From aa6f4fa980849d4b77d6bc3bc5aa56ccabad54b3 Mon Sep 17 00:00:00 2001 From: Kristof Schroeder Date: Mon, 16 Sep 2024 12:11:24 +0200 Subject: [PATCH 01/41] WIP: data-oob adaptation to pydvl.valuation --- notebooks/data_oob.ipynb | 10800 ++++++++++++++++++++++++- notebooks/least_core_basic.ipynb | 6 +- notebooks/least_core_basic_new.ipynb | 48 +- src/pydvl/reporting/plots.py | 4 +- src/pydvl/reporting/scores.py | 60 +- src/pydvl/valuation/result.py | 58 +- 6 files changed, 10821 insertions(+), 155 deletions(-) diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb index 5959a0c79..b9e63c8ca 100644 --- a/notebooks/data_oob.ipynb +++ b/notebooks/data_oob.ipynb @@ -53,7 +53,16 @@ "hide" ] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. It will be removed in v0.12.0.\n", + " warnings.warn(msg, FutureWarning)\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "\n", @@ -90,8 +99,13 @@ "outputs": [], "source": [ "%autoreload\n", - "from pydvl.utils import Dataset, Scorer, Seed, Utility, ensure_seed_sequence\n", - "from pydvl.value import ValuationResult, compute_data_oob" + "from pydvl.valuation import (\n", + " ValuationResult,\n", + " DataOOBValuation,\n", + " Dataset,\n", + " KNNClassifierUtility,\n", + ")\n", + "from pydvl.utils import Seed, ensure_seed_sequence" ] }, { @@ -106,15 +120,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide-input" - ] - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -131,15 +137,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide" - ] - }, + "metadata": {}, "outputs": [], "source": [ "if is_CI:\n", @@ -149,13 +147,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "metadata": {}, "outputs": [ { "data": { @@ -283,43 +275,638 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "metadata": {}, "outputs": [], "source": [ - "data = Dataset.from_arrays(\n", - " X=data_adult.drop(columns=[\"income\"]).values,\n", - " y=data_adult.loc[:, \"income\"].cat.codes.values,\n", + "train_data, test_data = Dataset.from_arrays(\n", + " data_adult.drop(columns=[\"income\"]).values,\n", + " data_adult.loc[:, \"income\"].cat.codes.values,\n", " random_state=random_state,\n", ")\n", - "\n", - "model = KNeighborsClassifier(n_neighbors=5)\n", - "\n", - "utility = Utility(model, data, Scorer(\"accuracy\", default=0.0))" + "model = KNeighborsClassifier(n_neighbors=5)" ] }, { "cell_type": "code", "execution_count": 7, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + 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"Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to 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+ "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n" + ] + } + ], "source": [ "n_estimators = [100, 500]\n", - "oob_values = [\n", - " compute_data_oob(utility, n_est=n_est, max_samples=0.95, seed=random_state)\n", - " for n_est in n_estimators\n", - "]" + "oob_values = []\n", + "for n_est in n_estimators:\n", + " valuation = DataOOBValuation(\n", + " model, n_estimators=n_est, max_samples=0.95, seed=random_state\n", + " )\n", + " valuation.fit(train_data)\n", + " oob_values.append(valuation.values())" ] }, { @@ -353,7 +940,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -431,36 +1018,35 @@ " max_samples: float = 0.95,\n", "):\n", " \"\"\"This is not very efficient, but it's just an example.\"\"\"\n", - " rng = np.random.default_rng(seed)\n", - " random_state = np.random.RandomState(rng.bit_generator)\n", - " data = Dataset.from_arrays(\n", - " X=data_adult.drop(columns=[\"income\"]).values,\n", - " y=data_adult.loc[:, \"income\"].cat.codes.values,\n", - " random_state=random_state.randint(0, 2**32 - 1),\n", + " data = Dataset(\n", + " data_adult.drop(columns=[\"income\"]).values,\n", + " data_adult.loc[:, \"income\"].cat.codes.values,\n", " )\n", "\n", " model = KNeighborsClassifier(n_neighbors=5)\n", - "\n", - " utility = Utility(model, data, Scorer(\"accuracy\", default=0.0))\n", + " valuation = DataOOBValuation(\n", + " model, n_estimators=n_est, max_samples=max_samples, seed=seed\n", + " )\n", "\n", " if method == \"random\":\n", - " values = ValuationResult.from_random(size=len(utility.data), seed=seed)\n", + " values = ValuationResult.from_random(size=len(data), seed=seed)\n", " else:\n", - " values = compute_data_oob(\n", - " u=utility, n_est=n_est, max_samples=max_samples, progress=False, seed=seed\n", - " )\n", + " valuation.fit(data)\n", + " values = valuation.values()\n", "\n", " best_scores = compute_removal_score(\n", - " u=utility,\n", - " values=values,\n", + " KNNClassifierUtility(model, data),\n", + " values,\n", + " data,\n", " percentages=removal_percentages,\n", " remove_best=True,\n", " )\n", " best_scores[\"method_name\"] = values.algorithm\n", "\n", " worst_scores = compute_removal_score(\n", - " u=utility,\n", - " values=values,\n", + " KNNClassifierUtility(model, data),\n", + " values,\n", + " data,\n", " percentages=removal_percentages,\n", " remove_best=False,\n", " )\n", @@ -482,10 +1068,20 @@ ] }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kristof/Projects/pyDVL/src/pydvl/parallel/config.py:31: FutureWarning: The `ParallelConfig` class was deprecated in v0.9.0 and will be removed in v0.10.0\n", + " warnings.warn(\n", + "/Users/kristof/Projects/pyDVL/src/pydvl/parallel/backends/joblib.py:79: FutureWarning: The `JoblibParallelBackend` uses deprecated arguments: `config`. They were deprecated since v0.9.0 and will be removed in v0.10.0.\n", + " warnings.warn(\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8896586289f44d7795df6df058f159fe", + "model_id": "dfb8897bce60474684afb41dac801129", "version_major": 2, "version_minor": 0 }, @@ -495,6 +1091,10036 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. It will be removed in v0.12.0.\n", + " warnings.warn(msg, FutureWarning)\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. It will be removed in v0.12.0.\n", + " warnings.warn(msg, FutureWarning)\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. 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ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. It will be removed in v0.12.0.\n", + " warnings.warn(msg, FutureWarning)\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. It will be removed in v0.12.0.\n", + " warnings.warn(msg, FutureWarning)\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. It will be removed in v0.12.0.\n", + " warnings.warn(msg, FutureWarning)\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. It will be removed in v0.12.0.\n", + " warnings.warn(msg, FutureWarning)\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n", + "Casting ValuationResult.names from object to object\n" + ] } ], "source": [ @@ -548,9 +11174,35 @@ ] }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n", + " ret = um.true_divide(\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n", + " arrmean = um.true_divide(arrmean, div, out=arrmean,\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n", + " ret = um.true_divide(\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n", + " ret = um.true_divide(\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n", + " return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n", + " arrmean = um.true_divide(arrmean, div, out=arrmean,\n", + "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n", + " ret = um.true_divide(\n" + ] + }, { "data": { - "image/png": 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", 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HrcGwhKP12aJewXZCnISd9VZqjOleAN/QP2+pt2CMaROR87Ejom/DBh43Ac+JyOXmaE2IoXC88+zv+ffkeOcnx9neX93fq2ji8WFsi/KedK9zcrxz6+859+d9eRw75f5G7M/KjdgkXWyNj1nAs8BubOOAYqATG8D9J0M0qOIFjh8Skf/BjoRWGGP2ikh0yWxftdSigddbCglHiS3m/BngcmOMKyLvAX7sjZQiIrdiGy987TiH2O19XdKPl6SUUkqdMt7n2vPAHdhk0A7sZ3Qltk5YT6q8xxrgnd4somuxS0t/AXxGRM70kmPx7FTFc2Df06c5mjjqbm+37wcbz/XJGPOa2CYRN2KXoF6LTby9OXDqNVB4AZtk+hJ2ALsdu6LiGwzhJBljzP+IyL3AImxX0W3iNUrjre9Pd/2J5zKxdZQ/a4xp8eK5R40xf/HufxT7M//8cQ6xHzuYq/GcGpE00abUqRGdoXYuNtH23Zj7NmHrO1yILYj/z5j7DmM/VOdgl6AC4BXtz/Lu75M3nf1Z7/Zpr1Dp/2KTb89w4omwORw7kjjbO9/CoTz/fhhMIi+qCjs93WeMeaavnQep3++LF5z8HXiXiHwamyR9yRhTGnO8a7GFba+LXU7ax0jhCfNmnlV4z+HD/uyu70eQH10eUtXLPvcCfzPGRP/P5GPr5kWVcnTJRk/ntldE9mCXSt8xAv7wUEopdXqJ/v0VXUJ4ADsz6OXYZYPHY4x5DVuo/n9E5GZsg6h3Y1cuDCaeezPh4c22msjRmDQal8zDztSK7peIbdwwVHHTUMRzYN/T9FMUz0H/35dHgDu8JNRN2BprsU0HLsTWvv03E9M4QERmDP2pvzmrcF3MpkuxteR29/yIN/UnnvsS9m+E33jf5wOhmPtLsQ0TjndurSLyHHCxiEyJmSWn1IigS0eVOjVex45IvRebJHhzRpsxpgPbZfF2bO222A+8aIBzZ7fjfdr7+o++nlh6aEOOLe4KRztJtnhfs/o6Xje3d/s+2l3oCe/roM+/n1oY5HR6b2bfY8ANXq2KY4hIzmCO381A35c/YAOUD2Fr/P2h2/3REdc3R1i9umwfGOyJ9sNnscF4dBkBIpIt3drQe/VkCrAz7XocvfQSg1djlxREVXBsTZIF2CYJvbkLG6j+LKYuXuzzXC4i1/RxDKWUUmpIeZ+Fl2M/C6MDbY9g64b9vx72T/BmOSEiY7vVa4W3xnOt3tesAZ7aR7rVffsYNiEYjeee8c75U93O4YPY+Gso47nozK7BeAQ4S0Su6H6HiGT1FBucoIG+L3/AXqtbsStMHul2f0/xXCLw8SE63+MSkZuA1cTUmxORzO5Lk73X+UXv2+618aL7zMV2zb0jZlnsicRzX8a+F7/uVtsu+jwrvZUOSsUdndGm1ClgjOkUkY3AedjZa5u67fIKdrkcHFsQdYuIPIQNgLKw08nPwH5A/8UYc7zp1rG+5C0d/Qd25C0X+4F9JOa5DmDren1URJqwgc56Y0xPdS9izRCRv2G7W56FLfT7W2PMliE8//7YBNwkIt8BNgLNxpjHT+A4BdhZfutF5KfYOirZ2Cn7l9LLFPmBOIH35Z/Y2Xb3YoOwx7rd/xQ20HtcRH6MHSX/MHYpykT6ICJrgQuMMb0uhRCRW7ANI17E1u+4FLsE4mfGmNhzug74orcs4BD2fbsZ2y7+v71adN2P7cPO9PyWObaN+6PAN0WkClvrZAnHX14DgDHmDyKyBNtMISAiv8P+7I/DBraXeOejlFJKnUxXiUg0uZCL/eyZAwS9hkcYY17wPru/ICLLsZ/pYW+/d2GXmT6KjRE+LiJ/xsZtGdjP+ka8ATyvXMhObEy0F9sFcrsxZnsf55kIPCsij2BnZ30cGyP+zTtulYh8HTuQ9S8v9ovut5GjNcQGKxoff09EngQixpjfn8BxvoWNRf4uIr/0jpuGjSHeia09Vz3Ykx3o+2KMeUNE9mNXlSTx1oHTV7C1iB8Ske9hZ/i9j34uVfVe663ADGNMYS/7nY+dcfYUtmHbmdjB2X8B98XsugL4nRdH7ccudX0HdnXOT4wxbxznKf4P2/xiQ8y2R4G/xixPvRboddDTGPOKiNyObd6wW0R+ja0nmIGd/XcdR5N+SsUXEwetT/Wmt9PhxtFW4S/3cN87vPsaiWm17d2XgP0wPIhNphR5x0rqtl8h8Pcejn0x8BdsV6MO7+tveWvL8+uwtULC3rnc1strudvbZwG2VlgjNpj7PpB8gue/Flgb8/2F3nO8s9t+07ufHzZ4+g02ODH00e6cXtp/YwPh+73z7MQWA34G+HA/zu02b/uq47xf4wf6vsTs/7B3jKePc/+1wBZsrZJD2FlhH/AeM/1477O37XWgrB8/w2dgk4K13vNsBv4DkG77rcQG50e8n7kmbMesd/Vy7I9ja36k9vDz823s8oRC4P0D+D8X/dmv8H6uK73zuq6Hn6fPnsz//3rTm970prfT5xYTD8Te2rBL5z7a/XPTe8yHvc/jVmxctRVbl2uid38AG78dxq6SqMDWcV3Z7Thnecfp8J737n6c5/nYzuK13mf2w0B2D/vfjp2J14mdjfRDIKvbPr8kJg7r7XO2+/lhZ/Z9z/u8dvFK0/Vy/oX0EPt696Vj46p93ntRhW0O9hnA39u5MfA4r8/3JWbfr3nH2Hec+88GXvV+Dkq8n4HLvcdceLz32dv2qPe4Hp87Zr9Z2NloVd7P0i7sYHNit/1mYGfdHcL+/LZ4P1tvif1iHnO19zM0sYf7CrzXVAr81wD+P63Axvkl3ntci43N3w843X6eeozv9aa3U30TY4ZqKbxS6nQhIndjR+9yjDGDHhFUw0dEMrABy53GmB8M9/kopZRS6tQQkduwHb5XG2NeH+bTUYMkIhXAr4wxnxvuc1HqdKc12pRS6vR2PnaE8KfDfSJKKaWUUmrgRGQRdmnnN4b7XJRSmmhTSqnTmjHmH8aY6caYzuE+F6WUUkopNXDGmB3GmExdaaJUfNBEm1JKKaWUUkoppZRSQ0BrtCmllFJKKaWUUkopNQR0RptSSimllFJKKaWUUkNAE21KKaWUUkoppZRSSg2BhOE+gXgkIgLkA03DfS5KKaWUGnEygFKj9TniksZ5SimllBqEPuM8TbT1LB84MtwnoZRSSqkRazJQMtwnoXqkcZ5SSimlBqPXOE8TbT2LjnBORkc7lVJKKdV/GdgkjsYP8UvjPKWUUkqdiH7FeZpo612TMaZxuE9CKaWUUiODXZWoRgiN85RSSinVb/2N87QZglJKKaWUUkoppZRSQ0ATbUoppZRSSimllFJKDYG4SLSJyO0iUigi7SKyXkTO6GXftSJierj94zj7P+Ddf+dJewFKKaWUUkoppZRS6rQ37DXaROQm4DvAR4H1wJ3AkyIyzxhT2cND/g1IjPl+HLAF+GMPx34HcCZQOsSnrZRSSp2QTZs2OcB4IIs4GfBS/eYC9UD1ypUr3WE+lxFDRG4HPgdMwMZsnzTGbDjOvmuBC3q465/GmLf1sP8DwH8A/2mM+e5QnbNSSil1IjTOG9GGLM4b9kQb8Gngp8aYBwFE5KPA24B/B4LddzbG1MZ+LyLvBlrplmgTkUnA94ErgB5nuymllFKn0qZNmyaJyFccx7lAROLhM1gNkDGmy3XdFzZt2vSllStXHretu7J0QFUppdTpQuO8kW+o4rxhvfgikgisBL4e3WaMcUXkGeCsfh7mg8DvjTEtMcd1gF8D3zLG7OirM4SIJAFJMZsy+vncSimlVL9s2rQp0XGcP6ekpEzNy8trTExMbNYOlSOLMYbOzs7EioqKt7W1tS3ZtGnTuStXruwc7vOKczqgqpRSatTTOG/kG8o4b7izrOMBH1DRbXsFML+vB3u13BZjk22xPg90Ad/r53l8Abirn/sqpZRSJ2Ka4zg5kydPrklPT28b7pNRJyYtLa3d7/eHDxw4kOO67lRg/3CfU7zSAVWllFKnEY3zRoGhivNG+prhDwLbYut8iMhK4A7gNmOM6edxvg6MiblNHuoTVUopddrzAeI4Tn8/m1Sc8q6hMPwDlvGutwHVCX09OGZA9Wfd7jqRAdWGmNuRfj5OKaWU6i+N80aJoYjzhjvRVg1EgLxu2/OA8t4eKCJpwLuBn3e76zwgFygSkS4R6QKmAd8WkcKejmWM6TDGNEZvQNOAX4lSSimllBpKOqCqlFJKqRFnWBNtxphOYBNwSXSbtxzgEuDVPh7+LuwygIe7bf81sBRYHnMrBb6FreOhlFJKKaVOPh1QVUoppdRpJx6WPHwHeEhEXgc2YLtRpQHRorm/AkqMMV/o9rgPAn8xxtTEbvS+P2abiISBcmPMnpPyCpRSSik1JG644YbpDQ0NvmeeeebAcJ+LGhxjTKeIRAdU/wLHDKje38fDextQfabbtie97Q8O8pSVUkopdRKdLnHesCfajDF/EJEc4CvYeh2bgSuNMdF6HlMBN/YxIjIPOBe4/BSeqlJKKaWUGhgdUFVKKaXUaWXYE20Axpj7Oc7IpjHmwh627cEWp+vv8aef6LmdDNPuIwVoP3wHWihRKaXUiNPe3i7Jycn6Gab6dDoOqE67j2SgQ+M8pZRSI5HGeYM33M0QTlc/AV6Zdh/XTrtPr4FSSp2uXANNnTin+uYOMHQ644wz5r3//e+f+u///u9Txo4du+yCCy6Yc/fdd+fNnTt3YUpKSmDChAlLb7nllqkNDQ1vfqZ973vfG5eRkbH8sccey5w5c+ai1NTUwHnnnTfn8OHD/ug+XV1dfOhDH5qckZGxPCsra/lHP/rRyd3r27e1tcltt902JTs7e1lSUtKKlStXznvhhRdSo/f//e9/zxCRlY899ljmggULFiYnJ68488wz55aUlCQ88sgjmTNnzlyUnp4euPbaa2c0NTXpZ+4wMMbcb4yZZoxJMsasMcasj7nvQmPMbd3232OMEWPM0/08/nRjzHeH9qwH5etAaNp9vH/afSQO98kopZQaHsMV5w001tM4b+jFxYy208m0+8gC3g6kA3/zidkz74d8sz0svzp8B13DenJKKaVOqZYwzuIfETjVz7v9Y4QyEo+dRdSXxx57bNz73ve+qrVr1+4G+Otf/zrm29/+dvHcuXM79uzZk3THHXdMvf322yc//PDDRdHHtLe3O9/+9rfzfvnLXx5yHIdbb711xic/+cnJf/vb3w4B3H333Xl//OMfx99///2FS5Ysaf/GN76R99RTT40988wzG6PH+PjHPz75n//859gHHnjg0KxZszrvueeeCdddd93cvXv3bsvLy4tE9/vqV7+af9999x1OS0tzb7755lnveMc7ZiUmJrq//vWvDzY1Nfne8573zAoGg7n/+7//22sRfqUGY9p9+LFNHCYAD4mYb8++nx+GI/L9w3dQPcynp5RS6hQarjgPBh7raZw3tDTRdoodvoP6afcxJzXR3NUe5taIkXmRMD9PcMz/rvq5+fHifPd7v7wqoXa4z1MppZSKNW3atPYHHnjgSPT7ZcuWVUb/PW/evM6amprST3/601OBNwOwrq4u+elPf1q0aNGiDoAPf/jDlffee29+9P6f/OQneZ/61KfKbr311nqAhx9++PCUKVPGRO9vbGx0Hn744Zzvf//7hTfeeGMjwG9/+9vDU6ZMybz//vvHf/WrX40uP+TLX/5yyeWXX94C8N73vrf661//+qQdO3ZsW7hwYSfAVVddVffiiy9m0Ee3S6UG4/AdhKfdx6IxKeYLzR38e8SV8eEIXxJMwfwf8kSij5eS/OaVOblm02+v8XUO9/kqpZRSoHHeUNNE2zA4fAflIB+78g/uV6ubzd21rfKeLlcmVDXLXS/tl0+t+rn783l55t7fXOOr6PtoSimlRqo0P+72jxEajucd6GOWLl3aGvv9X/7yl4xvfOMbEw8cOJDc0tLii0Qi0tHRIU1NTU5GRoYLkJyc7EaDL4D8/PxwbW1tAkBNTY2vqqrKf/bZZ7dE7/f7/SxZsqQluqxg165dSV1dXXLxxRc3R/dJSkoyy5Yta9m9e3dK7PmsXr26LfrvvLy8cHJyshsNvrxtXZs3b04b6OtWaqAO30EtyOeu/IP7pbpW86n6Nvloe1imt4W5vi3M9bQLVc2mY/4PzYHURF6dk+v+MDDF3VIQ8Ef6PrpSSqmRYrjivOhzD2R/jfOGlibahtG/bnJKg6HwfxTXyXe2lTifL22QfwtHZGxVs3y2ttX8x8qfm4dm57jf+MN1viN9H00ppdRI4wgMdAnncElNTX3zPPfs2ZN40003zbnllluqvva1r5Xk5OR0Pffcc+n/+Z//Ob2jo0MyMjIASEhIOKYQh4jQvTbHUElMTHzzwCLS43O7rtvvRkpKDda/bnLagG/ctT58/4ZC55aGdnl7U7vMae5gsmskqS3MwrYwC2sPObfuq3Se3VQU+c7Kqe66goC/tc+DK6WUinsa5w2dkRbnxU2xuNNVQcBvfnBxwu6rF0c+ftPKrivn5LoPJfpMXcSVjOpm+cTGw86Ocx5y7/7yxvCYvo+mlFJKnXyvvvpqquu6/OQnPym+5JJLWpYuXdpRWlo6oKLv48aNi+Tk5IRfeeWVN0cfw+Ew27dvf7MA7oIFCzr8fr957rnn0qPbOjo6ZOvWrWkLFixo635MpeLRl9f4W564yffj65ZGbn7vGV3XfPCcrisvnd9VsHBi5LdpiabIIAk1LXLFhkLnX79/PeG56x6N/PvXXg9PDobC/r6PrpRSSg0tjfMGT2e0xYmCgL8NeDWYHN7W2skP1hf6PnSoWv6to0vGH6mXu/6wSd69vTRy18qp7hMFAX9jnwdUSimlTpJ58+Z1dHV1yT333JN7ww031D///PPpDz30UM5Aj/PhD3+48vvf//7EuXPndixevLj9m9/8Zl5TU9ObsUlmZqZ7yy23VH3pS1+aPH78+K4ZM2Z03nPPPRPa29udT37yk1pYXo0oBQF/HVAH7A6Gwi/PyTXjIrPciVuOOO/cU+HcUN8m8+paWVPX6luzq9ypGZdmXl1/KPLYymnu8wkO5QUBf0dfz6GUUkoNlsZ5g6eJtjhTEPA3AxuDieEdq6by4xf3+z5fXCfvaOmQeRsKnd8eqZN/1TSHg+PS2QNUFwT8I2IqqlJKqdHjrLPOarv77ruLv//970+45557Jq1atar5S1/60pFPfOITMwZynLvvvru8rKzM//GPf3y6iHDTTTdVX3755XWNjY2+6D7333//Edd1+fCHPzyjtbXVt3jx4pa//e1ve3NycrSelRqxCgL+MLZgc3nQCe9bMdV9bHe5XLO91Hl7dYss6ozIuLJGuaaskWu2lzk1Oelm3Y7SyMOL8t3XgDKt56aUUupk0Thv8ORkraEdyUQkE2gAxhhjhnX2WDAUHrOvUq7bVOT7r7pWWQzgc0xbTrpZPzfX/HVRvvsMcKQg4K8fzvNUSinVu02bNs1PSEj415w5c5pTU1Pbh/t81IlrbW1N3rdvX3pXV9eVK1eu3B17XzzFEKpn8XqNgqFwJjC/uYPAjlLn7MJaZ0ldC4sM8uZynYxks3fSGPPUssmRh7PT2F8Q8NcM4ykrpZTyaJw3egxFnKcz2uJcQcDfEAyFfzsnt2vL+kPOh7eXOe/p7JJx5Y1yYXkjF24qcoonZZkXa1vCf8pOYytQrEsLlFJKKaVGFq80yIZgKLxzzQx37ZoZ7oyWDmZvLXHWHKx2Vja2M7+pXebubpe5+yrlg3mZZt3h2q6fTcs2rwIluspBKaWUig+aaBsBvOUBW4OE/3flVHfttlLnij0Vzsq6Vpa2dMqUvZXy3v1VcsOkLPPKiqnun4Oh8GtAEVBVEPDrlEWllFJKqRHCKyOyB9gTDIXHnDXTff6sme6c0npZsqXEOaekXlaGIzK+tEEuK2uQi/IyzSsrpri/CobCLwGFBQF/5zC/BKWUUuq0pom2EaQg4C8PhsL/CExx9wWmuAtrW5i1qci3qqhW1nRGZGJxnVxcXCcX5GaYN5ZPdv81K8e8FgyFdwNFBQF/13Cfv1JKKaWU6r+CgL8BaAiGwrvys8z6/KzIcxGXeZuPOGfvKHUuaumUmeWNcv4TO+Sc3AyzcfkU94/BUPhFoBio1AFXpZRS6tTTRNsIUxDwtwNbg6Hwruw0ply2IPK66/LUG8XO6h1lztmtnTKvsklWP7XLWZ1VaLYHpkSenj/BvBIMhXcAh7zHK6WUUkqpEcJbFloGlAVD4S0rp7rrA1Pcp7aXOBdtKXEuau6QeRVNcuaTO2VNdirbF050n1qU774YDIX3YQdcW4b5JSillFKnDU20jVBet6qDwVD4kOMwftU0d+uqae5ru8tl0RvFvjMb2lhd3yaLn9+bsHhTkbly2WT3+cX57mvBUHg7sFcDLqWUUkqpkcdbWrojGArvWzrZ3bF0srtuZ5mcFSr2ndfYLktrW1my7oBvSajYuWFenvviymnuc8FQeBuwryDgbxru81dKKaVGO020jXDekoAqoCoYCu+ZP8HsmT+ha1dRrTz36kHfWbWtnNvYLgte2u9b8EaRc1lgivvskknuq8FQeCs24dY6zC9BKaWUUkoNkFeLbUcwFD64cKLZvnBi14bCGlmy8bDvjOpmzm7plOlvFPumby91rpo53qxbPT3yZDAUfh2bcIubbqtKKaXUaKOJtlGkIOCvBl4IhsK7p2abJVOzu46U1suLLx/0nVndzEUtnTJn3QHfnC0lznlnzog8NTvHbA6GwluAA97oqFJKKaWUGkEKAv42YEswFN4/fZzZMX1c166aZta+fNC3rKxBLu6MSM7uCnnHvkq5fPJY8+qqae4/gqHwRuxS1Apd5aCUUkoNLU20jUIFAX9FMBSuBHbmZ5kl71rRVVXRKC+/uN93ZnUzlzW1y6KndyUs2nTYrD93duTZSVnmYDAUPgwcBko14FJKKaWUGlm8+G1TMBTeNy6dBdctjRS3drJh3QHf3MM1cnmXKxMP18qlRbVy4bg0ts7JdTcsnuS+EQyFj2C71VcANdpAQSmllBocTbSNUl6QVBoMhcuAXXmZZum7VnTVFdfJhpf2+y5qaJPzaltlzd+2+lZlpfDGnFw3tHyyuy/BR52XdNtVEPCXDPPLUEoppZRSA+AtC10fDIX3pSay6PIFkcqIy471hc7MPRXOxe1hmVvdworqQ74VGw87NRPHmNeXTXbfmDLWFGNLkRwAyoEq7VqvlFJKDZwm2kY5L+FW5I1WTpsy1iy/eXVXw+5yeXFDoe/qlk4J1LexeuNh3+o3ip263HTz6rLJ7rYZ483sYCi8AdiqQZZSSqnuzjjjjHmLFy9u/cUvflE83Odyovbs2ZM4f/78JS+//PLOs88+u224z0epoVQQ8NcCLwVD4W0+h+lnz3QXnjXD3bu3UvK3lThLa1rknC5XxhXXyRXFdc4VSQmmcEKm2TYvz907c7w5LEJ1MBTeBezXEiNKKXV60ThvcDTRdprw2sIf8hJuc+ZPMIH5E7oadpfLn7eV+FbWtnJRxJWxZY1yddlO5+rxaebFKxZ2jctMIS8YCr9WEPDXDfdrUEopNTL9/e9/z7j22mvnVlVVbR4/fnxkuM9HqdNJQcBfD2wOhsI7RZgyL8/Mm5cXKWwP8+yGQmdKYY1zdksnyzu6ZPrhWpl+uNbB7zMVOelmy8qp7huTx5o9MV1LtYmCUkqpY2ic91aaaDvNFAT8YWCntzx04fwJZtn8CV3rOrr4x2uHnBlFtc7FzR2yprpFzv/9poTFK6a4f1k1zc0JhsKvYkc0tW6HUkoppdQI43UpPRAMhQ8CE5L9zDt/jttw/hz3cFUTD75R7Jtb2SRrWjoIhCOSV9ogl5duk8uyUggtneS+tmCiuykYCu8AdhcE/A3D/HKUUkqpuOUM9wmo4VEQ8LcUBPwbgT8BLyYl0HHBHLfmfWu6fn3GtMiX/D5TGnEle+Nh37//YVPCJ2paeCdwaTAUzg+GwjLMp6+UUqOCMYZIW5tzqm/GDGzMpLGx0XnHO94xPTU1NZCTk7P0rrvuyou9/wc/+EH24sWLF6SlpQXGjx+/7Nprr51RUlKSAHba/rXXXjsXICcnZ7mIrLzhhhumAzz66KOZK1eunJeRkbE8Kytr+UUXXTR7x44dSf09rw0bNqSceeaZc5OTk1dkZWUtf8973jOtoaHhzdgmEonw2c9+dmJeXt7SxMTEFfPnz1/46KOPZnY/zvbt25MDgcD8pKSkFXPmzFn0j3/8I31Ab5BSI0hBwG8KAv6ygoB/LfAY8EpOBm1XLIwcft+art+894yujy+Y4H4jLdFsApH6Nlnx4n7fx3/1WsIXXz3ofLLL5Z3BUHhFMBROG+aXopRScW244ryBxnoa5w09GWiwfToQkUygARhjjDktpsgHQ+EUYBqwAJjcFibpie2+cyqa5DoQxxHTNDvHPHvOrMjaZD87gR1Amc5wU0qp/tm0adP8hISEf82ZM6c5NTW1HSDS1uZs+chHAqf6XJb95CchX0qK29/9b7nllqnPPffcmB/96EeFEydO7CooKJi0YcOGjBtvvLH6F7/4RfF3v/vdcfn5+eHFixe3l5WV+T/72c9OyczM7HrhhRf2d3V18Zvf/Cbrtttum7Vly5btWVlZkbS0NDNu3LjIL3/5yywRYeXKlW2NjY3OF7/4xUlHjhxJ3LVr106fz9frOTU2NjqzZ89eHAgEWr72ta+VlJWV+T/+8Y9PX7NmTdNjjz1WCPDlL3859957783/9re/fXjNmjWtDzzwwPif/exneW+88caOJUuWdERrd+Tl5YWDwWDR0qVL27/5zW/m/f3vf8/ev3//1gkTJvS4/KG1tTV537596V1dXVeuXLlyd+x9p2MMMdLoNXqrYCicCeQDU71bJuDuq5SkTUW+i+pbudggfoCkBFO2cKL77Opp7tM+hxCwtyDg7xi+s1dKqeEXT3EeDCzW0zjvWEMR5+nSUQVAQcDfBuwOhsJ7gUkpfpb/WyAS2VUuu1454Lu5MyLT9lbK2w9Wy3nz8txnzp7pvpzgY7dXJLfUqwGnlFJqlGloaHAeeeSR8Q888MCh66+/vgngd7/73aHp06cvje5z55131kT/vXDhws777ruv6IILLljQ0NDgjBkzxh03blwEID8/vyu2dsdtt91WH/tcv/nNbwrz8/OXvfHGG8mrV69u7+28fvrTn2Z3dHQ4f/zjHw9lZma6QHtbW1vRzTffPLu4uPjIlClTun7wgx9MuP3228s/8pGP1AH86Ec/Klm3bl3mN7/5zbxf//rXRdFjffCDH6yMnsuvf/3rw1OmTBlz//33j//a175WMYi3TqkRw6u91oiNBdOBCcDUOblm5pzcrmeqm3n65QO+VeWNck1Hl0wMFftu2VXmXLQo331m5VT3yWAovBU4VBDwtw7rC1FKKTUgGuedHJpoU8fwEmbFwVC4FJizYIJZMTun60cv7vPNO1At13W5Mm5Hme+mfZXO+Qsnus+cMd193edwKBgK7wQOa4dSpZTqPyc52V32k5+EhuN5+7vvzp07k8LhsJx33nkt0W15eXmR6dOnvzmD5aWXXkq966678nft2pXS2NiY4Lr28Pv3709cuXLlcQOpbdu2Jf33f/93figUSq+vr3/zcQcPHkzqKwDbtWtX8vz581u94AuASy+9tNl1XbZv356clpbWWlVV5T/vvPOO6ZZ4xhlnNO/YsSMldtu555775j5+v58lS5a07N69+5h9lDpdeB1G9wP7g6HwJmDa+HQWXr8s0tXYzpa1e30ryxrkqvYumbSpyHfrzjLnkrm57ourprnPeAm3g9pESymlhi/Oiz53f/bTOO/k0ESb6lFBwB/BjmoW+X0svmR+pPacMLuf3+NbXFQn13dGZOLmI7737SxzLp6X5z575gx3S4KPYq9I7n6v4K5SSqleiAgDWcIZjxobG53rrrtuznnnndf44IMPHsrLy+s6dOhQ4g033DCno6Oj15qe11133exJkyZ1/uAHPyicMmVK2HVdVq9evaizs1NrgSoVB7ymB1u9FQyTMpOZc93SSEND25sJtyvawjJ5S4nv5p3lzhUzxpmXz5geedrrUlqJnSXXBDTr6gel1OlG47zTN87TZgiqVwUBf2tBwL8B+HOyn9evWhxZ/94zuv43f4z7B0dMS2dEJm0r9b3/l68lfG7tXt+1nV1cA1wfDIVnBUNh/flSSqkRbuHChR0JCQnmpZdeerPweVVVla+wsDAJYMuWLcn19fUJ3/nOd45ceeWVzYFAoL28vPyYgbykpCQXoKvr6KTn8vJyX2FhYfIXv/jFsuuvv75pxYoV7TU1Nf0eAFywYEH77t27UxsbG9/8rHnmmWfSHcdh8eLF7dnZ2W5OTk74pZdeOqbg7YYNG9Lnzp17zCjqyy+//OZrC4fDbN++PXX+/Plt/T0XpUa7goA/XBDwFwLPAH8ek8K665dF/vXuVV3BKWPdR31i6sMRGbe30rnutxsTvvGXLb6vvXrQ+UJ5o3wQeDfw7mAofFEwFJ6sTbWUUip+aJx3cuiMNtUvBQF/dTAUfh7Ym57E8uuXRTqa2nn9+b2+ZWUNcl04IhN2lcv79lVK5YIJ7j/PnulOchz2BkPhzQUBf+lwn79SSqkTM2bMGPfGG2+s/n//7/9NHj9+fNfEiRPDBQUFkxzHxj2zZs3q9Pv95t5778371Kc+VRkKhVK++c1v5sceY/bs2Z0iwh//+MesG264oSEtLc3NycmJZGVldT3wwAM5kydPDh88eDDxf/7nfyb397w+8pGP1H7jG9/Iv/HGG6d/9atfLS0vL/d/9rOfnfL2t7+9ZsqUKV0An/jEJ8q/9a1v5c+aNavjjDPOaP3xj388fvfu3Sm//e1vD8Ye6xe/+EXu3LlzO5YsWdL+rW99K6+xsTHh9ttvr+n5mZU6fXlNsMqB8mAovCMrlfnXLImUdYTZtO6Ab/6hGrk0HJFJZQ1yblkD524+AgmOqclI5uDUbHf3GdPc1xN87PVKjhzRWW5KKTW8NM47OTTRpvrNC66OePXbZmQkE7huacRt6eD15/f6lpfWy9u7XMndVuq7bV+lc3DFVPdfyya7071gqgSoB+oLAv7wcL4OpZRSA/PDH/7wyK233uq76aabZqelpbkf+9jHypuamhLAFr79/ve/X/iVr3xl0oMPPpi7cOHC1nvuuaf4ve997+zo42fMmBH+zGc+U/qVr3xl0h133DH9He94R81jjz1W+OCDDx78zGc+M3XlypWLpk+f3v6d73yn6JprrpnXn3PKyMhw//73v+/71Kc+NeW8885bmJyc7F511VV1DzzwQHF0n//5n/+pbGho8H3xi1+cUltbmzBr1qz23/3ud/uXLFlyTIfEu+66q+Tee++dsHv37tSpU6d2/P73v98/ceJErTmqVC8KAv4qoCoYCm9P8jPnkvmRCtdl58bDzuTDtc6M5g4WdHQxo8uVcXWtjKtr9a3eWeZcNjfXff6sme56v4/9XsmRYq9kiVJKqWGgcd7QE2PMyTz+iKRt3/snGAr7gVnAcmBSaycdT+/yrSlrlBuMkRSAjCSzZcXUyNq5uaYkwUcr0AxUAaXYwEpnDCilTgs9tX1XI9NQtH1Xw0ev0ckRDIUzsHHhEiAHiDS107Gz3MktqZOFVc1ylWskEyDBMVWzc83zZ8+MrE9K4BCwCygsCPj1d6NSakTSOG/0GIo4T2e0qRPmzUzbHQyFDwGzUxNZfv2yyJbqZjY9tyfh0poWrmzqkGUv7EtY9uJ+05qWyO7xaWb/tHFu0dxcU5XgozEYCh/EdrY6og0UlFJKKaVGpoKAvwnYHAyF9wBTgfyMZKaume7WM52NLR285q2AuL7LlZzd5XLjvgq5YlKWeWX1dHdjboaJLiktxK6A0NkASimlRiRNtKlBKwj4O4AdXtJs7vh0lt24smvtoWp56ZWDvquaOlhtjKQ2d7CiuUNWFNY6rDtgavPHmGfOnhnJzk5jEVAZDIV3AxVAlXdMpZRSp6mCgoIJ3/ve9yb2dN+qVauaX3zxxX2n+pyUUn0rCPjbgD3AnmAonAiMB3LTkph1zZIIbWG2Pr/Ht/BIvVwfcWV8UZ1cVVQnl49N5Y2lkyIb5k8w2xyhOhgKF2JXQVTqTDellBpdRnucp0tHe6BLCgYnGAqnAwuApcDYLpfKHaVOdlGdLK5vlcWtnSxyjaQDCKZzbCprV02LrJuVY1qBLmwr+CPYum5VQI0Wy1VKjXS6pGBgKioqfFVVVT0OCKalpbkzZswYtnqfunR0ZNNrNDyCoXACMA27tHRaOIK8csCZcqjGuaItLIui+yX6TGlepgktznd3Th9nSrHXqhg70620IOBvHY7zV0qp3micNzCjPc7TRFsPNAAbGsFQOAtYBCwEMoEI0NAepvWl/b41xXVyfUeXzIzun5poNk/LNk+fNTOyOymBdMAPtGKTbQeAMuxsNy2Yq5QacTQAGz000Tay6TUaXsFQ2IddWroYmA4k7CqT1M0lvgsaWrnAIInRfRN9pigv02xcOdXdNnGMaQLqgIPYxFuJroBQSsULjfNGD63RpuJaQcBfD7zsdZTKBSYA05L95F62IFLiGr72+mFnwt4K59qmDs5s7ZTlu8pl+d4KqR+Xbp5ZOsl9ak6uacQuOZgGdADVwVB4L7Zgbu1wvTallDoBLmCMMTLcJ6IGx7uGBntNlVID4A2YHgqGwoeBfGDWgolmzoKJXU81tvOPjYW+GaUNcnZzB4HOiEwtrpOpxXXyjowkNsyf4L64cqo7RoQV2JhwBxoTKqXig8Z5o8RQxHk6o60HOtJ58nidSnOAydjZbuOBuoPVIpuLnUurm+WyiJGx0f1T/GbbxDHmuTOmRV4Zm4YLZGNnxzViZ7ntw45o6iw3pVRc27RpU6bP53s9Pz/fn5ubqx2XR7DKyspxpaWl4UgksnLlypVNsfdpDBH/9BrFn2AonImd3bYQyAOktoWWjYd9i8oa5OK2sCyJ7uv3mZKJY8wT58yMbM1KJYljY8IKneWmlBoOGueNHkMR52mirQcagJ0aXhv42dhaHblAY0cX1a8e9K0sqpUrWjpZASJga7llJPPa1Gz3+TXT3VCiXVqai80yHwG2Afu1lptSKp5t2rTpi36//2MTJkzoTEtLaxUR/RAeQYwx0tLSklpeXp4YDod/tHLlyq9130djiPin1yh+eQOyk7Dx4Uy8wdVd5ZK0tcR3RV0rFxsjKWBjwzEpvLRwovvS0kluowguNunWCtQC9UALUIMtPaIxolLqpNI4b2QbyjhPE2090ADs1AqGwqnALGzzhAnY977yULWM31riXFjVLBeFIzI5un+CYyryx5i/njs78syYFFzvMX5gN7CpIOCvGoaXoZRSfdq0aZMD/LfP53u/iPgBXV4wshhjTDgSifwKuGflypVv+cNdY4j4p9doZAiGwtnY0iELsLPcOuvbqHvlgO+8sga5qjMi06L7JvrM4YljzHMzxrkHZuWamkQfCUC01lsHUIqd8XbEK22ilFJDTuO8EW/I4jxNtPVAA7DhEQyFU4B52IRbHlANVLsGthxxZu+rdC6qa+VC10gGgCOmZXy6eeKM6e7fp4w1rcAUoBl4A9iureCVUvFq06ZNGcBEwBnuc1ED4gJl3ZcRxNIYIv7pNRpZgqFwIjAD2zxhKhB2DWVvFDkz9lU6Vza0cV5sAwXBhBMTOJSWyP6xaWbPmTMiocxkMoAUoAk4DOwoCPiPDMfrUUqNfhrnjVhDFudpoq0HGoANr2AonI4dvVyKrclWh026RZraSVy333dxSYO8PRyRfPsI05WbYf585cLI79OSyMDWgDsMbAWKCgL+tuF4HUoppU4/GkPEP71GI5O3rHQ6tuTIVGyh6qqqZtwNh3wXVTXLmR1dzHKNpMU+TsS0Zqfy7NJJkX/On2CasHFiJzZO3FwQ8Def2leilFJqpNJE2yBoABYfgqFwFnZJ6XzsDLcuoBJo6YrgrDvgW11YI+9oC8tCAL/PHFk+2b1v1TR3P7a+RxI2QbcXKMQWyNUfeKWUUieNxhDxT6/RyBYMhROwCbe52IRbGnamWk3EJXywWiYU1Tqz61qZXd8ma44OzEJqognNHO8+fs4sd68jTATKgdeBfdpYSymlVF800TYIGoDFl2AonIQNpOZ5X1OwCbd6gLV7fWfurZCPR4xkgTHj0/nblQu7fp2RTBd2Rlw20A4UATuxbeA1mFJKKTXkNIZ4KxG5HfgctqbqFuCTxpgNx9l3LXBBD3f90xjzNq/mzdeAq7HF8huAZ4ACY0xpP89Hr9EoEAyFBRvjTeHooKwP2wyhEWiOuPDaIWfZwWrnmuYOVkebbKX4zfbAFPfnyya7bUAqdlD2ELaJQm1BwN85DC9JKaVUnNNE2yBoABafvIAqFzuCOR8YA1QBtZVNkv7sbt8H69vkEoAEx5RNzjJ/PmNG5MVxabRig6hc7Dr5g9hAv1hnuCmllBpKGkMcS0RuAn4FfBRYD9wJvAuYZ4yp7GH/bI4WsQcYh/3M/pAx5pciMgZ4FPipt30scB/gM8as6uc56TUaZbxZbhOxybZp2J+bdOzy0lqg9kCV5IWKnaurm+VttqabMVkpPHPB3Mgj+WNMGpCAXVLaBFRgGyiUAdXasVQppRRoom1QNACLf14nqrnAImyQXQXUrtvvrNxZ7twecWU82NbvmSmsm5XjPr16mrvDEZKwy0pdYA+wtSDgLx+ml6GUUmqU0RjiWCKyHthojPmE970DFAPfN8YE+/H4O4GvABONMS3H2Wc1sAGYZowp6scx9RqNYt7AbCYwHpt8m4eNFWuAmsIaGffyAd+tje1yAYBg2nIyzN8nZppt8/Lcw+PSESADSAbasMm2/UAJdrab/vGklFKnKU20DYIGYCNHMBQeg024BbBLSg/XtZC47qDv8spGuawzIlOj+/p9pmRqtnnk4rmRFxJ8JAP52AAqhC2G2zEcr0EppdTooTHEUSKSiF3G905jzF9itj8EZBljru/HMbYBrxpjPtLLPpcCT3nHfMt7LiJJ2LqtURnAEfQanRZiYsXF2ORbHVD1+mFnztYS58MdXTI3dn+/z5Sk+tk3Ns3sXJzvbpsy1gi2DlwzdpbbIWxttxqd6aaUUqcXTbQNggbJI08wFM4HzsK2fy8DGl0DoWJn3r5K57L6Vs4zSArYpgkzx5vfXDAn8orPYQx2mcEB4FWd3aaUUmowNIY4SkTysbOAzjbGvBqz/ZvABcaYNX08/gzsctM1vdR0SwZeBnYbY957nH3uBu7q4a7T/hqdToKhcAYwG9u1NBfbPKFm7V7fOeWNcmZrJ3O6XJnY/XFJCebA2FSzfk6u2b5ootsoQgo2gVyFLUdSDlQWBPzhU/hylFJKDQNNtA2CBskjUzAUTgZWeDeDHa12AepbSXlxn+/qska5wTWSDpCYYA7OzXV/fd5sdwu2nkcrdunJ9oKAv2tYXoRSSqkRTWOIo4Yg0fZj4CxjzNLj3O8HHgMmAxce7/3WGW0qVjAUTsMuJ12GneFWia3jRkWjZOyrlDlVzTK3vk1WtIeZF22gAOATU5eWxLbcDLNrfp5bPHmsaRchjG3QVYgd7K0oCPj150oppUYhTbQNggbJI5dXl2M6dnbbRKADO+LYClDTQuqL+3zXVzTJ242xM9zGpJjnr1jY9eNxaaRia3jsArYCpVqHQyml1EBoDHHUYJaOikgadpnel4wx9/Vwvx94BNt59GJjTM0AzkuvkSIYCmcCC7Ez3DKBFu/Wim2KQGmDjNlW4qyqbJI1LR0EDBKbsCXBMZUZyWyblOXuXZLvlmSl0o7teFoC7AaKdPBWKaVGjxGVaNO272qoBUPhVOwstTnYke4U7DWtBiIVjZLx4n7fO6ubuR7ESXBM1dJJ7v+tmeHuBaYCYexygF3YICkyPK9EKaXUSKIxxLG8ZggbjDGf9L53gCLg/t6aIYjIbcADwKTuSbSYJNsc4CJjTNUAz0mvkXqT12BrDrZ2bxa2Hlu0820rNnHW3NaJb2uJM7+kQZY2tsnSNjvbzXf0SMZNTmD/uHSzY+kkd9f0caYKm3DbCRwqCPhbT92rUkopdTKMmESbtn1XJ5M3wy0HmALMxyZzW7D1NCKvH3bmhYqdT9uaHMbkpJs/vW1x5DcpifixM+LA/kGwEyjUhglKKaV6ozHEsbw47yHgP7DlGe4EbgTmG2MqRORXQIkx5gvdHveSt/3d3bb7sXHeCuAaoCLm7lpjTGc/zkmvkeqRV4YkExiDTbpNA7KBdG+XZu/W0tiGf2uJs6is0Qk0thGIbcAFkJZotsyf4L68aqp7yHGoxM5wKwSqdMWEUkqNTCMp0aZt39UpEQyFk4BZwFJgEl7CraEN/xM7Ej5U1yqXAyT6TNG0bPPH8+ZE1iUl4MMm55KwwfwubMKtdnhehVJKqXimMcRbicgnOLpyYTPwKWPMeu++tUChMea2mP3nYZMSlxtjnu52rOnYro89ucgYs7Yf56PXSPWLN2Cbia3lloNNvGViZ705QAQbTzYW1kja7gonUNUkZzZ3sCpa283vmPIp2Wbt2TMj2zOSqcMO4O7DrphoG4aXpZRS6gSNiESbtn1Xw6GHhFsDULF2r2/Nngr5pGskE8DnmOoJmebxc2ZFnhyXRgc2yMrCLiE4COwFinVUUimlVJQmceKfXiN1orzEWzo2HszCxobRJafJQBfQeLBakt4oci6vaZHLXSNpAIJpH5fOi6umRjbOGG9qsQ0Y9mATb+VapkQppeLfSEm0adt3NWy8hNtcYA02uXq4olGSXz3kXFXZKNdEjGQBiJi28WnmifNmu4/lZZom7EhmDnYUcyuwqSDgbx6eV6GUUiqeaBIn/uk1UkMpGAr7sMtLc7BlRyZjE2/S3EHjS/t9K0vr5ZrYpaWpiWbzvDz3hdXT3GKfQye2pEl0AFdXTSilVJw6XRJt2vZdDVowFM4FzsQWwq0E6ls68L90wHfhkTp5ezgiUwBETOuEDPPni+ZF/jomhXbsiOZkbFe09cABnd2mlFKnN03ixD+9RupkCobCidiE21xgBpDpGuo3FDoT91U6Vzd3sCa6rNQnpi47zby8ON/dOi/P1InQxNFmXKUFAb87bC9EKaXUW4yURJu2fVdxwQuKlgMrgQRsorUr4iIvH3BW7aty3tvZJTMBfGLqJ2WZRy6eF/lXSiIudvlpAkdntzUNz6tQSik13DSGiH96jdSp4nU0nQ4sBPKA9kPV4m4qci6raZFLXSMZ0X39PnMkL9O8ct6syNasVBqBA9hmXMW6rFQppeLDiEi0gbZ9V/ElGApPxS4lnYyts1EBtEVcZO1e37kHq+UW26EUEhxTtmCC++NzZ7tvYIviTsbOiNsC7NUCt0opdfrRGCL+6TVSp5pXrmQGsAiYArjtYarWFzoLj9Q5Fza1c4ZBEgEcMS0Tx5jHL50feSM1kU5sp9JCbI3gRqCxIOAPD8sLUUqp09xISrRp23cVV4KhsB/bVWqB99UPVAENHV34ntvju6y4Tt4dcSUbICPZvHze7MjPpmWbOiAX2xK+HDvDbZ8m3JRS6vShMUT802ukhkswFE4ApmITbtOxMWZdbQsdGw/7zjhSJ9d3RmQ62BUUU7LNo5fMi2xPTCABMEA7tstpHXb1RRVQVRDwt5/6V6OUUqefEZNoA237ruJTMBR2sDU25gDzgFTsbMvO+lZSnt6d8J7qZq4DcQTTPinL/PbyhZHHkxIwHE24lWGn/VcC1QUBf8fwvBqllFKngsYQ8U+vkRpuXoyZj53lNhPbTMFEXOrX7vUtOVgtN3e5MgEgwTHV6UlsHptq9kzNdgvn5pnqBIdUIAWvyym25nUJcKgg4G8dlhellFKngRGVaIs3GoCp7oKhcB5wBrawbQPeTMltJc7014ucj7WHZQHY+hqzcsyvL5gTedURHGw9jgyOBkKl3q0COwKp/wGVUmoU0Rgi/uk1UvEkGAonY5NuU7GJt3EdXdQ+u9u36kidvDtiZGzs/oJpS/Gzb0yq2Tp1rNm+ZJJb6feRDiRiB3g3Afu1rptSSg09TbQNggZgqifedP8FwCrsyGMxXv22Z3b7LimskQ9Ei9omJZi9Cye6D505w93mPTwByPRufuzU/0JsK/eigoC/zyXNSiml4p/GEPFPr5GKV8FQOB3bOGE5kN7cQeUbRc7c6maZ19Qh89rCzDVGUmIfI2Ja0xLZnpthNl48L7LT7yMJ2A+8URDwl5z6V6GUUqOXJtoGQQMw1Ruvg9QqbNKtCztDraumhdQX9vreUdkkbzdIEkBqogktmOD+YdU0d6cjxxwmDbu81Ied3bYLuyy1Tlu5K6XUyKUxRPzTa6TiXTAUzgECwHzAxdb+7eiK4OyucKYU1cqi2hZZ2tLJUtdIevRxfp8pXjzRffDMmW4d0AHswM5yawaagBZdTaGUUidOE22DoAGY6otXW2MWdsRxKnZZaAVgSuola90B3021LVwJ4gOvZXuGeWr1dPfZCZmmKeZQCUAOtp5bE1ALHObo0tKWU/ailFJKDZrGEPFPr5EaCbxYczqwEluKJAkIY+PFRqCzK4KzrdSZWVQrgfJGud41kgmQnmReO2925A/TxxkBHO9xbdhGChXYWtelmnRTSqmB0UTbIGgApvrLa9c+FzvqmIvt/lQLsL9K8l4/7HtXfSvnGyTZPsJ0ZSbz2ozx7pNrprtbfQ6x/wFTsQm3dGxnqUZsY4/dQJkGQ0opFf80hoh/eo3USOKVLhkHjMc26ZrE0VIkYWy82FTVTOLzexLeU9PCNV6jrnB2Gv+alu2+unyKuy8pgURsA4VMoBMbX24rCPgrh+N1KaXUSKSJtkHQAEwNVExNjWXYAKYEO2pIXQsprxX6zi9rkMs7umRO9DEJjinPzTBPr5zqPjN5rKnrdkjHO04Odur/fuzy0iO6tFQppeKXxhDxT6+RGsmCoXAiNvE2DttEIR/beCsJqNlZJikbD/s+3Nopy6OPccS0pCURyk03m5ZNdjfkZRrjPa4Z2AnsKAj4a0/1a1FKqZFGE22DoAGYOlHBUHg8Ntm2AFt/7Qh21BCArSXOjF3lzuX1rVzoGkmzW42bnsTGSVnmhVVTIxszU+jodtg0YAIQwTZQ2AkcLgj4u07261FKKTUwGkPEP71GajTxVleMw852Ww6MdQ0lLx9w5hXXOec3tbMyuqQUQDAdORnm7+fMcv80IdM42GWpTcBB4AB2UDd86l+JUkrFP020DYIGYGowgqGwAJOxwc5s7Iy0UmyiDICmdhJfPeQ7p7RermgLy8LodsF0pifx+sQxZt2qaZHXx6TQHnPoZGzCzYedMbcDOFgQ8Mfuo5RSahhpDBH/9Bqp0SoYCo/Fxp+LsPFicVeEyJYSZ/bhWllV1yJndkZkOoBg2nIyzOPnzXb/kpthfNhknQNUYldRFAPVWrpEKaWO0kTbIGgApoZCMBT2YRNtAWw9jSZsAdpjln7uKpPJO8udi2tb5JwuVyZGtwumMzuNp9bMiDw2LdvUxDzEjx19TPWOtwPYXxDwxzZZUEopNQw0hoh/eo3UaOYN+E7BNlGYjq3jVgm0uQbW7XdW769ybu7oklkAIqYtN9387dzZ7l9zM0w7thbcGOyy0iJgH1CsA7tKKaWJtkHRAEwNpZiGCcuxCbJabNOEY7gGtpU4M/dXyTl1LXJu+M2km+nKTuXpM6ZHHp0x3sQ+zsE2YMgCarBLSvcXBPzVJ/HlKKWU6oXGEPFPr5E6HQRDYT8wE5iPTbwlYuPFOtdgXtrnrNlf7dzc2SUz4M0Zbn8/Z5b7lwmZpgnbnCsHSMDGrXuAQwUB/1tiWKWUOl1oom0QNABTJ0MwFE4D5mFruI3DjhRWw1tqsuEaWH/IWbqnwnl3W1gW260mkpXKc6unRR6ZnWMqYnYXIBs7AtmEHXncA1RoHTellDq1NIaIf3qN1OkkGAo72NIjs7ADv2OxP/+VERfz4n7fmQer5d0xCbf2nAzzj7NmuH/NzzL12CWo47ADuy3YOm570AZdSqnTkCbaBkEDMHUyBUPhTGAGNtiZgB1hrMPOdIt03399obNod7lz09HuUcbNSuHZ1dPfknAD26k0F+jyjlmCXV5aC9RocVullDq5NIaIf3qN1OkqJgZdytEmCBURF/el/b4zDlTLezq7ZKbd23RlJvPK7Fz3idXT3B2OALa7aS62DEoxtnzJ4YKA/y2DxkopNRppom0QNABTp4JXw20Cdjr/XOxooYudnv+WemuvH3bm7yxz3t3SKSvsll4TbknYpFsGdsp/JzbxthXYowGRUkqdHBpDxD+9Rup0FwyFU7Az3JZiu5W2AGWuwX1pv3PGwSrnXe1dMi+6f6LPFE0YY544Z2bkuaxU2rBxZnSwuArYi028lessN6XUaKaJtkHQAEydasFQOBnbqXQ2MA2bIGvEBi/HLP98/bAzb2eZ857YhFtaIqEpY83TZ82MbEj209Ny0UTs8tJMbGHbzdiOpW+ZQaeUUurEaQwR//QaKWV5dYRnYsuaTMbGnbUAW444M3eVO1fXt3KBQZIAHDEt49PNP8+c4T4+6a3LSjuBMmwJk6KCgL/uVL8epZQ62TTRNggagKnhFAyFs4Gp2OK1+dgabqX0mXADR0xjdhrPL5jgPrM43z3cw+F92A6ofmA/NuFWqqOPSik1NDSGiH96jZQ6ljfDbQm2cVcacARoB6hpIfW1Q76LyhrkmnBEJtlHmK6xqTwbmBL587w8U+odJglbLzgdO1i8H5t0K9E4Uyk1WmiibRA0AFPxIKZb1FLs8tI2oJxuCbd9lTJxa4lzSU2LXBpxJTu6PdFnirLTzLr5ee66BRPNkW6HT8KOXEawo4/7sfXcqgsCfv2loJRSJ0hjiPin10ipngVD4VxgBbZ5Vxhb57cTIOIi6/Y7ZxyscW5oD8t8+whjUhPZPGWsefqsGZHXUhLfjFGzsEm3LmzSbhdQWBDwt5/K16OUUkNNE22DoAGYiidewm0WdpRxErZbaTm2ntubOrtw1hc6gcM1zmVNHZwBkhC9L9FnCnMzzHMXz4v8Iy2J2IYIydgp/+l49TmwSbeDBQF/68l8XUopNRppDBH/9BopdXxel9LZ2OWk+dju9pXE1A/eWOgs2F3h3NDcIWdEtzlimsamsnbBBPfpJZPcQm9zMrZ5QjI2xtwK7NeEm1JqpNJE2yBoAKbiUTAUTsQGPgFs4do6bC2Nt/wnrmkh9Y0i35ryRjm3uYNANOmW4JiyeXnuz86f427s4SlSsUm3VOwI5jZgX0HA33JyXpFSSo0+GkPEP71GSvUtGAonYFc/zMWusEjD62KPF3vuq5QJ20qcS6pb5JKIK+Ojj03xm22zc9zHzp7lvuF1K/Vhu5ymowk3pdQIpom2QdAATMUzr47GfOwMt/HYpNhxC85WN5P2+mHfOUV1cnN0aWlaonn9zBmRn87NM2U9PMQBcrDT/iuB7diE21s6oSqllDqWxhDxT6+RUgMTDIVzgBnAIuygbD12sNcF6IrgvHbIWV5Y61za1M5ZID6AxARzaFq2eez82ZF1iQm42IRbLrbpVzlHE25tp/xFKaXUCdBE2yBoAKZGgmAonAksxBavzcQ2TajDTu1/y3/s+lZSnt3ju7GySa63M9xM17g0nliSH3mihxpuYJcK5ABjsQFVEVAIlGnSTSmleqYxRPzTa6TUiQmGwhnY1RVLsAmzRuyg7Jtd7AtrZPzGw77ra5q5wiDJAAmOqZg4xjx+zszI02PTaMMO6uZhE26VHE246SoKpVRc00TbIGgApkaSYCg8FltDYwZ2SWkmtvhsFTH1NKL2VEj++kO+D7d0ysrothS/2Zk/xjx11szIuoxkW/Q2hgBjgGwgAft/owg4hC1sG0YppRSgMcRIoNdIqcEJhsKp2PrBS7AJswg2YfZmoqyySdJfOehcXdEo17pGxgCImNZxaTy1amrk8RnjTRU24ZaLjTOjqyj2aMJNKRWvNNE2CBqAqZHKm+U2EZjGsfU0aolpnuAaePWgEzhY7Vzd3MFqEAfAEdMyLs08ecHcyB9z0ukpyIlNujnYTlLbsc0TOk7ma1NKqZFAY4j4p9dIqaERDIWTgKnYLqVTOLaOmwvQ3EHiS/t9F5XUy/XhiEy2jzRuRjKvLJro/jkwxd2HjSnHY+PLUmAzsFcHc5VS8UYTbYOgAZgaDbx6GjOxy0uzsaOM5cRM7wcorpPsTUXOpVVNclmXK3kAjpjm/DHmD5fOj/wjplV7dwnABCAFGxRtRwvbKqVOcxpDxD+9RkoNrWAoHC03Mh1bRzi6rLQCL+EWcZFXDzorDlQ717d2yvLoY73GCX86e5a7yZFjlpQeAkLA4YKA30UppeKAJtoGQQMwNZoEQ+E0bOCzBNs9qgo72niMiIu8fMBZta/SeX9nRKaBrakxc7z51UXzIi95XaN6EttJqhzYgibclFKnKY0h4p9eI6VOHq9p12xgKXaVxTEJN4BtJc70baXO2xvauODNxgk+c3hqtnns4nmRF3wOPmzMKsAeYD9Qqk0TlFLDTRNtg6ABmBqNgqFwMjbZFsBO7S/GNlA4RlcE55k9vkuKauW90S6lfp8pHZ9unls2yX3eq6nRk9hRyDKOJtx0SalS6rShMUT802uk1MnnJdzmYGPPiUAzdrD3zVrAXuOEa2uaudIgKQBJCWbf0knuT1ZNc/dgV03kYxNu9cABbPxapgO6SqnhoIm2QdAATI1mwVA4D1gFzAVasbPQ3jIlv7GNpOf2+N5e3ij/Fg1+AFL8Zmv+GPP8WTMjL/XQOAGOneFWAmzD1nDTUUil1KinMUT802uk1KkTk3Cbj024OdgabvWAAahuJu3lA76ryxrlBmMkFWBMinn+3FmRh6Zmm1rvMWO9m/EeW4od2K0BanRgVyl1KmiibRA0AFOjXTAU9mETbSuxSbF2bMLtLYmzhjaS1x/ynV3aIBe3hWVpdLtPTF1+lvnjRXMjT6Yl0VOxWh+2hlsadgQzWsNN/08ppUYtjSHin14jpU69YCicgJ2dNgO7tDQLO+BbATaOLK2XrBf2+95X38qlICKY9txM85eVU90np2WbGu9QPu+xGUCi99gm7ODufuBIQcDf00CwUkoNmibaBkEDMHW68JaTTsc2TJjiba6AHjuOcqhacraUOBdVNcnlXa7kAvgcUzPZJtyeOk7jBAdbIHcstjbcLmww1AQ0aUcppdRoojFE/NNrpNTwCobCGdhupQuxyTeASuzyUkLFzuzNR5yPtIdlvr3LuGmJhCaPNc+cNSOyvlu86ccm3bKwS0wrsbFmYUHAX3cKXo5S6jSiibZB0ABMnW68GW6Tse3Z52A7ihZBzx1H28MkPL/Xd8mROrmpy5XxAAmOqZ4y1vz+onmRZ5MSju1sGiMb277dxc6ia8VO/6/ELi+tGMKXpZRSp5zGEPFPr5FS8cGb5TYFG3/OAFKxKywaXQPP7/GdW1wnV7eFZXH0MY6YprGprF040X1qcb57uNshE7CDu5nYJgwHvZvOclNKDQlNtA2CBmDqdOW1Z5+MreE2k6NJsB61dZLw3F7f5SX1cmO0cUKCY8qnjzO/u2hu5IUE31trv3kESMYWuU3B1nNrBDYC23WWm1JqpNIYIv7pNVIqvnjxZy424bYYO0utGG9J6b5KmbitxLmkukUuibgyLvq4pASzN3+MefLMGZF1Wal0rwU8BhiHjTmrgN3YQeTqgoBf/wBWSp0QTbQNggZg6nQXDIUTgQXACuwstBKOs5wUoKUD//N7fVeV1ss7I0ayAPw+c2TmePPbC+ZEXvY59OcXzXjvufYC6wsC/uMm+JRSKl5pDBH/9BopFb+CofBkbA3hWdilpOV4TRO6IjivHXKWF9Y4lzd1sAbEByCY9uw0njl3VuSR/CxT3+2QCdiE2xjsSorD2KWlRQUB//FWYCilVI800TYIGoApZQVD4bHYZNt8IAlbY60Wek6cNbaRtHav75qyRrnBNZIO4PeZksljzZ8umB15/jg13GL5gWnYwGojdrp/k448KqVGCo0h4p9eI6XiWzAU9nO0aVcutrNoLRxdKVHaIGNeP+xcXNkkl4cjMglswi03w/z1grmRP49Lo7WHQ6dhm4CBnd22A1vLTZeVKqX6RRNtg6ABmFJHedP5J2CXks7FzjprwzZN6DEwqWkh9cV9vusqm+R610gagM8xtRMyzd/OmRX513GCn1jjsaOPjUA1dvSxGjvd/7gz65RSarhpDBH/9BopNTIEQ+FMbMOEBdi4sAVb0uTN+NM18NohZ9nucud9HV0yF2wdt/wx5pGL5kWeSE/qMVZNBCZ6X0uBbcChgoC/r/hUKXWa00TbIGgAplTPgqFwGrZL6Xxs8dou7LLSHmeq1beS8tJ+3+VljfL2aE0NwXQk+9mXnmT2Zaexb/o4d+/0cabSkbc83MGOPGZ6X13s/8vdwO6CgL92yF+gUkoNksYQ8U+vkVIjS0z8uQCYhK27VgZHB25dAy/u8525v0reH47IZLAJt3Fp5qnAFPdfs3JMTw23ErAz3NKwddx2YptzaYyplOqRJtoGQQMwpXrndSmdDizHLvVsxQY8PTY/aA+T8OI+3wXFdfKOzohM7X5/gmOqZ+ean140N/JqL0/rYFu352D/f+7EJtxqBvFSlFJqSGkM8VYicjvwOezs6C3AJ40xG46z71rggh7u+qcx5m3ePgJ8Gfgw9nPhZeBjxph9/TwfvUZKjUBe/DkJm3Cbi40NjxAzw62zC+f5vb6Li2rlPV2u5NitxqQlsmnGePcfZ8903+ihdrCDnTGXjV1NsQdbM7hcy5copWJpom0QNABTqn+8GhqzsQm3SRztUtrjL5aIi+wqd6aW1Muc+laZ09LJnI4upoMkAGQmm5cunhf58cQxff6/G4ut2dHI0YRb9VC8JqWUGgyNIY4lIjcBvwI+CqwH7gTeBcwzxryl6Y2IZGOXc0WNwybnPmSM+aW3z+eBLwC3AoeArwJLgIXGmPZ+nJNeI6VGMK+syRRgGbZpQhi7BPTNFRadXTivHnRWF9Y6V7d2SiC6PdFnimaON789f07k1eM068rCxpht2GTbDjThppTyaKJtEDQAU2pggqFwMrYl+zLsFPwabE21Pn/BNHeQ+ORO342VTfJOEMcR0zA31/zoonmRV/rx1FnYYKgJO/q4S7uVKqWGk8YQxxKR9cBGY8wnvO8doBj4vjEm2I/H3wl8BZhojGnxZrOVAt82xtzr7TMGWzf0NmPM7/txTL1GSo0C3gy3mdgB36nYhglvGXjdWyETt5T4rq5t4dJo7eDEBHNo9nj3t+fNcdf3UL4EIB1bx60dG2PuBCoKAv4eV28opU4PmmgbBA3AlDoxXg2NaMJtHLbeRb/qXISKndlvFDl3dEZkGkB6ktmYl2nWzc9zt0zNNn0dYww2wdfC0YRb+Ym+DqWUOlEaQxwlIonY0gLvNMb8JWb7Q0CWMeb6fhxjG/CqMeYj3vczgQNAwBizOWa/F4DNxpg7ejhGErZzdlQGdrnZaX+NlBoNgqFwInY56Wpsbd/D9NCwq7qZtBf3+66vbJLrjZEUgKQEs39atvnTObMiryX7e6w5HJtwa8LOdGsFmr2vLdgVFk1AsybilBrdNNE2CBokKzU4Xpeo+cBS7KyzGu/W6y+ctk4Sntjhu6miSd4F4kS3+32mODOZLZPHuuvPnOFuOc7II9g/niZgg6DdwA6d4aaUOpU0hjhKRPKxDXPONsa8GrP9m8AFxpg1fTz+DOxy0zXRmm4icja2Jlu+MaYsZt9HAGOMuamH49wN3NXDU5z210ip0SQYCucAa7CDvg3Yma5vUdkk6S/td95e1STXGSQZwCembny6eXLlNPfJadmmp/q/aUAK4PduCYAP25ihi6MJuEps3eJKoLog4I8M5WtUSg0vTbQNggbJSg2NYCg8FpgDLALGA3XYWW69jvZtL3Wm7amQ8xvaZHlHF7NB3kytJSeYPfPy3N+cOdPd3EfCbSJ2lHEnNuGmTROUUiedxhBHDUGi7cfAWcaYpTHbTiTRpjPalDpNBEPhBOzstjOwg72V2BrCb1HWIJmvHnKuqW6WyyOuZNutxs1IYv2yye7vlkxyC/v5tD5sEi7VuyViZ8DVAoVAOVBWEPD3WUNSKRXfNNE2CBokKzW0gqFwBrZpwmLsEs8Ojk6zD/f22Kpm0naU+paUN8qK+lYuNkgiQIrf7Jw/wf3NmTPcbb08PLqktAlbzHaXtmxXSp1MGkMcNZiloyKShq3F9iVjzH0x2we8dLSHY+s1UmqUC4bC47Bx51xswi1av+0tg70dXfhePuA7q6hWrm4Ly2K71ZisFJ49d3bk4Slj+yxh0pNkbBya4X1fh/3ddRibdOs1/lVKxSdNtA2CBmBKnRzBUDgFW7R2MnbGWQZ2+n0YO+LY2tvjS+ol65UDvhtqWrjaIH6AFL/Zujjf/fWqae6eXh6ahW2a0ICd4barIOCvG+zrUUqp7jSGOJbXDGGDMeaT3vcOUATc31szBBG5DXgAmGTM0WVcMc0Q7jXGfNvblon9DNFmCEqpY3irK2ZiV1fkYmurlXGc1RU7Sp2pm484NzW2y3kAgunIyzR/umRe5E+ZKXSc4Gn4gLHYeNTFJvz2YWf8VhQE/D3VhlNKxSFNtA2CBmBKnXzBUDgJyMY2TZiMrafRgf0DqtdfTEW1kv3qId+7alu4AiQBID3JbFg+2X24j2n+2UAOdgnBdmA/UKf1M5RSQ0VjiGOJyE3AQ8B/ABuAO4EbgfnGmAoR+RVQYoz5QrfHveRtf3cPx/w8UADcChwCvoqtCbrQGNPn0iy9RkqdfoKhcCowA9uwayI2FqziODHnpiJn3tYS59/bw7IAwCemPj2Z0NhUs3N6trtj3gRzpJcSJr3xY+PRMdiB5mpsPKpJN6VGAE20DYIGYEqdWsFQ2MEuLV2NDX6OYOur9epQjYx/7ZDvPfWtXGKbJxiTmcxLZ0yPPDwn1/TWdTSacGvGLis9gg10arGFa3U6v1LqhGgM8VYi8gngc9hmNZuBTxlj1nv3rQUKjTG3xew/D9vQ5nJjzNM9HE+ALwMfwc4QWQd83Bizt5/no9dIqdOUt7piHhDADvZWcJwabq6BF/b6zt5fJbd1uTIh9j5HTFNqIjvHJJvdE8aYXQsnuvvTk97a6bQPidiYNBObdKsAtgEHCgL+E509p5Q6iTTRNggagCk1PLxupSuBJdgOTuX0UcMNYHe5TNpU5Ls5Zpp/OC/T/PHyBZHH0pJ6fXwKdvlqOrZ7VKf3nJuBgzrTTSk1UBpDxD+9RkopL+ZcjI0507GDrbX0sKS0PUzCG0XO4vJGWdjYLovaw8yL1gw+ynQlJXAwM9lsWzDBPL8o3y0a4CklYgeBo/Upt2ITbtpAQak4oom2QdAATKnhEwyFBZiFnd2Wg51i3479P9lELx1Lt5Y4M0LFzgdaO2U5gN9nShdOdH909kx3Sz+f3o+dUefHFqzdDBwpCPj1F6VSql80hoh/eo2UUlHBUDgHG3fOBcZjB3qrsKseetQeJmFHmTOrpF4WNrbJvNZOFkSMjI3dJynB7JuYaZ5ePT3y0vj0vldpxPBhG3mlYwd/t2IHfwdyDKXUSaKJtkHQAEyp4RcMhROxAU8OMBUbdGRytI5bjzUsXAPP7/Gde6BaPhRt1Z6ZbF44b3bkwanZ/e4alQRMAiLALu9WoTPclFJ90Rgi/uk1Ukp1FwyFk4EpwBxs3JmGLS3Sa6MusLFnYY3kHqxyFpQ3ytlNHZwB4gMQTGd6Eq+PTTNbp2eb7fMnuMU+p/daxB4ftnlDJlCDjUUPFAT8VSf2CpVSQ0ETbYOgAZhS8ScYCqcD+diC11OxgU85Nhn2FjUtpD67O+GWmhau9uq3dY1J4aWFE9y/LZ/iHujn06ZhE26d2NHNvdgkX5Um3ZRSPdEYIv7pNVJKHY+3smI8tmnCQmyceYReVlR0V9ogYzYVORdWNsplnRGZGnufI6YxLZEd49LNGyumuK/kZZqmPg4n2Fpy47Cz7A4Ce4DigoC/3+eklBoammgbBA3AlIpfwVDYj22csAybBGvCFo/tMdjYXOzM2nzE+VBbWBZFt6X4zY7p48xfz5oZ2ZiU0HOirptoh9QM7Iy6SmyQU1gQ8DcM6gUppUYVjSHin14jpVRfvEZdM7ClTCZjY7+6gRzDNbDliDP7cK2saGiTxW2dLDBI0tE9TFd6Em/kjzEvrJ4W2ZCZQl8NENKxs9zAJty2AUWacFPq1BlRiTYRuZ2j3ai2AJ80xmw4zr5rgQt6uOufxpi3eftEu1F9GNuN6mXgY8aYff08Hw3AlIpzwVA4CTu9fxl2plsVdmp9j0LFzuxd5c61DW2cHzOdvy01ke3ZaWbLjHFm84KJblE/WrUnczTpVo+d5XYAKNNARymlMUT802uklOovr0vpEmA5NvarwzZNGPDKhvYwCdtLndlH6mVpTYuc3dklM6P3CaZ9TAovL5zoPr5ssnuwj0MlY2NfAxwCtgOHNQ5V6uQbMYk2EbkJ+BXwUWA9cCfwLmCeMaayh/2zsV1ZosZhk3MfMsb80tvn88AXgFuxv3y+iv0FudAY02fnFg3AlBo5gqFwGnZq/zJsYr0UO8utR8V1kr2x0Lm6ulkujxjJir3PJ6ZuQqb52+ULI39N9vdcA66bsdjfQV1AERAqCPgH2mVKKTWKaAwR//QaKaUGKhgK52JnuM3Dxn4udpbbcZsm9GVnmUzZXe5cUNMiF3S5khfdnuw3u6aONY+fOzvyah8rL5KxTbwEG/8exK7yqCwI+DtP9LyUUsc3khJt64GNxphPeN87QDHwfWNMsB+PvxP4CjDRGNPizWYrBb5tjLnX22cM9pfObcaY3/fjmBqAKTXCBEPhbGz9tkVAAjbxddxkWcRFdpQ50wtrZHl9qyxr7WRxtFW732eKF+e7D5w5w93Wz6dPwc7IjQCbgM3ajl2p05PGEPFPr5FS6kQdp2lCF/Z3SgO9xJ7H4xrYVOQs2FvhvK2xnXOiKy98jqnNzTD/XDPd/dfEMb3+rkrCJv/SveevAwqxE06OFAT8w7+ETalRYkQk2kQkEVvQ/J3GmL/EbH8IyDLGXN+PY2wDXjXGfMT7fiZ2GVfAGLM5Zr8XgM3GmDt6OEYS9hdUVAa26KUGYEqNIF4B23xgFTYAKsf+IuxTWycJL+33XXCoRm5zjYwByEw2L14wJ/LzyWNNf2tyjMEm3A4BrxUE/CUDfhFKqRFNkzjxT6+RUmqwvJgzG1szbRK2jtsY7GBvPbakyYD/0D5SJ2M3HnaurGqSKyNGxoLtXJqVytol+e7fFuW7fa2c8HvnkQWEgR3AloKAv3ag56KUequRkmjLB0qAs40xr8Zs/yZwgTFmTR+PPwO73HRNtKabiJyNrcmWb4wpi9n3EcAYY27q4Th3A3f18BQagCk1Ann12wLASm9TMf0MdqqaSXtuT8L7alu4ylZ8NG256eZvZ810H+9jNDHKB0zDNk3YBGwtCPj7Km6rlBolNIkT//QaKaWGmhd75mAHXBd5/27ALi8dcO209jAJ6/b7zimqk+s6umROdHtqotk8N9d9dM0Md2s/6gqnY5OA9cAbwE5dcaHU4JwuibYfA2cZY5bGbDuRRJvOaFNqFAqGwjOAs7H1K4qAtv4+dnOxM+uNYudjHV0yF+xoYnYaT66eHvnzjHGmuh+HGIsd5SwEXscWqdWp+0qNcprEiX96jZRSJ5NXP3g2sBibeGvB1g9uBwZUOy26rHRPhXNdUztngTgAKX6zfeFE9+Ezprs7+3GY8dilpUeAXUA1UKNJN6UGbqQk2k546aiIpGFrsX3JGHNfzPYBLx3t4dgagCk1SgRD4UxgDbAAO9usFjuy1+foYsRFXtznO+tQjbyzo0tm260mkpXC2jm57pMrprq7+xhNTMAuJQDbESpUEPDXn9grUUqNBBpDxD+9RkqpU8HrWDoTO8MtEzuxIzq5w8XGo3X0c8bbwWrJff2w7+01LVwJkgCQmmhCS/Ldh1dMdff18XAHO/AcrSnXiP1bugw7GNyvUitKne5GRKIN3myGsMEY80nvewc78+T+3pohiMhtwAPAJGNMTcz2aDOEe40x3/a2ZWKn7WozBKVOQ8FQ2IedOj8NO8KYjR1RrKGXDqVRroHXDjrL91U672wNy5szaBMcUzY+3axdnO8+PyfXlPdyiDRswq0GO7ttV0HAP+BiuUqp+KcxRPzTa6SUOpW8em4p2HgwehsHzMKugOjCzjLrMyYFKKyR8a8d8t1Y18pl0cYJiQnm0NgUs3HaOLNx2SR3X4Kv1+RdAjbxlwkkYgeh3wB2a7kTpXo3khJtNwEPAf8BbADuBG4E5htjKkTkV0CJMeYL3R73krf93T0c8/NAAXArtij5V7HdCBcaY/qcIqsBmFKjVzAUTsV2i5qNTb5lYAOceuz/+97aqLOpyJm7p8K5urGNsw2SHN2e4jc71kyP/GDBRHOkl4fnYYOa3dhmCVqYVqlRRmOI+KfXSCkVD7yYdDI2Jp2CranWgh2Y7fNv1v1Vkrex0Pfu+jYuii4pBXDENGQm8/qyyZE/LZxoivtxKrnY5glFQAg4WBDwD7iunFKngxGTaAMQkU8An8OuYd8MfMoYs967by1QaIy5LWb/edg/VC83xjzdw/EE+DLwEewvjXXAx40xe/t5PhqAKTXKeaOLY7DJryneLQs7fb8Y26npuBrbSNpQ6DuztEEuaulkOYgjmI5ZOeYHly2IrO3loYnAdOzI5XpgrwYzSo0eGkPEP71GSql4EwyFs7Gx6Bzs38RJ2N9TNdgB4eMqa5DM7aXOysomWd3cQcA1kmbvMZGcdPOXyxZE/jAmpc/EXXT1hx/YCxz2nr8RaNZYVSlrRCXa4o0GYEqdfrw6GhOws19nY6fR96fpAYdrZdzavb47WztlGcDYVPOva5d0/TQtqddk3UTsMoItwMaCgL9lUC9AKRUXNIaIf3qNlFLxKhgKOxwdBJ6D7V7qAhXY2ua96ujCFyp2FuyrdK5v7pA1AAmOqZ6da356wZzIq/3oVJoM5GMTb673nE1AOTb5VlwQ8Pc6GK3UaKaJtkHQAEyp01cwFE7EFq1dhZ3CX0Q/OkR1RXAe3+a7qbxR3g0iiQnm4PmzI9+Yk3u0+3EP0rCBVDF26fwh7Uyq1MimMUT802uklBoJgqGwHxsnLsTWGU4CqrDlTvr04j5n9Z4K5yNdruQBpCWaTbNy3L+unuZuTUzoVwMGH0dry6UDgm2esAMoLAj4+1VTTqnRRBNtg6ABmFIqGArnAWcAc7EBTWV/HvfKAWf5tlLnM66RMYJpm5Rlfnf5wsjjSQnHrf3mYIMowc5u26Sz25QauTSGiH96jZRSI4lX7mQidobbPGy930psx9JeNbWT+PQu37sqmuSGaKdSn5i6sam8OCfXXbt0snugH7PcovzYem7p2JUf+7G1jZOxSUC/9/0ObCdTXW6qRh1NtA2CBmBKKXhzJHEhsBLbFaoEW6S2V0W1kv3cHt9n28KyGMDvM8WLJro/Pmumu7WXh2Vgp+qXYGe3HdTZbUqNPBpDxD+9Rkqpkcqr5TYfWIyNHcvoR7fSPRWSv7nYd119G+e5RjKi2xN9pmhRvvvAmTPc7QM4DcHGxdnY5aUuNsEWwSbcwNZ521oQ8JcO4LhKxT1NtA2CBmBKqVheUBPALil1gSP00Z004iLP7PZdUlgjt7lGMgEyks26c2dFfjF9nDle7TcH233KB2wFthUE/DVD9TqUUiefxhDxT6+RUmqkC4bC47HJtgXYGWUV9CPh1h4mYUOhEyiucy5sameNQRIBxqWZx69a1PVQRnLf5VL6IQXbWKED2InGs2oU0UTbIGgAppTqzpu2PwM7u20qNpipB9p6e1xVM2nP70l4b00LV3udSdunjTM/v2Jh5MlepuqnY2e3NQG7gJ0aoCg1MmgMEf/0GimlRotgKDwRWIKNUdOxHUobvFuv3Uqrm0l7ZnfCrXWtciWA32dKl092/2/VNHfPEJ1eBnbJayO2PMr2goC/z4YOSsUzTbQNggZgSqnjCYbCydjRw3nAOOwoYju2TsZxRxK3lTjTXy9yPtYelgVgC9JeNC/y/SljTW0vT5eF7TzViK13sReo125PSsUvjSHin14jpdRo4g0GZ2Hrp03CDghneXcX00dTr3X7nRU7y51PRlwZB8Ydn87jyyZF/jk3r9eGXgOR7Z1bKfAGsK8g4O81CahUvNJE2yBoAKaU6ovXfn08MAGYjh2xSwUOcZxlpV0RnCd2+K4rqZf3GcTviGmenWN+dMn8yEt9PF0WNuHW6t3qsF2nGoHqgoC/fAheklJqCGgMEf/0GimlRrNgKJyEjU+XYpt6NWCXlh5XVTNpT+9K+EhDm1wU3ZaUYA6MTzcvLsl3180Yb6oGeVoOR2PlA9iE2xGtR6xGGk20DYIGYEqpgfBGEnOBc4GZ2GRbx/H231kmU1475PvPji6ZDZCRbF6+cE7kJ5PHmr66R6V0uwk28fYGsKUg4D/ucyqlTg2NIeKfXiOl1OnAa+o1H1iNbV5QTB8lT17a76w6VO1c09LJchAnuj3Fb7Yun+w+uHyKe2CQp5WIrUfcBWwHNhcE/A2DPKZSp4wm2gZBAzCl1IkIhsLpwDnYWhml9LKUtKML3z+3+95V3ig3gfgcMS1Ts81Dly+IPOlzGMgv5rHYJN8BYL12d1JqeGkMEf/0GimlTideU69V2NInXUA5fSwnLWuQzM1HnLMrGuW8tjCLQQSMyUrhmfPnRH49KcvUD/K0MrD1iKuBTcBuLY2iRgJNtA2CBmBKqRPljR6egR09bMAGEMe1tcSZ8fph5xMdXTIHINlvdp0x3f3Boolu0QCeNgFbj6MdeB3b3WkoukYppQZIY4j4p9dIKXW68UqezMIOBk/xNpfRxww3gEM1Mv7Vg773RZeVipi2/DHm95fNjzyekth7w4U+RFeEjAEOApuxy0k14abilibaBkEDMKXUYHjBzGLgbCAJOEIvI4ddEZx/7fS97Uid3GKQFDBdOenmr+fPcR/LzTDNA3jqbCAH2Ae8WhDwVw7iZSilToDGEPFPr5FS6nQVDIV92MHZBdhyJ36ghH4k3F4/7MzbWuJ8JDo4DKYrwaEmwUet30dNko+a3Ex323mz3Q2ODOi0/Njkn2BrEO/DLnOtKAj43QEdSamTTBNtg6ABmFJqKARD4cnASmwg04qdqn/cgKGwRsa/tN/3H80dsgZAxLTmZZi/nj8n8tdxafS3HbofmIZdtroB2FEQ8PfYnEEpNfQ0hoh/eo2UUqc7r75wPrAQOzhcB9T09biIizy723dRYa28P+JKdk/7JCeYPYvy3QfPmO7uHOBpJQDjsDPcOrBx8wHvvGoLAv6BDD4rdVJoom0QNABTSg2VYCicAMwBVmC7LVUBtcfb3zWwbr+zel+Vc0tnl8wAcMQ0T8g0fzpnVuSJ8em09POpc7DdSncAGwoC/r4aLSilhoDGEPFPr5FSSlneDLclwJmADzuTrM8EQWcXzpF6GV/bItmN7TKutZNxLZ2SX9fCJQZJAkhPMhtWTHV/NcByKFEp2JUaaUAEaMYm3I4ABwsC/j6TgkqdDJpoGwQNwJRSQy0YCqcBi4Dl2KDhMHDcGhQRF3lhn+/sg9VyczgiXi0N4yYnsG9MitmSn2U2L5vk7u6jNkYSdnZbFbAR2K91L5Q6uTSGiH96jZRS6ljBUHg6tqHXBOAQvcSovTlSJ2PXHfC9p66Vy23XUuNmpfDcmTMjv50xzvRat7gXDpCObaCQiU22PaMlUtRw0ETbIGgAppQ6WYKhcB62WcJc7My2XoOOrgjO83t95xfVyjs7IzI19j7BdI5PN/942+LIr3tJuAl2Jl0aUIgtNFuoNS+UOjk0hoh/eo2UUuqtgqHwWOBcYB59rMDoy+5ymfT6Yd/7mjrkbLvFdI1P4x/nzo78ceKYQf3eFWxJlipssq1iEMdSasA00TYIGoAppU4mrzPpImxn0nSgiD7arIOt4ba3wllW3SzLmjtYHjGSBZDoM4VnznTv7WNqvh+YhB0V3A9sAUoKAn79EFBqCGkMEf/0GimlVM+CoXASsAy7nDQbm9A64WWam4qcudtKnPe3hWUpgGDa8jLNXy6aG/lLVmrfDRiOIzbZ9mxBwF9+ouen1EBpom0QNABTSp0KwVA4F1iDnd3WAFTSj7oYYGu5vbTPWbO7wvmkayRTMJ1Tss2DVy2K/KOPTk/J2IRbGFuH4xC2vXuNJt2UGjyNIeKfXiOllOpdMBTOws5sWwyMxa7AOKGEm2vgtYPO8t0Vzvs7umQ2gN8xZWtmuF9fMsktPMFTFGCGd07PaLJNnSqaaBsEDcCUUqeKN7ttPrY76XigFNsxtF9K6iXr2d2+O1o6ZSVAWqJ5/dL5ke/lZ5n6Ph6ahu3slAy0ABXAQeBQQcCvv/eUOkEaQ8Q/vUZKKdU/wVB4DDbhthRbI60Qeq0PfFyugRf2+s7eXyUf6nJlvGA6Z443P7x8YeS5Ezy9aLKtDngROKyDxupk00TbIGgAppQ61bxAZjl25DABO9usX4VoXQNPbPddU1wnHzCI3xHTODfX/PCieZFX+vn0adjRyjTsrLpNwJ6CgP+EAimlTmcaQ8Q/vUZKKTUwwVA4BzgLuwqjHPs79IRUNErGkzt9n44OEo9NNU9du6Trx2lJJ9aAAZiKTbptBUIFAX+/B6yVGihNtA2CBmBKqeEQDIUFmAyswo7QtWKDmUh/Hr+j1Jn6WqHzmc4umQGQmWxeuHxh1wM56bT08xQE220qHVvHbVNBwF8ywJeh1GlNY4j4p9dIKaUGzqvfFsDGqS62++cJJRMiLvL4Vt+NZY1yM4gkJZiDSye5D6yY6u7uowTK8WRgS6OUAq8D+7TxlzoZNNE2CBqAKaWGk7ecdDa2GO0k7FLSCmxQ06v2MAn/3O57d0WTvBPE8TmmdtFE975zZrmhAZxCIjbhFwa2AXuBKg1YlOqbxhDxT6+RUkqdGG9QeCZwNpAH1GN/n7afyPFeOeAs31bqfNY1kgng95kjuRnm6VVT3ef7UQalOwHysWVRdmGXuTZj4+hmjWPVUNBE2yBoAKaUigfBUDgZmINdUjoBW4Oimn7McNtU5MwNFTv/GY7IJLCz286dHfnltGwzkEK2mcBEoA1bbHY/tnFCZUHAf6LT+5Ua1TSGiH96jZRSanC8ZglLsMs2s7DJrS7s79ZaBjDT7XCtjHvlgO+WhjbONUiS3Wrc9CQ2zhjnPnHWTDfkcwY0cy4FO1DteOfUhl0lUgVsLQj4ywZwLKWOoYm2QdAATCkVT4KhcCpHOz+N8zbXYkcRj/tLvKmdxCd2JLy/poVrQUQwHRMyzaOXLoj8OT2JzgGcQjK2hlsGdpZbDd5IYUHAXzvwV6TU6KUxRPzTa6SUUkMjGAr7gGwgBzs4OxUbq1ZhY9V+q2shZX2h77yyBrmsvUvmRbf7faZ0Qqb5x1kzI8+OS6N1gKfowybekrEJwSbgFWC3znBTJ0ITbYOgAZhSKh55tTEmAdOw0/azsFP1j9DLstItR5yZoWLnI21hWQiQ4JjKWTnmwQvnRl4+gToYfmxAlQU0AgeAfUBJQcDfr1pySo1mGkPEP71GSil1cgRD4UxgAbZL6RjsSogB/57dWSZTtpf6rqht5VJjJBVAMO1j03juormRh3MzTPMJnmIetvlXCNhQEPCf0JJXdfrSRNsgaACmlIp3wVA4HVtHLYBNvh2ml/oYroHn9/jOPVgt/97lyniAFL/ZuWyy+7PAFHf/CZ5GJpCLTfIVA2+grdXVaU5jiPin10gppU6uYCicjV1augBIxTb3GnA30IY2kl8+4LuorEHe1hmRqWBnuJ03O/KVeXmm9ARPLx0bO+8D1hUE/AMpq6JOc5poGwQNwJRSI0UwFM7AtltfzNEabsfV1E7iM7t9/1bRKO80SCLAmBTz/DmzIr8aYP22WInY4rMAO7Gt1XVJqTotaQzxViJyO/A5bK3JLcAnjTEbetk/C/hf4N+wM2gPA3caY/7p3e8D7gZu8Y5ZCvwS+JrpR2Cr10gppU6NYCg8AVgEzOVowm3Av3ddA+sPOUu3lzqf6nIl1xHTsnSS+/WzZrpbT/DUErCrQ2qwXUr3aP1h1R+aaBsEDcCUUiOJVx9jCbAGm/Qqoo8OpYdrZdzLB3zvb2iTiwAE05mXaR67dH7ksYzkAdVvi5WGHSGsBzYBuwoC/o4TPJZSI5LGEMcSkZuAXwEfBdYDdwLvAuYZYyp72D8ReBmoBO4BSrDL5euNMVu8ff4b+DRwK7ADWAU8CPyPMeZ7/TgnvUZKKXUKBUPhPOzstnnYGWUnlHArbZAxT+70/Xd7WBaAcaePMw9ctSjyr0Gc2gTsCo2D2JUZRboyQ/VGE22DoAGYUmokCobCk4FzgSnYpZwtfT0mVOzM2XzE+ZANWCDBMRXz8tyfnD/H3TiIU8nBzkIpwv4RfLAg4G8bxPGUGjE0hjiWiKwHNhpjPuF972B/P33fGBPsYf+PYme/zTfG9Di7QET+DlQYYz4Ys+0xoM0Yc0s/zkmvkVJKDYNgKJyDTbgtw3YCfcuAS19aOvD/dUvCJxva5UKAcWnm8asXdz00wEZfsRKw5VjArszYrMtJ1fFoom0QNABTSo1UXu22ldgitBHsbJBeZ7e5Btbu9Z1zoEo+GK3flp5kNp49M/KTWTmm4gRPJQE7SpiK7Ty1AzhQEPDXn+DxlBoRNIY4ypud1gq80xjzl5jtDwFZxpjre3jMP7Gd6lqB67G/P34LfMMYE/H2+W/gI8Dlxpi9IrIMeAr4tDHmNz0cMwlIitmUgW0ic9pfI6WUOtWCobAAC4ELgC5sw4QBcQ38bavvxrIG5xYAn5i6/Czz6IVzI08OIuEWXZnRAOwC9hYE/FUneCw1SmmibRA0SFZKjWReADMLu5R0IvYPyj5ntzW2kfT0bt+7K5vk7SA+wXTmZJi/TB9nNizOdw8kJXAiXUUdYDwwlqOByxsFAf9A27MrNSJoDHGUiORjk/1nG2Nejdn+TeACY8yaHh6zG5gO/Ab4ITDb+/o9Y8yXvX0c7LLS/8IOKPiwy0a/fpzzuBu4q4e7TvtrpJRSwyUYCs8FLsT+Dj9yIsdYu9e3Zl+lfLjLlVwAn2NqJ42xCbe0JE605lo2NnZtAfYAu4FyXVKq4CQm2kSkEPgF8EtjTNFgTjJeaZCslBoNvEYJ0dltXfRjdhvArjKZvL7Q99G2sCyNbhNMe2oiO8ekmO0zx5vXl0xyC0/glLKwbdULgZcLAv4Bj2AqFe9GegwxlHHeCSba9gLJwIyYGWyfBj5njJnoff9u4FvYJaY7gOXAd7Ez2h7q4Zg6o00ppeJQMBSeCVyEXQFReCLHaA+T8Pxe3yVH6uTGLldywNYedoRWETpFCIvQkeKn8JJ5kZ/nZZr+dj/NxMatHcABbB23koKAv8/BazV6ncxE253AbdgOd88DPwf+bIwZNQWvR3qQrJRSUTGz21Zjp8NXYJsV9Cq6nLSkXi5s7WSRayQ99v6MJPPKymmRXy+YYEoGeEo+bGHzVuA1YEdBwN9n8k+pkWKkxxBDGeed4NLRF4CwMebSmG1XAf8EkowxnSJSDASNMT+I2eeLwC3GmPn9OK8RfY2UUmo0CYbCU4CLgXHYOPWEfi+3dZKwdq/v0uJ6uTHilULpLsEx5WfPdL+2KN8dyEBSGpAL+IE6bMKtCJt0GzU5ENU/J33pqIiswAZi78H+4fRb4BfGmDdO6IBxRAMwpdRoEwyFU7Ez25YDKdgAoV81LCIusrPMmXa4VhbXtcqy5g5Wgzhg3LGpPHPWzMjvpmWbgRaNzQHGAFuB9QUBf/MAH69UXBotMcRQxXleM4QNxphPet872N8/9x+nGcI9wM3ATGOM6227A/i8MSbf+74G+KIx5kcxj/sC8AFjzNx+nNOouEZKKTVaeF1JlwAzsTPJGrE1OrsGeqyOLnyHayS3MyKJnV0khl0S2zolfV+lfKjLlTzBtC2Y6H77gjnuhgEe2sGuzhgLCLae6EFsg5+y7km3YCjsYGPujoKAf8CvQ8WnU1ajTUT8wMeBb2CzvNuA7wEPmhFaAE4DMKXUaBUMhScCq7B1jxo5gW5PO8qcqW8UOe9r7pA1YKfnj083j18wN/JoTnrfteBipGBntxUCLxUE/OUDPRel4s1oiyEGG+eJyE3AQ8B/ABuAO4EbsV1FK0TkV0CJMeYL3v5TsMtBHwK+D8zBLmX9njHmf719fglc6h1zBxAAfoJNBH6+H+c0qq6RUkqNFsFQeCy2TucC7CyyCHap/6ATVRWNkvHEDt/nbWkUY/LHmIevXRr5oyMndDgfNuGWBRhs0u0A9rMlHVvnLRtbCqECeKEg4NfPm1HgVMxo8wPvAD4AXIZdAvRzbGvc24HnjDE3n9DBh5kGYEqp0SwYCidgA5izsHWLDmODhAHZeNhZsKPUubUtLAsBHDEtE8eYRy6eF/nHADo+OdiAqglYB+zTYrNqJBstMcRQxnki8glsPbUJwGbgU8aY9d59a4FCY8xtMfufBfwfdgZuife8sV1HM4CveueXC5QCvwO+Yozp83fPaLlGSik1WgVDYT8wFfs5MAOoAaoHe9yOLnx/2ZLwodoWeRtARrJ5+dL5kR9O6H/dtp4kYBNuWdiZbgZb163d+zoRG2u/UBDwD3iAW8WXk1mjbQU26HoPtqj2r4CfGWN2x+yzGNhojEk5gXMfdhqAKaVOB8FQeDJwPjYAOAQD787kGli331m9r9J5f2dEpgH4HFM9Ldv85pJ5kecTfH03X/DkA4nYP+ZDOsVejVQjPYbQOE8ppVS8CIbCSdjB4ZXYRNYRbO3PQXlih+/Kwhr5DxCfI6Zhdo758UXzIutOcHZbXxzsktgaYG1BwD8qG0qeLk5moi0CPI0dXfyLMeYtf5iJSBq29sYHBnTwOKEBmFLqdBEMhbOA84B52ODlhDopdUVwntvju/BwrdzS5RWgTXBM2YRM88QZ091n+9nhKQs7O2UL8EpBwD/oQEqpU22kxxAa5ymllIo3wVA4G5tsW4BdRnqEE1iNEeuNImdOqNi5ozMiUwHSk8z6C+ZEfjQ129QO+oR7No3/3959x7l1V/n/fx1pNOPeux3HcY1TrTi9kxDSIHSSpSwBNiz8KMtSlSzsLrCQgS/LApuQ3WXpG3oJBEiHhDSS2FHsOHHiEvfe6xSNdH5/fO4EZTIzlmakkTTzfj4e92HP1b1XnzuyPcfnfj7nhPrIf04lE88d6WCpTuVMtB3t7ut6Ob6qpgBMRAaS6GnhGYQA5iChlkSPgpdDLST++Hz8ys377M05t+EAhmdGDubPcyfm7kgelVtxhKeFgwlLBVYCf1I9C6k1tR5DKM4TEZFqFDUXmEmIWScTGus09eaazRnq7ngm/uat++3NYHVmfvjoMf7dV83P3h2P9S6R14VJhLptD6SSiaVluL6UWTkTbacBsfbaGnn7zwCy7r6oB+OtKgrARGSgiYKX+YRGCeMJdZF63Al0fxMNj7wQP3/zPruypc1mtu8fUu9PXTg3+/UjdCmtIwRSa4D7UsnEvp6OQ6Sv1XoMoThPRESqWWM6MwI4jdCl9DCwpbfXXLY5dvTja2MfbmmzOQCDEr78tKNz3zxhSq4cD57GEZJtd6eSidVluL6UUTkTbY8DX3b3X3TY/wZC6/UzejDeqqIATEQGqsZ0ZiSh8OwJhI5KG+hFp6ecQ3pDbO7z22JX7GvifLC6mPmhWeP9llcem/1zN6fGgVmEp5X3ppKJPT0dg0hfqvUYQnGeiIhUu+gB8TzgTEJ3z17XbmvLErvj2fhrNu2xtzk2CDw3fpjf9qr52R+PGExLCYadbxqhWcIdqWRia4mvLWVUzkTbQeAkd3+hw/5jgKXuPrwH460qCsBEZCBrTGcMOIowu20GoSD6YcL0/MNQcEfRl3h+m015eHX8Y+1PC0cM8j+/6ri2W8YP67IuXAyYTQie7k0lE93NghOpCrUeQyjOExGRWtGYzowlLCWdAQwh/Nu+k148JF67y8Y9tCr+dwda7GyAupjvmDsx998XzMk9XoIh55tJKNdyZyqZ2Fvia0uZlDPRtgt4tbs/2mH/2cDv3X10D8ZbVRSAiYhAYzpTT5hVNg6YCIwgBDGDCP9GFv0ErqWN+B+Wxd+ydb9dDRaLx3znyVNzXz3jmNyyLk5pr8exBfgzsCWVTJSjZoZISdR6DKE4T0REakn0gHg84SHxsdHvjVBvOJZ3aAzYT4FLTf+8Mnba89tif9+WswkAwxr8L+fNzv7PjLG+s0RDb3+gvBK4J5VM9KrenPSNcibafkwoPvhad98X7RsF3AZsd/e39HDMVUMBmIjIyzWmM4MIybaJhMYJYwlLS4sODBavj81Nr499LJOzyeC56WP8fy4/PvuHLholGCHZ1gqsBp4DNqaSiWzP7kSkfGo9hlCcJyIitaoxnUkAUwhJNyesxGiJtkHAWYTGW+sooPHXgWbq714e/5vtB+x1YHHDm6aO9h9dOj97e30duRIMuY7wUPsp4P5UMtHjmXjSN8qZaJtKmFUwFkhHuxcQpj1e4u4bejLgaqIATESke43pzGjC0tLjCTUmNlNkp9L9TTTc/nTdB/c32wUAo4f43a89qe2/Btd3Od1/KCHJB6F223JgTSqZKHXdDJEeq/UYQnGeiIj0V43pzDTgAmAC8AIUlix7ZnNs+uPrYh9ozth8gPo6f+Hkqbn/OvXo3HMlGNZgYDrhZ+5fUslEVyVVpAqULdEWXXwo8DbgZMJMhqXAj90907PhVhcFYCIiRxYVop0NnE6YAbMNKKppQc7hd0/HX79pr10LZoMSvvxV87M3Th3le7s5rZ7QHr2BMKNuCfCCngJKNegPMYTiPBER6a8a05lxwIWEum5rKLD2cDaH3bM8fsm63XZtzm0YwPAGf+TUo7M/PHaSb+rlsNqTbWuAh1LJxLZeXk/KpKyJtv5OAZiISOEa05nhwEnAfGAUoQjtboqY4fbQqtgpz2yJfSLnNjQe812nH537twVH5Y7U8jxOWB7QQHgquQRYn0omSjGVX6RHFENUP31GIiIDWxS7nk+IXTcBBws9d/M+G/nAyvjf7j3MxWAx8NyYIdx53uzsT6Z0/6D4SOKE5N9+4BHgedUlrj5lT7SZ2XGErGt9/n53/22PLlhFFICJiBQvWk46l7CcdAxhdttOCpyW//w2m/LgqvinM1mbZnjrvIn+1VfMyz5SwKn1hISbASuAJ1PJxPYe3YRIL/WXGEJxnoiI9GeN6UwDoWPpyUCGIsugPLMlNv3J9bF3Hmyx0wAMb6mLs93CNXIYHjcOzZ+cu/WMGblnihjaZEI9uUXAE6lkol/MJu8vylmjbSbwa+BEwh+i9tLVDuDu8Z4MuJooABMR6bnGdGYEMIeQcBsLZAkJtwNHOnfXIYb8/um6TxxqtYUAU0bmfvCak7K/6KJJQkeDgWmEJ4GPA8+oYYL0tVqPIRTniYjIQBF1LJ1NaJIwntAkoajav4+tiZ3wzJbYu1rabE5nrxvefNqM3KcXTs+tKOKywwkJt6eAB1PJREHLW6X8yplou53wn6a/I6whPp3wH6l/Bz7u7g8Web0PAJ8g1NtZAnzI3R/v5vhRwBeANxBmTKwDPuLuf4hejwP/Crw9uuZm4HvAv3mBN6sATESk96IupUcRuilNJ3Qs3U+o5dblLLfWNmK3Lan7u12H7NUAowb7fa9f0HbzoESXTRI6mgCMBJ4BHk8lE0XVjRPpjVqPIUod51WjWv+MRESktKJVGWcSlpK2r8goWM5h6cbYrJY2BjuYO+ZObOWO2BsOt9qCmPnBc2flUsdPya0v4rL5TRKUbKsS5Uy07QQucvelZrYPON3dnzezi4B/d/dkEde6GvgB8D7gMeAjwJuBee7+smU/ZlYPPAxsB75IWE99NLDX3ZdEx9wAfBR4J+E/WacC3wX+yd2/UeC4FICJiJRQYzozhpB0O4HwhO6I9TB+vyx+xfrd9l6w2OCEL7toXvYr08f47gLfsoHw82EH4efLStVuk75Q6zFEKeO8alXrn5GIiJReYzpTR4hTzyCUTVhPgeVPurKviUG/Std9vrnN5sVjvvviedlPzRrvxTQ6ULKtypQz0bYHOMXd15jZauDv3P1PZjYLeNrdhxRxrceAJ9z9g9HXMUIHuf9098ZOjn8fYfbbsV11vjKz3wHb3P09eft+CTS5+9sLHJcCMBGRMmhMZ4YBCwnNE3LARroJYh5aFTtl2ZbYJ91tiOGt44b578+bnfvFxBF+xGWokSmEIGUToX7bRmC3istKudR6DFHKOK9a1fpnJCIi5dOYzkwDziWUI1lLkUtJO9p+wIbdvjR+Y2vWjq6L+dbLj89+atpoL2a1RXuy7UlCsk012yqo0Bgi1oNrLyMUDIQwS+CTZnYO8M+Erm+FDrCe8J+te9v3uXsu+vqsLk67CngUuNnMtpnZMjO7IVou2u4R4GIzmxu9z8mEvyh3dDOWBjMb0b4R1kSLiEiJpZKJg8Cfgd8DuwjNE7r8N/fc2bknz5+d/cSghC93rH7Hwdjrf70k/q3bnopfvfcwgwt4y82E5NpY4BLCrOkrG9OZYxvTmUSvb0ik/ylJnCciIlKLUsnERkKc+jRhdcSY3lxvwnA/eOlx2X+ui/nWtpxNuuvZ+Oe2H7BhRVyiiTC77hTgvKiJg1S5nsxouxQY6u6/MrPZwO8I/1HaBVzt7n8s8DpTCDMMznb3R/P2fxm4wN3P6OSc5wgtb28FvkkoXPhN4Bvu/tnomBhhWeknCTVG4oRlozd2M5Z/Bf6lk5f0pFNEpEwa05mhhKAhSQgitnZ1bM7h4dWxhSu2x/62tc2OAYiZ75s0wn979qzsH8YP41CBbzsMGAckCAmFh1PJRKHnihxRrc+WKlWcV81q/TMSEZHyi5aSnkxYSmqEZFePrdphE//4fPxL2ZyNGVTnz78h2faZkYNpLuIS7TPbnifMbNvbm/FIz5Rt6WgXbzYG2FNos4HonJ4k2lYQWt0e4+7ZaN9HgU+4++To62uA/0dYYvoMsAD4GvBRd/9+F2NpINTzaTecMANCAZiISBlF3Z6OBc4BhhAa3HS5lDSbw+5fET93zU57eyZn4d99vGnsMO48fUb2t0eP8V0FvnUD4cHNGuDPqWRiR2/uQ6Rdf0zi9CTOq2b98TMSEZHyaExnjgHOI6yOeIFe1G17ZnNs+kOrY405t2FD6v2pt5zS9rnB9QU3+4LwoPgYwsPph1LJxLqejkV6piyJNjNLEGYdLHD3Zb0cYD1wGHiTu9+Wt//7wCh3f20n5zwAZNz9lXn7Lgf+ADS4e6uZbQAa3f3mvGM+Dbzd3Y8tcGwKwERE+lBjOjOVEMRMJSS/uq0/0dJG/M8r4+et321vaM3ajLDX20YN5v4L52a/N3lkQf92xwnByi7Ck8E1vbgFEaC2Y4hSxnnVrJY/IxER6XuN6cwE4BWEum0vQFHJsZdYvD4294m1sX9zbNDwBn/0LQvbvlRfV1Tyzggz29oIZbWWpZKJbE/HI8UpS422qAHBesJ/TnrF3VuBxcDF7fuiZZ8XE/7AdOZhYHZ0XLu5wJboehBmRHT8g5qlZ/XoRESkD6SSiU2EWporgJmEJZ5daqgje8n87P3vOrvtwydMyX52cMKXgdXtbbJX3v50/Bt/WRM7oYC3zQKrove6rDGdObkxnen1zzeRWlXKOE9ERKS/SCUT24G7CHHjbF66Gq4oC6fnVpw8Lfdv4G0HWuysX6brPpTNYUVcwgkrQJqBi4ALGtOZ+Y3pzIzGdGZiYzozUnWIK68nNdreA7wBeIe77+7Vm5tdDXwf+HvgceAjwFsIXUW3mdkPgE3ufn10/FGE5aDfB/4TmAN8h1Cj7QvRMd8DXhld8xlC7Z//Ab7j7p8qcFx60ikiUgFRgdcz+Gvdti2FnrtoXWzeUxtj/5DJ2jRwnzjCf/rqE7I/KfAp4ThgFOHnxhOpZKKYblAiL6r1GKKUcV61qvXPSEREKqMxnRlCWIFxIqHUVI/r/N6/In7G8q12PVhs7FD/7ZtOafvfWDHptmAIYTWIERJwGUKX1MOEmHZl1IhMSqRsNdrMLE3I4iYImdSX/OFy91OKvN4HCfXUJgFPAR9298ei1+4H1rr7tXnHnwX8B6H22ibg28CX8mq2DQc+D7wemEDoOPdj4HN5s96ONCYFYCIiFdKYzsSAecCZhOTXOo6wlLTd/iYafr+s7r17m+wSgMEJf+YV87JfKbB222DgKGAn4eHP86lkosd1OGRgqvUYotRxXjWq9c9IREQqpzGdqQfOAhYCewhxY4/cvTz+itU7Yv8IMGqI3/P6k9tuGZTo+bJUws/uesJqjVHR2JYSEm4HenFdiZQz0dZZd84XtXf/rGUKwEREKq8xnRlHCGTmEYq+7iv03Hufi5+/aod9wN0Gx8wPTRzhvz5vdvZ3Y4dyuIDTJwNDCU8CH1dXJylGrccQivNERES6F5UaOYGwCmMQR2jm1Z07nolftnaXvQ8sNjjhyy47PnvjpBFeiqSYEVZsjCHUI34aWJpKJgqafCSd69Ouo/2NAjARkeoQPTVMAqcSAoaNhNpqR7Ryu016cFX8Ey1tNgcgZn5ownD/zXmzs7ePG3bEqf7tLdT3ACsJAdTmVDJR0Mw6GbgUQ1Q/fUYiIlIKjenMNOAcwoqI9YSyJ0V7aFVs4bItsU+425C6mG89e1bu346fnFtfomEaoWPqWOB54JFUMlHISg/phBJtvaAATESkukSt1U8FjiZMgy8oQGjLEntgZfycNbvsmkzWjoKQcJs4wn/96hOyv6iLH/Hp45hoy0Xvu5JQkuAwIZhq0fJSyacYovrpMxIRkVJpTGeGE8qdnAgcJJRcaAaKmjn27BY76uHV8c+05WySmTcdPzn35fNm5xaXcKgJYAYhhn6UsJxUyaAilXPpaI5QaK9T7l7znaoUgImIVJ/GdGYQcDxhhttIYAMFPjmMEm5nr91l17RmbTrA8AZ/9HUL2v59WENBgVAd4UngSEI79da87SCwlzD77QCwHziQSiaai7g96SdqPYZQnCciIlKcvKWkxxHKjwwmJLYM2E2Bddy27rfhdz4Tv74pYyeA506ckvv8uaVNtkFonlAPLAYWpZKJlhJfv18rZ6LttR12JQj/6Xkn8C/u/u0ix1p1FICJiFSvqHbbKcB8QkJgByHZdUTZHHbvc/GLXthpHwCrG5Tw5Vccn/23icXVwogTApR6/lp0toGQjIPQ7amJ0LDnaWCLnhgOHLUeQyjOExER6ZnGdMYInUCHRdtYQh23PRRYa7g5Q92v0nUf3tdsF8bMD104N/uxeRN9c4mHOhyYQlip8Wgqmdhe4uv3W32+dNTM3gpc7e4dA7SaowBMRKS6RZ1JjyE0SphOCGb2EZ4YHrFb02NrY8c/tSH26Zzb0ETcN71ibvZfZ433bSUaXgPhSeY4woy35cDTqWRiR4muL1Wsv8YQivNERESKEyXeTgfOBdZS4HLS5gx1P15U94XmjM1PxH3DGxe0fXz00J7Vf+tGHWEp6X7gL8BylUM5skok2mYCS919WEkuWEEKwEREakMUwIwjFKE9FphIqIux8UjnPrMlNv2R1bF/bcvZuLj53tNn5D674Kjc6hIPcRjhieFBQhfT54BdmuHWf/XXGEJxnoiISPEa05kE8EpC+ZOVFNiddNNeG/X7ZfH/yOZs7LAGf/ytp7V9IR7rurRDL0wkzHBbCjyWSiYKWiUyUPVpos3MBgM3Ape7+7xeX7DCFICJiNSeKJCZAZxFSL6t4Qiz2zbssTF3Pxv/19aszTC8deY4v+VVx2XvK8PwRgETCAVy1wOrgQ2pZOJwGd5LKqg/xhCK80RERHquMZ0ZAVwBjCfMbCtIekNszmNrYo2OJSYOz/3kDcnsj8o0xMGEFSIbgSeA9alkIlOm96pp5azRtoeXFsk1Qgb0MPB2d/9t8cOtLgrARERqV2M6M4YwRX8eIWDo9sncnkMMvv3puk8darVTAEYN9vuuPKHtv0YMphzFYYcRkoD1hOK4qwlT9bWstJ+o9RhCcZ6IiEjpNaYz0wjJtjZCfeGC3L08/orVO2L/CHDspNyNr5ibfbRMQ4wRVojUAdsJqzDWo5UYL1HORNu1vDQAyxH+oDzm7nuKH2r1UQAmIlLbGtOZBkJNjFMIs8i6rb+WzWG3Px1/85Z99lawWH3c1581M/ul4yb7hjINMUaY5TaO0Gb9z6lk4oUyvZf0oVqPIRTniYiIlEdjOnMCYRnpNgps5AXws8V1f7frkF1leMsJU3I3njs792TZBhkSbeOAkYQxbgSWppKJ9WV8z5rR5zXa+hMFYCIitS+q3zYfOJswI2cDdD9L7S9rYics3Rj7RNZttOEt08f4dy6al71nUOLIDRZ6YRph1tAjhEBGhWhrmGKI6qfPSEREKiFq5nUucBqhO/2hQs5rbSP2k0V1nznUagvBc8eM9ZsvOz57TznHGhlKqOHWBDyQSiae74P3rGrlnNH2LuCgu/+8w/43A0Pc/fs9GG9VUQAmItJ/NKYzEwgBzTzCk7mt0HUx2c17bdQ9z8U/drjVTgaIx3znlJF+2/mzs3eVaTkphPbvo4HFhEK05XofKbNajyEU54mIiJRPYzpTR3gIfBqwmQJntjVnqPvVU3Uf3NdkFwFMHJ776esWZG+NWfnGmmcykAAeAJ4ZyEtJy5loWwH8vbv/qcP+C4D/UZFcERGpNo3pTJyQaDuVUIi226eIbVlidz4bv3LzPntjNmdjAGLmByYM99unjPKnLS9RZ4bPGZ9bO2pIr9uuDyXMblsOPAXsSCUTBbWBl+pR6zGE4jwREZHyipJtZ0TbVuBAIeflHG5bEn/rtv2xawBGDvY/vWFB23+WeeVFu/GEWsMPAksG6gqMcibamoFj3X1th/0zgOXuPrjo0VYZBWAiIv1TYzozklC37SQKqN3W1Erd/SvjF2/cY29oy9nkro4zvGnUEB6YPyl358nTcr2ptVYPHA1kgP2EIrRbCDWydg/kJ4i1otZjCMV5IiIi5Rc9BD4dOJMQ5+0r9Ny7no2/8oWd9kGwWF3Md8Zj7IkZLe3b0AZfmzwqd++Msb6zxMMeQ6gx/CiwaCAm28qZaFsPfLBj1ykzey1ws7tP68F4q4oCMBGR/iuq3XYccD6QJSSyutWWJfbAyvjZG/bYlW05RuW/lnMGt896A2io85VTRvmd587KPjCsgZ7OSEsQitCOIBSlPUhot55Wsq261XoMoThPRESkb0Q1204DziJ0oy+46dDDq2PJpzfFUo518QDMc8MaWHz0mNydZ8/MLa6LU6qk2EjC7LbFwKOpZCJTouvWhHIm2r4EXA28C/hztPsC4DvAL9z94z0acRVRACYi0v81pjNzgQuBOKGjUo/kHB5fGzth9Y7YZfubORusDiAe813Tx/iPLpqbva++rtfBTfsTxAdRsq2q1XoMoThPRESk70TJtiRwDnCYI6y2yLd1vw1fu8tmZLI0ZLLW0JaloTXLkJ0H7cymjJ3UflxdzHdOH+O3Xnpc9r4SDbu93MlS4KFUMnG4RNeteuVMtNUDPwTeDC+uBY4BPwDe5+41X09GAZiIyMDQmM7MBF4BDAHW9vZ6m/fZyEXrYhdv22+vbsvZOIBE3DfOHu8/OH9O9i+9LFg7OtqUbKtitR5DKM4TERHpW3mrLc4jdKLv8QPgds9ttalPb45fuvsQF+fchgNMG5379mtOzP6mt9eONADHAKuA+1PJRMGz8WpZ2RJteW8wB1hAaPX6tLuv69GFqpACMBGRgaMxnZlOSLaNAtbQTUfSQh1qIfHHFfErNu+1t7QHN4Pq/PmFR+e+edLU3JpeXFrJtirXX2IIxXkiIiJ9K3oAfCGh6cBaShST3vls/K3bD8TeCDBtVO47rzkpe1tvrxupA2YSmozdn0omtpboulWr7Im2/kwBmIjIwNKYzkwhJNsmEpJtJenetOsQQx5YEX/99gP2OscawNumj/bvXH5C9ne9mN2Wn2x7aiAWoq1miiFezsw+AHwCmAQsAT7k7o93c/wo4AvAGwjLptcBH3H3P+QdMxX4EnA5YUbqKuBd7r6ogPHoMxIRkarUmM5MJSTbJgAvQO9rq+Ucbnsq/tZtB0K30qmjct+96qTsr3t73YgRZrbtB+5KJRObS3TdqlRoDBHrwYV/aWaf6mT/J83s58VeT0REpNKioOBOQpJtFjCoFNcdO5TDb0hmb33NSdnrhjX4Y2B16/fE3nvr43X/tG1/mOnWA3ui7QLgksZ0ZlIpxioCpY/zzOxq4KvAZwkdf5cAd5nZhC6OrwfuAWYAbwLmAdcRnpa3HzMaeJjQnfdywnKbj1FEEWkREZFqlEomNhFi0k2ETvS9FjN4QzL7o0kjcj8G2LQ39q7fLIm/sRTXJsy6e4GwMmRBVHNuwOtJjbYdwEXu/nSH/ScC97r7xBKOryL0pFNEZGBqTGeGEIrRngRsJTydK4mcwx3L4q9ev8feDVYXj/nOBdNyXzl9Ru7ZHl5yMDCVsLTvWeDpgVIfo5rVegxR6jjPzB4DnnD3D0Zfx4ANwH+6e2Mnx7+PMPvtWHfvtJOZmTUC57j7ecWMJe/8mv6MRESk/2tMZ6YBrwYOAXtLdd1fPxW/Zuv+2FsBJg7P/fS1J2d/FI/1fokqIS6dCNyWSiY2lOB6ValsM9oI64U7K4SbAUb04HoiIiJVIeqa9CfCbJlxhIChJGIGV56Y/d3ZM3MfT8R9UzZn4xavj33x7mfjF/Xwkk2E5XIHgNOBNzamM6c1pjNjSjVmGZBKFudFs9MWAve273P3XPT1WV2cdhXwKHCzmW0zs2VmdoOZxTscs8jMfm5m280sbWbXdTOOBjMb0b4BPZ1NKiIi0idSycRG4ElCLFpXquu+fkH2J5NH5v4PYNuB2NU/eqLuhj2HGFyCSzcBceD4qLnDgNaTRNvThLbvHV1DeKIuIiJSs1LJRBvwGH9NDswl1IAqiZOn5V548ylt/zhysP8JLLZ6p334/hXxM3txyf3A80CWsJz0TY3pzKsa05nZjelMQ0kGLQNJKeO8cYSge1uH/dsI9do6M5OwZDQOXAF8nrAs9NMdjnk/sBK4FLgF+IaZvbOLa15PePrcvvW6m5uIiEgfWAKsBqaX8qKvOzn7s5njct8AbzvYYmf88qm6f39uq00twaW3ALOBKSW4Vk3rydLR1wC/An4E/DHafTHwVuBN7n5bKQdYCVpSICIiAI3pzHhC58X5hBoUGylRo4Scw08X1X14b5O9Erzt5Gm5z549M7ekBJceDownPEzbBTwHLItm60mZ1XoMUco4z8ymEGrMnO3uj+bt/zJwgbuf0ck5Kwg1Eo9x92y076PAJ9x9cvR1K7DI3c/OO+8bwGnu/rKZcmbWAOQnnYcT/i7X5GckIiIDR9Sw6zVAM7C7lNd+cn1szqL1sRuyORtr5oePm5T79/Pn5J7o5WVnA88A96SSiX7XebNsS0fd/XbgdYRv4DeBfyfUiLmIsIRFRESkX0glEzuA+4DbCU/pZgLTCE8WZwNzCDPe5lLksrqYwRuTbTcNb/BHwOqWboz90+L1sXklGPYBQlHaNUACOB94TWM6U3BB3cZ0JhHNiJurorYDS4njvJ2EmZYdl2BPJNRA7MwWYEV7ki2yHJgULUVtP6bj7LrldPHE391b3H1/+0b4OyIiIlL1ooZdiwizxBOlvPYp03Mrrzwh+4+DE/6suw15Zkv8Mz9+ou4flm6KHdOLy24hxMeTSzTMmlT0jLaXXSBk9P4GeA+w0N3jRzil6tX602gRESm9xnSmHjiWkIA4RFiy2UR4wjie0EAhRpGz3ppaqfvZk3X/fLjVFsTMD54zK3f9CVNy60o49BghAZEDFgNPpZKJ5s4OjJpBzARO4K8B0lLgsVQycbCEY+q3+lsM0ds4L2qG8Li7fyj6OgasB27qohnCFwmz52ZG9dwws38APuXuU6KvfwQcld8Mwcz+Azgjf5bbEe6p33xGIiLSv0Ux6OWEGK3kk5uaM9T9Zmnd3+0+ZFe07xuc8GeOHuO3nzMr+5f6OnJFXnI2IX78Y3+b1VZoDNHjRJuZnU8Iut4IbCYsM/ilu/d2qmHFKQATEZFiREVfjwJOA44BdlDE9P59TQz6Vbru881tNi9uvnfMUP/j+OH+7LETffnEEV6q2TcjCXWx1gBpQsH7WN42GjiekDQ8RKijlSAk6TYAD0eFeaUb/SWGKFWcZ2ZXA98H/h54HPgI8BZCV9FtZvYDYJO7Xx8dfxRhycn3gf8kPBX/DvANd/9CdMxpwCPAvwA/IzQD+RbwXne/tYAx9YvPSEREBo7GdGYiYQlpHaEsQ8ktXh+b99zW2Gv2N3MOhCZEdTHfMX9S7uZzZ+eeLOJSw4BRwK9TycSLM9gb05l4tH93rSbgypJoM7NJwLWEwGsEIbh5H3Cyu/ebRggKwEREpCei5gMnEDotDiUEQk2FnLvjIEN/u6Tuxtaszcjfn4j7hpGDWXTZcW23Dh/UaTfIYsSBowmJNeelJSSM0D5+J7zkyWX7jLgMoUnE01HDCOlELccQ5YrzzOyDwCcIid6ngA+7+2PRa/cDa9392rzjzwL+g1AfcRPwbeBL+ctJzezVwI2ERNwa4Kvu/q0Cx1Ozn5GIiAxcjenMLEIphwThIWhZrN9tYxati12x46BdlnMbAZ6dPd6/fsn87P1FXGYO4cHuQ4SSEVMIM/KGAPelkolSrt7oMyVPtJnZ7YQ6L78HbgXudPesmWVQok1ERORF0VPHBYTabe3LSY+YJNvXxKDH1sTP3n3Y5h9s4bhM1o5qf21ovT9x9altX2yoI9vdNQrUnmgr5mni2Gh7NJVMPFKCMfRLtRpDKM4TERGpfo3pzDGEJkWDgLImqw61kPjNkroP7Wu2CwGOGp3731efmP1tgacPJ8xeOwCMIczE209YYfF4Kpn4c8kH3AfKkWhrA74B3OLuK/P2KwATERHpIFpOOp1Qu20WYUbYZoqo37Z1vw1ftil2yqod9iHH6kcM8geuObXtq/FYUQmyUhoFDAZ+lUomdlZoDFWtVmMIxXkiIiK1oTGdmU5Itg0D1pbzvbI57JfpuvfsOmRXAUwYnvv56xdkfxizgk6fTGiKtIcQB0N4aGvAT1LJREGrPqpJObqOnkvISi42s8fM7INmNq6X4xQREemXUsmER9Pi/0CYJbSdkHCrK/Qak0b4gVfOzz5w/JTcjeDZ/c12wS/Sde/NVa6qxV5CLHB8xUYg5aI4T0REpAakkon1wD2EGWKzCPV1JxISW1OjbVgp3isew990Stv/ThmZ+wHA9gOxN/90cd2HWtsKyiVtIcS/mbx9ewgPbqeWYnzVquBEm7v/xd2vI3x4/w1cQ3gyHwMuMbPh5RmiiIhI7UolE9lUMrGSkGx7nhAQFdW58bzZucVzJvh/gPvuQ3blbUviby3HWAu0FZgfLY+VfkJxnoiISO2IGlTdQ6hl2kJIuu0i/OzeTqi1OheYQViuWdgctE7EDF57cvYXx4zN/Sd4bu9hu+S2JXXv7uHlctE2o6fjqQU97joKYGbzCAVz30HISt7j7leVZmiVoyUFIiJSDo3pzAjgEkJwsZLiaqTxh2Xxy9ftjr0fiq6TUWpzgCX0w7btvdWfYgjFeSIiItWtMZ2xjrFYVL5kNKEJ0dGE2WOjgRcooGZwd+57Ln7uiu2xTwLMmZD7yiuPzfak1toooAH4WSqZqKmfw2XpOtrNm8UJrWbfrQBMRESka43pzBjgUmACIeApym1L4m/Zsi/2doCxQ/32q05q++6gROF130pkGKGw7W2pZKIsLeZrVX+MIRTniYiI1K7oQe+5wHGEB7257s/o3i/T8bdvPxB7i+Et587OffyEKblimzIY4aHt71PJxHO9GUtfK0eNti65e9bdb+sPwZeIiEg5pZKJ3cAfCTUqZhR7/lUnZX82aUTuxwC7DtlrfvxE3Y1rd/V5La2DQD1wQvTUVPoxxXkiIiK1K5o19hCwkRIs2XztSdkfDan3tGMNf3khdsPOgwwt8hJOmFk3u7/GkSVJtImIiEjhUsnENuBPQBMwpZhzYwavX5D98fGTs5+PmR9qbrN5dz0b//rDq2MLyjHWbmwm1P44qo/fV0RERESKECXb/gwcItRj7bG6OLnLjst+pS7m2zM5m3zHM3X/mM0VXQNuF39d0trvKNEmIiJSAalkYgMh4KknFKwtyvlzck9cfGz2Iw11vjrnNnzppthnf5WOv+1QC4mSD7ZzhwlxxImN6YziCREREZEqlkomNhNmtg0iNEjosYkj/MDpM3JfNDxzsMVOv/3p+JuLvMQBQrfzftl9VIGxiIhI5awEFhOeLBadIJs93rddvbDtk6OH+J1gtu1A7OofP1F304OrYgtLPtLObSZ0UZ3RR+8nIiIiIj33PPAYMJHQkKDHTp6We+GYcf5NgC377G1PrIvNL/IShwnLR/tdXqrf3ZCIiEitiLpELSIEPcfQg9brQxvIXHNq2zfnTsh9OR7z3ZmcTV62Of4vP3ys7tOrdtjEUo+5g2YgC5zVmM70y6n/IiIiIv1FFHs+CSwjxJ71vbnepcdl7xs12O8Ds2c2x95W5Ok7CSVU+rrWcNkp0SYiIlJBqWSiFXgY2A5M6+l1Lj42+9Cbk23vHz8s92vw7MEWO/3e5fFv/mZJ/M09qJtRjA2E9vHnNaYzg8r4PiIiIiLSS6lkIkNYQvoMMBMY0pvrnTkzeyt4W1PGTnp8bey4Ik5tIixj7XH8W62UaBMREamwqBPpw4QZbaN6ep3RQ2l60ynZ7144J/uhIfW+xLHE5n2xd9z6eN1netARqlAOvEBojHBWYzoTL9P7iIiIiEgJpJKJQ8B9wBOERFfR9YLbHTPWd44ewr0Ay7fGriny9APA3MZ0pq6n71+NlGgTERGpAqlk4gVCsDOBUKC2xwmr+ZN94zvOaPvMrHG5rxveeqjVTv31U3VffWZzbHqpxttBG2Fm2wLg5DK9h4iIiIiUSCqZaAEeJMxuG0cvlnCeMSP7c/Ds4VZbsGhd7NgiTt1FWD56Qk/fuxop0SYiIlI90sDThGn0RwNzCM0GJgNFPemLGbzquOx9Z83MfbIu5tvbcjb5wVWxf//j8/FzuztvzU4bf8cz8Ut/urju7x9ZHVtQxFseBnYTZrXNLmasIiIiItL3UslEFngc+BNhCelR9OBh7zHjfMfoIdwH8MyW2N8UcWorIX48uzGdmVns+1Yrc/dKj6HqmNkIYB8w0t33V3o8IiIycDSmMwYMJcxqGwWMIbQ+nwqsAnLFXnPbfht+57PxTxxutQUAgxO+rL6OHfVx3zMowe76Og7sPWyz9zdzSiZrL2mzPnao//bKE9q+P7SBTIFvNxVoAX6fSia2FzvWWqcYovrpMxIREXm56EHpqYQZZs3AVig4/mP1Dpt49/L4f4HFT5+R/cTC6bnni3j7o4BDwB9SycSOYsbdlwqNIZRo64QCMBERqSaN6cxI4HJC04HVPblGW5bYb5bG3779QOxN3R/puUEJnq+Ps3N/s50HUB/3tWfOzH3l+Mm59QW+3WxgLXBHKplo7sl4a5ViiOqnz0hERKRzjelMA6Eb6YmE2m0ZYAth5tkR/fiJug/tbbJLhtb74r89s+2zRb79bGA9cGdUQ67qKNHWCwrARESk2jSmMxOBy4BhhCCkR57eFJuxdb8d05RhdEvGRrdmGZPJMmpQgk0TR3j6xCnZpeOGcQjgwVWxhc9uiX0k5zbS8Najxvh3Lj8++4fYkXuY1hGCpb8AD0Wt5AcExRDVT5+RiIhI9xrTmQQwg1A7bRawkTDjrFsrt9uke5+L/xdY7IwZ2Y+dMj23soi3jRPKpiwB/phKJtqKH3l5KdHWCwrARESkGjWmMzOAS4EssK0v3nPTXht133PxfzjUagsBJo/M/d/rTs7+rIBThxOK6t6ZSiZWlHWQVUQxRPXTZyQiIlKYKOF2FmFJ6Q5g75HO+fETdf+wt8kuHlrvi/72zLbPFfmWg4DphAYNj1fbw9pCYwg1QxAREakRqWRiLSHwGEyo31Z2U0f53red3va5KSNz3wfYus/esmaXFdKV6gChVttZjenM2LIOUkRERERKLpVMZICHCfHnGAroTLpwevZn4LlDrXbq9x6tu/GuZ+Ov3HuYwQW+ZTOwHTiFUDKlJinRJiIiUlueJSzJHE9YRlp28Rj+mpOyvxyc8GWO1T+yOv7OAk/dDIwldJKqL+MQRURERKQMos6kTwB/JHQmndzd8XMn+pZJI/wn4N6UseNf2Bn78E8W1f3gR0/UfSS9ITangLfcS3ioPL+3Y6+UiifazOwDZrbWzJrN7DEzO/0Ix48ys5vNbIuZtZjZCjO7osMxU83s/8xsl5k1mdnTZnZqee9ERESk/KIp9E8SAp4p9FGyLWaQPCr3v+C+v9kuWLQuNq/AU9cCc4Fk+UYnIiIiIuWSSiY8lUw8DdwNOCEG7dLrF2R/csUJ2XdPGZn7QSLumxxr2NdkF/1lTexLz2yJTS/gLbcCc6MaxTWnook2M7sa+CrwWcLUwCXAXWY2oYvj64F7CEX53gTMA64DNuUdM5owtTFD6NB2HPAxYE+57kNERKQvRU8WHyHMbJtCqIdWdidPy70wagj3AizdFLsum+PIbRHCz+PtwKmN6czcsg5QRERERMomlUysBP5MqKU2qLtjjx7ju157cvYX7z677f2nHZ39xKCELwerW7ox9roC3uoAMBQ4tteDroBKz2j7KPAtd/+uuz8LvA84DLy7i+PfTVgX/Dp3f9jd17r7A+6+JO+YTwEb3P1d7v64u69x97vdfXVZ70RERKQPRcm2vwCPEmpY9Emy7fzZ2R+aeVNLm8394/PxCws8bS/QBlwQNXQQERERkdq0ElgBTCvk4JjBqUfnnj9pau7bAHubuHDjHhtdwKnbgGMb05nxPR9qZVQs0RbNTlsI4ck4gLvnoq/P6uK0qwj/objZzLaZ2TIzu8HM4h2OWWRmPzez7WaWNrPrjjCWBjMb0b7RR/9ZERER6Y0o2fYYIeE2CRhR7vecOsr3ThnhPwNYs8veub+JhgJP3QzUA69oTGcKCsxEREREpLqkkokcoYzJYYpozrVwem5F+6y2x9bGXl3AKfsIJVJqblZbJWe0jQPihCxlvm103V1iJmHJaBy4Avg8YVnopzsc835ClvVS4BbgG2bWXeHm6wkfYvu2sZgbERERqZS8mW2PABMIM7/L6uJjs7+ti/m2bM7G3Ptc/I1FnLqeEDBd1JjO1GwnKREREZGBLJVMbAOeAiZSRF7pmLH+K4CdB+3yfU3dLz2NbAfm11oH+0ovHS1WjPCNfq+7L3b3nwJfICw5zT/mSXe/wd3T7v4/wLc6HNPRjcDIvE1P2kVEpGZETxYfB+4nJLLK+nNsaAOZ2RP8OwDbD9gb1uy0Yqb0rwVGE5JtR2wRLyIiIiJVaRmwhSN0Ic137qzsE4m4b865DXtoVfyVBZyyl7Bio9AmXFWhkom2nUCWkAHNN5HQYaIzW4AV7p7N27ccmBQtRW0/5tkO5y0Huuxs4e4t7r6/fSMU3hMREakZqWQil0om0sAdhKn8cwgzwMvigjnZRwcnfJlj9Q+sjP9DW7aomGIN4ef9KxrTmT7pmioiIiIipZNKJg4Bi4HBUFgpkbo4uSkj/TcAm/fZawuMH3cAxzWmM4XUdasKFUu0uXsr4UO5uH2fmcWirx/t4rSHgdnRce3mAlui67Uf0zHbORdYV4pxi4iIVLNUMvEC8AfCzLHZhOCn5GIGZ83M3mR4c1PGTvrdsvibijjdgReAo6MxioiIiEjtWRltRxV6wnmzs/fFzA+05Wzig6viZxZwym5CLbiamdVW6aWjXwWuM7N3mtl8Qj21ocB3AczsB2Z2Y97xtxBqz3zdzOaa2ZXADcDNecf8B3Bm1CRhtpm9FXhvh2NERET6rVQysQO4k1A74yjK1CRh3kTfPGu83wKwZZ+99fG1seOKOD1HqIt6fGM6U2hDBRERERGpElGt4MVAEwU2Rhg+iNbxw/33AGt22RtyXtBbbQdOa0xnzm5MZ8ryELmUKppoi2qsfRz4HOE/AwuAy9y9vUHCdPLW+7r7BkKDg9OApcA3gK8DjXnHPAG8HvgbwprhzwAfcfdby3s3IiIi1SOVTBwm1Gx7GBhPmZokXDI/+6eRg/2PYLGnNsY+vm2/FdO5ewdhCemMcoxNRERERMorlUxsIeReOpYF69KZx+R+b3impc3mLl4Xm1/AKXsIM9vOBl7dmM50WRqsGph7YenDgcTMRhCeso+MaraJiIjUpMZ0JgacBJwLtNB1HdQe29fEoJ8/Wfe1TNamDGvwx952etsXYlbw6UcTun3/NmrqUFaN6cwJQCKqZ1dyiiGqnz4jERGR0mpMZ8YAbyTEmnsKOecni+o+sOewXTqswR97xxltXyjwreKE1Ro5IA2kU8lEU0/G3BOFxhCVXjoqIiIiZRQ1SXgKuBcwumkO1FMjB9N82tG5L4O3HWyxM+5YFn91EadvIwRMU0s9ri5MAIb00XuJiIiI9HupZGI3oSnlhELPOXla9jaAgy2c/vSm2IwCT8sS6hDvBc4BLm1MZwYVMdQ+oUSbiIjIAJBKJp4D7iF0JJ0NjAYSpbr+ydNyL0wf7d8BWL/H3l1EwNQM1BEaF/WFsiyhFRERERnglhNmexXUHXT+JN80YpA/BGZPbYy9o8j32g+sAuYApzSmM4WvpegDSrSJiIgMEKlkYg2hScJKwtT7aYQAZSZhRtkMQhJuTrTNpYiupZefkP3dsAZ/HKzuiXWxD7e2FRxnbAdmNaYzYwt9r55oTGcSwLByvoeIiIjIQBTNanuGUBu4IKcenf0/8NzBFjvtibUF1WrL1wZsBhYCxxR5blkp0SYiIjKApJKJLalk4nfAj4FfAHcRGgxtITyJfBi4D/h9tH8qMKmQa8cMXjE3e3PM/FBLm82+69n4VQUOaz+hM+qsom6meEOBqlteICIiItJPPEeI6wqa1TZvom8ePYS7AZZtiV1bYAfSfPsJy0nPakxnRhR9dpmoGUInVCRXRETkxUYKxwJnEAKmdUDrkc6789n4JWt2xj5keOsr52c/MHv8i93EuzOeUNj2Z1HH1JJrTGemAm8CFqWSiYfL8R6KIaqfPiMREZHyaUxnziZ0B32+kOPX77Yxf1gW/x/H6k+Ykv3cebNzi4p8SyOswlgK3JdKJrJFnl/4G6kZgoiIiPRG1EjhWeC3hGBpBgU8oXzV/Ow9gxO+1LH6h1fHP1Dg08ldwFjKO/VfM9pEREREyus5QrOCguriTh/ju8cP99sBVmyL/W02R7H11pzwMPj4aKs4JdpERESkW6lkYhdwN/Bnwsyz+u6OjxmcMyt7s+Gth1ttwb3L4xcX8DY5QqOG4xrTmXkdtmOi2XW9pUSbiIiISBnldSAtuFbb+XOyv4iZH2rN2ow/PR8/vwdv20yYaXZGYzpTUMmTclKiTURERI4olUxkgDSwAph+pOPnTPAtU0b5jwDW7LL3bN5rowp4m23A0cCVnWzzejbylxhJaAIhIiIiIuWznDCrbUIhB48fxqFJI/wXAGt22dubM9T14D13AMOB43pwbkkp0SYiIiIFSSUTbcATwCEKWA5w2XHZ2xrq/IWc27A/rYhfV8BbZAhLVFd22FoJRW4LfjLahbJ2NRURERERSCUTe4DHCd3eC2qMcNG87O3xmO9uy9nE+56PX9rDt24BEj08t2SUaBMREZGCpZKJrYSZbeM4wuyw+jpyC6fnvgGe299s5/36qfg1PegmBaEj6kjg7MZ0pqEnF2hMZxKEp5zSx8zsA2a21syazewxMzv9CMePMrObzWyLmbWY2Qozu6KLY1Nm5mb2tbIMXkRERHrqaeAhwoPOI3YEHT6I1qNG+U8ANu2xt7S01e4qBCXaREREpFhPE4rOHnWkA0+elnth6ij/AcDW/bG3/nxx3fvbsj2KP9YCc4CFjelMsUVy4a/12Vp6cK70kJldDXwV+CxwCrAEuMvMOl1KYmb1wD2ExhtvIiwZvg7Y1MmxpwF/T+gyJiIiIlUklUw44eHso8AkQizWrYuOzd4TM9+XdRv9xNrYgjIPsWyUaBMREZGipJKJZsIS0hwFzBK76qTsr44ek/svcN992C7/yaK6Tx5qKXpafxthZttCYHbRgw7BXQOhWK70nY8C33L377r7s8D7CE0v3t3F8e8mLEt+nbs/7O5r3f0Bd1+Sf5CZDQNuJSTh9pRv+CIiItJTqWQiR4gZnwCmAoO7O76hjuzoITwIsG537MKyD7BMlGgTERGRnlhPmNk2lQLiiStOyP7h2In+JfC2Ay129s+frPvsrkMMKfI99xPquJ3VmM4U1DI+z1CgjpCwkz4QzU5bCNzbvs/dc9HXZ3Vx2lWEJ983m9k2M1tmZjeYWcflIzcDv3f3e19+iZeNo8HMRrRvaAmxiIhIn0klE1nCz/Y0oaFWt93rZ43P3Q9woJkz9zXVZrd4JdpERESkaNFygCeBzcDkQs55xbzsI8mjcv9i5k1NGTvhtiV1XzzY0n2w1YlNhHbxZxW5hPSIyxWk5Nrr+G3rsH8bYQlJZ2YSlozGgSuAzwMfAz7dfoCZXUNYhnp9geO4HtiXt20s8DwREREpgah7/cPAcxyhe33yqNyKuphvcazhibXxM/pkgCWmRJuIiIj0SCqZOEjoKNVAgYmsM4/JPX32zFwqZr6vtc1mPrAy/soevPUWYEqh7xkZgWaz1YIYsB14r7svdvefAl8gLDnFzI4Cvg68zd0LXQZ8I6GZRvs2reSjFhERkW6lkokWCuheHzMYO8wfANi8zy7sk8GVmBJtIiIi0hurCUtIpwEFzTA7aWpuzbTR/mOAzXvt9a1tRccjTYQaH8UsARyL6rP1tZ1AFpjYYf9EYGsX52wBVrh7Nm/fcmBS3lLUCcCTZtZmZm3ABcCHo69f1qHM3VvcfX/7Bhzo3W2JiIhIT6SSiW2EJaTj6aZ7/fGTw/LRQ60kN++zkX0zutJRok1ERER6LCpyu4i/zjIryPmzs/fGzPe35Wzig6vi5xT5tm1AggJaxQM0pjMJQlJOibY+5O6twGLg4vZ9ZhaLvn60i9MeBmZHx7WbC2yJrncfcCKwIG9bRGiMsKBDgk5ERESqzxG718+b6Jsb6nwlWCy9IXZu3w2tNJRoExERkV5JJRP7KXIJ6fBBtE4Y7r8DWLfb3pjzot/WKXxG2xBgENBS9LtIb30VuM7M3mlm84FbCH9GvgtgZj8wsxvzjr+FsJzk62Y218yuBG4gND/A3Q+4+7L8jbAEZVf0exEREalied3rAYZ1ddyE4WH56Pb9dmH5R1VaSrSJiIhIKawCllLEEtKzZ+Z+b3hLS5vN/MsLsQVFvl8LYQlhIYYSEm2a0dbHohprHwc+BzxFmIF2mbu3N0iYTl4zDXffAFwKnEb48/QNQk22xj4btIiIiJTbOsLP+al0ETcmj8r9GTzX3GbzVm63rpooVSUl2kRERKTXoi6k7UtIpxZyzsQRfmDMUO4GWLkj9oYi3/IwMLYxnemyvkeeoYQ6IFpWWAHufpO7H+3uDe5+hrs/lvfahe5+bYfjH3X3M919kLvPcvcvdrckNLrGR8p3ByIiIlJKUdyYJjRA6jSJNnWU7x1Sz1KAZZtjF/bd6HpPiTYREREpiVQycYCwhLSeApeQnnZ09jbw3OFWW/DUhtisIt6uibAktMslB3mK6U4qIiIiImUWlR55ghCn1Xd2zOQRfj/ArkN2QQ/KjFSMEm0iIiJSSqsIRW4LmtV2zDjfMXIQfwZ4ZktRs9raO48W0hBhBJrNJiIiIlJtVgAbCF1IX+a0o7OPGt6aydrUJRtjs/t2aD2nRJuIiIiUTLQU4ClgHzC2kHNOnJr7FcD+Zs4pogZHjhDHFNIQYSyqzyYiIiJSVVLJRBZ4ni5WH4weStPwQTwGsGp77IK+HFtvKNEmIiIiJZVKJnYT6m6Mo4BY48SpubVD630xWOzJDfHXFfFWDozs7oDGdKaOMKNNiTYRERGR6rOO8IB2VGcvHjU6dz/A7sNcvP2AFVIypOKUaBMREZFyWE5ojDD5SAcCzJ2Y+yXA7kNc/sPH6v7piXWx+QWc1sSRO4+2dxxtKWQcIiIiItJ3olptqwkPaF/mzGNyi+vjvi7nNuyBlfGr+3Z0PaNEm4iIiJRcKpk4DCwmNCzotMBtvtNn5JaNHuJ3gtnBFjtj0br4l777aN2X718RPzOb67ztOyHRNqoxnUl0c2kl2kRERESq2wuEsiAvi+nq68gdOyn3bYCdB7lyxTYr6CFuJSnRJiIiIuWyivCEctqRDowZXHNq2zcvnNP2/40a4veAtzVn7NjlW2M3fP8vdV/ZsMfGdHJae+fR7uq0DQXiQFtPbkBEREREym4TsI0umiKcMyv3VFRmpO7xtfFr+3RkPaBEm4iIiJRFKploI9Rqy9JFkduO5k/2jX9zatt/vubE7HsmDM/9PGZ+qKXN5tz5TPz/PbvFjupweDNhtlp3nUeHQJcz4kRERESkwqKYcTndPDw99ejsd8BzB1rsrMfWxE7ou9EVT4k2ERERKacNwLPA1GJOmjba97wxmf3hxcdmP5KI+6a2nI1/cFX8y4+tjR2fd5hz5M6jI9FsNhEREZFqtx7YTxcPUI+b7BvGDOFOgGe2xN7TTWmRilOiTURERMomlUw4sITQTaqoZBvA7PG+7TUnZj85KOHLc25Dn1wf+/x9z8XPzTskC4zu5hKjUX02ERERkaqWSib2AGvoYvkowHlzsj8288MtbTbrvufir+i70RVHiTYREREpq1QysQt4gJDwmgPUFXP+xBF+4M2ntH1meIM/Cla3Ynvsk3c8E780evkwXXQebUxn4oRW8c09HryIiIiI9JVV0a+dxopTRvq+KSP9ZwBrd9vf7m+ioc9GVgQl2kRERKTsUsnESuB2QgA1i+7rqr3MsAZarzm17Utjh/rvADbstrdFLzUBIxrTmUGdnDYUaECJNhEREZFasBHYDozr6oCL52Vvr4v5tmzOxtz3fPz1fTe0winRJiIiIn0ilUzsBO4CHgHGUORS0ro4uVfNb/shQNZt1M6DDCXMaBtM53XahhKaJWjpqIiIiEiVSyUTGeA5Qo3dTg1tIDNjrP8IYPchO7er4ypJiTYRERHpM6lkogV4FLgDyAATizl/1BCa4jHfDbBhT2wK0EqYtdbZDLmhhKUHaoYgIiIiUhvWAwfppmP97Am5pwFas0xtzhRXkqQvKNEmIiIifSqVTHgqmVgFLCbUUCuqa1R9nM0Auw7alGiX0WFGW2M6EwNmEjqTioiIiEht2B1tXZYZOXqM7zTzJrD46p2xKV0dVylKtImIiEilrCTU4eiyu1RnBiXYBLC/5cWlp63A2A6HzQaOg3CsiIiIiFS/VDKRI3Qf7awsCAAxg4Y46wG27bfpfTW2QinRJiIiIhWRSiYOAU8T6rUVPKttSL1vBmhqfXFGWxMwoTGdMYDGdGYocCqhNltTKccsIiIiImW3HcgB8a4OGFQfEm17D6NEm4iIiEielcBOuuku1dHIwb4JoKXtxRltTYQ6HkOir08GpkBYYioiIiIiNWUHsI9ulo8Ob/B1AIdbNaNNRERE5EWpZOIgsIQiZrVNGB4Sba1tTMmFCmztnUdHNKYzUwmJtq2EJ6EiIiIiUkNSycRhQvmPLruPjhni6wGa2zi6r8ZVKCXaREREpNJWArt4eZ21Th0z1reB5xwbvHmvjSZ0Fa0nJOtOjX6/r1yDFREREZGy20iI6To1fUyY0ZbJMvlQC4k+G1UBlGgTERGRikolEweAZYRE2xFntQ1K0FYXYxvApr3WvnzUgWMITRA2lGmoIiIiItI3dgDNwKDOXpwyyvfEzA+AxVbvjE3r26F1T4k2ERERqQbPE1q5FzSrraEu1F/bc/jFhggtwDTCTLZMOQYoIiIiIn1mFyE27HT5aMygoe7FzqNVtXxUiTYRERGpuFQysZ+/zmo7okEJNgEcbHmxIcIeoAHCTDcRERERqV2pZCILrAWGd3XM4ATrAPY3V1fnUSXaREREpFo8T0iUzeQIS0iHNfhmgKbMizPaDgAvlHV0IiIiItKXthFiwk5zV8MGhYYIh1s1o01ERETkZVLJxD7gXmAnMItu4pRRQ0Ln0Za2F2e0iYiIiEj/sh3YTxez2sYNjTqPZjSjTURERKRTqWRiC3AXodPUbKCus+MmjwyJtkyWSa1timdERERE+ptUMnEQ2EoXddqOHhs6j7blbOK+ps6bJlSCAlMRERGpKqlkYidwN7CKMLPtZa3dp4/2XYa3gtWt220T+nqMIiIiItIn1tNF59FJI/xA3HwvwAs7Y0f15aC6o0SbiIiIVJ28ZaTPAMcAE4F4++vxGJ6Ih86j2/ablo+KiIiI9E87gFY6efAKf+08uv2AVc3y0apItJnZB8xsrZk1m9ljZnb6EY4fZWY3m9kWM2sxsxVmdkUXx6bMzM3sa2UZvIiIiJRFKpk4BPwJ+CPQREi4HQMMA2ioC4m2vU0vNkQQERERkf5lJ6G7fKfLRwfXt3ceVaLtRWZ2NfBV4LPAKcAS4C6zzpeBmFk9cA8wA3gTMA+4DtjUybGnAX8PLC3H2EVERKS8UslEcyqZeBL4BXA7YTnpSGD24PpQp+1Qq2a0iYiIiPRHqWQiQ1g+OqKz14dHnUebWqmazqMVT7QBHwW+5e7fdfdngfcBh4F3d3H8u4ExwOvc/WF3X+vuD7j7kvyDzGwYcCshCbenfMMXERGRckslEy2pZGIVcAehWUJmxCC2ATRn0Iw2ERERkf5rCyF/ZR1fGD8sNERoblOiDXhxdtpCQg0WANw9F319VhenXQU8CtxsZtvMbJmZ3WBm8Q7H3Qz83t3vffklXjaOBjMb0b7RRetYERERqaxUMuGEWh0t44f5ToDWNjSjTURERKT/2grsp5PlozPG5jYAZHM2dl8Tg/t6YJ2p9Iy2cYTCxts67N8GTOrinJmEJaNx4Arg88DHgE+3H2Bm1xCWoV5f4DiuB/blbRsLPE9ERET63mGgZdro3G6AtpyNP9jSeYFcEREREaltqWTiALCWsLrxJcYN41A8Fh6+btgTm9zHQ+tUpRNtPREDtgPvdffF7v5T4AuEJaeY2VHA14G3uXtzgde8kZAZbd+mlXzUIiIiUhKpZCIH7BszlEzM/CDA2l3VEViJiIiISFmsI0y4elkea1BdaIiw44BVRTxY6UTbTiALTOywfyJhamBntgAr3D2bt285MClvKeoE4EkzazOzNuAC4MPR1x2XmOLuLe6+v30DDvTutkRERKTM9sSMhvp4aIa044AaIoiIiIj0Y5sI9fdHdXxhcD3rAfY1oUSbu7cCi4GL2/eZWSz6+tEuTnsYmB0d124usCW63n3AicCCvG0RoTHCgg4JOhEREalNB4B4Q4LNAPubj9wQoaWNeGsbL3vgJiIiIiLVLZVMHAZeoJPloyOjzqMHW60qGmRVekYbwFeB68zsnWY2H7gFGAp8F8DMfmBmN+YdfwvhG/t1M5trZlcCNxCaH+DuB9x9Wf4GHAJ2Rb8XERGR2ncYYEi9bwI43HrkGW0Pr46f9b1H6z7/s8V1f1fuwYmIiIhIya0HHF764HT88JBoa2qtjhltdZUegLv/1MzGA58jNEB4CrjM3dsbJEwHcnnHbzCzS4H/AJYSpg9+HfhSX45bREREKuow4CMGsWXLPmhuO/KMts177dys25Ccu2a1iYiIiNSezcBuYDShFBkAs8bl1v9lTZy2nA3fvNde1pm0r1U80Qbg7jcBN3Xx2oWd7HsUOLOI67/sGiIiIlLTDgMtE4b7jue3QWsb3c5o29fEoIMtnAowc1zuj2gFqYiIiEhNSSUTLY3pzGrgDPISbSMG01IX821tOZu4drcdXbkRBtWwdFRERESkWIeBlqPH5HYD5NxGbD9gw7o6eNG6+GmO1TfU+c5Tp+dW9tkoRURERKSUNhCaar5k4tigROg8uuuQzajAmF5CiTYRERGpRc1A0/BBEI/5LoANe7ougLtln50LMHG4p2OKfvqUmX3AzNaaWbOZPWZmpx/h+FFmdrOZbTGzFjNbYWZX5L1+vZk9YWYHzGy7md1mZvPKfyciIiJSBbYQZrONzd85pD5qiNCiGW0iIiIiRUslEw7sBRrq42wC2H2o84YIew8zuH3Z6LyJuXSfDVIws6sJja8+C5wCLAHuMrMJXRxfD9wDzADeBMwDriPU5G13AaEJ1pnAJUACuNvMhpbnLkRERKRapJKJDLAKeEkttpGDwoy25gxHVWJc+aqiRpuIiIhID+wBjh2UYHNThpMONHfeEGHR+vhpjiUScd80a7xv7uMxDnQfBb7l7u3d5N8HXAm8G2js5Ph3E7rLn+3umWjf2vwD3P2y/K/N7FpgO7AQ+HMJxy4iIiLVaSPQCtRHv3LyUdlFM8fn/m3KSH80PIOrHM1oExERkVp1CLCh9b4J4HCm8xltW/bZeQBjhvhDZn04ugEump22ELi3fZ+756Kvz+ritKuAR4GbzWybmS0zsxvMrLvuFe1PtHd3MY4GMxvRvgHDi70XERERqSpbgR3kLR8dP4xDM8f51kEJspUbVqBEm4iIiNSqw4CPHerrAA42s/CFnS9dkrjnEIMPtrAQYO5Ef6gCYxzIxhHau27rsH8bMKmLc2YSlozGgSuAzwMfAz7d2cFmFgO+Bjzs7su6uOb1wL68bWPBdyAiIiJVJ5VMZIEVVOnDMyXaREREpFYdBrJnHJNbNjjhzzo2+MFV8Q/l/K8HLFofPwOsLhH3DcdNzq2r2EilUDHCMtD3uvtid/8p8AXgfV0cfzNwAnBNN9e8kTDrrX2bVrrhioiISIXsBvyIR1WAEm0iIiJSqw4DLfEY9WfPzH7d8NbDrXbyXc/GX6zhtXV/6DY6dqg/FNOy0b62E8gCEzvsn0hY8tGZLcAKd89f9rEcmBQtRX2Rmd0EvBp4hbt3OUvN3VvcfX/7Bhwo8j5ERESk+jQDGaqw94ASbSIiIlKrDgMtQMPcib5l2mj/AcC6XfauF3bahJ0HGXqwhVMA5k70hys50IHI3VuBxcDF7fuipZ4XE+qwdeZhYHZ0XLu5wJboelhwE/B64CJ3X1OO8YuIiEhVayEk2irb+aATSrSJiIhITUolE62EhggNAJcdl/3doIQvb19C+uT6+JlgdfVxX3/85Nz6yo52wPoqcJ2ZvdPM5gO3AEOB9i6kPzCzG/OOv4XQdfTrZjbXzK4EbiAsEW13M/B24K3AATObFG2D++B+REREpDo0A20o0SYiIiJSUnuIEm11cXLnzMx+rX0J6Zpd9m6AMUPVBKFSohprHwc+BzwFLAAuc/f2BgnTgcl5x28ALgVOA5YC3wC+DjTmXfb9hFpr9xOWmrZvV5ftRkRERKTatACtQP2RDuxrVbeWVURERKQIe8l7kjl3om95frv/cOMee0/ObTjAsRNzSrRVkLvfBNzUxWsXdrLvUeDMbq6nansiIiIDXCqZyDWmM4cJM+Grima0iYiISC07DLwk8XL5cdnbByV8OUB93NfOn9x1oXwRERERqVkH0NJRERERkZI63HFHXZzcBXOy/z68wR85dlLuO5UYlIiIiIiU3UGqMNGmpaMiIiJSyw7z145TmfadM8f59pnj2hq7PEtEREREal0TEK/0IDrSjDYRERGpZYcJxXAbKj0QEREREelTzYBXehAdKdEmIiIitUyJNhEREZGBqaXSA+iMEm0iIiJSs1LJRBbYjxJtIiIiIgNNM5ClynJbVTUYERERkR7YgxJtIiIiIgNNC9BGlTVEUKJNREREat1+1OBJREREZKBpBlpRok1ERESkpA5XegAiIiIi0udaCF3n6ys9kHxKtImIiEita0+0WUVHISIiIiJ9JpVMZAjJNs1oExERESmh9s6jVfU0U0RERETK7gBKtImIiIiUVHuiTQ0RRERERAYWJdpERERESqw52pRoExERERlYDlFlTbGUaBMREZGalkomcsBelGgTERERGWhaKj2AjpRoExERkf5gD0q0iYiIiAw0zZUeQEdKtImIiEh/cBDFNSIiIiIDTfuMtqrpPq+AVERERPqDw1RRgCUiIiIifaIZyFBFddqUaBMREZH+YB/hiWZ9pQciIiIiIn2mBWilimJAJdpERESkP9hLWD46rMLjEBEREZG+00KY0Zao9EDaKdEmIiIiNS+VTLQC21GiTURERGQgaZ/RpkSbiIiISIltBQZVehAiIiIi0jdSyYQDh1CiTURERKTk9gA51BRBREREZCA5iGq0iYiIiJTcHkL30SGVHoiIiIiI9Jn9aEabiIiISMntjzbVaRMREREZOJqpohUNSrSJiIhIv5BKJnLAZmBopcciIiIiIn2mBfBKD6KdEm0iIiLSn+wE4pUehIiIiIj0mZZKDyCfEm0iIiLSn+wBskBdpQciIiIiIn2iGWijSuI/JdpERESkP9lL6DylOm0iIiIiA0MLkKFKGiIo0SYiIiL9RiqZOAzsQnXaRERERAaKZpRoExERESmbTcCQSg9CRERERPqEZrSJiIiIlNFeqqjFu4iIiIiUTyqZyAKHUY02ERERkbLYAzQBgys9EBERERHpEwcrPYB2SrSJiIhIf7OXEGypTpuIiIjIwKBEm4iIiEg5pJKJNmAr6jwqIiIiMlAcAnKVHgQo0SYiIiL90zagvtKDEBEREZE+0d4QoeKUaBMREZH+aA/hqaZiHREREZH+rxkl2v7KzD5gZmvNrNnMHjOz049w/Cgzu9nMtphZi5mtMLMr8l6/3syeMLMDZrbdzG4zs3nlvxMRERGpEnsJSwhUp01ERESk/2sBWis9CKiCRJuZXQ18FfgscAqwBLjLzCZ0cXw9cA8wA3gTMA+4DtiUd9gFwM3AmcAlQAK428wUbIuIiAwMBwjJNtVpExEREen/tHQ0z0eBb7n7d939WeB9wGHg3V0c/25gDPA6d3/Y3de6+wPuvqT9AHe/zN2/5+7PRPuvBaYDC8t6JyIiIlIVUsmEEx7C6SGbiIiISP+npaPw4uy0hcC97fvcPRd9fVYXp10FPArcbGbbzGyZmd1gZvFu3mpk9OvuLsbRYGYj2jdgeLH3IiIiIlVnG5Dlr3GAiIiIiPRPSrRFxgFxQiCcbxswqYtzZhKWjMaBK4DPAx8DPt3ZwWYWA74GPOzuy7q45vXAvrxtY8F3ICIiItVqNfAXYAKa2VYxpa7F25NrioiISP+WSiYyhGRbxVU60dYTMWA78F53X+zuPwW+QFhy2pmbgROAa7q55o2Ep93t27TSDVdEREQqIVo+uhhYRPjZPqiyIxp4ylGLt9hrioiIyIBxqNIDgMon2nYSlnRM7LB/IrC1i3O2ACvcPZu3bzkwKQrOXmRmNwGvBl7h7l3OUnP3Fnff374RCiiLiIhIjUslE1lCyYklhORNfbcnSKmVvBZvsddUiRAREZEBY3+lBwAVTrS5eyvhSfPF7fuipZ4XE4LizjwMzI6OazcX2BJdDwtuAl4PXOTua8oxfhEREal+0VKCh4DngMEVHs6AUY5avD28pkqEiIiIDAyHAK/0ICo9ow3C1P/rzOydZjYfuIVQR+W7AGb2AzO7Me/4WwhPOr9uZnPN7ErgBsIS0XY3A28H3gocMLNJ0abgWkREZABKJRNNwP3AM0CusqMZMMpRi7cn11SJEBERkYGhCWit9CDqKj0Ad/+pmY0HPkcIkJ4CLnP39gBqOnkBsbtvMLNLgf8AlhJqdnwd+FLeZd8f/Xp/h7d7F/C90t6BiIiI1IJUMnGgMZ25i+p40Cidy6/FmwUWm9lU4BOEmmxFc/cWoKX9azMrxThFRESk+qwCNlR6EBVPtAG4+03ATV28dmEn+x4FzuzmeoqgRERE5GVSycTBSo9hAOlpLd5MN7V4e3JNERERGQBSyUQzVdB5VE90RURERKTkylGLt4fXFBEREekzSrSJiIiISLmUoxZvt9cUERERqaSqWDoqIiIiIv1POWrxFnBNERERkYox94p3Pq06ZjaC0P59pLvvr/R4REREpDYohqh++oxERESkJwqNIbR0VEREREREREREpASUaBMRERERERERESkBJdpERERERERERERKQIk2ERERERERERGRElCiTUREREREREREpASUaBMRERERERERESkBJdpERERERERERERKQIk2ERERERERERGRElCiTUREREREREREpASUaBMRERERERERESmBukoPoMoNN7NKj0FERERqx/BKD0AKpjhPREREilFQnKdEW+fav3kbKzoKERERqVXDgf2VHoR0SnGeiIiI9Ea3cZ65ex+OpTZYeLw5BThQprcYTgjuppXxPcpN91AddA/VQfdQHXQP1aE/3ENvDQc2u4KsqqQ4ryC6h+qge6gOuofqoHuoDv3hHnrriHGeZrR1IvqGbSrX9fOWKRxw95p82q17qA66h+qge6gOuofq0B/uoQQG6n3XBMV5R6Z7qA66h+qge6gOuofq0B/uoQSOeN9qhiAiIiIiIiIiIlICSrSJiIiIiIiIiIiUgBJtldECfDb6tVbpHqqD7qE66B6qg+6hOvSHexDpjf7wd0D3UB10D9VB91AddA/VoT/cQ9mpGYKIiIiIiIiIiEgJaEabiIiIiIiIiIhICSjRJiIiIiIiIiIiUgJKtImIiIiIiIiIiJSAEm0iIiIiIiIiIiIloERbBZjZB8xsrZk1m9ljZnZ6pcfUFTM738xuN7PNZuZm9roOr5uZfc7MtphZk5nda2ZzKjTclzGz683sCTM7YGbbzew2M5vX4ZhBZnazme0ys4Nm9kszm1ipMXfGzN5vZkvNbH+0PWpml+e9XvX3kM/MUtGfp6/l7av6ezCzf43Gnb89l/d6LdzDVDP7v2iMTWb2tJmdmvd6Vf+dBoj+/ez4ObiZ3Ry9XtWfg5nFzezzZrYm+h6vNrPPmJnlHVMLn8NwM/uama2LxviImZ2W93rV34NIOSjO61v9IdZTnFcd+kOcB7Uf69V6nAf9I9ZTnNc7SrT1MTO7GvgqoSXuKcAS4C4zm1DRgXVtKGGMH+ji9U8CHwbeB5wBHCLcz6C+Gd4RXQDcDJwJXAIkgLvNbGjeMf8BvAZ4c3T8FOBXfTzOI9kIpICFwKnAH4HfmNnx0eu1cA8ARP9A/z2wtMNLtXIPzwCT87Zz816r6nsws9HAw0AGuBw4DvgYsCfvsGr/Ow1wGi/9DC6J9v88+rWqPwfgU8D7gQ8C86OvPwl8KO+YWvgc/pfwvX8HcCJwN3CvmU2NXq+FexApKcV5FdEfYj3FedWjZuM86DexXq3HedA/Yj3Feb3h7tr6cAMeA27K+zoGbAJSlR5bAWN34HV5XxuwBfh43r6RQDNwTaXH28U9jI/u4/y88bYCb8o75tjomDMrPd4j3Mtu4D21dA/AMGAF8ErgfuBrtfQ5AP8KPNXFa1V/D0Aj8GA3r9fc3+lojF8DVkXjr4XP4XfAtzvs+yXwf7XyOQCDgTbgyg77FwP/Vgv3oE1bOTbFeZXf+kuspzivIuOv6TgvGlO/i/VqLc6LxlTTsZ7ivN5vmtHWh8ysnvCk6t72fe6ei74+q1Lj6oVjgEm89H72EYLMar2fkdGvu6NfFxKefObfw3PAeqr0HqKpyNcQnkI/Sm3dw83A79393g77a+ke5lhYYvOCmd1qZtOj/bVwD1cBi8zs5xaW16TN7Lq812vu73T07+rbge94+ClfC5/DI8DFZjYXwMxOJjwxvyN6vRY+hzogTgio8jUR7qUW7kGkpBTnVY2ajvUU51VcLcd50M9ivRqN86D2Yz3Feb1UV+kBDDDjCH9gt3XYv42Qia81k6JfO7ufSVQZM4sRnog87O7Lot2TgFZ339vh8Kq7BzM7kRBwDQIOAq9392fNbAE1cA9R0HgKYTp4R7XyOTwGXAs8T5jK/i/Ag2Z2ArVxDzMJ09i/CnyR8Fl8w8xa3f371Njf6cjrgFHA96Kva+FzaARGAM+ZWZbwc+Gf3P3W6PWq/xzc/YCZPQp8xsyWE8b2N4TgahU1cA8iZaA4r8JqOdZTnFcVaj3Og/4X672O2ovzoMZjPcV5vadEmwwkNwMn8NJaC7XkeWAB4Untm4Dvm9kFFR1RgczsKODrwCXu3vHJSM1w9zvyvlxqZo8B64C3EJ7wVLsYsMjdb4i+TkfB4/uA71duWL3yHuAOd99c6YEU4S3A24C3EmrBLAC+ZmaboyC4VrwD+A5hWVwWeBL4MeFps4hIJdRyrKc4r8L6QZwH/S/Wq8U4D/pHrKc4rxe0dLRv7ST8Ie3YFWUisLXvh9Nr7WOu+vsxs5uAVwOvcPeNeS9tBerNbFSHU6ruHty91d1Xuftid7+eULz4H6iNe1gITACeNLM2M2sjFC/9cPT7bVT/PbxM9DRtBTCb2vgctgDPdti3HGhfFlEzf6cBzOxoQh2Y/83bXQufw/8DGt39J+7+tLv/kFDY9/ro9Zr4HNx9tbtfQKjJc5S7n05YzvECNXIPIiWmOK+Caj3WU5xXfWowzoN+FOvVcJwH/SDWU5zXO0q09SF3byUUELy4fV80xf1iwlTxWrOG8Bcp/35GELqOVMX9RG2HbwJeD1zk7ms6HLKY0JUn/x7mEX4YVcU9dCMGNFAb93AfoVvNgrxtEXBr3u+r/R5exsyGAbMIQU0tfA4PA/M67JtLeFoLNfB3uoN3AduB3+ftq4XPYQiQ67Avy19/JtfU5+Duh9x9S9Tp7FLgN9TYPYiUguK8yujHsZ7ivAqrwTgP+lesV6txHvSjWE9xXg9VuhvDQNuAqwlFBd9JaPX734R2yxMrPbYuxjuMv/7AdOAfo99Pj17/VDT+qwg/YG8jZLkHVXrs0fi+CewlPFWblLcNzjvmFsIPn1cQnsg9AjxS6bF3uI8bgfOBGdH3+UbCP96X1Mo9dHJP9xN1o6qVewC+Ev1ZmgGcDdwD7ADG18I9EOp0ZIAbCE9n30poxf22vGOq+u903jhj0fe6sZPXqv1z+B6wEbgy+rP0+ujP0Zdq6XMgBFuXEQriXgI8BfwFSNTKPWjTVuoNxXmVuIeaj/VQnFcVGzUe50Vj7BexHjUc50Vj/B41HuuhOK93379KD2AgbsAHo38cWghFN8+o9Ji6GeuFhMCr4/a96HUDPkfIaDcTOo/MrfS488bf2dgduDbvmEGEmh67ox9EvwImVXrsHe7j28Da6M/M9uj7fEkt3UMn93Q/Lw3Aqv4egJ8Am6PPYWP09awau4dXA09Hf1+XA9d1eL2q/07njfNV0d/ll42t2j8HYDihWPc6Qs2X1YRW6fW19DkQ6o+sjv4+bAFuAkbW0j1o01aODcV5fX0PNR/roTivKjb6QZwXjbPmYz1qOM6LxljzsR6K83q1WfRNEhERERERERERkV5QjTYREREREREREZESUKJNRERERERERESkBJRoExERERERERERKQEl2kREREREREREREpAiTYREREREREREZESUKJNRERERERERESkBJRoExERERERERERKQEl2kREREREREREREpAiTYRKTszu9DM3MxGVcFYzjGzp80sY2a3FXHeWjP7SPlGNrCZ2Yzoz8iCSo9FRERECqc4T45EcZ4MNEq0ifRjZva96Idax212Gd/zfjP7WofdjwCTgX3let8ifBV4CjgGuLZcb2Jm15rZ3nJdX0RERAY2xXmdUpwnIhWnRJtI/3cnIfjJ39Z0PMjM6ss1AHdvdfet7u7leo8izAL+6O4b3X1vpQfTE+X8rERERKSmKM57KcV5IlJxSrSJ9H8tUfCTv2WjJ5I3mdnXzGwncBeAmX00mnJ/yMw2mNk3zWxY/gWjafn3m9lhM9tjZneZ2Wgz+x5wAfAPeU9VZ3S2pMDM3mhmz5hZSzRd/2Md3mOtmd1gZt8xswNmtt7M3tvdjZpZg5l9w8y2m1mzmT1kZqdFr80wMwfGAt+JxnNtF9eZYGa3m1mTma0xs7d1ckyX3yczuxD4LjAy7/vwr9Fr7zCzRdE9bTWzH5nZhCPc11oz+4yZ/cDM9gP/E+0/18wejMa5Ibr3oR3O+3R03kEzW2dmV5nZeDP7TbRvqZmd2uH9uvxszOyLZvZYJ2NcYmb/nPf135nZ8uhzeM7M/r8Ox59uZuno9UVAsrvvgYiIiHRKcR6K8xTniVQZd9emTVs/3YDvAbd18dr9wAHgy8A8YF60/yPAK4AZwEXAc8A3885bADQD3wROBo4HPgiMA0YSlg/8DzAp2uLAhYADo6JrLASywGeAuYSp/YeBa/PeZy2wC/j/gNlAKjpnXjf3+3VgE3A5cFx0/7uBMdE4JhGWNfxD9PvBXVznD4RlB2dGY304Gt9H8o7p8vsE1EfvsS/v+zAseu3d0fhmRtd/BPjDET7HtdG1PkZ4Utu+HYzGMQc4G3gS+G4n38O/j475ZnSdO4A3R9/7XwPPAlbIZxN93g7Mynuf9n2zo6/fBmwG3kBYuvGGaBzvjF4fBmwHbo3OfTWwOrrGgkr/vdGmTZs2bdpqYUNx3vdQnKc4T5u2KtwqPgBt2rSVb4sCkLboB3X79vPotfuBJwu4xpuAnXlf/wh4qJvj7we+1mHfhbw0ALsVuLvDMV8Gnsn7ei3ww7yvDdgGvK+L9x0KtAJvzduXIARkn8jbt5e8QK+T68yNxnpa3r5jo30fKeL7dC2wt4Dv76nRtYd1c8xa4Ncd9v0v8N8d9p0bBU+DuvgeTore63N5+86M9k0q4rN5CvhM3tdfBP6S9/Uq4G86XOPTwCPR798L7GwfZ7TvfQrAtGnTpk2btsI3xXmK8xTnadNWnZuWjor0f38iPJ1s3z6c99rijgeb2SvN7D4z22RmB4AfAmPNbEh0yALgvl6OaT7h6WG+h4E5ZhbP27e0/Tfu7sBWoKvp97MIAdfDeedkgMej9ytmbG3kfW/c/TlC4PaiAr5PnTKzhdFyhfXReQ9EL00/wrgWdfj6ZODaaFnAQTM7SFgWEiM8XWy3NO/326Jfn+5kX/v3tZDP5lbgrdH9GPA30T6iJQ2zgG93GNuno/3t77HU3Zvz3uPRLu9cREREuqI4T3FeO8V5IlWirtIDEJGyO+Tuq7p6Lf8LM5sB/A64BfgnwnT8c4FvE6bJHwaayjbSl8t0+NqpgtqSBX6fOjtvKCFIuosw7X4HIfC6KzqvO4c6fD0M+G/gG50cuz7v9y9+D93dQ7z0ku+rR78W8339MfAlMzsFGAwcBfw0b1wA1wEda3xki3gPEREROTLFeSWmOE9xnkhvKdEmIvkWEn4Qf8zdcwBm9pYOxywFLgb+pYtrtBLqZHRnOXBOh33nACvcvac/pFdH730OsA7AzBLAacDXirjOc4R/GxcCT0TXmQeMyjumkO9TZ9+HYwlFelPuviE671R65knguG6C65464mfj7hvN7AFCEDkYuMfdt0evbTOzzcBMd7+1m/d4h5kNynvaeWaJ70NEREReSnGe4jzFeSJ9oOJPDESkqqwiTMv/kJnNNLN3EGoq5LsROM1C96WTzOxYM3u/mY2LXl8LnGGh+9M4M+vs35l/By6OOizNNbN3EgrtfqWnA3f3Q4Qnj//PzC4zs+OAbwFDCE8gC73O88CdwH+b2RlmtpBQJyP/CW8h36e1wDAzuzj6PgwhPIFszTvvKkIx2p74EnC2hY5iC8xsjpm91sxu6uH12hX62dwKXEMottsx0PoX4Hoz+3B0jRPN7F1m9tHo9R8RnrB+y8yOM7MrgI/3ctwiIiLSPcV5ivMU54n0ASXaRORF7r4E+CjwKWAZ4UnW9R2OWQG8ilA74nFCzYXXEupdQPhBnSV0OGqfMt/xfZ4E3kL4Ab4M+Bzwz+7+vV7eQgr4JaGOxpOELlaXuvueIq/zLkI3pQeAXxG6a23PG38h36dHgP8iTLXfAXzS3XcQiue+mfD9SdHDwMPdlwIXEIr6PgikCd/HzT25Xt51C/1sfkF4ajsEuK3DNf4X+DvC9/FpwvfxWmBN9PpB4DXAidG4v0D4XoqIiEiZKM57keI8xXkiZdXe5ldERERERERERER6QTPaRERERERERERESkCJNhERERERERERkRJQok1ERERERERERKQElGgTEREREREREREpASXaRERERERERERESkCJNhERERERERERkRJQok1ERERERERERKQElGgTEREREREREREpASXaRERERERERERESkCJNhERERERERERkRJQok1ERERERERERKQE/n9bEg57YHI3kQAAAABJRU5ErkJggg==", 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" ] @@ -587,7 +11239,7 @@ " label=method_name,\n", " ax=ax2,\n", " )\n", - "metric_name = utility.scorer._name.capitalize()\n", + "metric_name = \"Accuracy\"\n", "ax1.set_title(\"Worst point removal, 95% CI\")\n", "ax1.set_xlabel(\"Fraction of data removed\")\n", "ax1.set_ylabel(metric_name)\n", diff --git a/notebooks/least_core_basic.ipynb b/notebooks/least_core_basic.ipynb index 5967cfe0f..751ccf8c8 100644 --- a/notebooks/least_core_basic.ipynb +++ b/notebooks/least_core_basic.ipynb @@ -144,7 +144,7 @@ ")\n", "from pydvl.value import compute_least_core_values, LeastCoreMode, ValuationResult\n", "from pydvl.reporting.plots import shaded_mean_std\n", - "from pydvl.reporting.scores import compute_removal_score" + "from pydvl.reporting.scores import compute_removal_score_legacy" ] }, { @@ -573,7 +573,7 @@ " n_iterations=n_iterations,\n", " n_jobs=n_jobs,\n", " )\n", - " scores = compute_removal_score(\n", + " scores = compute_removal_score_legacy(\n", " u=utility,\n", " values=values,\n", " percentages=removal_percentages,\n", @@ -695,7 +695,7 @@ " n_iterations=n_iterations,\n", " n_jobs=n_jobs,\n", " )\n", - " scores = compute_removal_score(\n", + " scores = compute_removal_score_legacy(\n", " u=utility,\n", " values=values,\n", " percentages=removal_percentages,\n", diff --git a/notebooks/least_core_basic_new.ipynb b/notebooks/least_core_basic_new.ipynb index 1452e489e..0ba362f04 100644 --- a/notebooks/least_core_basic_new.ipynb +++ b/notebooks/least_core_basic_new.ipynb @@ -151,7 +151,7 @@ " SupervisedScorer,\n", ")\n", "from pydvl.reporting.plots import shaded_mean_std\n", - "from pydvl.valuation.types import Sample" + "from pydvl.reporting.scores import compute_removal_score" ] }, { @@ -479,52 +479,6 @@ }, "outputs": [], "source": [ - "def compute_removal_score(\n", - " u: ModelUtility,\n", - " values: ValuationResult,\n", - " training_data: Dataset,\n", - " percentages: NDArray[np.float_] | Iterable[float],\n", - " *,\n", - " remove_best: bool = False,\n", - " progress: bool = False,\n", - ") -> dict[float, float]:\n", - " r\"\"\"Fits model and computes score on the test set after incrementally removing\n", - " a percentage of data points from the training set, based on their values.\n", - "\n", - " Args:\n", - " u: Utility object with model, data, and scoring function.\n", - " values: Data values of data instances in the training set.\n", - " percentages: Sequence of removal percentages.\n", - " remove_best: If True, removes data points in order of decreasing valuation.\n", - " progress: If True, display a progress bar.\n", - "\n", - " Returns:\n", - " Dictionary that maps the percentages to their respective scores.\n", - " \"\"\"\n", - " u = u.with_dataset(training_data)\n", - "\n", - " # Sanity checks\n", - " if np.any([x >= 1.0 or x < 0.0 for x in percentages]):\n", - " raise ValueError(\"All percentages should be in the range [0.0, 1.0)\")\n", - "\n", - " if len(values) != len(training_data.indices):\n", - " raise ValueError(\n", - " f\"The number of values, {len(values) }, should be equal to the number of data indices, {len(training_data.indices)}\"\n", - " )\n", - "\n", - " scores = {}\n", - "\n", - " # We sort in descending order if we want to remove the best values\n", - " values.sort(reverse=remove_best)\n", - "\n", - " for pct in tqdm(percentages, disable=not progress, desc=\"Removal Scores\"):\n", - " n_removal = int(pct * len(training_data))\n", - " indices = values.indices[n_removal:]\n", - " score = u(Sample(idx=None, subset=indices))\n", - " scores[pct] = score\n", - " return scores\n", - "\n", - "\n", "all_scores = []\n", "\n", "for i in trange(5):\n", diff --git a/src/pydvl/reporting/plots.py b/src/pydvl/reporting/plots.py index 147ae1d7a..72be2cb8b 100644 --- a/src/pydvl/reporting/plots.py +++ b/src/pydvl/reporting/plots.py @@ -10,7 +10,7 @@ from numpy.typing import NDArray from scipy.stats import norm, t -from pydvl.value import ValuationResult +from pydvl.valuation.result import ValuationResult @deprecated(target=None, deprecated_in="0.7.1", remove_in="0.9.0") @@ -104,7 +104,7 @@ def plot_ci_array( means = np.mean(data, axis=0) variances = np.var(data, axis=0, ddof=1) - dummy = ValuationResult[np.int_, np.object_]( + dummy = ValuationResult( algorithm="dummy", values=means, variances=variances, diff --git a/src/pydvl/reporting/scores.py b/src/pydvl/reporting/scores.py index 5b1c09f07..d99228f5c 100644 --- a/src/pydvl/reporting/scores.py +++ b/src/pydvl/reporting/scores.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from typing import Dict, Iterable, Union import numpy as np @@ -5,14 +7,18 @@ from tqdm.auto import tqdm from pydvl.utils import Utility -from pydvl.value.result import ValuationResult +from pydvl.valuation.dataset import Dataset +from pydvl.valuation.result import ValuationResult +from pydvl.valuation.types import Sample +from pydvl.valuation.utility.modelutility import ModelUtility +from pydvl.value.result import ValuationResult as LegacyValuationResult -__all__ = ["compute_removal_score"] +__all__ = ["compute_removal_score_legacy", "compute_removal_score"] -def compute_removal_score( +def compute_removal_score_legacy( u: Utility, - values: ValuationResult, + values: LegacyValuationResult, percentages: Union[NDArray[np.float64], Iterable[float]], *, remove_best: bool = False, @@ -52,3 +58,49 @@ def compute_removal_score( score = u(indices) scores[pct] = score return scores + + +def compute_removal_score( + u: ModelUtility, + values: ValuationResult, + training_data: Dataset, + percentages: NDArray[np.float_] | Iterable[float], + *, + remove_best: bool = False, + progress: bool = False, +) -> dict[float, float]: + r"""Fits model and computes score on the test set after incrementally removing + a percentage of data points from the training set, based on their values. + + Args: + u: Utility object with model, data, and scoring function. + values: Data values of data instances in the training set. + percentages: Sequence of removal percentages. + remove_best: If True, removes data points in order of decreasing valuation. + progress: If True, display a progress bar. + + Returns: + Dictionary that maps the percentages to their respective scores. + """ + u = u.with_dataset(training_data) + + # Sanity checks + if np.any([x >= 1.0 or x < 0.0 for x in percentages]): + raise ValueError("All percentages should be in the range [0.0, 1.0)") + + if len(values) != len(training_data.indices): + raise ValueError( + f"The number of values, {len(values) }, should be equal to the number of data indices, {len(training_data.indices)}" + ) + + scores = {} + + # We sort in descending order if we want to remove the best values + values.sort(reverse=remove_best) + + for pct in tqdm(percentages, disable=not progress, desc="Removal Scores"): + n_removal = int(pct * len(training_data)) + indices = values.indices[n_removal:] + score = u(Sample(idx=None, subset=indices)) + scores[pct] = score + return scores diff --git a/src/pydvl/valuation/result.py b/src/pydvl/valuation/result.py index 5fea62f1c..284995223 100644 --- a/src/pydvl/valuation/result.py +++ b/src/pydvl/valuation/result.py @@ -249,22 +249,8 @@ def __init__( self._sort_order = None self._extra_values = extra_values or {} - # Yuk... - if data_names is None: - if indices is not None: - self._names = np.copy(indices) - else: - self._names = np.arange(len(self._values), dtype=np.int_) - else: - self._names = np.array(data_names, copy=True) - - if len(np.unique(self._names)) != len(self._names): - raise ValueError("Data names must be unique") - - if indices is None: - self._indices = np.arange(len(self._values), dtype=np.int_) - else: - self._indices = np.asarray(indices) + self._indices = self._create_indices_array(indices, len(self._values)) + self._names = self._create_names_array(data_names, self._indices) self._positions = {idx: pos for pos, idx in enumerate(self._indices)} @@ -798,15 +784,8 @@ def zeros( Returns: Object with the results. """ - if indices is None: - indices = np.arange(n_samples, dtype=np.int_) - else: - indices = np.array(indices, dtype=np.int_) - - if data_names is None: - data_names = np.array(indices) - else: - data_names = np.array(data_names) + indices = cls._create_indices_array(indices, n_samples) + data_names = cls._create_names_array(data_names, indices) return cls( algorithm=algorithm, @@ -817,3 +796,32 @@ def zeros( variances=np.zeros(len(indices)), counts=np.zeros(len(indices), dtype=np.int_), ) + + @staticmethod + def _create_indices_array( + indices: Sequence[IndexT] | NDArray[IndexT] | None, n_samples: int + ) -> NDArray[IndexT]: + + if indices is None: + index_array: NDArray[IndexT] = np.arange(n_samples, dtype=np.int_) + elif isinstance(indices, np.ndarray): + index_array = indices.copy() + else: + index_array = np.asarray(indices) + + return index_array + + @staticmethod + def _create_names_array( + data_names: Sequence[NameT] | NDArray[NameT] | None, indices: NDArray[IndexT] + ) -> NDArray[NameT]: + + if data_names is None: + names = np.array(indices, copy=True) + else: + names = np.array(data_names, copy=True) + + if len(np.unique(names)) != len(names): + raise ValueError("Data names must be unique") + + return names From fc0f16d3a9ac45d58a5564bb216d9ad3a64127b8 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 13 Nov 2024 13:47:52 +0100 Subject: [PATCH 02/41] Fix warning message --- src/pydvl/valuation/result.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/src/pydvl/valuation/result.py b/src/pydvl/valuation/result.py index 284995223..47a890fcf 100644 --- a/src/pydvl/valuation/result.py +++ b/src/pydvl/valuation/result.py @@ -557,10 +557,10 @@ def __add__(self, other: ValuationResult) -> ValuationResult: # taken from the result with the name. if self._names.dtype != other._names.dtype: if np.can_cast(other._names.dtype, self._names.dtype, casting="safe"): - other._names = other._names.astype(self._names.dtype) logger.warning( f"Casting ValuationResult.names from {other._names.dtype} to {self._names.dtype}" ) + other._names = other._names.astype(self._names.dtype) else: raise TypeError( f"Cannot cast ValuationResult.names from " @@ -801,7 +801,6 @@ def zeros( def _create_indices_array( indices: Sequence[IndexT] | NDArray[IndexT] | None, n_samples: int ) -> NDArray[IndexT]: - if indices is None: index_array: NDArray[IndexT] = np.arange(n_samples, dtype=np.int_) elif isinstance(indices, np.ndarray): @@ -815,9 +814,8 @@ def _create_indices_array( def _create_names_array( data_names: Sequence[NameT] | NDArray[NameT] | None, indices: NDArray[IndexT] ) -> NDArray[NameT]: - if data_names is None: - names = np.array(indices, copy=True) + names = np.array(indices, copy=True, dtype=object) else: names = np.array(data_names, copy=True) From d76dd02b5fb2fc26cb938b52b18370c2a535990e Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 13 Nov 2024 13:48:05 +0100 Subject: [PATCH 03/41] Missing type --- src/pydvl/valuation/scorers/knn.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/pydvl/valuation/scorers/knn.py b/src/pydvl/valuation/scorers/knn.py index dcfff1471..4e8b209b1 100644 --- a/src/pydvl/valuation/scorers/knn.py +++ b/src/pydvl/valuation/scorers/knn.py @@ -6,6 +6,7 @@ from numpy.typing import NDArray from sklearn.neighbors import KNeighborsClassifier +from pydvl.valuation import Dataset from pydvl.valuation.scorers import SupervisedScorer, SupervisedScorerCallable @@ -20,7 +21,7 @@ class KNNClassifierScorer(SupervisedScorer): """ - def __init__(self, test_data): + def __init__(self, test_data: Dataset): def scoring(model: KNeighborsClassifier, X: NDArray, y: NDArray) -> float: probs = model.predict_proba(X) label_to_pos = {label: i for i, label in enumerate(model.classes_)} From 37dba9d8cbeb21a1859c6b0cbc788ec2b0033405 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 13 Nov 2024 13:49:26 +0100 Subject: [PATCH 04/41] OOB notebook: fix text, update code to new api, use joblib --- notebooks/data_oob.ipynb | 11138 +------------------------------------ 1 file changed, 155 insertions(+), 10983 deletions(-) diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb index b9e63c8ca..0e01bcb54 100644 --- a/notebooks/data_oob.ipynb +++ b/notebooks/data_oob.ipynb @@ -11,19 +11,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook introduces the Data-OOB method, an implementation based on a publication from Kwon and Zou \"[Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://proceedings.mlr.press/v202/kwon23e.html)\" ICML 2023 , using pyDVL.\n", + "This notebook introduces the Data-OOB method, from Kwon and Zou \"[Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://proceedings.mlr.press/v202/kwon23e.html)\" (ICML 2023), using pyDVL.\n", "\n", + "The objective of this paper is mainly to overcome the computational bottleneck of Shapley-based data valuation methods that require fitting a significant number of models to accurately estimate marginal contributions.\n", + "Instead, Data-OOB computes data values from out-of-bag (OOB) error estimates of a bagging model. Originally, the method is therefore only designed for bagging models, but it can be adapted to other models as well.\n", "\n", - "The objective of this paper is mainly to overcome the computational bottleneck of shapley-based data valuation methods that require to fit a significant number of models to accurately estimate marginal contributions.\n", - "The algorithms compute data values from out of bag estimates using a bagging model.\n", - "\n", - "The value can be interpreted as a partition of the OOB estimate, which is originally introduced to estimate the prediction error. This OOB estimate is given as:\n", - "\n", + "For a bagging model with $B$ estimators $\\hat{f}_b, b \\in [B]$, we define $w_{bj}$ as the number of times that the $j$-th sample is in the training set of the $b$-th estimator. For a **fixed** choice of bootstrapped training sets, the Data-OOB value of sample $(x_i, y_i)$ is defined as:\n", + " \n", "$$\n", - "\\sum_{i=1}^n\\frac{\\sum_{b=1}^{B}\\mathbb{1}(w_{bi}=0)T(y_i, \\hat{f}_b(x_i))}{\\sum_{b=1}^{B}\n", - "\\mathbb{1}\n", - "(w_{bi}=0)}\n", - "$$\n" + "\\psi(x_i,y_i) := \\frac{\\sum_{b=1}^{B}\\mathbb{1}(w_{bi}=0)T(y_i, \\hat{f}_b(x_i))}{\\sum_{b=1}^{B}\n", + "\\mathbb{1} (w_{bi}=0)}.\n", + "$$\n", + "\n", + "It can therefore be interpreted as a per-sample partition of the standard OOB error estimate for a bagging model, which is: $\\frac{1}{n} \\sum_{i=1}^n \\psi(x_i,y_i)$." ] }, { @@ -43,7 +43,6 @@ }, { "cell_type": "code", - "execution_count": 1, "metadata": { "editable": true, "slideshow": { @@ -53,22 +52,11 @@ "hide" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kristof/Projects/pyDVL/src/pydvl/value/__init__.py:16: FutureWarning: The package pydvl.value was deprecated since v0.10.0 in favor of pydvl.valuation. It will be removed in v0.12.0.\n", - " warnings.warn(msg, FutureWarning)\n" - ] - } - ], "source": [ "%load_ext autoreload\n", "\n", "import os\n", "import random\n", - "from concurrent.futures import FIRST_COMPLETED, wait\n", "from typing import Literal\n", "\n", "import matplotlib\n", @@ -77,11 +65,10 @@ "import pandas as pd\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from tqdm.notebook import tqdm\n", - "from support.common import load_adult_data\n", "\n", - "from pydvl.parallel import init_executor\n", "from pydvl.reporting.plots import plot_ci_array, plot_ci_values\n", "from pydvl.reporting.scores import compute_removal_score\n", + "from support.common import load_adult_data\n", "\n", "matplotlib.rcParams[\"axes.facecolor\"] = (1, 1, 1, 0)\n", "plt.rcParams[\"axes.facecolor\"] = (1, 1, 1, 0)\n", @@ -89,14 +76,14 @@ "\n", "is_CI = os.environ.get(\"CI\")\n", "random_state = 24\n", - "random.seed(random_state)" - ] + "random.seed(random_state);" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 2, "metadata": {}, - "outputs": [], "source": [ "%autoreload\n", "from pydvl.valuation import (\n", @@ -104,160 +91,54 @@ " DataOOBValuation,\n", " Dataset,\n", " KNNClassifierUtility,\n", - ")\n", + " )\n", "from pydvl.utils import Seed, ensure_seed_sequence" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We will work with the [adult classification dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data) from the UCI repository. The objective is to predict whether a person earns more than 50k a year based on a set of features such as age, education, occupation, etc.\n", + "We will work with the [adult classification dataset](https://archive.ics.uci.edu/dataset/2/adult) from the UCI repository. The objective is to predict whether a person earns more than 50k a year based on a set of features such as age, education, occupation, etc.\n", "\n", "With a helper function we download the data and obtain the following pandas dataframe, where the categorical features have been removed:" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found cached file: adult_data.pkl.\n" - ] - } - ], + "metadata": { + "tags": [ + "hide-output" + ] + }, "source": [ "data_adult = load_adult_data()" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [ + "hide" + ] + }, "source": [ "if is_CI:\n", " data_adult = data_adult.sample(100, random_state=random_state)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " age fnlwgt education-num capital-gain capital-loss hours-per-week \\\n", - "0 39 77516 13 2174 0 40 \n", - "1 50 83311 13 0 0 13 \n", - "2 38 215646 9 0 0 40 \n", - "3 53 234721 7 0 0 40 \n", - "4 28 338409 13 0 0 40 \n", - "\n", - " income \n", - "0 <=50K \n", - "1 <=50K \n", - "2 <=50K \n", - "3 <=50K \n", - "4 <=50K " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_adult.head()" - ] + "source": "data_adult.head()", + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -265,649 +146,41 @@ "source": [ "## Computing the OOB values\n", "\n", - "The main idea of Data-OOB is to take an existing classifier or regression model and compute a per-sample out-of-bag performance estimate via bagging.\n", + "The main idea of Data-OOB is to use the out-of-bag error estimates of a bagging model to compute data values. In pyDVL, we provide a class [DataOOBValuation][pydvl.valuation.DataOOBValuation] that allows extending this idea to any classifier. The class is designed to take an existing classifier or regression model and compute a per-sample out-of-bag performance estimate via bagging.\n", "\n", - "For this example, we use a simple KNN classifier with $k=5$ neighbours on the data and compute the data-oob values with two choices for the number of estimators in the bagging. For that we construct a [Utility][pydvl.utils.utility.Utility] object using the [Scorer][pydvl.utils.score.Scorer] class to specify the metric to use for the evaluation. Note how we pass a random seed to [Dataset.from_arrays][pydvl.utils.dataset.Dataset.from_arrays] in order to ensure that we always get the same split when running this notebook multiple times. This will be particularly important when running the standard point removal experiments later.\n", + "For this example, we use a simple KNN classifier with $k=5$ neighbours on the data and compute the data-oob values with two choices for the number of estimators in the bagging.\n", "\n", - "We then use the [compute_data_oob][pydvl.value.oob.compute_data_oob] function to compute the data-oob values." + "We then use the [fit][pydvl.valuation.DataOOBValuation.fit] method to compute the values and store them in a [ValuationResult][pydvl.value.result.ValuationResult] object." ] }, { "cell_type": "code", - "execution_count": 6, "metadata": {}, - "outputs": [], "source": [ - "train_data, test_data = Dataset.from_arrays(\n", - " data_adult.drop(columns=[\"income\"]).values,\n", - " data_adult.loc[:, \"income\"].cat.codes.values,\n", - " random_state=random_state,\n", - ")\n", + "data = Dataset(\n", + " data_adult.drop(columns=[\"income\"]).values,\n", + " data_adult.loc[:, \"income\"].cat.codes.values,\n", + " )\n", "model = KNeighborsClassifier(n_neighbors=5)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - 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"Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n", - "Casting ValuationResult.names from object to object\n" - ] - } - ], "source": [ "n_estimators = [100, 500]\n", "oob_values = []\n", "for n_est in n_estimators:\n", " valuation = DataOOBValuation(\n", - " model, n_estimators=n_est, max_samples=0.95, seed=random_state\n", - " )\n", - " valuation.fit(train_data)\n", + " model, n_estimators=n_est, max_samples=0.95, seed=random_state\n", + " )\n", + " valuation.fit(data)\n", " oob_values.append(valuation.values())" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -925,8 +198,6 @@ ] }, { - "cell_type": "code", - "execution_count": 8, "metadata": { "editable": true, "slideshow": { @@ -937,39 +208,28 @@ "invertible-output" ] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "cell_type": "code", "source": [ - "mean_colors = [\"dodgerblue\", \"indianred\", \"limegreen\", \"darkorange\", \"darkorchid\"]\n", + "mean_colors = [\"dodgerblue\", \"indianred\", \"limegreen\", \"darkorange\",\n", + " \"darkorchid\"]\n", "shade_colors = [\"lightskyblue\", \"firebrick\", \"seagreen\", \"gold\", \"plum\"]\n", "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", "for (n_est, values, mean_color, shade_color) in zip(\n", - " n_estimators, oob_values, mean_colors, shade_colors\n", - "):\n", + " n_estimators, oob_values, mean_colors, shade_colors\n", + " ):\n", " values.sort(key=\"value\")\n", " plot_ci_values(\n", - " values,\n", - " level=0.01,\n", - " mean_color=mean_color,\n", - " shade_color=shade_color,\n", - " ax=ax1,\n", - " label=f\"{n_est} estimators\",\n", - " )\n", + " values,\n", + " level=0.01,\n", + " mean_color=mean_color,\n", + " shade_color=shade_color,\n", + " ax=ax1,\n", + " label=f\"{n_est} estimators\",\n", + " )\n", "\n", - " ax2.hist(values, bins=50, color=mean_color, alpha=0.5, label=f\"{n_est} estimators\")\n", + " ax2.hist(values, bins=50, color=mean_color, alpha=0.5,\n", + " label=f\"{n_est} estimators\")\n", "ax1.set_title(\"Point rank\")\n", "ax1.set_xlabel(\"Value\")\n", "ax1.set_ylabel(\"Data-OOB values\")\n", @@ -978,10 +238,11 @@ "ax2.set_title(\"Histogram of Data-OOB values\")\n", "ax2.legend()\n", "plt.plot();" - ] + ], + "outputs": [], + "execution_count": null }, { - "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { @@ -989,17 +250,16 @@ }, "tags": [] }, + "cell_type": "markdown", "source": [ "## Point removal experiments\n", "\n", - "The standard procedure for the evaluation of data valuation schemes is the point removal experiment. The objective is to measure the evolution of performance when the best/worst points are removed from the training set. This can be done with the function [compute_removal_score][pydvl.reporting.scores.compute_removal_score], which takes precomputed values and computes the performance of the model as points are removed.\n", + "The standard procedure for the evaluation of data valuation schemes is the point removal experiment. The objective is to measure the evolution of performance when the best/worst points are iteratively removed from the training set. This can be done with the function [compute_removal_score][pydvl.reporting.scores.compute_removal_score], which takes precomputed values and evaluates the performance of the model as points are removed from the training set.\n", "\n", - "In order to test the true performance of DataOOB, we repeat the whole task of computing the values and the point removal experiment multiple times, including the splitting of the dataset into training and valuation sets. It is important to remember to pass random state adequately for full reproducibility." + "We repeat the whole task of computing the values and the point removal experiment multiple times, including the splitting of the dataset into training and valuation sets. It is important to remember to pass random state adequately for full reproducibility." ] }, { - "cell_type": "code", - "execution_count": 9, "metadata": { "editable": true, "slideshow": { @@ -1009,55 +269,54 @@ "hide" ] }, - "outputs": [], + "cell_type": "code", "source": [ "def removal_job(\n", - " method: Literal[\"random\", \"oob\"],\n", - " seed: Seed,\n", - " n_est: int = 500,\n", - " max_samples: float = 0.95,\n", - "):\n", + " method: Literal[\"random\", \"data_oob\"],\n", + " seed: Seed,\n", + " n_est: int = 500,\n", + " max_samples: float = 0.95,\n", + " ):\n", " \"\"\"This is not very efficient, but it's just an example.\"\"\"\n", " data = Dataset(\n", - " data_adult.drop(columns=[\"income\"]).values,\n", - " data_adult.loc[:, \"income\"].cat.codes.values,\n", - " )\n", - "\n", + " data_adult.drop(columns=[\"income\"]).values,\n", + " data_adult.loc[:, \"income\"].cat.codes.values,\n", + " )\n", " model = KNeighborsClassifier(n_neighbors=5)\n", - " valuation = DataOOBValuation(\n", - " model, n_estimators=n_est, max_samples=max_samples, seed=seed\n", - " )\n", "\n", " if method == \"random\":\n", " values = ValuationResult.from_random(size=len(data), seed=seed)\n", " else:\n", + " valuation = DataOOBValuation(\n", + " model, n_estimators=n_est, max_samples=max_samples, seed=seed\n", + " )\n", " valuation.fit(data)\n", " values = valuation.values()\n", "\n", " best_scores = compute_removal_score(\n", - " KNNClassifierUtility(model, data),\n", - " values,\n", - " data,\n", - " percentages=removal_percentages,\n", - " remove_best=True,\n", - " )\n", - " best_scores[\"method_name\"] = values.algorithm\n", + " KNNClassifierUtility(model, data),\n", + " values,\n", + " data,\n", + " percentages=removal_percentages,\n", + " remove_best=True,\n", + " )\n", + " best_scores[\"method_name\"] = method\n", "\n", " worst_scores = compute_removal_score(\n", - " KNNClassifierUtility(model, data),\n", - " values,\n", - " data,\n", - " percentages=removal_percentages,\n", - " remove_best=False,\n", - " )\n", - " worst_scores[\"method_name\"] = values.algorithm\n", + " KNNClassifierUtility(model, data),\n", + " values,\n", + " data,\n", + " percentages=removal_percentages,\n", + " remove_best=False,\n", + " )\n", + " worst_scores[\"method_name\"] = method\n", "\n", " return best_scores, worst_scores" - ] + ], + "outputs": [], + "execution_count": null }, { - "cell_type": "code", - "execution_count": 10, "metadata": { "editable": true, "slideshow": { @@ -1067,10065 +326,14 @@ "hide" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kristof/Projects/pyDVL/src/pydvl/parallel/config.py:31: FutureWarning: The `ParallelConfig` class was deprecated in v0.9.0 and will be removed in v0.10.0\n", - " warnings.warn(\n", - "/Users/kristof/Projects/pyDVL/src/pydvl/parallel/backends/joblib.py:79: FutureWarning: The `JoblibParallelBackend` uses deprecated arguments: `config`. They were deprecated since v0.9.0 and will be removed in v0.10.0.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dfb8897bce60474684afb41dac801129", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/40 [00:00 0:\n", - " pbar.n = 2 * n_runs - len(pending) + 1 # HACK\n", - " pbar.refresh()\n", - " completed, pending = wait(pending, timeout=0.5, return_when=FIRST_COMPLETED)\n", - " for future in completed:\n", - " best_scores, worst_scores = future.result()\n", + " job = delayed(removal_job)\n", + " args = zip([\"random\", \"data_oob\"] * n_runs,\n", + " seed_seq.spawn(2 * n_runs),\n", + " [n_est] * 2 * n_runs,\n", + " [max_samples] * 2 * n_runs)\n", + " with Parallel(return_as=\"generator_unordered\") as parallel:\n", + " delayed_evals = parallel(\n", + " job(method=m, seed=s, n_est=n, max_samples=ms) for m, s, n, ms\n", + " in args)\n", + " for result in tqdm(delayed_evals, unit=\"%\", total=2 * n_runs):\n", + " best_scores, worst_scores = result\n", " all_best_scores.append(best_scores)\n", " all_worst_scores.append(worst_scores)\n", "\n", "best_scores_df = pd.DataFrame(all_best_scores)\n", "worst_scores_df = pd.DataFrame(all_worst_scores)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": 11, "metadata": { "editable": true, "slideshow": { @@ -11173,72 +376,32 @@ "invertible-output" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n", - " ret = um.true_divide(\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n", - " return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n", - " arrmean = um.true_divide(arrmean, div, out=arrmean,\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n", - " ret = um.true_divide(\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:184: RuntimeWarning: invalid value encountered in divide\n", - " ret = um.true_divide(\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3747: RuntimeWarning: Degrees of freedom <= 0 for slice\n", - " return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in divide\n", - " arrmean = um.true_divide(arrmean, div, out=arrmean,\n", - "/opt/homebrew/Caskroom/miniforge/base/envs/pydvl/lib/python3.8/site-packages/numpy/core/_methods.py:258: RuntimeWarning: invalid value encountered in divide\n", - " ret = um.true_divide(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], "source": [ "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", "for i, method_name in enumerate([\"random\", \"data_oob\"]):\n", " plot_ci_array(\n", - " data=worst_scores_df[worst_scores_df[\"method_name\"] == method_name]\n", - " .drop(columns=[\"method_name\"])\n", - " .values,\n", - " level=0.05,\n", - " abscissa=removal_percentages,\n", - " mean_color=mean_colors[i],\n", - " shade_color=shade_colors[i],\n", - " label=method_name,\n", - " ax=ax1,\n", - " )\n", + " data=worst_scores_df[worst_scores_df[\"method_name\"] == method_name]\n", + " .drop(columns=[\"method_name\"])\n", + " .values,\n", + " level=0.05,\n", + " abscissa=removal_percentages,\n", + " mean_color=mean_colors[i],\n", + " shade_color=shade_colors[i],\n", + " label=method_name,\n", + " ax=ax1,\n", + " )\n", " plot_ci_array(\n", - " best_scores_df[best_scores_df[\"method_name\"] == method_name]\n", - " .drop(columns=[\"method_name\"])\n", - " .values,\n", - " level=0.05,\n", - " abscissa=removal_percentages,\n", - " mean_color=mean_colors[i],\n", - " shade_color=shade_colors[i],\n", - " label=method_name,\n", - " ax=ax2,\n", - " )\n", + " best_scores_df[best_scores_df[\"method_name\"] == method_name]\n", + " .drop(columns=[\"method_name\"])\n", + " .values,\n", + " level=0.05,\n", + " abscissa=removal_percentages,\n", + " mean_color=mean_colors[i],\n", + " shade_color=shade_colors[i],\n", + " label=method_name,\n", + " ax=ax2,\n", + " )\n", "metric_name = \"Accuracy\"\n", "ax1.set_title(\"Worst point removal, 95% CI\")\n", "ax1.set_xlabel(\"Fraction of data removed\")\n", @@ -11252,7 +415,16 @@ "ax2.legend()\n", "\n", "plt.show();" - ] + ], + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "outputs": [], + "execution_count": null } ], "metadata": { From aae6b3b74b21df6e5d6c01b7bc00f2dba7087f9d Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 13 Nov 2024 14:04:25 +0100 Subject: [PATCH 05/41] Move legacy code outside core dist --- notebooks/least_core_basic.ipynb | 2 +- notebooks/support/legacy.py | 56 ++++++++++++++++++++++++++++++++ src/pydvl/reporting/scores.py | 50 ++-------------------------- 3 files changed, 59 insertions(+), 49 deletions(-) create mode 100644 notebooks/support/legacy.py diff --git a/notebooks/least_core_basic.ipynb b/notebooks/least_core_basic.ipynb index 751ccf8c8..2bbc140a2 100644 --- a/notebooks/least_core_basic.ipynb +++ b/notebooks/least_core_basic.ipynb @@ -144,7 +144,7 @@ ")\n", "from pydvl.value import compute_least_core_values, LeastCoreMode, ValuationResult\n", "from pydvl.reporting.plots import shaded_mean_std\n", - "from pydvl.reporting.scores import compute_removal_score_legacy" + "from support.legacy import compute_removal_score_legacy" ] }, { diff --git a/notebooks/support/legacy.py b/notebooks/support/legacy.py new file mode 100644 index 000000000..7c0ea431c --- /dev/null +++ b/notebooks/support/legacy.py @@ -0,0 +1,56 @@ +from __future__ import annotations + +from typing import Iterable + +import numpy as np +from numpy.typing import NDArray +from tqdm import tqdm + +from pydvl.utils import Utility +from pydvl.value.result import ValuationResult as LegacyValuationResult + +__all__ = ["compute_removal_score_legacy"] + + +def compute_removal_score_legacy( + u: Utility, + values: LegacyValuationResult, + percentages: NDArray[np.float64] | Iterable[float], + *, + remove_best: bool = False, + progress: bool = False, +) -> dict[float, float]: + r"""Fits model and computes score on the test set after incrementally removing + a percentage of data points from the training set, based on their values. + + Args: + u: Utility object with model, data, and scoring function. + values: Data values of data instances in the training set. + percentages: Sequence of removal percentages. + remove_best: If True, removes data points in order of decreasing valuation. + progress: If True, display a progress bar. + + Returns: + Dictionary that maps the percentages to their respective scores. + """ + + # Sanity checks + if np.any([x >= 1.0 or x < 0.0 for x in percentages]): + raise ValueError("All percentages should be in the range [0.0, 1.0)") + + if len(values) != len(u.data.indices): + raise ValueError( + f"The number of values, {len(values) }, should be equal to the number of data indices, {len(u.data.indices)}" + ) + + scores = {} + + # We sort in descending order if we want to remove the best values + values.sort(reverse=remove_best) + + for pct in tqdm(percentages, disable=not progress, desc="Removal Scores"): + n_removal = int(pct * len(u.data)) + indices = values.indices[n_removal:] + score = u(indices) + scores[pct] = score + return scores diff --git a/src/pydvl/reporting/scores.py b/src/pydvl/reporting/scores.py index d99228f5c..9f06614d7 100644 --- a/src/pydvl/reporting/scores.py +++ b/src/pydvl/reporting/scores.py @@ -1,63 +1,17 @@ from __future__ import annotations -from typing import Dict, Iterable, Union +from typing import Iterable import numpy as np from numpy.typing import NDArray from tqdm.auto import tqdm -from pydvl.utils import Utility from pydvl.valuation.dataset import Dataset from pydvl.valuation.result import ValuationResult from pydvl.valuation.types import Sample from pydvl.valuation.utility.modelutility import ModelUtility -from pydvl.value.result import ValuationResult as LegacyValuationResult -__all__ = ["compute_removal_score_legacy", "compute_removal_score"] - - -def compute_removal_score_legacy( - u: Utility, - values: LegacyValuationResult, - percentages: Union[NDArray[np.float64], Iterable[float]], - *, - remove_best: bool = False, - progress: bool = False, -) -> Dict[float, float]: - r"""Fits model and computes score on the test set after incrementally removing - a percentage of data points from the training set, based on their values. - - Args: - u: Utility object with model, data, and scoring function. - values: Data values of data instances in the training set. - percentages: Sequence of removal percentages. - remove_best: If True, removes data points in order of decreasing valuation. - progress: If True, display a progress bar. - - Returns: - Dictionary that maps the percentages to their respective scores. - """ - - # Sanity checks - if np.any([x >= 1.0 or x < 0.0 for x in percentages]): - raise ValueError("All percentages should be in the range [0.0, 1.0)") - - if len(values) != len(u.data.indices): - raise ValueError( - f"The number of values, {len(values) }, should be equal to the number of data indices, {len(u.data.indices)}" - ) - - scores = {} - - # We sort in descending order if we want to remove the best values - values.sort(reverse=remove_best) - - for pct in tqdm(percentages, disable=not progress, desc="Removal Scores"): - n_removal = int(pct * len(u.data)) - indices = values.indices[n_removal:] - score = u(indices) - scores[pct] = score - return scores +__all__ = ["compute_removal_score"] def compute_removal_score( From ce42b0cd8f6e4a78f11da94cd897605587809bdb Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 13 Nov 2024 14:07:24 +0100 Subject: [PATCH 06/41] Add deletion note --- notebooks/support/legacy.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/notebooks/support/legacy.py b/notebooks/support/legacy.py index 7c0ea431c..d3a42f31f 100644 --- a/notebooks/support/legacy.py +++ b/notebooks/support/legacy.py @@ -1,3 +1,8 @@ +""" +DELETE THIS FILE: +Temporary support for least_core_basic.ipynb until it no longer requires +compute_removal_score_legacy +""" from __future__ import annotations from typing import Iterable From 6ae19ebfdc87449ca97fdd27a2da37b2f0598661 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Thu, 14 Nov 2024 12:21:25 +0100 Subject: [PATCH 07/41] Minor text tweaks to nb --- notebooks/data_oob.ipynb | 316 ++++++++++++++++++++++++++++++++------ tests/utils/test_types.py | 37 +++++ 2 files changed, 310 insertions(+), 43 deletions(-) create mode 100644 tests/utils/test_types.py diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb index 0e01bcb54..2f5be972e 100644 --- a/notebooks/data_oob.ipynb +++ b/notebooks/data_oob.ipynb @@ -50,7 +50,11 @@ }, "tags": [ "hide" - ] + ], + "ExecuteTime": { + "end_time": "2024-11-14T11:09:08.038214Z", + "start_time": "2024-11-14T11:09:05.740790Z" + } }, "source": [ "%load_ext autoreload\n", @@ -63,6 +67,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", + "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from tqdm.notebook import tqdm\n", "\n", @@ -79,11 +84,16 @@ "random.seed(random_state);" ], "outputs": [], - "execution_count": null + "execution_count": 1 }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T11:09:10.178202Z", + "start_time": "2024-11-14T11:09:08.698435Z" + } + }, "source": [ "%autoreload\n", "from pydvl.valuation import (\n", @@ -95,7 +105,7 @@ "from pydvl.utils import Seed, ensure_seed_sequence" ], "outputs": [], - "execution_count": null + "execution_count": 2 }, { "cell_type": "markdown", @@ -111,34 +121,162 @@ "metadata": { "tags": [ "hide-output" - ] + ], + "ExecuteTime": { + "end_time": "2024-11-14T11:09:10.204197Z", + "start_time": "2024-11-14T11:09:10.198352Z" + } }, "source": [ - "data_adult = load_adult_data()" + "data_adult = load_adult_data()\n", + "data_adult = data_adult.sample(frac=0.8, random_state=random_state)" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found cached file: adult_data.pkl.\n" + ] + } + ], + "execution_count": 3 }, { "cell_type": "code", "metadata": { "tags": [ "hide" - ] + ], + "ExecuteTime": { + "end_time": "2024-11-14T11:09:10.693987Z", + "start_time": "2024-11-14T11:09:10.689873Z" + } }, "source": [ "if is_CI:\n", " data_adult = data_adult.sample(100, random_state=random_state)" ], "outputs": [], - "execution_count": null + "execution_count": 4 }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T11:09:11.250997Z", + "start_time": "2024-11-14T11:09:11.234385Z" + } + }, "source": "data_adult.head()", - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + " age fnlwgt education-num capital-gain capital-loss hours-per-week \\\n", + "1084 31 232475 10 0 0 40 \n", + "1410 48 329778 9 0 0 40 \n", + "1930 43 166740 10 0 0 48 \n", + "1270 47 200734 13 0 0 45 \n", + "1280 52 186785 9 0 1876 50 \n", + "\n", + " income \n", + "1084 <=50K \n", + "1410 <=50K \n", + "1930 <=50K \n", + "1270 <=50K \n", + "1280 <=50K " + ], + "text/html": [ + "
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agefnlwgteducation-numcapital-gaincapital-losshours-per-weekincome
108431232475100040<=50K
14104832977890040<=50K
193043166740100048<=50K
127047200734130045<=50K
12805218678590187650<=50K
\n", + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 5 }, { "cell_type": "markdown", @@ -155,7 +293,12 @@ }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T11:09:12.496742Z", + "start_time": "2024-11-14T11:09:12.489613Z" + } + }, "source": [ "data = Dataset(\n", " data_adult.drop(columns=[\"income\"]).values,\n", @@ -164,23 +307,30 @@ "model = KNeighborsClassifier(n_neighbors=5)" ], "outputs": [], - "execution_count": null + "execution_count": 6 }, { "cell_type": "code", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-14T11:09:25.935095Z", + "start_time": "2024-11-14T11:09:13.364079Z" + } + }, "source": [ "n_estimators = [100, 500]\n", "oob_values = []\n", "for n_est in n_estimators:\n", - " valuation = DataOOBValuation(\n", - " model, n_estimators=n_est, max_samples=0.95, seed=random_state\n", - " )\n", + " # model = RandomForestClassifier(n_estimators=n_est,\n", + " # max_samples=0.95,\n", + " # random_state=random_state)\n", + " # model.fit(data.x, data.y)\n", + " valuation = DataOOBValuation(model, n_estimators=n_est, max_samples=0.95, seed=random_state)\n", " valuation.fit(data)\n", " oob_values.append(valuation.values())" ], "outputs": [], - "execution_count": null + "execution_count": 7 }, { "cell_type": "markdown", @@ -206,7 +356,11 @@ "tags": [ "hide-input", "invertible-output" - ] + ], + "ExecuteTime": { + "end_time": "2024-11-14T11:09:27.725095Z", + "start_time": "2024-11-14T11:09:26.819728Z" + } }, "cell_type": "code", "source": [ @@ -239,25 +393,61 @@ "ax2.legend()\n", "plt.plot();" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "execution_count": 8 }, { "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] + "ExecuteTime": { + "end_time": "2024-11-14T11:09:50.385541Z", + "start_time": "2024-11-14T11:09:50.208032Z" + } }, - "cell_type": "markdown", + "cell_type": "code", "source": [ - "## Point removal experiments\n", - "\n", - "The standard procedure for the evaluation of data valuation schemes is the point removal experiment. The objective is to measure the evolution of performance when the best/worst points are iteratively removed from the training set. This can be done with the function [compute_removal_score][pydvl.reporting.scores.compute_removal_score], which takes precomputed values and evaluates the performance of the model as points are removed from the training set.\n", - "\n", - "We repeat the whole task of computing the values and the point removal experiment multiple times, including the splitting of the dataset into training and valuation sets. It is important to remember to pass random state adequately for full reproducibility." - ] + "obv, rv = oob_values\n", + "df = obv.to_dataframe(\"oob\")\n", + "df.to_csv(\"/tmp/data_oob_values.csv\")\n", + "df[\"oob_variances\"].hist(bins=50)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "execution_count": 10 }, { "metadata": { @@ -267,7 +457,11 @@ }, "tags": [ "hide" - ] + ], + "ExecuteTime": { + "end_time": "2024-11-13T18:41:00.769921Z", + "start_time": "2024-11-13T18:41:00.764054Z" + } }, "cell_type": "code", "source": [ @@ -314,7 +508,7 @@ " return best_scores, worst_scores" ], "outputs": [], - "execution_count": null + "execution_count": 10 }, { "metadata": { @@ -324,7 +518,11 @@ }, "tags": [ "hide" - ] + ], + "ExecuteTime": { + "end_time": "2024-11-13T18:41:35.175143Z", + "start_time": "2024-11-13T18:41:11.810975Z" + } }, "cell_type": "code", "source": [ @@ -361,8 +559,23 @@ "best_scores_df = pd.DataFrame(all_best_scores)\n", "worst_scores_df = pd.DataFrame(all_worst_scores)" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + " 0%| | 0/40 [00:00" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "execution_count": 13 }, { "metadata": {}, diff --git a/tests/utils/test_types.py b/tests/utils/test_types.py new file mode 100644 index 000000000..bd658ca1f --- /dev/null +++ b/tests/utils/test_types.py @@ -0,0 +1,37 @@ +import pytest +from sklearn.ensemble import ( + BaggingClassifier, + BaggingRegressor, + RandomForestClassifier, + RandomForestRegressor, + ExtraTreesClassifier, + ExtraTreesRegressor, +) +from pydvl.utils.types import is_bagging_model + + +@pytest.mark.parametrize( + "model_class", + [ + BaggingClassifier, + BaggingRegressor, + RandomForestClassifier, + RandomForestRegressor, + ExtraTreesClassifier, + ExtraTreesRegressor, + ], +) +def test_is_bagging_model(model_class): + model = model_class() + assert is_bagging_model( + model + ), f"{model_class.__name__} should be recognized as a bagging model" + + +def test_is_not_bagging_model(): + from sklearn.linear_model import LinearRegression + + model = LinearRegression() + assert not is_bagging_model( + model + ), "LinearRegression should not be recognized as a bagging model" From 3555960c3727038002d7270918df906030364e33 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Thu, 14 Nov 2024 12:21:52 +0100 Subject: [PATCH 08/41] Fix warning message --- src/pydvl/value/result.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pydvl/value/result.py b/src/pydvl/value/result.py index 6a714e1bf..924278b92 100644 --- a/src/pydvl/value/result.py +++ b/src/pydvl/value/result.py @@ -557,10 +557,10 @@ def __add__( # taken from the result with the name. if self._names.dtype != other._names.dtype: if np.can_cast(other._names.dtype, self._names.dtype, casting="safe"): - other._names = other._names.astype(self._names.dtype) logger.warning( f"Casting ValuationResult.names from {other._names.dtype} to {self._names.dtype}" ) + other._names = other._names.astype(self._names.dtype) else: raise TypeError( f"Cannot cast ValuationResult.names from " From 7266c03d2f6f82184d64de0d75f40eacc5fc9b3a Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Thu, 14 Nov 2024 12:22:52 +0100 Subject: [PATCH 09/41] Export both variances and counts to dataframe --- src/pydvl/valuation/result.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/pydvl/valuation/result.py b/src/pydvl/valuation/result.py index 47a890fcf..43cc7db1c 100644 --- a/src/pydvl/valuation/result.py +++ b/src/pydvl/valuation/result.py @@ -682,7 +682,8 @@ def to_dataframe( else self._indices[self._sort_positions], columns=[column], ) - df[column + "_stderr"] = self.stderr[self._sort_positions] + df[column + "_variances"] = self.variances[self._sort_positions] + df[column + "_counts"] = self.counts[self._sort_positions] return df @classmethod From c037e5acbee852dfc0c6f71d2abc59f989b04d88 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Thu, 14 Nov 2024 12:23:59 +0100 Subject: [PATCH 10/41] Add BaggingModel type and predicate --- src/pydvl/utils/types.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/src/pydvl/utils/types.py b/src/pydvl/utils/types.py index 563d28150..faf5bb29a 100644 --- a/src/pydvl/utils/types.py +++ b/src/pydvl/utils/types.py @@ -4,6 +4,7 @@ from __future__ import annotations +import sys from typing import Any, Optional, Protocol, TypeVar, Union, cast, runtime_checkable import numpy as np @@ -11,6 +12,7 @@ from numpy.typing import NDArray __all__ = [ + "BaggingModel", "BaseModel", "IndexT", "LossFunction", @@ -20,6 +22,7 @@ "Seed", "SupervisedModel", "ensure_seed_sequence", + "is_bagging_model", ] IndexT = TypeVar("IndexT", bound=np.int_) @@ -96,6 +99,20 @@ def score(self, x: NDArray, y: NDArray | None) -> float: pass +def is_bagging_model(model: SupervisedModel) -> bool: + """Any model with the attributes `n_estimators` and `max_samples` is considered a + bagging model.""" + return hasattr(model, "n_estimators") and hasattr(model, "max_samples") + + +if sys.version_info >= (3, 9): + from typing import Annotated + + BaggingModel = Annotated[SupervisedModel, is_bagging_model] +else: + BaggingModel = SupervisedModel + + def ensure_seed_sequence( seed: Optional[Union[Seed, SeedSequence]] = None ) -> SeedSequence: From 2bdbea2ba7d1e4c9e7ad306d08ee08ececbb1208 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Thu, 14 Nov 2024 14:26:27 +0100 Subject: [PATCH 11/41] Revert typo leading to bogus stderr computation --- src/pydvl/valuation/result.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pydvl/valuation/result.py b/src/pydvl/valuation/result.py index 43cc7db1c..8174c5ee0 100644 --- a/src/pydvl/valuation/result.py +++ b/src/pydvl/valuation/result.py @@ -314,7 +314,7 @@ def variances(self) -> NDArray[np.float_]: def stderr(self) -> NDArray[np.float_]: """Standard errors of the value estimates, possibly sorted.""" return cast( - NDArray[np.float_], np.sqrt(self._variances / np.maximum(1, self.counts)) + NDArray[np.float_], np.sqrt(self.variances / np.maximum(1, self.counts)) ) @property From 9d0ff804b0a10b6668118db71d1bef398e1b8637 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Thu, 14 Nov 2024 16:32:34 +0100 Subject: [PATCH 12/41] Simplify type check and improve tests --- src/pydvl/utils/types.py | 31 +++++++++++++------ tests/utils/test_types.py | 64 +++++++++++++++++++++++++++++++-------- 2 files changed, 73 insertions(+), 22 deletions(-) diff --git a/src/pydvl/utils/types.py b/src/pydvl/utils/types.py index faf5bb29a..c0a3978ca 100644 --- a/src/pydvl/utils/types.py +++ b/src/pydvl/utils/types.py @@ -4,7 +4,6 @@ from __future__ import annotations -import sys from typing import Any, Optional, Protocol, TypeVar, Union, cast, runtime_checkable import numpy as np @@ -22,7 +21,6 @@ "Seed", "SupervisedModel", "ensure_seed_sequence", - "is_bagging_model", ] IndexT = TypeVar("IndexT", bound=np.int_) @@ -99,18 +97,33 @@ def score(self, x: NDArray, y: NDArray | None) -> float: pass -def is_bagging_model(model: SupervisedModel) -> bool: +@runtime_checkable +class BaggingModel(Protocol): """Any model with the attributes `n_estimators` and `max_samples` is considered a bagging model.""" - return hasattr(model, "n_estimators") and hasattr(model, "max_samples") + n_estimators: int + max_samples: float -if sys.version_info >= (3, 9): - from typing import Annotated + def fit(self, x: NDArray, y: NDArray | None): + """Fit the model to the data + + Args: + x: Independent variables + y: Dependent variable + """ + pass - BaggingModel = Annotated[SupervisedModel, is_bagging_model] -else: - BaggingModel = SupervisedModel + def predict(self, x: NDArray) -> NDArray: + """Compute predictions for the input + + Args: + x: Independent variables for which to compute predictions + + Returns: + Predictions for the input + """ + pass def ensure_seed_sequence( diff --git a/tests/utils/test_types.py b/tests/utils/test_types.py index bd658ca1f..e5d4b6d7b 100644 --- a/tests/utils/test_types.py +++ b/tests/utils/test_types.py @@ -1,13 +1,25 @@ import pytest from sklearn.ensemble import ( + AdaBoostClassifier, + AdaBoostRegressor, BaggingClassifier, BaggingRegressor, - RandomForestClassifier, - RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor, + GradientBoostingClassifier, + GradientBoostingRegressor, + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, + IsolationForest, + RandomForestClassifier, + RandomForestRegressor, + StackingClassifier, + StackingRegressor, + VotingClassifier, + VotingRegressor, ) -from pydvl.utils.types import is_bagging_model + +from pydvl.utils.types import BaggingModel @pytest.mark.parametrize( @@ -15,23 +27,49 @@ [ BaggingClassifier, BaggingRegressor, - RandomForestClassifier, - RandomForestRegressor, ExtraTreesClassifier, ExtraTreesRegressor, + IsolationForest, + RandomForestClassifier, + RandomForestRegressor, ], ) def test_is_bagging_model(model_class): model = model_class() - assert is_bagging_model( - model + assert isinstance( + model, BaggingModel ), f"{model_class.__name__} should be recognized as a bagging model" -def test_is_not_bagging_model(): - from sklearn.linear_model import LinearRegression +@pytest.mark.parametrize( + "model_class", + [ + AdaBoostClassifier, + AdaBoostRegressor, + GradientBoostingClassifier, + GradientBoostingRegressor, + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, + ], +) +def test_is_not_bagging_model(model_class): + model = model_class() + assert not isinstance( + model, BaggingModel + ), f"{model_class.__name__} should not be recognized as a bagging model" + - model = LinearRegression() - assert not is_bagging_model( - model - ), "LinearRegression should not be recognized as a bagging model" +@pytest.mark.parametrize( + "model_class", + [ + StackingClassifier, + StackingRegressor, + VotingClassifier, + VotingRegressor, + ], +) +def test_is_not_bagging_model_other(model_class): + model = model_class(estimators=[("est", RandomForestClassifier())]) + assert not isinstance( + model, BaggingModel + ), f"{model_class.__name__} should not be recognized as a bagging model" From b7542278e984ddf5d4b348633fb1e916a4fbc727 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Thu, 14 Nov 2024 16:33:56 +0100 Subject: [PATCH 13/41] Remove support for implicit training of bagging models in data_oob --- src/pydvl/valuation/methods/data_oob.py | 132 ++++++++++++------------ 1 file changed, 66 insertions(+), 66 deletions(-) diff --git a/src/pydvl/valuation/methods/data_oob.py b/src/pydvl/valuation/methods/data_oob.py index d44edc2bf..c4d2647a9 100644 --- a/src/pydvl/valuation/methods/data_oob.py +++ b/src/pydvl/valuation/methods/data_oob.py @@ -1,11 +1,12 @@ r""" This module implements the method described in (Kwon and Zou, 2023)1. It fits a bagging classifier or regressor to the data -with a given model as base estimator. A data point's value is the average loss of the -estimators which were not fit on it. +href="kwon_data_2023">1. + +A data point's Data-OOB value is defined for bagging models. It is the average loss of +the estimators which were not fit on it. Let $w_{bj}\in Z$ be the number of times the j-th datum $(x_j, y_j)$ is selected -in the b-th bootstrap dataset. +in the b-th bootstrap dataset. The Data-OOB value is computed as follows: $$ \psi((x_i,y_i),\Theta_B):=\frac{\sum_{b=1}^{B}\mathbb{1}(w_{bi}=0)T(y_i, @@ -16,10 +17,6 @@ where $T: Y \times Y \rightarrow \mathbb{R}$ is a score function that represents the goodness of a weak learner $\hat{f}_b$ at the i-th datum $(x_i, y_i)$. -!!! Warning - This implementation is a placeholder and does not match exactly the method described - in the paper. - ## References [^1]: Kwon, Yongchan, and James Zou. [Data-OOB: Out-of-bag @@ -35,14 +32,20 @@ import numpy as np from numpy.typing import NDArray -from sklearn.base import is_classifier, is_regressor -from sklearn.ensemble import BaggingClassifier, BaggingRegressor +from sklearn.base import is_classifier + +# HACK: we use some private sklearn stuff to obtain the indices of the bootstrap samples +# in RandomForest and ExtraTrees, which do not have the `estimators_samples_` attribute. +from sklearn.ensemble._forest import ( + _generate_unsampled_indices, + _get_n_samples_bootstrap, +) +from sklearn.utils.validation import check_is_fitted -from pydvl.utils.types import Seed, SupervisedModel +from pydvl.utils.types import BaggingModel, LossFunction from pydvl.valuation.base import Valuation from pydvl.valuation.dataset import Dataset from pydvl.valuation.result import ValuationResult -from pydvl.valuation.types import LossFunction T = TypeVar("T", bound=np.number) @@ -52,90 +55,87 @@ class DataOOBValuation(Valuation): """Computes Data Out-Of-Bag values. - !!! tip - `n_estimators` and `max_samples` must be tuned jointly to ensure that all - samples are at least 1 time out-of-bag, otherwise the result could include a - NaN value for that datum. + This class implements the method described in (Kwon and Zou, + 2023)1. Args: - data: dataset - model: - n_estimators: Number of estimators used in the bagging procedure. - max_samples: The fraction of samples to draw to train each base estimator. - loss: A function taking as parameters model prediction and corresponding - data labels(y_true, y_pred) and returning an array of point-wise errors. - seed: Either an instance of a numpy random number generator or a seed - for it. + model: A fitted bagging model. Bagging models in sklearn include + [[BaggingClassifier]], [[BaggingRegressor]], [[IsolationForest]], RandomForest*, + ExtraTrees*, or any model which defines an attribute `estimators_` and uses + bootstrapped subsamples to compute predictions. + loss: A loss function to compare the true values with the predictions. If `None`, + it uses point-wise accuracy for classifiers and negative $l_2$ distance for + regressors. Returns: Object with the data values. - - FIXME: this is an extended pydvl implementation of the Data-OOB valuation method - which just bags whatever model is passed to it. The paper only considers bagging - models as input. """ def __init__( self, - model: SupervisedModel, - n_estimators: int, - max_samples: float = 0.8, + model: BaggingModel, loss: LossFunction | None = None, - seed: Seed | None = None, ): super().__init__() self.model = model - self.n_estimators = n_estimators - self.max_samples = max_samples self.loss = loss - self.rng = np.random.default_rng(seed) def fit(self, data: Dataset): # TODO: automate str representation for all Valuations algorithm_name = f"Data-OOB-{str(self.model)}" - self.result = ValuationResult.zeros( + self.result = ValuationResult.empty( algorithm=algorithm_name, indices=data.indices, data_names=data.data_names, ) - random_state = np.random.RandomState(self.rng.bit_generator) - - if is_classifier(self.model): - logger.info(f"Training BaggingClassifier using {self.model}") - bag = BaggingClassifier( - self.model, - n_estimators=self.n_estimators, - max_samples=self.max_samples, - random_state=random_state, - ) - if self.loss is None: - self.loss = point_wise_accuracy - elif is_regressor(self.model): - logger.info(f"Training BaggingRegressor using {self.model}") - bag = BaggingRegressor( - self.model, - n_estimators=self.n_estimators, - max_samples=self.max_samples, - random_state=random_state, - ) - if self.loss is None: - self.loss = neg_l2_distance - else: + # We depart from common practice in pyDVL and perform a runtime check because + # this is one of a few model-specific valuation methods. + if not isinstance(self.model, BaggingModel): raise Exception( - "Model has to be a classifier or a regressor in sklearn format." + "The model has to be an sklearn-compatible bagging model, including " + "BaggingClassifier, BaggingRegressor, IsolationForest, RandomForest*, " + "ExtraTrees*, and any model which defines n_estimators, max_samples, " + "and after fitting estimators_ and uses bootstrapped subsamples to " + "compute predictions." + ) + + check_is_fitted( + self.model, + msg="The bagging model has to be fitted before calling the valuation method.", + ) + # This should always be present after fitting + estimators = getattr(self.model, "estimators_") + + if self.loss is None: + self.loss = ( + point_wise_accuracy if is_classifier(self.model) else neg_l2_distance ) - bag.fit(data.x, data.y) - for est, samples in zip(bag.estimators_, bag.estimators_samples_): - oob_idx = np.setxor1d(data.indices, np.unique(samples)) + if hasattr(self.model, "estimators_samples_"): # Bagging(Classifier|Regressor) + unsampled_indices = [ + np.setxor1d(data.indices, np.unique(sampled)) + for sampled in self.model.estimators_samples_ + ] + else: # RandomForest*, ExtraTrees*, IsolationForest + n_samples_bootstrap = _get_n_samples_bootstrap( + len(data), self.model.max_samples + ) + unsampled_indices = [] + for i, est in enumerate(estimators): + oob_indices = _generate_unsampled_indices( + est.random_state, len(data.indices), n_samples_bootstrap + ) + unsampled_indices.append(oob_indices) + + for est, oob_indices in zip(estimators, unsampled_indices): array_loss = self.loss( - y_true=data.y[oob_idx], - y_pred=est.predict(data.x[oob_idx]), + y_true=data.y[oob_indices], + y_pred=est.predict(data.x[oob_indices]), ) self.result += ValuationResult( algorithm=algorithm_name, - indices=oob_idx, + indices=oob_indices, values=array_loss, counts=np.ones_like(array_loss, dtype=data.indices.dtype), ) From 2d9ce58aad0096dfae220c4d98a4c0a466de4352 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Thu, 14 Nov 2024 21:57:50 +0100 Subject: [PATCH 14/41] Missing type --- notebooks/support/common.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/support/common.py b/notebooks/support/common.py index 6a5242047..ea7a02fe8 100644 --- a/notebooks/support/common.py +++ b/notebooks/support/common.py @@ -609,7 +609,7 @@ def wrapper(*args, **kwargs): @filecache(path=Path("adult_data.pkl")) -def load_adult_data(): +def load_adult_data() -> pd.DataFrame: data_url = ( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data" ) From d49f6a3641a5bfc600856a1eba8696ee6ed144bf Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Sat, 16 Nov 2024 19:51:47 +0100 Subject: [PATCH 15/41] Fix plotting for oob --- src/pydvl/reporting/plots.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/src/pydvl/reporting/plots.py b/src/pydvl/reporting/plots.py index 72be2cb8b..c65ce3de7 100644 --- a/src/pydvl/reporting/plots.py +++ b/src/pydvl/reporting/plots.py @@ -73,7 +73,7 @@ def plot_ci_array( data: NDArray, level: float, type: Literal["normal", "t", "auto"] = "normal", - abscissa: Optional[Sequence[str]] = None, + abscissa: Optional[Sequence[Any]] = None, mean_color: Optional[str] = "dodgerblue", shade_color: Optional[str] = "lightblue", ax: Optional[plt.Axes] = None, @@ -110,15 +110,13 @@ def plot_ci_array( variances=variances, counts=np.ones_like(means, dtype=np.int_) * m, indices=np.arange(n), - data_names=np.array(abscissa, dtype=str) - if abscissa is not None - else np.arange(n, dtype=str), ) return plot_ci_values( dummy, level=level, type=type, + abscissa=abscissa, mean_color=mean_color, shade_color=shade_color, ax=ax, @@ -130,7 +128,7 @@ def plot_ci_values( values: ValuationResult, level: float, type: Literal["normal", "t", "auto"] = "auto", - abscissa: Optional[Sequence[str]] = None, + abscissa: Optional[Sequence[Any]] = None, mean_color: Optional[str] = "dodgerblue", shade_color: Optional[str] = "lightblue", ax: Optional[plt.Axes] = None, @@ -138,8 +136,6 @@ def plot_ci_values( ): """Plot values and a confidence interval. - Uses `values.data_names` for the x-axis. - Supported intervals are based on the normal and the t distributions. Args: @@ -176,7 +172,8 @@ def plot_ci_values( ) from None if abscissa is None: - abscissa = [str(i) for i, _ in enumerate(values)] + abscissa = range(len(values)) + bound = score * values.stderr if ax is None: @@ -190,6 +187,7 @@ def plot_ci_values( color=shade_color, ) ax.plot(abscissa, values.values, color=mean_color, **kwargs) + ax.set_xlim(left=min(abscissa), right=max(abscissa)) return ax From 01628ffdc1ba523aa48d1636929d80562517ae29 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Sat, 16 Nov 2024 19:52:10 +0100 Subject: [PATCH 16/41] Fix naming of OOB scoring function --- src/pydvl/utils/types.py | 4 ++-- src/pydvl/valuation/methods/data_oob.py | 32 ++++++++++++++----------- src/pydvl/value/oob/oob.py | 4 ++-- 3 files changed, 22 insertions(+), 18 deletions(-) diff --git a/src/pydvl/utils/types.py b/src/pydvl/utils/types.py index c0a3978ca..ec2a50cb5 100644 --- a/src/pydvl/utils/types.py +++ b/src/pydvl/utils/types.py @@ -14,7 +14,7 @@ "BaggingModel", "BaseModel", "IndexT", - "LossFunction", + "PointwiseScore", "MapFunction", "NameT", "ReduceFunction", @@ -39,7 +39,7 @@ def __call__(self, *args: Any, **kwargs: Any) -> R: ... -class LossFunction(Protocol): +class PointwiseScore(Protocol): def __call__(self, y_true: NDArray, y_pred: NDArray) -> NDArray: ... diff --git a/src/pydvl/valuation/methods/data_oob.py b/src/pydvl/valuation/methods/data_oob.py index c4d2647a9..f235d7e50 100644 --- a/src/pydvl/valuation/methods/data_oob.py +++ b/src/pydvl/valuation/methods/data_oob.py @@ -42,7 +42,7 @@ ) from sklearn.utils.validation import check_is_fitted -from pydvl.utils.types import BaggingModel, LossFunction +from pydvl.utils.types import BaggingModel, PointwiseScore from pydvl.valuation.base import Valuation from pydvl.valuation.dataset import Dataset from pydvl.valuation.result import ValuationResult @@ -63,9 +63,9 @@ class DataOOBValuation(Valuation): [[BaggingClassifier]], [[BaggingRegressor]], [[IsolationForest]], RandomForest*, ExtraTrees*, or any model which defines an attribute `estimators_` and uses bootstrapped subsamples to compute predictions. - loss: A loss function to compare the true values with the predictions. If `None`, - it uses point-wise accuracy for classifiers and negative $l_2$ distance for - regressors. + score: A callable for point-wise comparison of true values with the predictions. + If `None`, uses point-wise accuracy for classifiers and negative $l_2$ + distance for regressors. Returns: Object with the data values. @@ -74,11 +74,11 @@ class DataOOBValuation(Valuation): def __init__( self, model: BaggingModel, - loss: LossFunction | None = None, + score: PointwiseScore | None = None, ): super().__init__() self.model = model - self.loss = loss + self.score = score def fit(self, data: Dataset): # TODO: automate str representation for all Valuations @@ -107,8 +107,8 @@ def fit(self, data: Dataset): # This should always be present after fitting estimators = getattr(self.model, "estimators_") - if self.loss is None: - self.loss = ( + if self.score is None: + self.score = ( point_wise_accuracy if is_classifier(self.model) else neg_l2_distance ) @@ -129,33 +129,37 @@ def fit(self, data: Dataset): unsampled_indices.append(oob_indices) for est, oob_indices in zip(estimators, unsampled_indices): - array_loss = self.loss( + score_array = self.score( y_true=data.y[oob_indices], y_pred=est.predict(data.x[oob_indices]), ) self.result += ValuationResult( algorithm=algorithm_name, indices=oob_indices, - values=array_loss, - counts=np.ones_like(array_loss, dtype=data.indices.dtype), + values=score_array, + counts=np.ones_like(score_array, dtype=data.indices.dtype), ) def point_wise_accuracy(y_true: NDArray[T], y_pred: NDArray[T]) -> NDArray[T]: - r"""Point-wise 0-1 loss between two arrays + """Point-wise accuracy, or 0-1 score between two arrays. + + Higher is better. Args: y_true: Array of true values (e.g. labels) y_pred: Array of estimated values (e.g. model predictions) Returns: - Array with point-wise 0-1 losses between labels and model predictions + Array with point-wise 0-1 accuracy between labels and model predictions """ return np.array(y_pred == y_true, dtype=y_pred.dtype) def neg_l2_distance(y_true: NDArray[T], y_pred: NDArray[T]) -> NDArray[T]: - r"""Point-wise negative $l_2$ distance between two arrays + r"""Point-wise negative $l_2$ distance between two arrays. + + Higher is better. Args: y_true: Array of true values (e.g. labels) diff --git a/src/pydvl/value/oob/oob.py b/src/pydvl/value/oob/oob.py index 71e32ff2a..1d13614b3 100644 --- a/src/pydvl/value/oob/oob.py +++ b/src/pydvl/value/oob/oob.py @@ -15,7 +15,7 @@ from tqdm.auto import tqdm from pydvl.utils import Seed, Utility -from pydvl.utils.types import LossFunction +from pydvl.utils.types import PointwiseScore from pydvl.value.result import ValuationResult __all__ = ["compute_data_oob"] @@ -28,7 +28,7 @@ def compute_data_oob( *, n_est: int = 10, max_samples: float = 0.8, - loss: Optional[LossFunction] = None, + loss: Optional[PointwiseScore] = None, n_jobs: Optional[int] = None, seed: Optional[Seed] = None, progress: bool = False, From 1b3794fd98a5e929147fb610e8931d77210342f1 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Tue, 19 Nov 2024 10:05:01 +0100 Subject: [PATCH 17/41] Ensure sorting of values prior to plotting --- src/pydvl/reporting/plots.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/pydvl/reporting/plots.py b/src/pydvl/reporting/plots.py index c65ce3de7..6f6ad11f8 100644 --- a/src/pydvl/reporting/plots.py +++ b/src/pydvl/reporting/plots.py @@ -111,6 +111,7 @@ def plot_ci_array( counts=np.ones_like(means, dtype=np.int_) * m, indices=np.arange(n), ) + dummy.sort(key="value") return plot_ci_values( dummy, @@ -139,7 +140,8 @@ def plot_ci_values( Supported intervals are based on the normal and the t distributions. Args: - values: The valuation result. + values: The valuation result. The object must be sorted by calling + `ValuationResult.sort()`. level: The confidence level. type: The type of confidence interval to use. If "auto", uses "norm" if the minimum number of updates for all indices is greater than 30, @@ -155,6 +157,7 @@ def plot_ci_values( Returns: The matplotlib axes. """ + assert values._sort_order is not None, "Values must be sorted first." ppfs = { "normal": norm.ppf, From 611ae47184836831a83e097f32af65fbb3e79478 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Tue, 19 Nov 2024 10:14:55 +0100 Subject: [PATCH 18/41] Require sklearn >= 1.3 for TargetEncoder --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 0a08a506a..8e945c7ca 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,11 @@ pyDeprecate>=0.3.2 numpy>=1.20,<2 pandas>=1.3 -scikit-learn +scikit-learn>=1.3 scipy>=1.7.0 cvxpy>=1.3.0 joblib @ git+https://github.com/joblib/joblib@c2087dbdeec9824c45822670395a8a0c45be2211 cloudpickle tqdm matplotlib -typing_extensions \ No newline at end of file +typing_extensions From cfcd148b9cf1c336a416152cc972b6b203addb0e Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Tue, 19 Nov 2024 10:23:08 +0100 Subject: [PATCH 19/41] Update tests for ValuationResult --- tests/valuation/test_result.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/valuation/test_result.py b/tests/valuation/test_result.py index 844e39eed..46d190e73 100644 --- a/tests/valuation/test_result.py +++ b/tests/valuation/test_result.py @@ -86,12 +86,14 @@ def test_iter(names, ranks_asc, dummy_values): def test_todataframe(ranks_asc, dummy_values): df = dummy_values.to_dataframe() assert "dummy_valuator" in df.columns - assert "dummy_valuator_stderr" in df.columns + assert "dummy_valuator_variances" in df.columns + assert "dummy_valuator_counts" in df.columns assert np.alltrue(df.index.values == ranks_asc) df = dummy_values.to_dataframe(column="val") assert "val" in df.columns - assert "val_stderr" in df.columns + assert "val_variances" in df.columns + assert "val_counts" in df.columns assert np.alltrue(df.index.values == ranks_asc) df = dummy_values.to_dataframe(use_names=True) From f53e8a8861086df3638f33342879a1890456e7f7 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Fri, 22 Nov 2024 08:01:05 +0100 Subject: [PATCH 20/41] WIP --- docs/value/data-oob.md | 165 +++ mkdocs.yml | 5 +- notebooks/data_oob.ipynb | 1447 ++++++++++++++++------- notebooks/support/common.py | 203 +++- notebooks/support/removal_experiment.py | 156 +++ src/pydvl/reporting/scores.py | 2 +- src/pydvl/valuation/methods/data_oob.py | 8 +- src/pydvl/valuation/methods/random.py | 27 + src/pydvl/valuation/result.py | 5 +- 9 files changed, 1531 insertions(+), 487 deletions(-) create mode 100644 docs/value/data-oob.md create mode 100644 notebooks/support/removal_experiment.py create mode 100644 src/pydvl/valuation/methods/random.py diff --git a/docs/value/data-oob.md b/docs/value/data-oob.md new file mode 100644 index 000000000..8ad325244 --- /dev/null +++ b/docs/value/data-oob.md @@ -0,0 +1,165 @@ +from valuation.test_interface import datasets--- +title: Data-OOB +--- + +# Data valuation for bagged models with Data-OOB + +Data-OOB [@kwon_dataoob_2023] is a method for valuing data used to train bagged +models. It defines value as the out-of-bag (OOB) performance estimate for the +model, overcoming the computational bottleneck of Shapley-based data valuation +methods: Instead of fitting a large number of models to accurately estimate +marginal contributions like Shapley-based methods, Data-OOB evaluates each weak +learner in an ensemble over samples it hasn't seen during training, and averages +the performance across all weak learners. + +More precisely, for a bagging model with $B$ estimators $\hat{f}_b, b \in [B]$, +we define $w_{bj}$ as the number of times that the $j$-th sample is in the +training set of the $b$-th estimator. For a **fixed** choice of bootstrapped +training sets, the Data-OOB value of sample $(x_i, y_i)$ is defined as: + +$$ \psi_i := \frac{\sum_{b=1}^{B}\mathbb{1}(w_{bi}=0)T(y_i, + \hat{f}_b(x_i))}{\sum_{b=1}^{B} \mathbb{1} (w_{bi}=0)}, +$$ + +where $T: Y \times Y \rightarrow \mathbb{R}$ is a score function that represents +the goodness of weak learner $\hat{f}_b$ at the $i$-th datum $(x_i, y_i)$. + +$\psi$ can therefore be interpreted as a per-sample partition of the standard +OOB error estimate for a bagging model, which is: $\frac{1}{n} \sum_{i=1}^n +\psi_i$. + +## Computing values + +The main class is +[DataOOBValuation][pydvl.valuation.methods.data_oob.DataOOBValuation]. It takes +a *fitted* bagged model and uses data precomputed during training to calculate +the values. It is therefore very fast, and can be used to value large datasets. + +This is how you would use it with a [[RandomForestClassifier]]: + +```python +from sklearn.ensemble import RandomForestClassifier +from pydvl.valuation import DataOOBValuation, Dataset + +train, test = Dataset(...), Dataset(...) +model = RandomForestClassifier(...) +model.fit(train.x, train.y) +valuation = DataOOBValuation(model) +valuation.fit(train) +values = valuation.values() +``` + +`values` is then a [ValuationResult][pydvl.valuation.result.ValuationResult] to +be used for data inspection, cleaning, etc. + +Data-OOB is not limited to sklearn's [[RandomForest]], but can be used with +any bagging model that defines the attribute `estimators_` after fitting and +makes the list of bootstrapped samples available in some way. This includes +[[BaggingRegressor]], [[BaggingClassifier]], [[ExtraTreesClassifier]], +[[ExtraTreesRegressor]] and [[IsolationForest]]. + +## Bagging arbitrary models + +Through [[BaggingClassifier]] and [[BaggingRegressor]], one can compute values +for any model that can be bagged. Bagging in itself is not necessarily always +beneficial, and there are cases where it can be detrimental. However, for data +valuation we are not interested in the performance of the bagged model, but in +the valuation coming out of it, which can then be used to work on the original +model and data. + +```python +from sklearn.ensemble import BaggingClassifier +from pydvl.valuation import DataOOBValuation, Dataset + +train, test = Dataset(...), Dataset(...) +model = BaggingClassifier( + estimator=KNeighborsClassifier(n_neighbors=10), + n_estimators=20) +model.fit(train.x, train.y) +valuation = DataOOBValuation(model) +valuation.fit(train) +values = valuation.values() +values.sort() +low_values = values[:int(0.05*len(train))] # select lowest 5% + +# Inspect the data with lowest values: +... +``` + +### Off-topic: When not to use bagging as the main model + +Here are some guidelines for when bagging might not be beneficial: + +1. **Low-Variance Models**: Models like linear regression, support vector + machines, or other inherently stable algorithms typically have low variance. + In such cases, bagging may not provide significant benefits and could even + increase computational cost unnecessarily. + +2. **When the Model Is Already Highly Regularized**: If a model is regularized + (e.g., Lasso, Ridge, or Elastic Net), it is already tuned to avoid + overfitting and reduce variance. Bagging might be redundant or offer marginal + improvements at best. + +3. **When Data Is Limited**: Bagging works by creating multiple subsets of the + data via bootstrapping. If the dataset is too small, the bootstrap samples + might overlap significantly or exclude important patterns, reducing the + effectiveness of the approach. + +4. **When Features Are Highly Correlated**: If features are highly correlated, + the individual models trained on different bootstrap samples may end up being + too similar. This limits the diversity among the models, reducing the + effectiveness of bagging. + +5. **For Models That Are Not Easily Overfitted**: If the base model doesn't + overfit the data (e.g., nearest neighbors or models with strong pruning + mechanisms like heavily regularized decision trees), bagging might not yield + substantial variance reduction. + +6. **When Computational Resources Are Limited**: Bagging increases computational + cost because it involves training multiple models. For computationally + expensive algorithms or resource-constrained scenarios, bagging may be + impractical. + +7. **When Interpretability Is Critical**: Bagging produces an ensemble of + models, which makes the overall model less interpretable compared to a single + model. If interpretability is crucial for the application, bagging might not + be suitable. + +8. **When the Model Does Not Benefit from Resampling**: Some models, such as + nearest neighbors, depend heavily on the full dataset. Subsampling through + bootstrapping can reduce their performance instead of improving it. + +9. **When Overfitting Is Already Controlled by Other Means**: If + cross-validation, regularization, or pruning is already effectively + controlling overfitting, bagging may offer little additional benefit. + +## Transferring values + +As with any other valuation method, you can transfer the values to a different +model, and given the efficiency of Data-OOB, this can be done very quickly. A +simple workflow is to compute values using a random forest, then use them to +inspect the data and clean it, and finally train a more complex model on the +cleaned data. Whether this is a valid idea or not will depend on the specific +dataset. + +```python +... +``` +... + +## A comment about sampling + +One might fear that there is a problem because the computation of the value +$\psi_i$ requires at least some bootstrap samples *not* to include the $i$-th +sample. But we can see that this is rarely an issue, and its probability of +happening can be easily computed: For a training set of size $n$ and +bootstrapping sample size $m \le n$, the probability that index $i$ is not +included in a bootstrap sample is $\prod_{j=1}^m \mathbb{P}(i \text{ is not +drawn at pos. } j) = (1 - 1/n)^m$, i.e. for each of the $m$ draws, the number is +not picked (for $m=n$ this converges to $1/e \approx 0.368$). The probability +that across $B$ bootstrapped samples a point is not included is therefore $(1 - +1/n)^{mB}$, which is typically extremely low. + +Incidentally, this allows us to estimate the estimated number of unique indices +in a bootstrap sample of size $m$ as $m(1 - 1/n)^m$. + diff --git a/mkdocs.yml b/mkdocs.yml index 6c8732760..df66caa92 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -20,8 +20,9 @@ nav: - value/index.md - Shapley values: value/shapley.md - Semi-values: value/semi-values.md - - The Core: value/the-core.md - Class-wise Shapley: value/classwise-shapley.md + - Least Core: value/the-core.md + - Data-OOB: value/data-oob.md - The Influence Function: - influence/index.md - Influence Function Model: influence/influence_function_model.md @@ -32,9 +33,9 @@ nav: - Shapley values: examples/shapley_basic_spotify.ipynb - KNN Shapley: examples/shapley_knn_flowers.ipynb - Data utility learning: examples/shapley_utility_learning.ipynb + - Banzhaf semivalues: examples/msr_banzhaf_digits.ipynb - Least Core: examples/least_core_basic.ipynb - Data OOB: examples/data_oob.ipynb - - Banzhaf Semivalues: examples/msr_banzhaf_digits.ipynb - Influence Function: - For CNNs: examples/influence_imagenet.ipynb - For mislabeled data: examples/influence_synthetic.ipynb diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb index 2f5be972e..a9feda157 100644 --- a/notebooks/data_oob.ipynb +++ b/notebooks/data_oob.ipynb @@ -4,26 +4,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Bagging for data valuation" + "# TODO:\n", + "\n", + "* Fix the problems with the removal job:\n", + " - different initial value for the curves\n", + " - increasing by high-value removal??\n", + "* Remove old text fragments\n", + "* Factor out any boilerplate\n", + "* Remove leftovers\n", + "* Actually perform the analysis using the values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook introduces the Data-OOB method, from Kwon and Zou \"[Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://proceedings.mlr.press/v202/kwon23e.html)\" (ICML 2023), using pyDVL.\n", - "\n", - "The objective of this paper is mainly to overcome the computational bottleneck of Shapley-based data valuation methods that require fitting a significant number of models to accurately estimate marginal contributions.\n", - "Instead, Data-OOB computes data values from out-of-bag (OOB) error estimates of a bagging model. Originally, the method is therefore only designed for bagging models, but it can be adapted to other models as well.\n", - "\n", - "For a bagging model with $B$ estimators $\\hat{f}_b, b \\in [B]$, we define $w_{bj}$ as the number of times that the $j$-th sample is in the training set of the $b$-th estimator. For a **fixed** choice of bootstrapped training sets, the Data-OOB value of sample $(x_i, y_i)$ is defined as:\n", - " \n", - "$$\n", - "\\psi(x_i,y_i) := \\frac{\\sum_{b=1}^{B}\\mathbb{1}(w_{bi}=0)T(y_i, \\hat{f}_b(x_i))}{\\sum_{b=1}^{B}\n", - "\\mathbb{1} (w_{bi}=0)}.\n", - "$$\n", + "# Data-OOB for random forests and bagged classifiers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook illustrates the use of [Data-OOB][../../value/data-oob] from Kwon and Zou \"[Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://proceedings.mlr.press/v202/kwon23e.html)\" (ICML 2023), to compute values for bagged models.\n", "\n", - "It can therefore be interpreted as a per-sample partition of the standard OOB error estimate for a bagging model, which is: $\\frac{1}{n} \\sum_{i=1}^n \\psi(x_i,y_i)$." + "We will work with the [adult classification dataset](https://archive.ics.uci.edu/dataset/2/adult) from the UCI repository. It's an imbalanced dataset where the objective is to predict whether a person earns more than $50K a year (the \"positive\" class) based on a set of features such as age, education, occupation, etc. After training a random forest on this dataset, we will compute the Data-OOB values and analyze them.\n" ] }, { @@ -32,8 +37,6 @@ "source": [ "## Setup\n", "\n", - "We begin by importing the main libraries and setting some defaults.\n", - "\n", "
\n", "\n", "If you are reading this in the documentation, some boilerplate (including most plotting code) has been omitted for convenience.\n", @@ -41,620 +44,1152 @@ "
" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "hide" + ] + }, + "source": [ + "We begin by importing the main libraries and setting some defaults." + ] + }, { "cell_type": "code", + "execution_count": null, "metadata": { - "editable": true, + "ExecuteTime": { + "end_time": "2024-11-21T13:42:27.489342Z", + "start_time": "2024-11-21T13:42:25.637695Z" + }, "slideshow": { "slide_type": "" }, "tags": [ "hide" - ], - "ExecuteTime": { - "end_time": "2024-11-14T11:09:08.038214Z", - "start_time": "2024-11-14T11:09:05.740790Z" - } + ] }, + "outputs": [], "source": [ "%load_ext autoreload\n", "\n", "import os\n", "import random\n", - "from typing import Literal\n", "\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from tqdm.notebook import tqdm\n", "\n", "from pydvl.reporting.plots import plot_ci_array, plot_ci_values\n", - "from pydvl.reporting.scores import compute_removal_score\n", - "from support.common import load_adult_data\n", + "from support.common import ThresholdTunerCV, load_adult_data, \\\n", + " load_adult_data_raw\n", "\n", "matplotlib.rcParams[\"axes.facecolor\"] = (1, 1, 1, 0)\n", "plt.rcParams[\"axes.facecolor\"] = (1, 1, 1, 0)\n", "plt.rcParams[\"figure.facecolor\"] = (1, 1, 1, 0)\n", + "MEAN_COLORS = [\"dodgerblue\", \"indianred\", \"limegreen\", \"darkorange\",\n", + " \"darkorchid\"]\n", + "SHADE_COLORS = [\"lightskyblue\", \"firebrick\", \"seagreen\", \"gold\", \"plum\"]\n", "\n", "is_CI = os.environ.get(\"CI\")\n", "random_state = 24\n", "random.seed(random_state);" - ], - "outputs": [], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2024-11-14T11:09:10.178202Z", - "start_time": "2024-11-14T11:09:08.698435Z" + "end_time": "2024-11-21T13:42:43.748256Z", + "start_time": "2024-11-21T13:42:43.744196Z" } }, - "source": [ - "%autoreload\n", - "from pydvl.valuation import (\n", - " ValuationResult,\n", - " DataOOBValuation,\n", - " Dataset,\n", - " KNNClassifierUtility,\n", - " )\n", - "from pydvl.utils import Seed, ensure_seed_sequence" - ], "outputs": [], - "execution_count": 2 + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import f1_score\n", + "from pydvl.valuation import DataOOBValuation, Dataset" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We will work with the [adult classification dataset](https://archive.ics.uci.edu/dataset/2/adult) from the UCI repository. The objective is to predict whether a person earns more than 50k a year based on a set of features such as age, education, occupation, etc.\n", "\n", - "With a helper function we download the data and obtain the following pandas dataframe, where the categorical features have been removed:" + "With a helper function we download the data, encode the categorical variables using [TargetEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html), and split it into training and testing sets. We must be careful to stratify the split by the target variable (income)." ] }, { "cell_type": "code", + "execution_count": null, "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:42:53.999969Z", + "start_time": "2024-11-21T13:42:53.799936Z" + }, "tags": [ "hide-output" - ], - "ExecuteTime": { - "end_time": "2024-11-14T11:09:10.204197Z", - "start_time": "2024-11-14T11:09:10.198352Z" - } + ] }, + "outputs": [], "source": [ - "data_adult = load_adult_data()\n", - "data_adult = data_adult.sample(frac=0.8, random_state=random_state)" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found cached file: adult_data.pkl.\n" - ] - } - ], - "execution_count": 3 + "train, test = load_adult_data(train_size=0.6, random_state=random_state)" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:42:56.400972Z", + "start_time": "2024-11-21T13:42:56.396323Z" + }, "tags": [ "hide" - ], - "ExecuteTime": { - "end_time": "2024-11-14T11:09:10.693987Z", - "start_time": "2024-11-14T11:09:10.689873Z" - } + ] }, - "source": [ - "if is_CI:\n", - " data_adult = data_adult.sample(100, random_state=random_state)" - ], "outputs": [], - "execution_count": 4 + "source": [ + "if is_CI: # Subsample 1% of the data for faster testing\n", + " from support.common import subsample_dataset\n", + "\n", + " train = subsample_dataset(train, 0.01)\n", + " test = subsample_dataset(test, 0.01)" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2024-11-14T11:09:11.250997Z", - "start_time": "2024-11-14T11:09:11.234385Z" + "end_time": "2024-11-21T13:42:58.947427Z", + "start_time": "2024-11-21T13:42:58.936728Z" } }, - "source": "data_adult.head()", - "outputs": [ - { - "data": { - "text/plain": [ - " age fnlwgt education-num capital-gain capital-loss hours-per-week \\\n", - "1084 31 232475 10 0 0 40 \n", - "1410 48 329778 9 0 0 40 \n", - "1930 43 166740 10 0 0 48 \n", - "1270 47 200734 13 0 0 45 \n", - "1280 52 186785 9 0 1876 50 \n", - "\n", - " income \n", - "1084 <=50K \n", - "1410 <=50K \n", - "1930 <=50K \n", - "1270 <=50K \n", - "1280 <=50K " - ], - "text/html": [ - "
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agefnlwgteducation-numcapital-gaincapital-losshours-per-weekincome
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" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 5 + "outputs": [], + "source": [ + "from support.common import subsample_dataset\n", + "\n", + "train = subsample_dataset(train, 0.2)\n", + "test = subsample_dataset(test, 0.2)" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Computing the OOB values\n", - "\n", - "The main idea of Data-OOB is to use the out-of-bag error estimates of a bagging model to compute data values. In pyDVL, we provide a class [DataOOBValuation][pydvl.valuation.DataOOBValuation] that allows extending this idea to any classifier. The class is designed to take an existing classifier or regression model and compute a per-sample out-of-bag performance estimate via bagging.\n", - "\n", - "For this example, we use a simple KNN classifier with $k=5$ neighbours on the data and compute the data-oob values with two choices for the number of estimators in the bagging.\n", - "\n", - "We then use the [fit][pydvl.valuation.DataOOBValuation.fit] method to compute the values and store them in a [ValuationResult][pydvl.value.result.ValuationResult] object." + "Usually we would carefully look at the features, check for missing values, outliers, etc. But for the sake of this example, we will skip this step and jump straight into training a model. We will only look at the class distribution since it will matter later:" ] }, { "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2024-11-14T11:09:12.496742Z", - "start_time": "2024-11-14T11:09:12.489613Z" - } + "end_time": "2024-11-21T13:43:01.875396Z", + "start_time": "2024-11-21T13:43:01.870446Z" + }, + "tags": [ + "hide-input" + ] }, + "outputs": [], "source": [ - "data = Dataset(\n", - " data_adult.drop(columns=[\"income\"]).values,\n", - " data_adult.loc[:, \"income\"].cat.codes.values,\n", - " )\n", - "model = KNeighborsClassifier(n_neighbors=5)" - ], + "print(f\"Class distribution: {100 * train.y.mean():.1f}% positive, \"\n", + " f\"{100 * (1 - train.y.mean()):.1f}% negative\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a quick baseline, we train a standard sklearn [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). Since the dataset is imbalanced, besides the accuracy we look at the confusion matrix, and notice that despite weighting the class by their inverse frequency with `class_weight=\"balanced\"`, the model is not very good at predicting the minority (\"positive\", or \"1\") class: in the figure below we see a high rate of false negatives. This will play a role later in how we interpret the values that Data-OOB returns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:43:24.904085Z", + "start_time": "2024-11-21T13:43:24.555235Z" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [], - "execution_count": 6 + "source": [ + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "\n", + "n_est = 20\n", + "model = RandomForestClassifier(n_estimators=n_est,\n", + " class_weight=\"balanced\",\n", + " random_state=random_state)\n", + "model.fit(train.x, train.y)\n", + "accuracy = model.score(test.x, test.y)\n", + "print(f\"Accuracy of a Random Forest with {n_est} estimators: {accuracy:.4f}\")\n", + "disp = ConfusionMatrixDisplay.from_estimator(model, test.x, test.y,\n", + " labels=[0, 1], normalize=\"true\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "invertible-output" + ] + }, + "source": [ + "## Computing the OOB values\n", + "\n", + "The main idea of Data-OOB is to use the out-of-bag error estimates of a bagged model to compute data values. In pyDVL, we provide a class [DataOOBValuation][pydvl.valuation.DataOOBValuation] that takes an existing classification or regression bagging model and uses the per-sample out-of-bag performance estimate for the value of each point.\n", + "\n", + "Let's compute and compare the Data-OOB values with three choices for the number of estimators of a random forest. After fitting the random forest, we use the [fit][pydvl.valuation.DataOOBValuation.fit] method to compute the values and store them in two [ValuationResult][pydvl.value.result.ValuationResult] objects" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2024-11-14T11:09:25.935095Z", - "start_time": "2024-11-14T11:09:13.364079Z" + "end_time": "2024-11-21T13:45:22.030123Z", + "start_time": "2024-11-21T13:45:17.503487Z" } }, + "outputs": [], "source": [ - "n_estimators = [100, 500]\n", + "n_estimators = [50, 100, 200]\n", "oob_values = []\n", - "for n_est in n_estimators:\n", - " # model = RandomForestClassifier(n_estimators=n_est,\n", - " # max_samples=0.95,\n", - " # random_state=random_state)\n", - " # model.fit(data.x, data.y)\n", - " valuation = DataOOBValuation(model, n_estimators=n_est, max_samples=0.95, seed=random_state)\n", - " valuation.fit(data)\n", + "for i, n_est in enumerate(n_estimators, start=1):\n", + " classifier = RandomForestClassifier(n_estimators=n_est,\n", + " max_samples=0.7,\n", + " class_weight=\"balanced\",\n", + " random_state=random_state)\n", + " model = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\n", + " model.fit(train.x, train.y)\n", + " model.n_estimators = model.base_estimator.n_estimators\n", + " model.max_samples = model.base_estimator.max_samples\n", + " model.estimators_ = model.base_estimator.estimators_\n", + " # model.estimators_samples = model.base_estimator.estimators_samples_\n", + " accuracy = model.score(test.x, test.y)\n", + " print(f\"Accuracy with {n_est} estimators: {accuracy:.4f}\")\n", + " valuation = DataOOBValuation(model)\n", + " valuation.fit(train)\n", " oob_values.append(valuation.values())" - ], - "outputs": [], - "execution_count": 7 + ] }, { "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "metadata": {}, "source": [ - "The two results are stored in an array of [ValuationResult][pydvl.value.result.ValuationResult] objects. Here's their distribution. The left-hand side depicts value as it increases with rank and a 99% t-confidence interval. The right-hand side shows the histogram of values.\n", + "Even though it's not relevant to our discussion, notice how the accuracy barely changes with the number of estimators. Below, we will discuss using the values to identify \"easy\" or \"hard\" samples in the dataset, but first let's quickly look at the values themselves." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### The distribution of values\n", + "\n", + "The left-hand side of the figure below depicts value as it increases with rank and a 95% t-confidence interval. The right-hand side shows the histogram of values.\n", "\n", - "Observe how adding estimators reduces the variance of the values, but doesn't change their distribution much. " + "We observe a long tail of high values. This is because the score $T$ used in Data-OOB (accuracy in this case) is a binary variable, and the value $\\psi_i$ is the fraction of times that all weak learners not trained on the $i$-th point classify it correctly. Given the imbalance in the dataset, many learners will always predict the majority (\"negative\", < $50K earnings / year) class and be correct on 75% of the dataset, leading to this tail. As the number of estimators increases, the chance of all of them failing on the same points decreases. The same happens when we increase the maximum depth (try it!). This behaviour is then not a deep property of Data-OOB from which to gain new insights, but rather a consequence of the dataset and the model, as we further elaborate below.\n", + "\n", + "*Note that a symmetric CI is actually incorrect in this situation since all values are bounded between 0 and 1 (instead we could use a bootstrapped CI if we stored the scores of all estimators in the bagging model, but this is not implemented in pyDVL).*\n" ] }, { + "cell_type": "code", + "execution_count": null, "metadata": { - "editable": true, + "ExecuteTime": { + "end_time": "2024-11-21T13:45:53.664257Z", + "start_time": "2024-11-21T13:45:52.855319Z" + }, "slideshow": { "slide_type": "" }, "tags": [ "hide-input", "invertible-output" - ], - "ExecuteTime": { - "end_time": "2024-11-14T11:09:27.725095Z", - "start_time": "2024-11-14T11:09:26.819728Z" - } + ] }, - "cell_type": "code", + "outputs": [], "source": [ - "mean_colors = [\"dodgerblue\", \"indianred\", \"limegreen\", \"darkorange\",\n", - " \"darkorchid\"]\n", - "shade_colors = [\"lightskyblue\", \"firebrick\", \"seagreen\", \"gold\", \"plum\"]\n", "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", "for (n_est, values, mean_color, shade_color) in zip(\n", - " n_estimators, oob_values, mean_colors, shade_colors\n", - " ):\n", - " values.sort(key=\"value\")\n", - " plot_ci_values(\n", - " values,\n", - " level=0.01,\n", - " mean_color=mean_color,\n", - " shade_color=shade_color,\n", - " ax=ax1,\n", - " label=f\"{n_est} estimators\",\n", - " )\n", + " n_estimators, oob_values, MEAN_COLORS, SHADE_COLORS):\n", + " values.sort()\n", + " plot_ci_values(values,\n", + " level=0.05,\n", + " mean_color=mean_color,\n", + " shade_color=shade_color,\n", + " ax=ax1,\n", + " label=f\"{n_est} estimators\")\n", "\n", " ax2.hist(values, bins=50, color=mean_color, alpha=0.5,\n", " label=f\"{n_est} estimators\")\n", + "\n", "ax1.set_title(\"Point rank\")\n", - "ax1.set_xlabel(\"Value\")\n", + "ax1.set_xlabel(\"Rank\")\n", "ax1.set_ylabel(\"Data-OOB values\")\n", - "ax1.set_xticks(ax1.get_xticks()[::150])\n", "ax1.legend()\n", "ax2.set_title(\"Histogram of Data-OOB values\")\n", + "ax2.set_xlabel(\"Value\")\n", "ax2.legend()\n", "plt.plot();" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "execution_count": 8 + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Interpreting the variances and the long tail\n", + "\n", + "Besides the actual value $\\psi_i$, [ValuationResult][pydvl.value.result.ValuationResult] objects store the number of times a sample is OOB —the quantity $\\sum_{b=1}^{B} \\mathbb{1} (w_{bi}=0)$— in the `counts` attribute, and the variance of the OOB score in the `variances` attribute. We used the latter in the plot above in order to display the confidence intervals, but their interpretation varies from one valuation method to another.\n", + "\n", + "For Shapley-based valuation methods, the variance is that of the marginal changes in the performance of the model when trained on subsets of the data with and without a sample, evaluated on a fixed valuation dataset. But for Data-OOB, it is the variance of the performance of the ensemble of weak learners on the sample when it is OOB. Although similar in spirit, the construction is different and can be misleading. As a matter of fact, the interpretation of the vanishing variance at the tail has little to do with valuation and everything to do with our dataset.\n", + "\n", + "To see this, focus on fraction of then long tails with zero variance. These are samples for which the score $T(y_i, \\hat{f}_b(x_i)) = 1$ for *every* estimator $\\hat{f}_b$ not trained on them, that is: *every weak learner in the ensemble correctly classifies these samples*. As we said above, this can happen because it is likely for weak estimators to be fitted to always predict the majority (negative) class." + ] }, { + "cell_type": "code", + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2024-11-14T11:09:50.385541Z", - "start_time": "2024-11-14T11:09:50.208032Z" - } + "end_time": "2024-11-21T13:46:02.829503Z", + "start_time": "2024-11-21T13:46:02.821699Z" + }, + "tags": [ + "hide-input" + ] }, - "cell_type": "code", + "outputs": [], "source": [ - "obv, rv = oob_values\n", - "df = obv.to_dataframe(\"oob\")\n", - "df.to_csv(\"/tmp/data_oob_values.csv\")\n", - "df[\"oob_variances\"].hist(bins=50)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "execution_count": 10 + "v = oob_values[0]\n", + "from_index = \\\n", + " np.where(np.isclose(v.variances, 0.0) & np.isclose(v.values, 1))[0][0]\n", + "tail = values.indices[from_index:]\n", + "print(f\"There are {len(tail)} points with value 1 and zero variance\"\n", + " f\" ({100 * len(tail) / len(train):.2f}% of the data).\")\n", + "print(f\"Of these, {100 * (train.y[tail] == 0).sum() / len(tail):.2f}% \"\n", + " f\"are in the majority class.\")" + ] }, { + "cell_type": "markdown", "metadata": { - "editable": true, "slideshow": { "slide_type": "" }, + "tags": [] + }, + "source": [ + "Simply put, the ensemble is mostly good at classifying the majority class, and the variance of the OOB score for these samples is very low. This is a common issue in imbalanced datasets, and it is one of the reasons why the OOB score might not be a good metric for model performance in these cases. For us, it shows that the values reflect only poorly fit models.\n", + "\n", + "We test this below by ensembling a number of constant classifiers, but how does this affect our ability to use the values for data inspection, cleaning, etc.?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A detour to further interpret the OOB values\n", + "\n", + "We can verify that the OOB values in our case reflect the imbalance of the dataset by training a [BaggingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) with constant estimators. A fraction of `n_estimators` will always pick class 1, and the rest class 0. This leads to a clear jump in the value rank plot, either around 25% or 75% of them, since, as we saw above, 25% of the samples are in the positive (\"1\") class, and 75% in the negative (\"0\").\n", + "\n", + "We will use three different probabilities for the constant estimators to predict class 0: 0.01, 0.5, and 0.99. Again, the idea is that the OOB values will reflect the class distribution of the dataset, and we should see a clear jump in the values around 25% and 75% of the data.\n", + "\n", + "The code is analogous to the above when we fitted the random forest, so it is ommitted from the documentation, but we use a custom class `ConstantBinaryClassifier` to create the constant estimators." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:46:21.407834Z", + "start_time": "2024-11-21T13:46:20.151630Z" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import BaggingClassifier\n", + "from support.common import ConstantBinaryClassifier\n", + "\n", + "probs = [0.01, 0.5, 0.99]\n", + "all_values = []\n", + "for p in probs:\n", + " model = BaggingClassifier(ConstantBinaryClassifier(p, random_state),\n", + " n_estimators=100,\n", + " max_samples=1.0,\n", + " random_state=random_state)\n", + " model.fit(train.x, train.y)\n", + " score = model.score(test.x, test.y)\n", + " print(\n", + " f\"Accuracy when ~{100 * p:.1f}% of estimators always predict class 0: {score:.4f}\")\n", + "\n", + " valuation = DataOOBValuation(model)\n", + " valuation.fit(train)\n", + " all_values.append(valuation.values())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:46:21.773680Z", + "start_time": "2024-11-21T13:46:21.476589Z" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=[15, 5])\n", + "\n", + "for (p, values, mean_color, shade_color) in zip(probs, all_values, MEAN_COLORS,\n", + " SHADE_COLORS):\n", + " values.sort(key=\"value\")\n", + " plot_ci_values(\n", + " values,\n", + " ax=ax,\n", + " level=0.01,\n", + " mean_color=mean_color,\n", + " shade_color=shade_color,\n", + " label=f\"Data-OOB, p={p:.2f}\",\n", + " )\n", + "\n", + "ax.axvline(0.25 * len(all_values[0]), color=\"darkorange\", linestyle=\"--\",\n", + " label=\"25% data\")\n", + "ax.axvline(0.75 * len(all_values[0]), color=\"darkred\", linestyle=\"--\",\n", + " label=\"75% data\")\n", + "plt.ylabel(\"Data-OOB values\")\n", + "plt.xlabel(\"Rank\")\n", + "plt.title(f\"Model with constant estimators\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A bogus (?) conclusion\n", + "\n", + "The conclusion of this discussion seems to be that for this imbalanced dataset and poorly performing model, the usual intuition that extreme values characterize \"easy\" or \"hard\" points might be bogus.\n", + " \n", + "If we discard the datapoints with the highest values, i.e. those which the ensemble of weak learners classifies correctly every time, because we believe that those are trivial in some sense (e.g. repeated) and bias the ensemble towards stricter decisions boundaries, we obtain very mild changes. The same happens when removing low-valued points. Basically, the changes in test scores could be just noise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating Data-OOB values with data removal\n", + "\n", + "We can go beyond the simple analysis above and systematically evaluate the impact of removing data points with high or low values on the model's performance. This is a common practice in the literature. We can use the [compute_removal_score][pydvl.reporting.scores.compute_removal_score] function to do it. This function takes a [ModelUtility][pydvl.valuation.ModelUtility] object, a [ValuationResult][pydvl.value.result.ValuationResult] object, and a [Dataset][pydvl.data.dataset.Dataset] object, and computes the performance of the model after removing a fraction of the data with the highest or lowest values.\n", + "\n", + "Recall from above that we can construct these objects as follows:\n", + "\n", + "```python\n", + "model = RandomForestClassifier(n_estimators=n_est, max_samples=max_samples, random_state=seed)\n", + "utility = ModelUtility(model, SupervisedScorer(\"accuracy\", test, 0.0), clone_before_fit=False)\n", + "\n", + "valuation = DataOOBValuation(model)\n", + "valuation.fit(train)\n", + "values = valuation.values()\n", + "```\n", + "\n", + "The details are hidden in the rendered documentation, please refer to the notebook for the full code." + ] + }, + { + "cell_type": "markdown", + "metadata": { "tags": [ "hide" - ], + ] + }, + "source": [ + "In order to call `run_removal_experiment()` we need to define 3 types of factories:\n", + "\n", + "1. A factory that returns a train-test split of the data given a random state \n", + "2. A factory that returns a utility that evaluates a model on a given test set.\n", + " This is used for the performance evaluation. The model need not be the same\n", + " as the one used for the valuation.\n", + "4. A factory returning a valuation method. The training set is passed to the\n", + " factory, in case the valuation needs to train something. E.g. for Data-OOB\n", + " we need the bagging model to be fitted before the valuation is computed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "ExecuteTime": { - "end_time": "2024-11-13T18:41:00.769921Z", - "start_time": "2024-11-13T18:41:00.764054Z" + "end_time": "2024-11-21T18:16:26.124375Z", + "start_time": "2024-11-21T18:16:24.695754Z" } }, + "outputs": [], + "source": [ + "%autoreload\n", + "from support.common import ThresholdTunerCV\n", + "from support.removal_experiment import run_removal_experiment" + ] + }, + { "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T18:16:26.416582Z", + "start_time": "2024-11-21T18:16:26.406307Z" + }, + "tags": [ + "hide" + ] + }, + "outputs": [], "source": [ - "def removal_job(\n", - " method: Literal[\"random\", \"data_oob\"],\n", - " seed: Seed,\n", - " n_est: int = 500,\n", - " max_samples: float = 0.95,\n", - " ):\n", - " \"\"\"This is not very efficient, but it's just an example.\"\"\"\n", - " data = Dataset(\n", - " data_adult.drop(columns=[\"income\"]).values,\n", - " data_adult.loc[:, \"income\"].cat.codes.values,\n", - " )\n", - " model = KNeighborsClassifier(n_neighbors=5)\n", + "from pydvl.valuation import KNNClassifierUtility, ModelUtility, SupervisedScorer\n", "\n", - " if method == \"random\":\n", - " values = ValuationResult.from_random(size=len(data), seed=seed)\n", - " else:\n", - " valuation = DataOOBValuation(\n", - " model, n_estimators=n_est, max_samples=max_samples, seed=seed\n", - " )\n", - " valuation.fit(data)\n", - " values = valuation.values()\n", + "from support.removal_experiment import run_removal_experiment\n", + "from pydvl.valuation.methods.random import RandomValuation\n", + "from pydvl.valuation.methods.data_oob import point_wise_accuracy\n", "\n", - " best_scores = compute_removal_score(\n", - " KNNClassifierUtility(model, data),\n", - " values,\n", - " data,\n", - " percentages=removal_percentages,\n", - " remove_best=True,\n", - " )\n", - " best_scores[\"method_name\"] = method\n", "\n", - " worst_scores = compute_removal_score(\n", - " KNNClassifierUtility(model, data),\n", - " values,\n", - " data,\n", - " percentages=removal_percentages,\n", - " remove_best=False,\n", - " )\n", - " worst_scores[\"method_name\"] = method\n", + "def make_data(random_state: int) -> tuple[Dataset, Dataset]:\n", + " return load_adult_data(train_size=0.6, random_state=random_state)\n", + "\n", + "\n", + "def make_utility(test: Dataset, random_state: int) -> ModelUtility:\n", + " classifier = RandomForestClassifier(n_estimators=50,\n", + " max_samples=0.8,\n", + " class_weight=\"balanced\",\n", + " random_state=random_state + 1)\n", + " model = ThresholdTunerCV(classifier,\n", + " n_splits=5,\n", + " metric=f1_score,\n", + " n_jobs=1,\n", + " random_state=random_state)\n", + " return ModelUtility(model, SupervisedScorer(\"accuracy\", test, 0.0))\n", + "\n", + "\n", + "def make_oob(train: Dataset, random_state: int) -> DataOOBValuation:\n", + " classifier = RandomForestClassifier(n_estimators=50,\n", + " max_samples=0.8,\n", + " class_weight=\"balanced\",\n", + " random_state=random_state)\n", + " model = ThresholdTunerCV(classifier,\n", + " n_splits=5,\n", + " metric=f1_score,\n", + " n_jobs=1,\n", + " random_state=random_state)\n", + " model.fit(train.x, train.y)\n", + " return DataOOBValuation(model, point_wise_accuracy)\n", + "\n", + "\n", + "def make_random(train: Dataset, random_state: int) -> RandomValuation:\n", + " return RandomValuation(random_state=random_state)\n", + "\n", "\n", - " return best_scores, worst_scores" - ], + "removal_percentages = np.arange(0, 0.51, 0.02)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T18:20:07.969388Z", + "start_time": "2024-11-21T18:16:28.143579Z" + }, + "tags": [ + "hide" + ] + }, "outputs": [], - "execution_count": 10 + "source": [ + "low_scores_df, high_scores_df = \\\n", + " run_removal_experiment(\n", + " data_factory=make_data,\n", + " valuation_factories=[make_random, make_oob],\n", + " utility_factory=make_utility, # for evaluation\n", + " removal_percentages=removal_percentages,\n", + " n_runs=10,\n", + " n_jobs=30,\n", + " random_state=random_state)" + ] }, { + "cell_type": "code", + "execution_count": null, "metadata": { - "editable": true, + "ExecuteTime": { + "end_time": "2024-11-21T18:20:08.368069Z", + "start_time": "2024-11-21T18:20:08.051785Z" + }, "slideshow": { "slide_type": "" }, "tags": [ - "hide" - ], + "hide-input", + "invertible-output" + ] + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", + "\n", + "for i, scores_df in enumerate((low_scores_df, high_scores_df)):\n", + " for j, (method_name, df) in enumerate(scores_df.groupby(\"method_name\")):\n", + " plot_ci_array(\n", + " data=df.drop(columns=[\"method_name\"]).values,\n", + " level=0.05,\n", + " abscissa=np.round(removal_percentages, 2),\n", + " mean_color=MEAN_COLORS[j],\n", + " shade_color=SHADE_COLORS[j],\n", + " label=method_name,\n", + " ax=axs[i],\n", + " )\n", + " axs[i].legend()\n", + " axs[i].set_ylabel(\"Accuracy\")\n", + "\n", + "axs[0].set_title(\"Lowest value removal, 95% CI\")\n", + "axs[0].set_xlabel(\"Fraction of data removed\")\n", + "axs[1].set_title(\"Highest value removal, 95% CI\")\n", + "axs[1].set_xlabel(\"Fraction of data removed\")\n", + "\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FIXME: THERE'S A BUG ABOVE\n", + "\n", + "The accuracy curves do not start at the same value!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Data-OOB with arbitrary models\n", + "\n", + "Note that even though the method is designed for bagging models, in principle it can be used with any other estimator by fitting a bagging model on top of it. This can generally be quite expensive, but it might prove useful in some cases. Below is what happens when we do this with a k-nearest neighbors classifier.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "ExecuteTime": { - "end_time": "2024-11-13T18:41:35.175143Z", - "start_time": "2024-11-13T18:41:11.810975Z" + "end_time": "2024-11-21T22:46:03.978452Z", + "start_time": "2024-11-21T22:39:42.943659Z" } }, - "cell_type": "code", + "outputs": [], "source": [ - "from joblib import parallel_config, Parallel, delayed\n", - "\n", - "n_runs = 20\n", - "n_jobs = 20\n", - "n_est = 500\n", - "max_samples = 0.95\n", - "seed = random_state\n", - "\n", - "all_best_scores = []\n", - "all_worst_scores = []\n", - "removal_percentages = np.arange(0, 0.99, 0.01)\n", - "\n", - "pending = set()\n", - "\n", - "with parallel_config(n_jobs=n_jobs):\n", - " seed_seq = ensure_seed_sequence(seed)\n", - " job = delayed(removal_job)\n", - " args = zip([\"random\", \"data_oob\"] * n_runs,\n", - " seed_seq.spawn(2 * n_runs),\n", - " [n_est] * 2 * n_runs,\n", - " [max_samples] * 2 * n_runs)\n", - " with Parallel(return_as=\"generator_unordered\") as parallel:\n", - " delayed_evals = parallel(\n", - " job(method=m, seed=s, n_est=n, max_samples=ms) for m, s, n, ms\n", - " in args)\n", - " for result in tqdm(delayed_evals, unit=\"%\", total=2 * n_runs):\n", - " best_scores, worst_scores = result\n", - " all_best_scores.append(best_scores)\n", - " all_worst_scores.append(worst_scores)\n", - "\n", - "best_scores_df = pd.DataFrame(all_best_scores)\n", - "worst_scores_df = pd.DataFrame(all_worst_scores)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - " 0%| | 0/40 [00:00 ModelUtility:\n", + " return KNNClassifierUtility(KNeighborsClassifier(n_neighbors=10), test)\n", + "\n", + "\n", + "def make_oob_knn(train: Dataset, random_state: int) -> DataOOBValuation:\n", + " classifier = BaggingClassifier(\n", + " estimator=KNeighborsClassifier(n_neighbors=10),\n", + " max_samples=0.4,\n", + " n_estimators=10,\n", + " random_state=random_state)\n", + " model = ThresholdTunerCV(classifier,\n", + " n_splits=5,\n", + " metric=f1_score,\n", + " n_jobs=1,\n", + " random_state=random_state)\n", + " model.fit(train.x, train.y)\n", + " return DataOOBValuation(model, point_wise_accuracy)\n", + "\n", + "low_scores_df, high_scores_df = \\\n", + " run_removal_experiment(\n", + " data_factory=make_data,\n", + " utility_factory=make_knn_utility,\n", + " valuation_factories=[make_random, make_oob_knn],\n", + " removal_percentages=removal_percentages,\n", + " n_runs=20,\n", + " n_jobs=30)" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" + "ExecuteTime": { + "end_time": "2024-11-21T22:52:42.855127Z", + "start_time": "2024-11-21T22:52:42.545146Z" }, "tags": [ "hide-input", "invertible-output" - ], + ] + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", + "\n", + "for i, scores_df in enumerate((low_scores_df, high_scores_df)):\n", + " for j, (method_name, df) in enumerate(scores_df.groupby(\"method_name\")):\n", + " plot_ci_array(\n", + " data=df.drop(columns=[\"method_name\"]).values,\n", + " level=0.05,\n", + " abscissa=np.round(removal_percentages, 2),\n", + " mean_color=MEAN_COLORS[j],\n", + " shade_color=SHADE_COLORS[j],\n", + " label=method_name,\n", + " ax=axs[i],\n", + " )\n", + " axs[i].legend()\n", + " axs[i].set_ylabel(\"Accuracy\")\n", + "\n", + "axs[0].set_title(\"Lowest value removal, 95% CI\")\n", + "axs[0].set_xlabel(\"Fraction of data removed\")\n", + "axs[1].set_title(\"Highest value removal, 95% CI\")\n", + "axs[1].set_xlabel(\"Fraction of data removed\")\n", + "\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The impact of removing data on the three random forest models\n", + "\n", + "Manually removing and retraining:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "ExecuteTime": { - "end_time": "2024-11-13T18:41:45.060268Z", - "start_time": "2024-11-13T18:41:44.733845Z" + "end_time": "2024-11-21T13:11:13.175909Z", + "start_time": "2024-11-21T13:11:13.077926Z" } }, + "outputs": [], "source": [ - "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", + "df = load_adult_data_raw()\n", + "corr = df.corr(numeric_only=True)\n", + "corr.style.background_gradient(cmap='coolwarm')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:13:12.320216Z", + "start_time": "2024-11-21T13:12:58.953700Z" + } + }, + "outputs": [], + "source": [ + "train, test = load_adult_data(train_size=0.6, random_state=random_state)\n", + "for i, values in enumerate(oob_values):\n", + " zero_variances = np.isclose(values.variances, 0.0)\n", + " to_index = \\\n", + " np.where(zero_variances & np.isclose(values.values, 1))[0][0]\n", + " from_index = \\\n", + " np.where(zero_variances & np.isclose(values.values, 0))[0][-1]\n", + " model = RandomForestClassifier(n_estimators=n_estimators[i],\n", + " max_samples=1.0,\n", + " random_state=random_state)\n", + " model.fit(train.x, train.y)\n", + " full_training_score = model.score(test.x, test.y)\n", "\n", - "for i, method_name in enumerate([\"random\", \"data_oob\"]):\n", - " plot_ci_array(\n", - " data=worst_scores_df[worst_scores_df[\"method_name\"] == method_name]\n", - " .drop(columns=[\"method_name\"])\n", - " .values,\n", - " level=0.05,\n", - " abscissa=removal_percentages,\n", - " mean_color=mean_colors[i],\n", - " shade_color=shade_colors[i],\n", - " label=method_name,\n", - " ax=ax1,\n", - " )\n", - " plot_ci_array(\n", - " best_scores_df[best_scores_df[\"method_name\"] == method_name]\n", - " .drop(columns=[\"method_name\"])\n", - " .values,\n", - " level=0.05,\n", - " abscissa=removal_percentages,\n", - " mean_color=mean_colors[i],\n", - " shade_color=shade_colors[i],\n", - " label=method_name,\n", - " ax=ax2,\n", - " )\n", - "metric_name = \"Accuracy\"\n", - "ax1.set_title(\"Worst point removal, 95% CI\")\n", - "ax1.set_xlabel(\"Fraction of data removed\")\n", - "ax1.set_ylabel(metric_name)\n", - "ax1.set_xticks(ax1.get_xticks()[::10])\n", - "ax1.legend()\n", - "ax2.set_title(\"Best point removal, 95% CI\")\n", - "ax2.set_xlabel(\"Fraction of data removed\")\n", - "ax2.set_xticks(ax2.get_xticks()[::10])\n", - "ax2.set_ylabel(metric_name)\n", - "ax2.legend()\n", + " model.fit(train.x[:to_index], train.y[:to_index])\n", + " reduced_training_score = model.score(test.x, test.y)\n", + " score_change = 100 * (\n", + " reduced_training_score - full_training_score) / full_training_score\n", + " fraction_of_data = 100 * to_index / len(train)\n", + " print(f\"Model with {n_estimators[i]} estimators:\")\n", + " print(f\" Remove HIGH: Relative change in test score after training on \"\n", + " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\")\n", "\n", - "plt.show();" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + " model.fit(train.x[from_index:], train.y[from_index:])\n", + " reduced_training_score = model.score(test.x, test.y)\n", + " score_change = 100 * (\n", + " reduced_training_score - full_training_score) / full_training_score\n", + " fraction_of_data = 100 * (len(train) - from_index) / len(train)\n", + " print(f\" Remove LOW: Relative change in test score after training on \"\n", + " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Removing high variance points, or training just on them\n", + "\n", + "=> same result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:11:41.128330Z", + "start_time": "2024-11-21T13:11:39.044280Z" } - ], - "execution_count": 13 + }, + "outputs": [], + "source": [ + "variance_q95 = np.quantile(values.variances, 0.95)\n", + "\n", + "for i, values in enumerate(oob_values):\n", + " high_variance_indices = np.where(values.variances > variance_q95)[0]\n", + " model = RandomForestClassifier(n_estimators=n_estimators[i],\n", + " max_samples=1.0,\n", + " class_weight='balanced',\n", + " random_state=random_state)\n", + " model.fit(train.x, train.y)\n", + " full_score = model.score(test.x, test.y)\n", + "\n", + " selected_x, selected_y = train.x[high_variance_indices], train.y[\n", + " high_variance_indices]\n", + " model.fit(selected_x, selected_y)\n", + " reduced_score = model.score(test.x, test.y)\n", + "\n", + " score_change = 100 * (reduced_score - full_score) / full_score\n", + " fraction_of_data = 100 * len(selected_x) / len(train)\n", + "\n", + " print(f\"Model with {n_estimators[i]} estimators:\")\n", + " print(f\" Relative change in test score after training on \"\n", + " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\")\n", + "\n", + " random_indices = np.random.choice(len(train), len(high_variance_indices),\n", + " replace=False)\n", + " model.fit(train.x[random_indices], train.y[random_indices])\n", + " random_score = model.score(test.x, test.y)\n", + " score_change = 100 * (random_score - full_score) / full_score\n", + " print(f\" Relative change in test score after training on a random \"\n", + " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\")" + ] }, { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-20T17:49:10.832877Z", + "start_time": "2024-11-20T17:49:10.753592Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "model = RandomForestClassifier(n_estimators=100,\n", + " max_samples=1.0,\n", + " class_weight=\"balanced\")\n", + "model.fit(train.x, train.y)\n", + "full_score = model.score(test.x, test.y)\n", + "confusion_matrix(test.y, model.predict(test.x), normalize='true')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-20T17:52:11.207292Z", + "start_time": "2024-11-20T17:52:10.899909Z" + } + }, + "outputs": [], + "source": [ + "model.fit(train.x[high_variance_indices], train.y[high_variance_indices])\n", + "print(f\"Using {100 * len(high_variance_indices) / len(train):.2f}% of the data\")\n", + "confusion_matrix(test.y, model.predict(test.x), normalize='true')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Subsampling a random subset of the same size\n", + "\n", + "Yields similar accuracy (expected) but worse FNR and FPR" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T10:38:06.484426Z", + "start_time": "2024-11-21T10:38:06.018361Z" + } + }, + "outputs": [], + "source": [ + "random_indices = np.random.choice(len(train), len(high_variance_indices),\n", + " replace=False)\n", + "model.fit(train.x[random_indices], train.y[random_indices])\n", + "random_score = model.score(test.x, test.y)\n", + "score_change = 100 * (random_score - full_score) / full_score\n", + "fraction_of_data = 100 * len(random_indices) / len(train)\n", + "print(f\"Relative change in test score after training on a random \"\n", + " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\")\n", + "confusion_matrix(test.y, model.predict(test.x), normalize='true')" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "### Calibrating the model\n", + "\n", + "Fails miserably:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T09:59:41.745289Z", + "start_time": "2024-11-21T09:59:39.757510Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.calibration import CalibratedClassifierCV\n", + "\n", + "calibrated_model = CalibratedClassifierCV(estimator=model, method='isotonic')\n", + "calibrated_model.fit(train.x[random_indices], train.y[random_indices])\n", + "\n", + "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", + "\n", + "ConfusionMatrixDisplay.from_estimator(model, test.x, test.y,\n", + " labels=[0, 1], normalize=\"true\",\n", + " ax=axs[0])\n", + "ConfusionMatrixDisplay.from_estimator(calibrated_model, test.x, test.y,\n", + " labels=[0, 1], normalize=\"true\",\n", + " ax=axs[1])\n", + "\n", + "axs[0].set_title(\"Original model\")\n", + "axs[1].set_title(\"Calibrated model\")\n", + "plt.show();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Oversampling with imblearn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:23:32.967495Z", + "start_time": "2024-11-21T13:22:49.953376Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "from support.common import ThresholdTunerCV, load_adult_data_raw\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import clone\n", + "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.pipeline import make_pipeline\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import ConfusionMatrixDisplay, f1_score\n", + "from sklearn.preprocessing import TargetEncoder\n", + "\n", + "random_state = 42\n", + "\n", + "df = load_adult_data_raw()\n", + "column_names = df.columns.tolist()\n", + "\n", + "df[\"income\"] = df[\"income\"].cat.codes\n", + "df.drop(columns=[\"education\"], inplace=True) # education-num is enough\n", + "df.dropna(inplace=True)\n", + "column_names.remove(\"education\")\n", + "column_names.remove(\"income\")\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(\n", + " df.drop(columns=[\"income\"]).values,\n", + " df[\"income\"].values,\n", + " train_size=0.6,\n", + " random_state=random_state,\n", + " stratify=df[\"income\"].values,\n", + " )\n", + "\n", + "encoder = TargetEncoder(target_type=\"binary\", random_state=random_state)\n", + "classifier = RandomForestClassifier(n_estimators=400,\n", + " max_samples=0.7,\n", + " class_weight=\"balanced\",\n", + " random_state=random_state)\n", + "tuned_classifier = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\n", + "ros_sampler = RandomOverSampler(random_state=random_state)\n", + "# smote_sampler = SMOTE(random_state=random_state)\n", + "# adasyn_sampler = ADASYN(random_state=random_state)\n", + "\n", + "simple_pipeline = make_pipeline(encoder, clone(classifier))\n", + "ros_pipeline = make_pipeline(encoder, ros_sampler, clone(classifier))\n", + "tuned_pipeline = make_pipeline(encoder, clone(tuned_classifier))\n", + "tuned_oversampling_pipeline = make_pipeline(encoder, ros_sampler,\n", + " clone(tuned_classifier))\n", + "# smote_pipeline = make_pipeline(encoder, smote_sampler, clone(tuned_classifier))\n", + "# adasyn_pipeline = make_pipeline(encoder, adasyn_sampler, clone(tuned_classifier))\n", + "\n", + "simple_pipeline.fit(x_train, y_train)\n", + "full_score = simple_pipeline.score(x_test, y_test)\n", + "print(f\"Accuracy with no oversampling: {full_score:.4f}\")\n", + "\n", + "ros_pipeline.fit(x_train, y_train)\n", + "full_score = ros_pipeline.score(x_test, y_test)\n", + "print(f\"Accuracy with random oversampling: {full_score:.4f}\")\n", + "\n", + "tuned_pipeline.fit(x_train, y_train)\n", + "full_score = tuned_pipeline.score(x_test, y_test)\n", + "print(f\"Accuracy with tuned threshold: {full_score:.4f}\")\n", + "\n", + "tuned_oversampling_pipeline.fit(x_train, y_train)\n", + "full_score = tuned_oversampling_pipeline.score(x_test, y_test)\n", + "print(f\"Accuracy with tuned threshold and ROS: {full_score:.4f}\")\n", + "\n", + "# smote_pipeline.fit(x_train, y_train)\n", + "# full_score = smote_pipeline.score(x_test, y_test)\n", + "# print(f\"Accuracy with SMOTE: {full_score:.4f}\")\n", + "# \n", + "# adasyn_pipeline.fit(x_train, y_train)\n", + "# full_score = adasyn_pipeline.score(x_test, y_test)\n", + "# print(f\"Accuracy with ADASYN: {full_score:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:23:40.729686Z", + "start_time": "2024-11-21T13:23:37.804993Z" + } + }, + "outputs": [], + "source": [ + "# Sanity check: did we fit the training set well?\n", + "(simple_pipeline.score(x_train, y_train),\n", + " ros_pipeline.score(x_train, y_train),\n", + " tuned_pipeline.score(x_train, y_train),\n", + " tuned_oversampling_pipeline.score(x_train, y_train))" + ] + }, + { "cell_type": "code", - "source": "", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T13:24:13.731707Z", + "start_time": "2024-11-21T13:24:10.961185Z" + } + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=[12, 12])\n", + "\n", + "ConfusionMatrixDisplay.from_estimator(simple_pipeline, x_test, y_test,\n", + " labels=[0, 1], normalize=\"true\",\n", + " ax=axs[0][0])\n", + "ConfusionMatrixDisplay.from_estimator(ros_pipeline, x_test, y_test,\n", + " labels=[0, 1], normalize=\"true\",\n", + " ax=axs[0][1])\n", + "ConfusionMatrixDisplay.from_estimator(tuned_pipeline, x_test, y_test,\n", + " labels=[0, 1], normalize=\"true\",\n", + " ax=axs[1][0])\n", + "ConfusionMatrixDisplay.from_estimator(tuned_oversampling_pipeline, x_test,\n", + " y_test,\n", + " labels=[0, 1], normalize=\"true\",\n", + " ax=axs[1][1])\n", + "\n", + "axs[0][0].set_title(\"No oversampling\")\n", + "axs[0][1].set_title(\"ROS\")\n", + "axs[1][0].set_title(\"Tuned threshold\")\n", + "axs[1][1].set_title(\"Tuned threshold with ROS\")\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T12:25:06.580020Z", + "start_time": "2024-11-21T12:25:05.940483Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.metrics import precision_recall_curve\n", + "\n", + "y_proba = ros_pipeline.predict_proba(x_test)[:, 1]\n", + "\n", + "precision, recall, thresholds = precision_recall_curve(y_test, y_proba)\n", + "f_score = 2 * precision * recall / (precision + recall)\n", + "optimal_index = np.argmax(f_score)\n", + "chosen_threshold = thresholds[optimal_index]\n", + "y_pred = (y_proba >= chosen_threshold).astype(int)\n", + "print(\n", + " f\"Accuracy at chosen threshold {chosen_threshold:.2f}: {np.mean(y_pred == y_test):.4f}\")\n", + "ConfusionMatrixDisplay.from_predictions(y_test, y_pred, labels=[0, 1],\n", + " normalize=\"true\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-21T12:25:06.859513Z", + "start_time": "2024-11-21T12:25:06.854556Z" + } + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [] } ], "metadata": { @@ -674,7 +1209,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/notebooks/support/common.py b/notebooks/support/common.py index ea7a02fe8..22d5a8898 100644 --- a/notebooks/support/common.py +++ b/notebooks/support/common.py @@ -12,8 +12,12 @@ import pandas as pd from numpy.typing import NDArray from PIL.JpegImagePlugin import JpegImageFile +from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.metrics import f1_score +from sklearn.model_selection import StratifiedKFold, train_test_split, cross_val_predict +from sklearn.preprocessing import TargetEncoder -from pydvl.utils import Dataset +from pydvl.valuation.dataset import Dataset from .types import Losses @@ -579,10 +583,14 @@ def plot_corrupted_influences_distribution( def filecache(path: Path) -> Callable[[Callable], Callable]: """Wraps a function to cache its output on disk. - There is no hashing of the arguments of the function. This function merely + There is no hashing of the arguments of the function. This decorator merely checks whether `filename` exists and if so, loads the output from it, and if not it calls the function and saves the output to `filename`. + The decorated function accepts an additional keyword argument `_force_reload` + which, if set to `True`, calls the wrapped function to recompute the output and + overwrites the cached file. + Args: fun: Function to wrap. filename: Name of the file to cache the output to. @@ -592,10 +600,13 @@ def filecache(path: Path) -> Callable[[Callable], Callable]: def decorator(fun: Callable) -> Callable: @wraps(fun) - def wrapper(*args, **kwargs): + def wrapper(*args, _force_rebuild: bool = False, **kwargs) -> Any: try: with path.open("rb") as fd: print(f"Found cached file: {path.name}.") + if _force_rebuild: + print("Ignoring and rebuilding...") + raise FileNotFoundError return pickle.load(fd) except (FileNotFoundError, EOFError, pickle.UnpicklingError): result = fun(*args, **kwargs) @@ -608,8 +619,14 @@ def wrapper(*args, **kwargs): return decorator -@filecache(path=Path("adult_data.pkl")) -def load_adult_data() -> pd.DataFrame: +@filecache(path=Path("adult_data_raw.pkl")) +def load_adult_data_raw() -> pd.DataFrame: + """ + Downloads the adult dataset from UCI and returns it as a pandas DataFrame. + + Returns: + The adult dataset as a pandas DataFrame. + """ data_url = ( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data" ) @@ -637,7 +654,7 @@ def load_adult_data() -> pd.DataFrame: "workclass": "category", "fnlwgt": int, "education": "category", - "education-num": int, + "education-num": int, # increasing level of education "marital-status": "category", "occupation": "category", "relationship": "category", @@ -650,28 +667,172 @@ def load_adult_data() -> pd.DataFrame: "income": "category", } - data_adult = pd.read_csv( + return pd.read_csv( data_url, names=column_names, sep=",\s*", engine="python", na_values="?", dtype=data_types, - nrows=2000, ) - # Drop categorical columns - data_adult = data_adult.drop( - columns=[ - "workclass", - "education", - "marital-status", - "occupation", - "relationship", - "race", - "sex", - "native-country", - ] + +def load_adult_data( + train_size: float = 0.7, random_state: Optional[int] = None +) -> Tuple[Dataset, Dataset]: + """ + Loads the adult dataset from UCI and performs some preprocessing. + + The data is preprocessed by performing target encoding of the categorical variables, + dropping the "education" column and dropping NaNs + + Ideally the encoding would be done in a pipeline, but we are trying to remove as + much complexity from the notebooks as possible. + + Args: + train_size: fraction of the data to use for training + random_state: random state for reproducibility + + Returns: + A tuple with training and test datasets. + """ + + df = load_adult_data_raw() + column_names = df.columns.tolist() + + df["income"] = df["income"].cat.codes + df.drop(columns=["education"], inplace=True) # education-num is enough + df.dropna(inplace=True) + column_names.remove("education") + column_names.remove("income") + + x_train, x_test, y_train, y_test = train_test_split( + df.drop(columns=["income"]).values, + df["income"].values, + train_size=train_size, + random_state=random_state, + stratify=df["income"].values, + ) + + te = TargetEncoder(target_type="binary", random_state=random_state) + x_train = te.fit_transform(x_train, y_train) + x_test = te.transform(x_test) + + return ( + Dataset(x_train, y_train, feature_names=column_names, target_names=["income"]), + Dataset(x_test, y_test, feature_names=column_names, target_names=["income"]), + ) + + +def subsample_dataset(data: Dataset, fraction: float) -> Dataset: + """Subsamples a dataset at random. + Args: + data: Dataset to subsample + fraction: Fraction of the dataset to keep. Range [0,1) + + Returns: + The subsampled dataset + """ + assert 0 <= fraction < 1, "Fraction must be in the range [0,1)" + subsample_mask = np.random.rand(len(data)) < fraction + + return Dataset( + data.x[subsample_mask], + data.y[subsample_mask], + feature_names=data.feature_names, + target_names=data.target_names, + ) + + +def to_dataframe(dataset: Dataset) -> pd.DataFrame: + """ + Converts a dataset to a pandas DataFrame + + Args: + dataset: Dataset to convert + + Returns: + A pandas DataFrame + """ + y = dataset.y[:, np.newaxis] if dataset.y.ndim == 1 else dataset.y + df = pd.DataFrame(dataset.x, columns=dataset.feature_names).assign( + **{name: y[:, i] for i, name in enumerate(dataset.target_names)} ) + return df - return data_adult + +class ConstantBinaryClassifier(BaseEstimator): + def __init__(self, p: float, random_state: int | None = None): + """A classifier that always predicts class 0 with probability p and class 1 + with probability 1-p. + + The prediction is fixed upon fitting the model and constant for all + outputs, i.e. the same prediction is returned for all samples. Call + `fit()` again to resample the prediction. + + Args: + p: probability that this estimator always predicts class 0 + random_state: + """ + self.p = p + self.prediction: int | None = None + self.random_state = random_state + + def fit(self, X, y): + rng = np.random.default_rng(self.random_state) + n_outputs = y.shape[1] if y.ndim > 1 else 1 + self.prediction = (rng.random(n_outputs) > self.p).astype(int) + + def predict(self, X): + return np.repeat(self.prediction, len(X)) + + +class ThresholdTunerCV(BaseEstimator, ClassifierMixin): + """ + A wrapper that tunes the decision threshold of a binary classifier to maximize + a given metric, using cross-fitting on the training data. + """ + + def __init__( + self, + base_estimator, + n_splits: int = 5, + metric=f1_score, + n_jobs: int = -1, + random_state: int | None = None, + ): + self.base_estimator = base_estimator + self.n_splits = n_splits + self.metric = metric + self.optimal_threshold_: float | None = None + self.n_jobs = n_jobs + self.random_state = random_state + + def fit(self, X, y): + # We find an optimal decision threshold using out-of-sample predictions + cv_strategy = StratifiedKFold( + n_splits=self.n_splits, shuffle=True, random_state=self.random_state + ) + y_proba_cv = cross_val_predict( + self.base_estimator, + X, + y, + cv=cv_strategy, + method="predict_proba", + n_jobs=self.n_jobs, + )[:, 1] + + thresholds = np.linspace(0, 1, 100) + f1_scores = [self.metric(y, y_proba_cv >= t) for t in thresholds] + self.optimal_threshold = thresholds[np.argmax(f1_scores)] + + # Then we fit the model on the entire dataset + self.base_estimator.fit(X, y) + return self + + def predict(self, X): + y_proba = self.base_estimator.predict_proba(X)[:, 1] + return (y_proba >= self.optimal_threshold).astype(int) + + def __getattr__(self, item): + return getattr(self.base_estimator, item) diff --git a/notebooks/support/removal_experiment.py b/notebooks/support/removal_experiment.py new file mode 100644 index 000000000..d326f19be --- /dev/null +++ b/notebooks/support/removal_experiment.py @@ -0,0 +1,156 @@ +from typing import Literal, Protocol + +import pandas as pd +from joblib import Parallel, delayed, parallel_config +from numpy.typing import NDArray +from tqdm import tqdm + +from pydvl.reporting.scores import compute_removal_score +from pydvl.utils.types import BaseModel, ensure_seed_sequence +from pydvl.valuation import Dataset, ModelUtility, SupervisedScorer +from pydvl.valuation.base import Valuation + + +class ModelFactory(Protocol): + def __call__(self, random_state: int) -> BaseModel: + pass + + +class UtilityFactory(Protocol): + def __call__(self, *, test: Dataset, random_state: int) -> ModelUtility: + pass + + +class ValuationFactory(Protocol): + def __call__(self, *, train: Dataset, random_state: int) -> Valuation: + pass + + +class DataSplitFactory(Protocol): + def __call__(self, random_state: int) -> tuple[Dataset, Dataset]: + pass + + +def removal_job( + data_factory: DataSplitFactory, + valuation_factory: ValuationFactory, + utility_factory: UtilityFactory, + removal_percentages: NDArray, + random_state: int, +) -> tuple[dict, dict]: + """ + + Args: + data_factory: + valuation_factory: + utility_factory: + removal_percentages: + random_state: + Returns: + + + """ + + train, test = data_factory(random_state=random_state) + valuation = valuation_factory(train=train, random_state=random_state) + valuation.fit(train) + values = valuation.values() + + utility = utility_factory(test=test, random_state=random_state) + low_scores: dict = compute_removal_score( + utility, + values, + train, + percentages=removal_percentages, + remove_best=False, + ) + low_scores["method_name"] = valuation.__class__.__name__ + + high_scores: dict = compute_removal_score( + utility, + values, + train, + percentages=removal_percentages, + remove_best=True, + ) + + high_scores["method_name"] = valuation.__class__.__name__ + + return low_scores, high_scores + + +def run_removal_experiment( + data_factory: DataSplitFactory, + valuation_factories: list[ValuationFactory], + utility_factory: UtilityFactory, + removal_percentages: NDArray, + n_runs: int = 20, + n_jobs: int = 32, + random_state: int | None = None, +) -> tuple[pd.DataFrame, pd.DataFrame]: + """Run the sample removal experiment. + + Given the factories, the removal percentages, and the number of runs, this function + does the following in each run: + + 1. Sample a random state + 2. For each valuation method, compute the values and iteratively compute the scores + after retraining on subsets of the data. This is parallelized. Each job requires + 3 factories: + + - A factory that returns a train-test split of the data given a random state + - A factory returning a valuation method. The training set is passed to the + factory, in case the valuation needs to train something. E.g. for Data-OOB + we need the bagging model to be fitted before the valuation is computed. + - A factory that returns a utility that evaluates some model on a given test set. + This is used for the performance evaluation. The model need not be the same + as the one used for the valuation. + 3. It returns the scores in two DataFrames, one for the high value removals and one + for the low value removals. + + Args: + data_factory: A callable that returns a tuple of Datasets (train, test) given + a random state + valuation_factories: A list of callables that return Valuation objects given + a model, train data, and random state. The training data is typically not + needed for construction, but bagging models may require it + utility_factory: A callable that returns a ModelUtility given test + data, potentially with a scoring function + removal_percentages: The percentage of data to remove from the training set. + This should be a list of floats between 0 and 1. + n_runs: The number of repetitions of the experiment + n_jobs: The number of parallel jobs to use + random_state: The initial random state + Returns: + A tuple of DataFrames with the scores for the low and high value removals + """ + all_high_scores = [] + all_low_scores = [] + + with parallel_config(n_jobs=n_jobs): + seed_seq = ensure_seed_sequence(random_state).generate_state(n_runs) + job = delayed(removal_job) + + with Parallel(return_as="generator_unordered") as parallel: + delayed_evals = parallel( + job( + data_factory=data_factory, + valuation_factory=valuation_factory, + utility_factory=utility_factory, + removal_percentages=removal_percentages, + random_state=seed_seq[i], + ) + for valuation_factory in valuation_factories + for i in range(n_runs) + ) + for result in tqdm( + delayed_evals, unit="%", total=len(valuation_factories) * n_runs + ): + low_scores, high_scores = result + all_low_scores.append(low_scores) + all_high_scores.append(high_scores) + + low_scores_df = pd.DataFrame(all_low_scores) + high_scores_df = pd.DataFrame(all_high_scores) + + return low_scores_df, high_scores_df diff --git a/src/pydvl/reporting/scores.py b/src/pydvl/reporting/scores.py index 9f06614d7..8e70f97cc 100644 --- a/src/pydvl/reporting/scores.py +++ b/src/pydvl/reporting/scores.py @@ -23,7 +23,7 @@ def compute_removal_score( remove_best: bool = False, progress: bool = False, ) -> dict[float, float]: - r"""Fits model and computes score on the test set after incrementally removing + """Fits model and computes score on the test set after incrementally removing a percentage of data points from the training set, based on their values. Args: diff --git a/src/pydvl/valuation/methods/data_oob.py b/src/pydvl/valuation/methods/data_oob.py index f235d7e50..de91b6e76 100644 --- a/src/pydvl/valuation/methods/data_oob.py +++ b/src/pydvl/valuation/methods/data_oob.py @@ -121,12 +121,12 @@ def fit(self, data: Dataset): n_samples_bootstrap = _get_n_samples_bootstrap( len(data), self.model.max_samples ) - unsampled_indices = [] - for i, est in enumerate(estimators): - oob_indices = _generate_unsampled_indices( + unsampled_indices = [ + _generate_unsampled_indices( est.random_state, len(data.indices), n_samples_bootstrap ) - unsampled_indices.append(oob_indices) + for est in estimators + ] for est, oob_indices in zip(estimators, unsampled_indices): score_array = self.score( diff --git a/src/pydvl/valuation/methods/random.py b/src/pydvl/valuation/methods/random.py new file mode 100644 index 000000000..d8cd23ee1 --- /dev/null +++ b/src/pydvl/valuation/methods/random.py @@ -0,0 +1,27 @@ +""" +This module implements a trivial random valuation method. + +""" + +from pydvl.utils import BaseModel, Seed +from pydvl.valuation.base import Valuation +from pydvl.valuation.dataset import Dataset +from pydvl.valuation.result import ValuationResult + + +class RandomValuation(Valuation): + """ + A trivial valuation method that assigns random values to each data point. + + Values are in the range [0, 1), as generated by + [ValuationResult.from_random][pydvl.valuation.result.ValuationResult.from_random]. + """ + + def __init__(self, random_state: Seed): + super().__init__() + self.random_state = random_state + + def fit(self, train: Dataset): + self.result = ValuationResult.from_random( + size=len(train), seed=self.random_state + ) diff --git a/src/pydvl/valuation/result.py b/src/pydvl/valuation/result.py index 8174c5ee0..40cfefd29 100644 --- a/src/pydvl/valuation/result.py +++ b/src/pydvl/valuation/result.py @@ -670,9 +670,8 @@ def to_dataframe( DataFrame's index. Returns: - A dataframe with two columns, one for the values, with name - given as explained in `column`, and another with standard errors for - approximate algorithms. The latter will be named `column+'_stderr'`. + A dataframe with three columns: `name`, `name_variances` and + `name_counts`, where `name` is the value of argument `column`. """ column = column or self._algorithm df = pd.DataFrame( From df5023e18cd09a6dfb88179e38ca008eb677aa84 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Fri, 29 Nov 2024 14:37:08 +0100 Subject: [PATCH 21/41] WIP2 --- notebooks/data_oob.ipynb | 569 ++++++++++-------------- notebooks/support/common.py | 27 +- requirements-docs.txt | 1 + src/pydvl/valuation/methods/data_oob.py | 21 +- 4 files changed, 255 insertions(+), 363 deletions(-) diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb index a9feda157..af994a62b 100644 --- a/notebooks/data_oob.ipynb +++ b/notebooks/data_oob.ipynb @@ -12,7 +12,8 @@ "* Remove old text fragments\n", "* Factor out any boilerplate\n", "* Remove leftovers\n", - "* Actually perform the analysis using the values" + "* Actually perform the analysis using the values\n", + "* Add support for pipeline objects to DataOOBValuation" ] }, { @@ -57,12 +58,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:42:27.489342Z", - "start_time": "2024-11-21T13:42:25.637695Z" - }, "slideshow": { "slide_type": "" }, @@ -70,7 +66,6 @@ "hide" ] }, - "outputs": [], "source": [ "%load_ext autoreload\n", "\n", @@ -82,8 +77,8 @@ "import numpy as np\n", "\n", "from pydvl.reporting.plots import plot_ci_array, plot_ci_values\n", - "from support.common import ThresholdTunerCV, load_adult_data, \\\n", - " load_adult_data_raw\n", + "from sklearn.metrics import ConfusionMatrixDisplay, f1_score\n", + "from support.common import load_adult_data, load_adult_data_raw\n", "\n", "matplotlib.rcParams[\"axes.facecolor\"] = (1, 1, 1, 0)\n", "plt.rcParams[\"axes.facecolor\"] = (1, 1, 1, 0)\n", @@ -93,25 +88,21 @@ "SHADE_COLORS = [\"lightskyblue\", \"firebrick\", \"seagreen\", \"gold\", \"plum\"]\n", "\n", "is_CI = os.environ.get(\"CI\")\n", - "random_state = 24\n", + "random_state = 42\n", "random.seed(random_state);" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:42:43.748256Z", - "start_time": "2024-11-21T13:42:43.744196Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import f1_score\n", "from pydvl.valuation import DataOOBValuation, Dataset" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -123,58 +114,32 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:42:53.999969Z", - "start_time": "2024-11-21T13:42:53.799936Z" - }, "tags": [ "hide-output" ] }, - "outputs": [], "source": [ - "train, test = load_adult_data(train_size=0.6, random_state=random_state)" - ] + "train, test = load_adult_data(train_size=0.6, subsample=0.2,\n", + " random_state=random_state)" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:42:56.400972Z", - "start_time": "2024-11-21T13:42:56.396323Z" - }, "tags": [ "hide" ] }, - "outputs": [], "source": [ "if is_CI: # Subsample 1% of the data for faster testing\n", - " from support.common import subsample_dataset\n", - "\n", - " train = subsample_dataset(train, 0.01)\n", - " test = subsample_dataset(test, 0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:42:58.947427Z", - "start_time": "2024-11-21T13:42:58.936728Z" - } - }, + " train, test = load_adult_data(train_size=0.6, subsample=0.01,\n", + " random_state=random_state)" + ], "outputs": [], - "source": [ - "from support.common import subsample_dataset\n", - "\n", - "train = subsample_dataset(train, 0.2)\n", - "test = subsample_dataset(test, 0.2)" - ] + "execution_count": null }, { "cell_type": "markdown", @@ -185,55 +150,86 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:43:01.875396Z", - "start_time": "2024-11-21T13:43:01.870446Z" - }, "tags": [ "hide-input" ] }, - "outputs": [], "source": [ - "print(f\"Class distribution: {100 * train.y.mean():.1f}% positive, \"\n", - " f\"{100 * (1 - train.y.mean()):.1f}% negative\")" - ] + "print(f\"{len(train)} samples. Class distribution: {100 * np.mean(train.y == 1):.1f}% positive, \"\n", + " f\"{100 * np.mean(train.y == 0):.1f}% negative\")" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "As a quick baseline, we train a standard sklearn [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). Since the dataset is imbalanced, besides the accuracy we look at the confusion matrix, and notice that despite weighting the class by their inverse frequency with `class_weight=\"balanced\"`, the model is not very good at predicting the minority (\"positive\", or \"1\") class: in the figure below we see a high rate of false negatives. This will play a role later in how we interpret the values that Data-OOB returns." - ] + "source": "As a quick baseline, we train a standard sklearn [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). Since the dataset is imbalanced, besides the accuracy we look at the confusion matrix, and notice that despite weighting the class by their inverse frequency with `class_weight=\"balanced\"`, the model is not very good at predicting the minority (\"positive\", or \"1\") class: in the left hand side of the figure below we see a high rate of false negatives. This will play a role later in how we interpret the values that Data-OOB returns, and requires us to address the imbalance in the dataset. We do this with a simple random over sampling using `imblearn`'s [RandomOverSampler](...) class." }, { "cell_type": "code", - "execution_count": null, + "metadata": {}, + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "n_est = 100\n", + "max_samples = 0.2 # Use small bootstrap samples\n", + "model = RandomForestClassifier(n_estimators=n_est,\n", + " max_samples=max_samples,\n", + " class_weight=\"balanced\",\n", + " random_state=random_state)\n", + "model.fit(train.x, train.y)\n", + "base_predictions = model.predict(test.x)\n", + "\n", + "sampler = RandomOverSampler(random_state=random_state)\n", + "train.x, train.y = sampler.fit_resample(train.x, train.y)\n", + "\n", + "model.fit(train.x, train.y)\n", + "predictions_oversampled = model.predict(test.x) " + ], + "outputs": [], + "execution_count": null + }, + { "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:43:24.904085Z", - "start_time": "2024-11-21T13:43:24.555235Z" - }, "tags": [ "hide-input" ] }, + "cell_type": "code", + "source": [ + "print(f\"{len(train)} samples. Class distribution: {100 * train.y.mean():.1f}% positive, \"\n", + " f\"{100 * (1 - train.y.mean()):.1f}% negative\")" + ], "outputs": [], + "execution_count": null + }, + { + "metadata": { + "tags": [ + "hide-input" + ] + }, + "cell_type": "code", "source": [ - "from sklearn.metrics import ConfusionMatrixDisplay\n", + "from sklearn.metrics import accuracy_score\n", + "base_accuracy = accuracy_score(test.y, base_predictions)\n", + "accuracy_oversampled = accuracy_score(test.y, predictions_oversampled)\n", "\n", - "n_est = 20\n", - "model = RandomForestClassifier(n_estimators=n_est,\n", - " class_weight=\"balanced\",\n", - " random_state=random_state)\n", - "model.fit(train.x, train.y)\n", - "accuracy = model.score(test.x, test.y)\n", - "print(f\"Accuracy of a Random Forest with {n_est} estimators: {accuracy:.4f}\")\n", - "disp = ConfusionMatrixDisplay.from_estimator(model, test.x, test.y,\n", - " labels=[0, 1], normalize=\"true\")" - ] + "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n", + "ConfusionMatrixDisplay.from_predictions(test.y, base_predictions, ax=axs[0],\n", + " labels=[0, 1], normalize=\"true\")\n", + "ConfusionMatrixDisplay.from_predictions(test.y, predictions_oversampled, ax=axs[1],\n", + " labels=[0, 1], normalize=\"true\")\n", + "\n", + "axs[0].set_title(f\"Original dataset, {n_est} trees.\\nAccuracy: {base_accuracy:.2f}\")\n", + "axs[1].set_title(f\"Oversampled dataset, {n_est} trees.\\nAccuracy: {accuracy_oversampled:.2f}\")\n", + "\n", + "plt.show()" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -247,46 +243,39 @@ "\n", "The main idea of Data-OOB is to use the out-of-bag error estimates of a bagged model to compute data values. In pyDVL, we provide a class [DataOOBValuation][pydvl.valuation.DataOOBValuation] that takes an existing classification or regression bagging model and uses the per-sample out-of-bag performance estimate for the value of each point.\n", "\n", - "Let's compute and compare the Data-OOB values with three choices for the number of estimators of a random forest. After fitting the random forest, we use the [fit][pydvl.valuation.DataOOBValuation.fit] method to compute the values and store them in two [ValuationResult][pydvl.value.result.ValuationResult] objects" + "Let's compute and compare the Data-OOB values with three choices for the number of estimators of a random forest. After fitting the random forest, we use the [fit][pydvl.valuation.DataOOBValuation.fit] method to compute the values and store them in [ValuationResult][pydvl.value.result.ValuationResult] objects.\n", + "\n", + "Even though it's not relevant to our discussion, notice how the accuracy barely changes with the number of estimators. Below, we will discuss using the values to identify \"easy\" or \"hard\" samples in the dataset, but first let's quickly look at the values themselves." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:45:22.030123Z", - "start_time": "2024-11-21T13:45:17.503487Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "n_estimators = [50, 100, 200]\n", "oob_values = []\n", "for i, n_est in enumerate(n_estimators, start=1):\n", - " classifier = RandomForestClassifier(n_estimators=n_est,\n", - " max_samples=0.7,\n", - " class_weight=\"balanced\",\n", - " random_state=random_state)\n", - " model = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\n", + " model = RandomForestClassifier(n_estimators=n_est,\n", + " max_samples=max_samples,\n", + " class_weight=\"balanced\",\n", + " random_state=random_state)\n", + " \n", + " # Tuning messes everything up if we oversample the dataset\n", + " # model = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\n", " model.fit(train.x, train.y)\n", - " model.n_estimators = model.base_estimator.n_estimators\n", - " model.max_samples = model.base_estimator.max_samples\n", - " model.estimators_ = model.base_estimator.estimators_\n", - " # model.estimators_samples = model.base_estimator.estimators_samples_\n", " accuracy = model.score(test.x, test.y)\n", - " print(f\"Accuracy with {n_est} estimators: {accuracy:.4f}\")\n", + " print(f\"Accuracy with {n_est} estimators: {accuracy:.2f}\")\n", " valuation = DataOOBValuation(model)\n", " valuation.fit(train)\n", " oob_values.append(valuation.values())" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "Even though it's not relevant to our discussion, notice how the accuracy barely changes with the number of estimators. Below, we will discuss using the values to identify \"easy\" or \"hard\" samples in the dataset, but first let's quickly look at the values themselves." - ] + "source": "" }, { "cell_type": "markdown", @@ -297,19 +286,20 @@ "\n", "The left-hand side of the figure below depicts value as it increases with rank and a 95% t-confidence interval. The right-hand side shows the histogram of values.\n", "\n", - "We observe a long tail of high values. This is because the score $T$ used in Data-OOB (accuracy in this case) is a binary variable, and the value $\\psi_i$ is the fraction of times that all weak learners not trained on the $i$-th point classify it correctly. Given the imbalance in the dataset, many learners will always predict the majority (\"negative\", < $50K earnings / year) class and be correct on 75% of the dataset, leading to this tail. As the number of estimators increases, the chance of all of them failing on the same points decreases. The same happens when we increase the maximum depth (try it!). This behaviour is then not a deep property of Data-OOB from which to gain new insights, but rather a consequence of the dataset and the model, as we further elaborate below.\n", + "We observe a long tail of high values. This is because the score $T$ used in Data-OOB (accuracy in this case) is a binary variable, and the value $\\psi_i$ is the fraction of times that all weak learners not trained on the $i$-th point classify it correctly. Given the imbalance in the dataset, many learners will always predict the majority (\"negative\", < $50K earnings / year) class and be correct on 75% of the dataset, leading to this tail.\n", + "\n", + "Besides the actual value $\\psi_i$, [ValuationResult][pydvl.value.result.ValuationResult] objects store the number of times a sample is OOB —the quantity $\\sum_{b=1}^{B} \\mathbb{1} (w_{bi}=0)$— in the `counts` attribute, and the variance of the OOB score in the `variances` attribute. We use the latter in the plot below in order to display the confidence intervals, but it is important to note that the interpretation varies from one valuation method to another:\n", + "\n", + "For Shapley-based valuation methods, the variance is that of the marginal changes in the performance of the model when trained on subsets of the data with and without a sample, evaluated on a fixed valuation dataset, and could in principle be used to see whether values have (roughly) converged. But for Data-OOB, it is the variance of the performance of the ensemble of weak learners on the sample when it is OOB. Although similar in spirit, the construction is different and can be misleading.\n", + "\n", + "As a matter of fact, the interpretation of the vanishing variance at the tail has little to do with valuation and everything to do with our dataset, as introduced above: As the number of estimators increases, the chance of all of them failing on the same points decreases, up to a point. The same happens when we increase the maximum depth (try it!). This behaviour is then not a deep property of Data-OOB from which to gain new insights, but rather a consequence of the dataset and the model, as we further elaborate below.\n", "\n", "*Note that a symmetric CI is actually incorrect in this situation since all values are bounded between 0 and 1 (instead we could use a bootstrapped CI if we stored the scores of all estimators in the bagging model, but this is not implemented in pyDVL).*\n" ] }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:45:53.664257Z", - "start_time": "2024-11-21T13:45:52.855319Z" - }, "slideshow": { "slide_type": "" }, @@ -318,7 +308,6 @@ "invertible-output" ] }, - "outputs": [], "source": [ "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", @@ -343,34 +332,26 @@ "ax2.set_xlabel(\"Value\")\n", "ax2.legend()\n", "plt.plot();" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Interpreting the variances and the long tail\n", + "To see this, focus on the long tails with zero variance. These are samples for which the score $T(y_i, \\hat{f}_b(x_i)) = 1$ for *every* estimator $\\hat{f}_b$ not trained on them, that is: *every weak learner in the ensemble correctly classifies these samples*. As we said above, this can happen because it is likely for weak estimators to be fitted to always predict the majority (negative) class:\n", "\n", - "Besides the actual value $\\psi_i$, [ValuationResult][pydvl.value.result.ValuationResult] objects store the number of times a sample is OOB —the quantity $\\sum_{b=1}^{B} \\mathbb{1} (w_{bi}=0)$— in the `counts` attribute, and the variance of the OOB score in the `variances` attribute. We used the latter in the plot above in order to display the confidence intervals, but their interpretation varies from one valuation method to another.\n", - "\n", - "For Shapley-based valuation methods, the variance is that of the marginal changes in the performance of the model when trained on subsets of the data with and without a sample, evaluated on a fixed valuation dataset. But for Data-OOB, it is the variance of the performance of the ensemble of weak learners on the sample when it is OOB. Although similar in spirit, the construction is different and can be misleading. As a matter of fact, the interpretation of the vanishing variance at the tail has little to do with valuation and everything to do with our dataset.\n", - "\n", - "To see this, focus on fraction of then long tails with zero variance. These are samples for which the score $T(y_i, \\hat{f}_b(x_i)) = 1$ for *every* estimator $\\hat{f}_b$ not trained on them, that is: *every weak learner in the ensemble correctly classifies these samples*. As we said above, this can happen because it is likely for weak estimators to be fitted to always predict the majority (negative) class." + "## This analysis is now wrong: 73% of the points in the tail are in the minority class (probably have been oversampled)" ] }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:46:02.829503Z", - "start_time": "2024-11-21T13:46:02.821699Z" - }, "tags": [ "hide-input" ] }, - "outputs": [], "source": [ "v = oob_values[0]\n", "from_index = \\\n", @@ -380,7 +361,16 @@ " f\" ({100 * len(tail) / len(train):.2f}% of the data).\")\n", "print(f\"Of these, {100 * (train.y[tail] == 0).sum() / len(tail):.2f}% \"\n", " f\"are in the majority class.\")" - ] + ], + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "plt.hist(tail, bins=20)", + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -406,26 +396,23 @@ "\n", "We will use three different probabilities for the constant estimators to predict class 0: 0.01, 0.5, and 0.99. Again, the idea is that the OOB values will reflect the class distribution of the dataset, and we should see a clear jump in the values around 25% and 75% of the data.\n", "\n", - "The code is analogous to the above when we fitted the random forest, so it is ommitted from the documentation, but we use a custom class `ConstantBinaryClassifier` to create the constant estimators." + "The code is analogous to the above when we fitted the random forest, so it is ommitted from the documentation, but we use a custom class `ConstantBinaryClassifier` as base estimator." ] }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:46:21.407834Z", - "start_time": "2024-11-21T13:46:20.151630Z" - }, "tags": [ "hide-input" ] }, - "outputs": [], "source": [ "from sklearn.ensemble import BaggingClassifier\n", "from support.common import ConstantBinaryClassifier\n", "\n", + "train, test = load_adult_data(train_size=0.6, subsample=0.2,\n", + " random_state=random_state)\n", + "\n", "probs = [0.01, 0.5, 0.99]\n", "all_values = []\n", "for p in probs:\n", @@ -441,21 +428,17 @@ " valuation = DataOOBValuation(model)\n", " valuation.fit(train)\n", " all_values.append(valuation.values())" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:46:21.773680Z", - "start_time": "2024-11-21T13:46:21.476589Z" - }, "tags": [ "hide-input" ] }, - "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=[15, 5])\n", "\n", @@ -479,7 +462,9 @@ "plt.xlabel(\"Rank\")\n", "plt.title(f\"Model with constant estimators\")\n", "plt.legend();" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -535,33 +520,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T18:16:26.124375Z", - "start_time": "2024-11-21T18:16:24.695754Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "%autoreload\n", "from support.common import ThresholdTunerCV\n", "from support.removal_experiment import run_removal_experiment" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T18:16:26.416582Z", - "start_time": "2024-11-21T18:16:26.406307Z" - }, "tags": [ "hide" ] }, - "outputs": [], "source": [ "from pydvl.valuation import KNNClassifierUtility, ModelUtility, SupervisedScorer\n", "\n", @@ -575,8 +549,8 @@ "\n", "\n", "def make_utility(test: Dataset, random_state: int) -> ModelUtility:\n", - " classifier = RandomForestClassifier(n_estimators=50,\n", - " max_samples=0.8,\n", + " classifier = RandomForestClassifier(n_estimators=n_est,\n", + " max_samples=max_samples,\n", " class_weight=\"balanced\",\n", " random_state=random_state + 1)\n", " model = ThresholdTunerCV(classifier,\n", @@ -588,8 +562,8 @@ "\n", "\n", "def make_oob(train: Dataset, random_state: int) -> DataOOBValuation:\n", - " classifier = RandomForestClassifier(n_estimators=50,\n", - " max_samples=0.8,\n", + " classifier = RandomForestClassifier(n_estimators=n_est,\n", + " max_samples=max_samples,\n", " class_weight=\"balanced\",\n", " random_state=random_state)\n", " model = ThresholdTunerCV(classifier,\n", @@ -606,21 +580,17 @@ "\n", "\n", "removal_percentages = np.arange(0, 0.51, 0.02)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T18:20:07.969388Z", - "start_time": "2024-11-21T18:16:28.143579Z" - }, "tags": [ "hide" ] }, - "outputs": [], "source": [ "low_scores_df, high_scores_df = \\\n", " run_removal_experiment(\n", @@ -628,19 +598,16 @@ " valuation_factories=[make_random, make_oob],\n", " utility_factory=make_utility, # for evaluation\n", " removal_percentages=removal_percentages,\n", - " n_runs=10,\n", + " n_runs=6,\n", " n_jobs=30,\n", " random_state=random_state)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T18:20:08.368069Z", - "start_time": "2024-11-21T18:20:08.051785Z" - }, "slideshow": { "slide_type": "" }, @@ -649,7 +616,6 @@ "invertible-output" ] }, - "outputs": [], "source": [ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", @@ -673,7 +639,9 @@ "axs[1].set_xlabel(\"Fraction of data removed\")\n", "\n", "plt.show();" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -695,14 +663,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T22:46:03.978452Z", - "start_time": "2024-11-21T22:39:42.943659Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", @@ -726,6 +687,7 @@ " model.fit(train.x, train.y)\n", " return DataOOBValuation(model, point_wise_accuracy)\n", "\n", + "\n", "low_scores_df, high_scores_df = \\\n", " run_removal_experiment(\n", " data_factory=make_data,\n", @@ -734,22 +696,18 @@ " removal_percentages=removal_percentages,\n", " n_runs=20,\n", " n_jobs=30)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T22:52:42.855127Z", - "start_time": "2024-11-21T22:52:42.545146Z" - }, "tags": [ "hide-input", "invertible-output" ] }, - "outputs": [], "source": [ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", @@ -773,7 +731,9 @@ "axs[1].set_xlabel(\"Fraction of data removed\")\n", "\n", "plt.show();" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -786,40 +746,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:11:13.175909Z", - "start_time": "2024-11-21T13:11:13.077926Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "df = load_adult_data_raw()\n", "corr = df.corr(numeric_only=True)\n", "corr.style.background_gradient(cmap='coolwarm')" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:13:12.320216Z", - "start_time": "2024-11-21T13:12:58.953700Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "train, test = load_adult_data(train_size=0.6, random_state=random_state)\n", "for i, values in enumerate(oob_values):\n", " zero_variances = np.isclose(values.variances, 0.0)\n", " to_index = \\\n", " np.where(zero_variances & np.isclose(values.values, 1))[0][0]\n", - " from_index = \\\n", - " np.where(zero_variances & np.isclose(values.values, 0))[0][-1]\n", + " try:\n", + " from_index = \\\n", + " np.where(zero_variances & np.isclose(values.values, 0))[0][-1]\n", + " except IndexError:\n", + " from_index = 0\n", " model = RandomForestClassifier(n_estimators=n_estimators[i],\n", - " max_samples=1.0,\n", + " max_samples=max_samples,\n", " random_state=random_state)\n", " model.fit(train.x, train.y)\n", " full_training_score = model.score(test.x, test.y)\n", @@ -840,7 +791,9 @@ " fraction_of_data = 100 * (len(train) - from_index) / len(train)\n", " print(f\" Remove LOW: Relative change in test score after training on \"\n", " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -853,14 +806,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:11:41.128330Z", - "start_time": "2024-11-21T13:11:39.044280Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "variance_q95 = np.quantile(values.variances, 0.95)\n", "\n", @@ -892,18 +838,13 @@ " score_change = 100 * (random_score - full_score) / full_score\n", " print(f\" Relative change in test score after training on a random \"\n", " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-20T17:49:10.832877Z", - "start_time": "2024-11-20T17:49:10.753592Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", @@ -913,23 +854,20 @@ "model.fit(train.x, train.y)\n", "full_score = model.score(test.x, test.y)\n", "confusion_matrix(test.y, model.predict(test.x), normalize='true')" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-20T17:52:11.207292Z", - "start_time": "2024-11-20T17:52:10.899909Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "model.fit(train.x[high_variance_indices], train.y[high_variance_indices])\n", "print(f\"Using {100 * len(high_variance_indices) / len(train):.2f}% of the data\")\n", "confusion_matrix(test.y, model.predict(test.x), normalize='true')" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -942,14 +880,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T10:38:06.484426Z", - "start_time": "2024-11-21T10:38:06.018361Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "random_indices = np.random.choice(len(train), len(high_variance_indices),\n", " replace=False)\n", @@ -960,7 +891,9 @@ "print(f\"Relative change in test score after training on a random \"\n", " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\")\n", "confusion_matrix(test.y, model.predict(test.x), normalize='true')" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -973,14 +906,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T09:59:41.745289Z", - "start_time": "2024-11-21T09:59:39.757510Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "from sklearn.calibration import CalibratedClassifierCV\n", "\n", @@ -999,7 +925,9 @@ "axs[0].set_title(\"Original model\")\n", "axs[1].set_title(\"Calibrated model\")\n", "plt.show();" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -1010,14 +938,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:23:32.967495Z", - "start_time": "2024-11-21T13:22:49.953376Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -1025,14 +946,15 @@ "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import clone\n", - "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN\n", "from imblearn.pipeline import make_pipeline\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import ConfusionMatrixDisplay, f1_score\n", "from sklearn.preprocessing import TargetEncoder\n", "\n", "random_state = 42\n", - "\n", + "n_est = 50\n", + "max_samples = 0.2\n", "df = load_adult_data_raw()\n", "column_names = df.columns.tolist()\n", "\n", @@ -1051,22 +973,24 @@ " )\n", "\n", "encoder = TargetEncoder(target_type=\"binary\", random_state=random_state)\n", - "classifier = RandomForestClassifier(n_estimators=400,\n", - " max_samples=0.7,\n", + "classifier = RandomForestClassifier(n_estimators=n_est,\n", + " max_samples=max_samples,\n", " class_weight=\"balanced\",\n", " random_state=random_state)\n", - "tuned_classifier = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\n", + "tuned_classifier = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score,\n", + " n_jobs=6)\n", "ros_sampler = RandomOverSampler(random_state=random_state)\n", - "# smote_sampler = SMOTE(random_state=random_state)\n", - "# adasyn_sampler = ADASYN(random_state=random_state)\n", + "smote_sampler = SMOTE(random_state=random_state)\n", + "adasyn_sampler = ADASYN(random_state=random_state)\n", "\n", "simple_pipeline = make_pipeline(encoder, clone(classifier))\n", "ros_pipeline = make_pipeline(encoder, ros_sampler, clone(classifier))\n", "tuned_pipeline = make_pipeline(encoder, clone(tuned_classifier))\n", - "tuned_oversampling_pipeline = make_pipeline(encoder, ros_sampler,\n", + "tuned_oversampling_pipeline = make_pipeline(encoder,\n", + " ros_sampler,\n", " clone(tuned_classifier))\n", - "# smote_pipeline = make_pipeline(encoder, smote_sampler, clone(tuned_classifier))\n", - "# adasyn_pipeline = make_pipeline(encoder, adasyn_sampler, clone(tuned_classifier))\n", + "smote_pipeline = make_pipeline(encoder, smote_sampler, clone(tuned_classifier))\n", + "adasyn_pipeline = make_pipeline(encoder, adasyn_sampler, clone(tuned_classifier))\n", "\n", "simple_pipeline.fit(x_train, y_train)\n", "full_score = simple_pipeline.score(x_test, y_test)\n", @@ -1084,45 +1008,38 @@ "full_score = tuned_oversampling_pipeline.score(x_test, y_test)\n", "print(f\"Accuracy with tuned threshold and ROS: {full_score:.4f}\")\n", "\n", - "# smote_pipeline.fit(x_train, y_train)\n", - "# full_score = smote_pipeline.score(x_test, y_test)\n", - "# print(f\"Accuracy with SMOTE: {full_score:.4f}\")\n", - "# \n", - "# adasyn_pipeline.fit(x_train, y_train)\n", - "# full_score = adasyn_pipeline.score(x_test, y_test)\n", - "# print(f\"Accuracy with ADASYN: {full_score:.4f}\")\n" - ] + "smote_pipeline.fit(x_train, y_train)\n", + "full_score = smote_pipeline.score(x_test, y_test)\n", + "print(f\"Accuracy with SMOTE: {full_score:.4f}\")\n", + "\n", + "adasyn_pipeline.fit(x_train, y_train)\n", + "full_score = adasyn_pipeline.score(x_test, y_test)\n", + "print(f\"Accuracy with ADASYN: {full_score:.4f}\")\n" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:23:40.729686Z", - "start_time": "2024-11-21T13:23:37.804993Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "# Sanity check: did we fit the training set well?\n", "(simple_pipeline.score(x_train, y_train),\n", " ros_pipeline.score(x_train, y_train),\n", " tuned_pipeline.score(x_train, y_train),\n", - " tuned_oversampling_pipeline.score(x_train, y_train))" - ] + " tuned_oversampling_pipeline.score(x_train, y_train),\n", + " smote_pipeline.score(x_train, y_train),\n", + " adasyn_pipeline.score(x_train, y_train),\n", + " )" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T13:24:13.731707Z", - "start_time": "2024-11-21T13:24:10.961185Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ - "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=[12, 12])\n", + "fig, axs = plt.subplots(nrows=3, ncols=2, figsize=[12, 12])\n", "\n", "ConfusionMatrixDisplay.from_estimator(simple_pipeline, x_test, y_test,\n", " labels=[0, 1], normalize=\"true\",\n", @@ -1137,24 +1054,28 @@ " y_test,\n", " labels=[0, 1], normalize=\"true\",\n", " ax=axs[1][1])\n", + "ConfusionMatrixDisplay.from_estimator(smote_pipeline, x_test, y_test,\n", + " labels=[0, 1], normalize=\"true\",\n", + " ax=axs[2][0])\n", + "ConfusionMatrixDisplay.from_estimator(adasyn_pipeline, x_test,\n", + " y_test,\n", + " labels=[0, 1], normalize=\"true\",\n", + " ax=axs[2][1])\n", "\n", "axs[0][0].set_title(\"No oversampling\")\n", "axs[0][1].set_title(\"ROS\")\n", "axs[1][0].set_title(\"Tuned threshold\")\n", "axs[1][1].set_title(\"Tuned threshold with ROS\")\n", + "axs[2][0].set_title(\"SMOTE\")\n", + "axs[2][1].set_title(\"ADASYN\")\n", "plt.show();" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T12:25:06.580020Z", - "start_time": "2024-11-21T12:25:05.940483Z" - } - }, - "outputs": [], + "metadata": {}, "source": [ "import numpy as np\n", "from sklearn.metrics import precision_recall_curve\n", @@ -1170,26 +1091,16 @@ " f\"Accuracy at chosen threshold {chosen_threshold:.2f}: {np.mean(y_pred == y_test):.4f}\")\n", "ConfusionMatrixDisplay.from_predictions(y_test, y_pred, labels=[0, 1],\n", " normalize=\"true\");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-11-21T12:25:06.859513Z", - "start_time": "2024-11-21T12:25:06.854556Z" - } - }, + ], "outputs": [], - "source": [] + "execution_count": null }, { - "cell_type": "code", - "execution_count": null, "metadata": {}, + "cell_type": "code", + "source": "", "outputs": [], - "source": [] + "execution_count": null } ], "metadata": { diff --git a/notebooks/support/common.py b/notebooks/support/common.py index 22d5a8898..403f4497b 100644 --- a/notebooks/support/common.py +++ b/notebooks/support/common.py @@ -678,7 +678,7 @@ def load_adult_data_raw() -> pd.DataFrame: def load_adult_data( - train_size: float = 0.7, random_state: Optional[int] = None + train_size: float = 0.7, subsample: float = 1.0, random_state: Optional[int] = None ) -> Tuple[Dataset, Dataset]: """ Loads the adult dataset from UCI and performs some preprocessing. @@ -690,7 +690,8 @@ def load_adult_data( much complexity from the notebooks as possible. Args: - train_size: fraction of the data to use for training + subsample: fraction of the data to keep. Range [0,1] + train_size: fraction of the (subsampled) data to use for training random_state: random state for reproducibility Returns: @@ -698,6 +699,8 @@ def load_adult_data( """ df = load_adult_data_raw() + if subsample < 1: + df = df.sample(frac=subsample, random_state=random_state) column_names = df.columns.tolist() df["income"] = df["income"].cat.codes @@ -724,26 +727,6 @@ def load_adult_data( ) -def subsample_dataset(data: Dataset, fraction: float) -> Dataset: - """Subsamples a dataset at random. - Args: - data: Dataset to subsample - fraction: Fraction of the dataset to keep. Range [0,1) - - Returns: - The subsampled dataset - """ - assert 0 <= fraction < 1, "Fraction must be in the range [0,1)" - subsample_mask = np.random.rand(len(data)) < fraction - - return Dataset( - data.x[subsample_mask], - data.y[subsample_mask], - feature_names=data.feature_names, - target_names=data.target_names, - ) - - def to_dataframe(dataset: Dataset) -> pd.DataFrame: """ Converts a dataset to a pandas DataFrame diff --git a/requirements-docs.txt b/requirements-docs.txt index 662775a9f..bd097c710 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,4 +1,5 @@ griffe==0.42.1 +imblearn mike==2.0.0 markdown-captions mkdocs==1.5.3 diff --git a/src/pydvl/valuation/methods/data_oob.py b/src/pydvl/valuation/methods/data_oob.py index de91b6e76..9bb5d466b 100644 --- a/src/pydvl/valuation/methods/data_oob.py +++ b/src/pydvl/valuation/methods/data_oob.py @@ -89,23 +89,20 @@ def fit(self, data: Dataset): data_names=data.data_names, ) - # We depart from common practice in pyDVL and perform a runtime check because - # this is one of a few model-specific valuation methods. - if not isinstance(self.model, BaggingModel): - raise Exception( - "The model has to be an sklearn-compatible bagging model, including " - "BaggingClassifier, BaggingRegressor, IsolationForest, RandomForest*, " - "ExtraTrees*, and any model which defines n_estimators, max_samples, " - "and after fitting estimators_ and uses bootstrapped subsamples to " - "compute predictions." - ) - check_is_fitted( self.model, msg="The bagging model has to be fitted before calling the valuation method.", ) + # This should always be present after fitting - estimators = getattr(self.model, "estimators_") + try: + estimators = self.model.estimators_ + except AttributeError as e: + raise ValueError( + "The model has to be an sklearn-compatible bagging model, including " + "BaggingClassifier, BaggingRegressor, IsolationForest, RandomForest*, " + "and ExtraTrees*" + ) if self.score is None: self.score = ( From 6a0fd35745e6be7dcead0e851dc5ee40d7e4394c Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Tue, 14 Jan 2025 17:10:17 +0100 Subject: [PATCH 22/41] More requirement to correct file --- requirements-docs.txt | 1 - requirements-notebooks.txt | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements-docs.txt b/requirements-docs.txt index bd097c710..662775a9f 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,5 +1,4 @@ griffe==0.42.1 -imblearn mike==2.0.0 markdown-captions mkdocs==1.5.3 diff --git a/requirements-notebooks.txt b/requirements-notebooks.txt index ac589844b..9b442e51b 100644 --- a/requirements-notebooks.txt +++ b/requirements-notebooks.txt @@ -1,5 +1,6 @@ datasets==2.21.0 distributed==2023.5.0 +imblearn pillow==10.4.0 torch==2.2.0 torchvision==0.17.0 From cda1f99dae1ae4933b0ae05d430871b0e8b10adc Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Tue, 14 Jan 2025 17:51:18 +0100 Subject: [PATCH 23/41] Fixes for new dataset interface --- src/pydvl/valuation/methods/data_oob.py | 6 ++---- tests/valuation/test_dataset.py | 14 ++++++++++++++ tests/valuation/test_result.py | 4 ++-- 3 files changed, 18 insertions(+), 6 deletions(-) diff --git a/src/pydvl/valuation/methods/data_oob.py b/src/pydvl/valuation/methods/data_oob.py index 61a657561..61cbd1830 100644 --- a/src/pydvl/valuation/methods/data_oob.py +++ b/src/pydvl/valuation/methods/data_oob.py @@ -129,10 +129,8 @@ def fit(self, data: Dataset): unsampled_indices.append(oob_indices) for est, oob_indices in zip(estimators, unsampled_indices): - score_array = self.score( - y_true=data.y[oob_indices], - y_pred=est.predict(data.x[oob_indices]), - ) + subset = data[oob_indices].data() + score_array = self.score(y_true=subset.y, y_pred=est.predict(subset.x)) self.result += ValuationResult( algorithm=algorithm_name, indices=oob_indices, diff --git a/tests/valuation/test_dataset.py b/tests/valuation/test_dataset.py index a0274832f..410cd34d7 100644 --- a/tests/valuation/test_dataset.py +++ b/tests/valuation/test_dataset.py @@ -191,6 +191,20 @@ def test_getitem_returns_correct_dataset(idx, expected_x, expected_y, expected_n assert np.array_equal(sliced_dataset._data_names, expected_names) +@pytest.mark.parametrize( + "idx", + [[0], slice(0, 2), [0, 2]], +) +def test_dataset_slice_data_access(idx): + dataset = Dataset( + x=np.array([[1, 2], [3, 4], [5, 6]]), + y=np.array([0, 1, 0]), + data_names=["a", "b", "c"], + ) + assert np.array_equal(dataset[idx].data().x, dataset.data().x[idx]) + assert np.array_equal(dataset[idx].data().y, dataset.data().y[idx]) + + @pytest.mark.parametrize( "x, y, feature_names, target_names, multi_output, expected_exception", [ diff --git a/tests/valuation/test_result.py b/tests/valuation/test_result.py index a30247d68..3a27bfcdb 100644 --- a/tests/valuation/test_result.py +++ b/tests/valuation/test_result.py @@ -88,13 +88,13 @@ def test_todataframe(ranks_asc, dummy_values): assert "dummy_valuator" in df.columns assert "dummy_valuator_variances" in df.columns assert "dummy_valuator_counts" in df.columns - assert np.alltrue(df.index.values == ranks_asc) + assert np.all(df.index.values == ranks_asc) df = dummy_values.to_dataframe(column="val") assert "val" in df.columns assert "val_variances" in df.columns assert "val_counts" in df.columns - assert np.alltrue(df.index.values == ranks_asc) + assert np.all(df.index.values == ranks_asc) df = dummy_values.to_dataframe(use_names=True) assert np.all(df.index.values == [it.name for it in dummy_values]) From 59ddcc6f568558269c2cbfb2459cbcaed210a693 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 11:09:03 +0100 Subject: [PATCH 24/41] Fixes for new dataset interface --- notebooks/data_oob.ipynb | 1199 +------------------------------------- 1 file changed, 30 insertions(+), 1169 deletions(-) diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb index a46147332..e137c3b4c 100644 --- a/notebooks/data_oob.ipynb +++ b/notebooks/data_oob.ipynb @@ -1,1144 +1,5 @@ { "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "{\n", - " \"cells\": [\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"# TODO:\\n\",\n", - " \"\\n\",\n", - " \"* Fix the problems with the removal job:\\n\",\n", - " \" - different initial value for the curves\\n\",\n", - " \" - increasing by high-value removal??\\n\",\n", - " \"* Remove old text fragments\\n\",\n", - " \"* Factor out any boilerplate\\n\",\n", - " \"* Remove leftovers\\n\",\n", - " \"* Actually perform the analysis using the values\\n\",\n", - " \"* Add support for pipeline objects to DataOOBValuation\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"# Data-OOB for random forests and bagged classifiers\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"This notebook illustrates the use of [Data-OOB][../../value/data-oob] from Kwon and Zou \\\"[Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://proceedings.mlr.press/v202/kwon23e.html)\\\" (ICML 2023), to compute values for bagged models.\\n\",\n", - " \"\\n\",\n", - " \"We will work with the [adult classification dataset](https://archive.ics.uci.edu/dataset/2/adult) from the UCI repository. It's an imbalanced dataset where the objective is to predict whether a person earns more than $50K a year (the \\\"positive\\\" class) based on a set of features such as age, education, occupation, etc. After training a random forest on this dataset, we will compute the Data-OOB values and analyze them.\\n\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"## Setup\\n\",\n", - " \"\\n\",\n", - " \"
\\n\",\n", - " \"\\n\",\n", - " \"If you are reading this in the documentation, some boilerplate (including most plotting code) has been omitted for convenience.\\n\",\n", - " \"\\n\",\n", - " \"
\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"We begin by importing the main libraries and setting some defaults.\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"slideshow\": {\n", - " \"slide_type\": \"\"\n", - " },\n", - " \"tags\": [\n", - " \"hide\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"%load_ext autoreload\\n\",\n", - " \"\\n\",\n", - " \"import os\\n\",\n", - " \"import random\\n\",\n", - " \"\\n\",\n", - " \"import matplotlib\\n\",\n", - " \"import matplotlib.pyplot as plt\\n\",\n", - " \"import numpy as np\\n\",\n", - " \"\\n\",\n", - " \"from pydvl.reporting.plots import plot_ci_array, plot_ci_values\\n\",\n", - " \"from sklearn.metrics import ConfusionMatrixDisplay, f1_score\\n\",\n", - " \"from support.common import load_adult_data, load_adult_data_raw\\n\",\n", - " \"\\n\",\n", - " \"matplotlib.rcParams[\\\"axes.facecolor\\\"] = (1, 1, 1, 0)\\n\",\n", - " \"plt.rcParams[\\\"axes.facecolor\\\"] = (1, 1, 1, 0)\\n\",\n", - " \"plt.rcParams[\\\"figure.facecolor\\\"] = (1, 1, 1, 0)\\n\",\n", - " \"MEAN_COLORS = [\\\"dodgerblue\\\", \\\"indianred\\\", \\\"limegreen\\\", \\\"darkorange\\\",\\n\",\n", - " \" \\\"darkorchid\\\"]\\n\",\n", - " \"SHADE_COLORS = [\\\"lightskyblue\\\", \\\"firebrick\\\", \\\"seagreen\\\", \\\"gold\\\", \\\"plum\\\"]\\n\",\n", - " \"\\n\",\n", - " \"is_CI = os.environ.get(\\\"CI\\\")\\n\",\n", - " \"random_state = 42\\n\",\n", - " \"random.seed(random_state);\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"from sklearn.ensemble import RandomForestClassifier\\n\",\n", - " \"from pydvl.valuation import DataOOBValuation, Dataset\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"\\n\",\n", - " \"With a helper function we download the data, encode the categorical variables using [TargetEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.TargetEncoder.html), and split it into training and testing sets. We must be careful to stratify the split by the target variable (income).\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide-output\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"train, test = load_adult_data(train_size=0.6, subsample=0.2,\\n\",\n", - " \" random_state=random_state)\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"if is_CI: # Subsample 1% of the data for faster testing\\n\",\n", - " \" train, test = load_adult_data(train_size=0.6, subsample=0.01,\\n\",\n", - " \" random_state=random_state)\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"Usually we would carefully look at the features, check for missing values, outliers, etc. But for the sake of this example, we will skip this step and jump straight into training a model. We will only look at the class distribution since it will matter later:\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide-input\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"print(f\\\"{len(train)} samples. Class distribution: {100 * np.mean(train.y == 1):.1f}% positive, \\\"\\n\",\n", - " \" f\\\"{100 * np.mean(train.y == 0):.1f}% negative\\\")\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": \"As a quick baseline, we train a standard sklearn [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html). Since the dataset is imbalanced, besides the accuracy we look at the confusion matrix, and notice that despite weighting the class by their inverse frequency with `class_weight=\\\"balanced\\\"`, the model is not very good at predicting the minority (\\\"positive\\\", or \\\"1\\\") class: in the left hand side of the figure below we see a high rate of false negatives. This will play a role later in how we interpret the values that Data-OOB returns, and requires us to address the imbalance in the dataset. We do this with a simple random over sampling using `imblearn`'s [RandomOverSampler](...) class.\"\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"from imblearn.over_sampling import RandomOverSampler\\n\",\n", - " \"\\n\",\n", - " \"n_est = 100\\n\",\n", - " \"max_samples = 0.2 # Use small bootstrap samples\\n\",\n", - " \"model = RandomForestClassifier(n_estimators=n_est,\\n\",\n", - " \" max_samples=max_samples,\\n\",\n", - " \" class_weight=\\\"balanced\\\",\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \"model.fit(train.x, train.y)\\n\",\n", - " \"base_predictions = model.predict(test.x)\\n\",\n", - " \"\\n\",\n", - " \"sampler = RandomOverSampler(random_state=random_state)\\n\",\n", - " \"train.x, train.y = sampler.fit_resample(train.x, train.y)\\n\",\n", - " \"\\n\",\n", - " \"model.fit(train.x, train.y)\\n\",\n", - " \"predictions_oversampled = model.predict(test.x) \"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide-input\"\n", - " ]\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"print(f\\\"{len(train)} samples. Class distribution: {100 * train.y.mean():.1f}% positive, \\\"\\n\",\n", - " \" f\\\"{100 * (1 - train.y.mean()):.1f}% negative\\\")\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide-input\"\n", - " ]\n", - " },\n", - " \"cell_type\": \"code\",\n", - " \"source\": [\n", - " \"from sklearn.metrics import accuracy_score\\n\",\n", - " \"base_accuracy = accuracy_score(test.y, base_predictions)\\n\",\n", - " \"accuracy_oversampled = accuracy_score(test.y, predictions_oversampled)\\n\",\n", - " \"\\n\",\n", - " \"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\\n\",\n", - " \"ConfusionMatrixDisplay.from_predictions(test.y, base_predictions, ax=axs[0],\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\")\\n\",\n", - " \"ConfusionMatrixDisplay.from_predictions(test.y, predictions_oversampled, ax=axs[1],\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\")\\n\",\n", - " \"\\n\",\n", - " \"axs[0].set_title(f\\\"Original dataset, {n_est} trees.\\\\nAccuracy: {base_accuracy:.2f}\\\")\\n\",\n", - " \"axs[1].set_title(f\\\"Oversampled dataset, {n_est} trees.\\\\nAccuracy: {accuracy_oversampled:.2f}\\\")\\n\",\n", - " \"\\n\",\n", - " \"plt.show()\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"invertible-output\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"## Computing the OOB values\\n\",\n", - " \"\\n\",\n", - " \"The main idea of Data-OOB is to use the out-of-bag error estimates of a bagged model to compute data values. In pyDVL, we provide a class [DataOOBValuation][pydvl.valuation.DataOOBValuation] that takes an existing classification or regression bagging model and uses the per-sample out-of-bag performance estimate for the value of each point.\\n\",\n", - " \"\\n\",\n", - " \"Let's compute and compare the Data-OOB values with three choices for the number of estimators of a random forest. After fitting the random forest, we use the [fit][pydvl.valuation.DataOOBValuation.fit] method to compute the values and store them in [ValuationResult][pydvl.value.result.ValuationResult] objects.\\n\",\n", - " \"\\n\",\n", - " \"Even though it's not relevant to our discussion, notice how the accuracy barely changes with the number of estimators. Below, we will discuss using the values to identify \\\"easy\\\" or \\\"hard\\\" samples in the dataset, but first let's quickly look at the values themselves.\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"n_estimators = [50, 100, 200]\\n\",\n", - " \"oob_values = []\\n\",\n", - " \"for i, n_est in enumerate(n_estimators, start=1):\\n\",\n", - " \" model = RandomForestClassifier(n_estimators=n_est,\\n\",\n", - " \" max_samples=max_samples,\\n\",\n", - " \" class_weight=\\\"balanced\\\",\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" \\n\",\n", - " \" # Tuning messes everything up if we oversample the dataset\\n\",\n", - " \" # model = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\\n\",\n", - " \" model.fit(train.x, train.y)\\n\",\n", - " \" accuracy = model.score(test.x, test.y)\\n\",\n", - " \" print(f\\\"Accuracy with {n_est} estimators: {accuracy:.2f}\\\")\\n\",\n", - " \" valuation = DataOOBValuation(model)\\n\",\n", - " \" valuation.fit(train)\\n\",\n", - " \" oob_values.append(valuation.values())\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": \"\"\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"\\n\",\n", - " \"### The distribution of values\\n\",\n", - " \"\\n\",\n", - " \"The left-hand side of the figure below depicts value as it increases with rank and a 95% t-confidence interval. The right-hand side shows the histogram of values.\\n\",\n", - " \"\\n\",\n", - " \"We observe a long tail of high values. This is because the score $T$ used in Data-OOB (accuracy in this case) is a binary variable, and the value $\\\\psi_i$ is the fraction of times that all weak learners not trained on the $i$-th point classify it correctly. Given the imbalance in the dataset, many learners will always predict the majority (\\\"negative\\\", < $50K earnings / year) class and be correct on 75% of the dataset, leading to this tail.\\n\",\n", - " \"\\n\",\n", - " \"Besides the actual value $\\\\psi_i$, [ValuationResult][pydvl.value.result.ValuationResult] objects store the number of times a sample is OOB —the quantity $\\\\sum_{b=1}^{B} \\\\mathbb{1} (w_{bi}=0)$— in the `counts` attribute, and the variance of the OOB score in the `variances` attribute. We use the latter in the plot below in order to display the confidence intervals, but it is important to note that the interpretation varies from one valuation method to another:\\n\",\n", - " \"\\n\",\n", - " \"For Shapley-based valuation methods, the variance is that of the marginal changes in the performance of the model when trained on subsets of the data with and without a sample, evaluated on a fixed valuation dataset, and could in principle be used to see whether values have (roughly) converged. But for Data-OOB, it is the variance of the performance of the ensemble of weak learners on the sample when it is OOB. Although similar in spirit, the construction is different and can be misleading.\\n\",\n", - " \"\\n\",\n", - " \"As a matter of fact, the interpretation of the vanishing variance at the tail has little to do with valuation and everything to do with our dataset, as introduced above: As the number of estimators increases, the chance of all of them failing on the same points decreases, up to a point. The same happens when we increase the maximum depth (try it!). This behaviour is then not a deep property of Data-OOB from which to gain new insights, but rather a consequence of the dataset and the model, as we further elaborate below.\\n\",\n", - " \"\\n\",\n", - " \"*Note that a symmetric CI is actually incorrect in this situation since all values are bounded between 0 and 1 (instead we could use a bootstrapped CI if we stored the scores of all estimators in the bagging model, but this is not implemented in pyDVL).*\\n\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"slideshow\": {\n", - " \"slide_type\": \"\"\n", - " },\n", - " \"tags\": [\n", - " \"hide-input\",\n", - " \"invertible-output\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\\n\",\n", - " \"\\n\",\n", - " \"for (n_est, values, mean_color, shade_color) in zip(\\n\",\n", - " \" n_estimators, oob_values, MEAN_COLORS, SHADE_COLORS):\\n\",\n", - " \" values.sort()\\n\",\n", - " \" plot_ci_values(values,\\n\",\n", - " \" level=0.05,\\n\",\n", - " \" mean_color=mean_color,\\n\",\n", - " \" shade_color=shade_color,\\n\",\n", - " \" ax=ax1,\\n\",\n", - " \" label=f\\\"{n_est} estimators\\\")\\n\",\n", - " \"\\n\",\n", - " \" ax2.hist(values, bins=50, color=mean_color, alpha=0.5,\\n\",\n", - " \" label=f\\\"{n_est} estimators\\\")\\n\",\n", - " \"\\n\",\n", - " \"ax1.set_title(\\\"Point rank\\\")\\n\",\n", - " \"ax1.set_xlabel(\\\"Rank\\\")\\n\",\n", - " \"ax1.set_ylabel(\\\"Data-OOB values\\\")\\n\",\n", - " \"ax1.legend()\\n\",\n", - " \"ax2.set_title(\\\"Histogram of Data-OOB values\\\")\\n\",\n", - " \"ax2.set_xlabel(\\\"Value\\\")\\n\",\n", - " \"ax2.legend()\\n\",\n", - " \"plt.plot();\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"To see this, focus on the long tails with zero variance. These are samples for which the score $T(y_i, \\\\hat{f}_b(x_i)) = 1$ for *every* estimator $\\\\hat{f}_b$ not trained on them, that is: *every weak learner in the ensemble correctly classifies these samples*. As we said above, this can happen because it is likely for weak estimators to be fitted to always predict the majority (negative) class:\\n\",\n", - " \"\\n\",\n", - " \"## This analysis is now wrong: 73% of the points in the tail are in the minority class (probably have been oversampled)\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide-input\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"v = oob_values[0]\\n\",\n", - " \"from_index = \\\\\\n\",\n", - " \" np.where(np.isclose(v.variances, 0.0) & np.isclose(v.values, 1))[0][0]\\n\",\n", - " \"tail = values.indices[from_index:]\\n\",\n", - " \"print(f\\\"There are {len(tail)} points with value 1 and zero variance\\\"\\n\",\n", - " \" f\\\" ({100 * len(tail) / len(train):.2f}% of the data).\\\")\\n\",\n", - " \"print(f\\\"Of these, {100 * (train.y[tail] == 0).sum() / len(tail):.2f}% \\\"\\n\",\n", - " \" f\\\"are in the majority class.\\\")\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"code\",\n", - " \"source\": \"plt.hist(tail, bins=20)\",\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {\n", - " \"slideshow\": {\n", - " \"slide_type\": \"\"\n", - " },\n", - " \"tags\": []\n", - " },\n", - " \"source\": [\n", - " \"Simply put, the ensemble is mostly good at classifying the majority class, and the variance of the OOB score for these samples is very low. This is a common issue in imbalanced datasets, and it is one of the reasons why the OOB score might not be a good metric for model performance in these cases. For us, it shows that the values reflect only poorly fit models.\\n\",\n", - " \"\\n\",\n", - " \"We test this below by ensembling a number of constant classifiers, but how does this affect our ability to use the values for data inspection, cleaning, etc.?\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"### A detour to further interpret the OOB values\\n\",\n", - " \"\\n\",\n", - " \"We can verify that the OOB values in our case reflect the imbalance of the dataset by training a [BaggingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) with constant estimators. A fraction of `n_estimators` will always pick class 1, and the rest class 0. This leads to a clear jump in the value rank plot, either around 25% or 75% of them, since, as we saw above, 25% of the samples are in the positive (\\\"1\\\") class, and 75% in the negative (\\\"0\\\").\\n\",\n", - " \"\\n\",\n", - " \"We will use three different probabilities for the constant estimators to predict class 0: 0.01, 0.5, and 0.99. Again, the idea is that the OOB values will reflect the class distribution of the dataset, and we should see a clear jump in the values around 25% and 75% of the data.\\n\",\n", - " \"\\n\",\n", - " \"The code is analogous to the above when we fitted the random forest, so it is ommitted from the documentation, but we use a custom class `ConstantBinaryClassifier` as base estimator.\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide-input\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"from sklearn.ensemble import BaggingClassifier\\n\",\n", - " \"from support.common import ConstantBinaryClassifier\\n\",\n", - " \"\\n\",\n", - " \"train, test = load_adult_data(train_size=0.6, subsample=0.2,\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \"\\n\",\n", - " \"probs = [0.01, 0.5, 0.99]\\n\",\n", - " \"all_values = []\\n\",\n", - " \"for p in probs:\\n\",\n", - " \" model = BaggingClassifier(ConstantBinaryClassifier(p, random_state),\\n\",\n", - " \" n_estimators=100,\\n\",\n", - " \" max_samples=1.0,\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" model.fit(train.x, train.y)\\n\",\n", - " \" score = model.score(test.x, test.y)\\n\",\n", - " \" print(\\n\",\n", - " \" f\\\"Accuracy when ~{100 * p:.1f}% of estimators always predict class 0: {score:.4f}\\\")\\n\",\n", - " \"\\n\",\n", - " \" valuation = DataOOBValuation(model)\\n\",\n", - " \" valuation.fit(train)\\n\",\n", - " \" all_values.append(valuation.values())\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide-input\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"fig, ax = plt.subplots(figsize=[15, 5])\\n\",\n", - " \"\\n\",\n", - " \"for (p, values, mean_color, shade_color) in zip(probs, all_values, MEAN_COLORS,\\n\",\n", - " \" SHADE_COLORS):\\n\",\n", - " \" values.sort(key=\\\"value\\\")\\n\",\n", - " \" plot_ci_values(\\n\",\n", - " \" values,\\n\",\n", - " \" ax=ax,\\n\",\n", - " \" level=0.01,\\n\",\n", - " \" mean_color=mean_color,\\n\",\n", - " \" shade_color=shade_color,\\n\",\n", - " \" label=f\\\"Data-OOB, p={p:.2f}\\\",\\n\",\n", - " \" )\\n\",\n", - " \"\\n\",\n", - " \"ax.axvline(0.25 * len(all_values[0]), color=\\\"darkorange\\\", linestyle=\\\"--\\\",\\n\",\n", - " \" label=\\\"25% data\\\")\\n\",\n", - " \"ax.axvline(0.75 * len(all_values[0]), color=\\\"darkred\\\", linestyle=\\\"--\\\",\\n\",\n", - " \" label=\\\"75% data\\\")\\n\",\n", - " \"plt.ylabel(\\\"Data-OOB values\\\")\\n\",\n", - " \"plt.xlabel(\\\"Rank\\\")\\n\",\n", - " \"plt.title(f\\\"Model with constant estimators\\\")\\n\",\n", - " \"plt.legend();\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"## A bogus (?) conclusion\\n\",\n", - " \"\\n\",\n", - " \"The conclusion of this discussion seems to be that for this imbalanced dataset and poorly performing model, the usual intuition that extreme values characterize \\\"easy\\\" or \\\"hard\\\" points might be bogus.\\n\",\n", - " \" \\n\",\n", - " \"If we discard the datapoints with the highest values, i.e. those which the ensemble of weak learners classifies correctly every time, because we believe that those are trivial in some sense (e.g. repeated) and bias the ensemble towards stricter decisions boundaries, we obtain very mild changes. The same happens when removing low-valued points. Basically, the changes in test scores could be just noise.\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"## Evaluating Data-OOB values with data removal\\n\",\n", - " \"\\n\",\n", - " \"We can go beyond the simple analysis above and systematically evaluate the impact of removing data points with high or low values on the model's performance. This is a common practice in the literature. We can use the [compute_removal_score][pydvl.reporting.scores.compute_removal_score] function to do it. This function takes a [ModelUtility][pydvl.valuation.ModelUtility] object, a [ValuationResult][pydvl.value.result.ValuationResult] object, and a [Dataset][pydvl.data.dataset.Dataset] object, and computes the performance of the model after removing a fraction of the data with the highest or lowest values.\\n\",\n", - " \"\\n\",\n", - " \"Recall from above that we can construct these objects as follows:\\n\",\n", - " \"\\n\",\n", - " \"```python\\n\",\n", - " \"model = RandomForestClassifier(n_estimators=n_est, max_samples=max_samples, random_state=seed)\\n\",\n", - " \"utility = ModelUtility(model, SupervisedScorer(\\\"accuracy\\\", test, 0.0), clone_before_fit=False)\\n\",\n", - " \"\\n\",\n", - " \"valuation = DataOOBValuation(model)\\n\",\n", - " \"valuation.fit(train)\\n\",\n", - " \"values = valuation.values()\\n\",\n", - " \"```\\n\",\n", - " \"\\n\",\n", - " \"The details are hidden in the rendered documentation, please refer to the notebook for the full code.\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"In order to call `run_removal_experiment()` we need to define 3 types of factories:\\n\",\n", - " \"\\n\",\n", - " \"1. A factory that returns a train-test split of the data given a random state \\n\",\n", - " \"2. A factory that returns a utility that evaluates a model on a given test set.\\n\",\n", - " \" This is used for the performance evaluation. The model need not be the same\\n\",\n", - " \" as the one used for the valuation.\\n\",\n", - " \"4. A factory returning a valuation method. The training set is passed to the\\n\",\n", - " \" factory, in case the valuation needs to train something. E.g. for Data-OOB\\n\",\n", - " \" we need the bagging model to be fitted before the valuation is computed.\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"%autoreload\\n\",\n", - " \"from support.common import ThresholdTunerCV\\n\",\n", - " \"from support.removal_experiment import run_removal_experiment\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"from pydvl.valuation import KNNClassifierUtility, ModelUtility, SupervisedScorer\\n\",\n", - " \"\\n\",\n", - " \"from support.removal_experiment import run_removal_experiment\\n\",\n", - " \"from pydvl.valuation.methods.random import RandomValuation\\n\",\n", - " \"from pydvl.valuation.methods.data_oob import point_wise_accuracy\\n\",\n", - " \"\\n\",\n", - " \"\\n\",\n", - " \"def make_data(random_state: int) -> tuple[Dataset, Dataset]:\\n\",\n", - " \" return load_adult_data(train_size=0.6, random_state=random_state)\\n\",\n", - " \"\\n\",\n", - " \"\\n\",\n", - " \"def make_utility(test: Dataset, random_state: int) -> ModelUtility:\\n\",\n", - " \" classifier = RandomForestClassifier(n_estimators=n_est,\\n\",\n", - " \" max_samples=max_samples,\\n\",\n", - " \" class_weight=\\\"balanced\\\",\\n\",\n", - " \" random_state=random_state + 1)\\n\",\n", - " \" model = ThresholdTunerCV(classifier,\\n\",\n", - " \" n_splits=5,\\n\",\n", - " \" metric=f1_score,\\n\",\n", - " \" n_jobs=1,\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" return ModelUtility(model, SupervisedScorer(\\\"accuracy\\\", test, 0.0))\\n\",\n", - " \"\\n\",\n", - " \"\\n\",\n", - " \"def make_oob(train: Dataset, random_state: int) -> DataOOBValuation:\\n\",\n", - " \" classifier = RandomForestClassifier(n_estimators=n_est,\\n\",\n", - " \" max_samples=max_samples,\\n\",\n", - " \" class_weight=\\\"balanced\\\",\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" model = ThresholdTunerCV(classifier,\\n\",\n", - " \" n_splits=5,\\n\",\n", - " \" metric=f1_score,\\n\",\n", - " \" n_jobs=1,\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" model.fit(train.x, train.y)\\n\",\n", - " \" return DataOOBValuation(model, point_wise_accuracy)\\n\",\n", - " \"\\n\",\n", - " \"\\n\",\n", - " \"def make_random(train: Dataset, random_state: int) -> RandomValuation:\\n\",\n", - " \" return RandomValuation(random_state=random_state)\\n\",\n", - " \"\\n\",\n", - " \"\\n\",\n", - " \"removal_percentages = np.arange(0, 0.51, 0.02)\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"low_scores_df, high_scores_df = \\\\\\n\",\n", - " \" run_removal_experiment(\\n\",\n", - " \" data_factory=make_data,\\n\",\n", - " \" valuation_factories=[make_random, make_oob],\\n\",\n", - " \" utility_factory=make_utility, # for evaluation\\n\",\n", - " \" removal_percentages=removal_percentages,\\n\",\n", - " \" n_runs=6,\\n\",\n", - " \" n_jobs=30,\\n\",\n", - " \" random_state=random_state)\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"slideshow\": {\n", - " \"slide_type\": \"\"\n", - " },\n", - " \"tags\": [\n", - " \"hide-input\",\n", - " \"invertible-output\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\\n\",\n", - " \"\\n\",\n", - " \"for i, scores_df in enumerate((low_scores_df, high_scores_df)):\\n\",\n", - " \" for j, (method_name, df) in enumerate(scores_df.groupby(\\\"method_name\\\")):\\n\",\n", - " \" plot_ci_array(\\n\",\n", - " \" data=df.drop(columns=[\\\"method_name\\\"]).values,\\n\",\n", - " \" level=0.05,\\n\",\n", - " \" abscissa=np.round(removal_percentages, 2),\\n\",\n", - " \" mean_color=MEAN_COLORS[j],\\n\",\n", - " \" shade_color=SHADE_COLORS[j],\\n\",\n", - " \" label=method_name,\\n\",\n", - " \" ax=axs[i],\\n\",\n", - " \" )\\n\",\n", - " \" axs[i].legend()\\n\",\n", - " \" axs[i].set_ylabel(\\\"Accuracy\\\")\\n\",\n", - " \"\\n\",\n", - " \"axs[0].set_title(\\\"Lowest value removal, 95% CI\\\")\\n\",\n", - " \"axs[0].set_xlabel(\\\"Fraction of data removed\\\")\\n\",\n", - " \"axs[1].set_title(\\\"Highest value removal, 95% CI\\\")\\n\",\n", - " \"axs[1].set_xlabel(\\\"Fraction of data removed\\\")\\n\",\n", - " \"\\n\",\n", - " \"plt.show();\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"# FIXME: THERE'S A BUG ABOVE\\n\",\n", - " \"\\n\",\n", - " \"The accuracy curves do not start at the same value!\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"## Using Data-OOB with arbitrary models\\n\",\n", - " \"\\n\",\n", - " \"Note that even though the method is designed for bagging models, in principle it can be used with any other estimator by fitting a bagging model on top of it. This can generally be quite expensive, but it might prove useful in some cases. Below is what happens when we do this with a k-nearest neighbors classifier.\\n\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n", - " \"\\n\",\n", - " \"\\n\",\n", - " \"# We want to use the KNN classifier as model for the utility, not the bagged model \\n\",\n", - " \"def make_knn_utility(test: Dataset, random_state: int) -> ModelUtility:\\n\",\n", - " \" return KNNClassifierUtility(KNeighborsClassifier(n_neighbors=10), test)\\n\",\n", - " \"\\n\",\n", - " \"\\n\",\n", - " \"def make_oob_knn(train: Dataset, random_state: int) -> DataOOBValuation:\\n\",\n", - " \" classifier = BaggingClassifier(\\n\",\n", - " \" estimator=KNeighborsClassifier(n_neighbors=10),\\n\",\n", - " \" max_samples=0.4,\\n\",\n", - " \" n_estimators=10,\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" model = ThresholdTunerCV(classifier,\\n\",\n", - " \" n_splits=5,\\n\",\n", - " \" metric=f1_score,\\n\",\n", - " \" n_jobs=1,\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" model.fit(train.x, train.y)\\n\",\n", - " \" return DataOOBValuation(model, point_wise_accuracy)\\n\",\n", - " \"\\n\",\n", - " \"\\n\",\n", - " \"low_scores_df, high_scores_df = \\\\\\n\",\n", - " \" run_removal_experiment(\\n\",\n", - " \" data_factory=make_data,\\n\",\n", - " \" utility_factory=make_knn_utility,\\n\",\n", - " \" valuation_factories=[make_random, make_oob_knn],\\n\",\n", - " \" removal_percentages=removal_percentages,\\n\",\n", - " \" n_runs=20,\\n\",\n", - " \" n_jobs=30)\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {\n", - " \"tags\": [\n", - " \"hide-input\",\n", - " \"invertible-output\"\n", - " ]\n", - " },\n", - " \"source\": [\n", - " \"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\\n\",\n", - " \"\\n\",\n", - " \"for i, scores_df in enumerate((low_scores_df, high_scores_df)):\\n\",\n", - " \" for j, (method_name, df) in enumerate(scores_df.groupby(\\\"method_name\\\")):\\n\",\n", - " \" plot_ci_array(\\n\",\n", - " \" data=df.drop(columns=[\\\"method_name\\\"]).values,\\n\",\n", - " \" level=0.05,\\n\",\n", - " \" abscissa=np.round(removal_percentages, 2),\\n\",\n", - " \" mean_color=MEAN_COLORS[j],\\n\",\n", - " \" shade_color=SHADE_COLORS[j],\\n\",\n", - " \" label=method_name,\\n\",\n", - " \" ax=axs[i],\\n\",\n", - " \" )\\n\",\n", - " \" axs[i].legend()\\n\",\n", - " \" axs[i].set_ylabel(\\\"Accuracy\\\")\\n\",\n", - " \"\\n\",\n", - " \"axs[0].set_title(\\\"Lowest value removal, 95% CI\\\")\\n\",\n", - " \"axs[0].set_xlabel(\\\"Fraction of data removed\\\")\\n\",\n", - " \"axs[1].set_title(\\\"Highest value removal, 95% CI\\\")\\n\",\n", - " \"axs[1].set_xlabel(\\\"Fraction of data removed\\\")\\n\",\n", - " \"\\n\",\n", - " \"plt.show();\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"## The impact of removing data on the three random forest models\\n\",\n", - " \"\\n\",\n", - " \"Manually removing and retraining:\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"df = load_adult_data_raw()\\n\",\n", - " \"corr = df.corr(numeric_only=True)\\n\",\n", - " \"corr.style.background_gradient(cmap='coolwarm')\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"train, test = load_adult_data(train_size=0.6, random_state=random_state)\\n\",\n", - " \"for i, values in enumerate(oob_values):\\n\",\n", - " \" zero_variances = np.isclose(values.variances, 0.0)\\n\",\n", - " \" to_index = \\\\\\n\",\n", - " \" np.where(zero_variances & np.isclose(values.values, 1))[0][0]\\n\",\n", - " \" try:\\n\",\n", - " \" from_index = \\\\\\n\",\n", - " \" np.where(zero_variances & np.isclose(values.values, 0))[0][-1]\\n\",\n", - " \" except IndexError:\\n\",\n", - " \" from_index = 0\\n\",\n", - " \" model = RandomForestClassifier(n_estimators=n_estimators[i],\\n\",\n", - " \" max_samples=max_samples,\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" model.fit(train.x, train.y)\\n\",\n", - " \" full_training_score = model.score(test.x, test.y)\\n\",\n", - " \"\\n\",\n", - " \" model.fit(train.x[:to_index], train.y[:to_index])\\n\",\n", - " \" reduced_training_score = model.score(test.x, test.y)\\n\",\n", - " \" score_change = 100 * (\\n\",\n", - " \" reduced_training_score - full_training_score) / full_training_score\\n\",\n", - " \" fraction_of_data = 100 * to_index / len(train)\\n\",\n", - " \" print(f\\\"Model with {n_estimators[i]} estimators:\\\")\\n\",\n", - " \" print(f\\\" Remove HIGH: Relative change in test score after training on \\\"\\n\",\n", - " \" f\\\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\\\")\\n\",\n", - " \"\\n\",\n", - " \" model.fit(train.x[from_index:], train.y[from_index:])\\n\",\n", - " \" reduced_training_score = model.score(test.x, test.y)\\n\",\n", - " \" score_change = 100 * (\\n\",\n", - " \" reduced_training_score - full_training_score) / full_training_score\\n\",\n", - " \" fraction_of_data = 100 * (len(train) - from_index) / len(train)\\n\",\n", - " \" print(f\\\" Remove LOW: Relative change in test score after training on \\\"\\n\",\n", - " \" f\\\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\\\")\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"### Removing high variance points, or training just on them\\n\",\n", - " \"\\n\",\n", - " \"=> same result\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"variance_q95 = np.quantile(values.variances, 0.95)\\n\",\n", - " \"\\n\",\n", - " \"for i, values in enumerate(oob_values):\\n\",\n", - " \" high_variance_indices = np.where(values.variances > variance_q95)[0]\\n\",\n", - " \" model = RandomForestClassifier(n_estimators=n_estimators[i],\\n\",\n", - " \" max_samples=1.0,\\n\",\n", - " \" class_weight='balanced',\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \" model.fit(train.x, train.y)\\n\",\n", - " \" full_score = model.score(test.x, test.y)\\n\",\n", - " \"\\n\",\n", - " \" selected_x, selected_y = train.x[high_variance_indices], train.y[\\n\",\n", - " \" high_variance_indices]\\n\",\n", - " \" model.fit(selected_x, selected_y)\\n\",\n", - " \" reduced_score = model.score(test.x, test.y)\\n\",\n", - " \"\\n\",\n", - " \" score_change = 100 * (reduced_score - full_score) / full_score\\n\",\n", - " \" fraction_of_data = 100 * len(selected_x) / len(train)\\n\",\n", - " \"\\n\",\n", - " \" print(f\\\"Model with {n_estimators[i]} estimators:\\\")\\n\",\n", - " \" print(f\\\" Relative change in test score after training on \\\"\\n\",\n", - " \" f\\\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\\\")\\n\",\n", - " \"\\n\",\n", - " \" random_indices = np.random.choice(len(train), len(high_variance_indices),\\n\",\n", - " \" replace=False)\\n\",\n", - " \" model.fit(train.x[random_indices], train.y[random_indices])\\n\",\n", - " \" random_score = model.score(test.x, test.y)\\n\",\n", - " \" score_change = 100 * (random_score - full_score) / full_score\\n\",\n", - " \" print(f\\\" Relative change in test score after training on a random \\\"\\n\",\n", - " \" f\\\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\\\")\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"from sklearn.metrics import confusion_matrix\\n\",\n", - " \"\\n\",\n", - " \"model = RandomForestClassifier(n_estimators=100,\\n\",\n", - " \" max_samples=1.0,\\n\",\n", - " \" class_weight=\\\"balanced\\\")\\n\",\n", - " \"model.fit(train.x, train.y)\\n\",\n", - " \"full_score = model.score(test.x, test.y)\\n\",\n", - " \"confusion_matrix(test.y, model.predict(test.x), normalize='true')\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"model.fit(train.x[high_variance_indices], train.y[high_variance_indices])\\n\",\n", - " \"print(f\\\"Using {100 * len(high_variance_indices) / len(train):.2f}% of the data\\\")\\n\",\n", - " \"confusion_matrix(test.y, model.predict(test.x), normalize='true')\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"### Subsampling a random subset of the same size\\n\",\n", - " \"\\n\",\n", - " \"Yields similar accuracy (expected) but worse FNR and FPR\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"random_indices = np.random.choice(len(train), len(high_variance_indices),\\n\",\n", - " \" replace=False)\\n\",\n", - " \"model.fit(train.x[random_indices], train.y[random_indices])\\n\",\n", - " \"random_score = model.score(test.x, test.y)\\n\",\n", - " \"score_change = 100 * (random_score - full_score) / full_score\\n\",\n", - " \"fraction_of_data = 100 * len(random_indices) / len(train)\\n\",\n", - " \"print(f\\\"Relative change in test score after training on a random \\\"\\n\",\n", - " \" f\\\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\\\")\\n\",\n", - " \"confusion_matrix(test.y, model.predict(test.x), normalize='true')\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"### Calibrating the model\\n\",\n", - " \"\\n\",\n", - " \"Fails miserably:\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"from sklearn.calibration import CalibratedClassifierCV\\n\",\n", - " \"\\n\",\n", - " \"calibrated_model = CalibratedClassifierCV(estimator=model, method='isotonic')\\n\",\n", - " \"calibrated_model.fit(train.x[random_indices], train.y[random_indices])\\n\",\n", - " \"\\n\",\n", - " \"fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\\n\",\n", - " \"\\n\",\n", - " \"ConfusionMatrixDisplay.from_estimator(model, test.x, test.y,\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\",\\n\",\n", - " \" ax=axs[0])\\n\",\n", - " \"ConfusionMatrixDisplay.from_estimator(calibrated_model, test.x, test.y,\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\",\\n\",\n", - " \" ax=axs[1])\\n\",\n", - " \"\\n\",\n", - " \"axs[0].set_title(\\\"Original model\\\")\\n\",\n", - " \"axs[1].set_title(\\\"Calibrated model\\\")\\n\",\n", - " \"plt.show();\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"markdown\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"### Oversampling with imblearn\"\n", - " ]\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"import matplotlib.pyplot as plt\\n\",\n", - " \"\\n\",\n", - " \"from support.common import ThresholdTunerCV, load_adult_data_raw\\n\",\n", - " \"\\n\",\n", - " \"from sklearn.model_selection import train_test_split\\n\",\n", - " \"from sklearn import clone\\n\",\n", - " \"from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN\\n\",\n", - " \"from imblearn.pipeline import make_pipeline\\n\",\n", - " \"from sklearn.ensemble import RandomForestClassifier\\n\",\n", - " \"from sklearn.metrics import ConfusionMatrixDisplay, f1_score\\n\",\n", - " \"from sklearn.preprocessing import TargetEncoder\\n\",\n", - " \"\\n\",\n", - " \"random_state = 42\\n\",\n", - " \"n_est = 50\\n\",\n", - " \"max_samples = 0.2\\n\",\n", - " \"df = load_adult_data_raw()\\n\",\n", - " \"column_names = df.columns.tolist()\\n\",\n", - " \"\\n\",\n", - " \"df[\\\"income\\\"] = df[\\\"income\\\"].cat.codes\\n\",\n", - " \"df.drop(columns=[\\\"education\\\"], inplace=True) # education-num is enough\\n\",\n", - " \"df.dropna(inplace=True)\\n\",\n", - " \"column_names.remove(\\\"education\\\")\\n\",\n", - " \"column_names.remove(\\\"income\\\")\\n\",\n", - " \"\\n\",\n", - " \"x_train, x_test, y_train, y_test = train_test_split(\\n\",\n", - " \" df.drop(columns=[\\\"income\\\"]).values,\\n\",\n", - " \" df[\\\"income\\\"].values,\\n\",\n", - " \" train_size=0.6,\\n\",\n", - " \" random_state=random_state,\\n\",\n", - " \" stratify=df[\\\"income\\\"].values,\\n\",\n", - " \" )\\n\",\n", - " \"\\n\",\n", - " \"encoder = TargetEncoder(target_type=\\\"binary\\\", random_state=random_state)\\n\",\n", - " \"classifier = RandomForestClassifier(n_estimators=n_est,\\n\",\n", - " \" max_samples=max_samples,\\n\",\n", - " \" class_weight=\\\"balanced\\\",\\n\",\n", - " \" random_state=random_state)\\n\",\n", - " \"tuned_classifier = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score,\\n\",\n", - " \" n_jobs=6)\\n\",\n", - " \"ros_sampler = RandomOverSampler(random_state=random_state)\\n\",\n", - " \"smote_sampler = SMOTE(random_state=random_state)\\n\",\n", - " \"adasyn_sampler = ADASYN(random_state=random_state)\\n\",\n", - " \"\\n\",\n", - " \"simple_pipeline = make_pipeline(encoder, clone(classifier))\\n\",\n", - " \"ros_pipeline = make_pipeline(encoder, ros_sampler, clone(classifier))\\n\",\n", - " \"tuned_pipeline = make_pipeline(encoder, clone(tuned_classifier))\\n\",\n", - " \"tuned_oversampling_pipeline = make_pipeline(encoder,\\n\",\n", - " \" ros_sampler,\\n\",\n", - " \" clone(tuned_classifier))\\n\",\n", - " \"smote_pipeline = make_pipeline(encoder, smote_sampler, clone(tuned_classifier))\\n\",\n", - " \"adasyn_pipeline = make_pipeline(encoder, adasyn_sampler, clone(tuned_classifier))\\n\",\n", - " \"\\n\",\n", - " \"simple_pipeline.fit(x_train, y_train)\\n\",\n", - " \"full_score = simple_pipeline.score(x_test, y_test)\\n\",\n", - " \"print(f\\\"Accuracy with no oversampling: {full_score:.4f}\\\")\\n\",\n", - " \"\\n\",\n", - " \"ros_pipeline.fit(x_train, y_train)\\n\",\n", - " \"full_score = ros_pipeline.score(x_test, y_test)\\n\",\n", - " \"print(f\\\"Accuracy with random oversampling: {full_score:.4f}\\\")\\n\",\n", - " \"\\n\",\n", - " \"tuned_pipeline.fit(x_train, y_train)\\n\",\n", - " \"full_score = tuned_pipeline.score(x_test, y_test)\\n\",\n", - " \"print(f\\\"Accuracy with tuned threshold: {full_score:.4f}\\\")\\n\",\n", - " \"\\n\",\n", - " \"tuned_oversampling_pipeline.fit(x_train, y_train)\\n\",\n", - " \"full_score = tuned_oversampling_pipeline.score(x_test, y_test)\\n\",\n", - " \"print(f\\\"Accuracy with tuned threshold and ROS: {full_score:.4f}\\\")\\n\",\n", - " \"\\n\",\n", - " \"smote_pipeline.fit(x_train, y_train)\\n\",\n", - " \"full_score = smote_pipeline.score(x_test, y_test)\\n\",\n", - " \"print(f\\\"Accuracy with SMOTE: {full_score:.4f}\\\")\\n\",\n", - " \"\\n\",\n", - " \"adasyn_pipeline.fit(x_train, y_train)\\n\",\n", - " \"full_score = adasyn_pipeline.score(x_test, y_test)\\n\",\n", - " \"print(f\\\"Accuracy with ADASYN: {full_score:.4f}\\\")\\n\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"# Sanity check: did we fit the training set well?\\n\",\n", - " \"(simple_pipeline.score(x_train, y_train),\\n\",\n", - " \" ros_pipeline.score(x_train, y_train),\\n\",\n", - " \" tuned_pipeline.score(x_train, y_train),\\n\",\n", - " \" tuned_oversampling_pipeline.score(x_train, y_train),\\n\",\n", - " \" smote_pipeline.score(x_train, y_train),\\n\",\n", - " \" adasyn_pipeline.score(x_train, y_train),\\n\",\n", - " \" )\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"fig, axs = plt.subplots(nrows=3, ncols=2, figsize=[12, 12])\\n\",\n", - " \"\\n\",\n", - " \"ConfusionMatrixDisplay.from_estimator(simple_pipeline, x_test, y_test,\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\",\\n\",\n", - " \" ax=axs[0][0])\\n\",\n", - " \"ConfusionMatrixDisplay.from_estimator(ros_pipeline, x_test, y_test,\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\",\\n\",\n", - " \" ax=axs[0][1])\\n\",\n", - " \"ConfusionMatrixDisplay.from_estimator(tuned_pipeline, x_test, y_test,\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\",\\n\",\n", - " \" ax=axs[1][0])\\n\",\n", - " \"ConfusionMatrixDisplay.from_estimator(tuned_oversampling_pipeline, x_test,\\n\",\n", - " \" y_test,\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\",\\n\",\n", - " \" ax=axs[1][1])\\n\",\n", - " \"ConfusionMatrixDisplay.from_estimator(smote_pipeline, x_test, y_test,\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\",\\n\",\n", - " \" ax=axs[2][0])\\n\",\n", - " \"ConfusionMatrixDisplay.from_estimator(adasyn_pipeline, x_test,\\n\",\n", - " \" y_test,\\n\",\n", - " \" labels=[0, 1], normalize=\\\"true\\\",\\n\",\n", - " \" ax=axs[2][1])\\n\",\n", - " \"\\n\",\n", - " \"axs[0][0].set_title(\\\"No oversampling\\\")\\n\",\n", - " \"axs[0][1].set_title(\\\"ROS\\\")\\n\",\n", - " \"axs[1][0].set_title(\\\"Tuned threshold\\\")\\n\",\n", - " \"axs[1][1].set_title(\\\"Tuned threshold with ROS\\\")\\n\",\n", - " \"axs[2][0].set_title(\\\"SMOTE\\\")\\n\",\n", - " \"axs[2][1].set_title(\\\"ADASYN\\\")\\n\",\n", - " \"plt.show();\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"cell_type\": \"code\",\n", - " \"metadata\": {},\n", - " \"source\": [\n", - " \"import numpy as np\\n\",\n", - " \"from sklearn.metrics import precision_recall_curve\\n\",\n", - " \"\\n\",\n", - " \"y_proba = ros_pipeline.predict_proba(x_test)[:, 1]\\n\",\n", - " \"\\n\",\n", - " \"precision, recall, thresholds = precision_recall_curve(y_test, y_proba)\\n\",\n", - " \"f_score = 2 * precision * recall / (precision + recall)\\n\",\n", - " \"optimal_index = np.argmax(f_score)\\n\",\n", - " \"chosen_threshold = thresholds[optimal_index]\\n\",\n", - " \"y_pred = (y_proba >= chosen_threshold).astype(int)\\n\",\n", - " \"print(\\n\",\n", - " \" f\\\"Accuracy at chosen threshold {chosen_threshold:.2f}: {np.mean(y_pred == y_test):.4f}\\\")\\n\",\n", - " \"ConfusionMatrixDisplay.from_predictions(y_test, y_pred, labels=[0, 1],\\n\",\n", - " \" normalize=\\\"true\\\");\"\n", - " ],\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " },\n", - " {\n", - " \"metadata\": {},\n", - " \"cell_type\": \"code\",\n", - " \"source\": \"\",\n", - " \"outputs\": [],\n", - " \"execution_count\": null\n", - " }\n", - " ],\n", - " \"metadata\": {\n", - " \"celltoolbar\": \"Edit Metadata\",\n", - " \"kernelspec\": {\n", - " \"display_name\": \"Python 3 (ipykernel)\",\n", - " \"language\": \"python\",\n", - " \"name\": \"python3\"\n", - " },\n", - " \"language_info\": {\n", - " \"codemirror_mode\": {\n", - " \"name\": \"ipython\",\n", - " \"version\": 3\n", - " },\n", - " \"file_extension\": \".py\",\n", - " \"mimetype\": \"text/x-python\",\n", - " \"name\": \"python\",\n", - " \"nbconvert_exporter\": \"python\",\n", - " \"pygments_lexer\": \"ipython3\",\n", - " \"version\": \"3.10.12\"\n", - " },\n", - " \"vscode\": {\n", - " \"interpreter\": {\n", - " \"hash\": \"4e000971326892723e7f31ded70802f690c31c3620f59a0f99e594aaee3047ef\"\n", - " }\n", - " }\n", - " },\n", - " \"nbformat\": 4,\n", - " \"nbformat_minor\": 4\n", - "}\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1282,8 +143,8 @@ "outputs": [], "source": [ "print(\n", - " f\"{len(train)} samples. Class distribution: {100 * np.mean(train.y == 1):.1f}% positive, \"\n", - " f\"{100 * np.mean(train.y == 0):.1f}% negative\"\n", + " f\"{len(train)} samples. Class distribution: {100 * np.mean(train.data().y == 1):.1f}% positive, \"\n", + " f\"{100 * np.mean(train.data().y == 0):.1f}% negative\"\n", ")" ] }, @@ -1310,14 +171,14 @@ " class_weight=\"balanced\",\n", " random_state=random_state,\n", ")\n", - "model.fit(train.x, train.y)\n", - "base_predictions = model.predict(test.x)\n", + "model.fit(*train.data())\n", + "base_predictions = model.predict(test.data().x)\n", "\n", "sampler = RandomOverSampler(random_state=random_state)\n", - "train.x, train.y = sampler.fit_resample(train.x, train.y)\n", + "train = Dataset(*sampler.fit_resample(*train.data()))\n", "\n", - "model.fit(train.x, train.y)\n", - "predictions_oversampled = model.predict(test.x)" + "model.fit(*train.data())\n", + "predictions_oversampled = model.predict(test.data().x)" ] }, { @@ -1331,8 +192,8 @@ "outputs": [], "source": [ "print(\n", - " f\"{len(train)} samples. Class distribution: {100 * train.y.mean():.1f}% positive, \"\n", - " f\"{100 * (1 - train.y.mean()):.1f}% negative\"\n", + " f\"{len(train)} samples. Class distribution: {100 * train.data().y.mean():.1f}% positive, \"\n", + " f\"{100 * (1 - train.data().y.mean()):.1f}% negative\"\n", ")" ] }, @@ -1348,15 +209,15 @@ "source": [ "from sklearn.metrics import accuracy_score\n", "\n", - "base_accuracy = accuracy_score(test.y, base_predictions)\n", - "accuracy_oversampled = accuracy_score(test.y, predictions_oversampled)\n", + "base_accuracy = accuracy_score(test.data().y, base_predictions)\n", + "accuracy_oversampled = accuracy_score(test.data().y, predictions_oversampled)\n", "\n", "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n", "ConfusionMatrixDisplay.from_predictions(\n", - " test.y, base_predictions, ax=axs[0], labels=[0, 1], normalize=\"true\"\n", + " test.data().y, base_predictions, ax=axs[0], labels=[0, 1], normalize=\"true\"\n", ")\n", "ConfusionMatrixDisplay.from_predictions(\n", - " test.y, predictions_oversampled, ax=axs[1], labels=[0, 1], normalize=\"true\"\n", + " test.data().y, predictions_oversampled, ax=axs[1], labels=[0, 1], normalize=\"true\"\n", ")\n", "\n", "axs[0].set_title(f\"Original dataset, {n_est} trees.\\nAccuracy: {base_accuracy:.2f}\")\n", @@ -1402,8 +263,8 @@ "\n", " # Tuning messes everything up if we oversample the dataset\n", " # model = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\n", - " model.fit(train.x, train.y)\n", - " accuracy = model.score(test.x, test.y)\n", + " model.fit(*train.data())\n", + " accuracy = model.score(*test.data())\n", " print(f\"Accuracy with {n_est} estimators: {accuracy:.2f}\")\n", " valuation = DataOOBValuation(model)\n", " valuation.fit(train)\n", @@ -1563,8 +424,8 @@ " max_samples=1.0,\n", " random_state=random_state,\n", " )\n", - " model.fit(train.x, train.y)\n", - " score = model.score(test.x, test.y)\n", + " model.fit(*train.data())\n", + " score = model.score(*test.data())\n", " print(\n", " f\"Accuracy when ~{100 * p:.1f}% of estimators always predict class 0: {score:.4f}\"\n", " )\n", @@ -2006,9 +867,9 @@ "model = RandomForestClassifier(\n", " n_estimators=100, max_samples=1.0, class_weight=\"balanced\"\n", ")\n", - "model.fit(train.x, train.y)\n", - "full_score = model.score(test.x, test.y)\n", - "confusion_matrix(test.y, model.predict(test.x), normalize=\"true\")" + "model.fit(*train.data())\n", + "full_score = model.score(*test.data())\n", + "confusion_matrix(test.data().y, model.predict(test.data().x), normalize=\"true\")" ] }, { @@ -2017,9 +878,9 @@ "metadata": {}, "outputs": [], "source": [ - "model.fit(train.x[high_variance_indices], train.y[high_variance_indices])\n", + "model.fit(train[high_variance_indices].data().x, train[high_variance_indices].data().y)\n", "print(f\"Using {100 * len(high_variance_indices) / len(train):.2f}% of the data\")\n", - "confusion_matrix(test.y, model.predict(test.x), normalize=\"true\")" + "confusion_matrix(test.data().y, model.predict(test.data().x), normalize=\"true\")" ] }, { @@ -2038,15 +899,15 @@ "outputs": [], "source": [ "random_indices = np.random.choice(len(train), len(high_variance_indices), replace=False)\n", - "model.fit(train.x[random_indices], train.y[random_indices])\n", - "random_score = model.score(test.x, test.y)\n", + "model.fit(train[random_indices].data().x, train[random_indices].data().y)\n", + "random_score = model.score(*test.data())\n", "score_change = 100 * (random_score - full_score) / full_score\n", "fraction_of_data = 100 * len(random_indices) / len(train)\n", "print(\n", " f\"Relative change in test score after training on a random \"\n", " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\"\n", ")\n", - "confusion_matrix(test.y, model.predict(test.x), normalize=\"true\")" + "confusion_matrix(test.data().y, model.predict(test.data().x), normalize=\"true\")" ] }, { @@ -2067,15 +928,15 @@ "from sklearn.calibration import CalibratedClassifierCV\n", "\n", "calibrated_model = CalibratedClassifierCV(estimator=model, method=\"isotonic\")\n", - "calibrated_model.fit(train.x[random_indices], train.y[random_indices])\n", + "calibrated_model.fit(train[random_indices].data().x, train[random_indices].data().y)\n", "\n", "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", "ConfusionMatrixDisplay.from_estimator(\n", - " model, test.x, test.y, labels=[0, 1], normalize=\"true\", ax=axs[0]\n", + " model, *test.data(), labels=[0, 1], normalize=\"true\", ax=axs[0]\n", ")\n", "ConfusionMatrixDisplay.from_estimator(\n", - " calibrated_model, test.x, test.y, labels=[0, 1], normalize=\"true\", ax=axs[1]\n", + " calibrated_model, *test.data(), labels=[0, 1], normalize=\"true\", ax=axs[1]\n", ")\n", "\n", "axs[0].set_title(\"Original model\")\n", @@ -2096,7 +957,7 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", + "#### import matplotlib.pyplot as plt\n", "from imblearn.over_sampling import ADASYN, SMOTE, RandomOverSampler\n", "from imblearn.pipeline import make_pipeline\n", "from sklearn import clone\n", @@ -2272,7 +1133,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.21" }, "vscode": { "interpreter": { From fa0db1b0256809371916c873f8fae8a2894dc6da Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 11:09:49 +0100 Subject: [PATCH 25/41] Import future annotations --- notebooks/support/common.py | 2 ++ notebooks/support/removal_experiment.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/notebooks/support/common.py b/notebooks/support/common.py index e068786dd..1abbbffdf 100644 --- a/notebooks/support/common.py +++ b/notebooks/support/common.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import logging import os import pickle diff --git a/notebooks/support/removal_experiment.py b/notebooks/support/removal_experiment.py index 48b03dffb..1eb77d2eb 100644 --- a/notebooks/support/removal_experiment.py +++ b/notebooks/support/removal_experiment.py @@ -1,3 +1,5 @@ +from __future__ import annotations + from typing import Protocol import pandas as pd From f8994f33a0481a6ce22f2c034474ca6b1248ba43 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 11:49:55 +0100 Subject: [PATCH 26/41] Fix dtype of names in results --- src/pydvl/valuation/result.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/pydvl/valuation/result.py b/src/pydvl/valuation/result.py index bcdc19fe5..1d7e52263 100644 --- a/src/pydvl/valuation/result.py +++ b/src/pydvl/valuation/result.py @@ -561,8 +561,8 @@ def __add__(self, other: ValuationResult) -> ValuationResult: both_pos = np.intersect1d(this_pos, other_pos) if len(both_pos) > 0: - this_names: NDArray = np.empty_like(indices, dtype=object) - other_names: NDArray = np.empty_like(indices, dtype=object) + this_names: NDArray = np.empty_like(indices, dtype=np.str_) + other_names: NDArray = np.empty_like(indices, dtype=np.str_) this_names[this_pos] = self._names other_names[other_pos] = other._names @@ -808,7 +808,7 @@ def _create_names_array( data_names: Sequence[NameT] | NDArray[NameT] | None, indices: NDArray[IndexT] ) -> NDArray[NameT]: if data_names is None: - names = np.array(indices, copy=True, dtype=object) + names = np.array(indices, copy=True, dtype=np.str_) else: names = np.array(data_names, copy=True) From 5ec3a76af081bb9df814b0f1d8108628e5cfac7a Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 11:50:06 +0100 Subject: [PATCH 27/41] Comments --- notebooks/support/common.py | 4 ++++ src/pydvl/valuation/dataset.py | 3 +-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/notebooks/support/common.py b/notebooks/support/common.py index 1abbbffdf..32aaa3b12 100644 --- a/notebooks/support/common.py +++ b/notebooks/support/common.py @@ -776,6 +776,10 @@ class ThresholdTunerCV(BaseEstimator, ClassifierMixin): """ A wrapper that tunes the decision threshold of a binary classifier to maximize a given metric, using cross-fitting on the training data. + + This is used to counteract class imbalance in the dataset. Note however that + upsampling or downsampling the dataset can perform equally well or better, while + being simpler to implement and faster to train. """ def __init__( diff --git a/src/pydvl/valuation/dataset.py b/src/pydvl/valuation/dataset.py index e5c4b32f0..de3d30b73 100644 --- a/src/pydvl/valuation/dataset.py +++ b/src/pydvl/valuation/dataset.py @@ -174,8 +174,7 @@ def data( indices. This is used mainly by [Utility][pydvl.valuation.dataset.utility.Utility] to - retrieve subsets of the data from indices. It is typically **not needed in - valuation algorithms**. + retrieve subsets of the data from indices. Args: indices: Optional indices that will be used to select points from From c6648f3886e644058a4b3ae9881b4b1bf18c5a2c Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 13:38:16 +0100 Subject: [PATCH 28/41] Docstrings --- notebooks/support/removal_experiment.py | 68 ++++++++++++++++++++----- src/pydvl/reporting/scores.py | 9 ++-- 2 files changed, 59 insertions(+), 18 deletions(-) diff --git a/notebooks/support/removal_experiment.py b/notebooks/support/removal_experiment.py index 1eb77d2eb..f14cea212 100644 --- a/notebooks/support/removal_experiment.py +++ b/notebooks/support/removal_experiment.py @@ -1,3 +1,26 @@ +""" +This module implements the standard point removal experiment in data valuation. + +It is a method to evaluate the usefulness and stability of a valuation method. The idea +is to remove a percentage of the data points from the training set based on their +valuation, and then retrain the model and evaluate it on a test set. This is done for a +range of removal percentages, and the performance is measured as a function of the +percentage of data removed. By repeating this process multiple times, we can get an +estimate of the stability of the valuation method. + +The experiment can be run in parallel with the +[run_removal_experiment][pydvl.support.removal_experiment.run_removal_experiment] +function. In order to call it, we need to define 3 types of factories: + +1. A factory that returns a train-test split of the data given a random state +2. A factory that returns a utility that evaluates a model on a given test set. + This is used for the performance evaluation. The model need not be the same + as the one used for the valuation. +3. A factory returning a valuation method. The training set is passed to the + factory, in case the valuation needs to train something. E.g. for Data-OOB + we need the bagging model to be fitted before the valuation is computed. +""" + from __future__ import annotations from typing import Protocol @@ -12,6 +35,14 @@ from pydvl.valuation import Dataset, ModelUtility from pydvl.valuation.base import Valuation +__all__ = [ + "DataSplitFactory", + "ModelFactory", + "UtilityFactory", + "ValuationFactory", + "run_removal_experiment", +] + class ModelFactory(Protocol): def __call__(self, random_state: int) -> BaseModel: @@ -41,16 +72,23 @@ def removal_job( random_state: int, ) -> tuple[dict, dict]: """ + A job that computes the scores for a single run of the removal experiment. Args: - data_factory: - valuation_factory: - utility_factory: - removal_percentages: - random_state: + data_factory: A callable that returns a tuple of Datasets (train, test) to use + in the experiment. + valuation_factory: A callable that returns a Valuation object given a train + dataset and a random state. Computing values with this object is the goal + of the experiment + utility_factory: A callable that returns a ModelUtility object given a test + dataset and a random state. This object is used to evaluate the performance + of the valuation method by removing data points from the training set and + retraining the model, then scoring it on the test set. + removal_percentages: As sequence of percentages of data to remove from the + training set. + random_state: The random state to use in the experiment. Returns: - - + A tuple of dictionaries with the scores for the low and high value removals. """ train, test = data_factory(random_state=random_state) @@ -86,8 +124,8 @@ def run_removal_experiment( valuation_factories: list[ValuationFactory], utility_factory: UtilityFactory, removal_percentages: NDArray, - n_runs: int = 20, - n_jobs: int = 32, + n_runs: int = 1, + n_jobs: int = 1, random_state: int | None = None, ) -> tuple[pd.DataFrame, pd.DataFrame]: """Run the sample removal experiment. @@ -116,13 +154,15 @@ def run_removal_experiment( valuation_factories: A list of callables that return Valuation objects given a model, train data, and random state. The training data is typically not needed for construction, but bagging models may require it - utility_factory: A callable that returns a ModelUtility given test - data, potentially with a scoring function + utility_factory: A callable that returns a ModelUtility object given a test + dataset and a random state. This object is used to evaluate the performance + of the valuation method by removing data points from the training set and + retraining the model, then scoring it on the test set. removal_percentages: The percentage of data to remove from the training set. This should be a list of floats between 0 and 1. - n_runs: The number of repetitions of the experiment - n_jobs: The number of parallel jobs to use - random_state: The initial random state + n_runs: The number of repetitions of the experiment. + n_jobs: The number of parallel jobs to use. + random_state: The initial random state. Returns: A tuple of DataFrames with the scores for the low and high value removals """ diff --git a/src/pydvl/reporting/scores.py b/src/pydvl/reporting/scores.py index 2772dd13b..5c399265f 100644 --- a/src/pydvl/reporting/scores.py +++ b/src/pydvl/reporting/scores.py @@ -23,11 +23,12 @@ def compute_removal_score( remove_best: bool = False, progress: bool = False, ) -> dict[float, float]: - """Fits model and computes score on the test set after incrementally removing + """Fits a model and computes its score on a test set after incrementally removing a percentage of data points from the training set, based on their values. Args: - u: Utility object with model, data, and scoring function. + u: Utility object with model, test data, and scoring function. + training_data: Dataset from which to remove data points. values: Data values of data instances in the training set. percentages: Sequence of removal percentages. remove_best: If True, removes data points in order of decreasing valuation. @@ -42,9 +43,9 @@ def compute_removal_score( if np.any([x >= 1.0 or x < 0.0 for x in percentages]): raise ValueError("All percentages should be in the range [0.0, 1.0)") - if len(values) != len(training_data.indices): + if len(values) != len(training_data): raise ValueError( - f"The number of values, {len(values)}, should be equal to the number of data indices, {len(training_data.indices)}" + f"The number of values, {len(values)}, should be equal to the number of data points, {len(training_data)}" ) scores = {} From 727f5a4f970c4cfe20f68b884a5ffa943fab9e23 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 13:38:35 +0100 Subject: [PATCH 29/41] Fix bogus sorting of data in plot_ci_array --- src/pydvl/reporting/plots.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pydvl/reporting/plots.py b/src/pydvl/reporting/plots.py index e6fc5960a..c21754feb 100644 --- a/src/pydvl/reporting/plots.py +++ b/src/pydvl/reporting/plots.py @@ -116,7 +116,7 @@ def plot_ci_array( else np.arange(n, dtype=str) ), ) - dummy.sort(key="value") + dummy.sort(key="index") return plot_ci_values( dummy, From 943d6889cf77356b9d5ab15066a621073f3c6af0 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 13:49:19 +0100 Subject: [PATCH 30/41] Remove unnecessary type hint using private import --- src/pydvl/reporting/plots.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/pydvl/reporting/plots.py b/src/pydvl/reporting/plots.py index c21754feb..604e61f72 100644 --- a/src/pydvl/reporting/plots.py +++ b/src/pydvl/reporting/plots.py @@ -216,9 +216,7 @@ def spearman_correlation(vv: List[OrderedDict], num_values: int, pvalue: float): p: np.ndarray = np.ndarray((len(vv), len(vv))) for i, a in enumerate(vv): for j, b in enumerate(vv): - from scipy.stats._stats_py import SpearmanrResult - - spearman: SpearmanrResult = sp.stats.spearmanr( + spearman = sp.stats.spearmanr( list(a.keys())[:num_values], list(b.keys())[:num_values] ) r[i][j] = ( From 2209ba7ee3ba86c9e098187a6cfcc4fc2f635d70 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 13:49:49 +0100 Subject: [PATCH 31/41] Copy names and ignore type --- src/pydvl/valuation/methods/data_oob.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/pydvl/valuation/methods/data_oob.py b/src/pydvl/valuation/methods/data_oob.py index 9057e7bc5..57b7caa38 100644 --- a/src/pydvl/valuation/methods/data_oob.py +++ b/src/pydvl/valuation/methods/data_oob.py @@ -96,7 +96,7 @@ def fit(self, data: Dataset): # This should always be present after fitting try: - estimators = self.model.estimators_ + estimators = self.model.estimators_ # type: ignore except AttributeError: raise ValueError( "The model has to be an sklearn-compatible bagging model, including " @@ -131,6 +131,7 @@ def fit(self, data: Dataset): self.result += ValuationResult( algorithm=algorithm_name, indices=oob_indices, + names=data[oob_indices].names, values=score_array, counts=np.ones_like(score_array, dtype=data.indices.dtype), ) From 6cdd20799b44cf0493dde90710fbf7132a99d379 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 13:50:33 +0100 Subject: [PATCH 32/41] Extend accepted types for dataset slices --- src/pydvl/valuation/dataset.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/pydvl/valuation/dataset.py b/src/pydvl/valuation/dataset.py index de3d30b73..070341144 100644 --- a/src/pydvl/valuation/dataset.py +++ b/src/pydvl/valuation/dataset.py @@ -149,7 +149,9 @@ def make_names(s: str, a: np.ndarray) -> list[str]: else self._indices.astype(np.str_) ) - def __getitem__(self, idx: int | slice | Sequence[int]) -> Dataset: + def __getitem__( + self, idx: int | slice | Sequence[int] | NDArray[np.int_] + ) -> Dataset: if isinstance(idx, int): idx = [idx] return Dataset( @@ -462,7 +464,9 @@ def __init__( def __len__(self): return len(self._indices) - def __getitem__(self, idx: int | slice | Sequence[int]) -> GroupedDataset: + def __getitem__( + self, idx: int | slice | Sequence[int] | NDArray[np.int_] + ) -> GroupedDataset: if isinstance(idx, int): idx = [idx] return GroupedDataset( From 41ced28f971b90674f20b28999df9e0d5a389230 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 14:38:57 +0100 Subject: [PATCH 33/41] Cleanup --- notebooks/support/common.py | 4 ++++ src/pydvl/reporting/__init__.py | 3 +++ src/pydvl/reporting/plots.py | 9 +++++++++ .../pydvl/reporting/point_removal.py | 2 +- 4 files changed, 17 insertions(+), 1 deletion(-) rename notebooks/support/removal_experiment.py => src/pydvl/reporting/point_removal.py (98%) diff --git a/notebooks/support/common.py b/notebooks/support/common.py index 32aaa3b12..e1d92ed77 100644 --- a/notebooks/support/common.py +++ b/notebooks/support/common.py @@ -780,6 +780,10 @@ class ThresholdTunerCV(BaseEstimator, ClassifierMixin): This is used to counteract class imbalance in the dataset. Note however that upsampling or downsampling the dataset can perform equally well or better, while being simpler to implement and faster to train. + + !!! Note + This class is a left-over from a previous version of the Data-OOB notebook and + should probably be removed. """ def __init__( diff --git a/src/pydvl/reporting/__init__.py b/src/pydvl/reporting/__init__.py index e69de29bb..dea82f004 100644 --- a/src/pydvl/reporting/__init__.py +++ b/src/pydvl/reporting/__init__.py @@ -0,0 +1,3 @@ +from .plots import * +from .point_removal import * +from .scores import * diff --git a/src/pydvl/reporting/plots.py b/src/pydvl/reporting/plots.py index 604e61f72..cfc8dfcca 100644 --- a/src/pydvl/reporting/plots.py +++ b/src/pydvl/reporting/plots.py @@ -12,6 +12,15 @@ from pydvl.valuation.result import ValuationResult +__all__ = [ + "plot_ci_array", + "plot_ci_values", + "plot_shapley", + "plot_influence_distribution", + "plot_influence_distribution_by_label", + "spearman_correlation", +] + @deprecated(target=None, deprecated_in="0.7.1", remove_in="0.9.0") def shaded_mean_std( diff --git a/notebooks/support/removal_experiment.py b/src/pydvl/reporting/point_removal.py similarity index 98% rename from notebooks/support/removal_experiment.py rename to src/pydvl/reporting/point_removal.py index f14cea212..87edce83b 100644 --- a/notebooks/support/removal_experiment.py +++ b/src/pydvl/reporting/point_removal.py @@ -9,7 +9,7 @@ estimate of the stability of the valuation method. The experiment can be run in parallel with the -[run_removal_experiment][pydvl.support.removal_experiment.run_removal_experiment] +[run_removal_experiment][pydvl.reporting.point_removal.run_removal_experiment] function. In order to call it, we need to define 3 types of factories: 1. A factory that returns a train-test split of the data given a random state From 00a0f348bd74a2f98410051296c0871d183ad3f0 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 14:39:39 +0100 Subject: [PATCH 34/41] Finish notebook and analysis --- notebooks/data_oob.ipynb | 842 +++++++++++++-------------------------- 1 file changed, 280 insertions(+), 562 deletions(-) diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb index e137c3b4c..b9d31792a 100644 --- a/notebooks/data_oob.ipynb +++ b/notebooks/data_oob.ipynb @@ -31,18 +31,14 @@ }, { "cell_type": "markdown", - "metadata": { - "tags": [ - "hide" - ] - }, + "metadata": {}, "source": [ "We begin by importing the main libraries and setting some defaults." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "slideshow": { "slide_type": "" @@ -61,8 +57,8 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "from sklearn.metrics import ConfusionMatrixDisplay, f1_score\n", - "from support.common import load_adult_data, load_adult_data_raw\n", + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "from support.common import load_adult_data\n", "\n", "from pydvl.reporting.plots import plot_ci_array, plot_ci_values\n", "\n", @@ -79,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -98,20 +94,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "tags": [ "hide-output" ] }, - "outputs": [], - "source": [ - "train, test = load_adult_data(train_size=0.6, subsample=0.2, random_state=random_state)" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found cached file: adult_data_raw.pkl.\n" + ] + } + ], + "source": [ + "train_size = 0.6\n", + "train, test = load_adult_data(\n", + " train_size=train_size, subsample=0.2, random_state=random_state\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "tags": [ "hide" @@ -121,7 +128,7 @@ "source": [ "if is_CI: # Subsample 1% of the data for faster testing\n", " train, test = load_adult_data(\n", - " train_size=0.6, subsample=0.01, random_state=random_state\n", + " train_size=train_size, subsample=0.01, random_state=random_state\n", " )" ] }, @@ -134,16 +141,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "tags": [ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3602 samples. Class distribution: 25.0% positive, 75.0% negative\n" + ] + } + ], "source": [ "print(\n", - " f\"{len(train)} samples. Class distribution: {100 * np.mean(train.data().y == 1):.1f}% positive, \"\n", + " f\"{len(train)} samples. \"\n", + " f\"Class distribution: {100 * np.mean(train.data().y == 1):.1f}% positive, \"\n", " f\"{100 * np.mean(train.data().y == 0):.1f}% negative\"\n", ")" ] @@ -157,13 +173,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from imblearn.over_sampling import RandomOverSampler\n", "\n", - "n_est = 100\n", + "n_est = 50\n", "max_samples = 0.2 # Use small bootstrap samples\n", "model = RandomForestClassifier(\n", " n_estimators=n_est,\n", @@ -175,7 +191,8 @@ "base_predictions = model.predict(test.data().x)\n", "\n", "sampler = RandomOverSampler(random_state=random_state)\n", - "train = Dataset(*sampler.fit_resample(*train.data()))\n", + "resampled_x, resampled_y = sampler.fit_resample(*train.data())\n", + "train = Dataset(resampled_x, resampled_y, train.feature_names, train.target_names)\n", "\n", "model.fit(*train.data())\n", "predictions_oversampled = model.predict(test.data().x)" @@ -183,29 +200,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "tags": [ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5406 samples. Class distribution: 50.0% positive, 50.0% negative\n" + ] + } + ], "source": [ "print(\n", - " f\"{len(train)} samples. Class distribution: {100 * train.data().y.mean():.1f}% positive, \"\n", + " f\"{len(train)} samples. Class distribution: \"\n", + " f\"{100 * train.data().y.mean():.1f}% positive, \"\n", " f\"{100 * (1 - train.data().y.mean()):.1f}% negative\"\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "tags": [ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", @@ -230,11 +269,7 @@ }, { "cell_type": "markdown", - "metadata": { - "tags": [ - "invertible-output" - ] - }, + "metadata": {}, "source": [ "## Computing the OOB values\n", "\n", @@ -247,9 +282,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy with 50 estimators: 0.83\n", + "Accuracy with 100 estimators: 0.83\n", + "Accuracy with 200 estimators: 0.83\n" + ] + } + ], "source": [ "n_estimators = [50, 100, 200]\n", "oob_values = []\n", @@ -293,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "slideshow": { "slide_type": "" @@ -303,7 +348,20 @@ "invertible-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", @@ -336,20 +394,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To see this, focus on the long tails with zero variance. These are samples for which the score $T(y_i, \\hat{f}_b(x_i)) = 1$ for *every* estimator $\\hat{f}_b$ not trained on them, that is: *every weak learner in the ensemble correctly classifies these samples*. As we said above, this can happen because it is likely for weak estimators to be fitted to always predict the majority (negative) class:\n", - "\n", - "## This analysis is now wrong: 73% of the points in the tail are in the minority class (probably have been oversampled)" + "To see this, focus on the long tails with zero variance. These are samples for which the score $T(y_i, \\hat{f}_b(x_i)) = 1$ for *every* estimator $\\hat{f}_b$ not trained on them, that is: *every weak learner in the ensemble correctly classifies these samples*. As we said above, this can happen because it is likely for weak estimators to be fitted to always predict the majority (negative) class:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "tags": [ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 914 points with value 1 and zero variance (16.91% of the data).\n", + "Of these, 77.46% are in the majority class.\n" + ] + } + ], "source": [ "v = oob_values[0]\n", "from_index = np.where(np.isclose(v.variances, 0.0) & np.isclose(v.values, 1))[0][0]\n", @@ -359,126 +424,20 @@ " f\" ({100 * len(tail) / len(train):.2f}% of the data).\"\n", ")\n", "print(\n", - " f\"Of these, {100 * (train.y[tail] == 0).sum() / len(tail):.2f}% \"\n", + " f\"Of these, {100 * (train[tail].data().y == 0).sum() / len(tail):.2f}% \"\n", " f\"are in the majority class.\"\n", ")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.hist(tail, bins=20)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "Simply put, the ensemble is mostly good at classifying the majority class, and the variance of the OOB score for these samples is very low. This is a common issue in imbalanced datasets, and it is one of the reasons why the OOB score might not be a good metric for model performance in these cases. For us, it shows that the values reflect only poorly fit models.\n", - "\n", - "We test this below by ensembling a number of constant classifiers, but how does this affect our ability to use the values for data inspection, cleaning, etc.?" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### A detour to further interpret the OOB values\n", + "Simply put, the ensemble is mostly good at classifying the majority class, and the variance of the OOB score for these samples is very low. This is a common issue in imbalanced datasets, and it is one of the reasons why the OOB score might not be a good metric for model performance in these cases. For us, it shows that the values reflect only poorly fit models. We test this at the end of the notebook by ensembling a number of constant classifiers.\n", "\n", - "We can verify that the OOB values in our case reflect the imbalance of the dataset by training a [BaggingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) with constant estimators. A fraction of `n_estimators` will always pick class 1, and the rest class 0. This leads to a clear jump in the value rank plot, either around 25% or 75% of them, since, as we saw above, 25% of the samples are in the positive (\"1\") class, and 75% in the negative (\"0\").\n", - "\n", - "We will use three different probabilities for the constant estimators to predict class 0: 0.01, 0.5, and 0.99. Again, the idea is that the OOB values will reflect the class distribution of the dataset, and we should see a clear jump in the values around 25% and 75% of the data.\n", + "But first, let's evaluate the conclusion of the previous discussion: at first sight, it seems that for this imbalanced dataset and poorly performing model, the usual intuition that extreme values characterize \"easy\" or \"hard\" points might be bogus.\n", "\n", - "The code is analogous to the above when we fitted the random forest, so it is ommitted from the documentation, but we use a custom class `ConstantBinaryClassifier` as base estimator." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "from sklearn.ensemble import BaggingClassifier\n", - "from support.common import ConstantBinaryClassifier\n", - "\n", - "train, test = load_adult_data(train_size=0.6, subsample=0.2, random_state=random_state)\n", - "\n", - "probs = [0.01, 0.5, 0.99]\n", - "all_values = []\n", - "for p in probs:\n", - " model = BaggingClassifier(\n", - " ConstantBinaryClassifier(p, random_state),\n", - " n_estimators=100,\n", - " max_samples=1.0,\n", - " random_state=random_state,\n", - " )\n", - " model.fit(*train.data())\n", - " score = model.score(*test.data())\n", - " print(\n", - " f\"Accuracy when ~{100 * p:.1f}% of estimators always predict class 0: {score:.4f}\"\n", - " )\n", - "\n", - " valuation = DataOOBValuation(model)\n", - " valuation.fit(train)\n", - " all_values.append(valuation.values())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=[15, 5])\n", - "\n", - "for p, values, mean_color, shade_color in zip(\n", - " probs, all_values, MEAN_COLORS, SHADE_COLORS\n", - "):\n", - " values.sort(key=\"value\")\n", - " plot_ci_values(\n", - " values,\n", - " ax=ax,\n", - " level=0.01,\n", - " mean_color=mean_color,\n", - " shade_color=shade_color,\n", - " label=f\"Data-OOB, p={p:.2f}\",\n", - " )\n", - "\n", - "ax.axvline(\n", - " 0.25 * len(all_values[0]), color=\"darkorange\", linestyle=\"--\", label=\"25% data\"\n", - ")\n", - "ax.axvline(0.75 * len(all_values[0]), color=\"darkred\", linestyle=\"--\", label=\"75% data\")\n", - "plt.ylabel(\"Data-OOB values\")\n", - "plt.xlabel(\"Rank\")\n", - "plt.title(\"Model with constant estimators\")\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## A bogus (?) conclusion\n", - "\n", - "The conclusion of this discussion seems to be that for this imbalanced dataset and poorly performing model, the usual intuition that extreme values characterize \"easy\" or \"hard\" points might be bogus.\n", - " \n", - "If we discard the datapoints with the highest values, i.e. those which the ensemble of weak learners classifies correctly every time, because we believe that those are trivial in some sense (e.g. repeated) and bias the ensemble towards stricter decisions boundaries, we obtain very mild changes. The same happens when removing low-valued points. Basically, the changes in test scores could be just noise." + "In order to test this idea, we will use a standard experiment in the data valuation literature." ] }, { @@ -487,95 +446,78 @@ "source": [ "## Evaluating Data-OOB values with data removal\n", "\n", - "We can go beyond the simple analysis above and systematically evaluate the impact of removing data points with high or low values on the model's performance. This is a common practice in the literature. We can use the [compute_removal_score][pydvl.reporting.scores.compute_removal_score] function to do it. This function takes a [ModelUtility][pydvl.valuation.ModelUtility] object, a [ValuationResult][pydvl.value.result.ValuationResult] object, and a [Dataset][pydvl.data.dataset.Dataset] object, and computes the performance of the model after removing a fraction of the data with the highest or lowest values.\n", - "\n", - "Recall from above that we can construct these objects as follows:\n", - "\n", - "```python\n", - "model = RandomForestClassifier(n_estimators=n_est, max_samples=max_samples, random_state=seed)\n", - "utility = ModelUtility(model, SupervisedScorer(\"accuracy\", test, 0.0), clone_before_fit=False)\n", + "We can systematically evaluate the impact of removing data points with high or low values on the model's performance. If the values are meaningful, we should see a significant change in the model's performance. This is a common practice in the literature.\n", "\n", - "valuation = DataOOBValuation(model)\n", - "valuation.fit(train)\n", - "values = valuation.values()\n", - "```\n", + "PyDVL provides [run_removal_experiment][pydvl.reporting.point_removal.run_removal_experiment], which is a convenience function to compute the performance of the model after removing a fraction of the data with the highest or lowest values.\n", "\n", - "The details are hidden in the rendered documentation, please refer to the notebook for the full code." + "The details are hidden in the version of this notebook rendered for the documentation, please refer to the actual notebook for the full code." ] }, { "cell_type": "markdown", - "metadata": { - "tags": [ - "hide" - ] - }, + "metadata": {}, "source": [ - "In order to call `run_removal_experiment()` we need to define 3 types of factories:\n", + "In order to run the experiment, we need to define 3 types of factories:\n", "\n", - "1. A factory that returns a train-test split of the data given a random state \n", + "1. A factory that returns a train-test split of the data given a random state\n", "2. A factory that returns a utility that evaluates a model on a given test set.\n", " This is used for the performance evaluation. The model need not be the same\n", " as the one used for the valuation.\n", - "4. A factory returning a valuation method. The training set is passed to the\n", + "3. A factory returning a valuation method. The training set is passed to the\n", " factory, in case the valuation needs to train something. E.g. for Data-OOB\n", " we need the bagging model to be fitted before the valuation is computed." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "%autoreload\n", - "from support.common import ThresholdTunerCV\n", - "from support.removal_experiment import run_removal_experiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide" - ] - }, - "outputs": [], - "source": [ + "import warnings\n", + "\n", + "from pydvl.reporting.point_removal import run_removal_experiment\n", "from pydvl.valuation import KNNClassifierUtility, ModelUtility, SupervisedScorer\n", "from pydvl.valuation.methods.data_oob import point_wise_accuracy\n", "from pydvl.valuation.methods.random import RandomValuation\n", "\n", + "n_est = 50\n", + "max_samples = 0.2 # Small bootstrap samples\n", + "train_size = 0.6 # Fraction of data for training\n", + "random_state = 42\n", + "\n", "\n", "def make_data(random_state: int) -> tuple[Dataset, Dataset]:\n", - " return load_adult_data(train_size=0.6, random_state=random_state)\n", + " train, test = load_adult_data(train_size=train_size, random_state=random_state)\n", + " # suppress sklearn >= 1.6 warnings originating in imblearn\n", + " with warnings.catch_warnings():\n", + " warnings.simplefilter(\"ignore\")\n", + " sampler = RandomOverSampler(random_state=random_state)\n", + " resampled_x, resampled_y = sampler.fit_resample(*train.data())\n", + " train = Dataset(resampled_x, resampled_y, train.feature_names, train.target_names)\n", + " return train, test\n", "\n", "\n", "def make_utility(test: Dataset, random_state: int) -> ModelUtility:\n", - " classifier = RandomForestClassifier(\n", + " model = RandomForestClassifier(\n", " n_estimators=n_est,\n", " max_samples=max_samples,\n", " class_weight=\"balanced\",\n", " random_state=random_state + 1,\n", " )\n", - " model = ThresholdTunerCV(\n", - " classifier, n_splits=5, metric=f1_score, n_jobs=1, random_state=random_state\n", - " )\n", + "\n", " return ModelUtility(model, SupervisedScorer(\"accuracy\", test, 0.0))\n", "\n", "\n", "def make_oob(train: Dataset, random_state: int) -> DataOOBValuation:\n", - " classifier = RandomForestClassifier(\n", + " model = RandomForestClassifier(\n", " n_estimators=n_est,\n", " max_samples=max_samples,\n", " class_weight=\"balanced\",\n", " random_state=random_state,\n", " )\n", - " model = ThresholdTunerCV(\n", - " classifier, n_splits=5, metric=f1_score, n_jobs=1, random_state=random_state\n", - " )\n", - " model.fit(train.x, train.y)\n", + "\n", + " model.fit(*train.data())\n", " return DataOOBValuation(model, point_wise_accuracy)\n", "\n", "\n", @@ -588,28 +530,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "tags": [ "hide" ] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [01:01<00:00, 3.10s/%]\n" + ] + } + ], "source": [ "low_scores_df, high_scores_df = run_removal_experiment(\n", " data_factory=make_data,\n", " valuation_factories=[make_random, make_oob],\n", " utility_factory=make_utility, # for evaluation\n", " removal_percentages=removal_percentages,\n", - " n_runs=6,\n", - " n_jobs=30,\n", + " n_runs=10,\n", + " n_jobs=18,\n", " random_state=random_state,\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "slideshow": { "slide_type": "" @@ -619,7 +569,20 @@ "invertible-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", @@ -649,9 +612,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# FIXME: THERE'S A BUG ABOVE\n", + "We observe that if we discard the datapoints with the highest values, i.e. those which the ensemble of weak learners classifies correctly every time (maybe because we believe that those are trivial in some sense, e.g. repeated) and bias the ensemble towards stricter decisions boundaries, we obtain very mild changes in performance, even after removing 50% of the data. This is consistent with the fact that the ensemble is mostly good at classifying the majority class.\n", "\n", - "The accuracy curves do not start at the same value!" + "However, we see a more pronounced sensitivity of the model to the removal of low-valued points, with an average drop of 5% in accuracy after removing 50% of the data with the lowest value, as opposed to barely any change when removing data at random. This is consistent with the fact that the ensemble is not very good at classifying the minority class, and the low-valued points are those that the ensemble gets wrong most of the time.\n" ] }, { @@ -665,9 +628,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 20/20 [06:19<00:00, 18.96s/%] \n" + ] + } + ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", @@ -678,16 +649,13 @@ "\n", "\n", "def make_oob_knn(train: Dataset, random_state: int) -> DataOOBValuation:\n", - " classifier = BaggingClassifier(\n", + " model = BaggingClassifier(\n", " estimator=KNeighborsClassifier(n_neighbors=10),\n", " max_samples=0.4,\n", " n_estimators=10,\n", " random_state=random_state,\n", " )\n", - " model = ThresholdTunerCV(\n", - " classifier, n_splits=5, metric=f1_score, n_jobs=1, random_state=random_state\n", - " )\n", - " model.fit(train.x, train.y)\n", + " model.fit(*train.data())\n", " return DataOOBValuation(model, point_wise_accuracy)\n", "\n", "\n", @@ -696,21 +664,34 @@ " utility_factory=make_knn_utility,\n", " valuation_factories=[make_random, make_oob_knn],\n", " removal_percentages=removal_percentages,\n", - " n_runs=20,\n", - " n_jobs=30,\n", + " n_runs=10,\n", + " n_jobs=18,\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "tags": [ "hide-input", "invertible-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", @@ -740,379 +721,116 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## The impact of removing data on the three random forest models\n", - "\n", - "Manually removing and retraining:" + "As we can see by the changes in performance, bagging the model and looking at OOB information to rank data points with Data-OOB does provide valuable information for data inspection, cleaning, etc. even when the model is not a bagging model. However, the cost of fitting the bagging model might be prohibitive in some cases." ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = load_adult_data_raw()\n", - "corr = df.corr(numeric_only=True)\n", - "corr.style.background_gradient(cmap=\"coolwarm\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "train, test = load_adult_data(train_size=0.6, random_state=random_state)\n", - "for i, values in enumerate(oob_values):\n", - " zero_variances = np.isclose(values.variances, 0.0)\n", - " to_index = np.where(zero_variances & np.isclose(values.values, 1))[0][0]\n", - " try:\n", - " from_index = np.where(zero_variances & np.isclose(values.values, 0))[0][-1]\n", - " except IndexError:\n", - " from_index = 0\n", - " model = RandomForestClassifier(\n", - " n_estimators=n_estimators[i], max_samples=max_samples, random_state=random_state\n", - " )\n", - " model.fit(train.x, train.y)\n", - " full_training_score = model.score(test.x, test.y)\n", + "## Appendix: A detour to further interpret the OOB values\n", "\n", - " model.fit(train.x[:to_index], train.y[:to_index])\n", - " reduced_training_score = model.score(test.x, test.y)\n", - " score_change = (\n", - " 100 * (reduced_training_score - full_training_score) / full_training_score\n", - " )\n", - " fraction_of_data = 100 * to_index / len(train)\n", - " print(f\"Model with {n_estimators[i]} estimators:\")\n", - " print(\n", - " f\" Remove HIGH: Relative change in test score after training on \"\n", - " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\"\n", - " )\n", + "We can verify that the OOB values in our case reflect the imbalance of the dataset by training a [BaggingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) with constant estimators. A fraction of `n_estimators` will always pick class 1, and the rest class 0. This leads to a clear jump in the value rank plot, either around 25% or 75% of them, since, as we saw above, 25% of the samples are in the positive (\"1\") class, and 75% in the negative (\"0\").\n", "\n", - " model.fit(train.x[from_index:], train.y[from_index:])\n", - " reduced_training_score = model.score(test.x, test.y)\n", - " score_change = (\n", - " 100 * (reduced_training_score - full_training_score) / full_training_score\n", - " )\n", - " fraction_of_data = 100 * (len(train) - from_index) / len(train)\n", - " print(\n", - " f\" Remove LOW: Relative change in test score after training on \"\n", - " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Removing high variance points, or training just on them\n", + "We will use three different probabilities for the constant estimators to predict class 0: 0.01, 0.5, and 0.99. Again, the idea is that the OOB values will reflect the class distribution of the dataset, and we should see a clear jump in the values around 25% and 75% of the data.\n", "\n", - "=> same result" + "The code is analogous to the above when we fitted the random forest, so it is ommitted from the documentation, but we use a custom class `ConstantBinaryClassifier` as base estimator." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found cached file: adult_data_raw.pkl.\n", + "Accuracy when ~1.0% of estimators always predict class 0: 0.2498\n", + "Accuracy when ~50.0% of estimators always predict class 0: 0.7502\n", + "Accuracy when ~99.0% of estimators always predict class 0: 0.7502\n" + ] + } + ], "source": [ - "variance_q95 = np.quantile(values.variances, 0.95)\n", + "from sklearn.ensemble import BaggingClassifier\n", + "from support.common import ConstantBinaryClassifier\n", "\n", - "for i, values in enumerate(oob_values):\n", - " high_variance_indices = np.where(values.variances > variance_q95)[0]\n", - " model = RandomForestClassifier(\n", - " n_estimators=n_estimators[i],\n", + "train, test = load_adult_data(\n", + " train_size=train_size, subsample=0.2, random_state=random_state\n", + ")\n", + "\n", + "probs = [0.01, 0.5, 0.99]\n", + "all_values = []\n", + "for p in probs:\n", + " model = BaggingClassifier(\n", + " ConstantBinaryClassifier(p, random_state),\n", + " n_estimators=100,\n", " max_samples=1.0,\n", - " class_weight=\"balanced\",\n", " random_state=random_state,\n", " )\n", - " model.fit(train.x, train.y)\n", - " full_score = model.score(test.x, test.y)\n", - "\n", - " selected_x, selected_y = (\n", - " train.x[high_variance_indices],\n", - " train.y[high_variance_indices],\n", - " )\n", - " model.fit(selected_x, selected_y)\n", - " reduced_score = model.score(test.x, test.y)\n", - "\n", - " score_change = 100 * (reduced_score - full_score) / full_score\n", - " fraction_of_data = 100 * len(selected_x) / len(train)\n", - "\n", - " print(f\"Model with {n_estimators[i]} estimators:\")\n", + " model.fit(*train.data())\n", + " score = model.score(*test.data())\n", " print(\n", - " f\" Relative change in test score after training on \"\n", - " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\"\n", + " f\"Accuracy when ~{100 * p:.1f}% of estimators always predict class 0: {score:.4f}\"\n", " )\n", "\n", - " random_indices = np.random.choice(\n", - " len(train), len(high_variance_indices), replace=False\n", - " )\n", - " model.fit(train.x[random_indices], train.y[random_indices])\n", - " random_score = model.score(test.x, test.y)\n", - " score_change = 100 * (random_score - full_score) / full_score\n", - " print(\n", - " f\" Relative change in test score after training on a random \"\n", - " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import confusion_matrix\n", - "\n", - "model = RandomForestClassifier(\n", - " n_estimators=100, max_samples=1.0, class_weight=\"balanced\"\n", - ")\n", - "model.fit(*train.data())\n", - "full_score = model.score(*test.data())\n", - "confusion_matrix(test.data().y, model.predict(test.data().x), normalize=\"true\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(train[high_variance_indices].data().x, train[high_variance_indices].data().y)\n", - "print(f\"Using {100 * len(high_variance_indices) / len(train):.2f}% of the data\")\n", - "confusion_matrix(test.data().y, model.predict(test.data().x), normalize=\"true\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Subsampling a random subset of the same size\n", - "\n", - "Yields similar accuracy (expected) but worse FNR and FPR" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "random_indices = np.random.choice(len(train), len(high_variance_indices), replace=False)\n", - "model.fit(train[random_indices].data().x, train[random_indices].data().y)\n", - "random_score = model.score(*test.data())\n", - "score_change = 100 * (random_score - full_score) / full_score\n", - "fraction_of_data = 100 * len(random_indices) / len(train)\n", - "print(\n", - " f\"Relative change in test score after training on a random \"\n", - " f\"{fraction_of_data:.2f}% of the data: {score_change:.2f}%\"\n", - ")\n", - "confusion_matrix(test.data().y, model.predict(test.data().x), normalize=\"true\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calibrating the model\n", - "\n", - "Fails miserably:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.calibration import CalibratedClassifierCV\n", - "\n", - "calibrated_model = CalibratedClassifierCV(estimator=model, method=\"isotonic\")\n", - "calibrated_model.fit(train[random_indices].data().x, train[random_indices].data().y)\n", - "\n", - "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", - "\n", - "ConfusionMatrixDisplay.from_estimator(\n", - " model, *test.data(), labels=[0, 1], normalize=\"true\", ax=axs[0]\n", - ")\n", - "ConfusionMatrixDisplay.from_estimator(\n", - " calibrated_model, *test.data(), labels=[0, 1], normalize=\"true\", ax=axs[1]\n", - ")\n", - "\n", - "axs[0].set_title(\"Original model\")\n", - "axs[1].set_title(\"Calibrated model\")\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Oversampling with imblearn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#### import matplotlib.pyplot as plt\n", - "from imblearn.over_sampling import ADASYN, SMOTE, RandomOverSampler\n", - "from imblearn.pipeline import make_pipeline\n", - "from sklearn import clone\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import ConfusionMatrixDisplay\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import TargetEncoder\n", - "from support.common import load_adult_data_raw\n", - "\n", - "random_state = 42\n", - "n_est = 50\n", - "max_samples = 0.2\n", - "df = load_adult_data_raw()\n", - "column_names = df.columns.tolist()\n", - "\n", - "df[\"income\"] = df[\"income\"].cat.codes\n", - "df.drop(columns=[\"education\"], inplace=True) # education-num is enough\n", - "df.dropna(inplace=True)\n", - "column_names.remove(\"education\")\n", - "column_names.remove(\"income\")\n", - "\n", - "x_train, x_test, y_train, y_test = train_test_split(\n", - " df.drop(columns=[\"income\"]).values,\n", - " df[\"income\"].values,\n", - " train_size=0.6,\n", - " random_state=random_state,\n", - " stratify=df[\"income\"].values,\n", - ")\n", - "\n", - "encoder = TargetEncoder(target_type=\"binary\", random_state=random_state)\n", - "classifier = RandomForestClassifier(\n", - " n_estimators=n_est,\n", - " max_samples=max_samples,\n", - " class_weight=\"balanced\",\n", - " random_state=random_state,\n", - ")\n", - "tuned_classifier = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\n", - "ros_sampler = RandomOverSampler(random_state=random_state)\n", - "smote_sampler = SMOTE(random_state=random_state)\n", - "adasyn_sampler = ADASYN(random_state=random_state)\n", - "\n", - "simple_pipeline = make_pipeline(encoder, clone(classifier))\n", - "ros_pipeline = make_pipeline(encoder, ros_sampler, clone(classifier))\n", - "tuned_pipeline = make_pipeline(encoder, clone(tuned_classifier))\n", - "tuned_oversampling_pipeline = make_pipeline(\n", - " encoder, ros_sampler, clone(tuned_classifier)\n", - ")\n", - "smote_pipeline = make_pipeline(encoder, smote_sampler, clone(tuned_classifier))\n", - "adasyn_pipeline = make_pipeline(encoder, adasyn_sampler, clone(tuned_classifier))\n", - "\n", - "simple_pipeline.fit(x_train, y_train)\n", - "full_score = simple_pipeline.score(x_test, y_test)\n", - "print(f\"Accuracy with no oversampling: {full_score:.4f}\")\n", - "\n", - "ros_pipeline.fit(x_train, y_train)\n", - "full_score = ros_pipeline.score(x_test, y_test)\n", - "print(f\"Accuracy with random oversampling: {full_score:.4f}\")\n", - "\n", - "tuned_pipeline.fit(x_train, y_train)\n", - "full_score = tuned_pipeline.score(x_test, y_test)\n", - "print(f\"Accuracy with tuned threshold: {full_score:.4f}\")\n", - "\n", - "tuned_oversampling_pipeline.fit(x_train, y_train)\n", - "full_score = tuned_oversampling_pipeline.score(x_test, y_test)\n", - "print(f\"Accuracy with tuned threshold and ROS: {full_score:.4f}\")\n", - "\n", - "smote_pipeline.fit(x_train, y_train)\n", - "full_score = smote_pipeline.score(x_test, y_test)\n", - "print(f\"Accuracy with SMOTE: {full_score:.4f}\")\n", - "\n", - "adasyn_pipeline.fit(x_train, y_train)\n", - "full_score = adasyn_pipeline.score(x_test, y_test)\n", - "print(f\"Accuracy with ADASYN: {full_score:.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity check: did we fit the training set well?\n", - "(\n", - " simple_pipeline.score(x_train, y_train),\n", - " ros_pipeline.score(x_train, y_train),\n", - " tuned_pipeline.score(x_train, y_train),\n", - " tuned_oversampling_pipeline.score(x_train, y_train),\n", - " smote_pipeline.score(x_train, y_train),\n", - " adasyn_pipeline.score(x_train, y_train),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(nrows=3, ncols=2, figsize=[12, 12])\n", - "\n", - "ConfusionMatrixDisplay.from_estimator(\n", - " simple_pipeline, x_test, y_test, labels=[0, 1], normalize=\"true\", ax=axs[0][0]\n", - ")\n", - "ConfusionMatrixDisplay.from_estimator(\n", - " ros_pipeline, x_test, y_test, labels=[0, 1], normalize=\"true\", ax=axs[0][1]\n", - ")\n", - "ConfusionMatrixDisplay.from_estimator(\n", - " tuned_pipeline, x_test, y_test, labels=[0, 1], normalize=\"true\", ax=axs[1][0]\n", - ")\n", - "ConfusionMatrixDisplay.from_estimator(\n", - " tuned_oversampling_pipeline,\n", - " x_test,\n", - " y_test,\n", - " labels=[0, 1],\n", - " normalize=\"true\",\n", - " ax=axs[1][1],\n", - ")\n", - "ConfusionMatrixDisplay.from_estimator(\n", - " smote_pipeline, x_test, y_test, labels=[0, 1], normalize=\"true\", ax=axs[2][0]\n", - ")\n", - "ConfusionMatrixDisplay.from_estimator(\n", - " adasyn_pipeline, x_test, y_test, labels=[0, 1], normalize=\"true\", ax=axs[2][1]\n", - ")\n", - "\n", - "axs[0][0].set_title(\"No oversampling\")\n", - "axs[0][1].set_title(\"ROS\")\n", - "axs[1][0].set_title(\"Tuned threshold\")\n", - "axs[1][1].set_title(\"Tuned threshold with ROS\")\n", - "axs[2][0].set_title(\"SMOTE\")\n", - "axs[2][1].set_title(\"ADASYN\")\n", - "plt.show();" + " valuation = DataOOBValuation(model)\n", + " valuation.fit(train)\n", + " all_values.append(valuation.values())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "import numpy as np\n", - "from sklearn.metrics import precision_recall_curve\n", + "fig, ax = plt.subplots(figsize=[15, 5])\n", "\n", - "y_proba = ros_pipeline.predict_proba(x_test)[:, 1]\n", + "for p, values, mean_color, shade_color in zip(\n", + " probs, all_values, MEAN_COLORS, SHADE_COLORS\n", + "):\n", + " values.sort(key=\"value\")\n", + " plot_ci_values(\n", + " values,\n", + " ax=ax,\n", + " level=0.01,\n", + " mean_color=mean_color,\n", + " shade_color=shade_color,\n", + " label=f\"Data-OOB, p={p:.2f}\",\n", + " )\n", "\n", - "precision, recall, thresholds = precision_recall_curve(y_test, y_proba)\n", - "f_score = 2 * precision * recall / (precision + recall)\n", - "optimal_index = np.argmax(f_score)\n", - "chosen_threshold = thresholds[optimal_index]\n", - "y_pred = (y_proba >= chosen_threshold).astype(int)\n", - "print(\n", - " f\"Accuracy at chosen threshold {chosen_threshold:.2f}: {np.mean(y_pred == y_test):.4f}\"\n", + "ax.axvline(\n", + " 0.25 * len(all_values[0]), color=\"darkorange\", linestyle=\"--\", label=\"25% data\"\n", ")\n", - "ConfusionMatrixDisplay.from_predictions(\n", - " y_test, y_pred, labels=[0, 1], normalize=\"true\"\n", - ");" + "ax.axvline(0.75 * len(all_values[0]), color=\"darkred\", linestyle=\"--\", label=\"75% data\")\n", + "plt.ylabel(\"Data-OOB values\")\n", + "plt.xlabel(\"Rank\")\n", + "plt.title(\"Model with constant estimators\")\n", + "plt.legend();" ] } ], From 5cbee9ce81b55e02ad21c3abdeccb95734866d78 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 14:56:03 +0100 Subject: [PATCH 35/41] Fix doc --- docs/value/data-oob.md | 58 +++++++++++++++++------------------------- 1 file changed, 24 insertions(+), 34 deletions(-) diff --git a/docs/value/data-oob.md b/docs/value/data-oob.md index 8ad325244..667016b9f 100644 --- a/docs/value/data-oob.md +++ b/docs/value/data-oob.md @@ -1,4 +1,4 @@ -from valuation.test_interface import datasets--- +--- title: Data-OOB --- @@ -43,7 +43,7 @@ from pydvl.valuation import DataOOBValuation, Dataset train, test = Dataset(...), Dataset(...) model = RandomForestClassifier(...) -model.fit(train.x, train.y) +model.fit(*train.data()) valuation = DataOOBValuation(model) valuation.fit(train) values = valuation.values() @@ -75,7 +75,7 @@ train, test = Dataset(...), Dataset(...) model = BaggingClassifier( estimator=KNeighborsClassifier(n_neighbors=10), n_estimators=20) -model.fit(train.x, train.y) +model.fit(*train.data()) valuation = DataOOBValuation(model) valuation.fit(train) values = valuation.values() @@ -88,50 +88,40 @@ low_values = values[:int(0.05*len(train))] # select lowest 5% ### Off-topic: When not to use bagging as the main model -Here are some guidelines for when bagging might not be beneficial: +Here are some guidelines for when bagging might unnecessarily increase +computational cost, or even be detrimental: -1. **Low-Variance Models**: Models like linear regression, support vector +1. **Low-variance models**: Models like linear regression, support vector machines, or other inherently stable algorithms typically have low variance. - In such cases, bagging may not provide significant benefits and could even - increase computational cost unnecessarily. + However, even these models can benefit from bagging in certain scenarios, + particularly with noisy data or when there are influential outliers. -2. **When the Model Is Already Highly Regularized**: If a model is regularized +2. **When the model is already highly regularized**: If a model is regularized (e.g., Lasso, Ridge, or Elastic Net), it is already tuned to avoid - overfitting and reduce variance. Bagging might be redundant or offer marginal - improvements at best. + overfitting and reduce variance, so bagging might not provide much of a + benefit for its high computational cost. -3. **When Data Is Limited**: Bagging works by creating multiple subsets of the +3. **When data is limited**: Bagging works by creating multiple subsets of the data via bootstrapping. If the dataset is too small, the bootstrap samples might overlap significantly or exclude important patterns, reducing the - effectiveness of the approach. + effectiveness of bagging. -4. **When Features Are Highly Correlated**: If features are highly correlated, +4. **When features are highly correlated**: If features are highly correlated, the individual models trained on different bootstrap samples may end up being - too similar. This limits the diversity among the models, reducing the - effectiveness of bagging. + too similar. -5. **For Models That Are Not Easily Overfitted**: If the base model doesn't - overfit the data (e.g., nearest neighbors or models with strong pruning - mechanisms like heavily regularized decision trees), bagging might not yield - substantial variance reduction. +5. **For inherently stable models**: Models that are naturally resistant to + changes in the training data (like nearest neighbors) may not benefit + significantly from bagging's variance reduction properties. -6. **When Computational Resources Are Limited**: Bagging increases computational - cost because it involves training multiple models. For computationally - expensive algorithms or resource-constrained scenarios, bagging may be - impractical. - -7. **When Interpretability Is Critical**: Bagging produces an ensemble of +6. **When interpretability is critical**: Bagging produces an ensemble of models, which makes the overall model less interpretable compared to a single - model. If interpretability is crucial for the application, bagging might not - be suitable. - -8. **When the Model Does Not Benefit from Resampling**: Some models, such as - nearest neighbors, depend heavily on the full dataset. Subsampling through - bootstrapping can reduce their performance instead of improving it. + model. There are however manye techniques to maintain interpretability, like + partial dependence plots. -9. **When Overfitting Is Already Controlled by Other Means**: If - cross-validation, regularization, or pruning is already effectively - controlling overfitting, bagging may offer little additional benefit. +7. **When the bias-variance trade-off favors bias reduction**: If the model's + error is primarily due to bias rather than variance, techniques that address + bias (like boosting) might be more appropriate than bagging. ## Transferring values From 27224eef14a4a0bd6c437b3c954b20ad6822f2ff Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 15:04:18 +0100 Subject: [PATCH 36/41] Update CHANGELOG.md --- CHANGELOG.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 399da078a..7abfc15d8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,8 @@ ### Added +- Added `run_removal_experiment` for easy removal experiments + [PR #636](https://github.com/aai-institute/pyDVL/pull/636) - Refactor Classwise Shapley valuation with the interfaces and sampler architecture [PR #616](https://github.com/aai-institute/pyDVL/pull/616) - Refactor KNN Shapley values with the new sampler architecture @@ -41,6 +43,8 @@ ### Fixed +- Fixed the analysis of the adult dataset in the Data-OOB notebook + [PR #636](https://github.com/aai-institute/pyDVL/pull/636) - Replace `np.float_` with `np.float64` and `np.alltrue` with `np.all`, as the old aliases are removed in NumPy 2.0 [PR #604](https://github.com/aai-institute/pyDVL/pull/604) @@ -67,6 +71,8 @@ - Dropped black, isort and pylint from the CI pipeline, in favour of ruff [PR #633](https://github.com/aai-institute/pyDVL/pull/633) - **Breaking Changes** + - Changed `DataOOBValuation` to only accept bagged models + [PR #636](https://github.com/aai-institute/pyDVL/pull/636) - Dropped support for python 3.8 after EOL [PR #633](https://github.com/aai-institute/pyDVL/pull/633) - Rename parameter `hessian_regularization` of `DirectInfluence` From 70d095619d9c8cbb075be49bba631c2082647eee Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 15:13:05 +0100 Subject: [PATCH 37/41] Fix docs build --- requirements-docs.txt | 5 +++-- src/pydvl/valuation/scorers/knn.py | 8 +++++--- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/requirements-docs.txt b/requirements-docs.txt index 662775a9f..60842299d 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,7 +1,8 @@ griffe==0.42.1 mike==2.0.0 markdown-captions -mkdocs==1.5.3 +mistune==3.1.0 +mkdocs==1.6.1 mkdocstrings[python]>=0.24 mkdocstrings-python==1.9.0 # There is a bug in version 0.8.0 with type hints @@ -13,7 +14,7 @@ mkdocs-gen-files mkdocs-git-revision-date-localized-plugin mkdocs-glightbox==0.3.7 mkdocs-literate-nav -mkdocs-material[imaging]==9.5.16 +mkdocs-material[imaging]==9.5.49 mkdocs-section-index==0.3.8 mkdocs-macros-plugin mknotebooks==0.8.0 diff --git a/src/pydvl/valuation/scorers/knn.py b/src/pydvl/valuation/scorers/knn.py index 4e8b209b1..eb19dde59 100644 --- a/src/pydvl/valuation/scorers/knn.py +++ b/src/pydvl/valuation/scorers/knn.py @@ -1,4 +1,6 @@ -"""Specialized scorer for k-nearest neighbors models.""" +""" +Specialized scorer for k-nearest neighbors models. +""" from typing import cast @@ -6,7 +8,7 @@ from numpy.typing import NDArray from sklearn.neighbors import KNeighborsClassifier -from pydvl.valuation import Dataset +from pydvl.valuation.dataset import Dataset from pydvl.valuation.scorers import SupervisedScorer, SupervisedScorerCallable @@ -14,7 +16,7 @@ class KNNClassifierScorer(SupervisedScorer): """Scorer for KNN classifier models based on the KNN likelihood. Typically, users will not create instances of this class directly but indirectly - by using `pydvl.valuation.utility.KNNUtility`. + by using [KNNUtility][pydvl.valuation.utility.KNNUtility]. Args: test_data: The test data to evaluate the model on. From 705713eb49bb343b8823a616c27f04a8d8d29c6b Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 15:38:33 +0100 Subject: [PATCH 38/41] Revert fe03352bb56f29ae4f4d9628bbb6fa2818324458 --- tests/test_results.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_results.py b/tests/test_results.py index 76ba578e8..f81b7b1ca 100644 --- a/tests/test_results.py +++ b/tests/test_results.py @@ -11,7 +11,7 @@ import pytest from pydvl.utils.status import Status -from pydvl.valuation import ValuationResult +from pydvl.value.result import ValuationResult @pytest.fixture From 20863edec1993eae3688a6247cce0aea50b2cc65 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 15:38:55 +0100 Subject: [PATCH 39/41] Move test to its correct location --- tests/{ => value}/test_results.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tests/{ => value}/test_results.py (100%) diff --git a/tests/test_results.py b/tests/value/test_results.py similarity index 100% rename from tests/test_results.py rename to tests/value/test_results.py From 12c75343ad25d00d89d64f67bfd0fdd54d0de9e9 Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 15:45:15 +0100 Subject: [PATCH 40/41] Faster CI exec of data oob notebook, and a missing import --- notebooks/data_oob.ipynb | 156 ++++++++++----------------------------- 1 file changed, 40 insertions(+), 116 deletions(-) diff --git a/notebooks/data_oob.ipynb b/notebooks/data_oob.ipynb index b9d31792a..b2d0d93d1 100644 --- a/notebooks/data_oob.ipynb +++ b/notebooks/data_oob.ipynb @@ -95,11 +95,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "tags": [ - "hide-output" - ] - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -110,6 +106,10 @@ } ], "source": [ + "n_est = 50 # Number of estimators for the random forest\n", + "max_samples = 0.2 # Use small bootstrap samples\n", + "n_jobs = 18\n", + "n_runs = 10\n", "train_size = 0.6\n", "train, test = load_adult_data(\n", " train_size=train_size, subsample=0.2, random_state=random_state\n", @@ -119,17 +119,16 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "tags": [ - "hide" - ] - }, + "metadata": {}, "outputs": [], "source": [ "if is_CI: # Subsample 1% of the data for faster testing\n", " train, test = load_adult_data(\n", " train_size=train_size, subsample=0.01, random_state=random_state\n", - " )" + " )\n", + " n_jobs = 2\n", + " n_runs = 1\n", + " n_est = 10" ] }, { @@ -141,12 +140,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "tags": [ - "hide-input" - ] - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -173,14 +168,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from imblearn.over_sampling import RandomOverSampler\n", "\n", - "n_est = 50\n", - "max_samples = 0.2 # Use small bootstrap samples\n", "model = RandomForestClassifier(\n", " n_estimators=n_est,\n", " max_samples=max_samples,\n", @@ -200,12 +193,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [ - "hide-input" - ] - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -225,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "tags": [ "hide-input" @@ -282,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -306,8 +295,6 @@ " random_state=random_state,\n", " )\n", "\n", - " # Tuning messes everything up if we oversample the dataset\n", - " # model = ThresholdTunerCV(classifier, n_splits=6, metric=f1_score, n_jobs=6)\n", " model.fit(*train.data())\n", " accuracy = model.score(*test.data())\n", " print(f\"Accuracy with {n_est} estimators: {accuracy:.2f}\")\n", @@ -338,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "slideshow": { "slide_type": "" @@ -399,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "tags": [ "hide-input" @@ -470,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -481,11 +468,6 @@ "from pydvl.valuation.methods.data_oob import point_wise_accuracy\n", "from pydvl.valuation.methods.random import RandomValuation\n", "\n", - "n_est = 50\n", - "max_samples = 0.2 # Small bootstrap samples\n", - "train_size = 0.6 # Fraction of data for training\n", - "random_state = 42\n", - "\n", "\n", "def make_data(random_state: int) -> tuple[Dataset, Dataset]:\n", " train, test = load_adult_data(train_size=train_size, random_state=random_state)\n", @@ -530,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": { "tags": [ "hide" @@ -541,7 +523,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 20/20 [01:01<00:00, 3.10s/%]\n" + "100%|██████████| 20/20 [01:29<00:00, 4.50s/%]\n" ] } ], @@ -551,15 +533,15 @@ " valuation_factories=[make_random, make_oob],\n", " utility_factory=make_utility, # for evaluation\n", " removal_percentages=removal_percentages,\n", - " n_runs=10,\n", - " n_jobs=18,\n", + " n_runs=n_runs,\n", + " n_jobs=n_jobs,\n", " random_state=random_state,\n", ")" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": { "slideshow": { "slide_type": "" @@ -628,18 +610,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 20/20 [06:19<00:00, 18.96s/%] \n" - ] - } - ], + "outputs": [], "source": [ + "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "\n", @@ -664,34 +639,16 @@ " utility_factory=make_knn_utility,\n", " valuation_factories=[make_random, make_oob_knn],\n", " removal_percentages=removal_percentages,\n", - " n_runs=10,\n", - " n_jobs=18,\n", + " n_runs=n_runs,\n", + " n_jobs=n_jobs,\n", ")" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "tags": [ - "hide-input", - "invertible-output" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n", "\n", @@ -739,26 +696,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found cached file: adult_data_raw.pkl.\n", - "Accuracy when ~1.0% of estimators always predict class 0: 0.2498\n", - "Accuracy when ~50.0% of estimators always predict class 0: 0.7502\n", - "Accuracy when ~99.0% of estimators always predict class 0: 0.7502\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "from sklearn.ensemble import BaggingClassifier\n", "from support.common import ConstantBinaryClassifier\n", "\n", "train, test = load_adult_data(\n", @@ -770,7 +711,7 @@ "for p in probs:\n", " model = BaggingClassifier(\n", " ConstantBinaryClassifier(p, random_state),\n", - " n_estimators=100,\n", + " n_estimators=n_est,\n", " max_samples=1.0,\n", " random_state=random_state,\n", " )\n", @@ -787,26 +728,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=[15, 5])\n", "\n", From 61a6c1560cfa09ed8566285461c3a14f300d7d2b Mon Sep 17 00:00:00 2001 From: Miguel de Benito Delgado Date: Wed, 15 Jan 2025 16:06:38 +0100 Subject: [PATCH 41/41] Adapt notebook to new ValuationResult --- notebooks/least_core_basic_new.ipynb | 124 ++++++++++++++++++++++----- 1 file changed, 101 insertions(+), 23 deletions(-) diff --git a/notebooks/least_core_basic_new.ipynb b/notebooks/least_core_basic_new.ipynb index 2872a61e7..4582e7f03 100644 --- a/notebooks/least_core_basic_new.ipynb +++ b/notebooks/least_core_basic_new.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "f6656599", "metadata": { "tags": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "a6c212a517774f6b", "metadata": {}, "outputs": [], @@ -130,12 +130,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3155c98e", "metadata": {}, "outputs": [], "source": [ - "%autoreload\n", "from pydvl.reporting.plots import shaded_mean_std\n", "from pydvl.reporting.scores import compute_removal_score\n", "from pydvl.valuation import (\n", @@ -164,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "2e916a67", "metadata": {}, "outputs": [], @@ -180,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "4b128695", "metadata": {}, "outputs": [], @@ -199,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "ffce8661", "metadata": {}, "outputs": [], @@ -209,10 +208,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "270c1bf3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training accuracy: 86.25%\n", + "Testing accuracy: 70.00%\n" + ] + } + ], "source": [ "model.fit(*full_train_data.data())\n", "print(f\"Training accuracy: {100 * model.score(*full_train_data.data()):0.2f}%\")\n", @@ -221,10 +229,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "e539e5ed", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training accuracy: 100.00%\n", + "Testing accuracy: 47.89%\n" + ] + } + ], "source": [ "model.fit(*small_train_data.data())\n", "print(f\"Training accuracy: {100 * model.score(*small_train_data.data()):0.2f}%\")\n", @@ -243,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "98a22915", "metadata": {}, "outputs": [], @@ -260,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "aa91a124", "metadata": { "editable": true, @@ -271,7 +288,22 @@ "hide-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8ba1fa0f387b4c2084af5d0994abd1bd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1023 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "_ = shaded_mean_std(\n", " errors_df,\n", @@ -375,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "48bccf93", "metadata": { "editable": true, @@ -387,7 +447,20 @@ "invertible-output" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "mean_std_values_df = values_df.drop(columns=\"budget\").agg([\"mean\", \"std\"])\n", "df = pd.concat([exact_values_df, mean_std_values_df])\n", @@ -399,7 +472,13 @@ " ylabel=\"Value\",\n", " color=[\"dodgerblue\", \"indianred\"],\n", " y=[\"exact_value\", \"mean\"],\n", - " yerr=[exact_values_df.loc[\"exact_value_stderr\"], mean_std_values_df.loc[\"std\"]],\n", + " yerr=[\n", + " np.sqrt(\n", + " exact_values_df.loc[\"exact_value_variances\"]\n", + " / exact_values_df.loc[\"exact_value_counts\"]\n", + " ),\n", + " mean_std_values_df.loc[\"std\"],\n", + " ],\n", ")\n", "plt.legend([\"Exact\", \"Monte Carlo\"])\n", "plt.show()" @@ -433,7 +512,6 @@ ")\n", "utility = ModelUtility(model=model, scorer=scorer)\n", "\n", - "\n", "method_names = [\"Random\", \"Least Core\"]\n", "removal_percentages = np.arange(0, 0.41, 0.05)" ]