From 6d8bc687ff64b603bb09f57430f8d367fac42e83 Mon Sep 17 00:00:00 2001 From: Kevin Klein <7267523+kklein@users.noreply.github.com> Date: Fri, 2 Aug 2024 10:35:12 +0200 Subject: [PATCH 1/9] Move fixed propensity model to utils. (#72) * Move fixed propensity model to utils. * Add changelog entry. * Add tests. * Fix typo. * Fix class reference. --- CHANGELOG.rst | 6 ++- docs/examples/example_propensity.ipynb | 66 ++++++++++---------------- metalearners/utils.py | 32 +++++++++++++ tests/test_utils.py | 61 +++++++++++++++++++++++- 4 files changed, 120 insertions(+), 45 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index bd987b68..44f1abd4 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -12,7 +12,9 @@ Changelog **New features** -* Added :meth:`metalearners.metalearner.MetaLearner.init_params`. +* Add :meth:`metalearners.metalearner.MetaLearner.init_params`. + +* Add :class:`metalearners.utils.FixedBinaryPropensity`. 0.8.0 (2024-07-22) @@ -20,7 +22,7 @@ Changelog **New features** -* Added :meth:`metalearners.metalearner.MetaLearner.fit_all_nuisance` and +* Add :meth:`metalearners.metalearner.MetaLearner.fit_all_nuisance` and :meth:`metalearners.metalearner.MetaLearner.fit_all_treatment`. * Add optional ``store_raw_results`` and ``store_results`` parameters to :class:`metalearners.grid_search.MetaLearnerGridSearch`. diff --git a/docs/examples/example_propensity.ipynb b/docs/examples/example_propensity.ipynb index 3a356351..7fc280f8 100644 --- a/docs/examples/example_propensity.ipynb +++ b/docs/examples/example_propensity.ipynb @@ -59,8 +59,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Creating our own estimator\n", - "--------------------------\n", + "Using a dummy estimator\n", + "-----------------------\n", "\n", "In this tutorial we will assume that we know that all observations were assigned to the\n", "treatment with a fixed probability of 0.3, which is close to the fraction of the observations\n", @@ -89,43 +89,10 @@ "dataset, we just use it for illustrational purposes.\n", "```\n", "\n", - "Now we can define our custom ``sklearn``-like classifier. We recommend inheriting from\n", - "the ``sklearn`` base classes and following the rules explained in the\n", - "[sklearn documentation](https://scikit-learn.org/stable/developers/develop.html) to avoid\n", - "having to define helper functions and ensure the correct functionality of the ``metalearners``\n", - "library." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "vscode": { - "languageId": "plaintext" - } - }, - "outputs": [], - "source": [ - "from sklearn.base import BaseEstimator, ClassifierMixin\n", - "from typing import Any\n", - "from typing_extensions import Self\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "\n", - "class FixedPropensityModel(ClassifierMixin, BaseEstimator):\n", - " def __init__(self, propensity_score: float) -> None:\n", - " self.propensity_score = propensity_score\n", - "\n", - " def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:\n", - " self.classes_ = np.unique(y.to_numpy()) # sklearn requires this\n", - " return self\n", - "\n", - " def predict(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:\n", - " return np.argmax(self.predict_proba(X), axis=1)\n", - "\n", - " def predict_proba(self, X: pd.DataFrame) -> np.ndarray[Any, Any]:\n", - " return np.full((len(X), 2), [1 - self.propensity_score, self.propensity_score])" + "Now we can use a custom ``sklearn``-like classifier: {class}`~metalearners.utils.FixedBinaryPropensity`.\n", + "The latter can be used like any ``sklearn`` classifier but will always return the same propensity,\n", + "independently of the observed covariates. This propensity has to be provided at initialization via the\n", + "``propensity_score`` parameter." ] }, { @@ -149,11 +116,12 @@ "outputs": [], "source": [ "from metalearners import RLearner\n", + "from metalearners.utils import FixedBinaryPropensity\n", "from lightgbm import LGBMRegressor\n", "\n", "rlearner = RLearner(\n", " nuisance_model_factory=LGBMRegressor,\n", - " propensity_model_factory=FixedPropensityModel,\n", + " propensity_model_factory=FixedBinaryPropensity,\n", " treatment_model_factory=LGBMRegressor,\n", " nuisance_model_params={\"verbose\": -1},\n", " propensity_model_params={\"propensity_score\": 0.3},\n", @@ -205,10 +173,24 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/metalearners/utils.py b/metalearners/utils.py index 765e6c1d..587bef67 100644 --- a/metalearners/utils.py +++ b/metalearners/utils.py @@ -1,8 +1,14 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause +from typing import Any + import numpy as np +import pandas as pd +from sklearn.base import BaseEstimator, ClassifierMixin +from typing_extensions import Self +from metalearners._typing import Matrix, Vector from metalearners.drlearner import DRLearner from metalearners.metalearner import MetaLearner from metalearners.rlearner import RLearner @@ -73,3 +79,29 @@ def simplify_output(tensor: np.ndarray) -> np.ndarray: if n_outputs == 2: return tensor[:, :, 1].reshape(n_obs, n_variants) return tensor + + +class FixedBinaryPropensity(ClassifierMixin, BaseEstimator): + """Binary classifier propensity dummy model which outputs a fixed propensity, + independently of covariates.""" + + def __init__(self, propensity_score: float) -> None: + if not 0 <= propensity_score <= 1: + raise ValueError( + f"Expected a propensity score between 0 and 1 but got {propensity_score}." + ) + self.propensity_score = propensity_score + + def fit(self, X: Matrix, y: Vector) -> Self: + self.classes_ = np.unique(y) # sklearn requires this + if (n_classes := len(self.classes_)) > 2: + raise ValueError( + f"FixedBinaryPropensityModel only supports binary outcomes but {n_classes} were provided ." + ) + return self + + def predict(self, X: Matrix) -> np.ndarray[Any, Any]: + return np.argmax(self.predict_proba(X), axis=1) + + def predict_proba(self, X: pd.DataFrame) -> np.ndarray[Any, Any]: + return np.full((len(X), 2), [1 - self.propensity_score, self.propensity_score]) diff --git a/tests/test_utils.py b/tests/test_utils.py index c3f334ec..d88b5d14 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,11 +2,16 @@ # SPDX-License-Identifier: BSD-3-Clause import numpy as np +import pandas as pd import pytest from lightgbm import LGBMRegressor from metalearners.metalearner import MetaLearner -from metalearners.utils import metalearner_factory, simplify_output +from metalearners.utils import ( + FixedBinaryPropensity, + metalearner_factory, + simplify_output, +) @pytest.mark.parametrize("prefix", ["T"]) @@ -52,3 +57,57 @@ def test_simplify_output(input, expected): def test_simplify_output_raises(input): with pytest.raises(ValueError, match="needs to be 3-dimensional"): simplify_output(input) + + +@pytest.mark.parametrize("use_pd", [True, False]) +def test_fixed_binary_propensity(use_pd): + propensity_score = 0.3 + dominant_class = propensity_score >= 0.5 + + model = FixedBinaryPropensity(propensity_score=propensity_score) + + n_samples = 5 + X_train = np.ones((n_samples, 5)) + y_train = np.ones(n_samples) + if use_pd: + X_train = pd.DataFrame(X_train) + y_train = pd.Series(y_train) + + model.fit(X_train, y_train) + + n_test_samples = 3 + X_test = np.zeros(n_test_samples) + + class_predictions = model.predict(X_test) + assert np.array_equal( + class_predictions, np.array(np.ones(n_test_samples) * dominant_class) + ) + + probability_estimates = model.predict_proba(X_test) + assert np.array_equal( + probability_estimates, + np.column_stack( + ( + np.ones(n_test_samples) * (1 - propensity_score), + np.ones(n_test_samples) * propensity_score, + ) + ), + ) + + +@pytest.mark.parametrize("propensity_score", [-1, 100, 1.1]) +def test_fixed_binary_propensity_not_a_propbability(propensity_score): + with pytest.raises(ValueError, match="between 0 and 1 but got"): + FixedBinaryPropensity(propensity_score=propensity_score) + + +def test_fixed_binary_propensity_non_binary(): + propensity_score = 0.3 + + model = FixedBinaryPropensity(propensity_score=propensity_score) + + n_samples = 5 + X_train = np.ones((n_samples, 5)) + y_train = np.fromiter(range(n_samples), dtype=int) + with pytest.raises(ValueError, match="only supports binary outcomes"): + model.fit(X_train, y_train) From b0be07cb2e165bbd4f561ccf91c50beb23b76208 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Francesc=20Mart=C3=AD=20Escofet?= <154450563+FrancescMartiEscofetQC@users.noreply.github.com> Date: Fri, 2 Aug 2024 20:52:20 +0200 Subject: [PATCH 2/9] ONNX Interface (#39) * Draft Onnx TLearner * Add tests * Fix parameters to avoid some warnings * Add test for categorical features * Rename variable * Implement onnx XLearner and tests * Refactor * Fix import location * Implement onnx RLearner and tests * Fix atol * Remove skip as fixed * Remove old code * Refactor to not overwrite input * Update metalearners/_utils.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/_utils.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/_utils.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/_utils.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/_utils.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/_utils.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Consistent docs * Add return type * Implement onnx DRLearner and tests * Increase radius to avoid nan * Move data generation to fixture * Abstract method and raise error for SLearner * Fix * Add docstrings * Fix spacing * Update metalearners/rlearner.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/rlearner.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Fix variable names * Update metalearners/metalearner.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/metalearner.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/metalearner.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Update metalearners/metalearner.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Fix variable names * Reuse ONNX_PROBABILITIES_OUTPUTS * Rename _validate_feature_set_all and error * Update tests/test_rlearner.py Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> * Use itertools.repeat * Implement necessary_onnx_models * Raise error multiclass * Fix pchs * Add docstring * Add warning in docstring and build_onnx * Change import * Add tutorial * Modify error msg * Update CHANGELOG * Update lock file * Update metalearners/drlearner.py Co-authored-by: Christian Bourjau * Use const instead of constant * Update docs/examples/example_onnx.ipynb Co-authored-by: Christian Bourjau * Only check spox installed as depends on onnx * Avoid indexing * Fix XLearner float value * Make methods private * Clarify what _build_onnx does * Fix windows errors * Typo * Refactor code * Fix pchs * Add note about lightgbm * Fix text * Update docs/examples/example_onnx.ipynb Co-authored-by: Christian Bourjau * Update docs/examples/example_onnx.ipynb Co-authored-by: Christian Bourjau * Edit. * Add netron visualization. * Remove state. * Add return type annotation. * Adapt phrasing of doc string and error message. * Use md instead of html for image. --------- Co-authored-by: Kevin Klein <7267523+kklein@users.noreply.github.com> Co-authored-by: Christian Bourjau Co-authored-by: kklein --- CHANGELOG.rst | 5 + docs/examples/example_onnx.ipynb | 380 ++ docs/examples/index.rst | 1 + docs/examples/onnx_netron.png | Bin 0 -> 73943 bytes metalearners/_utils.py | 67 +- metalearners/drlearner.py | 50 + metalearners/metalearner.py | 106 +- metalearners/rlearner.py | 51 +- metalearners/slearner.py | 12 + metalearners/tlearner.py | 64 +- metalearners/xlearner.py | 97 +- pixi.lock | 6629 ++++++++++++++++++------------ pixi.toml | 4 + tests/conftest.py | 145 + tests/test_drlearner.py | 117 +- tests/test_metalearner.py | 8 + tests/test_rlearner.py | 114 +- tests/test_tlearner.py | 141 + tests/test_xlearner.py | 136 + 19 files changed, 5536 insertions(+), 2591 deletions(-) create mode 100644 docs/examples/example_onnx.ipynb create mode 100644 docs/examples/onnx_netron.png create mode 100644 tests/test_tlearner.py create mode 100644 tests/test_xlearner.py diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 44f1abd4..97802eb5 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -16,6 +16,11 @@ Changelog * Add :class:`metalearners.utils.FixedBinaryPropensity`. +* Added ``_build_onnx`` to :class:`metalearners.MetaLearner` abstract class and implement it + for :class:`metalearners.TLearner`, :class:`metalearners.XLearner`, :class:`metalearners.RLearner` + and :class:`metalearners.DRLearner`. + +* Added ``_necessary_onnx_models`` to :class:`metalearners.MetaLearner`. 0.8.0 (2024-07-22) ------------------ diff --git a/docs/examples/example_onnx.ipynb b/docs/examples/example_onnx.ipynb new file mode 100644 index 00000000..5e2a00fd --- /dev/null +++ b/docs/examples/example_onnx.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Converting a MetaLearner to ONNX\n", + "\n", + "```{warning}\n", + "This is a experimental feature which is not subject to deprecation cycles. Use\n", + "it at your own risk!\n", + "```\n", + "\n", + "ONNX is an open standard for representing trained machine learning models.\n", + "By converting a Metalearner\n", + "into an ONNX model, it becomes easier to leverage the model in different environments without\n", + "needing to worry about compatibility or performance issues.\n", + "\n", + "In particular, this conversion also allows models to be run on a variety of hardware setups. Also, ONNX\n", + "models are optimized for efficient computation, enabling faster inference compared to\n", + "the Python interface.\n", + "\n", + "For more information about ONNX, you can check the ONNX [website](https://onnx.ai/).\n", + "\n", + "In this example we will show how most MetaLearners can be converted to ONNX.\n", + "\n", + "## Installation\n", + "\n", + "In order to convert a MetaLearner to ONNX, we first need to install the following packages:\n", + "\n", + "* [onnx](https://github.com/onnx/onnx)\n", + "* [onnxmltools](https://github.com/onnx/onnxmltools)\n", + "* [onnxruntime](https://github.com/microsoft/onnxruntime)\n", + "* [spox](https://github.com/Quantco/spox)\n", + "\n", + "We can do so either via conda and conda-forge:\n", + "\n", + "```console\n", + "$ conda install onnx onnxmltools onnxruntime spox -c conda-forge\n", + "```\n", + "\n", + "or via pip and PyPI\n", + "\n", + "```console\n", + "$ pip install onnx onnxmltools onnxruntime spox\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage\n", + "\n", + "```{warning}\n", + "It is important to notice that this method only works for {class}`~metalearners.TLearner`,\n", + "{class}`~metalearners.XLearner`, {class}`~metalearners.RLearner` and {class}`~metalearners.DRLearner`.\n", + "Converting an {class}`~metalearners.SLearner` is highly dependent on the fact that the base\n", + "model supports categorical variables or not and it is not implemented yet. \n", + "```\n", + "\n", + "### Loading the data\n", + "\n", + "Just like in our {ref}`example on estimating CATEs with a MetaLearner `,\n", + "we will first load some experiment data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "from git_root import git_root\n", + "\n", + "df = pd.read_csv(git_root(\"data/learning_mindset.zip\"))\n", + "outcome_column = \"achievement_score\"\n", + "treatment_column = \"intervention\"\n", + "feature_columns = [\n", + " column for column in df.columns if column not in [outcome_column, treatment_column]\n", + "]\n", + "categorical_feature_columns = [\n", + " \"ethnicity\",\n", + " \"gender\",\n", + " \"frst_in_family\",\n", + " \"school_urbanicity\",\n", + " \"schoolid\",\n", + "]\n", + "# Note that explicitly setting the dtype of these features to category\n", + "# allows both lightgbm as well as shap plots to\n", + "# 1. Operate on features which are not of type int, bool or float\n", + "# 2. Correctly interpret categoricals with int values to be\n", + "# interpreted as categoricals, as compared to ordinals/numericals.\n", + "for categorical_feature_column in categorical_feature_columns:\n", + " df[categorical_feature_column] = df[categorical_feature_column].astype(\"category\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've loaded the experiment data, we can train a MetaLearner.\n", + "\n", + "\n", + "### Training a MetaLearner\n", + "\n", + "Again, mirroring our {ref}`example on estimating CATEs with a MetaLearner\n", + "`, we can train an\n", + "{class}`~metalearners.XLearner` as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from metalearners import XLearner\n", + "from lightgbm import LGBMRegressor, LGBMClassifier\n", + "\n", + "xlearner = XLearner(\n", + " nuisance_model_factory=LGBMRegressor,\n", + " propensity_model_factory=LGBMClassifier,\n", + " treatment_model_factory=LGBMRegressor,\n", + " is_classification=False,\n", + " n_variants=2,\n", + " nuisance_model_params={\"n_estimators\": 5, \"verbose\": -1},\n", + " propensity_model_params={\"n_estimators\": 5, \"verbose\": -1},\n", + " treatment_model_params={\"n_estimators\": 5, \"verbose\": -1},\n", + " n_folds=2,\n", + ")\n", + "\n", + "xlearner.fit(\n", + " X=df[feature_columns],\n", + " y=df[outcome_column],\n", + " w=df[treatment_column],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{note}\n", + "In this example, we used all ``lightgbm`` models because these are the only type of models\n", + "that we managed to get to work with categorical encodings from ``pandas``\n", + "while also being convertible to ONNX. Other ``sklearn`` models which support categoricals such as\n", + "``HistGradientBoostingRegressor`` or ``xgboost`` models do not have support for them\n", + "in their conversion to ONNX. See [this issue](https://github.com/onnx/sklearn-onnx/issues/1051)\n", + "and [this comment](https://github.com/onnx/onnxmltools/issues/469#issuecomment-1993880910).\n", + "```\n", + "\n", + "### Converting the base models to ONNX\n", + "\n", + "Before being able to convert the MetaLearner to ONXX we need to manually convert the necessary\n", + "base models for the prediction. To get the necessary base models that need to be\n", + "converted we can use {meth}`~metalearners.metalearner.MetaLearner._necessary_onnx_models`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "necessary_models = xlearner._necessary_onnx_models()\n", + "necessary_models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that we need to convert the ``\"propensity_model\"``, the ``\"control_effect_model\"``\n", + "and the ``\"treatment_effect_model\"``. We can do this with the following code where we\n", + "use the ``convert_lightgbm`` function from the ``onnxmltools`` package.\n", + "\n", + "```{note}\n", + "It is important to know that for classifiers we need to pass the ``zipmap=False`` option. This\n", + "is required so the output probabilities are a Matrix and not a list of dictionaries.\n", + "In the case of using a ``sklearn`` model and using the ``convert_sklearn`` function, this\n", + "option needs to be specified with the ``options={\"zipmap\": False}`` parameter.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import onnx\n", + "from onnxmltools import convert_lightgbm\n", + "from onnxconverter_common.data_types import FloatTensorType\n", + "\n", + "onnx_models: dict[str, list[onnx.ModelProto]] = {}\n", + "\n", + "for model_kind, models in necessary_models.items():\n", + " onnx_models[model_kind] = []\n", + " for model in models:\n", + " onnx_models[model_kind].append(\n", + " convert_lightgbm(\n", + " model,\n", + " initial_types=[(\"X\", FloatTensorType([None, len(feature_columns)]))],\n", + " zipmap=False,\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can call {meth}`~metalearners.metalearner.MetaLearner._build_onnx` which combines\n", + "the the converted ONNX base models into a single ONNX model.\n", + "This combined model has a single 2D input ``\"X\"`` and a single output named ``\"tau\"``.\n", + "The output name can be changed using the ``output_name`` parameter. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "onnx_model = xlearner._build_onnx(onnx_models)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can explore the input and output of the model and see that it expects a matrix with 11\n", + "columns and returns a three dimensional tensor with shape ``(..., 1, 1)`` which is expected\n", + "as there is only two treatment variants and one outcome as it is a regression problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"ONNX model input: \", onnx_model.graph.input)\n", + "print(\"ONNX model output: \", onnx_model.graph.output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also visualize the ONNX model with, e.g. [netron](https://netron.app/):\n", + "![](onnx_netron.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```{note}\n", + "We noticed that ``convert_lightgbm`` does not support using native pandas categorical variables.\n", + "This is because a numpy array needs to be passed when predicting, for this reason we need to\n", + "use the categories codes in the input matrix. For more context on this issue see\n", + "[here](https://github.com/onnx/onnxmltools/issues/309) and [here](https://github.com/microsoft/LightGBM/issues/5162).\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "X_onnx = df[feature_columns].copy(deep=True)\n", + "for c in categorical_feature_columns:\n", + " X_onnx[c] = df[c].cat.codes\n", + "X_onnx = X_onnx.to_numpy(np.float32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can finally use ``onnxruntime`` to perform predictions using our model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import onnxruntime as rt\n", + "\n", + "sess = rt.InferenceSession(\n", + " onnx_model.SerializeToString(), providers=rt.get_available_providers()\n", + ")\n", + "\n", + "(pred_onnx,) = sess.run(\n", + " [\"tau\"],\n", + " {\"X\": X_onnx},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "onnx.save_model(onnx_model, \"model.onnx\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We recommend always doing a final check with some data that the CATEs predicted by the python\n", + "implementation and the ONNX model are the same (up to some tolerance). This can be done with\n", + "the following code:\n", + "\n", + "```{note}\n", + "We have to use the data as if it was out-of-sample with ``oos_method = True`` as when we\n", + "converted the base models we used the ``_overall_estimtor``.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.testing.assert_allclose(\n", + " xlearner.predict(df[feature_columns], True, \"overall\"), pred_onnx, atol=1e-6\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further comments\n", + "\n", + "* It would be desirable to work with ``DoubleTensorType`` instead of ``FloatTensorType``\n", + " but we have noted that some converters have issues with it. We recommend try using\n", + " ``DoubleTensorType`` but switching to ``FloatTensorType`` in case the converter fails.\n", + "* In the case the final assertion fails we recommend first testing that the different\n", + " base models have the same base outputs as we discovered some issues with some converters,\n", + " see [this issue](https://github.com/onnx/sklearn-onnx/issues/1117) and\n", + " [this issue](https://github.com/onnx/sklearn-onnx/issues/1116)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/index.rst b/docs/examples/index.rst index ad950311..429c4ce3 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -14,3 +14,4 @@ Examples Generating data Estimating CATEs for survival analysis What if I know the propensity score? + Converting a MetaLearner to ONNX diff --git a/docs/examples/onnx_netron.png b/docs/examples/onnx_netron.png new file mode 100644 index 0000000000000000000000000000000000000000..56efd3c772106d2092ab58170592619d47ac3add GIT binary patch literal 73943 zcmeFZWmHt}8#X#KFvK8=NGJv)NDV0x0s@1Av@}SogfvJuGbk2HhXTSVNQZ!Qt4K*m zill@{ch~!DK!5-Bo^?K-FUPeMnc4B|`?>SFuX{gJy(LSDVnD%QFiLqjX>}Nk2myl; zlarBvPh9$|R>2Rrle+A6Sa!>~N$|@(v)l6K%E~Yd_&*s8{=^bS3>^agGJwC})Ud}e zQt*=+{8c5uVMO339QvMs_(q+-)78Q()KJMZte<&7AL|-ED2`oJ8HlnGTK+1^hsA zbK`Nl!ej4vpZBtehzKtqKQBK&H#maZ$-~b1t~*Ay#{?Ms%q(OW}_=@ zX$!;y?jdoR@5+^H2PgbLzWVQ$|8wf?|4tPa=KJ3>|L2?k`%En-GsheDw&0%568~+@ z(aHb&<4EiO@N?AgkYkh>HJ}VVPZl3fN|VQ zWM_Jgw%uFz^4-7ky7%?Kn}IhH3Co^YSDdCx=AGnQoK#9=-lETt!-=V|u%mxa3Iqiy zLvH#&7=jFqJNoAq4vRYr|L-t7f(A{+UVVKc?Bu@_snArz7ykPVOMoHCh*2NfN=*Of 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+++ b/metalearners/_utils.py @@ -1,6 +1,7 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause +import warnings from collections.abc import Callable from inspect import signature from operator import le, lt @@ -18,7 +19,7 @@ _PREDICT = "predict" _PREDICT_PROBA = "predict_proba" - +ONNX_PROBABILITIES_OUTPUTS = ["probabilities", "output_probability"] default_rng = np.random.default_rng() @@ -499,3 +500,67 @@ def default_metric(predict_method: PredictMethod) -> str: if predict_method == _PREDICT_PROBA: return "neg_log_loss" return "neg_root_mean_squared_error" + + +def check_onnx_installed() -> None: + """Ensures that ``onnx`` is available.""" + try: + import onnx # noqa F401 + except ImportError: + raise ImportError( + "onnx is not installed. Please install onnx to use this feature." + ) + + +def check_spox_installed() -> None: + """Ensures that ``spox`` is available.""" + try: + import spox # noqa F401 + except ImportError: + raise ImportError( + "spox is not installed. Please install spox to use this feature." + ) + + +def infer_dtype_and_shape_onnx(tensor) -> tuple[np.dtype, tuple]: + """Returns the ``np.dtype`` and shape of an ONNX tensor.""" + check_onnx_installed() + import onnx + + dtype = onnx.helper.tensor_dtype_to_np_dtype(tensor.type.tensor_type.elem_type) + shape = tuple( + d.dim_value if d.HasField("dim_value") else None + for d in tensor.type.tensor_type.shape.dim + ) + return dtype, shape + + +def infer_probabilities_output(model) -> tuple[int, str]: + """Returns the index and name of the output which contains the probabilities outcome + in a ONNX classifier.""" + check_onnx_installed() + for i, output in enumerate(model.graph.output): + if output.name in ONNX_PROBABILITIES_OUTPUTS: + return i, output.name + raise ValueError("No probabilities output was found.") + + +def infer_input_dict(model) -> dict: + """Returns a dict where the keys are the input names of the model and the values are + ``spox.Var`` with the corresponding shape and type.""" + check_spox_installed() + from spox import Tensor, Var, argument + + input_dict: dict[str, Var] = {} + for input_tensor in model.graph.input: + input_dtype, input_shape = infer_dtype_and_shape_onnx(input_tensor) + input_dict[input_tensor.name] = argument(Tensor(input_dtype, input_shape)) + + return input_dict + + +def warning_experimental_feature(function_name: str) -> None: + warnings.warn( + f"{function_name} is an experimental feature. Use it at your own risk!", + stacklevel=2, + ) diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index 1a29487e..8ec4b7e3 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -2,6 +2,8 @@ # SPDX-License-Identifier: BSD-3-Clause +from collections.abc import Mapping, Sequence + import numpy as np from joblib import Parallel, delayed from typing_extensions import Any, Self @@ -18,12 +20,16 @@ _ScikitModel, ) from metalearners._utils import ( + check_spox_installed, clip_element_absolute_value_to_epsilon, + copydoc, get_one, get_predict, get_predict_proba, index_matrix, + infer_input_dict, validate_valid_treatment_variant_not_control, + warning_experimental_feature, ) from metalearners.cross_fit_estimator import OVERALL, CrossFitEstimator from metalearners.metalearner import ( @@ -38,6 +44,7 @@ _fit_cross_fit_estimator_joblib, _ModelSpecifications, _ParallelJoblibSpecification, + get_overall_estimators, ) _EPSILON = 1e-09 @@ -402,3 +409,46 @@ def _pseudo_outcome( @property def init_args(self) -> dict[str, Any]: return super().init_args | {"adaptive_clipping": self.adaptive_clipping} + + def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]: + return { + TREATMENT_MODEL: get_overall_estimators( + self._treatment_models[TREATMENT_MODEL] + ) + } + + @copydoc(MetaLearner._build_onnx, sep="") + def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"): + """In the DRLearner case, the necessary models are: + + * ``"treatment_model"`` + """ + warning_experimental_feature("_build_onnx") + check_spox_installed() + import spox.opset.ai.onnx.v21 as op + from onnx.checker import check_model + from spox import Var, build, inline + + self._validate_feature_set_none() + self._validate_onnx_models(models, set(self._necessary_onnx_models().keys())) + + input_dict = infer_input_dict(models[TREATMENT_MODEL][0]) + + treatment_output_name = models[TREATMENT_MODEL][0].graph.output[0].name + + tau_hat: list[Var] = [] + for m in models[TREATMENT_MODEL]: + tau_hat_tv = inline(m)(**input_dict)[treatment_output_name] + tau_hat_tv = op.unsqueeze(tau_hat_tv, axes=op.constant(value_int=2)) + if self.is_classification: + if self._supports_multi_class(): + raise ValueError( + "ONNX conversion is not implemented for a multi-class output." + ) + tau_hat_tv = op.concat([op.neg(tau_hat_tv), tau_hat_tv], axis=-1) + tau_hat.append(tau_hat_tv) + + cate = op.concat(tau_hat, axis=1) + final_model = build(input_dict, {output_name: cate}) + check_model(final_model, full_check=True) + return final_model diff --git a/metalearners/metalearner.py b/metalearners/metalearner.py index 5bed116a..44b3aea9 100644 --- a/metalearners/metalearner.py +++ b/metalearners/metalearner.py @@ -2,7 +2,7 @@ # SPDX-License-Identifier: BSD-3-Clause from abc import ABC, abstractmethod -from collections.abc import Callable, Collection, Sequence +from collections.abc import Callable, Collection, Mapping, Sequence, Set from copy import deepcopy from dataclasses import dataclass from typing import Any, TypedDict @@ -27,6 +27,7 @@ _ScikitModel, ) from metalearners._utils import ( + ONNX_PROBABILITIES_OUTPUTS, default_metric, index_matrix, validate_model_and_predict_method, @@ -137,6 +138,17 @@ def _validate_n_folds_synchronize(n_folds: dict[str, int]) -> None: raise ValueError("Need at least two folds to use synchronization.") +def get_overall_estimators(cfes: list[CrossFitEstimator]) -> list[_ScikitModel]: + overall_estimators = [] + for cfe in cfes: + if cfe._overall_estimator is None: + raise ValueError( + "To use this functionality the overall models need to be fitted." + ) + overall_estimators.append(cfe._overall_estimator) + return overall_estimators + + def _evaluate_model_kind( cfes: Sequence[CrossFitEstimator], Xs: Sequence[Matrix], @@ -1172,6 +1184,98 @@ def init_args(self) -> dict[str, Any]: "random_state": self.random_state, } + def _validate_onnx_models( + self, models: Mapping[str, Sequence], necessary_models: Set[str] + ): + """Validates that the converted ONNX models are correct. + + Specifically it validates the following: + * The ``necessary_models`` are equal to the keys of the ``models``` dictionary + * The number of models for each model matches the cardinality in the MetaLearner + * All ONNX have the same input format + * The models with ``"predict"`` as ``predict_method`` have only one output + * The models with ``"predict_proba"`` as ``predict_method`` have a probabilities output + """ + if set(models.keys()) != necessary_models: + raise ValueError( + f"necessary_model ({necessary_models}) should equal to keys present in models dictionary." + ) + specifications = ( + self.nuisance_model_specifications() | self.treatment_model_specifications() + ) + input_format = None + for model_kind in necessary_models: + model_specification = specifications[model_kind] + if len(models[model_kind]) != model_specification["cardinality"](self): + raise ValueError( + f"{model_kind} cardinality does not match the expected cardinality." + ) + predict_method = model_specification["predict_method"](self) + for model_index, model in enumerate(models[model_kind]): + if input_format is None: + input_format = model.graph.input + elif input_format != model.graph.input: + raise ValueError( + "Some ONNX model has a different input, check that all models have " + "the same input format." + ) + if predict_method == "predict" and len(model.graph.output) != 1: + raise ValueError( + f"ONNX {model_kind} with index {model_index} has {len(model.graph.output)} " + "outputs and should have only one." + ) + elif predict_method == "predict_proba": + found_probabilities = False + for output in model.graph.output: + if output.name in ONNX_PROBABILITIES_OUTPUTS: + found_probabilities = True + if not found_probabilities: + raise ValueError( + f"ONNX {model_kind} model with index {model_index} needs to have an output " + f"with name in {ONNX_PROBABILITIES_OUTPUTS}." + ) + + def _validate_feature_set_none(self): + """Validates that the feature set for all the models is None, i.e. all models + use all features.""" + for feature_set in self.feature_set.values(): + if feature_set is not None: + raise ValueError( + "ONNX conversion can only be used if all base models have ``None`` " + "as feature set (and therefore use all the features)." + ) + + @abstractmethod + def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]: + """Return a dictionary with the necessary models to convert the MetaLearner to + ONNX. + + The returned dictionary keys will be strings and the values will be list of the + overall base models (trained on the complete dataset) which should be converted + to onnx. + """ + ... + + @abstractmethod + def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"): + """Convert the MetaLearner to an ONNX model. + + .. warning:: + This is a experimental feature which is not subject to deprecation cycles. Use + it at your own risk! + + ``output_name`` can be used to change the output name of the ONNX model. + + ``models`` should be a dictionary of sequences with the necessary base models converted to + ONNX. The necessary models for the specific MetaLearner can be accessed with + :meth:`~metalearners.metalearner.MetaLearner._necessary_onnx_models`. + + This method combines the the converted ONNX base models into a single ONNX model. + This combined model has a single 2D input ``"X"`` and a single output named + ``output_name``. + """ + ... + class _ConditionalAverageOutcomeMetaLearner(MetaLearner, ABC): diff --git a/metalearners/rlearner.py b/metalearners/rlearner.py index 965b928c..a43ff8ea 100644 --- a/metalearners/rlearner.py +++ b/metalearners/rlearner.py @@ -2,13 +2,16 @@ # SPDX-License-Identifier: BSD-3-Clause +from collections.abc import Mapping, Sequence + import numpy as np from joblib import Parallel, delayed from sklearn.metrics import root_mean_squared_error from typing_extensions import Self -from metalearners._typing import Matrix, OosMethod, Scoring, Vector +from metalearners._typing import Matrix, OosMethod, Scoring, Vector, _ScikitModel from metalearners._utils import ( + check_spox_installed, clip_element_absolute_value_to_epsilon, copydoc, function_has_argument, @@ -16,8 +19,10 @@ get_predict, get_predict_proba, index_matrix, + infer_input_dict, validate_all_vectors_same_index, validate_valid_treatment_variant_not_control, + warning_experimental_feature, ) from metalearners.cross_fit_estimator import OVERALL from metalearners.metalearner import ( @@ -30,6 +35,7 @@ _fit_cross_fit_estimator_joblib, _ModelSpecifications, _ParallelJoblibSpecification, + get_overall_estimators, ) OUTCOME_MODEL = "outcome_model" @@ -519,3 +525,46 @@ def _pseudo_outcome_and_weights( weights = np.square(w_residuals) return pseudo_outcomes, weights + + def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]: + return { + TREATMENT_MODEL: get_overall_estimators( + self._treatment_models[TREATMENT_MODEL] + ) + } + + @copydoc(MetaLearner._build_onnx, sep="") + def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"): + """In the RLearner case, the necessary models are: + + * ``"treatment_model"`` + """ + warning_experimental_feature("_build_onnx") + check_spox_installed() + import spox.opset.ai.onnx.v21 as op + from onnx.checker import check_model + from spox import Var, build, inline + + self._validate_feature_set_none() + self._validate_onnx_models(models, set(self._necessary_onnx_models().keys())) + + input_dict = infer_input_dict(models[TREATMENT_MODEL][0]) + + treatment_output_name = models[TREATMENT_MODEL][0].graph.output[0].name + + tau_hats: list[Var] = [] + for m in models[TREATMENT_MODEL]: + tau_hat_tv = inline(m)(**input_dict)[treatment_output_name] + tau_hat_tv = op.unsqueeze(tau_hat_tv, axes=op.constant(value_int=2)) + if self.is_classification: + if self._supports_multi_class(): + raise ValueError( + "ONNX conversion is not implemented for a multi-class output." + ) + tau_hat_tv = op.concat([op.neg(tau_hat_tv), tau_hat_tv], axis=-1) + tau_hats.append(tau_hat_tv) + + tau_hat = op.concat(tau_hats, axis=1) + final_model = build(input_dict, {output_name: tau_hat}) + check_model(final_model, full_check=True) + return final_model diff --git a/metalearners/slearner.py b/metalearners/slearner.py index 3e1d8078..e636c0f2 100644 --- a/metalearners/slearner.py +++ b/metalearners/slearner.py @@ -2,6 +2,7 @@ # SPDX-License-Identifier: BSD-3-Clause import warnings +from collections.abc import Mapping, Sequence import numpy as np import pandas as pd @@ -297,3 +298,14 @@ def predict_conditional_average_outcomes( return np.stack(conditional_average_outcomes_list, axis=1).reshape( n_obs, self.n_variants, -1 ) + + @classmethod + def _necessary_onnx_models(cls) -> dict[str, list[_ScikitModel]]: + raise ValueError( + "The SLearner does not implement this method. Please refer to comment in the tutorial." + ) + + def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"): + raise ValueError( + "The SLearner does not implement this method. Please refer to comment in the tutorial." + ) diff --git a/metalearners/tlearner.py b/metalearners/tlearner.py index d512ab12..c009e597 100644 --- a/metalearners/tlearner.py +++ b/metalearners/tlearner.py @@ -1,12 +1,21 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause +from collections.abc import Mapping, Sequence + import numpy as np from joblib import Parallel, delayed from typing_extensions import Self -from metalearners._typing import Matrix, OosMethod, Scoring, Vector -from metalearners._utils import index_matrix +from metalearners._typing import Matrix, OosMethod, Scoring, Vector, _ScikitModel +from metalearners._utils import ( + check_spox_installed, + copydoc, + index_matrix, + infer_input_dict, + infer_probabilities_output, + warning_experimental_feature, +) from metalearners.cross_fit_estimator import OVERALL from metalearners.metalearner import ( NUISANCE, @@ -17,6 +26,7 @@ _fit_cross_fit_estimator_joblib, _ModelSpecifications, _ParallelJoblibSpecification, + get_overall_estimators, ) @@ -140,3 +150,53 @@ def evaluate( is_treatment_model=False, feature_set=self.feature_set[VARIANT_OUTCOME_MODEL], ) + + def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]: + return { + VARIANT_OUTCOME_MODEL: get_overall_estimators( + self._nuisance_models[VARIANT_OUTCOME_MODEL] + ) + } + + @copydoc(MetaLearner._build_onnx, sep="") + def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"): + """In the TLearner case, the necessary models are: + + * ``"variant_outcome_model"`` + """ + warning_experimental_feature("_build_onnx") + check_spox_installed() + import spox.opset.ai.onnx.v21 as op + from onnx.checker import check_model + from spox import build, inline + + self._validate_feature_set_none() + self._validate_onnx_models(models, set(self._necessary_onnx_models().keys())) + + input_dict = infer_input_dict(models[VARIANT_OUTCOME_MODEL][0]) + + if not self.is_classification: + model_output_name = models[VARIANT_OUTCOME_MODEL][0].graph.output[0].name + else: + _, model_output_name = infer_probabilities_output( + models[VARIANT_OUTCOME_MODEL][0] + ) + + output_0 = inline(models[VARIANT_OUTCOME_MODEL][0])(**input_dict)[ + model_output_name + ] + + variant_cates = [] + for m in models[VARIANT_OUTCOME_MODEL][1:]: + variant_output = inline(m)(**input_dict)[model_output_name] + variant_cate = op.sub(variant_output, output_0) + variant_cates.append( + op.unsqueeze( + variant_cate, + axes=op.constant(value_int=1), + ) + ) + cate = op.concat(variant_cates, axis=1) + final_model = build(input_dict, {output_name: cate}) + check_model(final_model, full_check=True) + return final_model diff --git a/metalearners/xlearner.py b/metalearners/xlearner.py index 7e8a1b21..5965b96c 100644 --- a/metalearners/xlearner.py +++ b/metalearners/xlearner.py @@ -2,17 +2,24 @@ # SPDX-License-Identifier: BSD-3-Clause +from collections.abc import Mapping, Sequence + import numpy as np from joblib import Parallel, delayed from typing_extensions import Self -from metalearners._typing import Matrix, OosMethod, Scoring, Vector +from metalearners._typing import Matrix, OosMethod, Scoring, Vector, _ScikitModel from metalearners._utils import ( + check_spox_installed, + copydoc, get_one, get_predict, get_predict_proba, index_matrix, + infer_input_dict, + infer_probabilities_output, validate_valid_treatment_variant_not_control, + warning_experimental_feature, ) from metalearners.cross_fit_estimator import MEDIAN, OVERALL from metalearners.metalearner import ( @@ -26,6 +33,7 @@ _fit_cross_fit_estimator_joblib, _ModelSpecifications, _ParallelJoblibSpecification, + get_overall_estimators, ) CONTROL_EFFECT_MODEL = "control_effect_model" @@ -430,3 +438,90 @@ def _pseudo_outcome( imputed_te_control = treatment_outcome - y_filt[control_indices] return imputed_te_control, imputed_te_treatment + + def _necessary_onnx_models(self) -> dict[str, list[_ScikitModel]]: + return { + PROPENSITY_MODEL: get_overall_estimators( + self._nuisance_models[PROPENSITY_MODEL] + ), + CONTROL_EFFECT_MODEL: get_overall_estimators( + self._treatment_models[CONTROL_EFFECT_MODEL] + ), + TREATMENT_EFFECT_MODEL: get_overall_estimators( + self._treatment_models[TREATMENT_EFFECT_MODEL] + ), + } + + @copydoc(MetaLearner._build_onnx, sep="") + def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"): + """In the XLearner case, the necessary models are: + + * ``"propensity_model"`` + * ``"control_effect_model"`` + * ``"treatment_effect_model"`` + """ + warning_experimental_feature("_build_onnx") + check_spox_installed() + import spox.opset.ai.onnx.v21 as op + from onnx.checker import check_model + from spox import Var, build, inline + + self._validate_feature_set_none() + self._validate_onnx_models(models, set(self._necessary_onnx_models().keys())) + + input_dict = infer_input_dict(models[PROPENSITY_MODEL][0]) + + treatment_output_name = models[CONTROL_EFFECT_MODEL][0].graph.output[0].name + + tau_hat_control: list[Var] = [] + for m in models[CONTROL_EFFECT_MODEL]: + tau_hat_control.append(inline(m)(**input_dict)[treatment_output_name]) + tau_hat_effect: list[Var] = [] + for m in models[TREATMENT_EFFECT_MODEL]: + tau_hat_effect.append(inline(m)(**input_dict)[treatment_output_name]) + + _, propensity_output_name = infer_probabilities_output( + models[PROPENSITY_MODEL][0] + ) + + propensity_scores = inline(models[PROPENSITY_MODEL][0])(**input_dict)[ + propensity_output_name + ] + + slice_0 = op.slice( + propensity_scores, + starts=op.const(np.array([0])), + ends=op.const(np.array([1])), + axes=op.const(np.array([1])), + ) + + tau_hat = [] + for tv in range(self.n_variants - 1): + slice_tv = op.slice( + propensity_scores, + starts=op.const(np.array([tv + 1])), + ends=op.const(np.array([tv + 2])), + axes=op.const(np.array([1])), + ) + denominator = op.add(slice_0, slice_tv) + scaled_propensity = op.div(slice_tv, denominator) + tau_hat_tv = op.add( + op.mul(scaled_propensity, tau_hat_control[tv]), + op.mul( + op.sub(op.constant(value_float=1), scaled_propensity), + tau_hat_effect[tv], + ), + ) + tau_hat_tv = op.unsqueeze(tau_hat_tv, axes=op.constant(value_int=2)) + if self.is_classification: + if self._supports_multi_class(): + raise ValueError( + "ONNX conversion is not implemented for a multi-class output." + ) + tau_hat_tv = op.concat([op.neg(tau_hat_tv), tau_hat_tv], axis=-1) + tau_hat.append(tau_hat_tv) + + cate = op.concat(tau_hat, axis=1) + final_model = build(input_dict, {output_name: cate}) + check_model(final_model, full_check=True) + return final_model diff --git a/pixi.lock b/pixi.lock index 63a6f428..25f7b561 100644 --- a/pixi.lock +++ b/pixi.lock @@ -11,11 +11,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hd590300_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hbb29018_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/causalml-0.15.1-py310h2bcf4ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.6.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda - 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__glibc >=2.17,<3.0.a0 - cffi >=1.11 - libgcc-ng >=12 - python >=3.12,<3.13.0a0 @@ -27773,17 +29238,16 @@ packages: - zstd >=1.5.6,<1.6.0a0 license: BSD-3-Clause license_family: BSD - size: 415366 - timestamp: 1718866454481 + size: 416708 + timestamp: 1721044154409 - kind: conda name: zstandard - version: 0.22.0 - build: py312h721a963_1 - build_number: 1 + version: 0.23.0 + build: py312h721a963_0 subdir: osx-arm64 - url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.22.0-py312h721a963_1.conda - sha256: 3aea4c16de85cfe932ba523dc1bdec3d267e06ee5a8528e478e6258b2f419ea5 - md5: 13b5cc78a710f6f13ff3c5bee14355d2 + url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h721a963_0.conda + sha256: 6fc0d2f7a0a49a7c1453bb9eacd5456214b6cf000760067d72f0cce464975fa1 + md5: caf7f5b85615a132c0fa586b82bd59e6 depends: - __osx >=11.0 - cffi >=1.11 @@ -27794,17 +29258,16 @@ packages: - zstd >=1.5.6,<1.6.0a0 license: BSD-3-Clause license_family: BSD - size: 332966 - timestamp: 1718866670388 + size: 332489 + timestamp: 1721044244889 - kind: conda name: zstandard - version: 0.22.0 - build: py312h7606c53_1 - build_number: 1 + version: 0.23.0 + build: py312h7606c53_0 subdir: win-64 - url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.22.0-py312h7606c53_1.conda - sha256: 8e7a8188c0bea8b60d2010f2640e8d3ef0b8e63e4c3c0e1cedb73b9048eaeeab - md5: 786be87a7cee3e81dea86dd2783ce06c + url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py312h7606c53_0.conda + sha256: 907edf473419a5aff6151900d09bb3f2b2c2ede8964f20ae87cb6fae04d0cbb7 + md5: c405924e081cb476495ffe72c88e92c2 depends: - cffi >=1.11 - python >=3.12,<3.13.0a0 @@ -27816,8 +29279,8 @@ packages: - zstd >=1.5.6,<1.6.0a0 license: BSD-3-Clause license_family: BSD - size: 324422 - timestamp: 1718867030240 + size: 320649 + timestamp: 1721044547910 - kind: conda name: zstd version: 1.5.6 diff --git a/pixi.toml b/pixi.toml index a23b176f..d9d84d7c 100644 --- a/pixi.toml +++ b/pixi.toml @@ -34,6 +34,10 @@ shap = "*" matplotlib-base = "*" joblib = ">=1.2.0" lifelines = "*" +onnx = "*" +onnxmltools = "*" +onnxruntime = "*" +spox = "*" [feature.lint.dependencies] pre-commit = "*" diff --git a/tests/conftest.py b/tests/conftest.py index 9ef37d26..93ffe213 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -7,6 +7,65 @@ import pandas as pd import pytest from git_root import git_root +from sklearn.calibration import CalibratedClassifierCV +from sklearn.discriminant_analysis import ( + QuadraticDiscriminantAnalysis, +) +from sklearn.ensemble import ( + AdaBoostRegressor, + BaggingClassifier, + BaggingRegressor, + ExtraTreesClassifier, + ExtraTreesRegressor, + GradientBoostingClassifier, + GradientBoostingRegressor, + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, + RandomForestClassifier, + RandomForestRegressor, +) +from sklearn.gaussian_process import GaussianProcessRegressor +from sklearn.linear_model import ( + ARDRegression, + BayesianRidge, + ElasticNet, + ElasticNetCV, + HuberRegressor, + Lars, + LarsCV, + Lasso, + LassoCV, + LassoLars, + LassoLarsCV, + LassoLarsIC, + LinearRegression, + LogisticRegression, + LogisticRegressionCV, + OrthogonalMatchingPursuit, + OrthogonalMatchingPursuitCV, + PassiveAggressiveRegressor, + QuantileRegressor, + RANSACRegressor, + Ridge, + RidgeCV, + SGDRegressor, + TheilSenRegressor, + TweedieRegressor, +) +from sklearn.neighbors import ( + KNeighborsClassifier, + KNeighborsRegressor, + RadiusNeighborsClassifier, + RadiusNeighborsRegressor, +) +from sklearn.neural_network import MLPClassifier, MLPRegressor +from sklearn.svm import SVR, LinearSVR, NuSVR +from sklearn.tree import ( + DecisionTreeClassifier, + DecisionTreeRegressor, + ExtraTreeClassifier, + ExtraTreeRegressor, +) from metalearners._utils import get_linear_dimension, load_mindset_data, load_twins_data from metalearners.data_generation import ( @@ -22,6 +81,68 @@ _SEED = 1337 _SIGMA_TAU = 0.5 +all_sklearn_classifiers = [ + # AdaBoostClassifier, # The output probabilities are wrong when there are only two classes, see https://github.com/onnx/sklearn-onnx/issues/1117 + BaggingClassifier, + CalibratedClassifierCV, + DecisionTreeClassifier, + ExtraTreeClassifier, + ExtraTreesClassifier, + # GaussianProcessClassifier, # This raises an error com.microsoft:Solve(-1) is not a registered function/op when inference on onnx. TODO: investigate it further + GradientBoostingClassifier, + HistGradientBoostingClassifier, + KNeighborsClassifier, + # LinearDiscriminantAnalysis, # The output probabilities are wrong when there are only two classes, see https://github.com/onnx/sklearn-onnx/issues/1116 + LogisticRegression, + LogisticRegressionCV, + MLPClassifier, + QuadraticDiscriminantAnalysis, + RadiusNeighborsClassifier, + RandomForestClassifier, +] # extracted from all_estimators("classifier"), models which have predict_proba and convert_sklearn supports them + +all_sklearn_regressors = [ + ARDRegression, + AdaBoostRegressor, + BaggingRegressor, + BayesianRidge, + DecisionTreeRegressor, + ElasticNet, + ElasticNetCV, + ExtraTreeRegressor, + ExtraTreesRegressor, + GaussianProcessRegressor, + GradientBoostingRegressor, + HistGradientBoostingRegressor, + HuberRegressor, + KNeighborsRegressor, + Lars, + LarsCV, + Lasso, + LassoCV, + LassoLars, + LassoLarsCV, + LassoLarsIC, + LinearRegression, + LinearSVR, + MLPRegressor, + NuSVR, + OrthogonalMatchingPursuit, + OrthogonalMatchingPursuitCV, + # PLSRegression, # The output shape of the onnx converted model is wrong + PassiveAggressiveRegressor, + QuantileRegressor, + RANSACRegressor, + RadiusNeighborsRegressor, + RandomForestRegressor, + Ridge, + RidgeCV, + SGDRegressor, + SVR, + TheilSenRegressor, + TweedieRegressor, +] # regressors which are supported by convert_sklearn and support regression in the reals + def _generate_rct_experiment_data( covariates, @@ -246,3 +367,27 @@ def grid_search_data(): w_test = rng.integers(0, n_variants, n_test_samples) return X, y_class, y_reg, w, X_test, y_test_class, y_test_reg, w_test + + +@pytest.fixture(scope="session") +def onnx_dataset(): + rng = np.random.default_rng(_SEED) + n_samples = 300 + n_numerical_features = 5 + + X_numerical = rng.standard_normal((n_samples, n_numerical_features)) + + X_with_categorical = pd.DataFrame(X_numerical) + X_with_categorical[n_numerical_features] = pd.Series( + rng.integers(10, 13, n_samples), dtype="category" + ) # not start at 0 + X_with_categorical[n_numerical_features + 1] = pd.Series( + rng.choice([-5, 4, -10, -32], size=n_samples), dtype="category" + ) # not consecutive + + y_class = rng.integers(0, 2, size=n_samples) + y_reg = rng.standard_normal(n_samples) + + w = rng.integers(0, 3, n_samples) + + return X_numerical, X_with_categorical, y_class, y_reg, w diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py index 2bf611ad..75790327 100644 --- a/tests/test_drlearner.py +++ b/tests/test_drlearner.py @@ -1,9 +1,24 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause +from itertools import repeat + +import numpy as np +import onnxruntime as rt +import pytest +from lightgbm import LGBMClassifier, LGBMRegressor +from onnx import ModelProto +from onnxconverter_common.data_types import FloatTensorType +from onnxmltools import convert_lightgbm, convert_xgboost +from skl2onnx import convert_sklearn from sklearn.linear_model import LinearRegression, LogisticRegression +from sklearn.neighbors import RadiusNeighborsRegressor +from xgboost import XGBRegressor + +from metalearners import DRLearner +from metalearners._typing import Params -from metalearners.drlearner import DRLearner +from .conftest import all_sklearn_regressors def test_adaptive_clipping_smoke(dummy_dataset): @@ -18,3 +33,103 @@ def test_adaptive_clipping_smoke(dummy_dataset): adaptive_clipping=True, ) ml.fit(X, y, w) + + +@pytest.mark.parametrize( + "treatment_model_factory, onnx_converter", + ( + list( + zip( + all_sklearn_regressors, + repeat(convert_sklearn), + ) + ) + + [ + (LGBMRegressor, convert_lightgbm), + (XGBRegressor, convert_xgboost), + ] + ), +) +@pytest.mark.parametrize("is_classification", [True, False]) +def test_drlearner_onnx( + treatment_model_factory, onnx_converter, is_classification, onnx_dataset +): + supports_categoricals = treatment_model_factory in [ + LGBMRegressor, + # convert_sklearn does not support categoricals https://github.com/onnx/sklearn-onnx/issues/1051 + # HistGradientBoostingRegressor, + # convert_xgboost does not support categoricals https://github.com/onnx/onnxmltools/issues/469#issuecomment-1993880910 + # XGBRegressor, + ] + treatment_model_params: Params | None = None + if treatment_model_factory == RadiusNeighborsRegressor: + treatment_model_params = {"radius": 10} + + X_numerical, X_with_categorical, y_class, y_reg, w = onnx_dataset + n_numerical_features = X_numerical.shape[1] + + if supports_categoricals: + X = X_with_categorical + n_categorical_features = X.shape[1] - n_numerical_features + else: + X = X_numerical + n_categorical_features = 0 + n_variants = len(np.unique(w)) + if is_classification: + y = y_class + nuisance_model_factory = LogisticRegression + else: + y = y_reg + nuisance_model_factory = LinearRegression + + ml = DRLearner( + is_classification, + n_variants, + nuisance_model_factory=nuisance_model_factory, + propensity_model_factory=LGBMClassifier, + treatment_model_factory=treatment_model_factory, + propensity_model_params={"n_estimators": 1}, + treatment_model_params=treatment_model_params, + n_folds=2, + ) + ml.fit(X, y, w) + + necessary_models = ml._necessary_onnx_models() + onnx_models: dict[str, list[ModelProto]] = {} + + for model_kind, models in necessary_models.items(): + onnx_models[model_kind] = [] + for model in models: + onnx_models[model_kind].append( + onnx_converter( + model, + initial_types=[ + ( + "X", + FloatTensorType( + [None, n_numerical_features + n_categorical_features] + ), + ) + ], + ) + ) + + final = ml._build_onnx(onnx_models) + sess = rt.InferenceSession( + final.SerializeToString(), providers=rt.get_available_providers() + ) + + if supports_categoricals: + onnx_X = X.to_numpy(np.float32) + # This is needed for categoricals as LGBM uses the categorical codes, when + # other implementations support categoricals this may need to be changed + onnx_X[:, n_numerical_features] = X[n_numerical_features].cat.codes + onnx_X[:, n_numerical_features + 1] = X[n_numerical_features + 1].cat.codes + else: + onnx_X = X.astype(np.float32) + + (pred_onnx,) = sess.run( + ["tau"], + {"X": onnx_X}, + ) + np.testing.assert_allclose(ml.predict(X, True, "overall"), pred_onnx, atol=5e-4) diff --git a/tests/test_metalearner.py b/tests/test_metalearner.py index a65087cd..5721f5c6 100644 --- a/tests/test_metalearner.py +++ b/tests/test_metalearner.py @@ -1,6 +1,7 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause +from collections.abc import Mapping, Sequence from itertools import chain import matplotlib.pyplot as plt @@ -12,6 +13,7 @@ from sklearn.base import BaseEstimator from sklearn.linear_model import LinearRegression, LogisticRegression +from metalearners._typing import _ScikitModel from metalearners.cross_fit_estimator import CrossFitEstimator from metalearners.data_generation import insert_missing from metalearners.drlearner import DRLearner @@ -111,6 +113,12 @@ def evaluate(self, X, y, w, is_oos, oos_method=None): def predict_conditional_average_outcomes(self, X, is_oos, oos_method=None): return np.zeros((len(X), 2, 1)) + def _build_onnx(self, models: Mapping[str, Sequence], output_name: str = "tau"): ... + + @classmethod + def _necessary_onnx_models(cls) -> dict[str, list[_ScikitModel]]: + return {} + @pytest.mark.parametrize("is_classification", [True, False]) @pytest.mark.parametrize("nuisance_model_params", [None, {}, {"n_estimators": 5}]) diff --git a/tests/test_rlearner.py b/tests/test_rlearner.py index 69499e60..0455f60b 100644 --- a/tests/test_rlearner.py +++ b/tests/test_rlearner.py @@ -1,11 +1,24 @@ # Copyright (c) QuantCo 2024-2024 # SPDX-License-Identifier: BSD-3-Clause +from itertools import repeat + import numpy as np +import onnxruntime as rt import pandas as pd import pytest +from lightgbm import LGBMClassifier, LGBMRegressor +from onnx import ModelProto +from onnxconverter_common.data_types import FloatTensorType +from onnxmltools import convert_lightgbm, convert_xgboost +from skl2onnx import convert_sklearn +from sklearn.linear_model import LinearRegression, LogisticRegression +from xgboost import XGBRegressor + +from metalearners._utils import function_has_argument +from metalearners.rlearner import RLearner, r_loss -from metalearners.rlearner import r_loss +from .conftest import all_sklearn_regressors @pytest.mark.parametrize("use_pandas", [True, False]) @@ -26,3 +39,102 @@ def test_r_loss(use_pandas): propensity_scores=propensity_scores, ) assert result == pytest.approx(2, abs=1e-4, rel=1e-4) + + +@pytest.mark.parametrize( + "treatment_model_factory, onnx_converter", + ( + list( + zip( + all_sklearn_regressors, + repeat(convert_sklearn), + ) + ) + + [ + (LGBMRegressor, convert_lightgbm), + (XGBRegressor, convert_xgboost), + ] + ), +) +@pytest.mark.parametrize("is_classification", [True, False]) +def test_rlearner_onnx( + treatment_model_factory, onnx_converter, is_classification, onnx_dataset +): + if not function_has_argument(treatment_model_factory.fit, "sample_weight"): + pytest.skip() + + supports_categoricals = treatment_model_factory in [ + LGBMRegressor, + # convert_sklearn does not support categoricals https://github.com/onnx/sklearn-onnx/issues/1051 + # HistGradientBoostingRegressor, + # convert_xgboost does not support categoricals https://github.com/onnx/onnxmltools/issues/469#issuecomment-1993880910 + # XGBRegressor, + ] + + X_numerical, X_with_categorical, y_class, y_reg, w = onnx_dataset + n_numerical_features = X_numerical.shape[1] + + if supports_categoricals: + X = X_with_categorical + n_categorical_features = X.shape[1] - n_numerical_features + else: + X = X_numerical + n_categorical_features = 0 + n_variants = len(np.unique(w)) + if is_classification: + y = y_class + nuisance_model_factory = LogisticRegression + else: + y = y_reg + nuisance_model_factory = LinearRegression + + ml = RLearner( + is_classification, + n_variants, + nuisance_model_factory=nuisance_model_factory, + propensity_model_factory=LGBMClassifier, + treatment_model_factory=treatment_model_factory, + propensity_model_params={"n_estimators": 1}, + n_folds=2, + ) + ml.fit(X, y, w) + + necessary_models = ml._necessary_onnx_models() + onnx_models: dict[str, list[ModelProto]] = {} + + for model_kind, models in necessary_models.items(): + onnx_models[model_kind] = [] + for model in models: + onnx_models[model_kind].append( + onnx_converter( + model, + initial_types=[ + ( + "X", + FloatTensorType( + [None, n_numerical_features + n_categorical_features] + ), + ) + ], + ) + ) + + final = ml._build_onnx(onnx_models) + sess = rt.InferenceSession( + final.SerializeToString(), providers=rt.get_available_providers() + ) + + if supports_categoricals: + onnx_X = X.to_numpy(np.float32) + # This is needed for categoricals as LGBM uses the categorical codes, when + # other implementations support categoricals this may need to be changed + onnx_X[:, n_numerical_features] = X[n_numerical_features].cat.codes + onnx_X[:, n_numerical_features + 1] = X[n_numerical_features + 1].cat.codes + else: + onnx_X = X.astype(np.float32) + + (pred_onnx,) = sess.run( + ["tau"], + {"X": onnx_X}, + ) + np.testing.assert_allclose(ml.predict(X, True, "overall"), pred_onnx, atol=5e-4) diff --git a/tests/test_tlearner.py b/tests/test_tlearner.py new file mode 100644 index 00000000..9c8d33f0 --- /dev/null +++ b/tests/test_tlearner.py @@ -0,0 +1,141 @@ +# Copyright (c) QuantCo 2024-2024 +# SPDX-License-Identifier: BSD-3-Clause + +from functools import partial +from itertools import repeat + +import numpy as np +import onnxruntime as rt +import pytest +from lightgbm import LGBMClassifier, LGBMRegressor +from onnx import ModelProto +from onnxconverter_common.data_types import FloatTensorType +from onnxmltools import convert_lightgbm, convert_xgboost +from skl2onnx.convert import convert_sklearn +from sklearn.ensemble import ( + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) +from sklearn.neighbors import ( + RadiusNeighborsClassifier, + RadiusNeighborsRegressor, +) +from xgboost import XGBClassifier, XGBRegressor + +from metalearners import TLearner +from metalearners._typing import Params + +from .conftest import all_sklearn_classifiers, all_sklearn_regressors + + +@pytest.mark.parametrize( + "nuisance_model_factory, onnx_converter, is_classification", + ( + list( + zip( + all_sklearn_classifiers, + repeat(partial(convert_sklearn, options={"zipmap": False})), + repeat([True]), + ) + ) + + list(zip(all_sklearn_regressors, repeat(convert_sklearn), repeat(False))) + + [ + (LGBMClassifier, partial(convert_lightgbm, zipmap=False), True), + (LGBMRegressor, convert_lightgbm, False), + (XGBClassifier, convert_xgboost, True), + (XGBRegressor, convert_xgboost, False), + ] + ), +) +def test_tlearner_onnx( + nuisance_model_factory, onnx_converter, is_classification, onnx_dataset +): + supports_categoricals = nuisance_model_factory in [ + LGBMClassifier, + LGBMRegressor, + # convert_sklearn does not support categoricals https://github.com/onnx/sklearn-onnx/issues/1051 + # HistGradientBoostingClassifier, + # HistGradientBoostingRegressor, + # convert_xgboost does not support categoricals https://github.com/onnx/onnxmltools/issues/469#issuecomment-1993880910 + # XGBClassifier, + # XGBRegressor, + ] + + nuisance_model_params: Params | None + if nuisance_model_factory in [RadiusNeighborsClassifier, RadiusNeighborsRegressor]: + nuisance_model_params = {"radius": 10} + elif nuisance_model_factory in [ + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, + ]: + # This is unnecessary for now but if at some point convert_sklearn supports categoricals this is needed + nuisance_model_params = {"categorical_features": "from_dtype"} + elif nuisance_model_factory in [XGBClassifier, XGBRegressor]: + # This is unnecessary for now but if at some point convert_xgboost supports categoricals this is needed + nuisance_model_params = {"enable_categorical": True} + else: + nuisance_model_params = None + + X_numerical, X_with_categorical, y_class, y_reg, w = onnx_dataset + n_numerical_features = X_numerical.shape[1] + + if supports_categoricals: + X = X_with_categorical + n_categorical_features = X.shape[1] - n_numerical_features + else: + X = X_numerical + n_categorical_features = 0 + n_variants = len(np.unique(w)) + if is_classification: + y = y_class + else: + y = y_reg + + ml = TLearner( + is_classification, + n_variants, + nuisance_model_factory=nuisance_model_factory, + nuisance_model_params=nuisance_model_params, + n_folds=2, + ) + ml.fit(X, y, w) + + necessary_models = ml._necessary_onnx_models() + onnx_models: dict[str, list[ModelProto]] = {} + + for model_kind, models in necessary_models.items(): + onnx_models[model_kind] = [] + for model in models: + onnx_models[model_kind].append( + onnx_converter( + model, + initial_types=[ + ( + "X", + FloatTensorType( + [None, n_numerical_features + n_categorical_features] + ), + ) + ], + ) + ) + + final = ml._build_onnx(onnx_models) + sess = rt.InferenceSession( + final.SerializeToString(), providers=rt.get_available_providers() + ) + + if supports_categoricals: + onnx_X = X.to_numpy(np.float32) + # This is needed for categoricals as LGBM uses the categorical codes, when + # other implementations support categoricals this may need to be changed + for i in range(n_categorical_features): + onnx_X[:, n_numerical_features + i] = X[n_numerical_features + i].cat.codes + else: + onnx_X = X.astype(np.float32) + + (pred_onnx,) = sess.run( + ["tau"], + {"X": onnx_X}, + ) + np.testing.assert_allclose(ml.predict(X, True, "overall"), pred_onnx, atol=5e-4) diff --git a/tests/test_xlearner.py b/tests/test_xlearner.py new file mode 100644 index 00000000..64e22395 --- /dev/null +++ b/tests/test_xlearner.py @@ -0,0 +1,136 @@ +# Copyright (c) QuantCo 2024-2024 +# SPDX-License-Identifier: BSD-3-Clause + +import random +from functools import partial +from itertools import repeat + +import numpy as np +import onnxruntime as rt +import pytest +from lightgbm import LGBMClassifier, LGBMRegressor +from onnx import ModelProto +from onnxconverter_common.data_types import FloatTensorType +from onnxmltools import convert_lightgbm, convert_xgboost +from skl2onnx import convert_sklearn +from sklearn.neighbors import RadiusNeighborsClassifier, RadiusNeighborsRegressor +from xgboost import XGBClassifier, XGBRegressor + +from metalearners import XLearner +from metalearners._typing import Params +from metalearners.xlearner import CONTROL_EFFECT_MODEL, TREATMENT_EFFECT_MODEL + +from .conftest import all_sklearn_classifiers, all_sklearn_regressors + + +@pytest.mark.parametrize( + "treatment_model_factory, treatment_onnx_converter", + random.sample( # With this we run a different set of tests every time as running all of them is too costly (there are ~50 regressors and classifiers) + list( + zip( + all_sklearn_regressors, + repeat(convert_sklearn), + ) + ) + + [ + (LGBMRegressor, convert_lightgbm), + (XGBRegressor, convert_xgboost), + ], + 4, + ), +) +@pytest.mark.parametrize( + "propensity_model_factory, propensity_onnx_converter", + random.sample( + list( + zip( + all_sklearn_classifiers, + [partial(convert_sklearn, options={"zipmap": False})] + * len(all_sklearn_classifiers), + ) + ) + + [ + (LGBMClassifier, partial(convert_lightgbm, zipmap=False)), + (XGBClassifier, convert_xgboost), + ], + 4, + ), +) +@pytest.mark.parametrize("is_classification", [True, False]) +def test_xlearner_onnx( + treatment_model_factory, + propensity_model_factory, + treatment_onnx_converter, + propensity_onnx_converter, + is_classification, + onnx_dataset, +): + treatment_model_params: Params | None + if treatment_model_factory == RadiusNeighborsRegressor: + treatment_model_params = {"radius": 10} + else: + treatment_model_params = None + propensity_model_params: Params | None + if propensity_model_factory == RadiusNeighborsClassifier: + propensity_model_params = {"radius": 10} + else: + propensity_model_params = None + + X, _, y_class, y_reg, w = onnx_dataset + n_numerical_features = X.shape[1] + n_variants = len(np.unique(w)) + if is_classification: + y = y_class + nuisance_model_factory = LGBMClassifier + else: + y = y_reg + nuisance_model_factory = LGBMRegressor + + nuisance_model_params = {"n_estimators": 1} + + ml = XLearner( + is_classification, + n_variants, + nuisance_model_factory=nuisance_model_factory, + nuisance_model_params=nuisance_model_params, + propensity_model_factory=propensity_model_factory, + propensity_model_params=propensity_model_params, + treatment_model_factory=treatment_model_factory, + treatment_model_params=treatment_model_params, + n_folds=2, + ) + ml.fit(X, y, w) + + necessary_models = ml._necessary_onnx_models() + onnx_models: dict[str, list[ModelProto]] = {} + + for model_kind, models in necessary_models.items(): + onnx_models[model_kind] = [] + if model_kind in [CONTROL_EFFECT_MODEL, TREATMENT_EFFECT_MODEL]: + onnx_converter = treatment_onnx_converter + else: + onnx_converter = propensity_onnx_converter + for model in models: + onnx_models[model_kind].append( + onnx_converter( + model, + initial_types=[ + ( + "X", + FloatTensorType([None, n_numerical_features]), + ) + ], + ) + ) + + final = ml._build_onnx(onnx_models) + + sess = rt.InferenceSession( + final.SerializeToString(), providers=rt.get_available_providers() + ) + + (pred_onnx,) = sess.run( + ["tau"], + {"X": X.astype(np.float32)}, + ) + np.testing.assert_allclose(ml.predict(X, True, "overall"), pred_onnx, atol=5e-4) From 44a1526f4f7fc3670687ceb57476b2e16c354882 Mon Sep 17 00:00:00 2001 From: Kevin Klein <7267523+kklein@users.noreply.github.com> Date: Mon, 5 Aug 2024 10:17:55 +0200 Subject: [PATCH 3/9] Adapt CI trigger to run on PR from fork. (#74) * Adapt CI trigger to run on PR from fork. * Trigger on any tag, not only those prefixed with v. * Fix syntax. * Use default pull request types. --- .github/workflows/ci.yml | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index f9d98ef0..f455d9e9 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,5 +1,15 @@ name: CI -on: [push] +on: + # We would like to trigger for CI for any pull request action - + # both from QuantCo's branches as well as forks. + pull_request: + # In addition to pull requests, we want to run CI for pushes + # to the main branch and tags. + push: + branches: + - "main" + tags: + - "*" # Automatically stop old builds on the same branch/PR concurrency: From 246f7e8a25a732b86d7a51b353a3a19a55d0a517 Mon Sep 17 00:00:00 2001 From: Apoorva Lal Date: Mon, 5 Aug 2024 16:27:45 -0700 Subject: [PATCH 4/9] address review comments --- CHANGELOG.rst | 10 +- docs/examples/example_estimating_ates.ipynb | 559 +- docs/examples/index.rst | 1 + metalearners/drlearner.py | 12 +- pixi.lock | 7922 ++++++++----------- tests/test_drlearner.py | 20 + 6 files changed, 3948 insertions(+), 4576 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 7f30b505..a89a0839 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -7,15 +7,6 @@ Changelog ========= -0.9.1 (2024-07-xx) ------------------- - -**New features** - -* Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to compute AIPW point estimate and standard error without requiring a full model fit. - - - 0.9.0 (2024-07-xx) ------------------ @@ -25,6 +16,7 @@ Changelog * Add :class:`metalearners.utils.FixedBinaryPropensity`. +* Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to compute AIPW point estimate and standard error for _average treatment effects (ATE)_ without requiring a full model fit (which is required for CATE estimation). A new notebook contains examples. 0.8.0 (2024-07-22) ------------------ diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index e6539188..4a2c93a9 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -12,7 +12,9 @@ "Motivation\n", "----------\n", "\n", - "Estimating average treatment effects (ATEs) involves a subset of the tasks involved in estimating Conditional Average Treatment Effects (CATEs), so we can use methods that are designed for estimating CATEs to estimate ATEs. In this example, we simulate some data with confounding and demonstrate the `treatment_effect` method of the `DRLearner` class, which estimates the ATE, and compare it to estimates from some other popular libraries (`econML` and `doubleML`).\n", + "Estimating average treatment effects (ATEs) involves a subset of the tasks involved in estimating Conditional Average Treatment Effects (CATEs), so we can use methods that are designed for estimating CATEs to estimate ATEs. \n", + "\n", + "In this example, we simulate some data with a confounded binary treatment and demonstrate the `treatment_effect` method of the `DRLearner` class, which estimates the ATE, and compare it to estimates from some other popular libraries (`econML` and `doubleML`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n", "\n", "Example\n", "-------" @@ -26,14 +28,16 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", - "import statsmodels.formula.api as smf" + "import statsmodels.formula.api as smf\n", + "\n", + "np.random.seed(42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# DGP with confounding" + "## Binary Treatment with confounding" ] }, { @@ -42,11 +46,7 @@ "metadata": {}, "outputs": [], "source": [ - "# generate covariate matrix with mixture of continuous and categorical variables\n", - "np.random.seed(123)\n", - "\n", - "\n", - "def dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n", + "def binary_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n", " \"\"\"DGP for a confounded treatment assignment dgp\n", "\n", " Args:\n", @@ -55,9 +55,6 @@ " pscore_fn (lambda): propensity score function\n", " tau_fn (lambda): treatment effect function. Can be scalar for constant effect.\n", " outcome_fn (lambda): outcome DGP\n", - "\n", - " Returns:\n", - " _type_: _description_\n", " \"\"\"\n", " Sigma = np.random.uniform(-1, 1, (k, k))\n", " Sigma = Sigma @ Sigma.T\n", @@ -73,10 +70,6 @@ " return df\n", "\n", "\n", - "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n", - "\n", - "\n", - "# tau_fn = lambda x: 1 + 2 * x[:, 0] + 3 * x[:, 1] + 4 * x[:, 2] + 5 * x[:, 3]\n", "def outcome_fn(x, w, taufn):\n", " return (\n", " taufn(x) * w\n", @@ -90,7 +83,11 @@ "\n", "\n", "n, k = 10_000, 3\n", - "df = dgp(n, k, pscore_fn, tau_fn=lambda x: 1, outcome_fn=outcome_fn)\n", + "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n", + "# simulate constant_effects\n", + "# tau_fn = lambda x: 1 + 2 * x[:, 0] + 3 * x[:, 1] + 4 * x[:, 2] + 5 * x[:, 3]\n", + "tau_fn = lambda x: 1\n", + "df = binary_dgp(n, k, pscore_fn, tau_fn=tau_fn, outcome_fn=outcome_fn)\n", "outcome_column, treatment_column = \"Y\", \"W\"\n", "feature_columns = [f\"X{i}\" for i in range(k + 1)]" ] @@ -99,7 +96,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Linear Regression " + "### Linear Regression " ] }, { @@ -110,7 +107,7 @@ { "data": { "text/plain": [ - "(1.624943884109307, 0.04532725031682251)" + "(2.083595103597918, 0.06526671583747883)" ] }, "execution_count": 3, @@ -132,7 +129,7 @@ { "data": { "text/plain": [ - "(1.1326349969274776, 0.02972906033475406)" + "(2.1433722387308025, 0.06345124983351998)" ] }, "execution_count": 4, @@ -158,7 +155,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `metalearners`: `DRLearner`\n", + "### `metalearners`: `DRLearner`\n", "\n", "Point estimates and standard errors for treatment effects for the AIPW estimator can be computed by aggregating the pseudo-outcome computed by the `DRLearner` class." ] @@ -215,7 +212,7 @@ { "data": { "text/plain": [ - "array([[1.01467296, 0.03699228]])" + "array([[1.02931589, 0.06679633]])" ] }, "execution_count": 6, @@ -239,7 +236,6 @@ " y=df[outcome_column],\n", " w=df[treatment_column],\n", ")\n", - "\n", "metalearners_est = metalearners_dr.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", " X=df[feature_columns],\n", " w=df[treatment_column],\n", @@ -264,7 +260,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "est: 1.0146729600419584, se: 0.036994131087587\n" + "est: 1.0293158917468608, se: 0.06679966900982737\n" ] } ], @@ -285,9 +281,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `doubleml`: `DoubleMLIRM`\n", + "### `doubleml`: `DoubleMLIRM`\n", "\n", - "The `doubleML` library focuses on estimating average effects and has an 'interactive regression model (IRM)' class that estimates the ATE using the same pseudo-outcome method as the `DRLearner` class." + "The [`doubleML`](https://docs.doubleml.org/stable/index.html) library focuses on estimating average effects and has an 'interactive regression model (IRM)' class that estimates the ATE using the same pseudo-outcome method as the `DRLearner` class." ] }, { @@ -324,7 +320,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.01366331 0.03880938]\n" + "[1.08356716 0.05786543]\n" ] } ], @@ -336,7 +332,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## `econML`: `LinearDRLearner`" + "### `econML`: `LinearDRLearner`" ] }, { @@ -388,7 +384,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1.069 0.059]\n" + "[1.001 0.109]\n" ] } ], @@ -400,9 +396,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## comparison\n", + "### comparison\n", "\n", - "All ml-based estimators yield comparable results." + "All ml-based estimators yield comparable results (both point estimate and standard error)." ] }, { @@ -441,28 +437,28 @@ " \n", " \n", " est\n", - " 1.624944\n", - " 1.132635\n", - " 1.014673\n", - " 1.013663\n", - " 1.069\n", + " 2.083595\n", + " 2.143372\n", + " 1.029316\n", + " 1.083567\n", + " 1.001\n", " \n", " \n", - " 0.036994\n", - " 0.045327\n", - " 0.029729\n", - " 0.036992\n", - " 0.038809\n", - " 0.059\n", + " se\n", + " 0.065267\n", + " 0.063451\n", + " 0.066796\n", + " 0.057865\n", + " 0.109\n", " \n", " \n", "\n", "" ], "text/plain": [ - " naive linreg metalearners doubleml econml\n", - "est 1.624944 1.132635 1.014673 1.013663 1.069\n", - "0.036994 0.045327 0.029729 0.036992 0.038809 0.059" + " naive linreg metalearners doubleml econml\n", + "est 2.083595 2.143372 1.029316 1.083567 1.001\n", + "se 0.065267 0.063451 0.066796 0.057865 0.109" ] }, "execution_count": 14, @@ -478,10 +474,483 @@ " metalearners_est.flatten(),\n", " doubleml_est,\n", " econml_est,\n", - "], index = ['est', se],\n", + "], index = ['est', 'se'],\n", "columns = ['naive', 'linreg', 'metalearners', 'doubleml', 'econml']\n", ")\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple Treatment Arms\n", + "\n", + "Next, we demonstrate the use of the `treatment_effect` method of the `DRLearner` class in a setting with multiple discrete treatments." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Y X0 X1 X2 X3 W\n", + "0 8.514662 -0.104441 0.833485 1.802766 1.0 2\n", + "1 0.355338 -0.690561 -0.180011 -1.715710 0.0 2\n", + "2 2.033627 0.685568 -0.004838 0.671343 0.0 1\n", + "3 2.791453 1.984499 -0.433412 0.921716 0.0 1\n", + "4 2.080426 0.189912 -0.769235 -0.450760 1.0 1" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(123)\n", + "\n", + "def multi_pscore_fn(x, num_treatments=3):\n", + " logits = np.zeros((x.shape[0], num_treatments))\n", + " logits[:, 0] = -x[:, 0] - x[:, 1] - x[:, 2] + x[:, 3]\n", + " logits[:, 1] = -x[:, 1] - x[:, 2] - x[:, 3] + x[:, 0]\n", + " logits[:, 2] = -x[:, 2] - x[:, 3] - x[:, 0] + x[:, 1]\n", + " # Apply softmax\n", + " exp_logits = np.exp(logits - np.max(logits, axis=1, keepdims=True))\n", + " pscores = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n", + " return pscores\n", + "\n", + "# And then modify the multi_treatment_dgp function to use these probabilities:\n", + "\n", + "def multi_treatment_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1, num_treatments=3):\n", + " \"\"\"DGP for a confounded multiple treatment assignment\"\"\"\n", + " Sigma = np.random.uniform(-1, 1, (k, k))\n", + " Sigma = Sigma @ Sigma.T\n", + " Xnum = np.random.multivariate_normal(np.zeros(k), Sigma, n)\n", + " Xcat = np.random.binomial(1, 0.5, (n, k_cat))\n", + " X = np.c_[Xnum, Xcat]\n", + "\n", + " # Generate multiple treatments\n", + " pscores = pscore_fn(X, num_treatments)\n", + " W = np.zeros((n, num_treatments), dtype=int)\n", + " for i in range(n):\n", + " W[i, np.random.choice(num_treatments, p=pscores[i])] = 1\n", + " Y = outcome_fn(X, W, tau_fn)\n", + " df = pd.DataFrame(\n", + " np.c_[Y, X],\n", + " columns=[\"Y\"]\n", + " + [f\"X{i}\" for i in range(k + 1)],\n", + " ).assign(\n", + " W=np.argmax(W, axis=1)\n", + " ) # convert one-hot encoding to single column\n", + " return df\n", + "\n", + "\n", + "def multi_outcome_fn(x, w, taufn):\n", + " return (\n", + " np.sum(taufn(x) * w, axis=1) # sum over treatments\n", + " + x[:, 0]\n", + " + 2 * x[:, 1] ** 2\n", + " + 3 * x[:, 3] * x[:, 1]\n", + " + x[:, 2]\n", + " + x[:, 3]\n", + " + np.random.normal(0, 1, n)\n", + " )\n", + "\n", + "def multi_tau_fn(x):\n", + " return np.c_[ 0, 1, 2]\n", + "\n", + "n, k = 10_000, 3\n", + "\n", + "df_multi = multi_treatment_dgp(\n", + " n, k, multi_pscore_fn, tau_fn=multi_tau_fn, outcome_fn=multi_outcome_fn\n", + ")\n", + "feature_columns = [f\"X{i}\" for i in range(k + 1)]\n", + "outcome_column, treatment_column = \"Y\", \"W\"\n", + "df_multi.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Intercept 0.645659\n", + "C(W)[T.1] 1.777798\n", + "C(W)[T.2] 2.387949\n", + "dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lm_multi = smf.ols(\"Y ~ C(W)\", df_multi).fit()\n", + "lm_multi.params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These estimates are substantially biased, since the model is misspecified (the true outcome and propensity score contains quadratic terms)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.04071166, 0.14139908],\n", + " [2.25658364, 0.21346275]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metalearners_dr_2 = DRLearner(\n", + " nuisance_model_factory=LGBMRegressor,\n", + " treatment_model_factory=DummyRegressor, # not actually used since we don't fit treatment model\n", + " propensity_model_factory=LGBMClassifier,\n", + " is_classification=False,\n", + " n_variants=3,\n", + " nuisance_model_params={\"verbose\": -1},\n", + " propensity_model_params={\"verbose\": -1},\n", + ")\n", + "\n", + "metalearners_dr_2.fit_all_nuisance(\n", + " X=df_multi[feature_columns],\n", + " y=df_multi[outcome_column],\n", + " w=df_multi[treatment_column],\n", + ")\n", + "metalearners_est2 = metalearners_dr_2.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + " X=df_multi[feature_columns],\n", + " y=df_multi[outcome_column],\n", + " w=df_multi[treatment_column],\n", + ")\n", + "metalearners_est2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These estimates are less biased than those produced by the linear model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Binary Outcome\n", + "\n", + "Finally, we consider a case where the outcome is binary, in which case the DRLearner class is initialized with `is_classification=True` and the nuisance model is a classifier." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Y W X0 X1 X2 X3\n", + "0 0 1.0 1.138203 -1.439008 -1.981465 0.0\n", + "1 1 1.0 0.297749 -0.092953 1.455780 0.0\n", + "2 1 1.0 1.150090 0.545041 0.799303 0.0\n", + "3 1 0.0 0.832256 -0.591107 -1.076526 0.0\n", + "4 0 1.0 1.559507 -1.018031 -1.137247 0.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def classification_dgp(n, k, pscore_fn, tau_fn, outcome_fn, k_cat=1):\n", + " \"\"\"DGP for a confounded treatment assignment with binary outcome\"\"\"\n", + " Sigma = np.random.uniform(-1, 1, (k, k))\n", + " Sigma = Sigma @ Sigma.T\n", + " Xnum = np.random.multivariate_normal(np.zeros(k), Sigma, n)\n", + " Xcat = np.random.binomial(1, 0.5, (n, k_cat))\n", + " X = np.c_[Xnum, Xcat]\n", + " W = np.random.binomial(1, pscore_fn(X), n)\n", + " Y_prob = outcome_fn(X, W, tau_fn)\n", + " Y = np.random.binomial(1, Y_prob).astype(int)\n", + " df = pd.DataFrame(\n", + " np.c_[Y, W, X], columns=[\"Y\", \"W\"] + [f\"X{i}\" for i in range(k + 1)]\n", + " ).assign(Y = pd.to_numeric(Y, downcast='integer'))\n", + " return df\n", + "\n", + "\n", + "def classification_outcome_fn(x, w, taufn):\n", + " logits = (\n", + " taufn(x) * w\n", + " + x[:, 0]\n", + " + 2 * x[:, 1]\n", + " + 3 * x[:, 3] * x[:, 1]\n", + " + x[:, 2]\n", + " + x[:, 3]\n", + " )\n", + " return 1 / (1 + np.exp(-logits))\n", + "\n", + "\n", + "# Propensity score function\n", + "pscore_fn = lambda x: 1 / (1 + np.exp(-x[:, 0] - x[:, 1] - x[:, 2] ** 2 + x[:, 3]))\n", + "\n", + "# Treatment effect function - constant increase in probability of Y=1\n", + "classification_tau_fn = lambda x: 0.05\n", + "\n", + "n, k = 10_000, 3\n", + "df_class = classification_dgp(\n", + " n, k, pscore_fn, tau_fn=classification_tau_fn, outcome_fn=classification_outcome_fn\n", + ")\n", + "feature_columns = [f\"X{i}\" for i in range(k + 1)]\n", + "outcome_column, treatment_column = \"Y\", \"W\"\n", + "\n", + "df_class.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/latex": [ + "$\\displaystyle 0.081358650748229$" + ], + "text/plain": [ + "0.08135865074822901" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# naive TE\n", + "df_class.groupby(\"W\")[\"Y\"].mean().diff()[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.04373356, 0.04191263]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metalearners_dr_3 = DRLearner(\n", + " nuisance_model_factory=LGBMClassifier,\n", + " treatment_model_factory=DummyRegressor, # not actually used since we don't fit treatment model\n", + " propensity_model_factory=LGBMClassifier,\n", + " is_classification=True,\n", + " n_variants=2,\n", + " nuisance_model_params={\"verbose\": -1},\n", + " propensity_model_params={\"verbose\": -1},\n", + ")\n", + "\n", + "metalearners_dr_3.fit_all_nuisance(\n", + " X=df_class[feature_columns],\n", + " y=df_class[outcome_column],\n", + " w=df_class[treatment_column],\n", + ")\n", + "metalearners_est_3 = metalearners_dr_3.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + " X=df_class[feature_columns],\n", + " y=df_class[outcome_column],\n", + " w=df_class[treatment_column],\n", + ")\n", + "metalearners_est_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we have lower bias than the naive comparison." + ] } ], "metadata": { diff --git a/docs/examples/index.rst b/docs/examples/index.rst index ad950311..2ef14398 100644 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -6,6 +6,7 @@ Examples :maxdepth: 1 Estimating CATEs with MetaLearners + Estimating ATEs with DRLearner Reusing base models Explainability: Lime plots of MetaLearners Explainability: Feature importance and SHAP values diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index ae5f7c40..18df8af8 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -191,7 +191,7 @@ def fit_all_nuisance( ) self._assign_joblib_nuisance_results(results) - + self._nuisance_models_fit = True return self def fit_all_treatment( @@ -337,7 +337,11 @@ def treatment_effect( y: Vector, w: Vector, ) -> np.ndarray: - """Compute Average Treatment Effect (ATE) for each treatment variant using the Augmented IPW estimator (Robins et al 1994). Does not require fitting a second-stage treatment model: it uses the pseudo-outcome alone and computes the average and SE. Can be used following the `fit_all_nuisance` method. + """Compute Average Treatment Effect (ATE) for each treatment variant using the + Augmented IPW estimator (Robins et al 1994). Does not require fitting a second- + stage treatment model: it uses the pseudo-outcome alone and computes the average + and SE. Can be used following the + :meth:`~metalearners.drlearner.DRLearner.fit_all_nuisance` method. Args: X (Matrix): Covariate matrix. @@ -347,6 +351,10 @@ def treatment_effect( Returns: np.ndarray: Treatment effect and standard error for each treatment variant. """ + if not self._nuisance_models_fit: + raise ValueError( + "The nuisance models need to be fitted before computing the treatment effect." + ) gamma_matrix = np.zeros((len(X), self.n_variants - 1)) for treatment_variant in range(1, self.n_variants): gamma_matrix[:, treatment_variant - 1] = self._pseudo_outcome( diff --git a/pixi.lock b/pixi.lock index 8baa1d0e..7227f074 100644 --- a/pixi.lock +++ b/pixi.lock @@ -11,11 +11,11 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hd590300_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hbb29018_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/causalml-0.15.1-py310h2bcf4ab_1.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.6.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.0.0-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py310hd41b1e2_0.conda @@ -23,7 +23,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-0.29.34-py310heca2aa9_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dill-0.3.8-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/dowhy-0.7.1-pyhd8ed1ab_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/econml-0.15.0-py310hcc13569_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/econml-0.15.1-py310h27b3328_0.conda - 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__osx >=11.0 + - libzlib >=1.2.13,<2.0.0a0 + license: BSD-3-Clause + license_family: BSD + size: 405089 + timestamp: 1714723101397 From 62cf281a5c8a05e713e69d913a69078473d25ca4 Mon Sep 17 00:00:00 2001 From: kklein Date: Tue, 6 Aug 2024 12:21:32 +0200 Subject: [PATCH 6/9] Apply pchs. --- tests/test_drlearner.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py index e9d9e502..80c77029 100644 --- a/tests/test_drlearner.py +++ b/tests/test_drlearner.py @@ -2,7 +2,6 @@ # SPDX-License-Identifier: BSD-3-Clause from itertools import repeat -from sklearn.linear_model import LinearRegression, LogisticRegression import numpy as np import onnxruntime as rt From 9da63e96eabc90eeffe1f01ebe82f5f76e1760fb Mon Sep 17 00:00:00 2001 From: kklein Date: Tue, 6 Aug 2024 12:24:28 +0200 Subject: [PATCH 7/9] Rename method from treatment_effects to average_treatment_effects. --- CHANGELOG.rst | 2 +- docs/examples/example_estimating_ates.ipynb | 8 ++++---- metalearners/drlearner.py | 2 +- tests/test_drlearner.py | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 50acafdd..6fef175b 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -22,7 +22,7 @@ Changelog * Added ``_necessary_onnx_models`` to :class:`metalearners.MetaLearner`. -* Added :meth:`metalearners.metalearner.DRLearner.treatment_effect` to +* Added :meth:`metalearners.metalearner.DRLearner.average_treatment_effect` to compute AIPW point estimate and standard error for _average treatment effects (ATE)_ without requiring a full model fit (which is required for CATE diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index 4a2c93a9..d0120496 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -14,7 +14,7 @@ "\n", "Estimating average treatment effects (ATEs) involves a subset of the tasks involved in estimating Conditional Average Treatment Effects (CATEs), so we can use methods that are designed for estimating CATEs to estimate ATEs. \n", "\n", - "In this example, we simulate some data with a confounded binary treatment and demonstrate the `treatment_effect` method of the `DRLearner` class, which estimates the ATE, and compare it to estimates from some other popular libraries (`econML` and `doubleML`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n", + "In this example, we simulate some data with a confounded binary treatment and demonstrate the `average_treatment_effect` method of the `DRLearner` class, which estimates the ATE, and compare it to estimates from some other popular libraries (`econML` and `doubleML`). We then show how this approach generalizes to a setting with multiple discrete treatments, and finally to a setting with discrete-valued outcomes.\n", "\n", "Example\n", "-------" @@ -236,7 +236,7 @@ " y=df[outcome_column],\n", " w=df[treatment_column],\n", ")\n", - "metalearners_est = metalearners_dr.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + "metalearners_est = metalearners_dr.average_treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", " X=df[feature_columns],\n", " w=df[treatment_column],\n", " y=df[outcome_column],\n", @@ -248,7 +248,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Manual computation with pseudo outcome method produces the same estimate (`treatment_effect` does a generalisation of this under the hood) yields the same estimate" + "Manual computation with pseudo outcome method produces the same estimate (`average_treatment_effect` does a generalisation of this under the hood) yields the same estimate" ] }, { @@ -713,7 +713,7 @@ " y=df_multi[outcome_column],\n", " w=df_multi[treatment_column],\n", ")\n", - "metalearners_est2 = metalearners_dr_2.treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", + "metalearners_est2 = metalearners_dr_2.average_treatment_effect( # still need to pass data objects since DRLearner does not retain any data\n", " X=df_multi[feature_columns],\n", " y=df_multi[outcome_column],\n", " w=df_multi[treatment_column],\n", diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index a65735e6..1db92a19 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -338,7 +338,7 @@ def evaluate( return variant_outcome_evaluation | propensity_evaluation | treatment_evaluation - def treatment_effect( + def average_treatment_effect( self, X: Matrix, y: Vector, diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py index 80c77029..f5a942ac 100644 --- a/tests/test_drlearner.py +++ b/tests/test_drlearner.py @@ -150,5 +150,5 @@ def test_treatment_effect( n_folds=2, ) ml.fit_all_nuisance(X, Y, W) - est = ml.treatment_effect(X, Y, W) + est = ml.average_treatment_effect(X, Y, W) np.testing.assert_almost_equal(est[:, 0], tau.mean(), decimal=1) From 98d4408aca71b5def32975d4fbb3ab46afc91adc Mon Sep 17 00:00:00 2001 From: kklein Date: Tue, 6 Aug 2024 12:29:16 +0200 Subject: [PATCH 8/9] Add indicator for oos data. --- docs/examples/example_estimating_ates.ipynb | 2 ++ metalearners/drlearner.py | 8 +++++--- tests/test_drlearner.py | 2 +- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/docs/examples/example_estimating_ates.ipynb b/docs/examples/example_estimating_ates.ipynb index d0120496..9ba10c27 100644 --- a/docs/examples/example_estimating_ates.ipynb +++ b/docs/examples/example_estimating_ates.ipynb @@ -240,6 +240,7 @@ " X=df[feature_columns],\n", " w=df[treatment_column],\n", " y=df[outcome_column],\n", + " is_oos=False,\n", ")\n", "metalearners_est" ] @@ -717,6 +718,7 @@ " X=df_multi[feature_columns],\n", " y=df_multi[outcome_column],\n", " w=df_multi[treatment_column],\n", + " is_oos=False,\n", ")\n", "metalearners_est2" ] diff --git a/metalearners/drlearner.py b/metalearners/drlearner.py index 1db92a19..1bfc2ab5 100644 --- a/metalearners/drlearner.py +++ b/metalearners/drlearner.py @@ -343,6 +343,7 @@ def average_treatment_effect( X: Matrix, y: Vector, w: Vector, + is_oos: bool, ) -> np.ndarray: """Compute Average Treatment Effect (ATE) for each treatment variant using the Augmented IPW estimator (Robins et al 1994). Does not require fitting a second- @@ -351,9 +352,10 @@ def average_treatment_effect( :meth:`~metalearners.drlearner.DRLearner.fit_all_nuisance` method. Args: - X (Matrix): Covariate matrix. - y (Vector): Outcome vector. + X (Matrix): Covariate matrix + y (Vector): Outcome vector w (Vector): Treatment vector + is_oos (bool): indicator whether data is out of sample Returns: np.ndarray: Treatment effect and standard error for each treatment variant. @@ -369,7 +371,7 @@ def average_treatment_effect( w=w, y=y, treatment_variant=treatment_variant, - is_oos=False, + is_oos=is_oos, ) treatment_effect = gamma_matrix.mean(axis=0) standard_error = gamma_matrix.std(axis=0) / np.sqrt(len(X)) diff --git a/tests/test_drlearner.py b/tests/test_drlearner.py index f5a942ac..14298e21 100644 --- a/tests/test_drlearner.py +++ b/tests/test_drlearner.py @@ -150,5 +150,5 @@ def test_treatment_effect( n_folds=2, ) ml.fit_all_nuisance(X, Y, W) - est = ml.average_treatment_effect(X, Y, W) + est = ml.average_treatment_effect(X, Y, W, is_oos=False) np.testing.assert_almost_equal(est[:, 0], tau.mean(), decimal=1) From 0acba125a534242b5b28117b81f4a0bfa1dbf6b1 Mon Sep 17 00:00:00 2001 From: kklein Date: Tue, 6 Aug 2024 22:18:35 +0200 Subject: [PATCH 9/9] Update pixi lock. --- pixi.lock | 30329 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 30329 insertions(+) create mode 100644 pixi.lock diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 00000000..cb9aa727 --- /dev/null +++ b/pixi.lock @@ -0,0 +1,30329 @@ +version: 5 +environments: + benchmark: + channels: + - 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