diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 7f30b50..a89a083 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 e653918..4a2c93a 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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+ [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 ad95031..2ef1439 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 ae5f7c4..18df8af 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 8baa1d0..7227f07 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 - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.15.4-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 @@ -33,7 +33,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.53.0-py310hc51659f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.53.1-py310h5b4e0ec_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/forestci-0.6-pyha21a80b_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.10-h36c2ea0_0.tar.bz2 @@ -45,10 +45,10 @@ environments: - 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conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda @@ -56,49 +56,49 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2 - - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_h59595ed_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_he02047a_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_openblas.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libboost-1.84.0-hba137d9_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda - 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