{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Python: Choice of learners\n", "\n", "This notebooks contains some practical recommendations to choose the right learner and evaluate different learners for the corresponding nuisance components.\n", "\n", "For the example, we will work with a IRM, but all of the important components are directly usable for all other models too.\n", "\n", "To be able to compare the properties of different learners, we will start by setting the true treatment parameter to zero, fix some other parameters of the data generating process and generate several datasets \n", "to obtain some information about the distribution of the estimators." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import doubleml as dml\n", "\n", "from doubleml.datasets import make_irm_data\n", "\n", "theta = 0\n", "n_obs = 500\n", "dim_x = 5\n", "n_rep = 200\n", "\n", "np.random.seed(42)\n", "datasets = []\n", "for i in range(n_rep):\n", " data = make_irm_data(theta=theta, n_obs=n_obs, dim_x=dim_x, \n", " R2_d=0.8, R2_y=0.8, return_type='DataFrame')\n", " datasets.append(dml.DoubleMLData(data, 'y', 'd'))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing different learners\n", "For simplicity, we will restrict ourselves to the comparison of two different types and evaluate a learner of linear type and a tree based estimator for each nuisance component (with default hyperparameters)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression, LogisticRegressionCV\n", "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", "from sklearn.base import clone\n", "\n", "reg_learner_1 = LinearRegression()\n", "reg_learner_2 = GradientBoostingRegressor()\n", "class_learner_1 = LogisticRegressionCV()\n", "class_learner_2 = GradientBoostingClassifier()\n", "\n", "learner_list = [{'ml_g': reg_learner_1, 'ml_m': class_learner_1},\n", " {'ml_g': reg_learner_2, 'ml_m': class_learner_1},\n", " {'ml_g': reg_learner_1, 'ml_m': class_learner_2},\n", " {'ml_g': reg_learner_2, 'ml_m': class_learner_2}]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "In all combinations, we now can try to evaluate four different IRM models. To make the comparison fair, we will apply all different models to the same cross-fitting samples (usually this should not matter, we only consider this here to get slightly cleaner comparison)." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Standard approach\n", "\n", "At first, we will look at the most straightforward approach using the inbuild RMSE. The `rmses` attribute contains the out-of-sample RMSE for the nuisance functions. We will save all RMSEs and the corresponding treatment estimates for all combinations of learners over all repetitions." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing: 100.0 %\n", "Coverage: [0.935 0.65 0.975 0.95 ]\n" ] } ], "source": [ "from doubleml._utils_resampling import DoubleMLResampling\n", "\n", "coefs = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "rmses_ml_m = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "rmses_ml_g0 = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "rmses_ml_g1 = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "\n", "coverage = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "\n", "for i_rep in range(n_rep):\n", " print(f\"\\rProcessing: {round((i_rep+1)/n_rep*100, 3)} %\", end=\"\")\n", " dml_data = datasets[i_rep]\n", " # define the sample splitting\n", " smpls = DoubleMLResampling(n_folds=5, n_rep=1, n_obs=n_obs,\n", " apply_cross_fitting=True, stratify=dml_data.d).split_samples()\n", " \n", " for i_learners, learners in enumerate(learner_list):\n", " np.random.seed(42)\n", " dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=clone(learners['ml_g']),\n", " ml_m=clone(learners['ml_m']),\n", " draw_sample_splitting=False)\n", " dml_irm.set_sample_splitting(smpls)\n", " dml_irm.fit(n_jobs_cv=5)\n", "\n", " coefs[i_rep, i_learners] = dml_irm.coef[0]\n", " rmses_ml_m[i_rep, i_learners] = dml_irm.rmses['ml_m']\n", " rmses_ml_g0[i_rep, i_learners] = dml_irm.rmses['ml_g0']\n", " rmses_ml_g1[i_rep, i_learners] = dml_irm.rmses['ml_g1']\n", "\n", " confint = dml_irm.confint()\n", " coverage[i_rep, i_learners] = (confint['2.5 %'].iloc[0] <= theta) & (confint['97.5 %'].iloc[0] >= theta)\n", "\n", "print(f'\\nCoverage: {coverage.mean(0)}')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Next, let us take a look at the corresponding results" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "colnames = ['Linear + Logit','Boost + Logit', 'Linear + Boost', 'Boost + Boost']\n", "\n", "df_coefs = pd.DataFrame(coefs, columns=colnames)\n", "df_ml_m = pd.DataFrame(rmses_ml_m, columns=colnames)\n", "df_ml_g0 = pd.DataFrame(rmses_ml_g0, columns=colnames)\n", "df_ml_g1 = pd.DataFrame(rmses_ml_g1, columns=colnames)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(15, 5))\n", "fig.suptitle('Learner Comparison')\n", "\n", "\n", "sns.kdeplot(data=df_coefs,ax=axes[0][0], fill=True, alpha=0.3)\n", "sns.kdeplot(data=df_ml_m, ax=axes[0][1], fill=True, alpha=0.3, legend=False)\n", "sns.kdeplot(data=df_ml_g0, ax=axes[1][0], fill=True, alpha=0.3, legend=False)\n", "sns.kdeplot(data=df_ml_g1, ax=axes[1][1], fill=True, alpha=0.3)\n", "\n", "axes[0][0].title.set_text('Estimated Parameter')\n", "axes[0][1].title.set_text('RMSE ml_m')\n", "axes[1][0].title.set_text('RMSE ml_g0')\n", "axes[1][1].title.set_text('RMSE ml_g1')\n", "\n", "plt.subplots_adjust(left=0.1,\n", " bottom=0.1, \n", " right=0.9, \n", " top=0.9, \n", " wspace=0.4, \n", " hspace=0.4)\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We can now easily observe that in this setting, the linear learners are able to approximate the corresponding nuisance functions better than the boosting algorithm (as should be expected since the data is generated accordingly).\n", "\n", "Let us take a look at what would have happend if a each repetition for each nuisance element, we would have selected the learner with smallest out-of-sample rmse (in our example this corresponds to minimizing the product of rmses). \n", "Remark that we cannot select different learners for `ml_g0` and `ml_g1`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0, 2], dtype=int64), array([191, 9], dtype=int64))" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selected_learners = (rmses_ml_m * (rmses_ml_g0 + rmses_ml_g1)).argmin(axis=1)\n", "np.unique(selected_learners, return_counts=True)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Most of the time, we will use linear learners for both nuisance elements. Sometimes the tree-based estimator is chosen for the propensity score `ml_m`. \n", "Let us compare which learners, how the estimated coefficients would have performed with the selected learners." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coverage of selected learners: 0.94\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "print(f'Coverage of selected learners: {np.mean(np.array([coverage[i_rep, selected_learners[i_rep]] for i_rep in range(n_rep)]))}')\n", "\n", "selected_coefs = np.array([coefs[i_rep, selected_learners[i_rep]] for i_rep in range(n_rep)])\n", "df_coefs['Selected'] = selected_coefs\n", "sns.kdeplot(data=df_coefs, fill=True, alpha=0.3)\n", "plt.show()\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "This procedure will be generally valid as long as we do not compare a excessively large number of different learners." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Custom evaluation metrics\n", "\n", "If one wants to evaluate a learner based on some other metric/loss it is possible to use the inbuilt `evaluate_learners()` method.\n", "Without further arguments this will default to the RMSE for all nuisance components and result in the same output as the `rmses` attribute." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'ml_g0': array([[1.02649578]]), 'ml_g1': array([[1.06046108]]), 'ml_m': array([[0.34943161]])}\n", "{'ml_g0': array([[1.02649578]]), 'ml_g1': array([[1.06046108]]), 'ml_m': array([[0.34943161]])}\n" ] } ], "source": [ "print(dml_irm.evaluate_learners())\n", "print(dml_irm.rmses)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To evaluate a self-defined metric, the user has to hand over a callable. In this example, we define the mean absolute deviation as an error metric.\n", "\n", "Remark that the metric should be able to handle `nan` values, since e.g. in the IRM model the learner `ml_g` is used to onto two different subsamples. As a result, we have two different nuisance components for\n", "\n", "$$\n", "\\begin{aligned}\n", "g_0(x) &= \\mathbb{E}[Y|X=x, D=0] \\\\\n", "g_1(x) &= \\mathbb{E}[Y|X=x, D=1]\n", "\\end{aligned}\n", "$$\n", "\n", "which are both fitted with the learner `ml_g`. Of course, we can only observe the target value for $g_0(x)$ if $D=0$ and vice versa, resulting in `nan` values for all other observations." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ml_g0': array([[0.82261299]]),\n", " 'ml_g1': array([[0.853177]]),\n", " 'ml_m': array([[0.20148598]])}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import mean_absolute_error\n", "\n", "def mae(y_true, y_pred):\n", " subset = np.logical_not(np.isnan(y_true))\n", " return mean_absolute_error(y_true[subset], y_pred[subset])\n", "\n", "dml_irm.evaluate_learners(metric=mae)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Another option is to access the out-of-sample predictions and target values for the nuisance elements via the `nuisance_targets` and `predictions` attributes." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 1, 1)\n", "(500, 1, 1)\n" ] } ], "source": [ "print(dml_irm.nuisance_targets['ml_g1'].shape)\n", "print(dml_irm.predictions['ml_g1'].shape)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "For most models minimizing the RMSE for each learner should result in improved performance as the theoretical backbone of the DML Framework is build on $\\ell_2$-convergence rates for the nuisance estimates ([Chernozhukov et al. (2018)](https://doi.org/10.1111/ectj.12097)). But for some models (e.g. classification learners) it might be helpful to further check other error metrics (e.g. as in [scikit-learn](https://scikit-learn.org/stable/modules/model_evaluation.html#)) to gain a overview whether the nuisance function can be approximated sufficiently well. \n", "\n", "Of course, if one has some prior knowledge on functional form assumptions (e.g. linearity as in the IRM example above) using these learners will usually improve the performance of the estimator and might speed up computation time." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Computation time\n", "\n", "The choice of the learner has a huge impact on the computation time of the DoubleML models. As the largest part of the computation time is usually used to train the learners for the nuisance components, some clever choices of learners and hyperparameters can speed up the computation time. \n", "\n", "Resourcewise, most implementations support the `n_jobs_cv` argument, which can parallelize the k-fold estimation and might speed up the calculation nearly up to $k$-times if the resources are available." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time without parallelization of crossfitting: 5.7683 seconds\n", "Time with parallelization of crossfitting: 1.1492 seconds\n", "Speedup of factor 5.02\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", "from time import perf_counter\n", "\n", "np.random.seed(42)\n", "n_obs = 1000\n", "dml_data = dml.DoubleMLData(make_irm_data(theta=0, n_obs=n_obs, dim_x=20, return_type='DataFrame'), 'y', 'd')\n", "\n", "# define the sample splitting\n", "smpls = DoubleMLResampling(n_folds=5, n_rep=1, n_obs=n_obs, apply_cross_fitting=True, stratify=dml_data.d).split_samples()\n", "\n", "dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=RandomForestRegressor(),\n", " ml_m=RandomForestClassifier(),\n", " draw_sample_splitting=False)\n", "dml_irm.set_sample_splitting(smpls)\n", "\n", "np.random.seed(42)\n", "t_1_start = perf_counter()\n", "dml_irm.fit()\n", "t_1_stop = perf_counter()\n", "print(f'Time without parallelization of crossfitting: {round(t_1_stop - t_1_start, 4)} seconds')\n", "\n", "np.random.seed(42)\n", "t_2_start = perf_counter()\n", "dml_irm.fit(n_jobs_cv=5)\n", "t_2_stop = perf_counter()\n", "print(f'Time with parallelization of crossfitting: {round(t_2_stop - t_2_start, 4)} seconds')\n", "print(f'Speedup of factor {round((t_1_stop - t_1_start) / (t_2_stop - t_2_start), 2)}')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Other more helpful ways to improve computation time will largly depend on the implemented learner. Of course linear learners are quite fast, but if no functional form restrictions are known Boosting or Random Forest might be better default options to saveguard against wrong model assumptions. Especially Boosting performs very well as a default option for tabular data. As a general recommendation all popular Boosting frameworks (XGBoost, Lightgbm, Catboost, etc.) should improve computation time.\n", "But this might vary heavily with the number of features in your dataset.\n", "Let us compare the computation time with Boosting and Random Forest (we increase the sample size and the number of features)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time without RandomForest (Scikit-Learn): 11.7578 seconds\n", "Time with XGBoost: 0.7731 seconds\n", "Speedup of factor 15.21\n", "Time with LightGBM: 0.5987 seconds\n", "Speedup of factor 19.64\n" ] } ], "source": [ "from xgboost import XGBClassifier, XGBRegressor\n", "from lightgbm import LGBMClassifier, LGBMRegressor\n", "\n", "np.random.seed(42)\n", "n_obs = 1000\n", "dml_data = dml.DoubleMLData(make_irm_data(theta=0, n_obs=n_obs, dim_x=50, return_type='DataFrame'), 'y', 'd')\n", "\n", "# define the sample splitting\n", "smpls = DoubleMLResampling(n_folds=5, n_rep=1, n_obs=n_obs, apply_cross_fitting=True, stratify=dml_data.d).split_samples()\n", "\n", "np.random.seed(42)\n", "t_1_start = perf_counter()\n", "dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=RandomForestRegressor(),\n", " ml_m=RandomForestClassifier(),\n", " draw_sample_splitting=False)\n", "dml_irm.set_sample_splitting(smpls)\n", "dml_irm.fit()\n", "t_1_stop = perf_counter()\n", "print(f'Time without RandomForest (Scikit-Learn): {round(t_1_stop - t_1_start, 4)} seconds')\n", "\n", "np.random.seed(42)\n", "t_2_start = perf_counter()\n", "dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=XGBRegressor(),\n", " ml_m=XGBClassifier(),\n", " draw_sample_splitting=False)\n", "dml_irm.set_sample_splitting(smpls)\n", "dml_irm.fit()\n", "t_2_stop = perf_counter()\n", "print(f'Time with XGBoost: {round(t_2_stop - t_2_start, 4)} seconds')\n", "print(f'Speedup of factor {round((t_1_stop - t_1_start) / (t_2_stop - t_2_start), 2)}')\n", "\n", "np.random.seed(42)\n", "t_3_start = perf_counter()\n", "dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=LGBMRegressor(),\n", " ml_m=LGBMClassifier(),\n", " draw_sample_splitting=False)\n", "dml_irm.set_sample_splitting(smpls)\n", "dml_irm.fit()\n", "t_3_stop = perf_counter()\n", "print(f'Time with LightGBM: {round(t_3_stop - t_3_start, 4)} seconds')\n", "print(f'Speedup of factor {round((t_1_stop - t_1_start) / (t_3_stop - t_3_start), 2)}')" ] } ], "metadata": { "kernelspec": { "display_name": "doubleml", "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": "3.10.8" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }