{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using agnostic explainers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we'll create a simple IT expression and use popular model-agnostic explainer methods to obtain feature importances for the final model.\n", "\n", "The explainers we are going to use are SHAP, LIME, and PDP.\n", "\n", "To contrast with our model-specific explainer based on the Partial Effects, the same dataset used in the regression notebook will be used.\n", "\n", "Many explainers are still in development and can change in future versions. The versions used in this notebook are commented below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import shap #0.34.0\n", "import sage #https://github.com/iancovert/sage\n", "\n", "from sklearn import datasets\n", "\n", "from sklearn.model_selection import train_test_split\n", "from IPython.display import display\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from itea.regression import ITEA_regressor\n", "from itea.inspection import *\n", "\n", "from lime import lime_tabular #0.1.1.37\n", "\n", "from sklearn.inspection import plot_partial_dependence # 0.24.2\n", "\n", "from interpret.blackbox import MorrisSensitivity #0.2.5\n", "\n", "import warnings\n", "warnings.filterwarnings(action='ignore', module=r'itea')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Performing the evolution and retrieving the best solution." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gen | smallest fitness | mean fitness | highest fitness | remaining time\n", "----------------------------------------------------------------------------\n", " 0 | 0.879653 | 1.109206 | 1.153697 | 1min21sec \n", " 10 | 0.815819 | 0.852481 | 1.133898 | 4min29sec \n", " 20 | 0.785301 | 0.795977 | 0.872630 | 3min27sec \n", " 30 | 0.774451 | 0.780758 | 0.826534 | 3min16sec \n", " 40 | 0.769231 | 0.776211 | 0.800035 | 2min27sec \n", " 50 | 0.769231 | 0.774910 | 0.861931 | 1min55sec \n", " 60 | 0.763084 | 0.780482 | 0.995355 | 1min7sec \n", " 70 | 0.748003 | 0.773957 | 0.860793 | 0min20sec \n", "-15.231*log(MedInc * Population * AveOccup * Latitude^2) + 1.323*log(MedInc^2 * AveBedrms * Longitude^2) + 37.981*log(MedInc * Population * AveOccup * Latitude * Longitude^2) + -0.399*log(MedInc^2 * AveRooms^2 * AveBedrms * AveOccup * Latitude^2 * Longitude^2) + -11.439*log(MedInc^2 * Population^2 * AveOccup^2 * Latitude^2 * Longitude^2) + -202.481\n" ] } ], "source": [ "housing_data = datasets.fetch_california_housing() \n", "X, y = housing_data['data'], housing_data['target']\n", "labels = housing_data['feature_names']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.33, random_state=42)\n", "\n", "tfuncs = {\n", " 'log' : np.log,\n", " 'sqrt.abs' : lambda x: np.sqrt(np.abs(x)), \n", " 'id' : lambda x: x,\n", " 'sin' : np.sin,\n", " 'cos' : np.cos,\n", " 'exp' : np.exp\n", "}\n", "\n", "tfuncs_dx = {\n", " 'log' : lambda x: 1/x,\n", " 'sqrt.abs' : lambda x: x/( 2*(np.abs(x)**(3/2)) ),\n", " 'id' : lambda x: np.ones_like(x),\n", " 'sin' : np.cos,\n", " 'cos' : lambda x: -np.sin(x),\n", " 'exp' : np.exp,\n", "}\n", "\n", "reg = ITEA_regressor(\n", " gens = 75,\n", " popsize = 75,\n", " max_terms = 5,\n", " expolim = (0, 2),\n", " verbose = 10,\n", " tfuncs = tfuncs,\n", " tfuncs_dx = tfuncs_dx,\n", " labels = labels,\n", " random_state = 42,\n", " simplify_method = 'simplify_by_var'\n", ").fit(X_train, y_train)\n", "\n", "final_itexpr = reg.bestsol_\n", "\n", "print(final_itexpr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explaining with PDP\n", "\n", "Since ``ITExpr_regressor`` is a subclass of scikit-learn base classes ``RegressorMixin`` and ``BaseEstimator``, we can use the Partial Dependency Plot (PDP) and Individual Conditional Expectation (ICE), that works with any scikit estimator.\n", "\n", "The ``plot_partial_dependence`` function from scikit-learn package allows explaining any scikit machine learning model. It can be used to explain an ITEA model.\n", "\n", "The PDP is discussed and the ICE is proposed in paper _A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation, Journal of Computational and Graphical Statistics, 24(1): 44-65, Springer, 2015._" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(2, 4, figsize=(10, 5))\n", "\n", "plot_partial_dependence(\n", " estimator = final_itexpr,\n", " X = X_train,\n", " features = range(8),\n", " n_jobs = 3,\n", " kind = 'both',\n", " subsample = 25,\n", " grid_resolution = 100,\n", " ax = axs,\n", " method = 'auto'\n", ")\n", "\n", "for ax, label in zip(fig.axes, labels):\n", " ax.set_xlabel(label)\n", " ax.set_ylim([0, 7])\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The PDP function lets you specify two features to create a more interesting visualization of the relationship between them. Let us create this plot using the top 2 most important features." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n", "\n", "top1, top2 = (-final_itexpr.feature_importances_).argsort()[0, :2]\n", "\n", "plot_partial_dependence(\n", " estimator = final_itexpr,\n", " X = X,\n", " features = [(top1, top2)],\n", " n_jobs = 3,\n", " subsample = 10,\n", " grid_resolution = 50,\n", " ax = ax,\n", " method = 'auto'\n", ")\n", "\n", "plt.xlabel(labels[top1])\n", "plt.ylabel(labels[top2])\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explaining with SHAP\n", "\n", "The SHapley Additive exPlanations (SHAP) was proposed in _Lundberg, Scott & Lee, Su-In. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems. Curran Associates, Inc.._ \n", "\n", "SHAP package implements several visualization plots, including the bar plot." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a441518eaf324732932ec1ada5be2af7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap_explainer = shap.KernelExplainer(\n", " final_itexpr.predict,\n", " shap.sample(pd.DataFrame(X_train, columns=labels), 100)\n", ")\n", "\n", "shap.summary_plot(\n", " shap_explainer.shap_values(\n", " X_test[:100, :], l1_reg='num_features(10)'),\n", " shap.sample(\n", " pd.DataFrame(X_test[:100, :], columns=labels), 100),\n", " plot_type=\"bar\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can obtain explanations for a single instance with the ``force_plot``:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap.force_plot(\n", " shap_explainer.expected_value,\n", " shap_explainer.shap_values(\n", " X_test[0,:], l1_reg='num_features(10)'),\n", " pd.DataFrame(X_test, columns=labels).iloc[0,:],\n", " matplotlib=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SHAP also implements a dependence plot:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0ec628e3194c4e5eacd796138d4a9035", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "shap.dependence_plot(\n", " \"AveOccup\",\n", " shap_explainer.shap_values(\n", " X_test[:100, :], l1_reg='num_features(10)'),\n", " pd.DataFrame(X_test[:100, :], columns=labels)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explaining with LIME\n", "\n", "Local Interpretable Model-agnostic Explainer (LIME) was proposed in _Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. \"Why should I trust you?: Explaining the predictions of any classifier.\" Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016._" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", "
\n", " \n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explainer = lime_tabular.LimeTabularExplainer(\n", " X_train, mode=\"regression\", feature_names= labels)\n", "\n", "# LIME only supports local explanations.\n", "explanation = explainer.explain_instance(\n", " X_test[0], final_itexpr.predict, num_features=len(labels))\n", "\n", "explanation.show_in_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Morris Sensitivity\n", "\n", "InterpretML is an interpretability dashboard with the implementation of many model agnostic explainers created by Microsoft, reported in the paper _\"InterpretML: A Unified Framework for Machine Learning Interpretability\" (H. Nori, S. Jenkins, P. Koch, and R. Caruana 2019)_.\n", "\n", "While they implement the SHAP, LIME, and PDP, those were already used in the previous cells in this notebook (although they have interactive plots, which can be cool for deep interpretability analyses). One new model agnostic explainer that they provide and can be used with ITEA is the Morris Sensitivity explainer.\n", "\n", "This method changes one step at a time (OAT) to evaluate the sensitivity of the model regarding each variable independently (meaning that it [cannot differentiate interactions with non-linearities]([https://interpret.ml/docs/msa.html)), but have a fast computation of the feature importances in a global perspective.\n", "\n", "The plot function of the interpretML package uses plotly, making difficult to save and report the plot in this notebook. Thankfully, they have a method to access the data used in the plots. In te cell bellow we'll extract the importances and recreate the plot using matplotlib." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'names': ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], 'scores': masked_array(data=[5.338079860378901, 0.0, 3.234932254223549,\n", " 3.3656159672731563, 0.8078390455963547,\n", " 1.9215062891530326, 4.056309025452067,\n", " 4.454333671566609],\n", " mask=[False, False, False, False, False, False, False, False],\n", " fill_value=1e+20), 'convergence_index': 0.04371404122595719}\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "msa = MorrisSensitivity(\n", " predict_fn=final_itexpr.predict, data=X_test, feature_names=final_itexpr.labels.tolist(),\n", " feature_types=['continuous']*8\n", ")\n", "msa_global = msa.explain_global()\n", "\n", "data = msa_global.data()\n", "\n", "print(data)\n", "\n", "fig, ax = plt.subplots()\n", "\n", "features = np.array(data['names'])\n", "y_pos = np.arange(len(data['names']))\n", "importances = np.array(data['scores'])\n", "order = np.argsort(importances)[::-1]\n", "\n", "ax.barh(y_pos, importances[order], align='center')\n", "\n", "ax.set_yticks(y_pos)\n", "ax.set_yticklabels(features[order])\n", "ax.invert_yaxis()\n", "\n", "ax.set_title(\n", " f'Morris Sensitivity\\nConvergence Index: {np.round(data[\"convergence_index\"], 3)}')\n", "\n", "ax.grid(axis='y', zorder=-1, ls=':')\n", "ax.set_axisbelow(True)\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explaining with sage\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Setup and calculate\n", "imputer = sage.MarginalImputer(final_itexpr, X_test[:512])\n", "estimator = sage.PermutationEstimator(imputer, 'mse')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e30188c204ba46c0ae1c04844bd742f4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sage_values = estimator(X_test, y_test)\n", "sage_values.plot(final_itexpr.labels)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating model sensitivity (Shapley Effects, not SAGE)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6cdeb98b48e84e6d9313a66a905a7c98", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sensitivity = estimator(X_test)\n", "sensitivity.plot(final_itexpr.labels, title='Model Sensitivity')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating explanations with ``ITEA_summarizer``\n", "\n", "The ``ITEA_summarizer`` provides a method to automatically generate multiple interpretability plots given an ``ITEA`` instance.\n", "\n", "To compare the explanations from different explainers with the explanations through Partial Effects, we'll use the ``autoreport()`` method to create a pdf file.\n", "\n", "This file will have several explanations of the given ``itexpr``, and the file generated from the cell below can be downloaded [by clicking here](https://galdeia.github.io/itea-python/_static/files/Report.pdf)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "summarizer = (\n", " ITEA_summarizer(itea=reg)\n", " .fit(X_train, y_train)\n", " .autoreport(save_path='.')\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }