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648 lines
25 KiB
Plaintext
648 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Save and retrieve explanations via Azure Machine Learning Run History\n",
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"\n",
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"_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to save and retrieve classification model explanations to/from Azure Machine Learning Run History.**_\n",
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"\n",
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"\n",
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"## Table of Contents\n",
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"\n",
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"1. [Introduction](#Introduction)\n",
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"1. [Setup](#Setup)\n",
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"1. [Run model explainer locally at training time](#Explain)\n",
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" 1. Apply feature transformations\n",
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" 1. Train a binary classification model\n",
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" 1. Explain the model on raw features\n",
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" 1. Generate global explanations\n",
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" 1. Generate local explanations\n",
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"1. [Upload model explanations to Azure Machine Learning Run History](#Upload)\n",
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"1. [Download model explanations from Azure Machine Learning Run History](#Download)\n",
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"1. [Visualize explanations](#Visualize)\n",
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"1. [Next steps](#Next)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Introduction\n",
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"\n",
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"This notebook showcases how to explain a classification model predictions locally at training time, upload explanations to the Azure Machine Learning's run history, and download previously-uploaded explanations from the Run History.\n",
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"It demonstrates the API calls that you need to make to upload/download the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and downloaded explanations.\n",
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"\n",
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"We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
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"\n",
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"\n",
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"\n",
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"Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n",
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"\n",
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"1. Train a SVM classification model using Scikit-learn\n",
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"2. Run 'explain-model-sample' with AML Run History, which leverages run history service to store and manage the explanation data\n",
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"---\n",
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"\n",
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"Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
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"If you are using Jupyter Labs run the following command:\n",
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"```\n",
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"(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
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"```\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Explain\n",
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"\n",
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"### Run model explainer locally at training time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
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"from sklearn.svm import SVC\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"# Explainers:\n",
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"# 1. SHAP Tabular Explainer\n",
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"from interpret.ext.blackbox import TabularExplainer\n",
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"\n",
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"# OR\n",
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"\n",
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"# 2. Mimic Explainer\n",
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"from interpret.ext.blackbox import MimicExplainer\n",
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"# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
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"from interpret.ext.glassbox import LGBMExplainableModel\n",
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"from interpret.ext.glassbox import LinearExplainableModel\n",
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"from interpret.ext.glassbox import SGDExplainableModel\n",
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"from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
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"\n",
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"# OR\n",
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"\n",
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"# 3. PFI Explainer\n",
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"from interpret.ext.blackbox import PFIExplainer "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load the IBM employee attrition data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the IBM employee attrition dataset\n",
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"outdirname = 'dataset.6.21.19'\n",
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"try:\n",
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" from urllib import urlretrieve\n",
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"except ImportError:\n",
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" from urllib.request import urlretrieve\n",
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"import zipfile\n",
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"zipfilename = outdirname + '.zip'\n",
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"urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
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"with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
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" unzip.extractall('.')\n",
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"attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
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"\n",
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"# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
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"attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
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"# Dropping Employee Number since it is merely an identifier\n",
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"attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
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"\n",
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"attritionData = attritionData.drop(['Over18'], axis=1)\n",
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"\n",
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"# Since all values are 80\n",
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"attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
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"\n",
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"# Converting target variables from string to numerical values\n",
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"target_map = {'Yes': 1, 'No': 0}\n",
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"attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
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"target = attritionData[\"Attrition_numerical\"]\n",
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"\n",
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"attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Split data into train and test\n",
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"from sklearn.model_selection import train_test_split\n",
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"x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n",
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" target, \n",
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" test_size=0.2,\n",
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" random_state=0,\n",
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" stratify=target)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Creating dummy columns for each categorical feature\n",
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"categorical = []\n",
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"for col, value in attritionXData.iteritems():\n",
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" if value.dtype == 'object':\n",
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" categorical.append(col)\n",
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" \n",
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"# Store the numerical columns in a list numerical\n",
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"numerical = attritionXData.columns.difference(categorical) "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Transform raw features"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.compose import ColumnTransformer\n",
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"\n",
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"# We create the preprocessing pipelines for both numeric and categorical data.\n",
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"numeric_transformer = Pipeline(steps=[\n",
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" ('imputer', SimpleImputer(strategy='median')),\n",
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" ('scaler', StandardScaler())])\n",
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"\n",
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"categorical_transformer = Pipeline(steps=[\n",
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" ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
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" ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
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"\n",
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"transformations = ColumnTransformer(\n",
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" transformers=[\n",
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" ('num', numeric_transformer, numerical),\n",
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" ('cat', categorical_transformer, categorical)])\n",
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"\n",
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"# Append classifier to preprocessing pipeline.\n",
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"# Now we have a full prediction pipeline.\n",
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"clf = Pipeline(steps=[('preprocessor', transformations),\n",
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" ('classifier', SVC(C=1.0, probability=True))])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"'''\n",
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"# Uncomment below if sklearn-pandas is not installed\n",
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"#!pip install sklearn-pandas\n",
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"from sklearn_pandas import DataFrameMapper\n",
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"\n",
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"# Impute, standardize the numeric features and one-hot encode the categorical features. \n",
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"\n",
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"\n",
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"numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
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"\n",
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"categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
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"\n",
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"transformations = numeric_transformations + categorical_transformations\n",
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"\n",
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"# Append classifier to preprocessing pipeline.\n",
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"# Now we have a full prediction pipeline.\n",
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"clf = Pipeline(steps=[('preprocessor', transformations),\n",
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" ('classifier', SVC(C=1.0, probability=True))]) \n",
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"\n",
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"\n",
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"\n",
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"'''"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Train a SVM classification model, which you want to explain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = clf.fit(x_train, y_train)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Explain predictions on your local machine"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 1. Using SHAP TabularExplainer\n",
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"# clf.steps[-1][1] returns the trained classification model\n",
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"explainer = TabularExplainer(clf.steps[-1][1], \n",
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" initialization_examples=x_train, \n",
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" features=attritionXData.columns, \n",
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" classes=[\"Not leaving\", \"leaving\"], \n",
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" transformations=transformations)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"# 2. Using MimicExplainer\n",
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"# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model. Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
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"# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
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"# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
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"# explainer = MimicExplainer(clf.steps[-1][1], \n",
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"# x_train, \n",
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"# LGBMExplainableModel, \n",
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"# augment_data=True, \n",
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"# max_num_of_augmentations=10, \n",
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"# features=attritionXData.columns, \n",
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"# classes=[\"Not leaving\", \"leaving\"], \n",
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"# transformations=transformations)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"# 3. Using PFIExplainer\n",
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"\n",
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"# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
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"# Note that if a metric function is provided a higher value must be better.\n",
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"# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
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"# Default metrics: \n",
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"# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
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"# Mean absolute error for regression\n",
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"\n",
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"# explainer = PFIExplainer(clf.steps[-1][1], \n",
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"# features=x_train.columns, \n",
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"# transformations=transformations,\n",
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"# classes=[\"Not leaving\", \"leaving\"])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Generate global explanations\n",
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"Explain overall model predictions (global explanation)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
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"# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
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"global_explanation = explainer.explain_global(x_test)\n",
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"\n",
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"# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
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"# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Sorted SHAP values\n",
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"print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
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"# Corresponding feature names\n",
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"print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
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"# Feature ranks (based on original order of features)\n",
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"print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
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"\n",
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"# Note: PFIExplainer does not support per class explanations\n",
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"# Per class feature names\n",
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"print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
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"# Per class feature importance values\n",
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"print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Print out a dictionary that holds the sorted feature importance names and values\n",
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"print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Explain overall model predictions as a collection of local (instance-level) explanations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Feature shap values for all features and all data points in the training data\n",
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"print('local importance values: {}'.format(global_explanation.local_importance_values))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Generate local explanations\n",
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"Explain local data points (individual instances)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Note: PFIExplainer does not support local explanations\n",
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"# You can pass a specific data point or a group of data points to the explain_local function\n",
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"\n",
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"# E.g., Explain the first data point in the test set\n",
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"instance_num = 1\n",
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"local_explanation = explainer.explain_local(x_test[:instance_num])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the prediction for the first member of the test set and explain why model made that prediction\n",
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"prediction_value = clf.predict(x_test)[instance_num]\n",
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"\n",
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"sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
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"sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
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"\n",
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"print('local importance values: {}'.format(sorted_local_importance_values))\n",
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"print('local importance names: {}'.format(sorted_local_importance_names))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Upload\n",
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"Upload explanations to Azure Machine Learning Run History"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import azureml.core\n",
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"from azureml.core import Workspace, Experiment\n",
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"from azureml.interpret import ExplanationClient\n",
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"# Check core SDK version number\n",
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"print(\"SDK version:\", azureml.core.VERSION)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ws = Workspace.from_config()\n",
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"print('Workspace name: ' + ws.name, \n",
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" 'Azure region: ' + ws.location, \n",
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" 'Subscription id: ' + ws.subscription_id, \n",
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" 'Resource group: ' + ws.resource_group, sep = '\\n')"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"experiment_name = 'explain-model-sample'\n",
|
|
"experiment = Experiment(ws, experiment_name)\n",
|
|
"run = experiment.start_logging()\n",
|
|
"client = ExplanationClient.from_run(run)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Uploading model explanation data for storage or visualization in webUX\n",
|
|
"# The explanation can then be downloaded on any compute\n",
|
|
"# Multiple explanations can be uploaded\n",
|
|
"client.upload_model_explanation(global_explanation, comment='global explanation: all features')\n",
|
|
"# Or you can only upload the explanation object with the top k feature info\n",
|
|
"#client.upload_model_explanation(global_explanation, top_k=2, comment='global explanation: Only top 2 features')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Uploading model explanation data for storage or visualization in webUX\n",
|
|
"# The explanation can then be downloaded on any compute\n",
|
|
"# Multiple explanations can be uploaded\n",
|
|
"client.upload_model_explanation(local_explanation, comment='local explanation for test point 1: all features')\n",
|
|
"\n",
|
|
"# Alterntively, you can only upload the local explanation object with the top k feature info\n",
|
|
"#client.upload_model_explanation(local_explanation, top_k=2, comment='local explanation: top 2 features')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Download\n",
|
|
"Download explanations from Azure Machine Learning Run History"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# List uploaded explanations\n",
|
|
"client.list_model_explanations()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for explanation in client.list_model_explanations():\n",
|
|
" \n",
|
|
" if explanation['comment'] == 'local explanation for test point 1: all features':\n",
|
|
" downloaded_local_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n",
|
|
" # You can pass a k value to only download the top k feature importance values\n",
|
|
" downloaded_local_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n",
|
|
" \n",
|
|
" \n",
|
|
" elif explanation['comment'] == 'global explanation: all features':\n",
|
|
" downloaded_global_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n",
|
|
" # You can pass a k value to only download the top k feature importance values\n",
|
|
" downloaded_global_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Visualize\n",
|
|
"Load the visualization dashboard"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from interpret_community.widget import ExplanationDashboard"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ExplanationDashboard(downloaded_global_explanation, model, datasetX=x_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## End\n",
|
|
"Complete the run"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"run.complete()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Next\n",
|
|
"Learn about other use cases of the explain package on a:\n",
|
|
"1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb) \n",
|
|
"1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n",
|
|
"1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n",
|
|
"1. Explain models with engineered features:\n",
|
|
" 1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n",
|
|
" 1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n",
|
|
"1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
|
|
"1. Inferencing time: deploy a classification model and explainer:\n",
|
|
" 1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
|
|
" 1. [Deploy a locally-trained keras model and explainer](../scoring-time/train-explain-model-keras-locally-and-deploy.ipynb)\n",
|
|
" 1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"authors": [
|
|
{
|
|
"name": "mesameki"
|
|
}
|
|
],
|
|
"kernelspec": {
|
|
"display_name": "Python 3.6",
|
|
"language": "python",
|
|
"name": "python36"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
} |