MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.ipynb

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      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
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      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/tabular-data/explain-multiclass-classification-local.png)"
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      "source": [
        "# Explain multiclass classification model's predictions\n",
        "_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to explain and visualize a multiclass classification model predictions.**_\n",
        "\n",
        "\n",
        "\n",
        "## Table of Contents\n",
        "\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Run model explainer locally at training time](#Explain)\n",
        "    1. Train a multiclass classification model\n",
        "    1. Explain the model\n",
        "        1. Generate global explanations\n",
        "        1. Generate local explanations\n",
        "1. [Visualize results](#Visualize)\n",
        "1. [Next steps](#Next)"
      ]
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      "source": [
        "## Introduction\n",
        "\n",
        "This notebook illustrates how to explain a multiclass classification model predictions locally at training time without contacting any Azure services.\n",
        "It demonstrates the API calls that you need to make to get the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and explanations.\n",
        "\n",
        "We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
        "\n",
        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
        "|:--:|\n",
        "| *Interpretability Toolkit Architecture* |\n",
        "\n",
        "Problem: Iris flower classification with scikit-learn (run model explainer locally)\n",
        "\n",
        "1. Train a SVM classification model using Scikit-learn\n",
        "2. Run 'explain_model' globally and locally with full dataset in local mode, which doesn't contact any Azure services.\n",
        "3. Visualize the global and local explanations with the visualization dashboard.\n",
        "---\n",
        "\n",
        "Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
        "If you are using Jupyter Labs run the following command:\n",
        "```\n",
        "(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explain\n",
        "\n",
        "### Run model explainer locally at training time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn import svm\n",
        "\n",
        "# Explainers:\n",
        "# 1. SHAP Tabular Explainer\n",
        "from interpret.ext.blackbox import TabularExplainer\n",
        "\n",
        "# OR\n",
        "\n",
        "# 2. Mimic Explainer\n",
        "from interpret.ext.blackbox import MimicExplainer\n",
        "# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
        "from interpret.ext.glassbox import LGBMExplainableModel\n",
        "from interpret.ext.glassbox import LinearExplainableModel\n",
        "from interpret.ext.glassbox import SGDExplainableModel\n",
        "from interpret.ext.glassbox import DecisionTreeExplainableModel\n",
        "\n",
        "# OR\n",
        "\n",
        "# 3. PFI Explainer\n",
        "from interpret.ext.blackbox import PFIExplainer "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load the Iris flower dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iris = load_iris()\n",
        "X = iris['data']\n",
        "y = iris['target']\n",
        "classes = iris['target_names']\n",
        "feature_names = iris['feature_names']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Split data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Train a SVM classification model, which you want to explain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
        "model = clf.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Explain predictions on your local machine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# 1. Using SHAP TabularExplainer\n",
        "explainer = TabularExplainer(model, \n",
        "                             x_train, \n",
        "                             features=feature_names, \n",
        "                             classes=classes)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# 2. Using MimicExplainer\n",
        "# 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",
        "# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
        "# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
        "# explainer = MimicExplainer(model, \n",
        "#                            x_train, \n",
        "#                            LGBMExplainableModel, \n",
        "#                            augment_data=True, \n",
        "#                            max_num_of_augmentations=10, \n",
        "#                            features=feature_names, \n",
        "#                            classes=classes)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# 3. Using PFIExplainer\n",
        "\n",
        "# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
        "# Note that if a metric function is provided a higher value must be better.\n",
        "# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
        "# Default metrics: \n",
        "# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
        "# Mean absolute error for regression\n",
        "\n",
        "# explainer = PFIExplainer(model, \n",
        "#                          features=feature_names, \n",
        "#                          classes=classes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Generate global explanations\n",
        "Explain overall model predictions (global explanation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
        "# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
        "global_explanation = explainer.explain_global(x_test)\n",
        "\n",
        "# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
        "# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Sorted SHAP values\n",
        "print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
        "# Corresponding feature names\n",
        "print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
        "# Feature ranks (based on original order of features)\n",
        "print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
        "\n",
        "# Note: PFIExplainer does not support per class explanations\n",
        "# Per class feature names\n",
        "print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
        "# Per class feature importance values\n",
        "print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Print out a dictionary that holds the sorted feature importance names and values\n",
        "print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Explain overall model predictions as a collection of local (instance-level) explanations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# feature shap values for all features and all data points in the training data\n",
        "print('local importance values: {}'.format(global_explanation.local_importance_values))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Generate local explanations\n",
        "Explain local data points (individual instances)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Note: PFIExplainer does not support local explanations\n",
        "# You can pass a specific data point or a group of data points to the explain_local function\n",
        "\n",
        "# E.g., Explain the first data point in the test set\n",
        "instance_num = 0\n",
        "local_explanation = explainer.explain_local(x_test[instance_num,:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Get the prediction for the first member of the test set and explain why model made that prediction\n",
        "prediction_value = clf.predict(x_test)[instance_num]\n",
        "\n",
        "sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
        "sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
        "\n",
        "print('local importance values: {}'.format(sorted_local_importance_values))\n",
        "print('local importance names: {}'.format(sorted_local_importance_names))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Visualize\n",
        "Load the visualization dashboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.contrib.interpret.visualize import ExplanationDashboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ExplanationDashboard(global_explanation, model, x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Next\n",
        "Learn about other use cases of the explain package on a:\n",
        "\n",
        "1. [Training time: regression problem](./explain-regression-local.ipynb)       \n",
        "1. [Training time: binary classification problem](./explain-binary-classification-local.ipynb)\n",
        "1. Explain models with engineered features:\n",
        "    1. [Simple feature transformations](./simple-feature-transformations-explain-local.ipynb)\n",
        "    1. [Advanced feature transformations](./advanced-feature-transformations-explain-local.ipynb)\n",
        "1. [Save model explanations via Azure Machine Learning Run History](../azure-integration/run-history/save-retrieve-explanations-run-history.ipynb)\n",
        "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../azure-integration/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](../azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
        "    1. [Deploy a remotely-trained model and explainer](../azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)\n",
        "\u00e2\u20ac\u2039\n"
      ]
    }
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