{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L2X (learning to explain) for income prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of the L2X explainer. Different from LIME, SHAP or MACE, L2X trains a separate model for explanation. The advantage of L2X is that it generates explanations fast after the explanation model is trained. The disadvantage is that the quality of the explanations highly depend on the trained explanation model, which can be affected by multiple factors, e.g., the network structure of the explanation model, the training hyperparameters. \n",
    "\n",
    "For tabular data, we implement the default explanation model in `omnixai.explainers.tabular.agnostic.L2X.l2x`. One may implement other models by following the same interface (please refer to the docs for more details). If using this explainer, please cite the original work: \"Learning to Explain: An Information-Theoretic Perspective on Model Interpretation, Jianbo Chen, Le Song, Martin J. Wainwright, Michael I. Jordan, https://arxiv.org/abs/1802.07814\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This default renderer is used for sphinx docs only. Please delete this cell in IPython.\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"png\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sklearn\n",
    "import xgboost\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from omnixai.data.tabular import Tabular\n",
    "from omnixai.preprocessing.tabular import TabularTransform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset used in this example is for income prediction (https://archive.ics.uci.edu/ml/datasets/adult). We recommend using `Tabular` to represent a tabular dataset, which can be constructed from a pandas dataframe or a numpy array. To create a `Tabular` instance given a numpy array, one needs to specify the data, the feature names, the categorical feature names (if exists) and the target/label column name (if exists)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\n",
    "    \"Age\", \"Workclass\", \"fnlwgt\", \"Education\",\n",
    "    \"Education-Num\", \"Marital Status\", \"Occupation\",\n",
    "    \"Relationship\", \"Race\", \"Sex\", \"Capital Gain\",\n",
    "    \"Capital Loss\", \"Hours per week\", \"Country\", \"label\"\n",
    "]\n",
    "data = np.genfromtxt(os.path.join('../data', 'adult.data'), delimiter=', ', dtype=str)\n",
    "tabular_data = Tabular(\n",
    "    data,\n",
    "    feature_columns=feature_names,\n",
    "    categorical_columns=[feature_names[i] for i in [1, 3, 5, 6, 7, 8, 9, 13]],\n",
    "    target_column='label'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TabularTransform` is a special transform designed for tabular data. By default, it converts categorical features into one-hot encoding, and keeps continuous-valued features (if one wants to normalize continuous-valued features, set the parameter `cont_transform` in `TabularTransform` to `Standard` or `MinMax`). The `transform` method of `TabularTransform` will transform a `Tabular` instance into a numpy array. If the `Tabular` instance has a target/label column, the last column of the transformed numpy array will be the target/label. \n",
    "\n",
    "If some other transformations that are not supported in the library are necessary, one can simply convert the `Tabular` instance into a pandas dataframe by calling `Tabular.to_pd()` and try different transformations with it.\n",
    "\n",
    "After data preprocessing, we can train a XGBoost classifier for this task (one may try other classifiers)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape: (26048, 108)\n",
      "Test data shape:     (6513, 108)\n",
      "Test accuracy: 0.8668816213726394\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "transformer = TabularTransform().fit(tabular_data)\n",
    "class_names = transformer.class_names\n",
    "x = transformer.transform(tabular_data)\n",
    "train, test, labels_train, labels_test = \\\n",
    "    sklearn.model_selection.train_test_split(x[:, :-1], x[:, -1], train_size=0.80)\n",
    "print('Training data shape: {}'.format(train.shape))\n",
    "print('Test data shape:     {}'.format(test.shape))\n",
    "\n",
    "gbtree = xgboost.XGBClassifier(n_estimators=300, max_depth=5)\n",
    "gbtree.fit(train, labels_train)\n",
    "print('Test accuracy: {}'.format(\n",
    "    sklearn.metrics.accuracy_score(labels_test, gbtree.predict(test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction function takes a `Tabular` instance as its inputs, and outputs the class probabilities or logits for classification tasks or the estimated values for regression tasks. In this example, we simply call `transformer.transform` to do data preprocessing followed by the prediction function of `gbtree`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_function=lambda z: gbtree.predict_proba(transformer.transform(z))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To initialize a L2X explainer, we need to set:\n",
    "  \n",
    "  - `training_data`: The data used to train the explainer. ``training_data`` should be the training dataset for training the machine learning model.\n",
    "  - `predict_function`: The prediction function corresponding to the model.\n",
    "  - `mode`: The task type, e.g., \"classification\" or \"regression\".\n",
    "  - `selection_model`: A pytorch model class for estimating P(S|X) in L2X. If `selection_model = None`, a default model `DefaultSelectionModel` will be used.\n",
    "  - `prediction_model`: A pytorch model class for estimating Q(X_S) in L2X. If `prediction_model = None`, a default model `DefaultPredictionModel` will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " |████████████████████████████████████████| 100.0% Complete, Loss 0.1953\n",
      "L2X prediction model accuracy: 0.8647768803169436\n"
     ]
    }
   ],
   "source": [
    "from omnixai.explainers.tabular.agnostic.L2X.l2x import L2XTabular\n",
    "\n",
    "explainer = L2XTabular(\n",
    "    training_data=tabular_data,\n",
    "    predict_function=predict_function,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L2X generates local explanations, e.g. `explainer.explain` is called given the test instances. `ipython_plot` plots the generated explanations in IPython. Parameter `index` indicates which instance in `test_x` to plot, e.g., `index = 0` means plotting the first instance in `test_x`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1653\n",
    "test_x = tabular_data[i:i + 5]\n",
    "explanations = explainer.explain(test_x)\n",
    "explanations.ipython_plot(index=0, class_names=class_names)"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}