Chapter07/Recidivism_part1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criminal Recidivism Prediction (part 1)\n",
    "#### CHAPTER 07: _Anchor and Counterfactual Explanations_\n",
    "##### From **[Interpretable Machine Learning with Python](https://www.packtpub.com/product/interpretable-machine-learning-with-python/9781800203907)** by Serg Masís, published by Packt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Preparations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing the Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all already installed on Google Colab by default so install only if running elsewhere (and ***not already installed***):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade pandas numpy scikit-learn tensorflow matplotlib seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install these if running on Google Colab or ***not already installed***:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade machine-learning-datasets\n",
    "!pip install --upgrade catboost alibi shap witwidget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import machine_learning_datasets as mldatasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from catboost import CatBoostClassifier\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from alibi.utils.mapping import ohe_to_ord, ord_to_ohe\n",
    "from alibi.explainers import AnchorTabular\n",
    "from alibi.explainers import CEM\n",
    "from alibi.explainers import CounterFactualProto\n",
    "import shap\n",
    "import witwidget\n",
    "from witwidget.notebook.visualization import WitWidget, WitConfigBuilder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.1\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eager execution enabled:  False\n"
     ]
    }
   ],
   "source": [
    "tf.compat.v1.disable_eager_execution()\n",
    "print('Eager execution enabled: ', tf.executing_eagerly()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding and Preparing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://storage.googleapis.com/what-if-tool-resources/computefest2019/cox-violent-parsed_filt.csv downloaded to /Users/smasis/Documents/OTHER/InterpretableMLBook/programming/Chapter7/data/cox-violent-parsed_filt.csv\n",
      "1 dataset files found in /Users/smasis/Documents/OTHER/InterpretableMLBook/programming/Chapter7/data folder\n",
      "parsing /Users/smasis/Documents/OTHER/InterpretableMLBook/programming/Chapter7/data/cox-violent-parsed_filt.csv\n"
     ]
    }
   ],
   "source": [
    "recidivism_df = mldatasets.load(\"recidivism-risk\", prepare=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There should be almost 15,000 records and 23 columns. We can verify this was the case with `info()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14788 entries, 0 to 18315\n",
      "Data columns (total 23 columns):\n",
      " #   Column                 Non-Null Count  Dtype\n",
      "---  ------                 --------------  -----\n",
      " 0   age                    14788 non-null  int8 \n",
      " 1   juv_fel_count          14788 non-null  int8 \n",
      " 2   juv_misd_count         14788 non-null  int8 \n",
      " 3   juv_other_count        14788 non-null  int64\n",
      " 4   priors_count           14788 non-null  int8 \n",
      " 5   is_recid               14788 non-null  int8 \n",
      " 6   sex_Female             14788 non-null  uint8\n",
      " 7   sex_Male               14788 non-null  uint8\n",
      " 8   race_African-American  14788 non-null  uint8\n",
      " 9   race_Asian             14788 non-null  uint8\n",
      " 10  race_Caucasian         14788 non-null  uint8\n",
      " 11  race_Hispanic          14788 non-null  uint8\n",
      " 12  race_Native American   14788 non-null  uint8\n",
      " 13  race_Other             14788 non-null  uint8\n",
      " 14  c_charge_degree_(F1)   14788 non-null  uint8\n",
      " 15  c_charge_degree_(F2)   14788 non-null  uint8\n",
      " 16  c_charge_degree_(F3)   14788 non-null  uint8\n",
      " 17  c_charge_degree_(F7)   14788 non-null  uint8\n",
      " 18  c_charge_degree_(M1)   14788 non-null  uint8\n",
      " 19  c_charge_degree_(M2)   14788 non-null  uint8\n",
      " 20  c_charge_degree_(MO3)  14788 non-null  uint8\n",
      " 21  c_charge_degree_Other  14788 non-null  uint8\n",
      " 22  compas_score           14788 non-null  int64\n",
      "dtypes: int64(2), int8(5), uint8(16)\n",
      "memory usage: 649.9 KB\n"
     ]
    }
   ],
   "source": [
    "recidivism_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Examining Predictive Bias with Confusion Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_matrix = metrics.confusion_matrix(recidivism_df.is_recid,\\\n",
    "                                     recidivism_df.compas_score)\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True,\\\n",
    "            fmt='.2%', cmap='Blues', annot_kws={'size':16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tAfrican-American FPR:\t53.1%\n",
      "\tCaucasian FPR:\t\t28.8%\n",
      "\tRatio FPRs:\t\t1.84 x\n"
     ]
    }
   ],
   "source": [
    "recidivism_c_df = recidivism_df[recidivism_df['race_Caucasian'] == 1]\n",
    "recidivism_aa_df = recidivism_df[recidivism_df['race_African-American'] == 1]\n",
    "\n",
    "_ = mldatasets.\\\n",
    "      compare_confusion_matrices(recidivism_c_df.is_recid, \n",
    "                                 recidivism_c_df.compas_score,\\\n",
    "                                 recidivism_aa_df.is_recid,\\\n",
    "                                 recidivism_aa_df.compas_score,\\\n",
    "                                 'Caucasian', 'African-American', compare_fpr=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand = 9\n",
    "np.random.seed(rand)\n",
    "tf.random.set_seed(rand)\n",
    "\n",
    "y = recidivism_df['compas_score']\n",
    "X = recidivism_df.drop(['compas_score', 'is_recid'], axis=1).copy()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_train:  0.8793\t\tAccuracy_test:   0.8087\n",
      "Precision_test:  0.8294\t\tRecall_test:     0.8085\n",
      "ROC-AUC_test:    0.8904\t\tF1_test:         0.8188\t\tMCC_test: 0.6164\n"
     ]
    }
   ],
   "source": [
    "orig_plt_params = plt.rcParams\n",
    "sns.set()\n",
    "cb_mdl = CatBoostClassifier(iterations=500, learning_rate=0.5, depth=8)\n",
    "fitted_cb_mdl = cb_mdl.fit(X_train, y_train, verbose=False)\n",
    "y_train_cb_pred, y_test_cb_prob, y_test_cb_pred =\\\n",
    "        mldatasets.evaluate_class_mdl(fitted_cb_mdl, X_train,\\\n",
    "                                      X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/ops/resource_variable_ops.py:1659: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_train:  0.7527\t\tAccuracy_test:   0.7471\n",
      "Precision_test:  0.7653\t\tRecall_test:     0.7604\n",
      "ROC-AUC_test:    0.8320\t\tF1_test:         0.7628\t\tMCC_test: 0.4920\n"
     ]
    }
   ],
   "source": [
    "fitted_nn_mdl = keras.Sequential([\n",
    "    tf.keras.Input(shape=[len(X_train.keys())]),\n",
    "    layers.Dense(7, activation='relu'),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "  ])\n",
    "fitted_nn_mdl.compile(loss='mean_squared_error', optimizer='adam')\n",
    "nn_history = fitted_nn_mdl.fit(X_train.values, y_train.values, epochs=12,\\\n",
    "                               batch_size=32, validation_split=0.2, verbose=0)\n",
    "y_train_nn_pred, y_test_nn_prob, y_test_nn_pred =\\\n",
    "        mldatasets.evaluate_class_mdl(fitted_nn_mdl, X_train,\\\n",
    "                                      X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting Acquainted to our “Instance of Interest”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10127</th>\n",
       "      <th>2726</th>\n",
       "      <th>5231</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_pred</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>juv_fel_count</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>juv_misd_count</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>juv_other_count</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>priors_count</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_Female</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_Male</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_African-American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_Asian</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_Caucasian</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_Hispanic</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_Native American</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race_Other</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_charge_degree_(F1)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_charge_degree_(F2)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_charge_degree_(F3)</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_charge_degree_(F7)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_charge_degree_(M1)</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_charge_degree_(M2)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_charge_degree_(MO3)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_charge_degree_Other</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       10127  2726   5231 \n",
       "y                          0      0      1\n",
       "y_pred                     0      0      1\n",
       "age                       24     23     23\n",
       "juv_fel_count              0      0      0\n",
       "juv_misd_count             0      0      0\n",
       "juv_other_count            0      0      0\n",
       "priors_count               2      2      2\n",
       "sex_Female                 0      0      0\n",
       "sex_Male                   1      1      1\n",
       "race_African-American      0      0      1\n",
       "race_Asian                 0      0      0\n",
       "race_Caucasian             1      0      0\n",
       "race_Hispanic              0      1      0\n",
       "race_Native American       0      0      0\n",
       "race_Other                 0      0      0\n",
       "c_charge_degree_(F1)       0      0      0\n",
       "c_charge_degree_(F2)       0      0      0\n",
       "c_charge_degree_(F3)       0      1      0\n",
       "c_charge_degree_(F7)       0      0      1\n",
       "c_charge_degree_(M1)       1      0      0\n",
       "c_charge_degree_(M2)       0      0      0\n",
       "c_charge_degree_(MO3)      0      0      0\n",
       "c_charge_degree_Other      0      0      0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx1 = 5231\n",
    "idx2 = 2726\n",
    "idx3 = 10127\n",
    "eval_idxs = X_test.index.isin([idx1, idx2, idx3])\n",
    "X_test_evals = X_test[eval_idxs]\n",
    "eval_compare_df = pd.concat([\\\n",
    "            pd.DataFrame({'y':y_test[eval_idxs]},\\\n",
    "                         index=[idx3, idx2, idx1]),\\\n",
    "            pd.DataFrame({'y_pred':y_test_cb_pred[eval_idxs]},\\\n",
    "                         index=[idx3, idx2, idx1]),\\\n",
    "            X_test_evals], axis=1).transpose()\n",
    "eval_compare_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Anchor Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparations for Anchor and Counterfactual Explanations with Alibi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['Low Risk', 'Medium/High Risk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[23  0  0  0  2  0  1  1  0  0  0  0  0  0  0  0  1  0  0  0  0]]\n"
     ]
    }
   ],
   "source": [
    "X_test_eval = np.expand_dims(X_test.values[X_test.\\\n",
    "                                           index.get_loc(idx1)], axis=0)\n",
    "print(X_test_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[23  0  0  0  2  1  0  3]]\n"
     ]
    }
   ],
   "source": [
    "cat_vars_ohe = {5: 2, 7: 6, 13: 8}\n",
    "print(ohe_to_ord(X_test_eval, cat_vars_ohe)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_map = {\n",
    "    5: ['Female', 'Male'],\n",
    "    6: ['African-American', 'Asian', 'Caucasian',\\\n",
    "        'Hispanic', 'Native American', 'Other'],\n",
    "    7: ['Felony 1st Degree', 'Felony 2nd Degree',\\\n",
    "        'Felony 3rd Degree', 'Felony 7th Degree',\\\n",
    "        'Misdemeanor 1st Degree', 'Misdemeanor 2nd Degree',\\\n",
    "        'Misdemeanor 3rd Degree', 'Other Charge Degree']\n",
    "}\n",
    "feature_names = ['age', 'juv_fel_count', 'juv_misd_count',\\\n",
    "                 'juv_other_count', 'priors_count', \n",
    "                 'sex', 'race', 'c_charge_degree']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_map_ohe = {5: ['Not Female', 'Female'], 6: ['Not Male', 'Male'],\\\n",
    "        7:['Not African American', 'African American'],\\\n",
    "        8:['Not Asian', 'Asian'], 9:['Not Caucasian', 'Caucasian'],\\\n",
    "        10:['Not Hispanic', 'Hispanic'],\\\n",
    "        11:['Not Native American', 'Native American'],\\\n",
    "        12:['Not Other Race', 'Other Race'],\\\n",
    "        13:['Not Felony 1st Level', 'Felony 1st Level'],\\\n",
    "        14:['Not Felony 2nd Level', 'Felony 2nd Level'],\\\n",
    "        15:['Not Felony 3rd Level', 'Felony 3rd Level'],\\\n",
    "        16:['Not Felony 7th Level', 'Felony 7th Level'],\\\n",
    "        17:['Not Misdemeanor 1st Deg', 'Misdemeanor 1st Deg'],\\\n",
    "        18:['Not Misdemeanor 2nd Deg', 'Misdemeanor 2nd Deg'],\\\n",
    "        19:['Not Misdemeanor 3rd Deg', 'Misdemeanor 3rd Deg'],\\\n",
    "        20:['Not Other Charge Degree', 'Other Charge Degree']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Local Interpretations for Anchor Explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnchorTabular(meta={\n",
       "  'name': 'AnchorTabular',\n",
       "  'type': ['blackbox'],\n",
       "  'explanations': ['local'],\n",
       "  'params': {'seed': None, 'disc_perc': (25, 50, 75)}}\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_cb_fn = lambda x: fitted_cb_mdl.predict_proba(x)\n",
    "anchor_cb_explainer = AnchorTabular(predict_cb_fn, X_train.columns,\\\n",
    "                                    categorical_names=category_map_ohe)\n",
    "anchor_cb_explainer.fit(X_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: Medium/High Risk\n"
     ]
    }
   ],
   "source": [
    "print('Prediction: %s' %\\\n",
    "      class_names[anchor_cb_explainer.predictor(X_test.loc[idx1].values)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anchor: age <= 25.00 AND\n",
      "\tpriors_count > 0.00 AND\n",
      "\trace_African-American = African American\n",
      "Precision: 0.891\n",
      "Coverage: 0.290\n"
     ]
    }
   ],
   "source": [
    "anchor_cb_explanation =\\\n",
    "    anchor_cb_explainer.explain(X_test.loc[idx1].values, threshold=0.85,\\\n",
    "                                seed=rand)\n",
    "print('Anchor: %s' % (' AND\\r\\n\\t'.join(anchor_cb_explanation.anchor)))\n",
    "print('Precision: %.3f' % anchor_cb_explanation.precision)\n",
    "print('Coverage: %.3f' % anchor_cb_explanation.coverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anchor: age <= 25.00 AND\n",
      "\tpriors_count > 0.00 AND\n",
      "\trace_African-American = African American AND\n",
      "\trace_Other = Not Other Race AND\n",
      "\tsex_Female = Not Female AND\n",
      "\tc_charge_degree_(M2) = Not Misdemeanor 2nd Deg AND\n",
      "\trace_Hispanic = Not Hispanic AND\n",
      "\tc_charge_degree_(M1) = Not Misdemeanor 1st Deg\n",
      "Precision: 0.900\n",
      "Coverage: 0.290\n"
     ]
    }
   ],
   "source": [
    "anchor_cb_explanation =\\\n",
    "    anchor_cb_explainer.explain(X_test.loc[idx1].values, threshold=0.9,\\\n",
    "                                seed=rand)\n",
    "print('Anchor: %s' % (' AND\\r\\n\\t'.join(anchor_cb_explanation.anchor)))\n",
    "print('Precision: %.3f' % anchor_cb_explanation.precision)\n",
    "print('Coverage: %.3f' % anchor_cb_explanation.coverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnchorTabular(meta={\n",
       "  'name': 'AnchorTabular',\n",
       "  'type': ['blackbox'],\n",
       "  'explanations': ['local'],\n",
       "  'params': {'seed': None, 'disc_perc': (25, 50, 75)}}\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_nn_fn = lambda x: np.concatenate((1 - fitted_nn_mdl.predict(x),\\\n",
    "                                          fitted_nn_mdl.predict(x)), axis=1)\n",
    "anchor_nn_explainer = AnchorTabular(predict_nn_fn, X_train.columns,\\\n",
    "                                    categorical_names=category_map_ohe)\n",
    "anchor_nn_explainer.fit(X_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anchor: priors_count <= 2.00 AND\n",
      "\tc_charge_degree_(M1) = Misdemeanor 1st Deg\n",
      "Precision: 0.906\n",
      "Coverage: 0.578\n"
     ]
    }
   ],
   "source": [
    "anchor_nn_explanation =\\\n",
    "    anchor_nn_explainer.explain(X_test.loc[idx3].values, threshold=0.85,\\\n",
    "                                seed=rand)\n",
    "print('Anchor: %s' % (' AND\\r\\n\\t'.join(anchor_nn_explanation.anchor)))\n",
    "print('Precision: %.3f' % anchor_nn_explanation.precision)\n",
    "print('Coverage: %.3f' % anchor_nn_explanation.coverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anchor: priors_count <= 2.00 AND\n",
      "\trace_African-American = Not African American AND\n",
      "\trace_Caucasian = Not Caucasian\n",
      "Precision: 0.896\n",
      "Coverage: 0.578\n"
     ]
    }
   ],
   "source": [
    "anchor_nn_explanation =\\\n",
    "    anchor_nn_explainer.explain(X_test.loc[idx2].values, threshold=0.85,\\\n",
    "                                seed=rand)\n",
    "print('Anchor: %s' % (' AND\\r\\n\\t'.join(anchor_nn_explanation.anchor)))\n",
    "print('Precision: %.3f' % anchor_nn_explanation.precision)\n",
    "print('Coverage: %.3f' % anchor_nn_explanation.coverage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Counterfactual Explanations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counterfactual Explanations Guided by Prototypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[18.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
      "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]], dtype=float32), array([[96., 20., 13., 11., 38.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
      "         1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.]], dtype=float32))\n"
     ]
    }
   ],
   "source": [
    "feature_range = (X_train.values.min(axis=0).reshape(1,21).astype(np.float32),\\\n",
    "                 X_train.values.max(axis=0).reshape(1,21).astype(np.float32))\n",
    "print(feature_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/anaconda3/lib/python3.8/site-packages/alibi/utils/tf.py:26: The name tf.keras.backend.get_session is deprecated. Please use tf.compat.v1.keras.backend.get_session instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No encoder specified. Using k-d trees to represent class prototypes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CounterFactualProto(meta={\n",
       "  'name': 'CounterFactualProto',\n",
       "  'type': ['blackbox', 'tensorflow', 'keras'],\n",
       "  'explanations': ['local'],\n",
       "  'params': {\n",
       "              'kappa': 0.0,\n",
       "              'beta': 0.1,\n",
       "              'feature_range': (array([[18.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]], dtype=float32), array([[96., 20., 13., 11., 38.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "         1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.]], dtype=float32)),\n",
       "              'gamma': 0.0,\n",
       "              'theta': 5,\n",
       "              'cat_vars': None,\n",
       "              'ohe': False,\n",
       "              'use_kdtree': True,\n",
       "              'learning_rate_init': 0.01,\n",
       "              'max_iterations': 100,\n",
       "              'c_init': 10.0,\n",
       "              'c_steps': 10,\n",
       "              'eps': (0.001, 0.001),\n",
       "              'clip': (-1000.0, 1000.0),\n",
       "              'update_num_grad': 1,\n",
       "              'write_dir': None,\n",
       "              'shape': (1, 21),\n",
       "              'is_model': False,\n",
       "              'is_model_keras': False,\n",
       "              'is_ae': False,\n",
       "              'is_ae_keras': False,\n",
       "              'is_enc': False,\n",
       "              'is_enc_keras': False,\n",
       "              'enc_or_kdtree': True,\n",
       "              'is_cat': False,\n",
       "              'trustscore_kwargs': None,\n",
       "              'd_type': 'abdm-mvdm',\n",
       "              'w': None,\n",
       "              'disc_perc': (25, 50, 75),\n",
       "              'standardize_cat_vars': False,\n",
       "              'smooth': 1.0,\n",
       "              'center': True,\n",
       "              'update_feature_range': True}\n",
       "            }\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_nn_explainer = CounterFactualProto(predict_nn_fn,\n",
    "                                    X_test_eval.shape,\n",
    "                                    max_iterations=100,\n",
    "                                    feature_range=feature_range, \n",
    "                                    beta=.1, theta=5,\n",
    "                                    use_kdtree=True\n",
    "                                )\n",
    "cf_nn_explainer.fit(X_test.values, d_type='abdm-mvdm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instance Outcomes and Probabilities\n",
      "------------------------------------------------\n",
      "       original:  Medium/High Risk\n",
      "                  [0.46732193 0.53267807]\n",
      " counterfactual:  Low Risk\n",
      "                  [0.50025815 0.49974185]\n",
      "\n",
      "Categorical Feature Counterfactual Perturbations\n",
      "------------------------------------------------\n",
      "\t            sex:  Male  -->  Female\n",
      "\t           race:  African-American  -->  Asian\n",
      "\tc_charge_degree:  Felony 7th Degree  -->  Felony 1st Degree\n",
      "\n",
      "Numerical Feature Counterfactual Perturbations\n",
      "------------------------------------------------\n",
      "\t   priors_count:  2.00  -->  1.90\n"
     ]
    }
   ],
   "source": [
    "cf_nn_explanation = cf_nn_explainer.explain(X_test_eval)\n",
    "mldatasets.describe_cf_instance(X_test_eval, cf_nn_explanation, class_names,\\\n",
    "                  cat_vars_ohe, category_map, feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counterfactual Instances and much more with the What-If-Tool (WIT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configuring WIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "shap_cb_explainer = shap.TreeExplainer(fitted_cb_mdl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = recidivism_df.loc[y_test.index]\n",
    "test_np = test_df.values\n",
    "cols_l = test_df.columns\n",
    "delcol_idx = [cols_l.get_loc(\"is_recid\"),\\\n",
    "              cols_l.get_loc(\"compas_score\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_predict_with_shap(examples_np):\n",
    "    #For shap values we only need same features\n",
    "    #that were used for training\n",
    "    inputs_np = np.delete(np.array(examples_np),\\\n",
    "                          delcol_idx, axis=1)\n",
    "\n",
    "    #Get the model's class predictions\n",
    "    preds = predict_cb_fn(inputs_np)\n",
    "\n",
    "    #With test data generate SHAP values which converted\n",
    "    #to a list of dictionaries format\n",
    "    keepcols_l = [c for i, c in enumerate(cols_l)\\\n",
    "                  if i not in delcol_idx]\n",
    "    shap_output = shap_cb_explainer.shap_values(inputs_np)\n",
    "    attributions = []\n",
    "    for shap in shap_output:\n",
    "        attrs = {}\n",
    "        for i, col in enumerate(keepcols_l):\n",
    "            attrs[col] = shap[i]\n",
    "        attributions.append(attrs)\n",
    "        \n",
    "    #Prediction function must output \n",
    "    #predictions/attributions in dictionary\n",
    "    output = {'predictions': preds,\\\n",
    "              'attributions': attributions}\n",
    "    return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Displaying WIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2910\n"
     ]
    }
   ],
   "source": [
    "print(y_test.index.get_loc(5231))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b444f37f38e64f91bba07dec8d1cd4af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "WitWidget(config={'model_type': 'classification', 'label_vocab': ['Low Risk', 'Medium/High Risk'], 'feature_na…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wit_config_builder = WitConfigBuilder(\\\n",
    "                    test_np.tolist(), feature_names=cols_l.tolist()\n",
    "                ).set_custom_predict_fn(custom_predict_with_shap).\\\n",
    "                set_target_feature(\"is_recid\").set_label_vocab(class_names)\n",
    "WitWidget(wit_config_builder, height=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing with the Contrastive Explanation Method (CEM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train an Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 21)]              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                220       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 3)                 33        \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                40        \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 21)                231       \n",
      "=================================================================\n",
      "Total params: 524\n",
      "Trainable params: 524\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_layer = tf.keras.Input(shape=(21))\n",
    "encoder = tf.keras.layers.Dense(10, activation='relu')(input_layer)\n",
    "bottleneck = tf.keras.layers.Dense(3, activation='relu')(encoder)\n",
    "decoder = tf.keras.layers.Dense(10, activation='relu')(bottleneck)\n",
    "output_layer = tf.keras.layers.Dense(21, activation='linear')(decoder)\n",
    "autoencoder_mdl = tf.keras.Model(input_layer, output_layer)\n",
    "autoencoder_mdl.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder_mdl.compile(loss='mean_squared_error', optimizer='adam')\n",
    "autoencoder_history = autoencoder_mdl.fit(X_train.values, X_train.values, epochs=16,\\\n",
    "                               batch_size=32, validation_split=0.2, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit CEM Explainers & Extract Explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medium/High Risk -> Low Risk\n",
      "Probabilities: [0.5021912 0.4978088]\n",
      "Values: [2.3000000e+01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.0000000e+00\n",
      " 0.0000000e+00 9.9445111e-01 7.6429874e-01 0.0000000e+00 1.2006172e-01\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 1.0000000e+00 2.1608792e-02 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00]\n"
     ]
    }
   ],
   "source": [
    "cem_nn_explainer_pn = CEM(predict_nn_fn, 'PN',\\\n",
    "                          X_test_eval.shape,\\\n",
    "                          feature_range=feature_range,\\\n",
    "                          max_iterations=100, gamma=100,\\\n",
    "                          ae_model=autoencoder_mdl)\n",
    "cem_nn_explainer_pn.fit(X_train.values, no_info_type='median')\n",
    "cem_nn_explanation_pn = cem_nn_explainer_pn.explain(X_test_eval,\\\n",
    "                                                    verbose=False)\n",
    "print(\"%s -> %s\" % (class_names[cem_nn_explanation_pn.X_pred],\\\n",
    "                    class_names[cem_nn_explanation_pn.PN_pred]))\n",
    "print(\"Probabilities: %s\" % predict_nn_fn(cem_nn_explanation_pn.PN)[0])\n",
    "print(\"Values: %s\" % cem_nn_explanation_pn.PN[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Medium/High Risk -> Medium/High Risk\n",
      "Probabilities: [0.29961264 0.70038736]\n",
      "Values: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "cem_nn_explainer_pp = CEM(predict_nn_fn, 'PP',\\\n",
    "                          X_test_eval.shape,\\\n",
    "                          feature_range=feature_range,\\\n",
    "                          max_iterations=100, gamma=100,\\\n",
    "                          ae_model=autoencoder_mdl)\n",
    "cem_nn_explainer_pp.fit(X_train.values, no_info_type='median')\n",
    "cem_nn_explanation_pp = cem_nn_explainer_pp.explain(X_test_eval,\\\n",
    "                                                    verbose=False)\n",
    "print(\"%s -> %s\" % (class_names[cem_nn_explanation_pp.X_pred],\\\n",
    "                    class_names[cem_nn_explanation_pp.PP_pred]))\n",
    "print(\"Probabilities: %s\" % predict_nn_fn(cem_nn_explanation_pp.PP)[0])\n",
    "print(\"Values: %s\" % cem_nn_explanation_pp.PP[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing CEM Explanations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>x</th>\n",
       "      <th>PN</th>\n",
       "      <th>PN-x</th>\n",
       "      <th>PP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>23</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>priors_count</td>\n",
       "      <td>2</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sex_Male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.994451</td>\n",
       "      <td>-0.005549</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>race_African-American</td>\n",
       "      <td>1</td>\n",
       "      <td>0.764299</td>\n",
       "      <td>-0.235701</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>race_Caucasian</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120062</td>\n",
       "      <td>0.120062</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>c_charge_degree_(F7)</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>c_charge_degree_(M1)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.021609</td>\n",
       "      <td>0.021609</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Feature   x         PN      PN-x   PP\n",
       "0                     age  23  23.000000  0.000000  0.0\n",
       "4            priors_count   2   2.000000  0.000000  0.0\n",
       "6                sex_Male   1   0.994451 -0.005549  0.0\n",
       "7   race_African-American   1   0.764299 -0.235701  0.0\n",
       "9          race_Caucasian   0   0.120062  0.120062  0.0\n",
       "16   c_charge_degree_(F7)   1   1.000000  0.000000  0.0\n",
       "17   c_charge_degree_(M1)   0   0.021609  0.021609  0.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salients_df = pd.DataFrame({'Feature': X_test.columns,\\\n",
    "                     'x': cem_nn_explanation_pn.X[0],\\\n",
    "                     'PN': cem_nn_explanation_pn.PN[0],\\\n",
    "                     'PN-x': cem_nn_explanation_pn.PN[0]-\\\n",
    "                            cem_nn_explanation_pn.X[0],\\\n",
    "                     'PP': cem_nn_explanation_pp.PP[0]})\n",
    "salients_df = salients_df[(salients_df.PP != 0) |\\\n",
    "                          (salients_df.PN != 0)]\n",
    "salients_df"
   ]
  }
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