Interpret ML's classification notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Courtesy: [This notebook](https://github.com/microsoft/interpret/blob/master/examples/notebooks/Interpretable%20Classification%20Methods.ipynb) by the Microsoft Interpret team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup a classification experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
    "    header=None)\n",
    "df.columns = [\n",
    "    \"Age\", \"WorkClass\", \"fnlwgt\", \"Education\", \"EducationNum\",\n",
    "    \"MaritalStatus\", \"Occupation\", \"Relationship\", \"Race\", \"Gender\",\n",
    "    \"CapitalGain\", \"CapitalLoss\", \"HoursPerWeek\", \"NativeCountry\", \"Income\"\n",
    "]\n",
    "train_cols = df.columns[0:-1]\n",
    "label = df.columns[-1]\n",
    "X = df[train_cols]\n",
    "y = df[label].apply(lambda x: 0 if x == \" <=50K\" else 1) #Turning response into 0 and 1\n",
    "\n",
    "seed = 1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <iframe src=\"http://127.0.0.1:7302/140602073424168/\" width=100% height=800 frameBorder=\"0\"></iframe>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from interpret import show\n",
    "from interpret.data import ClassHistogram\n",
    "\n",
    "hist = ClassHistogram().explain_data(X_train, y_train, name = 'Train Data')\n",
    "show(hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Explainable Boosting Machine (EBM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It takes 246.30056929588318 seconds\n"
     ]
    }
   ],
   "source": [
    "from interpret.glassbox import ExplainableBoostingClassifier, LogisticRegression, ClassificationTree, DecisionListClassifier\n",
    "\n",
    "ebm = ExplainableBoostingClassifier(random_state=seed)\n",
    "start = time.time()\n",
    "ebm.fit(X_train, y_train)   \n",
    "print('It takes {} seconds'.format(time.time()-start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Explanations: What the model learned overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <iframe src=\"http://127.0.0.1:7302/140602031991608/\" width=100% height=800 frameBorder=\"0\"></iframe>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ebm_global = ebm.explain_global(name='EBM')\n",
    "show(ebm_global)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Local Explanations: How an individual prediction was made"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <iframe src=\"http://127.0.0.1:7302/140602031822720/\" width=100% height=800 frameBorder=\"0\"></iframe>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ebm_local = ebm.explain_local(X_test[:5], y_test[:5], name='EBM')\n",
    "show(ebm_local)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate EBM performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <iframe src=\"http://127.0.0.1:7302/140602033026440/\" width=100% height=800 frameBorder=\"0\"></iframe>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from interpret.perf import ROC\n",
    "\n",
    "ebm_perf = ROC(ebm.predict_proba).explain_perf(X_test, y_test, name='EBM')\n",
    "show(ebm_perf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In the training set [(0, 19694), (1, 6354)]\n",
      "\n",
      "In the testing set [(0, 5026), (1, 1487)]\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "print('In the training set', Counter(y_train).most_common())\n",
    "print('\\nIn the testing set', Counter(y_test).most_common())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "preds = ebm.predict(X_test)\n",
    "skplt.metrics.plot_confusion_matrix(y_test, preds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8713342545677875"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, preds)"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}