prediction_visualzied.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from predict import load_train, load_test\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from yellowbrick.classifier import ClassPredictionError, ClassificationReport, ConfusionMatrix\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_train()\n",
    "test_x, test_y = load_test()\n",
    "\n",
    "classes = [\"Not Occupied\", \"Occupied\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(nrows=1, ncols=5, sharey=True, figsize=(16, 4))\n",
    "\n",
    "# Instantiate the classification model and visualizer\n",
    "visualizer0 = ClassPredictionError(\n",
    "    LogisticRegression(), ax=ax0, classes=classes)\n",
    "\n",
    "visualizer1 = ClassPredictionError(\n",
    "    GaussianNB(), ax=ax1, classes=classes)\n",
    "\n",
    "visualizer2 = ClassPredictionError(\n",
    "    RandomForestClassifier(n_estimators=1000, n_jobs=6), ax=ax2, classes=classes)\n",
    "\n",
    "visualizer3 = ClassPredictionError(\n",
    "    GradientBoostingClassifier(), ax=ax3, classes=classes)\n",
    "\n",
    "visualizer4 = ClassPredictionError(\n",
    "    SVC(), ax=ax4, classes=classes)\n",
    "\n",
    "# Fit the training data to the visualizer\n",
    "visualizer0.fit(X, y)\n",
    "visualizer1.fit(X, y)\n",
    "visualizer2.fit(X, y)\n",
    "visualizer3.fit(X, y)\n",
    "visualizer4.fit(X, y)\n",
    "\n",
    "# Evaluate the model on the test data\n",
    "s0 = visualizer0.score(test_x, test_y)\n",
    "s1 = visualizer1.score(test_x, test_y)\n",
    "s2 = visualizer2.score(test_x, test_y)\n",
    "s3 = visualizer3.score(test_x, test_y)\n",
    "s4 = visualizer4.score(test_x, test_y)\n",
    "\n",
    "g0 = visualizer0.poof()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(12, 4))\n",
    "visualizer5 = ClassificationReport(LogisticRegression(), ax=ax0, classes=classes, support=True)\n",
    "visualizer6 = ClassificationReport(GradientBoostingClassifier(), ax=ax1, classes=classes, support=True)\n",
    "\n",
    "visualizer5.fit(X, y)\n",
    "visualizer6.fit(X, y)\n",
    "\n",
    "visualizer5.score(test_x, test_y)\n",
    "visualizer6.score(test_x, test_y)\n",
    "\n",
    "visualizer5.poof()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/ml/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(12,4))\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit_transform(y)\n",
    "\n",
    "cm1 = ConfusionMatrix(LogisticRegression(), ax=ax0, classes=[0,1])\n",
    "cm2 = ConfusionMatrix(GradientBoostingClassifier(), ax=ax1, classes=[0,1])\n",
    "\n",
    "cm1.fit(X, y)\n",
    "cm2.fit(X, y)\n",
    "\n",
    "cm1.score(test_x, test_y)\n",
    "cm2.score(test_x, test_y)\n",
    "\n",
    "cm1.poof()\n"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}