perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "# Environment settings\n",
    "data_path = 'Data/'\n",
    "\n",
    "# Deserialize previously saved data from \"preprocessing\"\n",
    "with open(data_path+'train_pp.obj', 'rb') as train_pp, \\\n",
    "open(data_path+'test_pp.obj', 'rb') as test_pp:\n",
    "    df_train = pickle.load(train_pp)\n",
    "    df_test = pickle.load(test_pp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train.drop([\"Survived\"], axis=1)\n",
    "y_train = df_train[\"Survived\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train,y_train, random_state=1, test_size=0.30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "perceptron = Perceptron( max_iter=50, eta0 = 0.01, random_state=0)\n",
    "perceptron.fit(X_train, y_train) \n",
    "predicted = perceptron.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7835820895522388"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy\n",
    "accuracy_score (y_test, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#confusion matrix\n",
    "cm=confusion_matrix(y_test, predicted)\n",
    "plt.figure()\n",
    "plt.imshow(cm, cmap= plt.cm.Blues)\n",
    "plt.title('Confusion matrix')\n",
    "plt.colorbar()\n",
    "plt.xticks([0,1], ['Dead', 'Survived'])\n",
    "plt.yticks([0,1], ['Dead', 'Survived'])\n",
    "for i,j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "    plt.text(j,i, format (cm[i,j]),\n",
    "            horizontalalignment=\"center\",\n",
    "            color=\"white\" if cm[i,j]> cm.max()/2 else \"black\")\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction on test set\n",
    "dv_test_X = df_test.drop([], axis=1).values\n",
    "\n",
    "test_prediction_results = pd.DataFrame(\n",
    "    data={'PassengerId': df_test['PassengerId'].values,\n",
    "          'Survived': perceptron.predict(dv_test_X).astype(int)})\n",
    "\n",
    "# Write results to a csv file\n",
    "test_prediction_results.to_csv(data_path+'outputs/perceptron.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}