Passive+Aggressive+Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lukwingsan/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n",
      "/Users/lukwingsan/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Name: predict(t), dtype: int64\n",
      "Train Accuracy ::  0.25\n",
      "Test Accuracy ::  0.118644067797\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a6455c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Import Library of Gaussian Naive Bayes model\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.patches as patches\n",
    "import matplotlib.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "#Create a Gaussian Classifier\n",
    "\n",
    "def dataset_headers(dataset):\n",
    "    \"\"\"\n",
    "    To get the dataset header names\n",
    "    :param dataset: loaded dataset into pandas DataFrame\n",
    "    :return: list of header names\n",
    "    \"\"\"\n",
    "    return list(dataset.columns.values)\n",
    "def model_accuracy(trained_model, features, targets):\n",
    "    \"\"\"\n",
    "    Get the accuracy score of the model\n",
    "    :param trained_model:\n",
    "    :param features:\n",
    "    :param targets:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    accuracy_score = trained_model.score(features, targets)\n",
    "    return accuracy_score\n",
    "\n",
    "dataset = pd.read_csv(\"WorkbookFYP2.csv\")\n",
    "headers = dataset_headers(dataset)\n",
    "\n",
    "training_features = [ 'trend(t-1)','trend(t-2)','value(t-1)','value(t-2)']\n",
    "target = 'predict(t)'\n",
    "train_x, test_x, train_y, test_y = train_test_split(dataset[training_features], dataset[target], train_size=0.3,shuffle=False)\n",
    "\n",
    "# Train the model using the training sets \n",
    "\n",
    "reg = PassiveAggressiveClassifier(random_state=0)\n",
    "reg.fit(train_x, train_y)\n",
    "\n",
    "k=[]\n",
    "x=[0]\n",
    "\n",
    "#Predict Output \n",
    "predicted=reg.predict(test_x)\n",
    "train_accuracy = model_accuracy(reg, train_x, train_y)\n",
    "test_accuracy = model_accuracy(reg, test_x, test_y)\n",
    "for i in range(0,(len(train_y)+len(test_y)- len(predicted))):\n",
    "    k.extend(x)\n",
    "    \n",
    "k.extend(predicted)              \n",
    "print (k)\n",
    "print (test_y)\n",
    "print (\"Train Accuracy :: \", train_accuracy)\n",
    "print (\"Test Accuracy :: \", test_accuracy)\n",
    "\n",
    "plt.plot(k) # plotting by columns\n",
    "#ax2 = plt.subplot(212)\n",
    "#ax2.set_xlim(33,len(predicted))\n",
    "plt.plot(test_y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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