Random+Forest+Classifier+.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "24    1\n",
      "25    0\n",
      "26    0\n",
      "27    0\n",
      "28    1\n",
      "29    1\n",
      "30    1\n",
      "31    1\n",
      "32    1\n",
      "33    1\n",
      "34    0\n",
      "35    0\n",
      "36    0\n",
      "37    0\n",
      "38    0\n",
      "39    0\n",
      "40    0\n",
      "41    0\n",
      "42    0\n",
      "43    0\n",
      "44    0\n",
      "45    0\n",
      "46    0\n",
      "47    0\n",
      "48    0\n",
      "49    0\n",
      "50    0\n",
      "51    0\n",
      "52    0\n",
      "53    0\n",
      "54    0\n",
      "55    0\n",
      "56    0\n",
      "57    0\n",
      "58    0\n",
      "59    0\n",
      "60    0\n",
      "61    0\n",
      "62    0\n",
      "63    0\n",
      "64    0\n",
      "65    0\n",
      "66    0\n",
      "67    0\n",
      "68    0\n",
      "69    0\n",
      "70    0\n",
      "71    0\n",
      "72    0\n",
      "73    0\n",
      "74    0\n",
      "75    0\n",
      "76    0\n",
      "77    0\n",
      "78    0\n",
      "79    0\n",
      "80    0\n",
      "81    0\n",
      "82    0\n",
      "Name: predict(t), dtype: int64\n",
      "Train Accuracy ::  0.958333333333\n",
      "Test Accuracy ::  0.949152542373\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSr1O5N4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a2a7278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import Library of Gaussian Naive Bayes model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\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",
    "clf=RandomForestClassifier(max_depth=15, random_state=0)\n",
    "clf.fit(train_x, train_y)\n",
    "\n",
    "k=[]\n",
    "x=[0]\n",
    "\n",
    "#Predict Output \n",
    "predicted= clf.predict(test_x)\n",
    "train_accuracy = model_accuracy(clf, train_x, train_y)\n",
    "test_accuracy = model_accuracy(clf, 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
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}