Extra+Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1]\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",
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      "47    0\n",
      "48    0\n",
      "49    0\n",
      "50    0\n",
      "51    0\n",
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      "80    0\n",
      "81    0\n",
      "82    0\n",
      "Name: predict(t), dtype: int64\n",
      "Train Accuracy ::  0.958333333333\n",
      "Test Accuracy ::  0.28813559322\n"
     ]
    },
    {
     "data": {
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KQVU8dyEmag9uPqKISWUApWXDL1v4WKHqvOXvFgRVFaagSkSYp8vVumXjsLD22kaMu7AL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\nx3aC1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a31e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import Library of decision tree model\n",
    "from sklearn import tree\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",
    "\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",
    "clf = tree.ExtraTreeClassifier()\n",
    "clf = 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()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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