Gradient+Boosting+.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
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      "Name: predict(t), dtype: int64\n",
      "Train Accuracy ::  0.958333333333\n",
      "Test Accuracy ::  0.525423728814\n"
     ]
    },
    {
     "data": {
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TuFvq3a3F0ju6HhgLCqpaYZmq0MrApu/G3QEwXYm4xHPfNyyTzL5XmigCKDIX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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a54e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Import Library of Gaussian Naive Bayes model\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\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 = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,max_depth=1, 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,
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   },
   "outputs": [],
   "source": []
  }
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