Gaussian+Process+Regressor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "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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      "24      4.660785\n",
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      "30     85.323752\n",
      "31     95.413713\n",
      "32     99.179408\n",
      "33    101.153265\n",
      "34     41.456240\n",
      "35     16.552995\n",
      "36      7.497706\n",
      "37      4.451091\n",
      "38      2.503619\n",
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      "51      0.674859\n",
      "52      0.757328\n",
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      "81      0.624278\n",
      "82      0.553993\n",
      "Name: value(t-1), dtype: float64\n",
      "Train Accuracy ::  0.954647270176\n",
      "Test Accuracy ::  -0.220517532714\n"
     ]
    },
    {
     "data": {
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UzQz06HEw2JPf/YrMMwp6mVJeR934Z2DUDah8IzINBb1MqT+pRu+tDJmFyBD4AuDPy6zY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\nzawE74THw0Vu06xgZoZXS93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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e130ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import Library of Gaussian Naive Bayes model\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\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",
    "#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 = [  'value(t-2)']\n",
    "target = 'value(t-1)'\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 = GaussianProcessRegressor()\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()\n",
    "\n"
   ]
  },
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   "source": []
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