svm-regression-rbf-kernel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance score of rbf: 0.4660645339769517\n",
      "Mean Squared Error of test data for rbf:  6.04177642887\n",
      "Mean Squared Error of train data for rbf:  0.00912540948396\n",
      "Real new dose :  [  4.  10.   8.   5.   2.   2.  14.   6.   6.   5.  12.   4.   8.   8.   4.]\n",
      "Predicted new dose using rbf:  [ 6.76701729  8.90919624  7.18987233  5.68176768  6.76701723  3.88906529\n",
      "  7.16080503  6.36245091  6.34004349  6.57432388  9.94382305  4.53849817\n",
      "  8.39496011  7.62319986  4.22444259]\n"
     ]
    },
    {
     "data": {
      "image/png": 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qF94rKirUllXKzs7WVTeIiKgOXuT3cm2hpfXgsbGxgZmZGfLy8lSW37lzB7a2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\nQ0REgmLwEBGRoBg8REQkKAYPEREJ6v8D2cqsrprSoCgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc4ee0f6940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "from sklearn.svm import SVR\n",
    "from sklearn import datasets, metrics\n",
    "import csv\n",
    "from copy import deepcopy\n",
    "\n",
    "def load_csv(filename):\n",
    "    raw_data = open(filename, 'rt')\n",
    "    reader = csv.reader(raw_data, delimiter=',', quoting=csv.QUOTE_NONE)\n",
    "    x = list(reader)\n",
    "    data = np.array(x).astype('float')\n",
    "    return data\n",
    "    \n",
    "def splitDataset(dataset, splitratio):\n",
    "    trainsize = int(np.round(dataset.shape[0]*splitratio))\n",
    "    trainset = np.zeros((trainsize,dataset.shape[1]))#array to store the training set.\n",
    "    testset = deepcopy(dataset)#create a copy of the dataset in test set.\n",
    "    for numsamples in range(trainsize):\n",
    "        indx = np.random.randint(0,testset.shape[0])#random index generation\n",
    "        trainset[numsamples,:] = testset[indx,:]#adding the randomly selected data vector to the training set\n",
    "        testset = np.delete(testset, indx, axis = 0)#delete the selected observation from the test set.\n",
    "    return trainset,testset\n",
    "\n",
    "data = load_csv('preprocessed_data.csv')\n",
    "trainset, testset = splitDataset(data,0.7)\n",
    "\n",
    "x_train = trainset[:,:-1]\n",
    "y_train = trainset[:,-1]\n",
    "\n",
    "x_test = testset[:,:-1]\n",
    "y_test = testset[:,-1]\n",
    "  \n",
    "# create linear regression object \n",
    "reg = SVR(kernel='rbf', C=1e3, gamma=0.1)\n",
    "  \n",
    "# train the model using the training sets \n",
    "reg.fit(x_train, y_train)\n",
    "\n",
    "# variance score: 1 means perfect prediction \n",
    "print('Variance score of rbf: {}'.format(reg.score(x_test, y_test)))\n",
    "\n",
    "# mean squared error\n",
    "print('Mean Squared Error of test data for rbf: ', metrics.mean_squared_error(y_test, reg.predict(x_test)))\n",
    "print('Mean Squared Error of train data for rbf: ', metrics.mean_squared_error(y_train, reg.predict(x_train)))\n",
    "\n",
    "# real and predicted new dose\n",
    "print('Real new dose : ', y_test)\n",
    "print('Predicted new dose using rbf: ', reg.predict(x_test))\n",
    "\n",
    "# plot for residual error of rbf\n",
    "  \n",
    "## setting plot style \n",
    "plt.style.use('fivethirtyeight') \n",
    "  \n",
    "## plotting residual errors in training data \n",
    "plt.scatter(reg.predict(x_train), reg.predict(x_train) - y_train, \n",
    "            color = \"green\", s = 10, label = 'Train data') \n",
    "  \n",
    "## plotting residual errors in test data \n",
    "plt.scatter(reg.predict(x_test), reg.predict(x_test) - y_test, \n",
    "            color = \"blue\", s = 10, label = 'Test data') \n",
    "  \n",
    "## plotting line for zero residual error \n",
    "plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2) \n",
    "  \n",
    "## plotting legend \n",
    "plt.legend(loc = 'upper right') \n",
    "  \n",
    "## plot title \n",
    "plt.title(\"Residual errors\") \n",
    "  \n",
    "## function to show plot \n",
    "plt.show()\n"
   ]
  }
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