cross_validation_demo_MN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recitation: Cross Validation + ROC Curve\n",
    "Code Authors: Emaad Manzoor, Mallory Nobles\n",
    "\n",
    "Data Source: Andrew Ng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_file = open(\"ex8a.csv\", \"r\") #2nd Dataset to Try\n",
    "X_file = open(\"ex8b_m.csv\", \"r\") \n",
    "X_file = X_file.readlines()\n",
    "y = []\n",
    "x1 = []\n",
    "x2 = []\n",
    "for l in range(0,len(X_file)):\n",
    "    l_text = X_file[l].rstrip()\n",
    "    [l_y, l_x1, l_x2] = l_text.split(\",\")\n",
    "    y.append(int(l_y))\n",
    "    x1.append(float(l_x1))\n",
    "    x2.append(float(l_x2))\n",
    "    \n",
    "y = [0 if b == -1 else b for b in y]\n",
    "y = np.array(y)\n",
    "X = np.column_stack((x1,x2))\n",
    "\n",
    "n_samples, n_features = X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.6513310808243729,\n",
       " 0.35179208082437285,\n",
       " -0.7346359504415011,\n",
       " 0.6501329504415009)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f51a80b438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the Data\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap='Set3')\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot different SVM Classifiers \n",
    "\n",
    "(Here, we are fitting the classifier on the whole dataset.  You wouldn't normally do this, it is just to demonstrate what the RBF kernel looks like, and show the need for model selection.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_meshgrid(x, y, h=.02):\n",
    "    \"\"\"Create a mesh of points to plot in\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: data to base x-axis meshgrid on\n",
    "    y: data to base y-axis meshgrid on\n",
    "    h: stepsize for meshgrid, optional\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    xx, yy : ndarray\n",
    "    \"\"\"\n",
    "    x_min, x_max = x.min() - .1, x.max() + .1\n",
    "    y_min, y_max = y.min() - .1, y.max() + .1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    return xx, yy\n",
    "\n",
    "\n",
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    \"\"\"Plot the decision boundaries for a classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib axes object\n",
    "    clf: a classifier\n",
    "    xx: meshgrid ndarray\n",
    "    yy: meshgrid ndarray\n",
    "    params: dictionary of params to pass to contourf, optional\n",
    "    \"\"\"\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f51a91d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C= 1.0\n",
    "from sklearn import svm\n",
    "models = (svm.SVC(kernel='rbf', gamma=1.0, C=C),\n",
    "          svm.SVC(kernel='rbf', gamma=10.0, C=C),\n",
    "          svm.SVC(kernel='rbf', gamma=100.0, C=C),\n",
    "          svm.SVC(kernel='rbf', gamma=1000.0, C=C))\n",
    "models = (clf.fit(X, y) for clf in models)# title for the plots\n",
    "\n",
    "titles = ('RBF kernel (gamma 1)',\n",
    "          'RBF kernel (gamma 10)',\n",
    "          'RBF kernel (gamma 100)',\n",
    "          'RBF kernel (gamma 1000)')\n",
    "\n",
    "# Set-up 2x2 grid for plotting.\n",
    "fig, sub = plt.subplots(2, 2)\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "X0, X1 = X[:, 0], X[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "for clf, title, ax in zip(models, titles, sub.flatten()):\n",
    "    plot_contours(ax, clf, xx, yy,\n",
    "                  cmap='Set3', alpha=0.8)\n",
    "    ax.scatter(X0, X1, c=y, cmap='Set3', s=20, edgecolors='k')\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xlabel('X1')\n",
    "    ax.set_ylabel('X2')\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hold out test data\n",
    "\n",
    "Hold out 25% of the data for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM: Create the hyperparameter grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cs = np.logspace(-4, 2, 10) #10 numbers evenly spaced between 10^-4 and 10^(2)\n",
    "Gammas = [1.0, 10.0, 100.0, 1000.0]\n",
    "parameter_list = [[C,G] for C in Cs for G in Gammas]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM: 5-fold cross-validation to select $C$ and $\\gamma$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = 0.0001 , Gamma = 1.0\n",
      "\t[fold 0] C: 0.00010, Gamma: 1.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00010, Gamma: 1.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.00010, Gamma: 1.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00010, Gamma: 1.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00010, Gamma: 1.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.0001 , Gamma = 10.0\n",
      "\t[fold 0] C: 0.00010, Gamma: 10.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00010, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 2] C: 0.00010, Gamma: 10.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00010, Gamma: 10.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00010, Gamma: 10.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5358870967741935\n",
      "C = 0.0001 , Gamma = 100.0\n",
      "\t[fold 0] C: 0.00010, Gamma: 100.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00010, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.00010, Gamma: 100.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00010, Gamma: 100.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00010, Gamma: 100.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.0001 , Gamma = 1000.0\n",
      "\t[fold 0] C: 0.00010, Gamma: 1000.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00010, Gamma: 1000.00000, accuracy: 0.65625\n",
      "\t[fold 2] C: 0.00010, Gamma: 1000.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00010, Gamma: 1000.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00010, Gamma: 1000.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.4858870967741936\n",
      "C = 0.00046415888336127773 , Gamma = 1.0\n",
      "\t[fold 0] C: 0.00046, Gamma: 1.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00046, Gamma: 1.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.00046, Gamma: 1.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00046, Gamma: 1.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00046, Gamma: 1.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.00046415888336127773 , Gamma = 10.0\n",
      "\t[fold 0] C: 0.00046, Gamma: 10.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00046, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 2] C: 0.00046, Gamma: 10.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00046, Gamma: 10.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00046, Gamma: 10.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5358870967741935\n",
      "C = 0.00046415888336127773 , Gamma = 100.0\n",
      "\t[fold 0] C: 0.00046, Gamma: 100.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00046, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.00046, Gamma: 100.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00046, Gamma: 100.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00046, Gamma: 100.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.00046415888336127773 , Gamma = 1000.0\n",
      "\t[fold 0] C: 0.00046, Gamma: 1000.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00046, Gamma: 1000.00000, accuracy: 0.65625\n",
      "\t[fold 2] C: 0.00046, Gamma: 1000.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00046, Gamma: 1000.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00046, Gamma: 1000.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.4858870967741936\n",
      "C = 0.002154434690031882 , Gamma = 1.0\n",
      "\t[fold 0] C: 0.00215, Gamma: 1.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00215, Gamma: 1.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.00215, Gamma: 1.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00215, Gamma: 1.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00215, Gamma: 1.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.002154434690031882 , Gamma = 10.0\n",
      "\t[fold 0] C: 0.00215, Gamma: 10.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00215, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 2] C: 0.00215, Gamma: 10.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00215, Gamma: 10.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00215, Gamma: 10.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5358870967741935\n",
      "C = 0.002154434690031882 , Gamma = 100.0\n",
      "\t[fold 0] C: 0.00215, Gamma: 100.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00215, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.00215, Gamma: 100.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00215, Gamma: 100.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00215, Gamma: 100.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.002154434690031882 , Gamma = 1000.0\n",
      "\t[fold 0] C: 0.00215, Gamma: 1000.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.00215, Gamma: 1000.00000, accuracy: 0.65625\n",
      "\t[fold 2] C: 0.00215, Gamma: 1000.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.00215, Gamma: 1000.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.00215, Gamma: 1000.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.4858870967741936\n",
      "C = 0.01 , Gamma = 1.0\n",
      "\t[fold 0] C: 0.01000, Gamma: 1.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.01000, Gamma: 1.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.01000, Gamma: 1.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.01000, Gamma: 1.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.01000, Gamma: 1.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.01 , Gamma = 10.0\n",
      "\t[fold 0] C: 0.01000, Gamma: 10.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.01000, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 2] C: 0.01000, Gamma: 10.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.01000, Gamma: 10.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.01000, Gamma: 10.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5358870967741935\n",
      "C = 0.01 , Gamma = 100.0\n",
      "\t[fold 0] C: 0.01000, Gamma: 100.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.01000, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.01000, Gamma: 100.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.01000, Gamma: 100.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.01000, Gamma: 100.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.01 , Gamma = 1000.0\n",
      "\t[fold 0] C: 0.01000, Gamma: 1000.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.01000, Gamma: 1000.00000, accuracy: 0.65625\n",
      "\t[fold 2] C: 0.01000, Gamma: 1000.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.01000, Gamma: 1000.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.01000, Gamma: 1000.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.4858870967741936\n",
      "C = 0.046415888336127774 , Gamma = 1.0\n",
      "\t[fold 0] C: 0.04642, Gamma: 1.00000, accuracy: 0.75000\n",
      "\t[fold 1] C: 0.04642, Gamma: 1.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.04642, Gamma: 1.00000, accuracy: 0.68750\n",
      "\t[fold 3] C: 0.04642, Gamma: 1.00000, accuracy: 0.41935\n",
      "\t[fold 4] C: 0.04642, Gamma: 1.00000, accuracy: 0.67742\n",
      "\tMean k-Fold score: 0.6756048387096774\n",
      "C = 0.046415888336127774 , Gamma = 10.0\n",
      "\t[fold 0] C: 0.04642, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 1] C: 0.04642, Gamma: 10.00000, accuracy: 0.93750\n",
      "\t[fold 2] C: 0.04642, Gamma: 10.00000, accuracy: 0.75000\n",
      "\t[fold 3] C: 0.04642, Gamma: 10.00000, accuracy: 0.77419\n",
      "\t[fold 4] C: 0.04642, Gamma: 10.00000, accuracy: 0.87097\n",
      "\tMean k-Fold score: 0.8477822580645162\n",
      "C = 0.046415888336127774 , Gamma = 100.0\n",
      "\t[fold 0] C: 0.04642, Gamma: 100.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.04642, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.04642, Gamma: 100.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.04642, Gamma: 100.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.04642, Gamma: 100.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.5233870967741936\n",
      "C = 0.046415888336127774 , Gamma = 1000.0\n",
      "\t[fold 0] C: 0.04642, Gamma: 1000.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.04642, Gamma: 1000.00000, accuracy: 0.65625\n",
      "\t[fold 2] C: 0.04642, Gamma: 1000.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.04642, Gamma: 1000.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.04642, Gamma: 1000.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.4858870967741936\n",
      "C = 0.21544346900318823 , Gamma = 1.0\n",
      "\t[fold 0] C: 0.21544, Gamma: 1.00000, accuracy: 0.87500\n",
      "\t[fold 1] C: 0.21544, Gamma: 1.00000, accuracy: 0.93750\n",
      "\t[fold 2] C: 0.21544, Gamma: 1.00000, accuracy: 0.75000\n",
      "\t[fold 3] C: 0.21544, Gamma: 1.00000, accuracy: 0.74194\n",
      "\t[fold 4] C: 0.21544, Gamma: 1.00000, accuracy: 0.87097\n",
      "\tMean k-Fold score: 0.8350806451612904\n",
      "C = 0.21544346900318823 , Gamma = 10.0\n",
      "\t[fold 0] C: 0.21544, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 1] C: 0.21544, Gamma: 10.00000, accuracy: 0.93750\n",
      "\t[fold 2] C: 0.21544, Gamma: 10.00000, accuracy: 0.78125\n",
      "\t[fold 3] C: 0.21544, Gamma: 10.00000, accuracy: 0.80645\n",
      "\t[fold 4] C: 0.21544, Gamma: 10.00000, accuracy: 0.90323\n",
      "\tMean k-Fold score: 0.8669354838709677\n",
      "C = 0.21544346900318823 , Gamma = 100.0\n",
      "\t[fold 0] C: 0.21544, Gamma: 100.00000, accuracy: 0.93750\n",
      "\t[fold 1] C: 0.21544, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 2] C: 0.21544, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 3] C: 0.21544, Gamma: 100.00000, accuracy: 0.83871\n",
      "\t[fold 4] C: 0.21544, Gamma: 100.00000, accuracy: 0.90323\n",
      "\tMean k-Fold score: 0.8733870967741936\n",
      "C = 0.21544346900318823 , Gamma = 1000.0\n",
      "\t[fold 0] C: 0.21544, Gamma: 1000.00000, accuracy: 0.50000\n",
      "\t[fold 1] C: 0.21544, Gamma: 1000.00000, accuracy: 0.65625\n",
      "\t[fold 2] C: 0.21544, Gamma: 1000.00000, accuracy: 0.53125\n",
      "\t[fold 3] C: 0.21544, Gamma: 1000.00000, accuracy: 0.35484\n",
      "\t[fold 4] C: 0.21544, Gamma: 1000.00000, accuracy: 0.38710\n",
      "\tMean k-Fold score: 0.4858870967741936\n",
      "C = 1.0 , Gamma = 1.0\n",
      "\t[fold 0] C: 1.00000, Gamma: 1.00000, accuracy: 0.90625\n",
      "\t[fold 1] C: 1.00000, Gamma: 1.00000, accuracy: 0.93750\n",
      "\t[fold 2] C: 1.00000, Gamma: 1.00000, accuracy: 0.78125\n",
      "\t[fold 3] C: 1.00000, Gamma: 1.00000, accuracy: 0.77419\n",
      "\t[fold 4] C: 1.00000, Gamma: 1.00000, accuracy: 0.90323\n",
      "\tMean k-Fold score: 0.860483870967742\n",
      "C = 1.0 , Gamma = 10.0\n",
      "\t[fold 0] C: 1.00000, Gamma: 10.00000, accuracy: 0.93750\n",
      "\t[fold 1] C: 1.00000, Gamma: 10.00000, accuracy: 0.87500\n",
      "\t[fold 2] C: 1.00000, Gamma: 10.00000, accuracy: 0.81250\n",
      "\t[fold 3] C: 1.00000, Gamma: 10.00000, accuracy: 0.90323\n",
      "\t[fold 4] C: 1.00000, Gamma: 10.00000, accuracy: 0.93548\n",
      "\tMean k-Fold score: 0.892741935483871\n",
      "C = 1.0 , Gamma = 100.0\n",
      "\t[fold 0] C: 1.00000, Gamma: 100.00000, accuracy: 0.87500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t[fold 1] C: 1.00000, Gamma: 100.00000, accuracy: 0.87500\n",
      "\t[fold 2] C: 1.00000, Gamma: 100.00000, accuracy: 0.90625\n",
      "\t[fold 3] C: 1.00000, Gamma: 100.00000, accuracy: 0.90323\n",
      "\t[fold 4] C: 1.00000, Gamma: 100.00000, accuracy: 0.90323\n",
      "\tMean k-Fold score: 0.8925403225806452\n",
      "C = 1.0 , Gamma = 1000.0\n",
      "\t[fold 0] C: 1.00000, Gamma: 1000.00000, accuracy: 0.78125\n",
      "\t[fold 1] C: 1.00000, Gamma: 1000.00000, accuracy: 0.78125\n",
      "\t[fold 2] C: 1.00000, Gamma: 1000.00000, accuracy: 0.81250\n",
      "\t[fold 3] C: 1.00000, Gamma: 1000.00000, accuracy: 0.48387\n",
      "\t[fold 4] C: 1.00000, Gamma: 1000.00000, accuracy: 0.67742\n",
      "\tMean k-Fold score: 0.707258064516129\n",
      "C = 4.641588833612772 , Gamma = 1.0\n",
      "\t[fold 0] C: 4.64159, Gamma: 1.00000, accuracy: 0.93750\n",
      "\t[fold 1] C: 4.64159, Gamma: 1.00000, accuracy: 0.90625\n",
      "\t[fold 2] C: 4.64159, Gamma: 1.00000, accuracy: 0.87500\n",
      "\t[fold 3] C: 4.64159, Gamma: 1.00000, accuracy: 0.90323\n",
      "\t[fold 4] C: 4.64159, Gamma: 1.00000, accuracy: 0.93548\n",
      "\tMean k-Fold score: 0.911491935483871\n",
      "C = 4.641588833612772 , Gamma = 10.0\n",
      "\t[fold 0] C: 4.64159, Gamma: 10.00000, accuracy: 0.93750\n",
      "\t[fold 1] C: 4.64159, Gamma: 10.00000, accuracy: 0.93750\n",
      "\t[fold 2] C: 4.64159, Gamma: 10.00000, accuracy: 0.87500\n",
      "\t[fold 3] C: 4.64159, Gamma: 10.00000, accuracy: 0.93548\n",
      "\t[fold 4] C: 4.64159, Gamma: 10.00000, accuracy: 0.90323\n",
      "\tMean k-Fold score: 0.917741935483871\n",
      "C = 4.641588833612772 , Gamma = 100.0\n",
      "\t[fold 0] C: 4.64159, Gamma: 100.00000, accuracy: 0.87500\n",
      "\t[fold 1] C: 4.64159, Gamma: 100.00000, accuracy: 0.87500\n",
      "\t[fold 2] C: 4.64159, Gamma: 100.00000, accuracy: 0.93750\n",
      "\t[fold 3] C: 4.64159, Gamma: 100.00000, accuracy: 0.90323\n",
      "\t[fold 4] C: 4.64159, Gamma: 100.00000, accuracy: 0.83871\n",
      "\tMean k-Fold score: 0.8858870967741936\n",
      "C = 4.641588833612772 , Gamma = 1000.0\n",
      "\t[fold 0] C: 4.64159, Gamma: 1000.00000, accuracy: 0.78125\n",
      "\t[fold 1] C: 4.64159, Gamma: 1000.00000, accuracy: 0.78125\n",
      "\t[fold 2] C: 4.64159, Gamma: 1000.00000, accuracy: 0.81250\n",
      "\t[fold 3] C: 4.64159, Gamma: 1000.00000, accuracy: 0.67742\n",
      "\t[fold 4] C: 4.64159, Gamma: 1000.00000, accuracy: 0.70968\n",
      "\tMean k-Fold score: 0.7524193548387096\n",
      "C = 21.54434690031882 , Gamma = 1.0\n",
      "\t[fold 0] C: 21.54435, Gamma: 1.00000, accuracy: 0.96875\n",
      "\t[fold 1] C: 21.54435, Gamma: 1.00000, accuracy: 0.93750\n",
      "\t[fold 2] C: 21.54435, Gamma: 1.00000, accuracy: 0.87500\n",
      "\t[fold 3] C: 21.54435, Gamma: 1.00000, accuracy: 0.90323\n",
      "\t[fold 4] C: 21.54435, Gamma: 1.00000, accuracy: 0.87097\n",
      "\tMean k-Fold score: 0.9110887096774194\n",
      "C = 21.54434690031882 , Gamma = 10.0\n",
      "\t[fold 0] C: 21.54435, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 1] C: 21.54435, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 2] C: 21.54435, Gamma: 10.00000, accuracy: 0.87500\n",
      "\t[fold 3] C: 21.54435, Gamma: 10.00000, accuracy: 0.93548\n",
      "\t[fold 4] C: 21.54435, Gamma: 10.00000, accuracy: 0.93548\n",
      "\tMean k-Fold score: 0.9116935483870968\n",
      "C = 21.54434690031882 , Gamma = 100.0\n",
      "\t[fold 0] C: 21.54435, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 1] C: 21.54435, Gamma: 100.00000, accuracy: 0.87500\n",
      "\t[fold 2] C: 21.54435, Gamma: 100.00000, accuracy: 0.90625\n",
      "\t[fold 3] C: 21.54435, Gamma: 100.00000, accuracy: 0.83871\n",
      "\t[fold 4] C: 21.54435, Gamma: 100.00000, accuracy: 0.77419\n",
      "\tMean k-Fold score: 0.8475806451612904\n",
      "C = 21.54434690031882 , Gamma = 1000.0\n",
      "\t[fold 0] C: 21.54435, Gamma: 1000.00000, accuracy: 0.78125\n",
      "\t[fold 1] C: 21.54435, Gamma: 1000.00000, accuracy: 0.78125\n",
      "\t[fold 2] C: 21.54435, Gamma: 1000.00000, accuracy: 0.81250\n",
      "\t[fold 3] C: 21.54435, Gamma: 1000.00000, accuracy: 0.67742\n",
      "\t[fold 4] C: 21.54435, Gamma: 1000.00000, accuracy: 0.70968\n",
      "\tMean k-Fold score: 0.7524193548387096\n",
      "C = 100.0 , Gamma = 1.0\n",
      "\t[fold 0] C: 100.00000, Gamma: 1.00000, accuracy: 0.96875\n",
      "\t[fold 1] C: 100.00000, Gamma: 1.00000, accuracy: 0.93750\n",
      "\t[fold 2] C: 100.00000, Gamma: 1.00000, accuracy: 0.90625\n",
      "\t[fold 3] C: 100.00000, Gamma: 1.00000, accuracy: 0.90323\n",
      "\t[fold 4] C: 100.00000, Gamma: 1.00000, accuracy: 0.90323\n",
      "\tMean k-Fold score: 0.9237903225806452\n",
      "C = 100.0 , Gamma = 10.0\n",
      "\t[fold 0] C: 100.00000, Gamma: 10.00000, accuracy: 0.93750\n",
      "\t[fold 1] C: 100.00000, Gamma: 10.00000, accuracy: 0.87500\n",
      "\t[fold 2] C: 100.00000, Gamma: 10.00000, accuracy: 0.90625\n",
      "\t[fold 3] C: 100.00000, Gamma: 10.00000, accuracy: 0.93548\n",
      "\t[fold 4] C: 100.00000, Gamma: 10.00000, accuracy: 0.90323\n",
      "\tMean k-Fold score: 0.911491935483871\n",
      "C = 100.0 , Gamma = 100.0\n",
      "\t[fold 0] C: 100.00000, Gamma: 100.00000, accuracy: 0.84375\n",
      "\t[fold 1] C: 100.00000, Gamma: 100.00000, accuracy: 0.87500\n",
      "\t[fold 2] C: 100.00000, Gamma: 100.00000, accuracy: 0.90625\n",
      "\t[fold 3] C: 100.00000, Gamma: 100.00000, accuracy: 0.80645\n",
      "\t[fold 4] C: 100.00000, Gamma: 100.00000, accuracy: 0.74194\n",
      "\tMean k-Fold score: 0.8346774193548387\n",
      "C = 100.0 , Gamma = 1000.0\n",
      "\t[fold 0] C: 100.00000, Gamma: 1000.00000, accuracy: 0.78125\n",
      "\t[fold 1] C: 100.00000, Gamma: 1000.00000, accuracy: 0.78125\n",
      "\t[fold 2] C: 100.00000, Gamma: 1000.00000, accuracy: 0.81250\n",
      "\t[fold 3] C: 100.00000, Gamma: 1000.00000, accuracy: 0.67742\n",
      "\t[fold 4] C: 100.00000, Gamma: 1000.00000, accuracy: 0.70968\n",
      "\tMean k-Fold score: 0.7524193548387096\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn import svm\n",
    "\n",
    "n_folds = 5\n",
    "k_fold = KFold(n_folds)\n",
    "\n",
    "c_g_scores = []\n",
    "for p in parameter_list:\n",
    "    C = p[0]\n",
    "    G = p[1]\n",
    "    fold_scores = []\n",
    "    print(\"C = \" + str(C) + \" , Gamma = \" + str(G))\n",
    "    for k, (train, val) in enumerate(k_fold.split(X_train, y_train)):\n",
    "        clf = svm.SVC(kernel='rbf', gamma=G, C=C)\n",
    "        clf.fit(X_train[train], y_train[train])\n",
    "        \n",
    "        ypred = clf.predict(X_train[val])\n",
    "        yval = y_train[val]\n",
    "        accuracy = np.sum(ypred==yval)/len(ypred)\n",
    "        fold_scores.append(accuracy)\n",
    "        \n",
    "        print(\"\\t[fold {0}] C: {1:.5f}, Gamma: {2:.5f}, accuracy: {3:.5f}\".\n",
    "                format(k, C, G, accuracy))\n",
    "    \n",
    "    c_g_score = np.mean(fold_scores)\n",
    "    c_g_scores.append(c_g_score)\n",
    "    print(\"\\tMean k-Fold score: \" + str(c_g_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best C: 100.0 Best G: 1.0 with score: 0.9237903225806452\n"
     ]
    }
   ],
   "source": [
    "best_score_idx = np.argmax(c_g_scores)\n",
    "best_p = parameter_list[best_score_idx]\n",
    "best_c = best_p[0]\n",
    "best_g = best_p[1]\n",
    "print(\"Best C: \" + str(best_p[0]) + \" Best G: \" + str(best_p[1]) + \" with score: \" + str(c_g_scores[best_score_idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit Final Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=1.0, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf_svm = svm.SVC(kernel='rbf', gamma=best_g, C=best_c)\n",
    "clf_svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph of Learned Decision Boundary + Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'RBF, Best Parameters')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f51a892780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_svm = svm.SVC(kernel='rbf', gamma=best_g, C=best_c)\n",
    "clf_svm.fit(X_train, y_train)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "X0_train, X1_train = X_train[:, 0], X_train[:, 1]\n",
    "xx_train, yy_train = make_meshgrid(X0_train, X1_train)\n",
    "\n",
    "plot_contours(plt, clf_svm, xx_train, yy_train,cmap='Set3', alpha=0.8)\n",
    "plt.scatter(X0_train, X1_train, c=y_train, cmap='Set3', s=20, edgecolors='k')\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')\n",
    "plt.title(\"RBF, Best Parameters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph of Learned Decision Boundary + Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'RBF, Best Parameters')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f51ca1fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_svm = svm.SVC(kernel='rbf', gamma=best_g, C=best_c)\n",
    "clf_svm.fit(X_train, y_train)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "X0_test, X1_test = X_test[:, 0], X_test[:, 1]\n",
    "xx_test, yy_test = make_meshgrid(X0_test, X1_test)\n",
    "\n",
    "plot_contours(plt, clf_svm, xx_test, yy_test,cmap='Set3', alpha=0.8)\n",
    "plt.scatter(X0_test, X1_test, c=y_test, cmap='Set3', s=20, edgecolors='k')\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('X2')\n",
    "plt.title(\"RBF, Best Parameters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Data \n",
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "Xi = iris.data\n",
    "yi = iris.target\n",
    "Xi, yi = Xi[yi != 2], yi[yi != 2]\n",
    "n_samples, n_features = Xi.shape\n",
    "\n",
    "# Add noisy features\n",
    "random_state = np.random.RandomState(0)\n",
    "Xi = np.c_[Xi, random_state.randn(n_samples, 200 * n_features)]\n",
    "\n",
    "#Hold Out Test Data\n",
    "from sklearn.model_selection import train_test_split\n",
    "Xi_train, Xi_test, yi_train, yi_test = train_test_split(Xi, yi, test_size=0.25, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f51cea0320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "#SVMs\n",
    "for C in Cs:\n",
    "    clf_svm = svm.LinearSVC(C=C)\n",
    "    clf_svm.fit(Xi_train, yi_train)\n",
    "    y_score = clf_svm.decision_function(Xi_test)\n",
    "    fpr, tpr, thresholds = roc_curve(yi_test, y_score, pos_label=1)\n",
    "    auc = np.trapz(tpr, fpr)\n",
    "    plt.plot(fpr, tpr, linestyle='-', alpha=0.5, color='C0')\n",
    "\n",
    "# Random Forests\n",
    "for ntrees in [1, 10, 20, 100, 1000]:\n",
    "    clf_rf = RandomForestClassifier(n_estimators=ntrees)\n",
    "    clf_rf.fit(Xi_train, yi_train)\n",
    "    y_score = clf_rf.predict_proba(Xi_test)[:,1]\n",
    "    fpr, tpr, thresholds = roc_curve(yi_test, y_score, pos_label=1)\n",
    "    auc = np.trapz(tpr, fpr)\n",
    "    plt.plot(fpr, tpr, linestyle='-', alpha=0.5, color='C1')\n",
    "\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.grid()\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py36",
   "language": "python",
   "name": "py36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

ex8a.csv

ex8b_m.csv

gistfile1.txt

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