{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "**Chapter 5 – Support Vector Machines**\n",
    "\n",
    "_This notebook contains all the sample code and solutions to the exercises in chapter 5._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Large margin classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The next few code cells generate the first figures in chapter 5. The first actual code sample comes after:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure large_margin_classification_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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FRXH8+HG2b99e8jdTlNjl/15mz317SL+cjl8PP0J+DMHBU76xzE9CSkL2H+JC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe682046978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bad models\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"large_margin_classification_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Sensitivity to feature scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_feature_scales_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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YDwFvAuOBLGANRlXBa0qpvsBCYAawC+iTP7kSoqSbMmUICQmRDmuwmjaNZMqU0W5umRCl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\nJ/8f4Vw1VcOg4J4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fd4d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_feature_scales_plot\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Sensitivity to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_outliers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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WravHjBmjT506lSPtCyEefg/bGCQbDQshhLirkJBRNr89bNp0FKtXZzx/rzxIGw3fb6+8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\nAex0nAGD+3CIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe682934978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "save_fig(\"sensitivity_to_outliers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Large margin *vs* margin violations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This is the first code example in chapter 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica\n",
    "\n",
    "svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n",
    "    ])\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5, 1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now let's generate the graph comparing different regularization settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('linear_svc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
    "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
    "\n",
    "scaled_svm_clf1 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf1),\n",
    "    ])\n",
    "scaled_svm_clf2 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf2),\n",
    "    ])\n",
    "\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Convert to unscaled parameters\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])\n",
    "\n",
    "# Find support vectors (LinearSVC does not do this automatically)\n",
    "t = y * 2 - 1\n",
    "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
    "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
    "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
    "svm_clf2.support_vectors_ = X[support_vectors_idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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lTPJY3johKkv+j+qW7378TrkOclVrflqjm7L0GJM1WTsm6vhEw+np6Wr9+vVq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\nCCGEEE7y/35+uT89wc8gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67f194240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Non-linear classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure higher_dimensions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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9mbDnJPl3SS5P8pUktyT50VU92GKxOPqnSo6RC8swX/pWncsgn0Dn5PbJpU8ufXLpOxO5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fb18320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZrhWLmW0DxoBWhw23AucBq5uuXwXsafV47n53w+ZtZvYR4CyqFU5LExMTjI6O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\nikVERIJSxSIiIkGpYhERkaD+PyxX+smTKkCdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67faf5c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('poly_features', PolynomialFeatures(degree=3, include_bias=True, interaction_only=False)), ('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', LinearSVC(C=10, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "     penalty='l2', random_state=42, tol=0.0001, verbose=0))))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
    "    ])\n",
    "\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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+ZU8TbuzwW5YSpZj6JXqNGpSLTK8t5br5X7noJ9Xs+g0N6z1WmB+2L6GeSND0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\nFMVK1KAURVEUK1GDUhRFUazk/wMZycrQM+SxOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fa95080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "save_fig(\"moons_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ])\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "  decision_function_shape=None, degree=10, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ])\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_kernelized_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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tDd6kuPg4wbkF+G66MycrL0WjA4g0sDp1ycnGI9OGOxONRnJM0+mxSc7yNPY1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\nCCk+6xwL9O/38zRywwO/X8KPF9zD3pMofG5IY/5+O+eGVoS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yROseZDDtVjhAmLmnltZA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\nASEiIiIiIpGpgBARERERkcj+H6axNTvFrlIEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fc26978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure kernel_method_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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MycT6Zz+/i4iIiJ/Vdfwe03cdiD3AEyLymTGmJTBNREpnpZsItMVa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\nTBkIERERERFJmDIQIiIiIiKSsP8Hz7NSdMHpeJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe68204a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi(-1.0, -2) = [ 0.74081822]\n",
      "Phi(-1.0, 1) = [ 0.30119421]\n"
     ]
    }
   ],
   "source": [
    "x1_example = X1D[3, 0]\n",
    "for landmark in (-2, 1):\n",
    "    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
    "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=(('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svm_clf', SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=5, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_rbf_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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p3SBD+BIoA2JU8IMH7xvqA3Fn1JHx5OXZ3wk6nR4JMucvHGnppbPrMZMjdyL6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\nAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67f9f2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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HjgE9yKvI+e3b6SboWfGQGSdJ6tBqtcTFxVG7dm1V2/3tt98YO3Ysp06dAqBn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\nEhMTHzluZGSk6tq1a06OatKkidq6datJYr5+/bry8PBQCxcuNMl4Qlg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0ia5du9K4ceN8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67f9171d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "  kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1)\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_with_polynomial_kernel_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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zZzRNI7B3U/ZHjsvkH5Mxyue1d9+12vUIgj1SunTptKGJ2IsX0wUc6H3OUocd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0AAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fd12198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "save_fig(\"svm_with_polynomial_kernel_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Under the hood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure iris_3D_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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CiD3kQBBEEhCq/Kqw2Vs8yK+SUU4QsSEcWVqxwu5oytIyfNOiKHpBENGDHAiCSGBCkV9l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hYXL74CANBqdbj11jfh3nvfh5aWYzCZjGK9vkajAcsKzoxWq0Nrawt4HhIjf3sODEOD5/kdmQnp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\nQgghhBBlJgGEEEIIIYQQosz+H2LIaRkqvabiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fd92fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def plot_3D_decision_function(ax, w, b, x1_lim=[4, 6], x2_lim=[0.8, 2.8]):\n",
    "    x1_in_bounds = (X[:, 0] > x1_lim[0]) & (X[:, 0] < x1_lim[1])\n",
    "    X_crop = X[x1_in_bounds]\n",
    "    y_crop = y[x1_in_bounds]\n",
    "    x1s = np.linspace(x1_lim[0], x1_lim[1], 20)\n",
    "    x2s = np.linspace(x2_lim[0], x2_lim[1], 20)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    xs = np.c_[x1.ravel(), x2.ravel()]\n",
    "    df = (xs.dot(w) + b).reshape(x1.shape)\n",
    "    m = 1 / np.linalg.norm(w)\n",
    "    boundary_x2s = -x1s*(w[0]/w[1])-b/w[1]\n",
    "    margin_x2s_1 = -x1s*(w[0]/w[1])-(b-1)/w[1]\n",
    "    margin_x2s_2 = -x1s*(w[0]/w[1])-(b+1)/w[1]\n",
    "    ax.plot_surface(x1s, x2, np.zeros_like(x1),\n",
    "                    color=\"b\", alpha=0.2, cstride=100, rstride=100)\n",
    "    ax.plot(x1s, boundary_x2s, 0, \"k-\", linewidth=2, label=r\"$h=0$\")\n",
    "    ax.plot(x1s, margin_x2s_1, 0, \"k--\", linewidth=2, label=r\"$h=\\pm 1$\")\n",
    "    ax.plot(x1s, margin_x2s_2, 0, \"k--\", linewidth=2)\n",
    "    ax.plot(X_crop[:, 0][y_crop==1], X_crop[:, 1][y_crop==1], 0, \"g^\")\n",
    "    ax.plot_wireframe(x1, x2, df, alpha=0.3, color=\"k\")\n",
    "    ax.plot(X_crop[:, 0][y_crop==0], X_crop[:, 1][y_crop==0], 0, \"bs\")\n",
    "    ax.axis(x1_lim + x2_lim)\n",
    "    ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=15)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=15)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=15)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^t \\cdot \\mathbf{x} + b$\", fontsize=18)\n",
    "    ax.legend(loc=\"upper left\", fontsize=16)\n",
    "\n",
    "fig = plt.figure(figsize=(11, 6))\n",
    "ax1 = fig.add_subplot(111, projection='3d')\n",
    "plot_3D_decision_function(ax1, w=svm_clf2.coef_[0], b=svm_clf2.intercept_[0])\n",
    "\n",
    "save_fig(\"iris_3D_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Small weight vector results in a large margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure small_w_large_margin_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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p02HwYDjhBJg/H9q1izoiERGJm6oqmDo1FFQTJ8Ihh4R26hMnQufOYWaEiDSe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\niIiI5Mj/B/7Ldc5B003JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fbb38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_2D_decision_function(w, b, ylabel=True, x1_lim=[-3, 3]):\n",
    "    x1 = np.linspace(x1_lim[0], x1_lim[1], 200)\n",
    "    y = w * x1 + b\n",
    "    m = 1 / w\n",
    "\n",
    "    plt.plot(x1, y)\n",
    "    plt.plot(x1_lim, [1, 1], \"k:\")\n",
    "    plt.plot(x1_lim, [-1, -1], \"k:\")\n",
    "    plt.axhline(y=0, color='k')\n",
    "    plt.axvline(x=0, color='k')\n",
    "    plt.plot([m, m], [0, 1], \"k--\")\n",
    "    plt.plot([-m, -m], [0, -1], \"k--\")\n",
    "    plt.plot([-m, m], [0, 0], \"k-o\", linewidth=3)\n",
    "    plt.axis(x1_lim + [-2, 2])\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=16)\n",
    "    if ylabel:\n",
    "        plt.ylabel(r\"$w_1 x_1$  \", rotation=0, fontsize=16)\n",
    "    plt.title(r\"$w_1 = {}$\".format(w), fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(12, 3.2))\n",
    "plt.subplot(121)\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.subplot(122)\n",
    "plot_2D_decision_function(0.5, 0, ylabel=False)\n",
    "save_fig(\"small_w_large_margin_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64) # Iris-Virginica\n",
    "\n",
    "svm_clf = SVC(kernel=\"linear\", C=1)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict([[5.3, 1.3]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure hinge_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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fqXR8ohJaTqHlEy8V4ACdeCIsWuRucbR1K1x+Odx1F+zc6TsyESlNLYiAWQv//d/wxz+6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fad4cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(-2, 4, 200)\n",
    "h = np.where(1 - t < 0, 0, 1 - t)  # max(0, 1-t)\n",
    "\n",
    "plt.figure(figsize=(5,2.8))\n",
    "plt.plot(t, h, \"b-\", linewidth=2, label=\"$max(0, 1 - t)$\")\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.yticks(np.arange(-1, 2.5, 1))\n",
    "plt.xlabel(\"$t$\", fontsize=16)\n",
    "plt.axis([-2, 4, -1, 2.5])\n",
    "plt.legend(loc=\"upper right\", fontsize=16)\n",
    "save_fig(\"hinge_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Training time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe67facc908>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R/8U3v4hSXmXp4uWNO0tFqdjDOO1YTRb19TUJpga/P1lSmCXk0b0Lys4miTe3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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67facc4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples=1000, noise=0.4, random_state=42)\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]0 0.1 0.24260568618774414\n",
      "[LibSVM]1 0.01 0.2221059799194336\n",
      "[LibSVM]2 0.001 0.2712545394897461\n",
      "[LibSVM]3 0.0001 0.49069976806640625\n",
      "[LibSVM]4 1e-05 0.7979280948638916\n",
      "[LibSVM]5 1.0000000000000002e-06 0.8101603984832764\n",
      "[LibSVM]6 1.0000000000000002e-07 6.707451581954956\n",
      "[LibSVM]7 1.0000000000000002e-08 0.893460750579834\n",
      "[LibSVM]8 1.0000000000000003e-09 0.8796014785766602\n",
      "[LibSVM]9 1.0000000000000003e-10 0.8945713043212891\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe67fb5cc50>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fc730f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tol = 0.1\n",
    "tols = []\n",
    "times = []\n",
    "for i in range(10):\n",
    "    svm_clf = SVC(kernel=\"poly\", gamma=3, C=10, tol=tol, verbose=1)\n",
    "    t1 = time.time()\n",
    "    svm_clf.fit(X, y)\n",
    "    t2 = time.time()\n",
    "    times.append(t2-t1)\n",
    "    tols.append(tol)\n",
    "    print(i, tol, t2-t1)\n",
    "    tol /= 10\n",
    "plt.semilogx(tols, times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Linear SVM classifier implementation using Batch Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Training set\n",
    "X = iris[\"data\"][:, (2, 3)] # petal length, petal width\n",
    "y = (iris[\"target\"] == 2).astype(np.float64).reshape(-1, 1) # Iris-Virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "\n",
    "class MyLinearSVC(BaseEstimator):\n",
    "    def __init__(self, C=1, eta0=1, eta_d=10000, n_epochs=1000, random_state=None):\n",
    "        self.C = C\n",
    "        self.eta0 = eta0\n",
    "        self.n_epochs = n_epochs\n",
    "        self.random_state = random_state\n",
    "        self.eta_d = eta_d\n",
    "\n",
    "    def eta(self, epoch):\n",
    "        return self.eta0 / (epoch + self.eta_d)\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        # Random initialization\n",
    "        if self.random_state:\n",
    "            np.random.seed(self.random_state)\n",
    "        w = np.random.randn(X.shape[1], 1) # n feature weights\n",
    "        b = 0\n",
    "\n",
    "        m = len(X)\n",
    "        t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "        X_t = X * t\n",
    "        self.Js=[]\n",
    "\n",
    "        # Training\n",
    "        for epoch in range(self.n_epochs):\n",
    "            support_vectors_idx = (X_t.dot(w) + t * b < 1).ravel()\n",
    "            X_t_sv = X_t[support_vectors_idx]\n",
    "            t_sv = t[support_vectors_idx]\n",
    "\n",
    "            J = 1/2 * np.sum(w * w) + self.C * (np.sum(1 - X_t_sv.dot(w)) - b * np.sum(t_sv))\n",
    "            self.Js.append(J)\n",
    "\n",
    "            w_gradient_vector = w - self.C * np.sum(X_t_sv, axis=0).reshape(-1, 1)\n",
    "            b_derivative = -C * np.sum(t_sv)\n",
    "                \n",
    "            w = w - self.eta(epoch) * w_gradient_vector\n",
    "            b = b - self.eta(epoch) * b_derivative\n",
    "            \n",
    "\n",
    "        self.intercept_ = np.array([b])\n",
    "        self.coef_ = np.array([w])\n",
    "        support_vectors_idx = (X_t.dot(w) + b < 1).ravel()\n",
    "        self.support_vectors_ = X[support_vectors_idx]\n",
    "        return self\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return X.dot(self.coef_[0]) + self.intercept_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return (self.decision_function(X) >= 0).astype(np.float64)\n",
    "\n",
    "C=2\n",
    "svm_clf = MyLinearSVC(C=C, eta0 = 10, eta_d = 1000, n_epochs=60000, random_state=2)\n",
    "svm_clf.fit(X, y)\n",
    "svm_clf.predict(np.array([[5, 2], [4, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 60000, 0, 100]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N+g6KiFgaEX8TEY9GxHRE3BURb+yaf25E3B8RuyPilohY12+bkqThaWOPYgLYBrw6M38M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\nI4NCktTIoJAkNTIoJEmN/j9s5sK29URsjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fae8b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(svm_clf.n_epochs), svm_clf.Js)\n",
    "plt.axis([0, svm_clf.n_epochs, 0, 100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56780998] [[[ 2.28129013]\n",
      "  [ 2.71597487]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.51721253] [[ 2.27128546  2.71287145]]\n"
     ]
    }
   ],
   "source": [
    "svm_clf2 = SVC(kernel=\"linear\", C=C)\n",
    "svm_clf2.fit(X, y.ravel())\n",
    "print(svm_clf2.intercept_, svm_clf2.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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79pqzpbTe4DsJKG7k8aJAkjkBWOKwp0SeIjo6Wn799deyZMmShoRerVo1+d//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\niqIoimIl/w86XTx9vEX6nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fa6aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\", label=\"Not Iris-Virginica\")\n",
    "plot_svc_decision_boundary(svm_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"MyLinearSVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"SVC\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.062485     2.24179316   1.79750198]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[4, 6, 0.8, 2.8]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkSNp1qwZ69aty9drDTKfBrooiUajweUtFzQajdJS8hWqQVeYIkWKMGvWLPbt20eFChU4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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67fbb3d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, n_iter = 50, random_state=42)\n",
    "sgd_clf.fit(X, y.ravel())\n",
    "\n",
    "m = len(X)\n",
    "t = y * 2 - 1  # -1 if t==0, +1 if t==1\n",
    "X_b = np.c_[np.ones((m, 1)), X]  # Add bias input x0=1\n",
    "X_b_t = X_b * t\n",
    "sgd_theta = np.r_[sgd_clf.intercept_[0], sgd_clf.coef_[0]]\n",
    "print(sgd_theta)\n",
    "support_vectors_idx = (X_b_t.dot(sgd_theta) < 1).ravel()\n",
    "sgd_clf.support_vectors_ = X[support_vectors_idx]\n",
    "sgd_clf.C = C\n",
    "\n",
    "plt.figure(figsize=(5.5,3.2))\n",
    "plt.plot(X[:, 0][yr==1], X[:, 1][yr==1], \"g^\")\n",
    "plt.plot(X[:, 0][yr==0], X[:, 1][yr==0], \"bs\")\n",
    "plot_svc_decision_boundary(sgd_clf, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.title(\"SGDClassifier\", fontsize=14)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "See appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Exercise: train a `LinearSVC` on a linearly separable dataset. Then train an `SVC` and a `SGDClassifier` on the same dataset. See if you can get them to produce roughly the same model._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's use the Iris dataset: the Iris Setosa and Iris Versicolor classes are linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [ 0.28480668] [[ 1.05542422  1.09851637]]\n",
      "SVC:                          [ 0.31933577] [[ 1.1223101   1.02531081]]\n",
      "SGDClassifier(alpha=0.00200): [ 0.32] [[ 1.12293103  1.02620763]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "C = 5\n",
    "alpha = 1 / (C * len(X))\n",
    "\n",
    "lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
    "svm_clf = SVC(kernel=\"linear\", C=C)\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
    "                        n_iter=100000, random_state=42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lin_clf.fit(X_scaled, y)\n",
    "svm_clf.fit(X_scaled, y)\n",
    "sgd_clf.fit(X_scaled, y)\n",
    "\n",
    "print(\"LinearSVC:                   \", lin_clf.intercept_, lin_clf.coef_)\n",
    "print(\"SVC:                         \", svm_clf.intercept_, svm_clf.coef_)\n",
    "print(\"SGDClassifier(alpha={:.5f}):\".format(sgd_clf.alpha), sgd_clf.intercept_, sgd_clf.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's plot the decision boundaries of these three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZM2eSmPji61vMnj0bW1tbmjVrBkDHjh25e/cu8+bNS7F8VFRUisd37txJ165d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\n8hs2bAAgKSmpzHGLhIpGPKVcClbpR3/KYE6ePJ5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z4GvgUf4zjX6y1wTwNPAycCmwFpjt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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe67f77b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the slope and bias of each decision boundary\n",
    "w1 = -lin_clf.coef_[0, 0]/lin_clf.coef_[0, 1]\n",
    "b1 = -lin_clf.intercept_[0]/lin_clf.coef_[0, 1]\n",
    "w2 = -svm_clf.coef_[0, 0]/svm_clf.coef_[0, 1]\n",
    "b2 = -svm_clf.intercept_[0]/svm_clf.coef_[0, 1]\n",
    "w3 = -sgd_clf.coef_[0, 0]/sgd_clf.coef_[0, 1]\n",
    "b3 = -sgd_clf.intercept_[0]/sgd_clf.coef_[0, 1]\n",
    "\n",
    "# Transform the decision boundary lines back to the original scale\n",
    "line1 = scaler.inverse_transform([[-10, -10 * w1 + b1], [10, 10 * w1 + b1]])\n",
    "line2 = scaler.inverse_transform([[-10, -10 * w2 + b2], [10, 10 * w2 + b2]])\n",
    "line3 = scaler.inverse_transform([[-10, -10 * w3 + b3], [10, 10 * w3 + b3]])\n",
    "\n",
    "# Plot all three decision boundaries\n",
    "plt.figure(figsize=(11, 4))\n",
    "plt.plot(line1[:, 0], line1[:, 1], \"k:\", label=\"LinearSVC\")\n",
    "plt.plot(line2[:, 0], line2[:, 1], \"b--\", linewidth=2, label=\"SVC\")\n",
    "plt.plot(line3[:, 0], line3[:, 1], \"r-\", label=\"SGDClassifier\")\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\") # label=\"Iris-Versicolor\"\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\") # label=\"Iris-Setosa\"\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper center\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Close enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Exercise: train an SVM classifier on the MNIST dataset. Since SVM classifiers are binary classifiers, you will need to use one-versus-all to classify all 10 digits. You may want to tune the hyperparameters using small validation sets to speed up the process. What accuracy can you reach?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "First, let's load the dataset and split it into a training set and a test set. We could use `train_test_split()` but people usually just take the first 60,000 instances for the training set, and the last 10,000 instances for the test set (this makes it possible to compare your model's performance with others): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "\n",
    "mnist = fetch_mldata(\"MNIST original\")\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"]\n",
    "\n",
    "X_train = X[:60000]\n",
    "y_train = y[:60000]\n",
    "X_test = X[60000:]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "rnd_idx = np.random.permutation(60000)\n",
    "X_train = X_train[rnd_idx]\n",
    "y_train = y_train[rnd_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's start simple, with a linear SVM classifier. It will automatically use the One-vs-All (also called One-vs-the-Rest, OvR) strategy, so there's nothing special we need to do. Easy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's make predictions on the training set and measure the accuracy (we don't want to measure it on the test set yet, since we have not selected and trained the final model yet):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82633333333333336"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = lin_clf.predict(X_train)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Wow, 82% accuracy on MNIST is a really bad performance. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float32))\n",
    "X_test_scaled = scaler.transform(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9224"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "That's much better (we cut the error rate in two), but still not great at all for MNIST. If we want to use an SVM, we will have to use a kernel. Let's try an `SVC` with an RBF kernel (the default).\n",
    "\n",
    "**Warning**: if you are using Scikit-Learn ≤ 0.19, the `SVC` class will use the One-vs-One (OvO) strategy by default, so you must explicitly set `decision_function_shape=\"ovr\"` if you want to use the OvR strategy instead (OvR is the default since 0.19)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(decision_function_shape=\"ovr\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94615000000000005"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "That's promising, we get better performance even though we trained the model on 6 times less data. Let's tune the hyperparameters by doing a randomized search with cross validation. We will do this on a small dataset just to speed up the process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] gamma=0.00176607465048, C=8.85231605842 .........................\n",
      "[CV] .......... gamma=0.00176607465048, C=8.85231605842, total=   0.8s\n",
      "[CV] gamma=0.00176607465048, C=8.85231605842 .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... gamma=0.00176607465048, C=8.85231605842, total=   0.8s\n",
      "[CV] gamma=0.00176607465048, C=8.85231605842 .........................\n",
      "[CV] .......... gamma=0.00176607465048, C=8.85231605842, total=   0.8s\n",
      "[CV] gamma=0.00184893979432, C=1.1070059007 ..........................\n",
      "[CV] ........... gamma=0.00184893979432, C=1.1070059007, total=   0.8s\n",
      "[CV] gamma=0.00184893979432, C=1.1070059007 ..........................\n",
      "[CV] ........... gamma=0.00184893979432, C=1.1070059007, total=   0.8s\n",
      "[CV] gamma=0.00184893979432, C=1.1070059007 ..........................\n",
      "[CV] ........... gamma=0.00184893979432, C=1.1070059007, total=   0.8s\n",
      "[CV] gamma=0.0408025396137, C=9.88773818971 ..........................\n",
      "[CV] ........... gamma=0.0408025396137, C=9.88773818971, total=   1.0s\n",
      "[CV] gamma=0.0408025396137, C=9.88773818971 ..........................\n",
      "[CV] ........... gamma=0.0408025396137, C=9.88773818971, total=   1.0s\n",
      "[CV] gamma=0.0408025396137, C=9.88773818971 ..........................\n",
      "[CV] ........... gamma=0.0408025396137, C=9.88773818971, total=   1.0s\n",
      "[CV] gamma=0.00464565003701, C=5.7997015194 ..........................\n",
      "[CV] ........... gamma=0.00464565003701, C=5.7997015194, total=   0.9s\n",
      "[CV] gamma=0.00464565003701, C=5.7997015194 ..........................\n",
      "[CV] ........... gamma=0.00464565003701, C=5.7997015194, total=   0.9s\n",
      "[CV] gamma=0.00464565003701, C=5.7997015194 ..........................\n",
      "[CV] ........... gamma=0.00464565003701, C=5.7997015194, total=   1.0s\n",
      "[CV] gamma=0.0157352905643, C=6.84899112775 ..........................\n",
      "[CV] ........... gamma=0.0157352905643, C=6.84899112775, total=   1.0s\n",
      "[CV] gamma=0.0157352905643, C=6.84899112775 ..........................\n",
      "[CV] ........... gamma=0.0157352905643, C=6.84899112775, total=   1.0s\n",
      "[CV] gamma=0.0157352905643, C=6.84899112775 ..........................\n",
      "[CV] ........... gamma=0.0157352905643, C=6.84899112775, total=   1.0s\n",
      "[CV] gamma=0.0294618722484, C=9.74491791678 ..........................\n",
      "[CV] ........... gamma=0.0294618722484, C=9.74491791678, total=   1.0s\n",
      "[CV] gamma=0.0294618722484, C=9.74491791678 ..........................\n",
      "[CV] ........... gamma=0.0294618722484, C=9.74491791678, total=   1.0s\n",
      "[CV] gamma=0.0294618722484, C=9.74491791678 ..........................\n",
      "[CV] ........... gamma=0.0294618722484, C=9.74491791678, total=   1.0s\n",
      "[CV] gamma=0.00614152194782, C=3.82271785963 .........................\n",
      "[CV] .......... gamma=0.00614152194782, C=3.82271785963, total=   1.0s\n",
      "[CV] gamma=0.00614152194782, C=3.82271785963 .........................\n",
      "[CV] .......... gamma=0.00614152194782, C=3.82271785963, total=   1.0s\n",
      "[CV] gamma=0.00614152194782, C=3.82271785963 .........................\n",
      "[CV] .......... gamma=0.00614152194782, C=3.82271785963, total=   1.0s\n",
      "[CV] gamma=0.0219465853153, C=3.7155233269 ...........................\n",
      "[CV] ............ gamma=0.0219465853153, C=3.7155233269, total=   1.0s\n",
      "[CV] gamma=0.0219465853153, C=3.7155233269 ...........................\n",
      "[CV] ............ gamma=0.0219465853153, C=3.7155233269, total=   1.0s\n",
      "[CV] gamma=0.0219465853153, C=3.7155233269 ...........................\n",
      "[CV] ............ gamma=0.0219465853153, C=3.7155233269, total=   1.0s\n",
      "[CV] gamma=0.00374501745191, C=5.22924613427 .........................\n",
      "[CV] .......... gamma=0.00374501745191, C=5.22924613427, total=   0.9s\n",
      "[CV] gamma=0.00374501745191, C=5.22924613427 .........................\n",
      "[CV] .......... gamma=0.00374501745191, C=5.22924613427, total=   0.9s\n",
      "[CV] gamma=0.00374501745191, C=5.22924613427 .........................\n",
      "[CV] .......... gamma=0.00374501745191, C=5.22924613427, total=   0.9s\n",
      "[CV] gamma=0.0224153322878, C=4.89867874863 ..........................\n",
      "[CV] ........... gamma=0.0224153322878, C=4.89867874863, total=   1.0s\n",
      "[CV] gamma=0.0224153322878, C=4.89867874863 ..........................\n",
      "[CV] ........... gamma=0.0224153322878, C=4.89867874863, total=   1.0s\n",
      "[CV] gamma=0.0224153322878, C=4.89867874863 ..........................\n",
      "[CV] ........... gamma=0.0224153322878, C=4.89867874863, total=   1.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   40.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe67f7a2470>, 'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe67f7a2908>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(svm_clf, param_distributions, n_iter=10, verbose=2)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.8523160584230869, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85599999999999998"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This looks pretty low but remember we only trained the model on 1,000 instances. Let's retrain the best estimator on the whole training set (run this at night, it will take hours):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.8523160584230869, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001766074650481071,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99965000000000004"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Ah, this looks good! Let's select this model. Now we can test it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97089999999999999"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Not too bad, but apparently the model is overfitting slightly. It's tempting to tweak the hyperparameters a bit more (e.g. decreasing `C` and/or `gamma`), but we would run the risk of overfitting the test set. Other people have found that the hyperparameters `C=5` and `gamma=0.005` yield even better performance (over 98% accuracy). By running the randomized search for longer and on a larger part of the training set, you may be able to find this as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "_Exercise: train an SVM regressor on the California housing dataset._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's load the dataset using Scikit-Learn's `fetch_california_housing()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "X = housing[\"data\"]\n",
    "y = housing[\"target\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Don't forget to scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's train a simple `LinearSVR` first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
       "     intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "     random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR(random_state=42)\n",
    "lin_svr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's see how it performs on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94996882221722923"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's look at the RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97466344048457532"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In this training set, the targets are tens of thousands of dollars. The RMSE gives a rough idea of the kind of error you should expect (with a higher weight for large errors): so with this model we can expect errors somewhere around $10,000. Not great. Let's see if we can do better with an RBF Kernel. We will use randomized search with cross validation to find the appropriate hyperparameter values for `C` and `gamma`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] gamma=0.0796945481864, C=4.74540118847 ..........................\n",
      "[CV] ........... gamma=0.0796945481864, C=4.74540118847, total=  11.4s\n",
      "[CV] gamma=0.0796945481864, C=4.74540118847 ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   15.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........... gamma=0.0796945481864, C=4.74540118847, total=  11.4s\n",
      "[CV] gamma=0.0796945481864, C=4.74540118847 ..........................\n",
      "[CV] ........... gamma=0.0796945481864, C=4.74540118847, total=  11.5s\n",
      "[CV] gamma=0.0157513204998, C=8.31993941811 ..........................\n",
      "[CV] ........... gamma=0.0157513204998, C=8.31993941811, total=  11.2s\n",
      "[CV] gamma=0.0157513204998, C=8.31993941811 ..........................\n",
      "[CV] ........... gamma=0.0157513204998, C=8.31993941811, total=  10.9s\n",
      "[CV] gamma=0.0157513204998, C=8.31993941811 ..........................\n",
      "[CV] ........... gamma=0.0157513204998, C=8.31993941811, total=  11.2s\n",
      "[CV] gamma=0.00205111041884, C=2.56018640442 .........................\n",
      "[CV] .......... gamma=0.00205111041884, C=2.56018640442, total=  10.6s\n",
      "[CV] gamma=0.00205111041884, C=2.56018640442 .........................\n",
      "[CV] .......... gamma=0.00205111041884, C=2.56018640442, total=   9.9s\n",
      "[CV] gamma=0.00205111041884, C=2.56018640442 .........................\n",
      "[CV] .......... gamma=0.00205111041884, C=2.56018640442, total=  10.5s\n",
      "[CV] gamma=0.0539948440979, C=1.58083612168 ..........................\n",
      "[CV] ........... gamma=0.0539948440979, C=1.58083612168, total=  10.4s\n",
      "[CV] gamma=0.0539948440979, C=1.58083612168 ..........................\n",
      "[CV] ........... gamma=0.0539948440979, C=1.58083612168, total=   9.9s\n",
      "[CV] gamma=0.0539948440979, C=1.58083612168 ..........................\n",
      "[CV] ........... gamma=0.0539948440979, C=1.58083612168, total=  10.0s\n",
      "[CV] gamma=0.0260702475837, C=7.01115011743 ..........................\n",
      "[CV] ........... gamma=0.0260702475837, C=7.01115011743, total=  10.0s\n",
      "[CV] gamma=0.0260702475837, C=7.01115011743 ..........................\n",
      "[CV] ........... gamma=0.0260702475837, C=7.01115011743, total=  11.8s\n",
      "[CV] gamma=0.0260702475837, C=7.01115011743 ..........................\n",
      "[CV] ........... gamma=0.0260702475837, C=7.01115011743, total=  11.5s\n",
      "[CV] gamma=0.087060208783, C=1.20584494296 ...........................\n",
      "[CV] ............ gamma=0.087060208783, C=1.20584494296, total=   9.9s\n",
      "[CV] gamma=0.087060208783, C=1.20584494296 ...........................\n",
      "[CV] ............ gamma=0.087060208783, C=1.20584494296, total=  10.4s\n",
      "[CV] gamma=0.087060208783, C=1.20584494296 ...........................\n",
      "[CV] ............ gamma=0.087060208783, C=1.20584494296, total=  10.1s\n",
      "[CV] gamma=0.00265875439833, C=9.324426408 ...........................\n",
      "[CV] ............ gamma=0.00265875439833, C=9.324426408, total=  10.4s\n",
      "[CV] gamma=0.00265875439833, C=9.324426408 ...........................\n",
      "[CV] ............ gamma=0.00265875439833, C=9.324426408, total=  10.7s\n",
      "[CV] gamma=0.00265875439833, C=9.324426408 ...........................\n",
      "[CV] ............ gamma=0.00265875439833, C=9.324426408, total=   9.8s\n",
      "[CV] gamma=0.00232706770838, C=2.81824967207 .........................\n",
      "[CV] .......... gamma=0.00232706770838, C=2.81824967207, total=  10.8s\n",
      "[CV] gamma=0.00232706770838, C=2.81824967207 .........................\n",
      "[CV] .......... gamma=0.00232706770838, C=2.81824967207, total=  10.3s\n",
      "[CV] gamma=0.00232706770838, C=2.81824967207 .........................\n",
      "[CV] .......... gamma=0.00232706770838, C=2.81824967207, total=  10.3s\n",
      "[CV] gamma=0.0112076062119, C=4.0424224296 ...........................\n",
      "[CV] ............ gamma=0.0112076062119, C=4.0424224296, total=  10.0s\n",
      "[CV] gamma=0.0112076062119, C=4.0424224296 ...........................\n",
      "[CV] ............ gamma=0.0112076062119, C=4.0424224296, total=  10.0s\n",
      "[CV] gamma=0.0112076062119, C=4.0424224296 ...........................\n",
      "[CV] ............ gamma=0.0112076062119, C=4.0424224296, total=  10.1s\n",
      "[CV] gamma=0.00382347522468, C=5.31945018642 .........................\n",
      "[CV] .......... gamma=0.00382347522468, C=5.31945018642, total=   9.8s\n",
      "[CV] gamma=0.00382347522468, C=5.31945018642 .........................\n",
      "[CV] .......... gamma=0.00382347522468, C=5.31945018642, total=   9.8s\n",
      "[CV] gamma=0.00382347522468, C=5.31945018642 .........................\n",
      "[CV] .......... gamma=0.00382347522468, C=5.31945018642, total=   9.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  7.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=None, error_score='raise',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=1,\n",
       "          param_distributions={'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe67f74ec50>, 'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe67f74eb00>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score=True, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import reciprocal, uniform\n",
    "\n",
    "param_distributions = {\"gamma\": reciprocal(0.001, 0.1), \"C\": uniform(1, 10)}\n",
    "rnd_search_cv = RandomizedSearchCV(SVR(), param_distributions, n_iter=10, verbose=2, random_state=42)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.7454011884736254, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma=0.079694548186439285, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now let's measure the RMSE on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785356"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Looks much better than the linear model. Let's select this model and evaluate it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59291683855287403"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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