05_support_vector_machines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\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 as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large margin classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, 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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure large_margin_classification_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\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.sca(axes[1])\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": {},
   "source": [
    "# Sensitivity to feature scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_feature_scales_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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=(9,2.7))\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.ylabel(\"$x'_1$  \", fontsize=20, rotation=0)\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": {},
   "source": [
    "# Sensitivity to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_outliers_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\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.sca(axes[1])\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": {},
   "source": [
    "# Large margin *vs* margin violations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the first code example in chapter 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     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": {},
   "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": {},
   "source": [
    "Now let's generate the graph comparing different regularization settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2b/lib/python3.7/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10,2.7), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\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, 5.9)\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, 5.9, 0.8, 2.8])\n",
    "\n",
    "plt.sca(axes[1])\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, 5.99)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 5.9, 0.8, 2.8])\n",
    "\n",
    "save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Non-linear classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure higher_dimensions_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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=(10, 3))\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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     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='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='poly', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, 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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     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='ovr', degree=10, gamma='auto_deprecated',\n",
       "  kernel='poly', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, 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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_kernelized_polynomial_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(10.5, 4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure kernel_method_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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=(10.5, 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": {},
   "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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     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='ovr', 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": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure moons_rbf_svc_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10.5, 7), sharex=True, sharey=True)\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.sca(axes[i // 2, i % 2])\n",
    "    plot_predictions(svm_clf, [-1.5, 2.45, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.45, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "    if i in (0, 1):\n",
    "        plt.xlabel(\"\")\n",
    "    if i in (1, 3):\n",
    "        plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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": {},
   "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": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True)\n",
    "plt.sca(axes[0])\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.sca(axes[1])\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": {},
   "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": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof, we set `gamma=\"scale\"`, as this will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='scale',\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, gamma=\"scale\")\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='scale',\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, gamma=\"scale\")\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1, gamma=\"scale\")\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure svm_with_polynomial_kernel_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True)\n",
    "plt.sca(axes[0])\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.sca(axes[1])\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": {},
   "source": [
    "# Under the hood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure iris_3D_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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=16)\n",
    "    ax.set_xlabel(r\"Petal length\", fontsize=16, labelpad=10)\n",
    "    ax.set_ylabel(r\"Petal width\", fontsize=16, labelpad=10)\n",
    "    ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18, labelpad=5)\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": {},
   "source": [
    "# Small weight vector results in a large margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure small_w_large_margin_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "fig, axes = plt.subplots(ncols=2, figsize=(9, 3.2), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_2D_decision_function(1, 0)\n",
    "plt.sca(axes[1])\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": {},
   "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": {},
   "source": [
    "# Hinge loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure hinge_plot\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x201.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "source": [
    "# Extra material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f985051ef90>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]0 0.1 0.19860410690307617\n",
      "[LibSVM]1 0.01 0.18686413764953613\n",
      "[LibSVM]2 0.001 0.20957088470458984\n",
      "[LibSVM]3 0.0001 0.3814828395843506\n",
      "[LibSVM]4 1e-05 0.6392810344696045\n",
      "[LibSVM]5 1.0000000000000002e-06 0.5971472263336182\n",
      "[LibSVM]6 1.0000000000000002e-07 0.6291971206665039\n",
      "[LibSVM]7 1.0000000000000002e-08 0.6234230995178223\n",
      "[LibSVM]8 1.0000000000000003e-09 0.6174850463867188\n",
      "[LibSVM]9 1.0000000000000003e-10 0.6262409687042236\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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, \"bo-\")\n",
    "plt.xlabel(\"Tolerance\", fontsize=16)\n",
    "plt.ylabel(\"Time (seconds)\", fontsize=16)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM classifier implementation using Batch Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "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": {},
   "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) + t * 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": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 60000, 0, 100]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-15.56761653] [[[2.28120287]\n",
      "  [2.71621742]]]\n"
     ]
    }
   ],
   "source": [
    "print(svm_clf.intercept_, svm_clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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": {},
   "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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\n",
      "text/plain": [
       "<Figure size 792x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yr = y.ravel()\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(11, 3.2), sharey=True)\n",
    "plt.sca(axes[0])\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",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.sca(axes[1])\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": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-14.32885908   2.27493198   1.98084355]\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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\n",
      "text/plain": [
       "<Figure size 396x230.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(loss=\"hinge\", alpha=0.017, max_iter=1000, tol=1e-3, 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": {},
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "See appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "Let's use the Iris dataset: the Iris Setosa and Iris Versicolor classes are linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [0.28474532] [[1.05364923 1.09903601]]\n",
      "SVC:                          [0.31896852] [[1.1203284  1.02625193]]\n",
      "SGDClassifier(alpha=0.00200): [0.117] [[0.77666262 0.72787608]]\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",
    "                        max_iter=1000, tol=1e-3, 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": {},
   "source": [
    "Let's plot the decision boundaries of these three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": {},
   "source": [
    "Close enough!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1, cache=True)\n",
    "\n",
    "X = mnist[\"data\"]\n",
    "y = mnist[\"target\"].astype(np.uint8)\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": {},
   "source": [
    "Many training algorithms are sensitive to the order of the training instances, so it's generally good practice to shuffle them first. However, the dataset is already shuffled, so we do not need to do it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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!\n",
    "\n",
    "**Warning**: this may take a few minutes depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2b/lib/python3.7/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state=42)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.895"
      ]
     },
     "execution_count": 49,
     "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": {},
   "source": [
    "Okay, 89.5% accuracy on MNIST is pretty bad. This linear model is certainly too simple for MNIST, but perhaps we just needed to scale the data first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "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": "markdown",
   "metadata": {},
   "source": [
    "**Warning**: this may take a few minutes depending on your hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2b/lib/python3.7/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "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": 51,
     "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92105"
      ]
     },
     "execution_count": 52,
     "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": {},
   "source": [
    "That's much better (we cut the error rate by about 25%), 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)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: to be future-proof we set `gamma=\"scale\"` since it will be the default value in Scikit-Learn 0.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "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='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(gamma=\"scale\")\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455333333333333"
      ]
     },
     "execution_count": 54,
     "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": {},
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=4.205364371116072, gamma=0.0020363543195523162 ................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=4.205364371116072, gamma=0.0020363543195523162, total=   0.6s\n",
      "[CV] C=4.205364371116072, gamma=0.0020363543195523162 ................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=4.205364371116072, gamma=0.0020363543195523162, total=   0.6s\n",
      "[CV] C=4.205364371116072, gamma=0.0020363543195523162 ................\n",
      "[CV] . C=4.205364371116072, gamma=0.0020363543195523162, total=   0.6s\n",
      "[CV] C=7.988626913672013, gamma=0.0017374050727190405 ................\n",
      "[CV] . C=7.988626913672013, gamma=0.0017374050727190405, total=   0.6s\n",
      "[CV] C=7.988626913672013, gamma=0.0017374050727190405 ................\n",
      "[CV] . C=7.988626913672013, gamma=0.0017374050727190405, total=   0.6s\n",
      "[CV] C=7.988626913672013, gamma=0.0017374050727190405 ................\n",
      "[CV] . C=7.988626913672013, gamma=0.0017374050727190405, total=   0.6s\n",
      "[CV] C=5.85175909599865, gamma=0.01842788363564703 ...................\n",
      "[CV] .... C=5.85175909599865, gamma=0.01842788363564703, total=   0.7s\n",
      "[CV] C=5.85175909599865, gamma=0.01842788363564703 ...................\n",
      "[CV] .... C=5.85175909599865, gamma=0.01842788363564703, total=   0.7s\n",
      "[CV] C=5.85175909599865, gamma=0.01842788363564703 ...................\n",
      "[CV] .... C=5.85175909599865, gamma=0.01842788363564703, total=   0.7s\n",
      "[CV] C=9.182267213548876, gamma=0.02323014864755092 ..................\n",
      "[CV] ... C=9.182267213548876, gamma=0.02323014864755092, total=   0.7s\n",
      "[CV] C=9.182267213548876, gamma=0.02323014864755092 ..................\n",
      "[CV] ... C=9.182267213548876, gamma=0.02323014864755092, total=   0.7s\n",
      "[CV] C=9.182267213548876, gamma=0.02323014864755092 ..................\n",
      "[CV] ... C=9.182267213548876, gamma=0.02323014864755092, total=   0.7s\n",
      "[CV] C=5.98561170053319, gamma=0.014913993724076518 ..................\n",
      "[CV] ... C=5.98561170053319, gamma=0.014913993724076518, total=   0.7s\n",
      "[CV] C=5.98561170053319, gamma=0.014913993724076518 ..................\n",
      "[CV] ... C=5.98561170053319, gamma=0.014913993724076518, total=   0.7s\n",
      "[CV] C=5.98561170053319, gamma=0.014913993724076518 ..................\n",
      "[CV] ... C=5.98561170053319, gamma=0.014913993724076518, total=   0.7s\n",
      "[CV] C=8.197542323569591, gamma=0.003288487329556284 .................\n",
      "[CV] .. C=8.197542323569591, gamma=0.003288487329556284, total=   0.7s\n",
      "[CV] C=8.197542323569591, gamma=0.003288487329556284 .................\n",
      "[CV] .. C=8.197542323569591, gamma=0.003288487329556284, total=   0.7s\n",
      "[CV] C=8.197542323569591, gamma=0.003288487329556284 .................\n",
      "[CV] .. C=8.197542323569591, gamma=0.003288487329556284, total=   0.7s\n",
      "[CV] C=6.46207319902158, gamma=0.006525528106404709 ..................\n",
      "[CV] ... C=6.46207319902158, gamma=0.006525528106404709, total=   0.7s\n",
      "[CV] C=6.46207319902158, gamma=0.006525528106404709 ..................\n",
      "[CV] ... C=6.46207319902158, gamma=0.006525528106404709, total=   0.7s\n",
      "[CV] C=6.46207319902158, gamma=0.006525528106404709 ..................\n",
      "[CV] ... C=6.46207319902158, gamma=0.006525528106404709, total=   0.7s\n",
      "[CV] C=2.7698462366793652, gamma=0.08694904406662167 .................\n",
      "[CV] .. C=2.7698462366793652, gamma=0.08694904406662167, total=   0.7s\n",
      "[CV] C=2.7698462366793652, gamma=0.08694904406662167 .................\n",
      "[CV] .. C=2.7698462366793652, gamma=0.08694904406662167, total=   0.7s\n",
      "[CV] C=2.7698462366793652, gamma=0.08694904406662167 .................\n",
      "[CV] .. C=2.7698462366793652, gamma=0.08694904406662167, total=   0.8s\n",
      "[CV] C=3.970183571076878, gamma=0.0037647629143775273 ................\n",
      "[CV] . C=3.970183571076878, gamma=0.0037647629143775273, total=   0.7s\n",
      "[CV] C=3.970183571076878, gamma=0.0037647629143775273 ................\n",
      "[CV] . C=3.970183571076878, gamma=0.0037647629143775273, total=   0.7s\n",
      "[CV] C=3.970183571076878, gamma=0.0037647629143775273 ................\n",
      "[CV] . C=3.970183571076878, gamma=0.0037647629143775273, total=   0.7s\n",
      "[CV] C=2.1619331759609963, gamma=0.0023091602640281797 ...............\n",
      "[CV]  C=2.1619331759609963, gamma=0.0023091602640281797, total=   0.6s\n",
      "[CV] C=2.1619331759609963, gamma=0.0023091602640281797 ...............\n",
      "[CV]  C=2.1619331759609963, gamma=0.0023091602640281797, total=   0.6s\n",
      "[CV] C=2.1619331759609963, gamma=0.0023091602640281797 ...............\n",
      "[CV]  C=2.1619331759609963, gamma=0.0023091602640281797, total=   0.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   28.9s finished\n",
      "/Users/ageron/miniconda3/envs/tf2b/lib/python3.7/site-packages/sklearn/model_selection/_search.py:842: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f9850502d90>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f98b046a550>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 55,
     "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, cv=3)\n",
    "rnd_search_cv.fit(X_train_scaled[:1000], y_train[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=7.988626913672013, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.0017374050727190405,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=7.988626913672013, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.0017374050727190405,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9995"
      ]
     },
     "execution_count": 59,
     "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": {},
   "source": [
    "Ah, this looks good! Let's select this model. Now we can test it on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9713"
      ]
     },
     "execution_count": 60,
     "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": {},
   "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": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Exercise: train an SVM regressor on the California housing dataset._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the dataset using Scikit-Learn's `fetch_california_housing()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "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": {},
   "source": [
    "Split it into a training set and a test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "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": {},
   "source": [
    "Don't forget to scale the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "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": {},
   "source": [
    "Let's train a simple `LinearSVR` first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ageron/miniconda3/envs/tf2b/lib/python3.7/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "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": 64,
     "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": {},
   "source": [
    "Let's see how it performs on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.954517044073374"
      ]
     },
     "execution_count": 65,
     "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": {},
   "source": [
    "Let's look at the RMSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.976993881287582"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n",
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.4s\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.5s\n",
      "[CV] C=4.745401188473625, gamma=0.07969454818643928 ..................\n",
      "[CV] ... C=4.745401188473625, gamma=0.07969454818643928, total=   5.5s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.3s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.2s\n",
      "[CV] C=8.31993941811405, gamma=0.015751320499779724 ..................\n",
      "[CV] ... C=8.31993941811405, gamma=0.015751320499779724, total=   5.1s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=2.560186404424365, gamma=0.002051110418843397 .................\n",
      "[CV] .. C=2.560186404424365, gamma=0.002051110418843397, total=   4.6s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.7s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.8s\n",
      "[CV] C=1.5808361216819946, gamma=0.05399484409787431 .................\n",
      "[CV] .. C=1.5808361216819946, gamma=0.05399484409787431, total=   4.8s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.4s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.2s\n",
      "[CV] C=7.011150117432088, gamma=0.026070247583707663 .................\n",
      "[CV] .. C=7.011150117432088, gamma=0.026070247583707663, total=   5.4s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.8s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.8s\n",
      "[CV] C=1.2058449429580245, gamma=0.0870602087830485 ..................\n",
      "[CV] ... C=1.2058449429580245, gamma=0.0870602087830485, total=   4.9s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   5.0s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   4.9s\n",
      "[CV] C=9.324426408004218, gamma=0.0026587543983272693 ................\n",
      "[CV] . C=9.324426408004218, gamma=0.0026587543983272693, total=   5.0s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.9s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.9s\n",
      "[CV] C=2.818249672071006, gamma=0.0023270677083837795 ................\n",
      "[CV] . C=2.818249672071006, gamma=0.0023270677083837795, total=   4.8s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   4.9s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   5.1s\n",
      "[CV] C=4.042422429595377, gamma=0.011207606211860567 .................\n",
      "[CV] .. C=4.042422429595377, gamma=0.011207606211860567, total=   4.8s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   4.8s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   4.7s\n",
      "[CV] C=5.319450186421157, gamma=0.003823475224675185 .................\n",
      "[CV] .. C=5.319450186421157, gamma=0.003823475224675185, total=   5.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  3.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "          estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12fc2e390>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x12fc2eac8>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 67,
     "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, cv=3, random_state=42)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.745401188473625, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma=0.07969454818643928, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's measure the RMSE on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5727524770785359"
      ]
     },
     "execution_count": 69,
     "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": {},
   "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5929168385528734"
      ]
     },
     "execution_count": 70,
     "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": {},
   "outputs": [],
   "source": []
  }
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