SVM recap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "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",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dateset(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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Linear SVM classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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=None, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('linear_svc', LinearSVC(C=1,loss='hinge'))\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### unlike logistic regression classifiers, SVM clfs do not output probabilities for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alternatively, we use the SGDClassifier(loss='hinge', alpha =1/(m*C)) to train a linear SVM classifer as well\n",
    "# it does not converge as fast as te LinearSVC, but it can be useful to handle huge datasets that do not \n",
    "# fit in memory( out of core) or online classification tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nonlinear SVM classification\n",
    "even though linear svm are efficient and work well in many case, many datasets are not even close to being linearly separable.\n",
    "One approach to handling nonlinear datasets is to add more features, such as polynomial features; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_moons(n_samples= 100, noise = 0.15, random_state = 123)\n",
    "plot_dateset(X, y, [-1.5,2.5,-1,1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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=123, tol=0.0001, verbose=0))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial_svm_clf = Pipeline([\n",
    "    ('poly_features', PolynomialFeatures(degree = 3)),\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', LinearSVC(C=10, loss='hinge',random_state = 123))\n",
    "])\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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_dateset(X, y, [-1.5,2.5,-1,1.5])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternative poly SVC ( the same as above)\n",
    "from sklearn.svm import SVC\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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(poly_kernel_svm_clf, [-1.5,2.5,-1,1.5])\n",
    "plot_dateset(X, y, [-1.5,2.5,-1,1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try larger degree\n",
    "poly_kernel_svm_clf = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', SVC(kernel='poly', degree = 10, coef0= 1, C= 5))\n",
    "])\n",
    "poly_kernel_svm_clf.fit(X, y)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5,2.5,-1,1.5])\n",
    "plot_dateset(X, y, [-1.5,2.5,-1,1.5])\n",
    "plt.show()  ## overfitting?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## another technique to handle nonlinear problems is to add features computed using a similarity function \n",
    "## that measures how much each instance resembles a particular landmark\n",
    "def gaussian_rbf(x, landmark, gamma):  # gaussian radial basis function with gamma = 0.3\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) # set the landmark of the gaussian_rbf\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": 24,
   "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": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternative\n",
    "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)\n",
    "plot_predictions(rbf_kernel_svm_clf, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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_dateset(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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM Regression\n",
    "SVM algorithm is quite versatile: not only does it support linear and nonlinear classification, but it also supports linear and nonlinear regression. The trick is to reverse the objective: instead of trying to fit the largest possible street between two classes while limiting margin violations, SVM Regression tries to fit as many instances as possible on the street while limiting margin violations (i.e., instances off the street)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "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()\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "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",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC:                    [0.28473933] [[1.05364493 1.09902926]]\n",
      "SVC:                          [0.31896852] [[1.1203284  1.02625193]]\n",
      "SGDClassifier(alpha=0.00200): [0.319] [[1.12098182 1.02688982]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "iris = datasets.load_iris()\n",
    "X = iris['data'][:,(2,3)]\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",
    "C = 5\n",
    "alpha = 1/(C *len(X))\n",
    "\n",
    "lin_clf = LinearSVC(loss='hinge', C=C, random_state = 123)\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 = 100000, tol = -np.infty, random_state = 123)\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": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', cache = False)\n",
    "def sort_by_target(mnist):\n",
    "    reorder_train = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[:60000])]))[:, 1]\n",
    "    reorder_test = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[60000:])]))[:, 1]\n",
    "    mnist.data[:60000] = mnist.data[reorder_train]\n",
    "    mnist.target[:60000] = mnist.target[reorder_train]\n",
    "    mnist.data[60000:] = mnist.data[reorder_test + 60000]\n",
    "    mnist.target[60000:] = mnist.target[reorder_test + 60000]\n",
    "mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings\n",
    "sort_by_target(mnist)\n",
    "X, y = mnist[\"data\"], mnist[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X[:60000]\n",
    "X_test = X[60000:]\n",
    "y_train = y[:60000]\n",
    "y_test = y[60000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_idx = np.random.permutation(60000)\n",
    "X_train = X_train[rnd_idx]\n",
    "y_train = y_train[rnd_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:922: 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=123, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_clf = LinearSVC(random_state = 123)\n",
    "lin_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8886"
      ]
     },
     "execution_count": 43,
     "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)\n",
    "# bad at MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.92135"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "lin_clf = LinearSVC(random_state = 123)\n",
    "lin_clf.fit(X_train_scaled, y_train)\n",
    "y_pred = lin_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9468666666666666"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(decision_function_shape ='ovr', gamma = 'auto')\n",
    "svm_clf.fit(X_train_scaled[:10000], y_train[:10000])\n",
    "y_pred = svm_clf.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=8.672598575312348, gamma=0.006929609303267635 .................\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=8.672598575312348, gamma=0.006929609303267635, total=   0.8s\n",
      "[CV] C=8.672598575312348, gamma=0.006929609303267635 .................\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] .. C=8.672598575312348, gamma=0.006929609303267635, total=   0.9s\n",
      "[CV] C=8.672598575312348, gamma=0.006929609303267635 .................\n",
      "[CV] .. C=8.672598575312348, gamma=0.006929609303267635, total=   0.9s\n",
      "[CV] C=8.925471905182384, gamma=0.001262542307589235 .................\n",
      "[CV] .. C=8.925471905182384, gamma=0.001262542307589235, total=   0.6s\n",
      "[CV] C=8.925471905182384, gamma=0.001262542307589235 .................\n",
      "[CV] .. C=8.925471905182384, gamma=0.001262542307589235, total=   0.6s\n",
      "[CV] C=8.925471905182384, gamma=0.001262542307589235 .................\n",
      "[CV] .. C=8.925471905182384, gamma=0.001262542307589235, total=   0.6s\n",
      "[CV] C=8.655274047166163, gamma=0.0187333598108448 ...................\n",
      "[CV] .... C=8.655274047166163, gamma=0.0187333598108448, total=   0.8s\n",
      "[CV] C=8.655274047166163, gamma=0.0187333598108448 ...................\n",
      "[CV] .... C=8.655274047166163, gamma=0.0187333598108448, total=   0.9s\n",
      "[CV] C=8.655274047166163, gamma=0.0187333598108448 ...................\n",
      "[CV] .... C=8.655274047166163, gamma=0.0187333598108448, total=   0.9s\n",
      "[CV] C=6.6666504120151195, gamma=0.004328139384576848 ................\n",
      "[CV] . C=6.6666504120151195, gamma=0.004328139384576848, total=   0.8s\n",
      "[CV] C=6.6666504120151195, gamma=0.004328139384576848 ................\n",
      "[CV] . C=6.6666504120151195, gamma=0.004328139384576848, total=   0.8s\n",
      "[CV] C=6.6666504120151195, gamma=0.004328139384576848 ................\n",
      "[CV] . C=6.6666504120151195, gamma=0.004328139384576848, total=   0.8s\n",
      "[CV] C=2.1411260843123205, gamma=0.01969707028717445 .................\n",
      "[CV] .. C=2.1411260843123205, gamma=0.01969707028717445, total=   0.9s\n",
      "[CV] C=2.1411260843123205, gamma=0.01969707028717445 .................\n",
      "[CV] .. C=2.1411260843123205, gamma=0.01969707028717445, total=   0.9s\n",
      "[CV] C=2.1411260843123205, gamma=0.01969707028717445 .................\n",
      "[CV] .. C=2.1411260843123205, gamma=0.01969707028717445, total=   0.9s\n",
      "[CV] C=5.716709337994133, gamma=0.002727028699403942 .................\n",
      "[CV] .. C=5.716709337994133, gamma=0.002727028699403942, total=   0.7s\n",
      "[CV] C=5.716709337994133, gamma=0.002727028699403942 .................\n",
      "[CV] .. C=5.716709337994133, gamma=0.002727028699403942, total=   0.7s\n",
      "[CV] C=5.716709337994133, gamma=0.002727028699403942 .................\n",
      "[CV] .. C=5.716709337994133, gamma=0.002727028699403942, total=   0.8s\n",
      "[CV] C=1.4336669271308269, gamma=0.011954470230652681 ................\n",
      "[CV] . C=1.4336669271308269, gamma=0.011954470230652681, total=   0.8s\n",
      "[CV] C=1.4336669271308269, gamma=0.011954470230652681 ................\n",
      "[CV] . C=1.4336669271308269, gamma=0.011954470230652681, total=   0.9s\n",
      "[CV] C=1.4336669271308269, gamma=0.011954470230652681 ................\n",
      "[CV] . C=1.4336669271308269, gamma=0.011954470230652681, total=   0.9s\n",
      "[CV] C=2.262963019825576, gamma=0.072250153408599 ....................\n",
      "[CV] ..... C=2.262963019825576, gamma=0.072250153408599, total=   0.9s\n",
      "[CV] C=2.262963019825576, gamma=0.072250153408599 ....................\n",
      "[CV] ..... C=2.262963019825576, gamma=0.072250153408599, total=   0.9s\n",
      "[CV] C=2.262963019825576, gamma=0.072250153408599 ....................\n",
      "[CV] ..... C=2.262963019825576, gamma=0.072250153408599, total=   0.9s\n",
      "[CV] C=2.5063185849267247, gamma=0.0011589128872050239 ...............\n",
      "[CV]  C=2.5063185849267247, gamma=0.0011589128872050239, total=   0.6s\n",
      "[CV] C=2.5063185849267247, gamma=0.0011589128872050239 ...............\n",
      "[CV]  C=2.5063185849267247, gamma=0.0011589128872050239, total=   0.6s\n",
      "[CV] C=2.5063185849267247, gamma=0.0011589128872050239 ...............\n",
      "[CV]  C=2.5063185849267247, gamma=0.0011589128872050239, total=   0.6s\n",
      "[CV] C=3.62047514032945, gamma=0.002238273876925364 ..................\n",
      "[CV] ... C=3.62047514032945, gamma=0.002238273876925364, total=   0.7s\n",
      "[CV] C=3.62047514032945, gamma=0.002238273876925364 ..................\n",
      "[CV] ... C=3.62047514032945, gamma=0.002238273876925364, total=   0.7s\n",
      "[CV] C=3.62047514032945, gamma=0.002238273876925364 ..................\n",
      "[CV] ... C=3.62047514032945, gamma=0.002238273876925364, total=   0.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   37.8s finished\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='auto', 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 0x00000157CFD45CF8>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000157CFD45C50>},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 46,
     "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=8.925471905182384, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.001262542307589235,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.873"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_.fit(X_train_scaled, y_train)\n",
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "accuracy_score(y_train, y_pred)   ### seems pretty legit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "train an SVM regressor on the California housing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": "code",
   "execution_count": 66,
   "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": "code",
   "execution_count": 67,
   "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": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:922: 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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#try LinearSVC first\n",
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "lin_svr = LinearSVR(random_state=42)\n",
    "lin_svr.fit(X_train_scaled, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse:  0.9612806653297273\n",
      "rmse:  0.9804492160890983\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "y_pred = lin_svr.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "print('mse: ',mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print('rmse: ', rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "[CV] C=7.964691855978616, gamma=0.0037348973478010345 ................\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=7.964691855978616, gamma=0.0037348973478010345, total=   4.3s\n",
      "[CV] C=7.964691855978616, gamma=0.0037348973478010345 ................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=7.964691855978616, gamma=0.0037348973478010345, total=   4.3s\n",
      "[CV] C=7.964691855978616, gamma=0.0037348973478010345 ................\n",
      "[CV] . C=7.964691855978616, gamma=0.0037348973478010345, total=   4.3s\n",
      "[CV] C=3.268514535642031, gamma=0.01266570994661668 ..................\n",
      "[CV] ... C=3.268514535642031, gamma=0.01266570994661668, total=   4.2s\n",
      "[CV] C=3.268514535642031, gamma=0.01266570994661668 ..................\n",
      "[CV] ... C=3.268514535642031, gamma=0.01266570994661668, total=   4.4s\n",
      "[CV] C=3.268514535642031, gamma=0.01266570994661668 ..................\n",
      "[CV] ... C=3.268514535642031, gamma=0.01266570994661668, total=   4.4s\n",
      "[CV] C=8.19468969785563, gamma=0.007017992831138442 ..................\n",
      "[CV] ... C=8.19468969785563, gamma=0.007017992831138442, total=   4.5s\n",
      "[CV] C=8.19468969785563, gamma=0.007017992831138442 ..................\n",
      "[CV] ... C=8.19468969785563, gamma=0.007017992831138442, total=   4.4s\n",
      "[CV] C=8.19468969785563, gamma=0.007017992831138442 ..................\n",
      "[CV] ... C=8.19468969785563, gamma=0.007017992831138442, total=   4.6s\n",
      "[CV] C=10.807641983846155, gamma=0.02342391466254441 .................\n",
      "[CV] .. C=10.807641983846155, gamma=0.02342391466254441, total=   5.0s\n",
      "[CV] C=10.807641983846155, gamma=0.02342391466254441 .................\n",
      "[CV] .. C=10.807641983846155, gamma=0.02342391466254441, total=   5.0s\n",
      "[CV] C=10.807641983846155, gamma=0.02342391466254441 .................\n",
      "[CV] .. C=10.807641983846155, gamma=0.02342391466254441, total=   5.1s\n",
      "[CV] C=5.809319014843609, gamma=0.006084642077147051 .................\n",
      "[CV] .. C=5.809319014843609, gamma=0.006084642077147051, total=   4.3s\n",
      "[CV] C=5.809319014843609, gamma=0.006084642077147051 .................\n",
      "[CV] .. C=5.809319014843609, gamma=0.006084642077147051, total=   4.3s\n",
      "[CV] C=5.809319014843609, gamma=0.006084642077147051 .................\n",
      "[CV] .. C=5.809319014843609, gamma=0.006084642077147051, total=   4.3s\n",
      "[CV] C=4.431780161508694, gamma=0.028714378103928375 .................\n",
      "[CV] .. C=4.431780161508694, gamma=0.028714378103928375, total=   4.4s\n",
      "[CV] C=4.431780161508694, gamma=0.028714378103928375 .................\n",
      "[CV] .. C=4.431780161508694, gamma=0.028714378103928375, total=   4.4s\n",
      "[CV] C=4.431780161508694, gamma=0.028714378103928375 .................\n",
      "[CV] .. C=4.431780161508694, gamma=0.028714378103928375, total=   4.5s\n",
      "[CV] C=5.385722446796244, gamma=0.0013163027639428407 ................\n",
      "[CV] . C=5.385722446796244, gamma=0.0013163027639428407, total=   4.2s\n",
      "[CV] C=5.385722446796244, gamma=0.0013163027639428407 ................\n",
      "[CV] . C=5.385722446796244, gamma=0.0013163027639428407, total=   4.2s\n",
      "[CV] C=5.385722446796244, gamma=0.0013163027639428407 ................\n",
      "[CV] . C=5.385722446796244, gamma=0.0013163027639428407, total=   4.1s\n",
      "[CV] C=4.980442553304314, gamma=0.029922013287804066 .................\n",
      "[CV] .. C=4.980442553304314, gamma=0.029922013287804066, total=   4.4s\n",
      "[CV] C=4.980442553304314, gamma=0.029922013287804066 .................\n",
      "[CV] .. C=4.980442553304314, gamma=0.029922013287804066, total=   4.4s\n",
      "[CV] C=4.980442553304314, gamma=0.029922013287804066 .................\n",
      "[CV] .. C=4.980442553304314, gamma=0.029922013287804066, total=   4.6s\n",
      "[CV] C=2.824917304535, gamma=0.0022433834533274752 ...................\n",
      "[CV] .... C=2.824917304535, gamma=0.0022433834533274752, total=   4.1s\n",
      "[CV] C=2.824917304535, gamma=0.0022433834533274752 ...................\n",
      "[CV] .... C=2.824917304535, gamma=0.0022433834533274752, total=   4.3s\n",
      "[CV] C=2.824917304535, gamma=0.0022433834533274752 ...................\n",
      "[CV] .... C=2.824917304535, gamma=0.0022433834533274752, total=   4.1s\n",
      "[CV] C=6.3155137384183835, gamma=0.011578576628023892 ................\n",
      "[CV] . C=6.3155137384183835, gamma=0.011578576628023892, total=   4.3s\n",
      "[CV] C=6.3155137384183835, gamma=0.011578576628023892 ................\n",
      "[CV] . C=6.3155137384183835, gamma=0.011578576628023892, total=   4.5s\n",
      "[CV] C=6.3155137384183835, gamma=0.011578576628023892 ................\n",
      "[CV] . C=6.3155137384183835, gamma=0.011578576628023892, total=   4.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:  3.1min 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 0x00000157CFE18E48>, 'C': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000157CFE18BA8>},\n",
       "          pre_dispatch='2*n_jobs', random_state=123, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 71,
     "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 =123)\n",
    "rnd_search_cv.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=4.980442553304314, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma=0.029922013287804066, kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse:  0.37146706768867105\n",
      "rmse:  0.6094809822206687\n"
     ]
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_train_scaled)\n",
    "mse = mean_squared_error(y_train, y_pred)\n",
    "print('mse: ', mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print('rmse: ', rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse:  0.388332654930362\n",
      "rmse:  0.623163425539691\n"
     ]
    }
   ],
   "source": [
    "y_pred = rnd_search_cv.best_estimator_.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print('mse: ', mse)\n",
    "rmse = np.sqrt(mse)\n",
    "print('rmse: ', rmse)"
   ]
  }
 ],
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