6110S_and_T_class10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SVM demo\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import sklearn\n",
    "\n",
    "theme = 'seismic'\n",
    "\n",
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=1.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary+margin\n",
    "    gutter_down = decision_boundary-margin\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k-\", linewidth=2)\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1], c=y, s=50)\n",
    "\n",
    "svm_clf = SVC(kernel='linear', C=100)\n",
    "svm_clf.fit(X,y)\n",
    "\n",
    "plt.xlim(0,4)\n",
    "plot_svc_decision_boundary(svm_clf,0,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17f8ad3a0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], c=y, s=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=100, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=100, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=100, kernel='linear')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(kernel='linear', C=100)\n",
    "svm_clf.fit(X,y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim(0,4)\n",
    "plot_svc_decision_boundary(svm_clf,0,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_iris()\n",
    "X,y = data.data[:, (2,3)], data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "setosa_and_versicolor = (y==0) | (y==1)\n",
    "\n",
    "X = X[setosa_and_versicolor]\n",
    "y = y[setosa_and_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], c=y, marker='o')\n",
    "\n",
    "svm_clf = SVC(kernel='linear', C=100)\n",
    "svm_clf.fit(X,y)\n",
    "\n",
    "plot_svc_decision_boundary(svm_clf, 1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# polynomial kernel\n",
    "from sklearn.datasets import make_moons\n",
    "X,y = make_moons(n_samples=100, noise=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "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='seismic', alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap='seismic', alpha=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], c=y, cmap='seismic')\n",
    "svm_clf = SVC(kernel='poly', degree=9, coef0=7, C=1000)\n",
    "svm_clf.fit(X,y)\n",
    "plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RBF kernel\n",
    "\n",
    "X,y = make_moons(n_samples=100, noise=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "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=theme, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=theme, alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], c=y, cmap='seismic')\n",
    "\n",
    "svm_clf = SVC(kernel='rbf', gamma=100, C=20)\n",
    "svm_clf.fit(X,y)\n",
    "plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], c=y, cmap='seismic')\n",
    "\n",
    "svm_clf = SVC(kernel='rbf', gamma=0.4, C=20)\n",
    "svm_clf.fit(X,y)\n",
    "\n",
    "plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM REGRESSION\n",
    "\n",
    "from sklearn.svm import LinearSVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(50)\n",
    "m=100\n",
    "X=3*np.random.rand(m,1)\n",
    "y=(5+2*X+np.random.randn(m,1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "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)\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, 'k--')\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, 'k--')\n",
    "    plt.plot(X,y,'bo')\n",
    "    plt.axis(axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_reg = LinearSVR(epsilon=0.3)\n",
    "svm_reg.fit(X,y)\n",
    "plot_svm_regression(svm_reg, X,y, [0,2,3,11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "bikes = pd.read_csv('https://raw.githubusercontent.com/delinai/schulich_ds1/main/Datasets/bikes_sharing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81        0.0       3          13     16  \n",
       "1        80        0.0       8          32     40  \n",
       "2        80        0.0       5          27     32  \n",
       "3        75        0.0       3          10     13  \n",
       "4        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head(\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = bikes[['temp','atemp','humidity','windspeed']]\n",
    "y = bikes['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "linreg = LinearRegression()\n",
    "reg_model = linreg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155.5643482887117"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "lin_SVR = LinearSVR(epsilon=10)\n",
    "linsvr = lin_SVR.fit(X_train,y_train)\n",
    "pred2 = linsvr.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160.50244970756438"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'epsilon':np.linspace(0,1,10),\n",
    "          'max_iter':[1000,5000,10000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search = RandomizedSearchCV(estimator=linsvr,\n",
    "                                   param_distributions=params,\n",
    "                                   n_iter=50,\n",
    "                                   scoring='neg_mean_absolute_error',\n",
    "                                   cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/model_selection/_search.py:318: UserWarning: The total space of parameters 30 is smaller than n_iter=50. Running 30 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
      "  warnings.warn(\n",
      "/Users/delinaivanova/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LinearSVR(epsilon=10), n_iter=50,\n",
       "                   param_distributions={&#x27;epsilon&#x27;: array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
       "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ]),\n",
       "                                        &#x27;max_iter&#x27;: [1000, 5000, 10000]},\n",
       "                   scoring=&#x27;neg_mean_absolute_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=LinearSVR(epsilon=10), n_iter=50,\n",
       "                   param_distributions={&#x27;epsilon&#x27;: array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
       "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ]),\n",
       "                                        &#x27;max_iter&#x27;: [1000, 5000, 10000]},\n",
       "                   scoring=&#x27;neg_mean_absolute_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LinearSVR</label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVR(epsilon=10)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearSVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.LinearSVR.html\">?<span>Documentation for LinearSVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVR(epsilon=10)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LinearSVR(epsilon=10), n_iter=50,\n",
       "                   param_distributions={'epsilon': array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
       "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ]),\n",
       "                                        'max_iter': [1000, 5000, 10000]},\n",
       "                   scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_iter': 1000, 'epsilon': 0.0}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10886.000000\n",
       "mean       191.574132\n",
       "std        181.144454\n",
       "min          1.000000\n",
       "25%         42.000000\n",
       "50%        145.000000\n",
       "75%        284.000000\n",
       "max        977.000000\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes['count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikes['binary_output'] = bikes['count'].apply(lambda x: 1 if x >= 145 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'C':np.linspace(1,4,20),\n",
    "    'gamma': np.linspace(1,4,20),\n",
    "    'kernel':['linear','rbf','poly']\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_iris()\n",
    "X,y = data.data[:, (2,3)], data.target\n",
    "setosa_and_versicolor = (y==0) | (y==1)\n",
    "\n",
    "X = X[setosa_and_versicolor]\n",
    "y = y[setosa_and_versicolor]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'C':np.logspace(-4,4,20),\n",
    "    'gamma': np.logspace(-4,4,20),\n",
    "    'kernel':['linear','rbf','poly']\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search = RandomizedSearchCV(\n",
    "    estimator=svc,\n",
    "    param_distributions=params,\n",
    "    n_iter=50,\n",
    "    scoring='accuracy',\n",
    "    cv=5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=SVC(), n_iter=50,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+0...\n",
       "                                        &#x27;gamma&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;]},\n",
       "                   scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=SVC(), n_iter=50,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+0...\n",
       "                                        &#x27;gamma&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;]},\n",
       "                   scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=SVC(), n_iter=50,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+0...\n",
       "                                        'gamma': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'kernel': ['linear', 'rbf', 'poly']},\n",
       "                   scoring='accuracy')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.23357214690901212, gamma=1.623776739188721)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=0.23357214690901212, gamma=1.623776739188721)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.23357214690901212, gamma=1.623776739188721)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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