hayperameter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  ...  petal width (cm)      flower\n",
       "47                 4.6               3.2  ...               0.2      setosa\n",
       "48                 5.3               3.7  ...               0.2      setosa\n",
       "49                 5.0               3.3  ...               0.2      setosa\n",
       "50                 7.0               3.2  ...               1.4  versicolor\n",
       "51                 6.4               3.2  ...               1.5  versicolor\n",
       "..                 ...               ...  ...               ...         ...\n",
       "145                6.7               3.0  ...               2.3   virginica\n",
       "146                6.3               2.5  ...               1.9   virginica\n",
       "147                6.5               3.0  ...               2.0   virginica\n",
       "148                6.2               3.4  ...               2.3   virginica\n",
       "149                5.9               3.0  ...               1.8   virginica\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df['flower'] = iris.target\n",
    "df['flower'] = df['flower'].apply(lambda x: iris.target_names[x])\n",
    "df[47:150]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf',C=30,gamma='auto')\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00124402, 0.00098462, 0.00099974, 0.00074406, 0.00090442,\n",
       "        0.00121698]),\n",
       " 'std_fit_time': array([1.98705282e-04, 5.24111402e-04, 1.51090427e-06, 6.21714075e-04,\n",
       "        4.89838606e-04, 1.79856036e-04]),\n",
       " 'mean_score_time': array([0.00080533, 0.00069237, 0.00080042, 0.00059409, 0.00113716,\n",
       "        0.0008894 ]),\n",
       " 'std_score_time': array([0.00040269, 0.00058448, 0.00040021, 0.00072767, 0.00016168,\n",
       "        0.00045599]),\n",
       " 'param_C': masked_array(data=[1, 1, 10, 10, 20, 20],\n",
       "              mask=[False, False, False, False, False, False],\n",
       "        fill_value=999999),\n",
       " 'param_kernel': masked_array(data=['rbf', 'linear', 'rbf', 'linear', 'rbf', 'linear'],\n",
       "              mask=[False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 1, 'kernel': 'rbf'},\n",
       "  {'C': 1, 'kernel': 'linear'},\n",
       "  {'C': 10, 'kernel': 'rbf'},\n",
       "  {'C': 10, 'kernel': 'linear'},\n",
       "  {'C': 20, 'kernel': 'rbf'},\n",
       "  {'C': 20, 'kernel': 'linear'}],\n",
       " 'split0_test_score': array([0.96666667, 0.96666667, 0.96666667, 1.        , 0.96666667,\n",
       "        1.        ]),\n",
       " 'split1_test_score': array([1., 1., 1., 1., 1., 1.]),\n",
       " 'split2_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.9       , 0.9       ,\n",
       "        0.9       ]),\n",
       " 'split3_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.96666667, 0.96666667,\n",
       "        0.93333333]),\n",
       " 'split4_test_score': array([1., 1., 1., 1., 1., 1.]),\n",
       " 'mean_test_score': array([0.98      , 0.98      , 0.98      , 0.97333333, 0.96666667,\n",
       "        0.96666667]),\n",
       " 'std_test_score': array([0.01632993, 0.01632993, 0.01632993, 0.03887301, 0.03651484,\n",
       "        0.0421637 ]),\n",
       " 'rank_test_score': array([1, 1, 1, 4, 5, 6])}"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "clf = GridSearchCV(svm.SVC(gamma='auto'),{\n",
    "      'C':[1,10,20],\n",
    "      'kernel' : ['rbf','linear']\n",
    "\n",
    "\n",
    "},cv=5,return_train_score=False\n",
    "                   )\n",
    "clf.fit(iris.data,iris.target)\n",
    "clf.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001244</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.000805</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.016330</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000985</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>0.000692</td>\n",
       "      <td>0.000584</td>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.016330</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.016330</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000744</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000728</td>\n",
       "      <td>10</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.038873</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000904</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>20</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 20, 'kernel': 'rbf'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.036515</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.001217</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.000889</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>20</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 20, 'kernel': 'linear'}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.042164</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
       "0       0.001244      0.000199  ...        0.016330                1\n",
       "1       0.000985      0.000524  ...        0.016330                1\n",
       "2       0.001000      0.000002  ...        0.016330                1\n",
       "3       0.000744      0.000622  ...        0.038873                4\n",
       "4       0.000904      0.000490  ...        0.036515                5\n",
       "5       0.001217      0.000180  ...        0.042164                6\n",
       "\n",
       "[6 rows x 15 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.973333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_C param_kernel  mean_test_score\n",
       "0        1          rbf         0.980000\n",
       "1        1       linear         0.980000\n",
       "2       10          rbf         0.980000\n",
       "3       10       linear         0.973333\n",
       "4       20          rbf         0.966667\n",
       "5       20       linear         0.966667"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['param_C','param_kernel','mean_test_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__abstractmethods__',\n",
       " '__annotations__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
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       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__sklearn_clone__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_abc_impl',\n",
       " '_build_request_for_signature',\n",
       " '_check_feature_names',\n",
       " '_check_n_features',\n",
       " '_check_refit_for_multimetric',\n",
       " '_doc_link_module',\n",
       " '_doc_link_template',\n",
       " '_doc_link_url_param_generator',\n",
       " '_estimator_type',\n",
       " '_format_results',\n",
       " '_get_default_requests',\n",
       " '_get_doc_link',\n",
       " '_get_metadata_request',\n",
       " '_get_param_names',\n",
       " '_get_routed_params_for_fit',\n",
       " '_get_scorers',\n",
       " '_get_tags',\n",
       " '_more_tags',\n",
       " '_parameter_constraints',\n",
       " '_repr_html_',\n",
       " '_repr_html_inner',\n",
       " '_repr_mimebundle_',\n",
       " '_required_parameters',\n",
       " '_run_search',\n",
       " '_select_best_index',\n",
       " '_sk_visual_block_',\n",
       " '_validate_data',\n",
       " '_validate_params',\n",
       " 'best_estimator_',\n",
       " 'best_index_',\n",
       " 'best_params_',\n",
       " 'best_score_',\n",
       " 'classes_',\n",
       " 'cv',\n",
       " 'cv_results_',\n",
       " 'decision_function',\n",
       " 'error_score',\n",
       " 'estimator',\n",
       " 'fit',\n",
       " 'get_metadata_routing',\n",
       " 'get_params',\n",
       " 'inverse_transform',\n",
       " 'multimetric_',\n",
       " 'n_features_in_',\n",
       " 'n_jobs',\n",
       " 'n_splits_',\n",
       " 'param_grid',\n",
       " 'pre_dispatch',\n",
       " 'predict',\n",
       " 'predict_log_proba',\n",
       " 'predict_proba',\n",
       " 'refit',\n",
       " 'refit_time_',\n",
       " 'return_train_score',\n",
       " 'score',\n",
       " 'score_samples',\n",
       " 'scorer_',\n",
       " 'scoring',\n",
       " 'set_params',\n",
       " 'transform',\n",
       " 'verbose']"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to tackle lagrge datASET we have randamize test cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.973448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.960376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.966912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.960376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.979984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_C param_kernel  mean_test_score\n",
       "0        1          rbf         0.973448\n",
       "1       20       linear         0.960376\n",
       "2       20          rbf         0.966912\n",
       "3       10          rbf         0.960376\n",
       "4        1       linear         0.979984"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rs = RandomizedSearchCV(svm.SVC(gamma='auto'), {\n",
    "        'C': [1,10,20],\n",
    "        'kernel': ['rbf','linear']\n",
    "    }, \n",
    "    cv=9, \n",
    "    return_train_score=False, \n",
    "    n_iter=5\n",
    ")\n",
    "rs.fit(iris.data, iris.target)\n",
    "pd.DataFrame(rs.cv_results_)[['param_C','param_kernel','mean_test_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model_params = {\n",
    "    'svm': {\n",
    "        'model': svm.SVC(gamma='auto'),\n",
    "        'params' : {\n",
    "            'C': [1,10,20],\n",
    "            'kernel': ['rbf','linear']\n",
    "        }  \n",
    "    },\n",
    "    'random_forest': {\n",
    "        'model': RandomForestClassifier(),\n",
    "        'params' : {\n",
    "            'n_estimators': [1,5,10]\n",
    "        }\n",
    "    },\n",
    "    'logistic_regression' : {\n",
    "        'model': LogisticRegression(solver='liblinear',multi_class='auto'),\n",
    "        'params': {\n",
    "            'C': [1,5,10]\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "c:\\Users\\RAJA\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>{'kernel': 'linear', 'C': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>{'n_estimators': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>logistic_regression</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>{'C': 5}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  best_score                   best_params\n",
       "0                  svm    0.980000  {'kernel': 'linear', 'C': 1}\n",
       "1        random_forest    0.960000           {'n_estimators': 1}\n",
       "2  logistic_regression    0.966667                      {'C': 5}"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \n",
    "scores = []\n",
    "\n",
    "for model_name, mp in model_params.items():\n",
    "    clf =  RandomizedSearchCV(mp['model'], mp['params'], cv=5, n_iter=3,return_train_score=False)\n",
    "    clf.fit(iris.data, iris.target)\n",
    "    scores.append({\n",
    "        'model': model_name,\n",
    "        'best_score': clf.best_score_,\n",
    "        'best_params': clf.best_params_\n",
    "    })\n",
    "    \n",
    "df = pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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