pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Linear Algebra and Plots\n",
    "\n",
    "import pandas                        as pd\n",
    "import numpy                         as np\n",
    "import seaborn                       as sns\n",
    "import matplotlib.pyplot             as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.preprocessing           import normalize, StandardScaler, MinMaxScaler, label, binarize\n",
    "from sklearn.model_selection         import train_test_split, GridSearchCV, RandomizedSearchCV, KFold, cross_val_score\n",
    "from sklearn.metrics                 import *\n",
    "\n",
    "# ML Libraries\n",
    "\n",
    "from sklearn.base                    import BaseEstimator\n",
    "from sklearn.ensemble                import RandomForestClassifier\n",
    "from sklearn.decomposition           import PCA\n",
    "from xgboost                         import XGBClassifier\n",
    "from sklearn.linear_model            import LogisticRegression, SGDClassifier\n",
    "from lightgbm                        import LGBMClassifier\n",
    "from sklearn.tree                    import DecisionTreeClassifier\n",
    "from sklearn.neighbors               import KNeighborsClassifier\n",
    "from sklearn.svm                     import SVC\n",
    "from sklearn.naive_bayes             import MultinomialNB\n",
    "\n",
    "# Pipeline\n",
    "\n",
    "from sklearn.pipeline                import make_pipeline\n",
    "\n",
    "# Datasets\n",
    "\n",
    "from sklearn                         import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "breast_cancer = datasets.load_breast_cancer()\n",
    "X = breast_cancer.data\n",
    "y = breast_cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0      1       2       3        4        5       6        7       8   \\\n",
      "0  17.99  10.38  122.80  1001.0  0.11840  0.27760  0.3001  0.14710  0.2419   \n",
      "1  20.57  17.77  132.90  1326.0  0.08474  0.07864  0.0869  0.07017  0.1812   \n",
      "2  19.69  21.25  130.00  1203.0  0.10960  0.15990  0.1974  0.12790  0.2069   \n",
      "3  11.42  20.38   77.58   386.1  0.14250  0.28390  0.2414  0.10520  0.2597   \n",
      "4  20.29  14.34  135.10  1297.0  0.10030  0.13280  0.1980  0.10430  0.1809   \n",
      "\n",
      "        9   ...     20     21      22      23      24      25      26      27  \\\n",
      "0  0.07871  ...  25.38  17.33  184.60  2019.0  0.1622  0.6656  0.7119  0.2654   \n",
      "1  0.05667  ...  24.99  23.41  158.80  1956.0  0.1238  0.1866  0.2416  0.1860   \n",
      "2  0.05999  ...  23.57  25.53  152.50  1709.0  0.1444  0.4245  0.4504  0.2430   \n",
      "3  0.09744  ...  14.91  26.50   98.87   567.7  0.2098  0.8663  0.6869  0.2575   \n",
      "4  0.05883  ...  22.54  16.67  152.20  1575.0  0.1374  0.2050  0.4000  0.1625   \n",
      "\n",
      "       28       29  \n",
      "0  0.4601  0.11890  \n",
      "1  0.2750  0.08902  \n",
      "2  0.3613  0.08758  \n",
      "3  0.6638  0.17300  \n",
      "4  0.2364  0.07678  \n",
      "\n",
      "[5 rows x 30 columns]\n",
      "   0\n",
      "0  0\n",
      "1  0\n",
      "2  0\n",
      "3  0\n",
      "4  0\n"
     ]
    }
   ],
   "source": [
    "df_X  = pd.DataFrame(X)\n",
    "df_y = pd.DataFrame(y)\n",
    "print(df_X.head())\n",
    "print(df_y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_X, df_y, test_size=0.20, random_state=7, shuffle=True, stratify=df_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0      1       2       3        4        5        6         7   \\\n",
      "285  12.58  18.40   79.83   489.0  0.08393  0.04216  0.00186  0.002924   \n",
      "535  20.55  20.86  137.80  1308.0  0.10460  0.17390  0.20850  0.132200   \n",
      "521  24.63  21.60  165.50  1841.0  0.10300  0.21060  0.23100  0.147100   \n",
      "339  23.51  24.27  155.10  1747.0  0.10690  0.12830  0.23080  0.141000   \n",
      "429  12.72  17.67   80.98   501.3  0.07896  0.04522  0.01402  0.018350   \n",
      "..     ...    ...     ...     ...      ...      ...      ...       ...   \n",
      "508  16.30  15.70  104.70   819.8  0.09427  0.06712  0.05526  0.045630   \n",
      "203  13.81  23.75   91.56   597.8  0.13230  0.17680  0.15580  0.091760   \n",
      "297  11.76  18.14   75.00   431.1  0.09968  0.05914  0.02685  0.035150   \n",
      "390  10.26  12.22   65.75   321.6  0.09996  0.07542  0.01923  0.019680   \n",
      "125  13.85  17.21   88.44   588.7  0.08785  0.06136  0.01420  0.011410   \n",
      "\n",
      "         8        9   ...     20     21      22      23      24       25  \\\n",
      "285  0.1697  0.05855  ...  13.50  23.08   85.56   564.1  0.1038  0.06624   \n",
      "535  0.2127  0.06251  ...  24.30  25.48  160.20  1809.0  0.1268  0.31350   \n",
      "521  0.1991  0.06739  ...  29.92  26.93  205.70  2642.0  0.1342  0.41880   \n",
      "339  0.1797  0.05506  ...  30.67  30.73  202.40  2906.0  0.1515  0.26780   \n",
      "429  0.1459  0.05544  ...  13.82  20.96   88.87   586.8  0.1068  0.09605   \n",
      "..      ...      ...  ...    ...    ...     ...     ...     ...      ...   \n",
      "508  0.1711  0.05657  ...  17.32  17.76  109.80   928.2  0.1354  0.13610   \n",
      "203  0.2251  0.07421  ...  19.20  41.85  128.50  1153.0  0.2226  0.52090   \n",
      "297  0.1619  0.06287  ...  13.36  23.39   85.10   553.6  0.1137  0.07974   \n",
      "390  0.1800  0.06569  ...  11.38  15.65   73.23   394.5  0.1343  0.16500   \n",
      "125  0.1614  0.05890  ...  15.49  23.58  100.30   725.9  0.1157  0.13500   \n",
      "\n",
      "           26        27      28       29  \n",
      "285  0.005579  0.008772  0.2505  0.06431  \n",
      "535  0.443300  0.214800  0.3077  0.07569  \n",
      "521  0.465800  0.247500  0.3157  0.09671  \n",
      "339  0.481900  0.208900  0.2593  0.07738  \n",
      "429  0.034690  0.036120  0.2165  0.06025  \n",
      "..        ...       ...     ...      ...  \n",
      "508  0.194700  0.135700  0.2300  0.07230  \n",
      "203  0.464600  0.201300  0.4432  0.10860  \n",
      "297  0.061200  0.071600  0.1978  0.06915  \n",
      "390  0.086150  0.066960  0.2937  0.07722  \n",
      "125  0.081150  0.051040  0.2364  0.07182  \n",
      "\n",
      "[455 rows x 30 columns]\n",
      "\n",
      "         0      1       2       3        4        5         6         7   \\\n",
      "167  16.780  18.80  109.30   886.3  0.08865  0.09182  0.084220  0.065760   \n",
      "283  16.240  18.77  108.80   805.1  0.10660  0.18020  0.194800  0.090520   \n",
      "152   9.731  15.34   63.78   300.2  0.10720  0.15990  0.410800  0.078570   \n",
      "156  17.680  20.74  117.40   963.7  0.11150  0.16650  0.185500  0.105400   \n",
      "97    9.787  19.94   62.11   294.5  0.10240  0.05301  0.006829  0.007937   \n",
      "..      ...    ...     ...     ...      ...      ...       ...       ...   \n",
      "251  11.500  18.45   73.28   407.4  0.09345  0.05991  0.026380  0.020690   \n",
      "280  19.160  26.60  126.20  1138.0  0.10200  0.14530  0.192100  0.096640   \n",
      "216  11.890  18.35   77.32   432.2  0.09363  0.11540  0.066360  0.031420   \n",
      "66    9.465  21.01   60.11   269.4  0.10440  0.07773  0.021720  0.015040   \n",
      "151   8.219  20.70   53.27   203.9  0.09405  0.13050  0.132100  0.021680   \n",
      "\n",
      "         8        9   ...      20     21      22      23      24       25  \\\n",
      "167  0.1893  0.05534  ...  20.050  26.30  130.70  1260.0  0.1168  0.21190   \n",
      "283  0.1876  0.06684  ...  18.550  25.09  126.90  1031.0  0.1365  0.47060   \n",
      "152  0.2548  0.09296  ...  11.020  19.49   71.04   380.5  0.1292  0.27720   \n",
      "156  0.1971  0.06166  ...  20.470  25.11  132.90  1302.0  0.1418  0.34980   \n",
      "97   0.1350  0.06890  ...  10.920  26.29   68.81   366.1  0.1316  0.09473   \n",
      "..      ...      ...  ...     ...    ...     ...     ...     ...      ...   \n",
      "251  0.1834  0.05934  ...  12.970  22.46   83.12   508.9  0.1183  0.10490   \n",
      "280  0.1902  0.06220  ...  23.720  35.90  159.80  1724.0  0.1782  0.38410   \n",
      "216  0.1967  0.06314  ...  13.250  27.10   86.20   531.2  0.1405  0.30460   \n",
      "66   0.1717  0.06899  ...  10.410  31.56   67.03   330.7  0.1548  0.16640   \n",
      "151  0.2222  0.08261  ...   9.092  29.72   58.08   249.8  0.1630  0.43100   \n",
      "\n",
      "          26       27      28       29  \n",
      "167  0.23180  0.14740  0.2810  0.07228  \n",
      "283  0.50260  0.17320  0.2770  0.10630  \n",
      "152  0.82160  0.15710  0.3108  0.12590  \n",
      "156  0.35830  0.15150  0.2463  0.07738  \n",
      "97   0.02049  0.02381  0.1934  0.08988  \n",
      "..       ...      ...     ...      ...  \n",
      "251  0.08105  0.06544  0.2740  0.06487  \n",
      "280  0.57540  0.18720  0.3258  0.09720  \n",
      "216  0.28060  0.11380  0.3397  0.08365  \n",
      "66   0.09412  0.06517  0.2878  0.09211  \n",
      "151  0.53810  0.07879  0.3322  0.14860  \n",
      "\n",
      "[114 rows x 30 columns]\n",
      "\n",
      "     0\n",
      "285  1\n",
      "535  0\n",
      "521  0\n",
      "339  0\n",
      "429  1\n",
      "..  ..\n",
      "508  1\n",
      "203  0\n",
      "297  0\n",
      "390  1\n",
      "125  1\n",
      "\n",
      "[455 rows x 1 columns]\n",
      "\n",
      "     0\n",
      "167  0\n",
      "283  0\n",
      "152  1\n",
      "156  0\n",
      "97   1\n",
      "..  ..\n",
      "251  1\n",
      "280  0\n",
      "216  1\n",
      "66   1\n",
      "151  1\n",
      "\n",
      "[114 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print()\n",
    "print(X_test)\n",
    "print()\n",
    "print(y_train)\n",
    "print()\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class clf_switcher(BaseEstimator):\n",
    "    def __init__(self, estimator=SVC(random_state=7, probability=True)):\n",
    "        \"\"\"\n",
    "        A Custom BaseEstimator that can switch between classifiers.\n",
    "        Default Support Vector Machine Classifier\n",
    "        :param estimator: <sklearn classifier object>\n",
    "        \"\"\"\n",
    "        self.estimator = estimator\n",
    "\n",
    "    def fit(self, X, y=None, **kwargs):\n",
    "        self.estimator.fit(X, y)\n",
    "        return self\n",
    "\n",
    "    def predict(self, X, y=None):\n",
    "        return self.estimator.predict(X)\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        return self.estimator.predict_proba(X)\n",
    "\n",
    "    def score(self, X, y):\n",
    "        return self.estimator.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# it takes a list of tuples as parameter. The last entry is the call to the modelling algorithm\n",
    "pipeline = make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), clf_switcher()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline steps:\n",
      " [('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('minmaxscaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
      "    svd_solver='auto', tol=0.0, whiten=False)), ('clf_switcher', clf_switcher(estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
      "                           class_weight=None, coef0=0.0,\n",
      "                           decision_function_shape='ovr', degree=3,\n",
      "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
      "                           probability=True, random_state=7, shrinking=True,\n",
      "                           tol=0.001, verbose=False)))]\n"
     ]
    }
   ],
   "source": [
    "print(\"Pipeline steps:\\n {}\".format(pipeline.steps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 715 candidates, totalling 7150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=12)]: Done   8 tasks      | elapsed:    3.0s\n",
      "[Parallel(n_jobs=12)]: Done 146 tasks      | elapsed:    3.7s\n",
      "[Parallel(n_jobs=12)]: Done 786 tasks      | elapsed:    6.0s\n",
      "[Parallel(n_jobs=12)]: Done 1682 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=12)]: Done 2834 tasks      | elapsed:   13.1s\n",
      "[Parallel(n_jobs=12)]: Done 4242 tasks      | elapsed:   18.0s\n",
      "[Parallel(n_jobs=12)]: Done 5491 tasks      | elapsed:   33.8s\n",
      "[Parallel(n_jobs=12)]: Done 6424 tasks      | elapsed:   44.1s\n",
      "[Parallel(n_jobs=12)]: Done 7150 out of 7150 | elapsed:   45.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('standardscaler',\n",
       "                                        StandardScaler(copy=True,\n",
       "                                                       with_mean=True,\n",
       "                                                       with_std=True)),\n",
       "                                       ('minmaxscaler',\n",
       "                                        MinMaxScaler(copy=True,\n",
       "                                                     feature_range=(0, 1))),\n",
       "                                       ('pca',\n",
       "                                        PCA(copy=True, iterated_power='auto',\n",
       "                                            n_components=None,\n",
       "                                            random_state=None,\n",
       "                                            svd_solver='auto', tol=0.0,\n",
       "                                            whiten=False)),\n",
       "                                       ('clf_switcher',\n",
       "                                        clf_sw...\n",
       "                                                                           n_neighbors=5,\n",
       "                                                                           p=2,\n",
       "                                                                           weights='uniform')],\n",
       "                          'clf_switcher__estimator__algorithm': ['ball_tree',\n",
       "                                                                 'kd_tree',\n",
       "                                                                 'brute'],\n",
       "                          'clf_switcher__estimator__leaf_size': (10, 20, 30, 40,\n",
       "                                                                 50),\n",
       "                          'clf_switcher__estimator__n_neighbors': (1, 3, 5, 10),\n",
       "                          'clf_switcher__estimator__weights': ['uniform',\n",
       "                                                               'distance']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyper_parameter_space = [\n",
    "    {\n",
    "        'clf_switcher__estimator': [SGDClassifier(random_state=7)], \n",
    "        'clf_switcher__estimator__penalty': ('l2', 'elasticnet', 'l1'),\n",
    "        'clf_switcher__estimator__max_iter': [50, 80],\n",
    "        'clf_switcher__estimator__tol': [1e-4],\n",
    "        'clf_switcher__estimator__loss': ['hinge', 'log', 'modified_huber'],\n",
    "    },\n",
    "    #{\n",
    "    #    'clf_switcher__estimator': [MultinomialNB()],\n",
    "    #    'clf_switcher__estimator__alpha': (1e-2, 1e-3, 1e-1),\n",
    "    #},\n",
    "    {\n",
    "        'clf_switcher__estimator': [SVC(random_state=7, probability=True)], # SVM if hinge loss\n",
    "        'clf_switcher__estimator__kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'],\n",
    "        'clf_switcher__estimator__C': (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),\n",
    "        'clf_switcher__estimator__degree': (1, 2, 3, 4, 5),\n",
    "        'clf_switcher__estimator__max_iter': [50, 80]\n",
    "    },\n",
    "    {\n",
    "        'clf_switcher__estimator': [LGBMClassifier(random_state=7)],\n",
    "    },\n",
    "    {\n",
    "        'clf_switcher__estimator': [LogisticRegression(random_state=7)], # Logreg if log loss\n",
    "    },\n",
    "    {\n",
    "        'clf_switcher__estimator': [XGBClassifier(random_state=7)],\n",
    "    },\n",
    "    {\n",
    "        'clf_switcher__estimator': [RandomForestClassifier(random_state=7)],\n",
    "        'clf_switcher__estimator__criterion': ['entropy', 'gini'],\n",
    "        'clf_switcher__estimator__max_depth': [10, 20, 30],\n",
    "        #'clf_switcher__estimator__bootstrap': [True, False],\n",
    "        #'clf_switcher__estimator__max_features': ['auto', 'sqrt'],\n",
    "        'clf_switcher__estimator__min_samples_leaf': [1, 2, 4],\n",
    "        'clf_switcher__estimator__min_samples_split': [2, 5, 10],\n",
    "        #'clf_switcher__estimator__n_estimators': [200, 400, 600]\n",
    "    },\n",
    "    {\n",
    "        'clf_switcher__estimator': [DecisionTreeClassifier(random_state=7)],\n",
    "        'clf_switcher__estimator__criterion': ['entropy', 'gini'],\n",
    "        'clf_switcher__estimator__splitter': ['best', 'random'],\n",
    "        'clf_switcher__estimator__max_depth': (1, 2, 3, 4, 5),\n",
    "    },\n",
    "    {\n",
    "        'clf_switcher__estimator': [KNeighborsClassifier()],\n",
    "        'clf_switcher__estimator__algorithm': ['ball_tree', 'kd_tree', 'brute'],\n",
    "        'clf_switcher__estimator__weights': ['uniform', 'distance'],\n",
    "        'clf_switcher__estimator__n_neighbors': (1, 3, 5, 10),\n",
    "        'clf_switcher__estimator__leaf_size': (10, 20, 30, 40, 50),\n",
    "    },\n",
    "]\n",
    "\n",
    "gscv = GridSearchCV(estimator=pipeline, param_grid=hyper_parameter_space, cv=10, n_jobs=12, verbose=3)\n",
    "gscv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9802415458937197"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('standardscaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('minmaxscaler', MinMaxScaler(copy=True, feature_range=(0, 1))),\n",
       "                ('pca',\n",
       "                 PCA(copy=True, iterated_power='auto', n_components=None,\n",
       "                     random_state=None, svd_solver='auto', tol=0.0,\n",
       "                     whiten=False)),\n",
       "                ('clf_switcher',\n",
       "                 clf_switcher(estimator=SVC(C=0.4, break_ties=False,\n",
       "                                            cache_size=200, class_weight=None,\n",
       "                                            coef0=0.0,\n",
       "                                            decision_function_shape='ovr',\n",
       "                                            degree=1, gamma='scale',\n",
       "                                            kernel='rbf', max_iter=80,\n",
       "                                            probability=True, random_state=7,\n",
       "                                            shrinking=True, tol=0.001,\n",
       "                                            verbose=False)))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf_switcher__estimator': SVC(C=0.4, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "     decision_function_shape='ovr', degree=1, gamma='scale', kernel='rbf',\n",
       "     max_iter=80, probability=True, random_state=7, shrinking=True, tol=0.001,\n",
       "     verbose=False),\n",
       " 'clf_switcher__estimator__C': 0.4,\n",
       " 'clf_switcher__estimator__degree': 1,\n",
       " 'clf_switcher__estimator__kernel': 'rbf',\n",
       " 'clf_switcher__estimator__max_iter': 80}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.95\n"
     ]
    }
   ],
   "source": [
    "print(\"Test score: {:.2f}\".format(gscv.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19360e92a48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = gscv.best_estimator_.predict(X_test)\n",
    "sns.scatterplot(data=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.93      0.93        42\n",
      "           1       0.96      0.96      0.96        72\n",
      "\n",
      "    accuracy                           0.95       114\n",
      "   macro avg       0.94      0.94      0.94       114\n",
      "weighted avg       0.95      0.95      0.95       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation accuracy: 0.98\n",
      "Test set accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "print(\"Best cross-validation accuracy: {:.2f}\".format(gscv.best_score_)) \n",
    "print(\"Test set accuracy: {:.2f}\".format(gscv.best_estimator_.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_threshold(fpr, tpr, thresholds, threshold):\n",
    "    \"\"\"\n",
    "    define a function that accepts a threshold and prints sensitivity and specificity\n",
    "    \"\"\"\n",
    "    sensitivity = (tpr[thresholds > threshold][-1])\n",
    "    specificity = (1 - fpr[thresholds > threshold][-1])\n",
    "    return {\n",
    "        sensitivity: sensitivity,\n",
    "        specificity: specificity\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def roc_plot(fpr, tpr):\n",
    "    \"\"\"\n",
    "    Predefined ROC Plot Function\n",
    "    \"\"\"\n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    plt.plot(fpr, tpr, color='darkorange',\n",
    "            lw=lw, label='ROC curve (area = %0.2f)' % auc(fpr, tpr))\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    return (plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\n",
    "    ('RandomForestClassifier_Entropy', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), RandomForestClassifier(criterion=\"entropy\", random_state=7))),\n",
    "    ('RandomForestClassifier_Gini', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), RandomForestClassifier(criterion=\"gini\", random_state=7))),\n",
    "    ('XGBClassifier', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), XGBClassifier(random_state=7))),\n",
    "    ('LGBMClassifier', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), LGBMClassifier(random_state=7))),\n",
    "    ('SGDClassifier', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
    "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
    "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
    "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
    "              power_t=0.5, random_state=7, shuffle=True, tol=0.001,\n",
    "              validation_fraction=0.1, verbose=0, warm_start=False))),\n",
    "    ('DecisionTreeClassifier_Entropy', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), DecisionTreeClassifier(criterion=\"entropy\", random_state=7))),\n",
    "    ('DecisionTreeClassifier_Gini', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), DecisionTreeClassifier(criterion=\"gini\", random_state=7))),\n",
    "    ('LogisticRegression', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), LogisticRegression(random_state=7))),\n",
    "    ('KNeighborsClassifier', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), KNeighborsClassifier(n_neighbors=10))),\n",
    "    ('SVC', make_pipeline(StandardScaler(), MinMaxScaler(), PCA(), SVC(C=0.4, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
    "     decision_function_shape='ovr', degree=1, gamma='scale', kernel='rbf',\n",
    "     max_iter=80, probability=True, random_state=7, shrinking=True, tol=0.001,\n",
    "     verbose=False)))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking with Multiple accuracy metrics and check for Overfitting\n",
    "def check_all_models():\n",
    "    model_list = pd.DataFrame(\n",
    "        columns=(\n",
    "            \"Model\",\n",
    "            \"Accuracy\",\n",
    "            \"F1Score\",\n",
    "            \"AUC\",\n",
    "            \"Accuracy_Train\",\n",
    "            \"F1Score_Train\",\n",
    "            \"AUC_Train\",\n",
    "            \"Mean_Accuracy\",\n",
    "            \"Mean_Accuracy_Std\",\n",
    "            \"TPR\",\n",
    "            \"TNR\",\n",
    "            \"FNR\",\n",
    "            \"FPR\",\n",
    "            \"Precision\",\n",
    "            \"Sensitivity\",\n",
    "            \"Specificity\"\n",
    "        )\n",
    "    )\n",
    "    row_number = 0\n",
    "    \n",
    "    for name, model in models:\n",
    "        #print(model)\n",
    "        model_list.loc[row_number, \"Model\"] = name\n",
    "        \n",
    "        classifier = model\n",
    "        classifier.fit(X_train, y_train)\n",
    "        \n",
    "        Y_train_predict = classifier.predict(X_train)\n",
    "        \n",
    "        model_list.loc[row_number, \"Accuracy_Train\"] = round(((accuracy_score(y_train, Y_train_predict))), 2)\n",
    "        model_list.loc[row_number, \"F1Score_Train\"] = round((f1_score(y_train, Y_train_predict)), 2)\n",
    "        model_list.loc[row_number, \"AUC_Train\"] = round((roc_auc_score(y_train, Y_train_predict)), 2)\n",
    "        \n",
    "        Y_test_predict = classifier.predict(X_test)\n",
    "        \n",
    "        fpr, tpr, thresholds = roc_curve(y_test, Y_test_predict, pos_label=None)\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, Y_test_predict).ravel()\n",
    "        \n",
    "        recall = round(tp/(tp+fn), 2)\n",
    "        precision = round(tp/(tp+fp), 2)\n",
    "        #specificity = round(tn/(tn+fp), 2)\n",
    "        \n",
    "        threshold = 0.5\n",
    "        sensitivity, specificity = evaluate_threshold(fpr, tpr, thresholds, threshold)\n",
    "        \n",
    "        model_list.loc[row_number, \"TPR\"] = round(recall, 2)\n",
    "        model_list.loc[row_number, \"TNR\"] = round(specificity, 2)\n",
    "        model_list.loc[row_number, \"FNR\"] = round(1-recall, 2)\n",
    "        model_list.loc[row_number, \"FPR\"] = round(1-specificity, 2)\n",
    "        \n",
    "        model_list.loc[row_number, \"Precision\"] = round(precision, 2)\n",
    "        model_list.loc[row_number, \"Sensitivity\"] = round(sensitivity, 2)\n",
    "        model_list.loc[row_number, \"Specificity\"] = round(specificity, 2)\n",
    "        \n",
    "        #roc_plot(fpr, tpr)\n",
    "        \n",
    "        model_list.loc[row_number, \"Accuracy\"] = float(round(((accuracy_score(y_test, Y_test_predict))), 2))\n",
    "        model_list.loc[row_number, \"F1Score\"]= round((f1_score(y_test, Y_test_predict)), 2)\n",
    "        model_list.loc[row_number, \"AUC\"] = round((roc_auc_score(y_test, Y_test_predict)), 2)        \n",
    "        \n",
    "        kfold = KFold(n_splits=10, random_state=7)\n",
    "        results_test = cross_val_score(classifier, X_test, Y_test_predict, cv=kfold)\n",
    "        \n",
    "        model_list.loc[row_number, \"Mean_Accuracy\"] = round(results_test.mean(), 2)\n",
    "        model_list.loc[row_number, \"Mean_Accuracy_Std\"] = round(results_test.std(), 2)\n",
    "\n",
    "        row_number += 1\n",
    "    return (model_list.sort_values(by=[\"Accuracy\", \"Mean_Accuracy\", \"AUC\", \"Precision\"], ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\texttt{accuracy}(y, \\hat{y}) = \\frac{1}{n_\\text{samples}} \\sum_{i=0}^{n_\\text{samples}-1} 1(\\hat{y}_i = y_i)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\texttt{balanced-accuracy}(y, \\hat{y}, w) = \\frac{1}{\\sum{\\hat{w}_i}} \\sum_i 1(\\hat{y}_i = y_i) \\hat{w}_i$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\text{precision} = \\frac{tp}{tp + fp},$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\text{recall} = \\frac{tp}{tp + fn},$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$F_\\beta = (1 + \\beta^2) \\frac{\\text{precision} \\times \\text{recall}}{\\beta^2 \\text{precision} + \\text{recall}}.$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sensitivity:** When the actual value is positive, how often is the prediction correct?\n",
    "\n",
    "- How \"sensitive\" is the classifier to detecting positive instances?\n",
    "- Also known as \"True Positive Rate\" or \"Recall\"\n",
    "\n",
    "**Specificity:** When the actual value is negative, how often is the prediction correct?\n",
    "\n",
    "- How \"specific\" (or \"selective\") is the classifier in predicting positive instances?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Accuracy</th>\n",
       "      <th>F1Score</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Accuracy_Train</th>\n",
       "      <th>F1Score_Train</th>\n",
       "      <th>AUC_Train</th>\n",
       "      <th>Mean_Accuracy</th>\n",
       "      <th>Mean_Accuracy_Std</th>\n",
       "      <th>TPR</th>\n",
       "      <th>TNR</th>\n",
       "      <th>FNR</th>\n",
       "      <th>FPR</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestClassifier_Gini</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>RandomForestClassifier_Entropy</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>LGBMClassifier</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.93</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>DecisionTreeClassifier_Entropy</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>DecisionTreeClassifier_Gini</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model Accuracy F1Score   AUC Accuracy_Train  \\\n",
       "8            KNeighborsClassifier     0.96    0.97  0.96           0.97   \n",
       "7              LogisticRegression     0.96    0.97  0.95           0.97   \n",
       "4                   SGDClassifier     0.96    0.96  0.96           0.98   \n",
       "1     RandomForestClassifier_Gini     0.95    0.96  0.94              1   \n",
       "9                             SVC     0.95    0.96  0.94           0.98   \n",
       "0  RandomForestClassifier_Entropy     0.94    0.95  0.94              1   \n",
       "3                  LGBMClassifier     0.94    0.95  0.94              1   \n",
       "2                   XGBClassifier     0.93    0.94  0.93              1   \n",
       "5  DecisionTreeClassifier_Entropy     0.91    0.93  0.91              1   \n",
       "6     DecisionTreeClassifier_Gini     0.89    0.92  0.89              1   \n",
       "\n",
       "  F1Score_Train AUC_Train Mean_Accuracy Mean_Accuracy_Std   TPR   TNR   FNR  \\\n",
       "8          0.97      0.96          0.97              0.06  0.99  0.93  0.01   \n",
       "7          0.97      0.96          0.95              0.06  0.99   0.9  0.01   \n",
       "4          0.98      0.98          0.94              0.05  0.94  0.98  0.06   \n",
       "1             1         1          0.96              0.04  0.96  0.93  0.04   \n",
       "9          0.98      0.98          0.95              0.06  0.96  0.93  0.04   \n",
       "0             1         1          0.96              0.04  0.94  0.93  0.06   \n",
       "3             1         1          0.92              0.06  0.93  0.95  0.07   \n",
       "2             1         1          0.97              0.04  0.93  0.93  0.07   \n",
       "5             1         1          0.92              0.09  0.93  0.88  0.07   \n",
       "6             1         1          0.89              0.07   0.9  0.88   0.1   \n",
       "\n",
       "    FPR Precision Sensitivity Specificity  \n",
       "8  0.07      0.96        0.99        0.93  \n",
       "7   0.1      0.95        0.99         0.9  \n",
       "4  0.02      0.99        0.94        0.98  \n",
       "1  0.07      0.96        0.96        0.93  \n",
       "9  0.07      0.96        0.96        0.93  \n",
       "0  0.07      0.96        0.94        0.93  \n",
       "3  0.05      0.97        0.93        0.95  \n",
       "2  0.07      0.96        0.93        0.93  \n",
       "5  0.12      0.93        0.93        0.88  \n",
       "6  0.12      0.93         0.9        0.88  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for any overfitting\n",
    "all_model_results = check_all_models()\n",
    "all_model_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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_clf_switcher__estimator</th>\n",
       "      <th>param_clf_switcher__estimator__loss</th>\n",
       "      <th>param_clf_switcher__estimator__max_iter</th>\n",
       "      <th>param_clf_switcher__estimator__penalty</th>\n",
       "      <th>param_clf_switcher__estimator__tol</th>\n",
       "      <th>param_clf_switcher__estimator__C</th>\n",
       "      <th>...</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split5_test_score</th>\n",
       "      <th>split6_test_score</th>\n",
       "      <th>split7_test_score</th>\n",
       "      <th>split8_test_score</th>\n",
       "      <th>split9_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
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       "  <tbody>\n",
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       "      <td>173</td>\n",
       "      <td>0.032099</td>\n",
       "      <td>0.022686</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.980242</td>\n",
       "      <td>0.018083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.026300</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.980242</td>\n",
       "      <td>0.018083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.025600</td>\n",
       "      <td>0.002010</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.980242</td>\n",
       "      <td>0.018083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>0.027099</td>\n",
       "      <td>0.004989</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.980242</td>\n",
       "      <td>0.018083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213</td>\n",
       "      <td>0.025399</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.980242</td>\n",
       "      <td>0.018083</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>416</td>\n",
       "      <td>0.006900</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>417</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.007200</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>387</td>\n",
       "      <td>0.006500</td>\n",
       "      <td>0.001204</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>357</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>0.001020</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>SVC(C=0.4, break_ties=False, cache_size=200, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>715 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "173       0.032099      0.022686           0.0023        0.000458   \n",
       "183       0.026300      0.001552           0.0022        0.000400   \n",
       "193       0.025600      0.002010           0.0024        0.000490   \n",
       "203       0.027099      0.004989           0.0021        0.000300   \n",
       "213       0.025399      0.000800           0.0022        0.000400   \n",
       "..             ...           ...              ...             ...   \n",
       "416       0.006900      0.000700           0.0000        0.000000   \n",
       "417       0.007000      0.000774           0.0000        0.000000   \n",
       "66        0.007200      0.000400           0.0000        0.000000   \n",
       "387       0.006500      0.001204           0.0000        0.000000   \n",
       "357       0.005400      0.001020           0.0000        0.000000   \n",
       "\n",
       "                         param_clf_switcher__estimator  \\\n",
       "173  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "183  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "193  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "203  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "213  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "..                                                 ...   \n",
       "416  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "417  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "66   SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "387  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "357  SVC(C=0.4, break_ties=False, cache_size=200, c...   \n",
       "\n",
       "    param_clf_switcher__estimator__loss  \\\n",
       "173                                 NaN   \n",
       "183                                 NaN   \n",
       "193                                 NaN   \n",
       "203                                 NaN   \n",
       "213                                 NaN   \n",
       "..                                  ...   \n",
       "416                                 NaN   \n",
       "417                                 NaN   \n",
       "66                                  NaN   \n",
       "387                                 NaN   \n",
       "357                                 NaN   \n",
       "\n",
       "    param_clf_switcher__estimator__max_iter  \\\n",
       "173                                      80   \n",
       "183                                      80   \n",
       "193                                      80   \n",
       "203                                      80   \n",
       "213                                      80   \n",
       "..                                      ...   \n",
       "416                                      50   \n",
       "417                                      80   \n",
       "66                                       50   \n",
       "387                                      80   \n",
       "357                                      80   \n",
       "\n",
       "    param_clf_switcher__estimator__penalty param_clf_switcher__estimator__tol  \\\n",
       "173                                    NaN                                NaN   \n",
       "183                                    NaN                                NaN   \n",
       "193                                    NaN                                NaN   \n",
       "203                                    NaN                                NaN   \n",
       "213                                    NaN                                NaN   \n",
       "..                                     ...                                ...   \n",
       "416                                    NaN                                NaN   \n",
       "417                                    NaN                                NaN   \n",
       "66                                     NaN                                NaN   \n",
       "387                                    NaN                                NaN   \n",
       "357                                    NaN                                NaN   \n",
       "\n",
       "    param_clf_switcher__estimator__C  ... split3_test_score split4_test_score  \\\n",
       "173                              0.4  ...          0.934783               1.0   \n",
       "183                              0.4  ...          0.934783               1.0   \n",
       "193                              0.4  ...          0.934783               1.0   \n",
       "203                              0.4  ...          0.934783               1.0   \n",
       "213                              0.4  ...          0.934783               1.0   \n",
       "..                               ...  ...               ...               ...   \n",
       "416                              0.8  ...               NaN               NaN   \n",
       "417                              0.8  ...               NaN               NaN   \n",
       "66                               0.1  ...               NaN               NaN   \n",
       "387                              0.8  ...               NaN               NaN   \n",
       "357                              0.7  ...               NaN               NaN   \n",
       "\n",
       "    split5_test_score split6_test_score split7_test_score split8_test_score  \\\n",
       "173          0.977778          0.977778               1.0          0.977778   \n",
       "183          0.977778          0.977778               1.0          0.977778   \n",
       "193          0.977778          0.977778               1.0          0.977778   \n",
       "203          0.977778          0.977778               1.0          0.977778   \n",
       "213          0.977778          0.977778               1.0          0.977778   \n",
       "..                ...               ...               ...               ...   \n",
       "416               NaN               NaN               NaN               NaN   \n",
       "417               NaN               NaN               NaN               NaN   \n",
       "66                NaN               NaN               NaN               NaN   \n",
       "387               NaN               NaN               NaN               NaN   \n",
       "357               NaN               NaN               NaN               NaN   \n",
       "\n",
       "    split9_test_score mean_test_score std_test_score rank_test_score  \n",
       "173          0.977778        0.980242       0.018083               1  \n",
       "183          0.977778        0.980242       0.018083               1  \n",
       "193          0.977778        0.980242       0.018083               1  \n",
       "203          0.977778        0.980242       0.018083               1  \n",
       "213          0.977778        0.980242       0.018083               1  \n",
       "..                ...             ...            ...             ...  \n",
       "416               NaN             NaN            NaN             711  \n",
       "417               NaN             NaN            NaN             712  \n",
       "66                NaN             NaN            NaN             713  \n",
       "387               NaN             NaN            NaN             714  \n",
       "357               NaN             NaN            NaN             715  \n",
       "\n",
       "[715 rows x 35 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(gscv.cv_results_).sort_values(by=[\"rank_test_score\", \"mean_test_score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x193611c4388>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,15))\n",
    "sns.scatterplot(x=gscv.cv_results_['rank_test_score'], y=gscv.cv_results_['mean_test_score'], hue=gscv.cv_results_['param_clf_switcher__estimator'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}