assignment_3_sol.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from time import time\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will directly pass the best pipeline which is already trained\n",
    "def benchmark(clf, X_test, y_test, categories, name= 'classifier'):\n",
    "#     print(\"parameters:\", params)\n",
    "#     t0 = time()\n",
    "#     clf = clf_class(**params).fit(X_train, y_train)\n",
    "#     print(\"done in %fs\" % (time() - t0))\n",
    "\n",
    "    print(\"Predicting the outcomes of the testing set\")\n",
    "    t0 = time()\n",
    "    pred = clf.predict(X_test)\n",
    "    print(\"done in %fs\" % (time() - t0))\n",
    "    \n",
    "    print (\" Printing Accuracy of the classifier\")\n",
    "    print (accuracy_score(y_test, pred))\n",
    "    print ()\n",
    "\n",
    "    print(\"Classification report on test set for classifier:\")\n",
    "    print(clf)\n",
    "    print()\n",
    "    print(classification_report(y_test, pred, target_names=categories))\n",
    "    \n",
    "    print ()\n",
    "    print (\"FI score metrics\")\n",
    "    print (precision_recall_fscore_support(y_test, pred, average='weighted'))\n",
    "\n",
    "    cm = confusion_matrix(y_test, pred)\n",
    "    print(\"Confusion matrix:\")\n",
    "    print(cm)\n",
    "\n",
    "    # Show confusion matrix\n",
    "    plt.matshow(cm)\n",
    "    plt.title('CM of the %s ' % name)\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 4)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "iris = load_iris()\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size= 0.2,random_state=42 )\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.6 3.6 1.  0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.4 3.2 1.3 0.2]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(X_train[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DummyEstimator(BaseEstimator):\n",
    "    def fit(self): pass\n",
    "    def score(self): pass\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('minmax', MinMaxScaler()),\n",
    "    ('clf', DummyEstimator()),\n",
    "])\n",
    "\n",
    "# A fit transform followed by an estimator\n",
    "\n",
    "search_space = [\n",
    "    {\n",
    "        'minmax': (MinMaxScaler(),),\n",
    "        'clf': (LogisticRegression(),),\n",
    "        'clf__C': ( 0.1, 1, 10),\n",
    "        'clf__penalty': ('l2', 'l1'),\n",
    "    }, \n",
    "    {\n",
    "        'minmax': (MinMaxScaler(),),\n",
    "        'clf': (RandomForestClassifier(),),\n",
    "        'clf__n_estimators': (10,20,30,50),\n",
    "        'clf__max_depth': (5,10,20),\n",
    "    },\n",
    "    {\n",
    "        'minmax': (MinMaxScaler(),),\n",
    "        'clf': (DecisionTreeClassifier(),),\n",
    "        'clf__max_depth': (1,5,10,25,50),\n",
    "    }, \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_clf = GridSearchCV(pipeline, search_space, cv=5, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 23 candidates, totalling 115 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 115 out of 115 | elapsed:    1.3s finished\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/s2917623/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('minmax',\n",
       "                                        MinMaxScaler(copy=True,\n",
       "                                                     feature_range=(0, 1))),\n",
       "                                       ('clf', DummyEstimator())],\n",
       "                                verbose=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid=[{'clf': (LogisticRegression(C=10, class_weight=None,\n",
       "                                                     dual=False,\n",
       "                                                     fit_intercept=True,\n",
       "                                                     intercept_scaling=1,\n",
       "                                                     l1_ratio=None,\n",
       "                                                     max_iter=100,\n",
       "                                                     mult...\n",
       "                                                         max_leaf_nodes=None,\n",
       "                                                         min_impurity_decrease=0.0,\n",
       "                                                         min_impurity_split=None,\n",
       "                                                         min_samples_leaf=1,\n",
       "                                                         min_samples_split=2,\n",
       "                                                         min_weight_fraction_leaf=0.0,\n",
       "                                                         presort=False,\n",
       "                                                         random_state=None,\n",
       "                                                         splitter='best'),),\n",
       "                          'clf__max_depth': (1, 5, 10, 25, 50),\n",
       "                          'minmax': (MinMaxScaler(copy=True,\n",
       "                                                  feature_range=(0, 1)),)}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=1)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " best estimator is {'clf': LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='warn', n_jobs=None, penalty='l1',\n",
      "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
      "                   warm_start=False), 'clf__C': 10, 'clf__penalty': 'l1', 'minmax': MinMaxScaler(copy=True, feature_range=(0, 1))} and score is 0.95\n"
     ]
    }
   ],
   "source": [
    "gs_clf.best_params_\n",
    "# {'clf__C': 0.1}\n",
    "\n",
    "gs_clf.best_score_\n",
    "# 0.702290076336\n",
    "\n",
    "print (f\" best estimator is {gs_clf.best_params_} and score is {gs_clf.best_score_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_pipeline= gs_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = selected_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting the outcomes of the testing set\n",
      "done in 0.000515s\n",
      " Printing Accuracy of the classifier\n",
      "1.0\n",
      "\n",
      "Classification report on test set for classifier:\n",
      "Pipeline(memory=None,\n",
      "         steps=[('minmax', MinMaxScaler(copy=True, feature_range=(0, 1))),\n",
      "                ('clf',\n",
      "                 LogisticRegression(C=10, class_weight=None, dual=False,\n",
      "                                    fit_intercept=True, intercept_scaling=1,\n",
      "                                    l1_ratio=None, max_iter=100,\n",
      "                                    multi_class='warn', n_jobs=None,\n",
      "                                    penalty='l1', random_state=None,\n",
      "                                    solver='warn', tol=0.0001, verbose=0,\n",
      "                                    warm_start=False))],\n",
      "         verbose=False)\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        10\n",
      "  versicolor       1.00      1.00      1.00         9\n",
      "   virginica       1.00      1.00      1.00        11\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n",
      "\n",
      "FI score metrics\n",
      "(1.0, 1.0, 1.0, None)\n",
      "Confusion matrix:\n",
      "[[10  0  0]\n",
      " [ 0  9  0]\n",
      " [ 0  0 11]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "benchmark(selected_pipeline, X_test, y_test, iris.target_names, name= 'pipeline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "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": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}