assignment_2_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "from time import time\n",
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import scipy.sparse as sp\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ALL major classifiers are imported below\n",
    "\n",
    "##### USE ANY 2 of the below classifiers, Make sure to understand the documentation and parameters before using them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### USE ANY OF THE BELOW VECTORIZERS, Again read the documentation to understand the parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### USE EITHER GRIDSEARCHCV or RANDOMSEARCHCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Use the below function for benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def benchmark(clf_class, params, 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": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset and choose 10 categories\n",
    "\n",
    "categories_all = ['alt.atheism',\n",
    " 'comp.graphics',\n",
    " 'comp.os.ms-windows.misc',\n",
    " 'comp.sys.ibm.pc.hardware',\n",
    " 'comp.sys.mac.hardware',\n",
    " 'comp.windows.x',\n",
    " 'misc.forsale',\n",
    " 'rec.autos',\n",
    " 'rec.motorcycles',\n",
    " 'rec.sport.baseball',\n",
    " 'rec.sport.hockey',\n",
    " 'sci.crypt',\n",
    " 'sci.electronics',\n",
    " 'sci.med',\n",
    " 'sci.space',\n",
    " 'soc.religion.christian',\n",
    " 'talk.politics.guns',\n",
    " 'talk.politics.mideast',\n",
    " 'talk.politics.misc',\n",
    " 'talk.religion.misc']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting 10 random categories\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(categories_all) # Randomly shuffle the categories, pick any 10\n",
    "categories = [categories_all.pop() for i in range(10)]\n",
    "twenty_train = fetch_20newsgroups(subset='train', shuffle=True, \n",
    "                                  remove=('headers', 'footers', 'quotes'), categories= categories)\n",
    "twenty_test = fetch_20newsgroups(subset='test', shuffle=True, remove=('headers', 'footers', 'quotes'),\n",
    "                                 categories=categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'filenames', 'target_names', 'target', 'DESCR'])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets use TF-IDF vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5790, 2448)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec = TfidfVectorizer(stop_words = {'english'}, lowercase=True, analyzer ='word', binary =False,min_df = 0.005)\n",
    "\n",
    "X_train = vec.fit_transform(twenty_train.data)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3853, 2448)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = vec.transform(twenty_test.data)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = twenty_train.target\n",
    "y_test = twenty_test.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MyMultiNomialNB(X_train, y_train):\n",
    "\tclf = MultinomialNB()\n",
    "\tparam_grid = {'alpha': [0.01,0.1, 1.0, 10, 100]}\n",
    "\t# Five fold Cross Validation\n",
    "\tclassifier= GridSearchCV(estimator=clf, cv=3 ,param_grid=param_grid)\n",
    "\tclassifier.fit(X_train, y_train)\n",
    "\treturn classifier.cv_results_, classifier.best_estimator_, classifier.best_params_, classifier.best_score_\n",
    "\n",
    "\n",
    "def Mylinear_svm(X_train, y_train):\n",
    "\tlinear_svc = svm.SVC(kernel='linear')\n",
    "\tparam_grid = {'C': np.logspace(-3, 2, 6)} # C is the regularizer, widens or shortens the width of the hyper plane\n",
    "\tclassifier= GridSearchCV(estimator=linear_svc, cv=3 ,param_grid=param_grid)\n",
    "\ty_train= np.array(y_train)\n",
    "\tclassifier.fit(X_train, y_train)\n",
    "\treturn classifier.cv_results_, classifier.best_estimator_, classifier.best_params_, classifier.best_score_\n",
    "\n",
    "\n",
    "def MyRandomForest(X_train, y_train):\n",
    "\tclf = RandomForestClassifier()\n",
    "\tparam_grid = {'n_estimators': [10,20,30,50,70,100],'max_depth': [5,10,20]}\n",
    "\tclassifier= GridSearchCV(estimator=clf, cv=3 ,param_grid=param_grid)\n",
    "\tclassifier.fit(X_train, y_train)\n",
    "\treturn classifier.cv_results_, classifier.best_estimator_, classifier.best_params_, classifier.best_score_\n",
    "\n",
    "def MyDecisionTree(X_train, y_train):\n",
    "\tclf = DecisionTreeClassifier(min_samples_split=2,random_state=0)\n",
    "\tparam_grid = {'max_depth': [1,5,10,25,50, 100, 200]}\n",
    "\tclassifier= GridSearchCV(estimator=clf, cv=3 ,param_grid=param_grid)\n",
    "\tclassifier.fit(X_train, y_train)\n",
    "\treturn classifier.cv_results_, classifier.best_estimator_, classifier.best_params_, classifier.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results, best_estimator, best_params_rf, best_score = MyRandomForest(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mean_fit_time': array([0.05367963, 0.07865437, 0.11440309, 0.18482526, 0.26211627,\n",
      "       0.37743704, 0.07213799, 0.12688478, 0.19498666, 0.32726081,\n",
      "       0.43387779, 0.60710462, 0.12930576, 0.25062466, 0.4467086 ,\n",
      "       0.63684638, 0.95579354, 1.36600431]), 'std_fit_time': array([0.00856218, 0.00374679, 0.003134  , 0.00619636, 0.0058333 ,\n",
      "       0.01333775, 0.0021728 , 0.00358072, 0.0111302 , 0.00675405,\n",
      "       0.00837295, 0.01475472, 0.00698181, 0.01080245, 0.00272734,\n",
      "       0.02774893, 0.06964255, 0.0601725 ]), 'mean_score_time': array([0.01403228, 0.01685389, 0.0214444 , 0.03282634, 0.04129632,\n",
      "       0.05561741, 0.01360591, 0.01814389, 0.02788273, 0.03543067,\n",
      "       0.04884354, 0.07147884, 0.01398794, 0.02178701, 0.03808713,\n",
      "       0.04455551, 0.07245008, 0.0949889 ]), 'std_score_time': array([0.0036187 , 0.00170732, 0.00139396, 0.00128166, 0.00182288,\n",
      "       0.00114851, 0.00133919, 0.00096235, 0.00556181, 0.00089693,\n",
      "       0.00557787, 0.00931245, 0.00093254, 0.0021198 , 0.00681545,\n",
      "       0.00286913, 0.01372614, 0.01519477]), 'param_max_depth': masked_array(data=[5, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10, 10, 20, 20, 20,\n",
      "                   20, 20, 20],\n",
      "             mask=[False, False, False, False, False, False, False, False,\n",
      "                   False, False, False, False, False, False, False, False,\n",
      "                   False, False],\n",
      "       fill_value='?',\n",
      "            dtype=object), 'param_n_estimators': masked_array(data=[10, 20, 30, 50, 70, 100, 10, 20, 30, 50, 70, 100, 10,\n",
      "                   20, 30, 50, 70, 100],\n",
      "             mask=[False, False, False, False, False, False, False, False,\n",
      "                   False, False, False, False, False, False, False, False,\n",
      "                   False, False],\n",
      "       fill_value='?',\n",
      "            dtype=object), 'params': [{'max_depth': 5, 'n_estimators': 10}, {'max_depth': 5, 'n_estimators': 20}, {'max_depth': 5, 'n_estimators': 30}, {'max_depth': 5, 'n_estimators': 50}, {'max_depth': 5, 'n_estimators': 70}, {'max_depth': 5, 'n_estimators': 100}, {'max_depth': 10, 'n_estimators': 10}, {'max_depth': 10, 'n_estimators': 20}, {'max_depth': 10, 'n_estimators': 30}, {'max_depth': 10, 'n_estimators': 50}, {'max_depth': 10, 'n_estimators': 70}, {'max_depth': 10, 'n_estimators': 100}, {'max_depth': 20, 'n_estimators': 10}, {'max_depth': 20, 'n_estimators': 20}, {'max_depth': 20, 'n_estimators': 30}, {'max_depth': 20, 'n_estimators': 50}, {'max_depth': 20, 'n_estimators': 70}, {'max_depth': 20, 'n_estimators': 100}], 'split0_test_score': array([0.43322981, 0.49585921, 0.53364389, 0.57194617, 0.58643892,\n",
      "       0.58333333, 0.48188406, 0.56935818, 0.59213251, 0.59886128,\n",
      "       0.61853002, 0.62836439, 0.54917184, 0.59886128, 0.63664596,\n",
      "       0.64906832, 0.64337474, 0.65993789]), 'split1_test_score': array([0.44461698, 0.4689441 , 0.5326087 , 0.56832298, 0.563147  ,\n",
      "       0.56935818, 0.51501035, 0.54347826, 0.57712215, 0.59006211,\n",
      "       0.61128364, 0.60766046, 0.54865424, 0.5942029 , 0.61438923,\n",
      "       0.64182195, 0.63250518, 0.64389234]), 'split2_test_score': array([0.41692627, 0.46105919, 0.50934579, 0.53634476, 0.55711319,\n",
      "       0.55970924, 0.49688474, 0.54153686, 0.55970924, 0.58722741,\n",
      "       0.60124611, 0.60695742, 0.5238837 , 0.60020768, 0.62616822,\n",
      "       0.63187954, 0.63032191, 0.63499481]), 'mean_test_score': array([0.43160622, 0.47530225, 0.52521589, 0.55889465, 0.56891192,\n",
      "       0.57081174, 0.49792746, 0.55146805, 0.57633851, 0.59205527,\n",
      "       0.61036269, 0.61433506, 0.54058722, 0.59775475, 0.62573402,\n",
      "       0.64093264, 0.63540587, 0.6462867 ]), 'std_test_score': array([0.01135999, 0.01489898, 0.01121239, 0.01598904, 0.01264522,\n",
      "       0.0096967 , 0.01355077, 0.01268484, 0.01324491, 0.00495333,\n",
      "       0.00708433, 0.00993209, 0.01179471, 0.00257286, 0.00909615,\n",
      "       0.00704363, 0.00570919, 0.01032036]), 'rank_test_score': array([18, 17, 15, 12, 11, 10, 16, 13,  9,  8,  6,  5, 14,  7,  4,  2,  3,\n",
      "        1], dtype=int32)}\n"
     ]
    }
   ],
   "source": [
    "print(cv_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " best estimator is {'max_depth': 20, 'n_estimators': 100} and score is 0.646286701208981\n"
     ]
    }
   ],
   "source": [
    "print (f\" best estimator is {best_params_rf} and score is {best_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decison Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results, best_estimator, best_params_dt, best_score = MyDecisionTree(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " best estimator is {'max_depth': 100} and score is 0.4896373056994819\n"
     ]
    }
   ],
   "source": [
    "print (f\" best estimator is {best_params_dt} and score is {best_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear SVM ( Takes a long time, skip for now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.e-03 1.e-02 1.e-01 1.e+00 1.e+01 1.e+02]\n"
     ]
    }
   ],
   "source": [
    "#cv_results, best_estimator, best_params_svm, best_score = Mylinear_svm(X_train, y_train)\n",
    "print (np.logspace(-3, 2, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print (f\" best estimator is {best_params_svm} and score is {best_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MultiNomial Naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results, best_estimator, best_params_nb, best_score = MyMultiNomialNB(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " best estimator is {'alpha': 0.1} and score is 0.7224525043177893\n"
     ]
    }
   ],
   "source": [
    "print (f\" best estimator is {best_params_nb} and score is {best_score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Best accuracy for now is Multinomial NB, You can call the benchmark function to evaluate on test and get the metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parameters: {'alpha': 0.1}\n",
      "done in 0.015710s\n",
      "Predicting the outcomes of the testing set\n",
      "done in 0.007800s\n",
      " Printing Accuracy of the classifier\n",
      "0.6921879055281599\n",
      "\n",
      "Classification report on test set for classifier:\n",
      "MultinomialNB(alpha=0.1, class_prior=None, fit_prior=True)\n",
      "\n",
      "                          precision    recall  f1-score   support\n",
      "\n",
      "            misc.forsale       0.65      0.50      0.57       319\n",
      "comp.sys.ibm.pc.hardware       0.62      0.65      0.64       389\n",
      "           comp.graphics       0.62      0.57      0.59       394\n",
      "      rec.sport.baseball       0.63      0.62      0.62       392\n",
      "  soc.religion.christian       0.68      0.62      0.65       385\n",
      "          comp.windows.x       0.75      0.67      0.71       395\n",
      "               sci.space       0.83      0.80      0.81       390\n",
      " comp.os.ms-windows.misc       0.86      0.82      0.84       397\n",
      "             alt.atheism       0.75      0.75      0.75       394\n",
      "   comp.sys.mac.hardware       0.59      0.88      0.70       398\n",
      "\n",
      "                accuracy                           0.69      3853\n",
      "               macro avg       0.70      0.69      0.69      3853\n",
      "            weighted avg       0.70      0.69      0.69      3853\n",
      "\n",
      "\n",
      "FI score metrics\n",
      "(0.6980841291512382, 0.6921879055281599, 0.6907138107291952, None)\n",
      "Confusion matrix:\n",
      "[[161   7   3   0   4   3   1  14  22 104]\n",
      " [  9 253  25  14  15  34   6   4  21   8]\n",
      " [ 10  31 223  50  11  28   1   4  14  22]\n",
      " [  0  19  43 242  54   7  13   3   2   9]\n",
      " [  4  16  25  51 237   4  19   0  14  15]\n",
      " [  1  53  33   6   5 265  14   3   4  11]\n",
      " [  1   3   3  22  15   2 312  13   8  11]\n",
      " [ 12   5   1   1   1   2  10 326   5  34]\n",
      " [ 23  15   4   0   3   5   2  11 297  34]\n",
      " [ 28   3   1   0   1   3   0   3   8 351]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "benchmark(MultinomialNB,best_params_nb )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks decent and results could be improved with nlp techniques such as proper preprocessing, stop words removal\n",
    "or other algos. But this is okay for our experiements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For just one classifier with one vectorizer, the performance of GridSearch and RandomSearch are nearly the same.\n",
    "\n",
    "### But there are many other classifiers with their own parameters ?\n",
    "### We could even use a different vectorizer ( count, tf-idf, hashing with its own parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE ANSWER IS BUILDING A PIPELINE AND DO A GRIDSEARCH ON THEM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Simple example pipeline to to a classifier, its parameters, vectorizer and GridSearchCV\n",
    "##### lets do this again with 4 categories for time saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load 2 categories and get train and test data\n",
    "\n",
    "categories = ['alt.atheism', 'rec.sport.baseball']\n",
    "twenty_train = fetch_20newsgroups(subset='train', shuffle=True, \n",
    "                                  remove=('headers', 'footers', 'quotes'), categories= categories)\n",
    "twenty_test = fetch_20newsgroups(subset='test', shuffle=True, remove=('headers', 'footers', 'quotes'),\n",
    "                                 categories=categories)\n",
    "\n",
    "y_train = twenty_train.target\n",
    "y_test = twenty_test.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets initialize a pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.base import TransformerMixin\n",
    "\n",
    "class DummyEstimator(BaseEstimator):\n",
    "    def fit(self): pass\n",
    "    def score(self): pass\n",
    "\n",
    "class DummyTransformer(TransformerMixin):\n",
    "    def fit_transform(self, X, y=None, **fit_params): pass\n",
    "    def fit(self): pass\n",
    "    def tranform(self): pass\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('vect', TfidfVectorizer()),\n",
    "    ('clf', DummyEstimator()),\n",
    "])\n",
    "\n",
    "# A fit transform followed by an estimator\n",
    "\n",
    "search_space = [\n",
    "    {\n",
    "        'vect': (TfidfVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (LogisticRegression(),),\n",
    "        'clf__C': ( 0.1, 1, 10),\n",
    "        'clf__penalty': ('l2', 'l1'),\n",
    "    }, \n",
    "    {\n",
    "        'vect': (TfidfVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (RandomForestClassifier(),),\n",
    "        'clf__n_estimators': (10,20,30,50),\n",
    "        'clf__max_depth': (5,10,20),\n",
    "    },\n",
    "    {\n",
    "        'vect': (TfidfVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (DecisionTreeClassifier(),),\n",
    "        'clf__max_depth': (1,5,10,25,50),\n",
    "    }, \n",
    "    {\n",
    "        'vect': (TfidfVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (MultinomialNB(),),\n",
    "        'clf__alpha': (0.01,0.1, 1.0, 10, 100),\n",
    "    }   \n",
    "]\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 56 candidates, totalling 280 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "[Parallel(n_jobs=1)]: Done 280 out of 280 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('vect',\n",
       "                                        TfidfVectorizer(analyzer='word',\n",
       "                                                        binary=False,\n",
       "                                                        decode_error='strict',\n",
       "                                                        dtype=<class 'numpy.float64'>,\n",
       "                                                        encoding='utf-8',\n",
       "                                                        input='content',\n",
       "                                                        lowercase=True,\n",
       "                                                        max_df=1.0,\n",
       "                                                        max_features=None,\n",
       "                                                        min_df=1,\n",
       "                                                        ngram_range=(1, 1),\n",
       "                                                        norm='l2',\n",
       "                                                        preprocessor=None,\n",
       "                                                        smooth_idf=True,\n",
       "                                                        stop_words...\n",
       "                                                   lowercase=True, max_df=0.5,\n",
       "                                                   max_features=None, min_df=1,\n",
       "                                                   ngram_range=(1, 1),\n",
       "                                                   norm='l2', preprocessor=None,\n",
       "                                                   smooth_idf=True,\n",
       "                                                   stop_words=None,\n",
       "                                                   strip_accents=None,\n",
       "                                                   sublinear_tf=False,\n",
       "                                                   token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                                   tokenizer=None, use_idf=True,\n",
       "                                                   vocabulary=None),),\n",
       "                          'vect__max_df': (0.5, 0.75)}],\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(twenty_train.data, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " best estimator is {'clf': MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True), 'clf__alpha': 0.01, 'vect': TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
      "                dtype=<class 'numpy.float64'>, encoding='utf-8',\n",
      "                input='content', lowercase=True, max_df=0.5, max_features=None,\n",
      "                min_df=1, ngram_range=(1, 1), norm='l2', preprocessor=None,\n",
      "                smooth_idf=True, stop_words=None, strip_accents=None,\n",
      "                sublinear_tf=False, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
      "                tokenizer=None, use_idf=True, vocabulary=None), 'vect__max_df': 0.5} and score is 0.9591457753017641\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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can add even more parameters and search your classifiers with even more vectorizers like below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('vect', TfidfVectorizer()),\n",
    "    ('clf', DummyEstimator()),\n",
    "])\n",
    "\n",
    "# A fit transform followed by an estimator\n",
    "\n",
    "search_space = [\n",
    "    {\n",
    "        'vect': (TfidfVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (LogisticRegression(),),\n",
    "        'clf__C': ( 0.1, 1, 10),\n",
    "        'clf__penalty': ('l2', 'l1'),\n",
    "    }, \n",
    "    {\n",
    "        'vect': (TfidfVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (RandomForestClassifier(),),\n",
    "        'clf__n_estimators': (10,20,30,50),\n",
    "        'clf__max_depth': (5,10,20),\n",
    "    },\n",
    "    {\n",
    "        'vect': (TfidfVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (DecisionTreeClassifier(),),\n",
    "        'clf__max_depth': (1,5,10,25,50),\n",
    "    }, \n",
    "    {\n",
    "        'vect': (TfidfVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (MultinomialNB(),),\n",
    "        'clf__alpha': (0.01,0.1, 1.0, 10, 100),\n",
    "    },\n",
    "        {\n",
    "        'vect': (CountVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (LogisticRegression(),),\n",
    "        'clf__C': ( 0.1, 1, 10),\n",
    "        'clf__penalty': ('l2', 'l1'),\n",
    "    }, \n",
    "    {\n",
    "        'vect': (CountVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (RandomForestClassifier(),),\n",
    "        'clf__n_estimators': (10,20,30,50),\n",
    "        'clf__max_depth': (5,10,20),\n",
    "    },\n",
    "    {\n",
    "        'vect': (CountVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (DecisionTreeClassifier(),),\n",
    "        'clf__max_depth': (1,5,10,25,50),\n",
    "    }, \n",
    "    {\n",
    "        'vect': (CountVectorizer(),),\n",
    "        'vect__max_df': (0.5, 0.75),\n",
    "        'clf': (MultinomialNB(),),\n",
    "        'clf__alpha': (0.01,0.1, 1.0, 10, 100),\n",
    "    }\n",
    "    \n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 112 candidates, totalling 560 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
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      "  FutureWarning)\n",
      "/opt/anaconda3/lib/python3.7/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",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "/opt/anaconda3/lib/python3.7/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",
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      "/opt/anaconda3/lib/python3.7/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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "/opt/anaconda3/lib/python3.7/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",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "/opt/anaconda3/lib/python3.7/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",
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      "  FutureWarning)\n",
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      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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",
      "/opt/anaconda3/lib/python3.7/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " best estimator is {'clf': MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True), 'clf__alpha': 0.01, 'vect': TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
      "                dtype=<class 'numpy.float64'>, encoding='utf-8',\n",
      "                input='content', lowercase=True, max_df=0.5, max_features=None,\n",
      "                min_df=1, ngram_range=(1, 1), norm='l2', preprocessor=None,\n",
      "                smooth_idf=True, stop_words=None, strip_accents=None,\n",
      "                sublinear_tf=False, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
      "                tokenizer=None, use_idf=True, vocabulary=None), 'vect__max_df': 0.5} and score is 0.9591457753017641\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 560 out of 560 | elapsed:  1.9min finished\n"
     ]
    }
   ],
   "source": [
    "gs_clf = GridSearchCV(pipeline, search_space, cv=5, verbose=1)\n",
    "gs_clf.fit(twenty_train.data, twenty_train.target)\n",
    "print (f\" best estimator is {gs_clf.best_params_} and score is {gs_clf.best_score_}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next More into pipelines, base estimator and transformer classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before that Practise pipeline with GridSearch on iris dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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