Project code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data from Assignment-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.cluster import KMeans\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.model_selection import KFold, train_test_split\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, silhouette_score, adjusted_rand_score, \\\n",
    "    homogeneity_score, completeness_score, adjusted_mutual_info_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "import tensorflow as tf\n",
    "\n",
    "DataFrameColumns = ['Headline', 'Text', 'Bip_Topics', 'Date_Published', 'Item_ID', 'File_Name']\n",
    "biptopiclist = []\n",
    "rows = []\n",
    "DataFrameCustom = []\n",
    "stopwords = set(stopwords.words('english'))\n",
    "lists = []\n",
    "\n",
    "\n",
    "def dataextraction():\n",
    "    for file in glob.iglob('/Users/santoshkumarmedisetty/Downloads/testing/*/*.xml'):\n",
    "        bip_topic = []\n",
    "        mytree = ET.parse(file)\n",
    "        myroot = mytree.getroot()\n",
    "        path, filename = os.path.split(file)\n",
    "        itemid = myroot.attrib.get('itemid')\n",
    "        hl = myroot.find('headline').text\n",
    "        dc = myroot.findall(\"./metadata/dc[@element='dc.date.published']\")\n",
    "        datepublished = dc[0].attrib.get(\"value\")\n",
    "        paragraph = \"\"\n",
    "        textnode = myroot.find(\"text\")\n",
    "        for node in textnode:\n",
    "            paragraph = paragraph + node.text\n",
    "\n",
    "        LemmatizedSentence = stemlemm(paragraph)\n",
    "\n",
    "        Sentencewostopwords = stopword(LemmatizedSentence)\n",
    "        Sentencewostopwords = ' '.join(Sentencewostopwords)\n",
    "        cd = myroot.findall(\"./metadata/codes[@class='bip:topics:1.0']/code\")\n",
    "\n",
    "        for i in cd:\n",
    "            k = i.attrib.get(\"code\")\n",
    "            biptopiclist.append(k)\n",
    "            rows.append(\n",
    "                {\"Headline\": hl, \"Text\": Sentencewostopwords, \"Bip_Topics\": k, \"Date_Published\": datepublished,\n",
    "                 \"Item_ID\": itemid,\n",
    "                 \"File_Name\": filename})\n",
    "            break\n",
    "\n",
    "    DataFrameCustom = pd.DataFrame(rows, columns=DataFrameColumns)\n",
    "    DataFrameCustom.to_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/FirstDF.csv')\n",
    "    return DataFrameCustom\n",
    "\n",
    "def stopword(paragraph):\n",
    "    words_tokens = word_tokenize(paragraph)\n",
    "    sentence = []\n",
    "    for w in words_tokens:\n",
    "        if w not in stopwords:\n",
    "            sentence.append(w)\n",
    "    return sentence\n",
    "\n",
    "\n",
    "def uniquebipcodes(biptopiclist):\n",
    "    unique_codes = []\n",
    "    for code in biptopiclist:\n",
    "        if code not in unique_codes:\n",
    "            unique_codes.append(code)\n",
    "    print(unique_codes)\n",
    "    return unique_codes\n",
    "\n",
    "\n",
    "def stemlemm(sentence):\n",
    "    words_tokens = word_tokenize(sentence)\n",
    "    filtered_sentence_list = [w for w in words_tokens if w not in stopwords]\n",
    "\n",
    "    StemSentence = []\n",
    "    LemmSentence = []\n",
    "    Stemmed_Sentence = []\n",
    "    for word in filtered_sentence_list:\n",
    "        StemSentence.append(PorterStemmer().stem(word))\n",
    "\n",
    "    for wor in StemSentence:\n",
    "        LemmSentence.append(WordNetLemmatizer().lemmatize(wor))\n",
    "\n",
    "    Stemmed_Sentence = \" \".join(StemSentence)\n",
    "    Lemmatized_Sentence = \" \".join(LemmSentence)\n",
    "\n",
    "    return Lemmatized_Sentence\n",
    "\n",
    "FirstDF=dataextraction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Headline</th>\n",
       "      <th>Text</th>\n",
       "      <th>Bip_Topics</th>\n",
       "      <th>Date_Published</th>\n",
       "      <th>Item_ID</th>\n",
       "      <th>File_Name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Care Group Inc Q4 shr loss vs profit.</td>\n",
       "      <td>( 000 's omit ) year end decemb 31 ( audit ) 1...</td>\n",
       "      <td>C15</td>\n",
       "      <td>1997-03-31</td>\n",
       "      <td>476242</td>\n",
       "      <td>476242newsML.xml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>China economy to grow at more than 10 pct in 1...</td>\n",
       "      <td>china see econom growth 10 percent year , infl...</td>\n",
       "      <td>E11</td>\n",
       "      <td>1997-03-31</td>\n",
       "      <td>476768</td>\n",
       "      <td>476768newsML.xml</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Africans say global debt relief plan too rigid.</td>\n",
       "      <td>african financi leader criticis monday new int...</td>\n",
       "      <td>E51</td>\n",
       "      <td>1997-03-31</td>\n",
       "      <td>476074</td>\n",
       "      <td>476074newsML.xml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Argentine export grain prices - March 31.</td>\n",
       "      <td>export offer/bid price u.s. dollar per tonn , ...</td>\n",
       "      <td>M14</td>\n",
       "      <td>1997-03-31</td>\n",
       "      <td>476597</td>\n",
       "      <td>476597newsML.xml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Yemen says kidnappers not harming German hosta...</td>\n",
       "      <td>four german tourist kidnap mountain tribesman ...</td>\n",
       "      <td>GCAT</td>\n",
       "      <td>1997-03-31</td>\n",
       "      <td>476845</td>\n",
       "      <td>476845newsML.xml</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",
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       "    <tr>\n",
       "      <td>2799</td>\n",
       "      <td>California ordered to hire welfare recipients.</td>\n",
       "      <td>california governor pete wilson friday order s...</td>\n",
       "      <td>E41</td>\n",
       "      <td>1997-03-29</td>\n",
       "      <td>475206</td>\n",
       "      <td>475206newsML.xml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2800</td>\n",
       "      <td>Dutch blaze victims buried in eastern Turkey.</td>\n",
       "      <td>bodi turkish woman five child die suspect raci...</td>\n",
       "      <td>GCAT</td>\n",
       "      <td>1997-03-29</td>\n",
       "      <td>475340</td>\n",
       "      <td>475340newsML.xml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2801</td>\n",
       "      <td>Court paves way for Oklahoma trial to start.</td>\n",
       "      <td>A feder appeal court friday deni three request...</td>\n",
       "      <td>GCAT</td>\n",
       "      <td>1997-03-29</td>\n",
       "      <td>475389</td>\n",
       "      <td>475389newsML.xml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2802</td>\n",
       "      <td>NBA BASKETBALL-STANDINGS AFTER FRIDAY'S GAMES.</td>\n",
       "      <td>stand nationalbasketbal associ team game play ...</td>\n",
       "      <td>GCAT</td>\n",
       "      <td>1997-03-29</td>\n",
       "      <td>475176</td>\n",
       "      <td>475176newsML.xml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2803</td>\n",
       "      <td>Bangladesh taka closes firmer as call rates up.</td>\n",
       "      <td>An abrupt rise call money rate help bangladesh...</td>\n",
       "      <td>M13</td>\n",
       "      <td>1997-03-29</td>\n",
       "      <td>475495</td>\n",
       "      <td>475495newsML.xml</td>\n",
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       "<p>2804 rows × 6 columns</p>\n",
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      "text/plain": [
       "                                               Headline  \\\n",
       "0                 Care Group Inc Q4 shr loss vs profit.   \n",
       "1     China economy to grow at more than 10 pct in 1...   \n",
       "2       Africans say global debt relief plan too rigid.   \n",
       "3             Argentine export grain prices - March 31.   \n",
       "4     Yemen says kidnappers not harming German hosta...   \n",
       "...                                                 ...   \n",
       "2799     California ordered to hire welfare recipients.   \n",
       "2800      Dutch blaze victims buried in eastern Turkey.   \n",
       "2801       Court paves way for Oklahoma trial to start.   \n",
       "2802     NBA BASKETBALL-STANDINGS AFTER FRIDAY'S GAMES.   \n",
       "2803    Bangladesh taka closes firmer as call rates up.   \n",
       "\n",
       "                                                   Text Bip_Topics  \\\n",
       "0     ( 000 's omit ) year end decemb 31 ( audit ) 1...        C15   \n",
       "1     china see econom growth 10 percent year , infl...        E11   \n",
       "2     african financi leader criticis monday new int...        E51   \n",
       "3     export offer/bid price u.s. dollar per tonn , ...        M14   \n",
       "4     four german tourist kidnap mountain tribesman ...       GCAT   \n",
       "...                                                 ...        ...   \n",
       "2799  california governor pete wilson friday order s...        E41   \n",
       "2800  bodi turkish woman five child die suspect raci...       GCAT   \n",
       "2801  A feder appeal court friday deni three request...       GCAT   \n",
       "2802  stand nationalbasketbal associ team game play ...       GCAT   \n",
       "2803  An abrupt rise call money rate help bangladesh...        M13   \n",
       "\n",
       "     Date_Published Item_ID         File_Name  \n",
       "0        1997-03-31  476242  476242newsML.xml  \n",
       "1        1997-03-31  476768  476768newsML.xml  \n",
       "2        1997-03-31  476074  476074newsML.xml  \n",
       "3        1997-03-31  476597  476597newsML.xml  \n",
       "4        1997-03-31  476845  476845newsML.xml  \n",
       "...             ...     ...               ...  \n",
       "2799     1997-03-29  475206  475206newsML.xml  \n",
       "2800     1997-03-29  475340  475340newsML.xml  \n",
       "2801     1997-03-29  475389  475389newsML.xml  \n",
       "2802     1997-03-29  475176  475176newsML.xml  \n",
       "2803     1997-03-29  475495  475495newsML.xml  \n",
       "\n",
       "[2804 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FirstDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Clusters and assigning Cluster IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 words in each cluster are\n",
      "Cluster  0:\n",
      " cup\n",
      " match\n",
      " game\n",
      " saturday\n",
      " second\n",
      " play\n",
      " world\n",
      " beat\n",
      " team\n",
      " indi\n",
      "Cluster  1:\n",
      " percent\n",
      " said\n",
      " market\n",
      " rate\n",
      " stock\n",
      " trade\n",
      " price\n",
      " trader\n",
      " dealer\n",
      " close\n",
      "Cluster  2:\n",
      " compani\n",
      " said\n",
      " share\n",
      " million\n",
      " inc\n",
      " stock\n",
      " corp\n",
      " quarter\n",
      " monday\n",
      " 1996\n",
      "Cluster  3:\n",
      " loss\n",
      " net\n",
      " incom\n",
      " share\n",
      " 1996\n",
      " shr\n",
      " 31\n",
      " profit\n",
      " per\n",
      " 000\n",
      "Cluster  4:\n",
      " congress\n",
      " parti\n",
      " gowda\n",
      " front\n",
      " deve\n",
      " india\n",
      " said\n",
      " govern\n",
      " kashmir\n",
      " support\n",
      "Cluster  5:\n",
      " 00\n",
      " 50\n",
      " 01\n",
      " 97\n",
      " pct\n",
      " 10\n",
      " 15\n",
      " amount\n",
      " 70\n",
      " 03\n",
      "Cluster  6:\n",
      " arab\n",
      " israel\n",
      " palestinian\n",
      " isra\n",
      " peac\n",
      " netanyahu\n",
      " jerusalem\n",
      " minist\n",
      " settlement\n",
      " arafat\n",
      "Cluster  7:\n",
      " said\n",
      " th\n",
      " state\n",
      " tonn\n",
      " china\n",
      " new\n",
      " would\n",
      " monday\n",
      " year\n",
      " govern\n",
      "Cluster  8:\n",
      " bank\n",
      " billion\n",
      " said\n",
      " rate\n",
      " million\n",
      " bond\n",
      " percent\n",
      " year\n",
      " central\n",
      " issu\n",
      "Cluster  9:\n",
      " said\n",
      " polic\n",
      " rebel\n",
      " peopl\n",
      " forc\n",
      " th\n",
      " albanian\n",
      " refuge\n",
      " albania\n",
      " govern\n"
     ]
    }
   ],
   "source": [
    "def clustering(DataFrameCustom):\n",
    "\n",
    "    textdata = DataFrameCustom[\"Text\"]\n",
    "    vectorizer = TfidfVectorizer()\n",
    "    X = vectorizer.fit_transform(textdata)\n",
    "    n_clusters = 10\n",
    "\n",
    "    model = KMeans(n_clusters)\n",
    "\n",
    "    model.fit(X)\n",
    "    order_centroids = model.cluster_centers_.argsort()[:, ::-1]\n",
    "    terms = vectorizer.get_feature_names()\n",
    "    \n",
    "    print('Top 10 words in each cluster are')\n",
    "    for i in range(n_clusters):\n",
    "        print('Cluster % d:' % i)\n",
    "        for ind in order_centroids[i, :10]:\n",
    "            print(' % s' % terms[ind])\n",
    "\n",
    "\n",
    "    results = pd.DataFrame()\n",
    "    results['FileName'] = DataFrameCustom[\"File_Name\"]\n",
    "    results['text'] = DataFrameCustom[\"Text\"]\n",
    "    clusters = model.labels_\n",
    "\n",
    "    results['Cluster'] = clusters\n",
    "    results['Bip_Topics'] = DataFrameCustom[\"Bip_Topics\"]\n",
    "    results.to_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/clusteredDF.csv')\n",
    "    \n",
    "clusteredDF=clustering(FirstDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying different Classifier to each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest is applied for cluster number 0\n",
      "Metrics of Random Forest are\n",
      "Confusion Matrix :[[83]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "        GCAT       1.00      1.00      1.00        83\n",
      "\n",
      "    accuracy                           1.00        83\n",
      "   macro avg       1.00      1.00      1.00        83\n",
      "weighted avg       1.00      1.00      1.00        83\n",
      "\n",
      "Accuracy is: 1.0\n",
      "KNN is applied for cluster number 1\n",
      "Metrics of KNN\n",
      "Confusion Matrix :[[ 1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
      " [ 0  0  0  0  0  1  1  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  1  0  0  0  0  0  1  1  0  1  0  0  0]\n",
      " [ 1  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  2  0  0  1  0  0  1  0  0  1  0  1 16  3  5  0]\n",
      " [ 0  0  0  0  1  0  1  0  3  0  0  0  0  1  2  0  2]\n",
      " [ 0  2  0  1  0  3  1  1  0  2  0  0  0  6  5  6  3]\n",
      " [ 0  0  1  1  0  0  0  0  0  0  0  0  0  0  2  1 12]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C13       0.50      0.33      0.40         3\n",
      "         C15       0.00      0.00      0.00         0\n",
      "         C21       0.00      0.00      0.00         0\n",
      "         C24       0.00      0.00      0.00         0\n",
      "         C31       0.00      0.00      0.00         1\n",
      "         E11       0.25      0.50      0.33         2\n",
      "         E12       0.00      0.00      0.00         4\n",
      "         E13       0.33      0.50      0.40         2\n",
      "         E14       0.00      0.00      0.00         0\n",
      "         E41       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         0\n",
      "        E512       0.00      0.00      0.00         0\n",
      "        GCAT       0.00      0.00      0.00         0\n",
      "         M11       0.67      0.53      0.59        30\n",
      "         M12       0.17      0.20      0.18        10\n",
      "         M13       0.46      0.20      0.28        30\n",
      "         M14       0.67      0.71      0.69        17\n",
      "\n",
      "    accuracy                           0.39        99\n",
      "   macro avg       0.18      0.17      0.17        99\n",
      "weighted avg       0.50      0.39      0.43        99\n",
      "\n",
      "Accuracy is: 0.3939393939393939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM is applied for cluster number 2\n",
      "Metrics of SVM are\n",
      "Confusion Matrix :[[ 3  0  3  0  2  0  0  1  2  0  0  0  0  0  0  1  0  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  1  4  4 34  0  1  9 13  1  0  2  3  1  7  1  2  0]\n",
      " [ 0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  1  0  0  0  1  0  1  0  0  0  0  0  0  0]\n",
      " [ 2  1  1  0  2  0  0  0  4  0  0  0  1  0  2  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C11       0.38      0.23      0.29        13\n",
      "         C12       0.00      0.00      0.00         1\n",
      "         C13       0.00      0.00      0.00         0\n",
      "         C14       0.00      0.00      0.00         0\n",
      "         C15       0.85      0.40      0.54        86\n",
      "        C152       0.00      0.00      0.00         1\n",
      "         C16       0.00      0.00      0.00         0\n",
      "         C17       0.00      0.00      0.00         3\n",
      "         C18       0.20      0.31      0.24        13\n",
      "        C181       0.00      0.00      0.00         0\n",
      "         C23       0.00      0.00      0.00         0\n",
      "         C24       0.00      0.00      0.00         0\n",
      "         C31       0.00      0.00      0.00         0\n",
      "         C33       0.00      0.00      0.00         0\n",
      "         C41       0.00      0.00      0.00         0\n",
      "        GCAT       0.00      0.00      0.00         0\n",
      "         M11       0.00      0.00      0.00         0\n",
      "         M12       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.35       117\n",
      "   macro avg       0.08      0.05      0.06       117\n",
      "weighted avg       0.69      0.35      0.46       117\n",
      "\n",
      "Accuracy is: 0.3504273504273504\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Classifier is applied for cluster number 3\n",
      "Metrics of Naive Bayes are\n",
      "Confusion Matrix :[[60]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C15       1.00      1.00      1.00        60\n",
      "\n",
      "    accuracy                           1.00        60\n",
      "   macro avg       1.00      1.00      1.00        60\n",
      "weighted avg       1.00      1.00      1.00        60\n",
      "\n",
      "Accuracy is: 1.0\n",
      "Decision Tree Classifier is applied for cluster number 4\n",
      "Metrics of DT are\n",
      "Confusion Matrix :[[ 0  0  0  0  0]\n",
      " [ 0  0  0  0  0]\n",
      " [ 1  1 22  1  3]\n",
      " [ 0  0  0  0  0]\n",
      " [ 0  0  0  0  0]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C31       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         0\n",
      "        GCAT       1.00      0.79      0.88        28\n",
      "        GPOL       0.00      0.00      0.00         0\n",
      "         M11       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.79        28\n",
      "   macro avg       0.20      0.16      0.18        28\n",
      "weighted avg       1.00      0.79      0.88        28\n",
      "\n",
      "Accuracy is: 0.7857142857142857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stochastic Gradient Descent Classifier is applied for cluster number 5\n",
      "Metrics of SGD are\n",
      "Confusion Matrix :[[ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  4  0  0  3  0  0  0  1  0  0]\n",
      " [ 0  0  0  0  0  2  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  2  1  1  1  2]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 2  0  0  0  1  0  0  0  1  1  0]\n",
      " [ 5  0  1  1  2  1  0  2  2  2 10]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C15       0.00      0.00      0.00         0\n",
      "         C17       0.00      0.00      0.00         0\n",
      "         C21       0.00      0.00      0.00         0\n",
      "        CCAT       0.00      0.00      0.00         0\n",
      "         E21       0.50      0.38      0.43         8\n",
      "         E71       0.67      1.00      0.80         2\n",
      "        GCAT       1.00      0.29      0.44         7\n",
      "         M11       0.00      0.00      0.00         0\n",
      "         M12       0.00      0.00      0.00         0\n",
      "         M13       0.25      0.20      0.22         5\n",
      "         M14       0.83      0.38      0.53        26\n",
      "\n",
      "    accuracy                           0.38        48\n",
      "   macro avg       0.30      0.20      0.22        48\n",
      "weighted avg       0.73      0.38      0.48        48\n",
      "\n",
      "Accuracy is: 0.375\n",
      "Neural Network Classifier is applied for cluster number 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics of NN are\n",
      "Confusion Matrix :[[ 0  0  0]\n",
      " [ 0  0  4]\n",
      " [ 1  1 16]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C18       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         4\n",
      "        GCAT       0.80      0.89      0.84        18\n",
      "\n",
      "    accuracy                           0.73        22\n",
      "   macro avg       0.27      0.30      0.28        22\n",
      "weighted avg       0.65      0.73      0.69        22\n",
      "\n",
      "Accuracy is: 0.7272727272727273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adaboost  Classifier is applied for cluster number 7\n",
      "Metrics of Adaboost are\n",
      "Confusion Matrix :[[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  1  1  1  0  0  2  0  0  0  0  2  0  0  2  0  0  0  0  0  0  0  3  0\n",
      "   0  0  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  1  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 8  3 19  2  6  1  1  4  1  7  1 11 10  2  3  2  1  6  6  6  5  1 62  1\n",
      "   0  0  7]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0\n",
      "   0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0]\n",
      " [ 1  0  1  0 11  0  1  0  0  0  0  1  2  1  1  0  0  0  0  0  2  0  2  0\n",
      "   1  1  9]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C11       0.00      0.00      0.00         0\n",
      "         C12       0.00      0.00      0.00         0\n",
      "         C13       0.00      0.00      0.00         0\n",
      "         C14       0.00      0.00      0.00         0\n",
      "         C15       0.00      0.00      0.00         0\n",
      "        C151       0.00      0.00      0.00         0\n",
      "         C17       0.00      0.00      0.00         0\n",
      "         C18       0.00      0.00      0.00        13\n",
      "        C183       0.00      0.00      0.00         0\n",
      "         C21       0.00      0.00      0.00         0\n",
      "         C23       0.00      0.00      0.00         0\n",
      "         C24       0.00      0.00      0.00         0\n",
      "         C31       0.00      0.00      0.00         0\n",
      "         C33       0.00      0.00      0.00         0\n",
      "         C41       0.00      0.00      0.00         0\n",
      "         C42       0.00      0.00      0.00         0\n",
      "        CCAT       0.00      0.00      0.00         0\n",
      "         E12       0.00      0.00      0.00         0\n",
      "         E21       0.00      0.00      0.00         0\n",
      "         E41       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         2\n",
      "        E512       0.00      0.00      0.00         0\n",
      "        GCAT       0.91      0.35      0.51       176\n",
      "        GDIP       0.00      0.00      0.00         0\n",
      "         M11       0.00      0.00      0.00         1\n",
      "         M13       0.00      0.00      0.00         0\n",
      "         M14       0.53      0.26      0.35        34\n",
      "\n",
      "    accuracy                           0.31       226\n",
      "   macro avg       0.05      0.02      0.03       226\n",
      "weighted avg       0.79      0.31      0.45       226\n",
      "\n",
      "Accuracy is: 0.3141592920353982\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Process  Classifier is applied for cluster number 8\n",
      "Metrics of GPC are\n",
      "Confusion Matrix :[[1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 1 0 0 8 0 0 0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 1 2 0 0 1 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0]\n",
      " [2 0 0 1 0 0 0 2 0 2 3 0 0 0 0 1]\n",
      " [0 0 0 0 0 2 0 0 0 0 5 0 0 0 0 2]\n",
      " [1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 1 0 4 1 0 0 1 1 3 2 0 1 0 5]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C11       0.14      0.25      0.18         4\n",
      "         C12       0.00      0.00      0.00         0\n",
      "         C13       0.33      0.20      0.25         5\n",
      "         C14       0.00      0.00      0.00         0\n",
      "         C15       0.57      0.73      0.64        11\n",
      "         C17       0.29      0.50      0.36         4\n",
      "         C18       0.50      1.00      0.67         1\n",
      "         C31       0.00      0.00      0.00         0\n",
      "         E11       0.33      0.50      0.40         2\n",
      "         E12       0.40      0.18      0.25        11\n",
      "         E21       0.38      0.56      0.45         9\n",
      "         E51       0.00      0.00      0.00         5\n",
      "         E71       1.00      1.00      1.00         2\n",
      "        GCAT       0.33      1.00      0.50         1\n",
      "         M12       0.00      0.00      0.00         1\n",
      "         M13       0.62      0.26      0.37        19\n",
      "\n",
      "    accuracy                           0.39        75\n",
      "   macro avg       0.31      0.39      0.32        75\n",
      "weighted avg       0.44      0.39      0.38        75\n",
      "\n",
      "Accuracy is: 0.38666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting  Classifier is applied for cluster number 9\n",
      "Metrics of Gradient Boosting are\n",
      "Confusion Matrix :[[ 0  0  0  1]\n",
      " [ 0  0  0  0]\n",
      " [ 0  0  0  0]\n",
      " [ 0  2  8 76]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C24       0.00      0.00      0.00         1\n",
      "        CCAT       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         0\n",
      "        GCAT       0.99      0.88      0.93        86\n",
      "\n",
      "    accuracy                           0.87        87\n",
      "   macro avg       0.25      0.22      0.23        87\n",
      "weighted avg       0.98      0.87      0.92        87\n",
      "\n",
      "Accuracy is: 0.8735632183908046\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/clusteredDF.csv')\n",
    "#     performanceevaluation(results,X)\n",
    "n_clusters=10\n",
    "for i in range(n_clusters):\n",
    "    lists.append(df.loc[df[\"Cluster\"] == i])\n",
    "    # print(lists)\n",
    "    # print(type(lists[4]))\n",
    "\n",
    "def featureextraction(FirstDF):\n",
    "    vectorizer = CountVectorizer()\n",
    "    textdata = FirstDF[\"text\"]\n",
    "    bip = FirstDF[\"Bip_Topics\"]\n",
    "    biparray = pd.Series(bip).values\n",
    "    filename = FirstDF[\"FileName\"]\n",
    "    FeatureData = vectorizer.fit_transform(textdata).toarray()\n",
    "    FeatureDataFrameData = np.column_stack((FeatureData, biparray))\n",
    "    dataFrameColumns = vectorizer.get_feature_names()\n",
    "    dataFrameColumns.insert(len(dataFrameColumns), 'Bip_Topics')\n",
    "    FeatureDataFrame = pd.DataFrame(data=FeatureDataFrameData, columns=dataFrameColumns, index=filename)\n",
    "    FeatureDataFrame.to_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/FeaturedataDF.csv')\n",
    "    return FeatureDataFrame\n",
    "\n",
    "\n",
    "def datasplitter(DF,clusternumber):\n",
    "\n",
    "    X = DF.iloc[:, :-1].values\n",
    "\n",
    "    y = DF[\"Bip_Topics\"]\n",
    "\n",
    "\n",
    "        \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)\n",
    "\n",
    "    sc = StandardScaler()\n",
    "    X_train = sc.fit_transform(X_train)\n",
    "    X_test = sc.transform(X_test)\n",
    "\n",
    "    # PCA\n",
    "    pca = PCA(n_components=10)\n",
    "    X_train = pca.fit_transform(X_train)\n",
    "    X_test = pca.transform(X_test)\n",
    "\n",
    "\n",
    "    if(clusternumber==1):\n",
    "        print(\"KNN is applied for cluster number 1\")\n",
    "        # KNN\n",
    "        KNN_model = KNeighborsClassifier(n_neighbors=10)\n",
    "        KNN_model.fit(X_train, y_train)\n",
    "        KNN_prediction = KNN_model.predict(X_test)\n",
    "        print(\"Metrics of KNN\")\n",
    "        qualityofclassifier(KNN_prediction, y_test)\n",
    "\n",
    "    if (clusternumber == 2):\n",
    "        print(\"SVM is applied for cluster number 2\")\n",
    "        # #SVM\n",
    "        SVC_model = svm.SVC(kernel='linear', C=0.1)\n",
    "        SVC_model.fit(X_train, y_train)\n",
    "        SVC_prediction = SVC_model.predict(X_test)\n",
    "        print(\"Metrics of SVM are\")\n",
    "        qualityofclassifier(SVC_prediction, y_test)\n",
    "\n",
    "    if (clusternumber == 0):\n",
    "        print(\"Random Forest is applied for cluster number 0\")\n",
    "        # RandomForest\n",
    "        RF = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n",
    "        RF.fit(X_train, y_train)\n",
    "        RF_Prediction = RF.predict(X_test)\n",
    "        print(\"Metrics of Random Forest are\")\n",
    "        qualityofclassifier(RF_Prediction, y_test)\n",
    "\n",
    "    if (clusternumber == 4):\n",
    "        print(\"Decision Tree Classifier is applied for cluster number 4\")\n",
    "        # DecisionTreeClassifier\n",
    "        DT_gini = DecisionTreeClassifier(criterion=\"gini\", random_state=100, max_depth=3, min_samples_leaf=10)\n",
    "        DT_gini.fit(X_train, y_train)\n",
    "        DT_pred = DT_gini.predict(X_test)\n",
    "        print(\"Metrics of DT are\")\n",
    "        qualityofclassifier(DT_pred, y_test)\n",
    "\n",
    "    if (clusternumber == 6):\n",
    "        print(\"Neural Network Classifier is applied for cluster number 6\")\n",
    "        # NeuralNetwork\n",
    "        p = MLPClassifier(random_state=42, tol=0.001, max_iter=500)\n",
    "        p.fit(X_train, y_train)\n",
    "        NN_Pred = p.predict(X_test)\n",
    "        print(\"Metrics of NN are\")\n",
    "        qualityofclassifier(NN_Pred, y_test)\n",
    "\n",
    "    if (clusternumber == 3):\n",
    "        print(\"Naive Bayes Classifier is applied for cluster number 3\")\n",
    "        # Naive Bayes\n",
    "        GNB = GaussianNB()\n",
    "        GNB.fit(X_train, y_train)\n",
    "        GNB_Pred = GNB.predict(X_test)\n",
    "        print(\"Metrics of Naive Bayes are\")\n",
    "        qualityofclassifier(GNB_Pred, y_test)\n",
    "\n",
    "    if(clusternumber==5):\n",
    "        print(\"Stochastic Gradient Descent Classifier is applied for cluster number 5\")\n",
    "        #Stochastic Gradient Descent\n",
    "        SGD=SGDClassifier()\n",
    "        SGD.fit(X_train,y_train)\n",
    "        SGD_Pred=SGD.predict(X_test)\n",
    "        print(\"Metrics of SGD are\")\n",
    "        qualityofclassifier(SGD_Pred, y_test)\n",
    "\n",
    "    if (clusternumber == 8):\n",
    "        print(\"Gaussian Process  Classifier is applied for cluster number 8\")\n",
    "        # Gaussian Process  Classifier\n",
    "        GPC = GaussianProcessClassifier()\n",
    "        GPC.fit(X_train, y_train)\n",
    "        GPC_Pred = GPC.predict(X_test)\n",
    "        print(\"Metrics of GPC are\")\n",
    "        qualityofclassifier(GPC_Pred, y_test)\n",
    "\n",
    "    if (clusternumber == 7):\n",
    "        print(\"Adaboost  Classifier is applied for cluster number 7\")\n",
    "        # Adaboost  Classifier\n",
    "        ADA = AdaBoostClassifier()\n",
    "        ADA.fit(X_train, y_train)\n",
    "        ADA_Pred = ADA.predict(X_test)\n",
    "        print(\"Metrics of Adaboost are\")\n",
    "        qualityofclassifier(ADA_Pred, y_test)\n",
    "\n",
    "    if (clusternumber == 9):\n",
    "        print(\"Gradient Boosting  Classifier is applied for cluster number 9\")\n",
    "        # Gradient Boosting  Classifier\n",
    "        GB = GradientBoostingClassifier()\n",
    "        GB.fit(X_train, y_train)\n",
    "        GB_Pred = GB.predict(X_test)\n",
    "        print(\"Metrics of Gradient Boosting are\")\n",
    "        qualityofclassifier(GB_Pred, y_test)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def qualityofclassifier(prediction, testlabel):\n",
    "        print(\"Confusion Matrix :\" + str(confusion_matrix(prediction, testlabel)))\n",
    "        print(\"Classification Report: \" + str(classification_report(prediction, testlabel)))\n",
    "        print(\"Accuracy is: \" + str(accuracy_score(prediction, testlabel)))\n",
    "\n",
    "\n",
    "for j in range(n_clusters):\n",
    "    datasplitter(featureextraction(lists[j]),j)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the performance of each cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completeness score0.39439488090209435\n",
      "Homogenity score is0.32712930655632644\n",
      "Silhoutte score is0.014990561527312865\n",
      "Rand Score is0.15047623103002636\n",
      "Mutual Info Score is0.30970460032500696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/cluster/supervised.py:746: FutureWarning: The behavior of AMI will change in version 0.22. To match the behavior of 'v_measure_score', AMI will use average_method='arithmetic' by default.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "results=pd.read_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/clusteredDF.csv')\n",
    "X = TfidfVectorizer().fit_transform(FirstDF[\"Text\"])\n",
    "def performanceevaluation(clusteredDF,fitdata):\n",
    "    \n",
    "    print(\"Completeness score\" + str(completeness_score(clusteredDF[\"Bip_Topics\"], clusteredDF[\"Cluster\"])));\n",
    "    print(\"Homogenity score is\" + str(homogeneity_score(clusteredDF[\"Bip_Topics\"], clusteredDF[\"Cluster\"])));\n",
    "    print(\"Silhoutte score is\" + str(silhouette_score(fitdata, clusteredDF[\"Cluster\"], metric='euclidean')));\n",
    "    print(\"Rand Score is\" + str(adjusted_rand_score(clusteredDF[\"Bip_Topics\"], clusteredDF[\"Cluster\"])));\n",
    "    print(\"Mutual Info Score is\" + str(adjusted_mutual_info_score(clusteredDF[\"Bip_Topics\"], clusteredDF[\"Cluster\"])));\n",
    "    \n",
    "performanceevaluation(results,X)\n",
    "\n",
    "\n",
    "# Performance evaluation:\n",
    "# In general, Silhoutte score is the best parameter to evaluate the performance of the cluster.\n",
    "\n",
    "# Silhouette score : \n",
    "# It evaluates the consistency of data within the cluster by calculating the distance between each object of a cluster to other objects of the same cluster as well as the objects from the neighboring clusters.\n",
    "# Unlike other evaluation techniques that require true labels of a dataset, Silhouette works only with data and clustering results.\n",
    "# The score is the measure of how similar an object is in its cluster when compared to an object in another cluster.\n",
    "# It ranges from -1 to +1, where the value near +1 indicates that the object is well-matched with the own cluster and doesn't belong to the neighboring cluster and the vale near -1 indicates that the assignment of that object to the cluster should be changed(i.e. the object doesn't belong to that cluster).\n",
    "# If the silhouette score is 0, then the object is in between the two clusters.\n",
    "\n",
    "# adjusted rand score:\n",
    "# Rand index computes the similarity between two clusterings by considering all pairs of samples and counting pairs that are assigned in the same or different clusters in the predicted and true clusterings.\n",
    "# The adjusted rand index is the modified version of the rand index which finds similarity of the cluster assignments of the objects ignoring permutations and normalizations.\n",
    "# Its boundary ranges from -1 to +1 where negative values are bad and +1 is a perfect match score.\n",
    "# It is symmetric (i.e. swapping the argument doesn't change the score)\n",
    "# It doesn't depend on cluster structures (i.e. two clusters with varying structure can be compared)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying AutoEncoder to enhance the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Train on 193 samples, validate on 83 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "Random Forest is applied for cluster number 0\n",
      "Metrics of Random Forest are\n",
      "Confusion Matrix :[[83]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "        GCAT       1.00      1.00      1.00        83\n",
      "\n",
      "    accuracy                           1.00        83\n",
      "   macro avg       1.00      1.00      1.00        83\n",
      "weighted avg       1.00      1.00      1.00        83\n",
      "\n",
      "Accuracy is: 1.0\n",
      "Train on 231 samples, validate on 99 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "KNN is applied for cluster number 1\n",
      "Metrics of KNN\n",
      "Confusion Matrix :[[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  1  2  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  1  0  1  0  1  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  2  0  0  0  0  0  0  0  0]\n",
      " [ 1  0  0  0  1  1  0  2  1  0  1  0  1  0  1  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  2  0  0  1  2  1  0  0  0  0  1  0 18  3  1  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  1  0  0  0  0  1  1  0]\n",
      " [ 0  2  0  0  2  0  1  0  0  0  1  0  0  5  6 11  4]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  0 13]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C13       0.00      0.00      0.00         0\n",
      "         C15       0.00      0.00      0.00         0\n",
      "         C21       1.00      0.33      0.50         3\n",
      "         C24       0.00      0.00      0.00         0\n",
      "         C31       0.00      0.00      0.00         3\n",
      "         E11       0.00      0.00      0.00         1\n",
      "         E12       0.00      0.00      0.00         2\n",
      "         E13       0.67      0.22      0.33         9\n",
      "         E14       0.00      0.00      0.00         0\n",
      "         E41       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         0\n",
      "        E512       0.00      0.00      0.00         0\n",
      "        GCAT       0.00      0.00      0.00         0\n",
      "         M11       0.75      0.58      0.65        31\n",
      "         M12       0.08      0.33      0.13         3\n",
      "         M13       0.85      0.34      0.49        32\n",
      "         M14       0.72      0.87      0.79        15\n",
      "\n",
      "    accuracy                           0.46        99\n",
      "   macro avg       0.24      0.16      0.17        99\n",
      "weighted avg       0.71      0.46      0.53        99\n",
      "\n",
      "Accuracy is: 0.46464646464646464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 272 samples, validate on 117 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "SVM is applied for cluster number 2\n",
      "Metrics of SVM are\n",
      "Confusion Matrix :[[ 1  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0]\n",
      " [ 1  0  0  1  2  0  0  0  3  0  0  0  0  0  0  0  0  1]\n",
      " [ 4  0  2  2 36  0  1  3  7  1  1  2  3  0  4  2  2  0]\n",
      " [ 0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  2  2  1  1  0  0  6  4  0  0  0  0  0  2  0  0  0]\n",
      " [ 2  0  1  0  0  0  0  1  6  0  0  0  1  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C11       0.12      0.25      0.17         4\n",
      "         C12       0.00      0.00      0.00         0\n",
      "         C13       0.00      0.00      0.00         1\n",
      "         C14       0.25      0.12      0.17         8\n",
      "         C15       0.90      0.51      0.65        70\n",
      "        C152       0.00      0.00      0.00         1\n",
      "         C16       0.00      0.00      0.00         0\n",
      "         C17       0.60      0.33      0.43        18\n",
      "         C18       0.30      0.55      0.39        11\n",
      "        C181       0.00      0.00      0.00         0\n",
      "         C23       0.00      0.00      0.00         0\n",
      "         C24       0.00      0.00      0.00         0\n",
      "         C31       0.00      0.00      0.00         0\n",
      "         C33       1.00      1.00      1.00         1\n",
      "         C41       0.33      1.00      0.50         3\n",
      "        GCAT       0.00      0.00      0.00         0\n",
      "         M11       0.00      0.00      0.00         0\n",
      "         M12       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.46       117\n",
      "   macro avg       0.19      0.21      0.18       117\n",
      "weighted avg       0.70      0.46      0.53       117\n",
      "\n",
      "Accuracy is: 0.46153846153846156\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 139 samples, validate on 60 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "Naive Bayes Classifier is applied for cluster number 3\n",
      "Metrics of Naive Bayes are\n",
      "Confusion Matrix :[[60]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C15       1.00      1.00      1.00        60\n",
      "\n",
      "    accuracy                           1.00        60\n",
      "   macro avg       1.00      1.00      1.00        60\n",
      "weighted avg       1.00      1.00      1.00        60\n",
      "\n",
      "Accuracy is: 1.0\n",
      "Train on 64 samples, validate on 28 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "Decision Tree Classifier is applied for cluster number 4\n",
      "Metrics of DT are\n",
      "Confusion Matrix :[[ 0  0  0  0  0]\n",
      " [ 0  0  0  0  0]\n",
      " [ 1  1 22  1  3]\n",
      " [ 0  0  0  0  0]\n",
      " [ 0  0  0  0  0]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C31       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         0\n",
      "        GCAT       1.00      0.79      0.88        28\n",
      "        GPOL       0.00      0.00      0.00         0\n",
      "         M11       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.79        28\n",
      "   macro avg       0.20      0.16      0.18        28\n",
      "weighted avg       1.00      0.79      0.88        28\n",
      "\n",
      "Accuracy is: 0.7857142857142857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 109 samples, validate on 48 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "Stochastic Gradient Descent Classifier is applied for cluster number 5\n",
      "Metrics of SGD are\n",
      "Confusion Matrix :[[ 5  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  4  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  6  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  2  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  2  0  0  0  2]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  3  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 2  0  1  1  0  1  0  3  2  4 10]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C15       0.71      1.00      0.83         5\n",
      "         C17       1.00      1.00      1.00         4\n",
      "         C21       0.00      0.00      0.00         0\n",
      "        CCAT       0.00      0.00      0.00         0\n",
      "         E21       1.00      1.00      1.00         6\n",
      "         E71       0.67      1.00      0.80         2\n",
      "        GCAT       1.00      0.50      0.67         4\n",
      "         M11       0.00      0.00      0.00         0\n",
      "         M12       0.60      1.00      0.75         3\n",
      "         M13       0.00      0.00      0.00         0\n",
      "         M14       0.83      0.42      0.56        24\n",
      "\n",
      "    accuracy                           0.67        48\n",
      "   macro avg       0.53      0.54      0.51        48\n",
      "weighted avg       0.85      0.67      0.71        48\n",
      "\n",
      "Accuracy is: 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 49 samples, validate on 22 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "Neural Network Classifier is applied for cluster number 6\n",
      "Metrics of NN are\n",
      "Confusion Matrix :[[ 0  0  0]\n",
      " [ 0  0  2]\n",
      " [ 1  1 18]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C18       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         2\n",
      "        GCAT       0.90      0.90      0.90        20\n",
      "\n",
      "    accuracy                           0.82        22\n",
      "   macro avg       0.30      0.30      0.30        22\n",
      "weighted avg       0.82      0.82      0.82        22\n",
      "\n",
      "Accuracy is: 0.8181818181818182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 527 samples, validate on 226 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "Adaboost  Classifier is applied for cluster number 7\n",
      "Metrics of Adaboost are\n",
      "Confusion Matrix :[[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 7  1 17  1  2  1  1  1  0  3  1  8  3  2  1  2  1  0  6  6  5  4  0 63\n",
      "   1  0  0  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   0  0  0  0]\n",
      " [ 2  3  3  2 15  0  3  3  1  2  0  6  9  1  5  0  0  0  0  0  1  4  1  4\n",
      "   0  1  1 16]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C11       0.00      0.00      0.00         0\n",
      "         C12       0.00      0.00      0.00         0\n",
      "         C13       0.00      0.00      0.00         0\n",
      "         C14       0.00      0.00      0.00         0\n",
      "         C15       0.00      0.00      0.00         0\n",
      "        C151       0.00      0.00      0.00         0\n",
      "         C17       0.00      0.00      0.00         0\n",
      "         C18       0.00      0.00      0.00         0\n",
      "        C183       0.00      0.00      0.00         0\n",
      "         C21       0.00      0.00      0.00         1\n",
      "         C23       0.00      0.00      0.00         0\n",
      "         C24       0.07      1.00      0.12         1\n",
      "         C31       0.00      0.00      0.00         1\n",
      "         C33       0.00      0.00      0.00         0\n",
      "         C41       0.00      0.00      0.00         0\n",
      "         C42       0.00      0.00      0.00         0\n",
      "        CCAT       0.00      0.00      0.00         0\n",
      "         E11       0.00      0.00      0.00         1\n",
      "         E12       0.00      0.00      0.00         0\n",
      "         E21       0.00      0.00      0.00         0\n",
      "         E41       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         1\n",
      "        E512       0.00      0.00      0.00         0\n",
      "        GCAT       0.93      0.46      0.61       138\n",
      "        GDIP       0.00      0.00      0.00         0\n",
      "         M11       0.00      0.00      0.00         0\n",
      "         M13       0.00      0.00      0.00         0\n",
      "         M14       0.94      0.19      0.32        83\n",
      "\n",
      "    accuracy                           0.35       226\n",
      "   macro avg       0.07      0.06      0.04       226\n",
      "weighted avg       0.91      0.35      0.49       226\n",
      "\n",
      "Accuracy is: 0.35398230088495575\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 172 samples, validate on 75 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "Gaussian Process  Classifier is applied for cluster number 8\n",
      "Metrics of GPC are\n",
      "Confusion Matrix :[[ 2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  0  0  0 10  1  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
      " [ 1  0  0  0  0  2  2  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  6  0  0  0  0  0]\n",
      " [ 0  0  0  0  1  0  0  0  0  1  3  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  2  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0]\n",
      " [ 3  0  2  1  3  3  0  0  3  2  2  7  2  0  2  0  6]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "         C11       0.29      1.00      0.44         2\n",
      "         C12       1.00      1.00      1.00         1\n",
      "         C13       0.33      0.50      0.40         2\n",
      "         C14       0.00      0.00      0.00         0\n",
      "         C15       0.71      0.83      0.77        12\n",
      "         C17       0.00      0.00      0.00         1\n",
      "         C18       1.00      0.40      0.57         5\n",
      "         C21       0.00      0.00      0.00         1\n",
      "         C31       0.00      0.00      0.00         0\n",
      "         E11       0.00      0.00      0.00         0\n",
      "         E12       0.00      0.00      0.00         1\n",
      "         E21       0.46      1.00      0.63         6\n",
      "         E51       0.00      0.00      0.00         5\n",
      "         E71       1.00      1.00      1.00         2\n",
      "        GCAT       0.00      0.00      0.00         0\n",
      "         M12       0.00      0.00      0.00         1\n",
      "         M13       0.75      0.17      0.27        36\n",
      "\n",
      "    accuracy                           0.40        75\n",
      "   macro avg       0.33      0.35      0.30        75\n",
      "weighted avg       0.63      0.40      0.41        75\n",
      "\n",
      "Accuracy is: 0.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 203 samples, validate on 87 samples\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Metrics after enhancement:\n",
      "Gradient Boosting  Classifier is applied for cluster number 9\n",
      "Metrics of Gradient Boosting are\n",
      "Confusion Matrix :[[ 0  0  0]\n",
      " [ 0  0  0]\n",
      " [ 2  8 77]]\n",
      "Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "        CCAT       0.00      0.00      0.00         0\n",
      "         E51       0.00      0.00      0.00         0\n",
      "        GCAT       1.00      0.89      0.94        87\n",
      "\n",
      "    accuracy                           0.89        87\n",
      "   macro avg       0.33      0.30      0.31        87\n",
      "weighted avg       1.00      0.89      0.94        87\n",
      "\n",
      "Accuracy is: 0.8850574712643678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/santoshkumarmedisetty/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "def clustering(DataFrameCustom):\n",
    "    textdata = DataFrameCustom[\"Text\"]\n",
    "    vectorizer = TfidfVectorizer()\n",
    "    X = vectorizer.fit_transform(textdata)\n",
    "    n_clusters = 10\n",
    "\n",
    "    model = KMeans(n_clusters)\n",
    "\n",
    "    model.fit(X)\n",
    "    order_centroids = model.cluster_centers_.argsort()[:, ::-1]\n",
    "    terms = vectorizer.get_feature_names()\n",
    "\n",
    "\n",
    "    for i in range(n_clusters):\n",
    "        top_words = [terms[ind] for ind in order_centroids[i, :20]]\n",
    "    print('\\n')\n",
    "\n",
    "    results = pd.DataFrame()\n",
    "    results['FileName'] = DataFrameCustom[\"File_Name\"]\n",
    "    results['text'] = DataFrameCustom[\"Text\"]\n",
    "    clusters = model.labels_\n",
    "\n",
    "    results['Cluster'] = clusters\n",
    "    results['Bip_Topics'] = DataFrameCustom[\"Bip_Topics\"]\n",
    "    results.to_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/clusteredDF.csv')\n",
    "    df=pd.read_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/clusteredDF.csv')\n",
    "#     performanceevaluation(results,X)\n",
    "    for i in range(n_clusters):\n",
    "        lists.append(df.loc[df[\"Cluster\"] == i])\n",
    "\n",
    "\n",
    "    def featureextraction(FirstDF):\n",
    "        vectorizer = CountVectorizer()\n",
    "        textdata = FirstDF[\"text\"]\n",
    "        bip = FirstDF[\"Bip_Topics\"]\n",
    "        biparray = pd.Series(bip).values\n",
    "        filename = FirstDF[\"FileName\"]\n",
    "        FeatureData = vectorizer.fit_transform(textdata).toarray()\n",
    "        FeatureDataFrameData = np.column_stack((FeatureData, biparray))\n",
    "        dataFrameColumns = vectorizer.get_feature_names()\n",
    "        dataFrameColumns.insert(len(dataFrameColumns), 'Bip_Topics')\n",
    "        FeatureDataFrame = pd.DataFrame(data=FeatureDataFrameData, columns=dataFrameColumns, index=filename)\n",
    "        FeatureDataFrame.to_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/FeaturedataDF.csv')\n",
    "        return FeatureDataFrame\n",
    "\n",
    "\n",
    "    def datasplitter(DF,clusternumber):\n",
    "\n",
    "        X = DF.iloc[:, :-1].values\n",
    "\n",
    "        y = DF[\"Bip_Topics\"]\n",
    "\n",
    "        input_layer = Input(shape=(X.shape[1],))\n",
    "        encoded = Dense(50, activation='relu')(input_layer)\n",
    "        decoded = Dense(X.shape[1], activation='softmax')(encoded)\n",
    "        autoencoder = Model(input_layer, decoded)\n",
    "        autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "\n",
    "        X_train, X_test, y_train, y_test=train_test_split(X, X, test_size=0.3, random_state=101)\n",
    "\n",
    "        global X_ae\n",
    "        autoencoder.fit(X_train, y_train,\n",
    "                        epochs=50,\n",
    "                        batch_size=100,\n",
    "                        shuffle=True,\n",
    "                        verbose=30,\n",
    "                        validation_data=(X_test, y_test))\n",
    "        encoder = Model(input_layer, encoded)\n",
    "        X_ae = encoder.predict(X)\n",
    "        \n",
    "\n",
    "\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X_ae, y, test_size=0.3, random_state=101)\n",
    "        print('Metrics after enhancement:')\n",
    "\n",
    "        if(clusternumber==1):\n",
    "            print(\"KNN is applied for cluster number 1\")\n",
    "            # KNN\n",
    "            KNN_model = KNeighborsClassifier(n_neighbors=10)\n",
    "            KNN_model.fit(X_train, y_train)\n",
    "            KNN_prediction = KNN_model.predict(X_test)\n",
    "            print(\"Metrics of KNN\")\n",
    "            qualityofclassifier(KNN_prediction, y_test)\n",
    "\n",
    "        if (clusternumber == 2):\n",
    "            print(\"SVM is applied for cluster number 2\")\n",
    "            # #SVM\n",
    "            SVC_model = svm.SVC(kernel='linear', C=0.1)\n",
    "            SVC_model.fit(X_train, y_train)\n",
    "            SVC_prediction = SVC_model.predict(X_test)\n",
    "            print(\"Metrics of SVM are\")\n",
    "            qualityofclassifier(SVC_prediction, y_test)\n",
    "\n",
    "        if (clusternumber == 0):\n",
    "            print(\"Random Forest is applied for cluster number 0\")\n",
    "            # RandomForest\n",
    "            RF = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n",
    "            RF.fit(X_train, y_train)\n",
    "            RF_Prediction = RF.predict(X_test)\n",
    "            print(\"Metrics of Random Forest are\")\n",
    "            qualityofclassifier(RF_Prediction, y_test)\n",
    "\n",
    "        if (clusternumber == 4):\n",
    "            print(\"Decision Tree Classifier is applied for cluster number 4\")\n",
    "            # DecisionTreeClassifier\n",
    "            DT_gini = DecisionTreeClassifier(criterion=\"gini\", random_state=100, max_depth=3, min_samples_leaf=10)\n",
    "            DT_gini.fit(X_train, y_train)\n",
    "            DT_pred = DT_gini.predict(X_test)\n",
    "            print(\"Metrics of DT are\")\n",
    "            qualityofclassifier(DT_pred, y_test)\n",
    "\n",
    "        if (clusternumber == 6):\n",
    "            print(\"Neural Network Classifier is applied for cluster number 6\")\n",
    "            # NeuralNetwork\n",
    "            p = MLPClassifier(random_state=42, tol=0.001, max_iter=500)\n",
    "            p.fit(X_train, y_train)\n",
    "            NN_Pred = p.predict(X_test)\n",
    "            print(\"Metrics of NN are\")\n",
    "            qualityofclassifier(NN_Pred, y_test)\n",
    "\n",
    "        if (clusternumber == 3):\n",
    "            print(\"Naive Bayes Classifier is applied for cluster number 3\")\n",
    "            # Naive Bayes\n",
    "            GNB = GaussianNB()\n",
    "            GNB.fit(X_train, y_train)\n",
    "            GNB_Pred = GNB.predict(X_test)\n",
    "            print(\"Metrics of Naive Bayes are\")\n",
    "            qualityofclassifier(GNB_Pred, y_test)\n",
    "\n",
    "        if(clusternumber==5):\n",
    "            print(\"Stochastic Gradient Descent Classifier is applied for cluster number 5\")\n",
    "            #Stochastic Gradient Descent\n",
    "            SGD=SGDClassifier()\n",
    "            SGD.fit(X_train,y_train)\n",
    "            SGD_Pred=SGD.predict(X_test)\n",
    "            print(\"Metrics of SGD are\")\n",
    "            qualityofclassifier(SGD_Pred, y_test)\n",
    "\n",
    "        if (clusternumber == 8):\n",
    "            print(\"Gaussian Process  Classifier is applied for cluster number 8\")\n",
    "            # Gaussian Process  Classifier\n",
    "            GPC = GaussianProcessClassifier()\n",
    "            GPC.fit(X_train, y_train)\n",
    "            GPC_Pred = GPC.predict(X_test)\n",
    "            print(\"Metrics of GPC are\")\n",
    "            qualityofclassifier(GPC_Pred, y_test)\n",
    "\n",
    "        if (clusternumber == 7):\n",
    "            print(\"Adaboost  Classifier is applied for cluster number 7\")\n",
    "            # Adaboost  Classifier\n",
    "            ADA = AdaBoostClassifier()\n",
    "            ADA.fit(X_train, y_train)\n",
    "            ADA_Pred = ADA.predict(X_test)\n",
    "            print(\"Metrics of Adaboost are\")\n",
    "            qualityofclassifier(ADA_Pred, y_test)\n",
    "\n",
    "        if (clusternumber == 9):\n",
    "            print(\"Gradient Boosting  Classifier is applied for cluster number 9\")\n",
    "            # Gradient Boosting  Classifier\n",
    "            GB = GradientBoostingClassifier()\n",
    "            GB.fit(X_train, y_train)\n",
    "            GB_Pred = GB.predict(X_test)\n",
    "            print(\"Metrics of Gradient Boosting are\")\n",
    "            qualityofclassifier(GB_Pred, y_test)\n",
    "\n",
    "\n",
    "\n",
    "    def qualityofclassifier(prediction, testlabel):\n",
    "            print(\"Confusion Matrix :\" + str(confusion_matrix(prediction, testlabel)))\n",
    "            print(\"Classification Report: \" + str(classification_report(prediction, testlabel)))\n",
    "            print(\"Accuracy is: \" + str(accuracy_score(prediction, testlabel)))\n",
    "\n",
    "\n",
    "    for j in range(n_clusters):\n",
    "        datasplitter(featureextraction(lists[j]),j)\n",
    "clustering(FirstDF)\n",
    "\n",
    "\n",
    "\n",
    "# Autoencoder:\n",
    "# An Autoencoder mentioned in paper-1 is implemented with 50 input nodes for the encoder is created and then this encoded output is then decoded to obtain enhanced features.\n",
    "\n",
    "# Autoencoder is a feed-forward neural network with the goal of dimensionality reduction.\n",
    "# The input and output layer has the same number of features whereas it varies for the hidden layer(i.e. it has fewer units than input and output layer).\n",
    "# The hidden layer constitutes of two major parts called encoder that maps inputs to the code and decoder that converts the code to the original input.\n",
    "# We used relu and softmax activation functions in encoder and decoder. An activation function calculates a weighted sum of inputs, adds bias to it for each element.\n",
    "# we have employed adam optimizer and binary cross-entropy loss function during compilation.\n",
    "# The training of autoencoder is the same as that of conventional neural networks with backpropagation except that the error is calculated by comparing the output with its input itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras import Sequential\n",
    "df2=pd.read_csv('/Users/santoshkumarmedisetty/Downloads/ML assignment/FeaturedataDF.csv')\n",
    "y=df2[\"Bip_Topics\"]\n",
    "\n",
    "\n",
    "def deepneuralclassifier(X_ae,y):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_ae, y, test_size=0.3, random_state=101)\n",
    "    labelencoder = LabelEncoder()\n",
    "    y1 = labelencoder.fit_transform(y_train)\n",
    "    cvb = np.unique(y1)\n",
    "    si = cvb.size\n",
    "\n",
    "\n",
    "    classifier = Sequential()\n",
    "    # First Hidden Layer\n",
    "    classifier.add(Dense(8, activation='relu', kernel_initializer='random_normal', input_dim=X_ae.shape[1]))\n",
    "    # Second  Hidden Layer\n",
    "    classifier.add(Dense(8, activation='relu', kernel_initializer='random_normal'))\n",
    "    # Output Layer\n",
    "    classifier.add(Dense(si, activation='sigmoid', kernel_initializer='random_normal'))\n",
    "    classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    classifier.fit(X_train, y1)\n",
    "    \n",
    "deepneuralclassifier(X_ae,y)\n",
    "\n",
    "# deep neural network:\n",
    "\n",
    "# A Neural Network with 2 hidden layers is created and advanced features are given as input.\n",
    "# Since we have large sets of data here, a deep neural network performs better than conventional machine learning classifiers as deep learning can model large data and perform complex computations.\n",
    "# It doesn't depend solely on feature engineering.\n",
    "# It doesn't require labels or structured data as each layer depends on the output processed by the previous layer.\n",
    "# A conventional classifier learns the model and then use those features to produce outputs. However, if the output is not the desired one, the features extracted needs to be retained manually.\n",
    "# Whereas deep neural networks did not require manual intervention as the nested layers retain the data which will aid in learning from their errors."
   ]
  }
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