Text_Classification_Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code : Imports\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "import zipfile\n",
    "import json\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as  sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [
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   "outputs": [],
   "source": [
    "#Code to Load Tweet Data in a DataFrame\n",
    "datafolder ='Data/'\n",
    "jsonfolder='/JSON/'\n",
    "zippedfilepath = 'JSON.zip'\n",
    "if zippedfilepath:\n",
    "    zippedFolder = zipfile.ZipFile(datafolder + zippedfilepath, 'r')\n",
    "    tweetjsonfiles = zippedFolder.infolist()\n",
    "else:\n",
    "    tweetjsonfiles = os.listdir(datafolder + jsonfolder)\n",
    "\n",
    "tweetsDF = pd.DataFrame()\n",
    "for tweetfile in tweetjsonfiles:\n",
    "    if zippedfilepath:\n",
    "        currjson = json.loads(zippedFolder.open(tweetfile).read())    \n",
    "    else:\n",
    "        currjson = json.loads(open(datafolder + jsonfolder + tweetfile).read())    \n",
    "    currtweetDF = json_normalize(currjson)\n",
    "    tweetsDF = tweetsDF.append(currtweetDF)\n",
    "\n",
    "tweetsDF.index = range(len(tweetsDF.index))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find Sentiment of Each Tweet Based on Number of Likes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "code_folding": [
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    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code to set Sentiment Class for each tweet\n",
    "def getClass(fav):\n",
    "    if fav<=4:\n",
    "        return \"NEG\"\n",
    "    elif fav>10:\n",
    "        return \"POS\"\n",
    "    else:\n",
    "        return \"NEU\"\n",
    "tweetsDF['sentiment'] = tweetsDF['favorite_count'].apply(getClass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract Features and Labels for Text Classifcation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get Tweets in English Language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code: Filter Languages\n",
    "englishfilter = tweetsDF['lang']=='en'\n",
    "eng_tweets = tweetsDF[englishfilter]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions for extracting features from text\n",
    "-  NLTK Tweet Tokenizer user\n",
    "-  User Names and URLS removed\n",
    "- Punctuation removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "#Helper Functions for Text Feature Extraction\n",
    "punct_num_rem = re.compile('[^0-9a-z #+_]')\n",
    "repl_space = re.compile('[/(){}\\[\\]\\|@,;]')\n",
    "\n",
    "def basic_text_preprocessing(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(repl_space,'',text)    \n",
    "    text = re.sub(punct_num_rem  ,'',text)\n",
    "    return text\n",
    "\n",
    "def ProcessTextNormal(tweet_text):\n",
    "    tokens = tweet_text.replace('\\n','').split()\n",
    "    txt_features = []\n",
    "    for t in tokens:\n",
    "        tfinal = t.lower()\n",
    "        \n",
    "        #RemoveURLS and Username\n",
    "        if tfinal[0]=='@':\n",
    "            pass\n",
    "        else:\n",
    "            #Handle HTS\n",
    "            if tfinal[0]=='#':\n",
    "                tfinal = tfinal[1:]\n",
    "            txt_features.append(tfinal)\n",
    "    return txt_features\n",
    "\n",
    "def tokenize_tweet(tweet_text):\n",
    "    tknzr = TweetTokenizer(strip_handles=True, preserve_case= False, reduce_len=True)\n",
    "    tweet_tokens = tknzr.tokenize(tweet_text)\n",
    "    #print(tweet_tokens)\n",
    "    temptokens= []  \n",
    "    for t in tweet_tokens:\n",
    "        if t[0:5]=='https':\n",
    "            pass\n",
    "        elif t[0]=='#':\n",
    "            tf= t[1:]\n",
    "            temptokens.append(tf)\n",
    "        else:\n",
    "            temptokens.append(t)\n",
    "    finalstr =  basic_text_preprocessing(\" \".join(temptokens))\n",
    "    return finalstr\n",
    "    \n",
    "def tokenize_tweets_tweets(tweet_texts):\n",
    "    \n",
    "    tknzr = TweetTokenizer(strip_handles=True, preserve_case= False, reduce_len=True)\n",
    "    tweet_tokens = [tknzr.tokenize(tweet_txt) for tweet_txt in tweet_texts]\n",
    "    tweet_tokens_final = []\n",
    "    for tokens in tweet_tokens:\n",
    "        temptokens =[]\n",
    "        for t in tokens:\n",
    "            if len(t)==1 or t[0:5]=='https':\n",
    "                pass\n",
    "            elif t[0]=='#':\n",
    "                tf= \" \"+ t[1:]\n",
    "                temptokens.append(tf)\n",
    "            else:\n",
    "                temptokens.append(t)\n",
    "        tweet_tokens_final.append(temptokens)\n",
    "    return tweet_tokens_final\n",
    "\n",
    "\n",
    "# TestCode\n",
    "# X =candidate_data['full_text'].tolist()[0:1]\n",
    "# X\n",
    "# tokenize_tweets_tweets(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text_features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sharing my #Walk with #K9Jinky🐾 \\nMy #Service,...</td>\n",
       "      <td>POS</td>\n",
       "      <td>sharing my walk with k9jinky  my service  bran...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>With this new IoT-based crash detection #servi...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>with this new iotbased crash detection service...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Simple Truths\\nhttps://t.co/Gm4g1R2oaH\\n#custo...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>simple truths customers service problems truth...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do you serve each other? Part of any relations...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>do you serve each other  part of any relations...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@HermanMashaba I hear you Mr mayor how Capetow...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>i hear you mr mayor how capetown looks is way ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           full_text sentiment  \\\n",
       "0  Sharing my #Walk with #K9Jinky🐾 \\nMy #Service,...       POS   \n",
       "1  With this new IoT-based crash detection #servi...       NEG   \n",
       "2  Simple Truths\\nhttps://t.co/Gm4g1R2oaH\\n#custo...       NEG   \n",
       "3  Do you serve each other? Part of any relations...       NEG   \n",
       "4  @HermanMashaba I hear you Mr mayor how Capetow...       NEG   \n",
       "\n",
       "                                       text_features  \n",
       "0  sharing my walk with k9jinky  my service  bran...  \n",
       "1  with this new iotbased crash detection service...  \n",
       "2  simple truths customers service problems truth...  \n",
       "3  do you serve each other  part of any relations...  \n",
       "4  i hear you mr mayor how capetown looks is way ...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code : Extract Text Features for POS and Neg Classes\n",
    "tknzr = TweetTokenizer()\n",
    "normal_features = ['full_text','sentiment']\n",
    "candidate_data = eng_tweets[normal_features]\n",
    "classfilter = candidate_data.sentiment.isin(['POS','NEG'])\n",
    "candidate_data = candidate_data[classfilter]\n",
    "candidate_data.index = range(len(candidate_data.index))\n",
    "candidate_data['text_features'] = candidate_data['full_text'].apply(tokenize_tweet)\n",
    "candidate_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    "collapsed": true
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   "outputs": [],
   "source": [
    "# #Save_Data\n",
    "# def single(r):\n",
    "#     return r.replace('\\n',' ').replace('\\r', '')\n",
    "\n",
    "# candidate_data['text_singleline'] = candidate_data['full_text'].apply(single)\n",
    "# candidate_data['text_singleline'].to_csv('Data/DL/tweetssingle.txt', index = False)\n",
    "# #candidate_data['full_text'].to_csv('Data/DL/tweets.txt', index = False)\n",
    "# #candidate_data['sentiment'].to_csv('Data/DL/Labels.txt', index = False)\n",
    "# #candidate_data['text_features'].to_csv('Data/DL/tweets_org_proccessed.txt', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experimental Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "source": [
    "# Import Classifiers and Helper Librries to Evaluate\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler,MaxAbsScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report as ClfRep\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.metrics import precision_score, \\\n",
    "    recall_score, confusion_matrix, classification_report, \\\n",
    "    accuracy_score, f1_score\n",
    "\n",
    "classifiers = {\n",
    "                'MNB' :  {'obj': MultinomialNB(), 'accuracy':0, 'prec':0, 'rec':0, 'fmeasure':0}  ,        \n",
    "                'NN' :  {'obj': MLPClassifier(), 'accuracy':0, 'prec':0, 'rec':0, 'fmeasure':0} ,         \n",
    "                'SGD' :  {'obj': SGDClassifier(loss='hinge', penalty='l2',\\\n",
    "                                 alpha=1e-3, random_state=42, max_iter=5, tol=None),\\\n",
    "                                'accuracy':0, 'prec':0, 'rec':0, 'fmeasure':0}   ,       \n",
    "                'LogReg' :  {'obj': LogisticRegression(random_state=0), 'accuracy':0, 'prec':0, 'rec':0, 'fmeasure':0}  ,        \n",
    "                'SVC' :  {'obj': LinearSVC(), 'accuracy':0, 'prec':0, 'rec':0, 'fmeasure':0}  ,        \n",
    "                 'RF' :  {'obj':  RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0), 'accuracy':0, 'prec':0, 'rec':0, 'fmeasure':0}      \n",
    "              \n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text_features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>What has @JHBHope been up to? Giving #life thr...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>what has been up to  giving life through the g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>topBrass inform (https://t.co/JLFhVfUxS4) #ceo...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>topbrass inform   ceos cmos cios cfos look no ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            full_text sentiment  \\\n",
       "17  What has @JHBHope been up to? Giving #life thr...       NEG   \n",
       "33  topBrass inform (https://t.co/JLFhVfUxS4) #ceo...       NEG   \n",
       "\n",
       "                                        text_features  \n",
       "17  what has been up to  giving life through the g...  \n",
       "33  topbrass inform   ceos cmos cios cfos look no ...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split Data into K FOlds and perform all experiments on this split only\n",
    "def GetKFoldData(df,labelsIN, k):\n",
    "    folds = {}\n",
    "    labels = LabelEncoder()\n",
    "    y =  labels.fit_transform(labelsIN)\n",
    "    kfolds = KFold(n_splits=k, shuffle = True, random_state = 4) \n",
    "    foldidx = 0\n",
    "    for train_idx, test_idx in kfolds.split(df.index):\n",
    "        folds[foldidx] = {\n",
    "                              'train': df.iloc[train_idx], 'test':df.iloc[test_idx], \n",
    "                              'y_train': y[train_idx], 'y_test': y[test_idx]\n",
    "                         }\n",
    "        foldidx+=1\n",
    "    return folds\n",
    "\n",
    "\n",
    "exp_data_k_folds = GetKFoldData(candidate_data, candidate_data['sentiment'] , 10)\n",
    "idx_list_org = exp_data_k_folds[0]['test'].index.tolist()\n",
    "exp_data_k_folds[0]['test'][0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "source": [
    "# Code: Helper Functions For Experiments\n",
    "\n",
    "\n",
    "def GetCountVectorizerFeatures(X_tr, X_test):\n",
    "    count_vectorizer = CountVectorizer()\n",
    "    X_tr_vec = count_vectorizer.fit_transform(X_tr.values)\n",
    "    X_test_vec = count_vectorizer.transform(X_test.values)\n",
    "    return X_tr_vec,X_test_vec\n",
    "    \n",
    "    \n",
    "def Evaluate(clfIN, X_train, X_test, y_train, y_test):\n",
    "    #X_tr_final = preprocessing.normalize(X_train)\n",
    "    #X_test_final = preprocessing.normalize(X_test)\n",
    "    scaler = MaxAbsScaler()\n",
    "    X_tr_final = scaler.fit_transform(X_train)\n",
    "    X_test_final = scaler.fit_transform(X_test)\n",
    "    \n",
    "    trained_model =clfIN.fit(X_tr_final,y_train)\n",
    "    pred = trained_model.predict(X_test_final)\n",
    "    return accuracy_score(y_test,pred), precision_score(y_test,pred,average ='macro'),\\\n",
    "                            recall_score(y_test,pred,average ='macro'),f1_score(y_test,pred,average ='macro')\n",
    "    \n",
    "def RunExpCountVec(classifiers,exp_data_k_folds, featurecol):\n",
    "    #print(['CLF', 'prec','rec','fmeasure','accuracy'])\n",
    "    \n",
    "    for clfkey,valkey in classifiers.items():\n",
    "        valkey['accuracy']= 0\n",
    "        valkey['prec']= 0\n",
    "        valkey['rec']= 0\n",
    "        valkey['fmeasure']= 0\n",
    "\n",
    "        accuracy = 0\n",
    "        precision = 0\n",
    "        recall = 0 \n",
    "        fmsr = 0\n",
    "        for fold in exp_data_k_folds:\n",
    "            train_features = exp_data_k_folds[fold]['train'][featurecol]\n",
    "            test_features = exp_data_k_folds[fold]['test'][featurecol]\n",
    "            X_tr_vec, X_test_vec = GetCountVectorizerFeatures(train_features, test_features)\n",
    "            y_train, y_test = exp_data_k_folds[fold]['y_train'], exp_data_k_folds[fold]['y_test']\n",
    "            acc, prec, rec, fmeasure = Evaluate(valkey['obj'], X_tr_vec, X_test_vec, y_train, y_test)\n",
    "            accuracy += acc\n",
    "            precision += prec\n",
    "            recall += rec\n",
    "            fmsr += fmeasure\n",
    "            #folds+=1\n",
    "        foldlen = len(exp_data_k_folds.items())\n",
    "        valkey['accuracy']= float(accuracy/foldlen)\n",
    "        valkey['prec']= float(precision/foldlen)\n",
    "        valkey['rec']= float(recall/foldlen)\n",
    "        valkey['fmeasure']= float(fmsr/foldlen)\n",
    "\n",
    "        #print([clfkey, valkey['prec'],valkey['rec'],valkey['fmeasure'],valkey['accuracy']])\n",
    "    return classifiers\n",
    "\n",
    "def Dicttodataframe(dictin, idxname, columnsin = None):\n",
    "    df =pd.DataFrame.from_dict(dictin, orient='index')\n",
    "    if columnsin:\n",
    "        df.columns = columnsin\n",
    "    df.index.name = idxname\n",
    "    df= df.reset_index()\n",
    "    return df\n",
    "\n",
    "def PlotRes(df, val,fontin=10,):\n",
    "    g= sns.factorplot(\"Classifier\", val, \"Exp_Type\" , data=df, kind=\"bar\", size=8, aspect=2, \\\n",
    "                      palette=\"muted\", legend=True)\n",
    "    ax = plt.gca()\n",
    "    ax.yaxis.set_tick_params(labelsize=12)\n",
    "    ax.xaxis.set_tick_params(labelsize=12)\n",
    "    ax.set_xlabel(\"Classifier\", fontsize= '16') \n",
    "    ax.set_ylabel(val, fontsize= '16') \n",
    "    #ax.set_title(title, fontsize = '16')\n",
    "    for p in ax.patches:\n",
    "        ax.text(p.get_x() + p.get_width()/4., p.get_height(),  round(p.get_height(),3), \n",
    "                fontsize=fontin, color='red', ha='center', va='bottom')\n",
    "    plt.show()"
   ]
  },
  {
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      "text/plain": [
       "<Figure size 1266x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run Baseline Approach\n",
    "Res1 = Dicttodataframe(RunExpCountVec(classifiers,exp_data_k_folds,'text_features'),'Classifier')\n",
    "Res1['Exp_Type']  = 'Orig_CountVect'\n",
    "PlotRes(Res1,'fmeasure')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Old Code: Helper Functions  to Run Experiments (OLD)\n",
    "# def Evaluate(clfIN, X_train, X_test, y_train, y_test,labels, vectorizer):\n",
    "#     model_pipeline = Pipeline([\n",
    "#                                 ('vectorizer', vectorizer),\n",
    "#                                 ('clf', clfIN)\n",
    "#                             ])\n",
    "#     trained_model = model_pipeline.fit(X_train.values,y_train)\n",
    "#     pred = trained_model.predict(X_test.values)\n",
    "#     return accuracy_score(y_test,pred), precision_score(y_test,pred),\\\n",
    "#                             recall_score(y_test,pred),f1_score(y_test,pred)\n",
    "\n",
    "# def RunExp(classifiers, Xcol, labelsIN, vectorizer):\n",
    "#     labels = LabelEncoder()\n",
    "#     X =  Xcol\n",
    "#     y =  labels.fit_transform(labelsIN)\n",
    "#     kfolds = KFold(n_splits=10) \n",
    "    \n",
    "    \n",
    "    \n",
    "#     for clfkey,valkey in classifiers.items():\n",
    "#         accuracy = 0\n",
    "#         precision = 0\n",
    "#         recall = 0 \n",
    "#         fmsr = 0\n",
    "#         folds = 1\n",
    "#         for train_idx, test_idx in kfolds.split(X):\n",
    "#             X_train, X_test = X[train_idx], X[test_idx]\n",
    "#             y_train, y_test = y[train_idx], y[test_idx]\n",
    "#             acc, prec, rec, fmeasure = Evaluate(valkey['obj'], X_train, X_test, y_train, y_test,labels, vectorizer)\n",
    "#             accuracy += acc\n",
    "#             precision += prec\n",
    "#             recall += rec\n",
    "#             fmsr += fmeasure\n",
    "#             folds+=1\n",
    "\n",
    "#         valkey['accuracy']= float(accuracy/folds)\n",
    "#         valkey['prec']= float(precision/folds)\n",
    "#         valkey['rec']= float(recall/folds)\n",
    "#         valkey['fmeasure']= float(fmsr/folds)\n",
    "\n",
    "#         print([clfkey, valkey['prec'],valkey['rec'],valkey['fmeasure'],valkey['accuracy']])\n",
    "\n",
    "\n",
    "\n",
    "#     return folds\n",
    "        \n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improve Perfromance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the ways accuracy can be improved are (I have focused on F-Measure metric but results can be easily plotted for other  )\n",
    "- Adding New Classifiers ( Have tested 5 classifiers already so ignoring it in further experiments)\n",
    "- More/Different Preprocessing of Text such as  \n",
    "    - ignoring 1 character features\n",
    "    - lematization \n",
    " \n",
    "- Using a Different Representation\n",
    "    - Using Representation such as TFIDF\n",
    "    - Using Representatin such as Word2vec (Not implemented )\n",
    "    \n",
    "-  Adding Features Other Than Text \n",
    "    - Popularity features : How many times the tweet was retweeted\n",
    "    - User importance features : How popular are they"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Features Modified (More preprocessing added)\n",
    "- Stemming Added\n",
    "- Remove Length 1 tokens\n",
    "- Stop Word (Pending)\n",
    "\n",
    "#### (Very Minor Improvement Achieved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text_features</th>\n",
       "      <th>text_features_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sharing my #Walk with #K9Jinky🐾 \\nMy #Service,...</td>\n",
       "      <td>POS</td>\n",
       "      <td>sharing my walk with k9jinky  my service  bran...</td>\n",
       "      <td>sharing walk k9jinky service branchmanaging k9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>With this new IoT-based crash detection #servi...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>with this new iotbased crash detection service...</td>\n",
       "      <td>new iotbased crash detection service safety pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Simple Truths\\nhttps://t.co/Gm4g1R2oaH\\n#custo...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>simple truths customers service problems truth...</td>\n",
       "      <td>simple truth customer service problem truth ho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do you serve each other? Part of any relations...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>do you serve each other  part of any relations...</td>\n",
       "      <td>serve part relationship marriage sacrifice alw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@HermanMashaba I hear you Mr mayor how Capetow...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>i hear you mr mayor how capetown looks is way ...</td>\n",
       "      <td>hear mr mayor capetown look way better city an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           full_text sentiment  \\\n",
       "0  Sharing my #Walk with #K9Jinky🐾 \\nMy #Service,...       POS   \n",
       "1  With this new IoT-based crash detection #servi...       NEG   \n",
       "2  Simple Truths\\nhttps://t.co/Gm4g1R2oaH\\n#custo...       NEG   \n",
       "3  Do you serve each other? Part of any relations...       NEG   \n",
       "4  @HermanMashaba I hear you Mr mayor how Capetow...       NEG   \n",
       "\n",
       "                                       text_features  \\\n",
       "0  sharing my walk with k9jinky  my service  bran...   \n",
       "1  with this new iotbased crash detection service...   \n",
       "2  simple truths customers service problems truth...   \n",
       "3  do you serve each other  part of any relations...   \n",
       "4  i hear you mr mayor how capetown looks is way ...   \n",
       "\n",
       "                                   text_features_new  \n",
       "0  sharing walk k9jinky service branchmanaging k9...  \n",
       "1  new iotbased crash detection service safety pr...  \n",
       "2  simple truth customer service problem truth ho...  \n",
       "3  serve part relationship marriage sacrifice alw...  \n",
       "4  hear mr mayor capetown look way better city an...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do More Text Processing\n",
    "import nltk\n",
    "from nltk.corpus import stopwords \n",
    "stopwords = set(stopwords.words('english'))\n",
    "\n",
    "def MoreTextProcessing(sentence):\n",
    "    stemmer = nltk.stem.WordNetLemmatizer()\n",
    "    tokens = sentence.split()\n",
    "    tokens = [t for t in tokens if len(t)>1]\n",
    "    tokens = [t for t in tokens if t not in stopwords]\n",
    "    tokens= \" \".join(stemmer.lemmatize(t) for t in tokens)\n",
    "    return tokens\n",
    " \n",
    "candidate_data['text_features_new'] = candidate_data['text_features'].apply(MoreTextProcessing)\n",
    "candidate_data.head()                                                                           \n",
    "# X= candidate_data['text_features'].tolist()[0]\n",
    "# print(X)\n",
    "# print(MoreTextProcessing(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split Data Also Smalll Check to See if it is same as original experiment\n",
    "exp_data_k_folds = GetKFoldData(candidate_data, candidate_data['sentiment'] , 10)\n",
    "idx_list_new = exp_data_k_folds[0]['test'].index.tolist()\n",
    "assert(sum([i-j for i,j in zip(idx_list_new,idx_list_org)])==0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1288.25x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run after more preprocessing and Compare Against Previous\n",
    "Res2 = Dicttodataframe(RunExpCountVec(classifiers,exp_data_k_folds,'text_features_new'),'Classifier')\n",
    "Res2['Exp_Type'] = 'More_Preprocessing'\n",
    "DF = pd.concat([Res1,Res2])\n",
    "DF.index = range(len(DF.index))\n",
    "PlotRes(DF,'fmeasure')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking If TFIDF Represenation Improves Performance\n",
    "\n",
    "### Does not help much"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "code_folding": [
     0,
     1,
     9
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Edited Helper Code \n",
    "def GetTFIDFFeatures(X_tr, X_test, vectorizer =TfidfVectorizer()):\n",
    "    tfidf = vectorizer\n",
    "    X_tr_vec = count_vectorizer.fit_transform(X_tr.values)\n",
    "    X_test_vec = count_vectorizer.transform(X_test.values)\n",
    "    return X_tr_vec,X_test_vec\n",
    "    \n",
    "\n",
    "    \n",
    "def RunAnyVec(classifiers,exp_data_k_folds, featurecol,vectorizer):\n",
    "    vectype = str(type(vectorizer))\n",
    "    for clfkey,valkey in classifiers.items():\n",
    "        valkey['accuracy']= 0\n",
    "        valkey['prec']= 0\n",
    "        valkey['rec']= 0\n",
    "        valkey['fmeasure']= 0\n",
    "\n",
    "        accuracy = 0\n",
    "        precision = 0\n",
    "        recall = 0 \n",
    "        fmsr = 0\n",
    "        for fold in exp_data_k_folds:\n",
    "            train_features = exp_data_k_folds[fold]['train'][featurecol]\n",
    "            test_features = exp_data_k_folds[fold]['test'][featurecol]\n",
    "            if vectype =='sklearn.feature_extraction.text.TfidfVectorizer':\n",
    "                X_tr_vec, X_test_vec = GetTFIDFFeatures(train_features, test_features, vectorizer)\n",
    "            else:\n",
    "                X_tr_vec, X_test_vec = GetCountVectorizerFeatures(train_features, test_features)\n",
    "            y_train, y_test = exp_data_k_folds[fold]['y_train'], exp_data_k_folds[fold]['y_test']\n",
    "            acc, prec, rec, fmeasure = Evaluate(valkey['obj'], X_tr_vec, X_test_vec, y_train, y_test)\n",
    "            accuracy += acc\n",
    "            precision += prec\n",
    "            recall += rec\n",
    "            fmsr += fmeasure\n",
    "            #folds+=1\n",
    "        foldlen = len(exp_data_k_folds.items())\n",
    "        valkey['accuracy']= float(accuracy/foldlen)\n",
    "        valkey['prec']= float(precision/foldlen)\n",
    "        valkey['rec']= float(recall/foldlen)\n",
    "        valkey['fmeasure']= float(fmsr/foldlen)\n",
    "\n",
    "        #print([clfkey, valkey['prec'],valkey['rec'],valkey['fmeasure'],valkey['accuracy']])\n",
    "    return classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SD9GPye8GA7wyybWttV+MaIuBdZaA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\nQAkAAAAAdOb/BwHgJVyqdIPGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1321.38x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code: TFIDF WITH NeW FEATURES\n",
    "vectype = TfidfVectorizer(min_df=2, max_df=0.5, ngram_range=(1, 2))\n",
    "Res3 = Dicttodataframe(RunAnyVec(classifiers,exp_data_k_folds,'text_features_new', vectype),'Classifier')\n",
    "Res3['Exp_Type'] = 'TFIDF_More_Preprocessing'\n",
    "DF = pd.concat([DF,Res3])\n",
    "DF.index = range(len(DF.index))\n",
    "PlotRes(DF,'fmeasure', fontin = 11)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lLC6lPFlKOWk15d5cSvlhKWVBKWVO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\nANAYU7wBAAAAgMYIKAEAAACAxggoAQAAAIDGCCgBgC1SKeWIUsq/l1J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      "text/plain": [
       "<Figure size 1321.38x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code: TFIDF WITH OLD FEATURES\n",
    "Res4 = Dicttodataframe(RunAnyVec(classifiers,exp_data_k_folds,'text_features', vectype),'Classifier')\n",
    "Res4['Exp_Type'] = 'TFIDF_OrgFeatures'\n",
    "#PlotRes(Res4,'fmeasure')\n",
    "DF = pd.concat([DF,Res4])\n",
    "DF.index = range(len(DF.index))\n",
    "PlotRes(DF,'fmeasure', fontin = 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1366.25x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code: TFIDF WITH NeW FEATURES\n",
    "vectype = TfidfVectorizer()\n",
    "Res5 = Dicttodataframe(RunAnyVec(classifiers,exp_data_k_folds,'text_features_new', vectype),'Classifier')\n",
    "Res5['Exp_Type'] = 'Default_ TFIDF_More_Preprocessing'\n",
    "DF = pd.concat([DF,Res5])\n",
    "DF.index = range(len(DF.index))\n",
    "PlotRes(DF,'fmeasure', fontin = 11)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check if adding  more Features helps improve performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features Considered\n",
    "-  Popularity Features such as retweet count\n",
    "- User Features such as Number of Friends and Followers of the Person who tweeted\n",
    "- Sentiment Score from Libraries such as textblob\n",
    "\n",
    "### (Popularity and Sentiment Features Do Help)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Helper Function for Adding More Feature\n",
    "from textblob import TextBlob\n",
    "def GetSS(text):\n",
    "    try:\n",
    "        return TextBlob(text).sentiment\n",
    "    except:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>user.friends_count</th>\n",
       "      <th>user.followers_count</th>\n",
       "      <th>user.listed_count</th>\n",
       "      <th>text_features</th>\n",
       "      <th>text_features_new</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sharing my #Walk with #K9Jinky🐾 \\nMy #Service,...</td>\n",
       "      <td>POS</td>\n",
       "      <td>23</td>\n",
       "      <td>9930</td>\n",
       "      <td>9925</td>\n",
       "      <td>28</td>\n",
       "      <td>sharing my walk with k9jinky  my service  bran...</td>\n",
       "      <td>sharing walk k9jinky service branchmanaging k9...</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>With this new IoT-based crash detection #servi...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>6465</td>\n",
       "      <td>96</td>\n",
       "      <td>with this new iotbased crash detection service...</td>\n",
       "      <td>new iotbased crash detection service safety pr...</td>\n",
       "      <td>0.534091</td>\n",
       "      <td>0.427273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           full_text sentiment  retweet_count  \\\n",
       "0  Sharing my #Walk with #K9Jinky🐾 \\nMy #Service,...       POS             23   \n",
       "1  With this new IoT-based crash detection #servi...       NEG              1   \n",
       "\n",
       "   user.friends_count  user.followers_count  user.listed_count  \\\n",
       "0                9930                  9925                 28   \n",
       "1                  10                  6465                 96   \n",
       "\n",
       "                                       text_features  \\\n",
       "0  sharing my walk with k9jinky  my service  bran...   \n",
       "1  with this new iotbased crash detection service...   \n",
       "\n",
       "                                   text_features_new  Polarity  Subjectivity  \n",
       "0  sharing walk k9jinky service branchmanaging k9...  0.675000      0.650000  \n",
       "1  new iotbased crash detection service safety pr...  0.534091      0.427273  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Data with New Features\n",
    "normal_features = ['full_text','sentiment']\n",
    "pop_features = ['retweet_count']\n",
    "user_features = ['user.friends_count','user.followers_count','user.listed_count']\n",
    "candidate_data_new = eng_tweets[normal_features + pop_features + user_features]\n",
    "classfilter = candidate_data_new.sentiment.isin(['POS','NEG'])\n",
    "candidate_data_new = candidate_data_new[classfilter]\n",
    "candidate_data_new.index = range(len(candidate_data_new.index))\n",
    "candidate_data_new['text_features'] = candidate_data['full_text'].apply(tokenize_tweet)\n",
    "candidate_data_new['text_features_new'] = candidate_data['text_features'].apply(MoreTextProcessing)\n",
    "\n",
    "#Polarity Features\n",
    "candidate_data_new['Polarity'] = candidate_data['text_features'].apply(GetSS).apply(lambda x: x[0])\n",
    "candidate_data_new['Subjectivity'] = candidate_data['text_features'].apply(GetSS).apply(lambda x: x[1])\n",
    "\n",
    "PolDif = candidate_data_new['Polarity'].max()  - candidate_data_new['Polarity'].min()\n",
    "candidate_data_new['Polarity'] =(candidate_data_new['Polarity'] - candidate_data_new['Polarity'].min())/PolDif\n",
    "\n",
    "SubDif = candidate_data_new['Subjectivity'].max()  - candidate_data_new['Subjectivity'].min()\n",
    "candidate_data_new['Subjectivity'] =(candidate_data_new['Subjectivity'] - candidate_data_new['Subjectivity'].min())/SubDif\n",
    "Senti_features = ['Polarity', 'Subjectivity']\n",
    "\n",
    " \n",
    "candidate_data_new.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>user.friends_count</th>\n",
       "      <th>user.followers_count</th>\n",
       "      <th>user.listed_count</th>\n",
       "      <th>text_features</th>\n",
       "      <th>text_features_new</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Subjectivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>What has @JHBHope been up to? Giving #life thr...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>841</td>\n",
       "      <td>58</td>\n",
       "      <td>what has been up to  giving life through the g...</td>\n",
       "      <td>giving life gift water course make difference ...</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>topBrass inform (https://t.co/JLFhVfUxS4) #ceo...</td>\n",
       "      <td>NEG</td>\n",
       "      <td>2</td>\n",
       "      <td>5015</td>\n",
       "      <td>2743</td>\n",
       "      <td>10</td>\n",
       "      <td>topbrass inform   ceos cmos cios cfos look no ...</td>\n",
       "      <td>topbrass inform ceo cmos cio cfo look contact ...</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            full_text sentiment  \\\n",
       "17  What has @JHBHope been up to? Giving #life thr...       NEG   \n",
       "33  topBrass inform (https://t.co/JLFhVfUxS4) #ceo...       NEG   \n",
       "\n",
       "    retweet_count  user.friends_count  user.followers_count  \\\n",
       "17              2                  58                   841   \n",
       "33              2                5015                  2743   \n",
       "\n",
       "    user.listed_count                                      text_features  \\\n",
       "17                 58  what has been up to  giving life through the g...   \n",
       "33                 10  topbrass inform   ceos cmos cios cfos look no ...   \n",
       "\n",
       "                                    text_features_new  Polarity  Subjectivity  \n",
       "17  giving life gift water course make difference ...     0.825           0.7  \n",
       "33  topbrass inform ceo cmos cio cfo look contact ...     0.500           0.5  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split Data Also Smalll Check to See if it is same as original experiment\n",
    "exp_data_k_folds = GetKFoldData(candidate_data_new, candidate_data['sentiment'] , 10)\n",
    "idx_list_new = exp_data_k_folds[0]['test'].index.tolist()\n",
    "assert(sum([i-j for i,j in zip(idx_list_new,idx_list_org)])==0)\n",
    "exp_data_k_folds[0]['test'][0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "code_folding": [
     0,
     4
    ],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Edited Helper Code For Handling Extra Features \n",
    "#from sklearn_pandas import DataFrameMapper\n",
    "import scipy as sp\n",
    "\n",
    "def RunAnyVecNew(classifiers, exp_data_k_folds, featurecol,vectorizer, added_features = None):\n",
    "    vectype = str(type(vectorizer))\n",
    "    for clfkey,valkey in classifiers.items():\n",
    "        valkey['accuracy']= 0\n",
    "        valkey['prec']= 0\n",
    "        valkey['rec']= 0\n",
    "        valkey['fmeasure']= 0\n",
    "\n",
    "        accuracy = 0\n",
    "        precision = 0\n",
    "        recall = 0 \n",
    "        fmsr = 0\n",
    "        for fold in exp_data_k_folds:\n",
    "            train_features = exp_data_k_folds[fold]['train'][featurecol]\n",
    "            test_features = exp_data_k_folds[fold]['test'][featurecol]\n",
    "            \n",
    "            if vectype =='sklearn.feature_extraction.text.TfidfVectorizer':\n",
    "                X_tr_vec, X_test_vec = GetTFIDFFeatures(train_features, test_features, vectorizer)\n",
    "            else:\n",
    "                X_tr_vec, X_test_vec = GetCountVectorizerFeatures(train_features, test_features)\n",
    "            \n",
    "            \n",
    "            y_train, y_test = exp_data_k_folds[fold]['y_train'], exp_data_k_folds[fold]['y_test']\n",
    "            if added_features:\n",
    "                other_features_train = exp_data_k_folds[fold]['train'][added_features]\n",
    "                other_features_test = exp_data_k_folds[fold]['test'][added_features]                \n",
    "                X_tr_vec = sp.sparse.hstack((X_tr_vec,other_features_train.values),format='csr')\n",
    "                X_test_vec = sp.sparse.hstack((X_test_vec,other_features_test.values),format='csr')\n",
    "\n",
    "            \n",
    "            acc, prec, rec, fmeasure = Evaluate(valkey['obj'], X_tr_vec, X_test_vec, y_train, y_test)\n",
    "            accuracy += acc\n",
    "            precision += prec\n",
    "            recall += rec\n",
    "            fmsr += fmeasure\n",
    "            #folds+=1\n",
    "        foldlen = len(exp_data_k_folds.items())\n",
    "        valkey['accuracy']= float(accuracy/foldlen)\n",
    "        valkey['prec']= float(precision/foldlen)\n",
    "        valkey['rec']= float(recall/foldlen)\n",
    "        valkey['fmeasure']= float(fmsr/foldlen)\n",
    "\n",
    "        #print([clfkey, valkey['prec'],valkey['rec'],valkey['fmeasure'],valkey['accuracy']])\n",
    "    return classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
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UAXgLwJqeD4+IiIiIiIiIiH5vTp48aR4XF+fe\nss3ExESfmZmpude529px3p+03mFO9HvW5eS2KIo/A/i5xef/BfC/vREUERERERERERH9foWEhNRq\nNBr1/Y6DiB5sku4OEARBIgiCryAIjwmCMKA3giIiIiIiIiIiIiIi6ki3ktuCIDwHoBTAeQBfAVDc\naf9UEIQXej48IiIiIiIiIiIiIqJf63JyWxCEeQA24PYLJacDEFoc/hZATM+GRkRERERERERERETU\ntu68UHIxgPWiKL4sCIK01TENgCU9FxYREREREREREXXVv198Mbgn5xu1YcPpnpyPiKg3dKcsiTuA\nI+0cuwVg4L2HQ0RERERERERED7rS0lKpUqlUKZVK1aBBgwKGDBnib/gsCEKw4XulUqnKzc01ycjI\nsBw3bpwnAKSkpNjZ2NgEeHt7q1xdXX3HjBnj9eWXXza/1y0mJsbN0dHRzzB+7dq1Q9qLw9HR0S84\nOFjRsk2pVKq8vLx8eu/sAalUGmxYZ/LkyQ9rtdpuv9fuQXT8+HFZQkKC8/2Og6irurNz+2cAbu0c\nUwAovudoiIiIiIiIiIjogTd06NAmjUajBoDFixc7WFhYNK1Zs6YMAGQyWaDhmEF+fr5py8+RkZE3\n09LSLgNAenq65YwZMzyPHj2aGxQUVAcAa9euvTJ79uybXYnl1q1b0osXLxp7eno2njlzxqy759LY\n2AhjY+NujTE1NdUbzjEqKsp9/fr1g1977bUyw3G9Xg9RFCGVti5+0Ltx3atHH3205tFHH63p00WJ\n7kF3/qqUDmClIAgPt2gTBUEYBGARbtfiJiIiIiIiIiIi6rLIyEjtrFmzrm/atGnw3YyfMmXKjbS0\nNFsASEtLs42JiblhOFZTUyM8+eSTbnK5XOXt7a1KT0+3BG7vHp88efLDjz/+uGdYWJgcAF599VV7\nX19fb7lcrlq0aJFDV9cfM2ZM9cWLF01zc3NNHn74YZ9Zs2a5+Pj4qAoKCkz2799vNWzYMKVKpfKe\nPHnyw5WVlRLg9o7zpKQkRz8/P28/Pz/vrKwsU+D2rvW5c+c6jRw5Uv7HP/7RqaysTDphwgQPuVyu\nCggIUJ44ccIcACorKyWG85LL5arU1NSBANDeen/84x8dPTw8fORyuWr+/PlOAPD+++/beHl5+SgU\nCtXw4cMVANByh/3ixYsdpk2b5hYSEqJwcnLya7mDfsmSJQ+5u7v7PPLII16RkZHuK1eutL+be0d0\nr7qT3F4BoB5AFoBjAEQAKQByADQBWNPj0RERERERERERUb9SX18vMZQUCQ8P9+jKmODg4Jr8/Pzm\nXdcrVqxwMsxx8uRJ847GxsbG3kxPT7cBgCNHjgyMjo6uMBxbt27dEADIy8tT7969+8f58+e71dTU\nCABw5swZiz179lz697//nbd//36rixcvmmVmZubk5OSoz507Jzt8+LBFZ3E3NjbiyJEjVn5+frUA\nUFhYaDZ79uzynJwctaWlpf7NN9986Pjx43lqtTonKCio5vXXX29OAltZWTVduHAhJzEx8drzzz/f\nXAqkoKDA7Lvvvsvbvn37laVLlzoEBATU5OXlqV9//fXi+Ph4dwB45ZVXHrKysmrKy8tT5+Xlqf/w\nhz9or169atTWemVlZdJDhw7Z5OfnZ+fl5anffPPNqwDwP//zPw8dPXo0Lzc3V/3FF19cbOv8Ll68\naPZ///d/eT/88EPOX/7yF4f6+nrh+PHjsvT0dJsLFy6oP//884LMzMwBbY0l6gtdLksiimK5IAjD\nAfwJwBMACu6M3wjgr6IoVvVOiERERERERERE1F+0LNnRVaIo/uJzd8qSDB48uMna2lr3t7/9zcbT\n07PWwsJCbzj2/fffWzz//PPXACAwMLDOwcGh4cKFC2YAEBYWVmVvb98EAF988YXV8ePHrVQqlQoA\nampqJBqNxmzy5MnVba1pSOADwMiRI7Uvvvjizz/99JPxQw891DB+/PhbAPDNN98MKCgoMAsJCVEC\nQGNjoxAcHNw8X3x8/A0AmDdv3o0VK1Y0J7ejo6NvGhndTtmdPHnSct++fRcBICoqSjt//nyj8vJy\n6fHjx60++uijH1tegz179li3tZ6trW2Tqamp/umnn3b9wx/+UDl9+vRKABg+fHj1zJkz3WJiYm7O\nnDmzzWs9ceLECnNzc9Hc3Fxna2vbeOXKFaNvvvnGYvLkyRUWFhYiADE8PLyirbFEfaE7NbchiqIW\nwOt3voiIiIiIiIiIiO7ZmTNnZHK5vPZuxz/55JM3ly5d6rp58+ZLLdtbJ81bkslk+pb9/vSnP11d\nsmTJz11Zr70Efus5x4wZU5Wenn6pdT8AkEj+U1BBEITmQFsm59uKXxAEURRFCILwi/aO1jt37lzO\nwYMHrT766CObLVu2DPn3v/+dt3v37stfffXVgIMHD1oPGzbM59y5c9ltnGdzAFKpFDqdTujomhL1\ntW4lt4mIiIiIiIiI6MEzasOG0/c7hrv1+eefW3z44YeDjx49mnu3c8ycOfPm1atXjaOjo6t++umn\n5rcwjhkzpvrDDz+0jYqK0mZmZppevXrVxN/fv+7EiROyluMnT55c9dprrznMnz//hrW1tf7SpUvG\nJiYmoqOjo+5uYxo7duytl156ySUrK8vU19e3XqvVSi5dumTs7+9fD9yuD/7mm2+WvvfeezaBgYG3\n2ppj1KhR2h07dtglJydfzcjIsLSxsdHZ2trqx44dW/X2228Pef/994sA4Pr169L21nN1dW2srq6W\nTJ8+vXLs2LHVcrncDwCys7NNH3/88VuPP/74rSNHjgz88ccfTbp4XtVJSUmuNTU1VxsbG4Vjx44N\njIuLu36314noXnQ5uS0IggTAfADTADgDaP32WVEURdcejI2IiIiIiIiIiH6D0tPTbZRKpUVdXZ3E\nycmpfvfu3ReDgoLq7nY+Gxsb/RtvvFHaun3p0qXXnnnmGVe5XK6SSqXYtm1bobm5+a+2HkdHR1dl\nZ2ebjRgxQgnc3oG9a9euS/eS3HZwcNBt27at8Omnn364oaFBAIBVq1YVG5Lb9fX1gr+/v1Kv1wst\nS4y0tG7dupLY2Fg3uVyuMjc316empl4CgP/+7/++Onv2bBcvLy8fiUQiLlu2rCQ+Pr6irfWsra31\nERERnvX19QIArF27tggAFi1a5FRYWGgqiqIwZsyYqlGjRtUeOnTIsrPzeuyxx2omTZpUqVKpfBwd\nHev9/f1vWVtbN93tdSK6F93Zuf0WgMUAzgL4AUBDr0RERERERERERET9xttvv13S8nNNTc3Z1n0i\nIiK0ERERWgB44YUXyl944YXy9ubbt29fYVfXLi4uvtC6TaFQNOTn52cDgEwmE9ua7876v4jh1Vdf\nvfbqq69e68q6bZ1jy3UNoqKitFFRUTltzfHnP//5+vr166+2bGsdq729fdM//vGPgtZjra2t9fv3\n7y9s3d7eehcuXPhV29GjR381b8v71Pq+tjy3VatWlb799tslWq1WEhoaqli6dGlZW+dI1Nu6k9ye\nBeB1URRX9VYwRERERERERERE9GCbNWuWa35+vnl9fb3w9NNPl48ZM6bmfsdEv0/dSW4bATjeW4EQ\nERERERERERG1xd/fX9nQ0CBp2ZaWlnYpJCTkrl9C2ZnS0lLp2LFjFa3bv/nmm9yhQ4feUxmOtnac\n9yftvSSTqK91J7n9CYAnAPyjl2IhIiIiIiIiIiL6lczMTE1frzl06NAmjUaj7ut1iajrupPcXgxg\nlyAIfwNwBMDN1h1EUfyqpwIjIiIiIiIiIiIiImpPd5LbDwF4GMB/AZjbol0EINz5V9pzoRERERER\nERERERERta07ye0dAAYBeBGABkBDr0RERERERERERERERNSJ7iS3hwOIE0Xxk94KhoiIiIiIiIiI\nuu/TN78O7sn5piwbd7on5yMi6g2Szrs0uwzu1iYiIiIiIiIi+t0rLS2VKpVKlVKpVA0aNChgyJAh\n/obPgiAEG75XKpWq3Nxck4yMDMtx48Z5AkBKSoqdjY1NgLe3t8rV1dV3zJgxXl9++eUAw9wxMTFu\njo6Ofobxa9euHdJeHOXl5dKpU6e6OTs7+zo7O/tOnTrVrby8vNtlc7uzZkdSUlLsCgsLje9mLBF1\nX3d2bq8F8LIgCF+JoljdWwEREREREREREdGDbejQoU0ajUYNAIsXL3awsLBoWrNmTRkAyGSyQMMx\ng/z8fNOWnyMjI2+mpaVdBoD09HTLGTNmeB49ejQ3KCioDgDWrl17Zfbs2Tc7i2PmzJmuKpWq7sCB\nA1kAsGjRIodZs2a5Hj58+MeW/XQ6HYyMOk6DdXXNjnz44YeDhg0bVuvm5tbY1TGNjY0wNmY+nOhu\ndGfn9hMAnAAUCoKQLghCWquvnb0UIxERERERERER/UZFRkZqZ82adX3Tpk2DuzMuKyvL9MKFCwPe\neuutEkNbcnJySWZm5oDs7GzTjIwMy5EjR8ojIyPdFQqFDwAsWbLkIXd3d59HHnnEKzIy0n3lypX2\nHa2xf/9+q2HDhilVKpX35MmTH66srJQAwJ///OeHfH19vb28vHxmzJjhqtfrsWPHDpusrCxZXFzc\nw0qlUlVdXS04Ojr6Xb161QgAjh8/LgsJCVEAt/8gMGPGDNfRo0d7RUdHu+t0OiQmJjr5+vp6y+Vy\nVXJy8iAA+Omnn4yHDx+uUCqVKi8vL58vvvjContXl+i3rTvJ7TEA9AC0AHwBhLXxRURERERERERE\nv2P19fUSQ3mP8PBwj66MCQ4OrsnPzzczfF6xYoWTYY6TJ0+atzXm/PnzZiqVqqbljmwjIyOoVKqa\nc+fOmQFAZmbmgOTk5OKCgoLs48ePy9LT020uXLig/vzzzwsyMzMHtJyv9ZpXr141evPNNx86fvx4\nnlqtzgkKCqp5/fXX7QFgyZIl17KysnLy8/Oza2trJR999JH17Nmzb/r6+takpaX9qNFo1BYWFmJH\n55yZmSk7cuTIxfT09EvvvPPOIGtr66asrKyc8+fP5+zcuXOwRqMxef/9923Hjx9fqdFo1Dk5Odkj\nR46s6cr1JPq96PD/YwiCcAPABFEUzwD4BsAaURQv9UVgRERERERERETU/5iamupblyXpjCj+Mg/c\nlRIhoigKgiC0OZeh3d/f/5ZSqWwAgG+++cZi8uTJFXeSzmJ4eHhFR2vu2bPHuqCgwCwkJEQJAI2N\njUJwcHA1ABw+fNjy7bffHlpXVyepqKgwUqlUtQAqu3POkyZNMsSCY8eOWWk0GtnBgwdtAECr1UrV\narXZqFGjbiUmJro1NjZKnnzyyZuPPPJIbXfWIPqt66zm9gAAhppIcQC2AGBym4iIiIiIiIiIesyZ\nM2dkcrm8W4nbYcOG1b788suypqYmSKW33yHZ1NSEnJwcmb+/f11hYaGJTCbTG/q3TqB3RhRFjBkz\npio9Pf0XubCamhrhpZdecj1x4oTa09OzcfHixQ51dXVtVkeQSqWiXn87hNra2l/0GTBgQMvYhPXr\n11+OiYmpaj3H8ePHc/ft22edkJDg/sILL5QtXLiwvFsnQvQb1lly+ycA8wRBMAUgAAgUBMGsvc6i\nKB7vyeCIiIiIiIiIiKhzU5aNO32/Y7hbn3/+ucWHH344+OjRo7ndGefr61vv4+NT8/LLLz/0l7/8\n5SoAvPzyyw/5+vrW+Pr61hcWFpq07D927NjqpKQk15qamquNjY3CsWPHBsbFxV1vb/6xY8feeuml\nl1yysrJMfX1967VareTSpUvGDg4OOgAYOnSorrKyUpKenm4TGRl5EwAsLCyaKisrpYY5nJycGr77\n7jvZU089VfX3v//dpr21wsPDK7ds2TI4IiJCa2pqKmZmZpq6ubk1lpaWGrm7uze89NJLP9+6dUty\n5swZGQAmt4nu6Cy5/T8AtgGIByAC2NxOP+HOcWk7x4mIiIiIiIiIiAAA6enpNkql0qKurk7i5ORU\nv3v37otBQUF13Z1n165dhXPnznVxcXHxFUURQUFBt3bt2lXYVt/HHnusZtKkSZUqlcrH0dGx3t/f\n/5a1tXVTe3M7ODjotm3bVvj0008/3NDQIADAqlWriv39/Stnzpx5XaVS+Tg5OTUEBATcMoyJi4v7\n+fnnn3ddsmSJ/tSpUzkrV64sWbBggdu6desag4ODb7W31qJFi34uLCw09fPz8xZFUbC1tW08dOhQ\nwZEjRyxTUlKGGhkZiTKZrGnXrl2sqEDUQofJbVEU3xcE4TAAOYCvAbwAIKcvAiMiIiIiIiIiogff\n22+/XdLyc01NzdnWfSIiIrQRERFaAHjhhRfKX3jhhXZ3H+/bt6+wq2sPHjy46bPPPmsz4dtyTYNV\nq1aVvv322yVarVYSGhqqWLp0aVlHa0ZFRWmjoqJ+lQtLSUkpSUlJKWndnpCQUJGQkNBcy3vSpEnV\nhYWFWa37tb5mUqkUGzduLAZQ3LL9+eefL3/++ee5U5uoHZ3t3IYoilcBXBUEYSeAz/lCSSIiIiIi\nIiIi6o9mzZrlmp+fb15fXy88/fTT5WPGjKm53zER0d3rNLltIIri7N4MhIiIiIiIiIiIqC3+/v7K\nhoaGX7yQMS0t7VJISEi3XkLZ+uWQRNS/dTm5TUREREREREREdD9kZmZq7ncMRPTgkXTehYiIiIiI\niIiIiIjowcLkNhERERERERERERH1O0xuExEREREREREREVG/w5rbRERERERERET93JGPXgruyfme\neHr96Z6cj4ioN3DnNhERERERERERdZtUKg1WKpUqT09PH4VCoXrttdfsm5qaOh2XmJjo5Onp6ZOY\nmOh0N+vKZLJAAMjNzTXZunWrbVfGnDx50lypVKqUSqXK2tp6mKOjo59SqVQ98sgj8tzcXBMzM7Mg\nw3GlUqmqq6sTUlJS7OLi4lwAYPHixQ5DhgzxVyqVKldXV9+JEyd6nD592swwf0hIiMLNzc3XMH7H\njh027cUiCELwlClT3A2fGxsbYWNjEzBu3DjPu7keXdHyHD08PHxiY2NdunKv+oNdu3ZZL1u2bOj9\njoPuD+7cJiIiIiIiIiKibjM1NdVrNBo1ABQXFxtNmzbt4crKSulf//rXko7G7dq1a/D169fPmZub\ni/eyfn5+vunevXttFyxYcKOzviEhIbWGWGNiYtwiIiIqZ8+efRO4nfh1dnauNxxvz4IFC8rWrFlT\nBgDbt2+3eeKJJxSZmZnZDg4OOgBIS0v78dFHH63pLBZzc3N9bm6ueXV1tWBhYSEeOHDAyt7evrEr\n52zQ2NgIY2Pj7gyB4RwbGxsRGhqq+PDDDwfGx8dXGI7rdDoYGd1bqvBu4rpXM2fOrARQ2aeL0gOD\nO7eJiIiIiIiIiOieODo66t59993CHTt2DNHr9dDpdEhMTHTy9fX1lsvlquTk5EEA8Pjjj3vW1tZK\nAgMDvbdv326ze/dua39/f6W3t7fqkUcekRcVFRkBt3dKr1y50t4wv5eXl09ubq5JyzWXL1/ueOrU\nKQulUqlavXr1kL4833nz5t0MCwurfO+997q0c7y18ePHV3788ccDAWDPnj22MTExzQn6srIy6YQJ\nEzzkcrkqICBAeeLECXPg9jWZMWOG6+jRo72io6Pd27vGnTE2NkZISEh1fn6+aUZGhuXIkSPlkZGR\n7gqFwgcANm/ebOvn5+etVCpVsbGxrjqdDsDtHfPz5s1zUqlU3qGhofKSkhIj4Pau9YULFzqOGDFC\nsXbtWvu8vDyT0NBQuVwuV4WGhsrz8/NNAKCoqMgoPDzcQ6FQqBQKherLL78c0N56Op0OMTExbl5e\nXj5yubz5/q5du3aIh4eHj1wuV0VERDwMAC132MfExLglJCQ4BwYGKp2cnPwMO+ibmpowa9YsF09P\nT59x48Z5PvbYY54d7a6n/oPJbSIiIiIiIiIiumcqlapBr9ejuLjY6J133hlkbW3dlJWVlXP+/Pmc\nnTt3DtZoNCZfffXVRcOO73nz5t0MDw+vPnfunCYnJ0f95JNP3lizZk2Xy0u88cYbxcOHD6/WaDTq\nVatWXbuX2IuKikwNJUWeeeYZl66MCQwMrNFoNM2lSeLi4h42zFFaWirtaOwzzzxzY+/evTY1NTVC\nTk6OLDQ09Jbh2NKlSx0CAgJq8vLy1K+//npxfHx8cwmTzMxM2ZEjRy6mp6dfau8adxa3VquVHD9+\n3Mrf37/2zpwDkpOTiwsKCrLPnDlj9sknn9ieOnVKo9Fo1BKJRNy6dasdANTW1kqCgoJq1Gp1zujR\no7WvvPKKg2HOiooK6Q8//JC7evXqsgULFrjExsaW5+XlqadPn16elJTkDAALFixwCQsL0+bm5qqz\ns7PVQUFBde2t969//Ut29epV4/z8/Oy8vDz1c889Vw4AKSkpQ7OystR5eXnq1NTUn9o6v7KyMuNT\np05pPvvss/xVq1Y5AkBaWppNUVGRSW5ubvbOnTsLz549a9HZdaL+gWVJiIiIiIiIiIioR4ji7Uoj\nx44ds9JoNLKDBw/aAIBWq5Wq1WozpVLZ0LL/pUuXTKZMmeJ0/fp144aGBomzs3P9fQgbXSlL0prh\nXA26WpYEAEaOHFl75coV0+3bt9tOmDDhFyU1Tp48ablv376LABAVFaWdP3++UXl5uRQAJk2aVGFh\nYSECXb/GBoYE3JQEHQAAIABJREFUviAImDx5csVTTz1VlZGRYenv73/LMOaLL76wzMrKkgUEBHgD\nQF1dnWTIkCE6AJBIJJg7d+4NAJgzZ055dHR0c43wGTNmNO88P3v27IDDhw8XAEBSUtKN1atXOwHA\n999/b/nJJ59cAgAjIyPY2dk1bdu2zbat9aZPn15RVFRkGh8f7xwZGVk5derUKgBQKBS1U6dOdY+K\niqqYOXNmc0mVlqKioiqkUimCg4PrysvLjQHg22+/tYiOjr4plUrh4uKiGzVqlLYr94kefExuExER\nERERERHRPVOr1SZSqRSOjo46URSF9evXX46JianqaMzChQtdXnzxxdKZM2dWZmRkWK5Zs8YBAIyM\njES9Xt/cr76+Xujl8Lvt3LlzsuDg4C4ls9syadKkilWrVjkfPXo099q1a805utZJcwAQBEEEgAED\nBuhb9OvSNTZoL4Evk8l+Mee0adPKN23aVNzZfILwn1tiaWmp76BruzpaLysrS33gwAGrzZs3D9m7\nd6/txx9/XPj111/nHz582PLTTz8d+NZbbznk5+dntR5nZmbWfAEN17Kta0q/DX2e3BYEwRbAewAm\nAvgZwP8TRXF3G/3+BOAFAIMAVAPYC2CJKIq6PgyXiIiIiIiIiOiB98TT60/fz/VLSkqM5s2b5zp7\n9uxrEokE4eHhlVu2bBkcERGhNTU1FTMzM03d3NwaraysfpEE1Wq1UhcXl0YASE1NtTO0u7m51R86\ndGggAPzzn/+UFRcXm7Ze09rauqm6urrD8h+9JTU1deC3335rvWnTpit3O0dSUtLP1tbWTSEhIbUZ\nGRmWhvZRo0Zpd+zYYZecnHw1IyPD0sbGRmdra/ur5HFXr3F3TJo0qSo6Otpz2bJlZY6OjrqysjJp\nZWWlVC6XN+j1euzYscNm/vz5N1NTU+1CQkLa3P0cGBh4691337V57rnnbmzbts12+PDh1QAwevRo\nbXJy8uCVK1de0+l0qKqqkrS3nqWlpd7U1FSfkJBQIZfL6+fMmePe1NSEgoICk8jISO3EiROrHRwc\nbCsrK7t0/8PCwqo/+OADu4ULF5aXlJQYnThxwrLlbnPqv+7Hzu1NABoA2AMYBuBzQRDOi6KY3apf\nOoBUURQr7iTEP8HtZPfbfRotERERERERERH9Sn19vUSpVKp0Op0glUrF6dOnl69ataoMABYtWvRz\nYWGhqZ+fn7coioKtrW3joUOHClrPsXz58pIZM2Z42NvbNwwfPvzW5cuXTQEgLi7u5q5du+yUSqVq\n2LBht1xdXetajw0JCak1MjISFQqFKjY29ud7rbvdma1bt9r//e9/t6utrZXI5fLaI0eO5Do4ONz1\nJkwPD4/GV1999Vcxr1u3riQ2NtZNLperzM3N9ampqZfaGt/Va9wdwcHBdStWrCgeP368XK/Xw9jY\nWExJSbksl8sbzM3N9dnZ2eY+Pj5DLS0tm/bv3/9jW3Ns2bLlcnx8vNuGDRuG2tnZ6dLS0goN7QkJ\nCa5yuXyQRCLBxo0bf5owYcKtttaTyWT6Z5991k2v1wsAsGbNmis6nU6IjY1112q1UlEUhcTExLJB\ngwY1deW84uPjbx47dsxSLpf7uLu71wUEBNwaOHBgl8bSg03oy235giAMAHATgK8oinl32j4AUCyK\n4isdjLPD7Z3beaIo/rGjNYYPHy6eOnXqnmONXfn1Pc/REefHM3p1/qkHeneDe6n9lF6d3/zh3r0+\np2yf6dX5l08c1qvzdwef5Y7xWe7Yg/QsA3yeO9Ofn2c+yz2Lz3LH+Lu5Z/Xn55nPcsf4LPcs/m7u\nGJ/nZl0uvXH+/PnCgICAn3tqYaLOyGSywJqamrP3O467VVlZKbG2ttaXlpZKR4wY4f3dd99pXFxc\nWCGinzl//vyggIAAN8Pnvt65LQfQZEhsG2IC8FhbnQVBiAWwFYAlbpcweamdfvMBzAcAF5cuvdCW\niIiIiIiIiIiIfifCw8O9qqqqpI2NjcKSJUuuMrH929DXyW0LAJWt2ipxO3n9K3dqce8WBMELQByA\nsnb6/Q3A34DbO7d7LFoiIiIiIiIiInrg7du3z2r58uVOLducnZ3rv/zyy3sq03G3SktLpWPHjlW0\nbv/mm29yhw4d2mvlME6ePGkeFxfn3rLNxMREn5mZqbnXufvzrm0AOHnyZO79joF6Xl8nt6sBWLVq\nswLQZgF6A1EU8wVByAawGUB0L8VGRERERERERET9UExMTFVMTIz6fsdhMHTo0CaNRtPn8YSEhNTe\nj3WJ7hdJH6+XB8Dozk5sgwAArV8m2RYjAB69EhURERERERERERER9St9mtwWRfEWgP0A1giCMEAQ\nhNEA/gvAB637CoIwVxCEIXe+VwH4fwD+0ZfxEhEREREREREREdGDqa93bgPAHwGYA7gGYA+AJFEU\nswVBCBMEobpFv9EALgiCcAvAoTtfy/o8WiIiIiIiIiIiIiJ64PR1zW2IongDwJQ22r/F7RdOGj7P\n7su4iIiIiIiIiIj6qzeOngvuyfmWTxx2uifnIyLqDfdj5zYREREREREREfVzUqk0WKlUqjw9PX0U\nCoXqtddes29qaup0XGJiopOnp6dPYmKi092sK5PJAgEgNzfXZOvWrbZdGXPy5ElzpVKpUiqVKmtr\n62GOjo5+SqVS9cgjj8hzc3NNzMzMggzHlUqlqq6uTkhJSbGLi4tzAYDFixc7DBkyxF+pVKpcXV19\nJ06c6HH69Gkzw/whISEKNzc3X8P4HTt22LQXiyAIwVOmTHE3fG5sbISNjU3AuHHjPO/menRFy3P0\n8PDwiY2NdenKveoPdu3aZb1s2bKhPTmn4X4qFApVUFCQ8vz586Y9OX9ubq6Jl5eXT3fHBQYGKg3j\nu/rst5aSkmJnY2MTYHhWp06d6nY382RkZFh++eWXA+5mbE/q853bRERERERERETU/5mamuo1Go0a\nAIqLi42mTZv2cGVlpfSvf/1rSUfjdu3aNfj69evnzM3NxXtZPz8/33Tv3r22CxYsuNFZ35CQkFpD\nrDExMW4RERGVs2fPvgncThQ6OzvXG463Z8GCBWVr1qwpA4Dt27fbPPHEE4rMzMxsBwcHHQCkpaX9\n+Oijj9Z0Fou5ubk+NzfXvLq6WrCwsBAPHDhgZW9v39iVczZobGyEsbFxd4bAcI6NjY0IDQ1VfPjh\nhwPj4+MrDMd1Oh2MjO4tVXg3cd2rmTNnVgKo7Ol5DffzL3/5y6BFixY5f/XVVxd7eo2uMtybs2fP\naoDuPfttiYyMvJmWlnb5XmL66quvLC0sLJrCw8NvdXVMTzxjrXHnNhERERERERER3RNHR0fdu+++\nW7hjx44her0eOp0OiYmJTr6+vt5yuVyVnJw8CAAef/xxz9raWklgYKD39u3bbXbv3m3t7++v9Pb2\nVj3yyCPyoqIiI+D2TumVK1faG+b38vLyyc3NNWm55vLlyx1PnTploVQqVatXrx7Sl+c7b968m2Fh\nYZXvvffeXe2eHT9+fOXHH388EAD27NljGxMT05ykLCsrk06YMMFDLperAgIClCdOnDAHbl+TGTNm\nuI4ePdorOjravb1r3BljY2OEhIRU5+fnm2ZkZFiOHDlSHhkZ6a5QKHwAYPPmzbZ+fn7eSqVSFRsb\n66rT6QDc3jE/b948J5VK5R0aGiovKSkxAm7vcl64cKHjiBEjFGvXrrXPy8szCQ0NlcvlclVoaKg8\nPz/fBACKioqMwsPDPRQKhUqhUKgMu37bWk+n0yEmJsbNy8vLRy6XN9/ftWvXDvHw8PCRy+WqiIiI\nh4HbO5ENO+xjYmLcEhISnAMDA5VOTk5+hh30TU1NmDVrlounp6fPuHHjPB977DHPjnbXt7pX1T/9\n9JMpAHz22WeW3t7eKrlcrpo2bZpbbW2tAACOjo5+SUlJjn5+ft5+fn7eWVlZpoZ4Wq5j+F8HLeXm\n5poEBwcrVCqVt0ql8jZcl7bujWF862c/ODhY8f3335sb5gwKCmp+broqOzvbNCwszMvHx8c7ODhY\ncfbsWTMAaOtnNDc31yQtLW3w1q1b7ZVKpeqLL76waO9cu/qMtXfPO8PkNhERERERERER3TOVStWg\n1+tRXFxs9M477wyytrZuysrKyjl//nzOzp07B2s0GpOvvvrqomHH97x5826Gh4dXnzt3TpOTk6N+\n8sknb6xZs6bL5SXeeOON4uHDh1drNBr1qlWrrt1L7EVFRaaGMg3PPPOMS1fGBAYG1mg0mubSJHFx\ncQ8b5igtLZV2NPaZZ565sXfvXpuamhohJydHFhoa2rz7denSpQ4BAQE1eXl56tdff704Pj6+uYRJ\nZmam7MiRIxfT09MvtXeNO4tbq9VKjh8/buXv7197Z84BycnJxQUFBdlnzpwx++STT2xPnTql0Wg0\naolEIm7dutUOAGprayVBQUE1arU6Z/To0dpXXnnFwTBnRUWF9IcffshdvXp12YIFC1xiY2PL8/Ly\n1NOnTy9PSkpyBoAFCxa4hIWFaXNzc9XZ2dnqoKCguvbW+9e//iW7evWqcX5+fnZeXp76ueeeKweA\nlJSUoVlZWeq8vDx1amrqT22dX1lZmfGpU6c0n332Wf6qVascASAtLc2mqKjIJDc3N3vnzp2FZ8+e\ntWhrbFv2799vrVQqa2tqaoTExET3vXv3FuTl5al1Oh2Sk5MHG/pZWVk1XbhwIScxMfHa888/79zV\n+R0cHHTffvttnlqtztm7d++PixYtan7+Wt6blmNaP/sJCQk/v/vuu4PujDFtaGgQRo4cWdvemunp\n6TaGZ3XDhg12ADB37lzXzZs3X87Ozs5JTk6+kpSU5AIAbf2MKhSKhri4uOsLFiwo02g06kmTJlV3\ndI5decbau+edYVkSIiIiIiIiIiLqEaJ4u9LIsWPHrDQajezgwYM2AKDVaqVqtdpMqVQ2tOx/6dIl\nkylTpjhdv37duKGhQeLs7Fx/H8JGV8qStGY4V4OuliUBgJEjR9ZeuXLFdPv27bYTJkz4RUmNkydP\nWu7bt+8iAERFRWnnz59vVF5eLgWASZMmVVhYWIhA16+xgSGBLwgCJk+eXPHUU09VZWRkWPr7+98y\njPniiy8ss7KyZAEBAd4AUFdXJxkyZIgOACQSCebOnXsDAObMmVMeHR3dXCN8xowZzTvPz549O+Dw\n4cMFAJCUlHRj9erVTgDw/fffW37yySeXAMDIyAh2dnZN27Zts21rvenTp1cUFRWZxsfHO0dGRlZO\nnTq1CgAUCkXt1KlT3aOioipmzpzZXFKlpaioqAqpVIrg4OC68vJyYwD49ttvLaKjo29KpVK4uLjo\nRo0ape3sHsXFxT1sZmamd3Jyqt+6devl8+fPmzk5OdX7+/vXA0BCQkL5pk2bhgC4BgDx8fE3AGDe\nvHk3VqxY0eXkdkNDg/Dss8+6qtVqc4lEAsMucQBoeW86kpCQcDM5Ofmh+vr6K1u3bh0UGxv7c0f9\nW5clqayslJw9e9Zi2rRpHi3jAnrmZ7Qrz1h797wzTG4TEREREREREdE9U6vVJlKpFI6OjjpRFIX1\n69dfjomJ6TBBtXDhQpcXX3yxdObMmZUZGRmWa9ascQAAIyMjUa/XN/err68Xejn8bjt37pwsODi4\nS8nstkyaNKli1apVzkePHs29du1ac46uddIcAARBEAFgwIAB+hb9unSNDdpL4Mtksl/MOW3atPJN\nmzYVdzafIPznllhaWuo76NqujtbLyspSHzhwwGrz5s1D9u7da/vxxx8Xfv311/mHDx+2/PTTTwe+\n9dZbDvn5+Vmtx5mZmTVfQMO1bOuadqb1Hyta3qO2SCT/KZBhuF9GRkai4cWder0ejY2Nv3qO33jj\nDfshQ4Y07tu375Jer4e5uXmw4VjLe9MRS0tLfVhYWNXu3bsHHjx40Pb06dPd+kNNU1MTLC0tdW09\nH+39jLbW0bl29Rlr6553FjuT20RERERERERE/dzyicNO38/1S0pKjObNm+c6e/bsaxKJBOHh4ZVb\ntmwZHBERoTU1NRUzMzNN3dzcGq2srH6RrNNqtVIXF5dGAEhNTbUztLu5udUfOnRoIAD885//lBUX\nF5uiFWtr66bq6uoOy3/0ltTU1IHffvut9aZNm67c7RxJSUk/W1tbN4WEhNRmZGRYGtpHjRql3bFj\nh11ycvLVjIwMSxsbG52tre2vkpxdvcbdMWnSpKro6GjPZcuWlTk6OurKysqklZWVUrlc3qDX67Fj\nxw6b+fPn30xNTbULCQlpc/dzYGDgrXfffdfmueeeu7Ft2zbb4cOHVwPA6NGjtcnJyYNXrlx5TafT\noaqqStLeepaWlnpTU1N9QkJChVwur58zZ457U1MTCgoKTCIjI7UTJ06sdnBwsK2srOzS/Q8LC6v+\n4IMP7BYuXFheUlJidOLECcuWu827YtiwYXXFxcUmWVlZpr6+vvVpaWl2YWFhzdcgLS3N9s033yx9\n7733bAIDA28BgKura8Pp06dlc+fOvblr166BOp3uV8ntyspKqZOTU4NUKsXGjRvtDAnijrT17C9Y\nsODnmJgYzxEjRlTb29t3PkkLtra2eicnp4b333/fZs6cOTf1ej1OnDhhHhoaWtvez6ilpWVTVVVV\ncwxdOVeg/WesrXveldiZ3CYiIiIiIiIiom6rr6+XKJVKlU6nE6RSqTh9+vTyVatWlQHAokWLfi4s\nLDT18/PzFkVRsLW1bTx06FBB6zmWL19eMmPGDA97e/uG4cOH37p8+bIpAMTFxd3ctWuXnVKpVA0b\nNuyWq6trXeuxISEhtUZGRqJCoVDFxsb+fK91tzuzdetW+7///e92tbW1ErlcXnvkyJFcBwcH3d3O\n5+Hh0fjqq6/+KuZ169aVxMbGusnlcpW5ubk+NTX1Ulvju3qNuyM4OLhuxYoVxePHj5fr9XoYGxuL\nKSkpl+VyeYO5ubk+Ozvb3MfHZ6ilpWXT/v37f2xrji1btlyOj49327Bhw1A7OztdWlpaoaE9ISHB\nVS6XD5JIJNi4ceNPEyZMuNXWejKZTP/ss8+66fV6AQDWrFlzRafTCbGxse5arVYqiqKQmJhYNmjQ\noC4lcePj428eO3bMUi6X+7i7u9cFBATcGjhwYLcSwDKZTNy6dWvhtGnTPJqamhAQEFDz5z//+brh\neH19veDv76/U6/XCRx999CMAPP/889cjIiI8/fz8vB999NEqc3PzX/3h4U9/+tO1mJgYj08//dRm\nzJgx2rb6tNbWsx8WFlYzYMCAptmzZ3dYkqQ9e/bs+XHevHmu69ate0in0wlTp069ERoaWtvez2hM\nTEzFk08+6XH48OGB77zzzuWunCvQ/jPW1j3vStzC3WzLf5ANHz5cPHXq1D3PE7vy6x6Ipn3Oj2f0\n6vxTD9z179YuKbWf0qvzmz/cu9fnlO0zvTr/8onDenX+7uCz3DE+yx17kJ5lgM9zZ/rz88xnuWfx\nWe4Yfzf3rP78PPNZ7hif5Z7F380d4/PcrMulN86fP18YEBBwV0ksorshk8kCa2pqzt7vOO5WZWWl\nxNraWl9aWiodMWKE93fffadxcXHpkV+ejo6OfqdOncp56KGHeveXcQcKCwuNx44dqygoKMiSSu/L\nf2joE+fPnx8UEBDgZvjMndtERERERERERET0mxYeHu5VVVUlbWxsFJYsWXK1pxLbD4KNGzfarV27\n1vHNN98s+i0nttvC5DYREREREREREfVr+/bts1q+fLlTyzZnZ+f6L7/88p7KdNyt0tJS6dixYxWt\n27/55pvcoUOHdqscRnecPHnSPC4u7he1ik1MTPSZmZmae527P+/aBoCTJ0/mtm4LDw/3KCoq+kU9\n9zfeeONKV1/SaVBcXHzhXuO7FwsXLixfuHBhecu2DRs22G3ZssW+ZduIESOqP/jgg8t9G13vYnKb\niIiIiIiIiKj/0ev1ekEikfy26s3epZiYmKqYmBj1/Y7DYOjQoU0ajabP4wkJCam9H+v2V/frjx99\n4cUXXyx/8cUXyzvv2X/cqcf9i1rekvsUCxERERERERER3b2s69evWxtevkZE9Fum1+uF69evWwPI\natnOndtERERERERERP2MTqebW1pa+m5paakvuHmRiH779ACydDrd3JaNTG4TEREREREREfUzwcHB\n1wBE3e84iIjuJ/5lj4iIiIiIiIiIiIj6HSa3iYiIiIiIiIiIiKjfYXKbiIiIiIiIiIiIiPodJreJ\niIiIiIiIiIiIqN9hcpuIiIiIiIiIiIiI+h0mt4mIiIiIiIiIiIio32Fym4iIiIiIiIiIiIj6HSa3\niYiIiIiIiIiIiKjfYXKbiIiIiIiIiIiIiPodJreJiIiIiIiIiIiIqN9hcpuIiIiIiIiIiIiI+h0m\nt4mIiIiIiIiIiIio32Fym4iIiIiIiIiIiIj6HSa3iYiIiIiIiIiIiKjfYXKbiIiIiIiIiIiIiPod\nJreJiIiIiIiIiIiIqN9hcpuIiIiIiIiIiIiI+h0mt4mIiIiIiIiIiIio32Fym4iIiIiIiIiIiIj6\nHSa3iYiIiIiIiIiIiKjfYXKbiIiIiIiIiIiIiPodJreJiIiIiIiIiIiIqN9hcpuIiIiIiIiIiIiI\n+h0mt4mIiIiIiIiIiIio32Fym4iIiIiIiIiIiIj6HSa3iYiIiIiIiIiIiKjfYXKbiIiIiIiIiIiI\niPodJreJiIiIiIiIiIiIqN9hcpuIiIiIiIiIiIiI+h0mt4mIiIiIiIiIiIio32Fym4iIiIiIiIiI\niIj6HSa3iYiIiIiIiIiIiKjfYXKbiIiIiIiIiIiIiPodJreJiIiIiIiIiIiIqN9hcpuIiIiIiIiI\niIiI+h0mt4mIiIiIiIiIiIio32Fym4iIiIiIiIiIiIj6HSa3iYiIiIiIiIiIiKjfYXKbiIiIiIiI\niIiIiPodJreJiIiIiIiIiIiIqN9hcpuIiIiIiIiIiIiI+h0mt4mIiIiIiIiIiIio32Fym4iIiIiI\niIiIiIj6nT5PbguCYCsIwgFBEG4JgvCTIAix7fRbIghCliAIWkEQLgmCsKSvYyUiIiIiIiIiIiKi\nB5PRfVhzE4AGAPYAhgH4XBCE86IoZrfqJwCIA5AJwAPAUUEQikRR/KhPoyUiIiIiIiIiIiKiB06f\n7twWBGEAgBgAr4qiWC2K4j8BHATwTOu+oii+JYriGVEUdaIo5gL4DMDovoyXiIiIiIiIiIiIiB5M\nfV2WRA6gSRTFvBZt5wH4dDRIEAQBQBiA1ru7DcfnC4JwShCEU9evX++xYImIiIiIiIiIiIjowdTX\nyW0LAJWt2ioBWHYy7jXcjnVHWwdFUfybKIrDRVEcPnjw4HsOkoiIiIiIiIiIiIgebH1dc7sagFWr\nNisA2vYGCIKwELdrb4eJoljfi7ERERERERERERERUT/R1zu38wAYCYLg1aItAO2XG5kD4BUA40VR\nvNIH8RERERERERERERFRP9CnyW1RFG8B2A9gjSAIAwRBGA3gvwB80LqvIAgzAbwJIFwUxR/7Mk4i\nIiIiIiIiIiIierD19c5tAPgjAHMA1wDsAZAkimK2IAhhgiBUt+i3FoAdgB8EQai+87X1PsRLRERE\nRERERERERA+Yvq65DVEUbwCY0kb7t7j9wknDZ/e+jIuIiIiIiIiIiIiI+o/7sXObiIiIiIiIiIiI\niOieMLlNRERERERERERERP0Ok9tERERERERERERE9P/bu/uo3cq6TuDfHxCCB5A3U0ANc1BzVNTB\nlAylEkxXBGaWQSTOqBPmWCaOL6OAZKamLlMpNUKURqRF4DBJJSaQRGDMKCqjEBqkIsmLHDjEi+Bv\n/tj7xM3Tcw5wXu7n7PN8Pmvd6zz33te+7mufda1n7ed7X/u3J0e4DQAAAADA5Ai3AQAAAACYHOE2\nAAAAAACTI9wGAAAAAGByhNsAAAAAAEyOcBsAAAAAgMkRbgMAAAAAMDnCbQAAAAAAJke4DQAAAADA\n5Ai3AQAAAACYHOE2AAAAAACTI9wGAAAAAGByhNsAAAAAAEyOcBsAAAAAgMkRbgMAAAAAMDnCbQAA\nAAAAJke4DQAAAADA5Ai3AQAAAACYHOE2AAAAAACTI9wGAAAAAGByhNsAAAAAAEyOcBsAAAAAgMkR\nbgMAAAAAMDnCbQAAAAAAJke4DQAAAADA5Ai3AQAAAACYHOE2AAAAAACTI9wGAAAAAGByhNsAAAAA\nAEyOcBsAAAAAgMkRbgMAAAAAMDnCbQAAAAAAJke4DQAAAADA5Ai3AQAAAACYHOE2AAAAAACTI9wG\nAAAAAGByhNsAAAAAAEyOcBsAAAAAgMkRbgMAAAAAMDnCbQAAAAAAJke4DQAAAADA5Ai3AQAAAACY\nHOE2AAAAAACTI9wGAAAAAGByhNsAAAAAAEyOcBsAAAAAgMkRbgMAAAAAMDnCbQAAAAAAJke4DQAA\nAADA5Ai3AQAAAACYHOE2AAAAAACTI9wGAAAAAGByhNsAAAAAAEyOcBsAAAAAgMkRbgMAAAAAMDlz\nD7eraueqOqOqbqmqq6rq0DW0+6mqOqeqVlbVlXMeJgAAAAAAm7ClWLl9fJI7kjwkyWFJ/qiq/uMi\n7W5JcmKS185xbAAAAAAATMBcw+2qWpHkBUne3N2ruvv8JGcmOXxh2+7+fHefnOQb8xwjAAAAAACb\nvnmv3H50kru6+/KZbZckWWzlNgAAAAAALGre4fZ2SVYu2LYyyfbr02lVvbyqLq6qi6+99tr16QoA\nAAAAgAmYd7i9KskOC7btkOTm9em0uz/c3ft09z4PfvCD16crAAAAAAAmYN7h9uVJtqqqvWa27Z3k\n0jmPAwAAAACACZtruN3dtyQ5PclxVbWiqp6R5OAkJy9sW1VbVNU2SX5oeFvbVNXW8xwvAAA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      "text/plain": [
       "<Figure size 1470.38x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code: TFIDF WITH POPULATION FEATURES ADDED\n",
    "vectype = TfidfVectorizer()\n",
    "Res6 = Dicttodataframe(RunAnyVecNew(classifiers,exp_data_k_folds,'text_features_new', vectype, pop_features),'Classifier')\n",
    "Res6['Exp_Type'] = 'Default_ TFIDF_More_Preprocessing_Popularity_Features'\n",
    "DF = pd.concat([DF,Res6])\n",
    "DF.index = range(len(DF.index))\n",
    "PlotRes(DF,'fmeasure', fontin = 11)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
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sWZ4JCQkVBQUFqokTJ1bMnj3bAwBmzZrlOXTo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TI+ycuT/CDJ4PB6w+68n5rke0m+XlUzhuMaYKBlzOl5SX6TZPqg9rskmZLkK91NlyQ5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\nQM/5/wFNfWUD+6ChoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1470.38x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code: TFIDF WITH USER FEATURES ADDED\n",
    "vectype = TfidfVectorizer()\n",
    "Res7 = Dicttodataframe(RunAnyVecNew(classifiers,exp_data_k_folds,'text_features_new', vectype, user_features),'Classifier')\n",
    "Res7['Exp_Type'] = 'Default_ TFIDF_More_Preprocessing_User_Features'\n",
    "DF = pd.concat([DF,Res7])\n",
    "DF.index = range(len(DF.index))\n",
    "PlotRes(DF,'fmeasure', fontin = 11)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zxkRGRtYVFRVd23U9f/58D9Mc+/fv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      "text/plain": [
       "<Figure size 1470.38x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code: TFIDF WITH USER FEATURES ADDED\n",
    "vectype = TfidfVectorizer()\n",
    "Res8 = Dicttodataframe(RunAnyVecNew(classifiers,exp_data_k_folds,'text_features_new', vectype, Senti_features),'Classifier')\n",
    "Res8['Exp_Type'] = 'Default_ TFIDF_More_Preprocessing_SS_Features'\n",
    "DF = pd.concat([DF,Res8])\n",
    "DF.index = range(len(DF.index))\n",
    "PlotRes(DF,'fmeasure', fontin = 11)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
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TplxydnY2RkdHV2ZmZjqa27t166Zf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      "text/plain": [
       "<Figure size 1470.38x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code: TFIDF WITH ALL FEATURES ADDED\n",
    "vectype = TfidfVectorizer()\n",
    "Res9 = Dicttodataframe(RunAnyVecNew(classifiers,exp_data_k_folds,'text_features_new', vectype, pop_features + user_features + Senti_features),'Classifier')\n",
    "Res9['Exp_Type'] = 'Default_ TFIDF_More_Preprocessing_ALL_Features'\n",
    "DF = pd.concat([DF,Res9])\n",
    "DF.index = range(len(DF.index))\n",
    "PlotRes(DF,'fmeasure', fontin = 11)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "candidate_data_new.to_csv('AllTweetInfo2.csv', index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}