codeLiar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#columnNames = [\"id\",\"text\", \"topic\", \"author\", \"pos\", \"location\", \"party\", \"false\", \"barely\", \"half\", \"mostly\", \"pantsonfire\", \"context\"]\n",
    "df = pd.read_csv(\"liar.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6750"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['party'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finds the word count of article text and appends to the dataframe\n",
    "\n",
    "count_list = []\n",
    "for i in df['text']:\n",
    "    count = len(i.split())\n",
    "    count_list.append(count)\n",
    "    \n",
    "df.loc[:,'word_count'] = count_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create binary ground truth for possible eval\n",
    "\n",
    "truth = []\n",
    "for i in df['label']:\n",
    "    \n",
    "    if (i == \"FALSE\" or i == \"pants-fire\"):\n",
    "        truth.append(0)\n",
    "    else:\n",
    "        truth.append(1)\n",
    "df.loc[:,'truthval'] = truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "6747      0      0       0    0     0          0       1     0           0   \n",
       "6748      0      0       0    0     0          0       0     0           0   \n",
       "6749      0      0       0    0     0          0       0     0           0   \n",
       "\n",
       "      spending  tax  taxes  texas  time  united  voted  wisconsin  \n",
       "0            0    0      0      0     0       0      0          0  \n",
       "1            0    0      0      0     0       0      0          0  \n",
       "2            0    0      0      0     0       0      0          0  \n",
       "3            0    0      0      0     0       0      0          0  \n",
       "4            0    0      0      0     0       0      0          0  \n",
       "...        ...  ...    ...    ...   ...     ...    ...        ...  \n",
       "6745         0    0      0      0     0       0      0          0  \n",
       "6746         0    0      0      0     0       0      0          0  \n",
       "6747         0    0      0      0     0       0      0          0  \n",
       "6748         0    0      0      0     0       0      0          1  \n",
       "6749         0    0      0      0     0       0      0          0  \n",
       "\n",
       "[6750 rows x 34 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tokenize for LDA\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction import text\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "skl_stopwords = list(text.ENGLISH_STOP_WORDS)\n",
    "\n",
    "all_skl_stopwords = skl_stopwords + [\"00\", \"10\", \"000\", \"year\", \"years\", \"says\", \"just\", \"said\", 'state', 'states', 'percent', 'people', 'new']\n",
    "\n",
    "binary = CountVectorizer(binary=False, stop_words = all_skl_stopwords, min_df= 0.0199) \n",
    "binary_dm = binary.fit_transform(df['text'])\n",
    "\n",
    "\n",
    "pd.DataFrame(binary_dm.toarray(),columns = binary.get_feature_names())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Implement LDA and retain topics\n",
    "\n",
    "def create_topics(model, feature_names, nob):\n",
    "    topics = []\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        topics.append(topic_idx)\n",
    "        topics.append(([feature_names[i] for i in topic.argsort()[:-nob - 1:-1]]))\n",
    "        \n",
    "        \n",
    "    return topics\n",
    "from sklearn.decomposition import LatentDirichletAllocation as lda\n",
    "\n",
    "LDA = lda(n_components = 5, max_iter=5, learning_method='online', learning_offset=50.,random_state=0).fit(binary_dm)\n",
    "\n",
    "topics = create_topics(LDA, binary.get_feature_names(), 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['jobs', 'million', 'americans']\n",
      "['texas', 'united', 'voted']\n",
      "['federal', 'government', 'law']\n",
      "['obama', 'health', 'president']\n",
      "['tax', 'billion', 'taxes']\n"
     ]
    }
   ],
   "source": [
    "# drop topic indices\n",
    "for x in topics:\n",
    "    if (type(x) == int):\n",
    "        topics.remove(x)\n",
    "        \n",
    "for topic in topics:\n",
    "    print(topic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "from spacy import displacy\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = nlp(\"This is a good example!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#displacy.serve(sample, style = 'dep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DET', 'AUX', 'DET', 'ADJ', 'NOUN', 'PUNCT']\n"
     ]
    }
   ],
   "source": [
    "print ([token.pos_ for token in sample])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assignSpeech(listoftext):\n",
    "    \n",
    "    nouns = []\n",
    "    adjectives = []\n",
    "    adverbs = []\n",
    "    proper_nouns = []\n",
    "    for text in listoftext:\n",
    "        spacydText = nlp(text)\n",
    "        nounCount = 0\n",
    "        adjCount = 0\n",
    "        advCount = 0\n",
    "        pnounCount = 0\n",
    "        for token in spacydText:\n",
    "            if token.pos_ == 'NOUN':\n",
    "                nounCount += 1\n",
    "            if token.pos_ == 'ADJ':\n",
    "                adjCount += 1\n",
    "            if token.pos_ == 'ADV':\n",
    "                advCount += 1\n",
    "            if token.pos_ == 'PROPN':\n",
    "                pnounCount += 1\n",
    "        nouns.append(nounCount)\n",
    "        adjectives.append(adjCount)\n",
    "        adverbs.append(advCount)\n",
    "        proper_nouns.append(pnounCount)\n",
    "    \n",
    "    return nouns, adjectives, adverbs, proper_nouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "nouns, adjectives, adverbs, proper_nouns = assignSpeech(df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'adjs'] = adjectives\n",
    "df.loc[:,'advbs'] = adverbs\n",
    "df.loc[:,'nouns'] = nouns\n",
    "df.loc[:,'pnouns'] = proper_nouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def toPercent(nouns):\n",
    "    #nounsPercent = []\n",
    "    #adjectivesPercent = []\n",
    "    #adverbsPercent = []\n",
    "    #proper_nounsPercent = []\n",
    "df['nounsPercent'] = df['nouns']/df['word_count']\n",
    "df['adjsPercent'] = df['adjs']/df['word_count']\n",
    "df['advbsPercent'] = df['advbs']/df['word_count']\n",
    "df['pnounsPercent'] = df['pnouns']/df['word_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label encoding for location\n",
    "from sklearn import preprocessing\n",
    "\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "df['location']= label_encoder.fit_transform(df['location']) \n",
    "\n",
    "df['topic'] = label_encoder.fit_transform(df['topic'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "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>text</th>\n",
       "      <th>sentiment_token_compound</th>\n",
       "      <th>sentiment_result_token</th>\n",
       "      <th>sentiment_stem_compound</th>\n",
       "      <th>sentiment_result_stem</th>\n",
       "      <th>label</th>\n",
       "      <th>topic</th>\n",
       "      <th>author</th>\n",
       "      <th>pos</th>\n",
       "      <th>location</th>\n",
       "      <th>party</th>\n",
       "      <th>false</th>\n",
       "      <th>barely</th>\n",
       "      <th>half</th>\n",
       "      <th>mostly</th>\n",
       "      <th>pantsonfire</th>\n",
       "      <th>context</th>\n",
       "      <th>word_count</th>\n",
       "      <th>truthval</th>\n",
       "      <th>textCluster</th>\n",
       "      <th>adjs</th>\n",
       "      <th>advbs</th>\n",
       "      <th>nouns</th>\n",
       "      <th>pnouns</th>\n",
       "      <th>nounsPercent</th>\n",
       "      <th>adjsPercent</th>\n",
       "      <th>advbsPercent</th>\n",
       "      <th>pnounsPercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Says the Annies List political group supports ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>16</td>\n",
       "      <td>dwayne-bohac</td>\n",
       "      <td>State representative</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a mailer</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>When did the decline of coal start? It started...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>half-true</td>\n",
       "      <td>1792</td>\n",
       "      <td>scott-surovell</td>\n",
       "      <td>State delegate</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a floor speech.</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hillary Clinton agrees with John McCain \"by vo...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>mostly-true</td>\n",
       "      <td>2001</td>\n",
       "      <td>barack-obama</td>\n",
       "      <td>President</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Denver</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Chicago Bears have had more starting quart...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>TRUE</td>\n",
       "      <td>1620</td>\n",
       "      <td>robin-vos</td>\n",
       "      <td>Wisconsin Assembly speaker</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>a an online opinion-piece</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I'm the only person on this stage who has work...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>half-true</td>\n",
       "      <td>1850</td>\n",
       "      <td>barack-obama</td>\n",
       "      <td>President</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>a Democratic debate in Philadelphia, Pa.</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  Says the Annies List political group supports ...   \n",
       "1  When did the decline of coal start? It started...   \n",
       "2  Hillary Clinton agrees with John McCain \"by vo...   \n",
       "3  The Chicago Bears have had more starting quart...   \n",
       "4  I'm the only person on this stage who has work...   \n",
       "\n",
       "   sentiment_token_compound sentiment_result_token  sentiment_stem_compound  \\\n",
       "0                       0.0                neutral                      0.0   \n",
       "1                       0.0                neutral                      0.0   \n",
       "2                       0.0                neutral                      0.0   \n",
       "3                       0.0                neutral                      0.0   \n",
       "4                       0.0                neutral                      0.0   \n",
       "\n",
       "  sentiment_result_stem        label  topic          author  \\\n",
       "0               neutral        FALSE     16    dwayne-bohac   \n",
       "1               neutral    half-true   1792  scott-surovell   \n",
       "2               neutral  mostly-true   2001    barack-obama   \n",
       "3               neutral         TRUE   1620       robin-vos   \n",
       "4               neutral    half-true   1850    barack-obama   \n",
       "\n",
       "                          pos  location  party  false  barely   half  mostly  \\\n",
       "0        State representative        56      1    0.0     1.0    0.0     0.0   \n",
       "1              State delegate        63      1    0.0     0.0    1.0     1.0   \n",
       "2                   President        18      1   70.0    71.0  160.0   163.0   \n",
       "3  Wisconsin Assembly speaker        73      1    0.0     3.0    2.0     5.0   \n",
       "4                   President        18      1   70.0    71.0  160.0   163.0   \n",
       "\n",
       "   pantsonfire                                   context  word_count  \\\n",
       "0          0.0                                  a mailer          11   \n",
       "1          0.0                           a floor speech.          24   \n",
       "2          9.0                                    Denver          19   \n",
       "3          1.0                 a an online opinion-piece          27   \n",
       "4          9.0  a Democratic debate in Philadelphia, Pa.          27   \n",
       "\n",
       "   truthval  textCluster  adjs  advbs  nouns  pnouns  nounsPercent  \\\n",
       "0         0            3     2      0      4       2      0.363636   \n",
       "1         1            0     1      2      5       4      0.208333   \n",
       "2         1            0     0      0      2       7      0.105263   \n",
       "3         1            0     3      1      5       3      0.185185   \n",
       "4         1            0     3      2      5       3      0.185185   \n",
       "\n",
       "   adjsPercent  advbsPercent  pnounsPercent  \n",
       "0     0.181818      0.000000       0.181818  \n",
       "1     0.041667      0.083333       0.166667  \n",
       "2     0.000000      0.000000       0.368421  \n",
       "3     0.111111      0.037037       0.111111  \n",
       "4     0.111111      0.074074       0.111111  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes: \n",
    "- How many classes should be evaluated? T/F vs pants on fire, false, barely true, etc.\n",
    "- How to incorporate tokenized vectors?\n",
    "- How to incorporate the topic models? Assign each ob to a cluster\n",
    "- All features must be numeric for at least KMeans\n",
    "\n",
    "Clustering methods to try:\n",
    "    K-means\n",
    "    Spectral clustering\n",
    "    Hierarchical clustering\n",
    "    DBSCAN\n",
    "    OPTICS\n",
    "    Birch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "vec = TfidfVectorizer(stop_words=\"english\")\n",
    "vec.fit(df.text.values)\n",
    "features = vec.transform(df.text.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfrom sklearn.cluster import KMeans\\n\\n\\nSum_of_squared_distances = []\\nK = range(1,15)\\nfor k in K:\\n    km = KMeans(n_clusters=k)\\n    km = km.fit(features)\\n    Sum_of_squared_distances.append(km.inertia_)\\n'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "Sum_of_squared_distances = []\n",
    "K = range(1,15)\n",
    "for k in K:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km = km.fit(features)\n",
    "    Sum_of_squared_distances.append(km.inertia_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nplt.plot(K, Sum_of_squared_distances, 'bx-')\\nplt.xlabel('k')\\nplt.ylabel('Sum_of_squared_distances')\\nplt.title('Elbow Method For Optimal k')\\nplt.show()\\n\""
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(K, Sum_of_squared_distances, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Sum_of_squared_distances')\n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clustering of text vectors to create textCluster\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "cls = KMeans(n_clusters=8)\n",
    "cls.fit(features)\n",
    "preds = cls.predict(features)\n",
    "#print(len(cls.labels_))\n",
    "\n",
    "df['textCluster'] = preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['textCluster'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfrom sklearn.decomposition import PCA\\n\\npca = PCA(n_components=2)\\nreduced_features = pca.fit_transform(features.toarray())\\n\\nreduced_cluster_centers = pca.transform(cls.cluster_centers_)\\n'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "reduced_features = pca.fit_transform(features.toarray())\n",
    "\n",
    "reduced_cluster_centers = pca.transform(cls.cluster_centers_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nplt.scatter(reduced_features[:,0], reduced_features[:,1], c=cls.predict(features))\\nplt.scatter(reduced_cluster_centers[:, 0], reduced_cluster_centers[:,1], marker='x', s=100, c='b')\\n\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(reduced_features[:,0], reduced_features[:,1], c=cls.predict(features))\n",
    "plt.scatter(reduced_cluster_centers[:, 0], reduced_cluster_centers[:,1], marker='x', s=100, c='b')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "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>location</th>\n",
       "      <th>party</th>\n",
       "      <th>word_count</th>\n",
       "      <th>textCluster</th>\n",
       "      <th>truthval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015640</td>\n",
       "      <td>0.039576</td>\n",
       "      <td>-0.049402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>party</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word_count</th>\n",
       "      <td>0.015640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015040</td>\n",
       "      <td>0.070266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>textCluster</th>\n",
       "      <td>0.039576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.015040</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truthval</th>\n",
       "      <td>-0.049402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070266</td>\n",
       "      <td>0.018520</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             location  party  word_count  textCluster  truthval\n",
       "location     1.000000    NaN    0.015640     0.039576 -0.049402\n",
       "party             NaN    NaN         NaN          NaN       NaN\n",
       "word_count   0.015640    NaN    1.000000    -0.015040  0.070266\n",
       "textCluster  0.039576    NaN   -0.015040     1.000000  0.018520\n",
       "truthval    -0.049402    NaN    0.070266     0.018520  1.000000"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlation matrix of numeric feature\n",
    "\n",
    "df[['location','party','word_count','textCluster','truthval']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "#nltk.download('stopwords')\n",
    "#nltk.download('punkt')\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.sentiment.util import *\n",
    "from nltk import tokenize\n",
    "snowballstemmer = SnowballStemmer(\"english\")\n",
    "stopwords = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text'] = df['text'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\ndef text_cleaner(text):\\n    semiclean_text = []\\n    for body in text.split():\\n        body = re.sub(r\\'@[A-Za-z0-9_]+\\',\\'\\',body)\\n        body = re.sub(r\"http\\\\S+\", \"\", body)\\n        body = re.sub(r\"[0-9]*\", \"\", body)\\n        body = re.sub(r\"(”|“|-|\\\\+|`|#|,|;|\\\\|)*\", \"\", body)\\n        body = re.sub(r\"&amp\", \"\", body)\\n        body = body.lower()\\n        semiclean_text.append(body)\\n        #print(semiclean_text)\\n        \\n    return semiclean_text\\n\\ndef tokenization_and_stem(semiclean_text):\\n    total_token_ls = []\\n    total_snowballstemmer_token_ls = []\\n    #semiclean_text = \\'\\'.join(semiclean_text)\\n    #print(semiclean_text)\\n    for sentence in semiclean_text:\\n        #print(sentence)\\n        #sentence = \\'\\'.join(sentence)\\n        token_ls = []\\n        snowballstemmer_token_ls = []\\n        tokens = nltk.word_tokenize(sentence)\\n        #print(tokens)\\n        for token in tokens:\\n            #print(token)\\n            if token not in stopwords:\\n                token_ls.append(token)\\n                snowballstemmer_token_ls.append(snowballstemmer.stem(token))\\n        total_token_ls.append(token_ls)\\n        total_snowballstemmer_token_ls.append(snowballstemmer_token_ls)\\n        #print(total_token_ls)\\n   \\n        return total_token_ls, total_snowballstemmer_token_ls\\n\\n# Create lists with same length as dataframe column\\ndf_text = [0]*len(df[\\'text\\'])\\ntoken_ls = [0]*len(df[\\'text\\'])\\nsnowstemmer_token_ls = [0]*len(df[\\'text\\'])\\n\\nfor num, text in enumerate(df[\\'text\\']):\\n    result = tokenization_and_stem(text_cleaner(text))\\n    if result is not None:\\n        token_ls[num], snowstemmer_token_ls[num] = tokenization_and_stem(text_cleaner(text))\\n#print(token_ls)\\n\\ndef back_to_clean_sent(token_ls):\\n    \"\"\"\\n    In order to perform sentiment analysis,\\n    here we put the words back into sentences. \\n    \"\"\"\\n    clean_sent_ls = []\\n    for word_ls in token_ls:\\n        clean_sent = \"\"\\n        for word in word_ls:\\n            clean_sent += (word + \" \")\\n        clean_sent_ls.append(clean_sent)\\n    return clean_sent_ls\\n\\n\\nsentence_tokenized = [0]*len(df[\"text\"])\\nfor num, token in enumerate(token_ls):\\n    if isinstance(token, int) == False:\\n        sentence_tokenized[num] = back_to_clean_sent(token)\\n#print(sentence_tokenized)\\n    \\nsentence_snowstemmeed = [0]*len(df[\"text\"])\\nfor num, token in enumerate(snowstemmer_token_ls):\\n    if isinstance(token, int) == False:\\n        sentence_snowstemmeed[num] = back_to_clean_sent(token)\\n        \\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\\nanalyser = SentimentIntensityAnalyzer()\\n\\ndef sentiment_analysis(insert_processed_sentence):\\n    \"\"\"\\n    Do the sentiment analysis\\n    \"\"\"\\n    sentiment = [0]*len(df[\"text\"])\\n    for num, player_sent in enumerate(insert_processed_sentence):\\n        ll = []\\n        if isinstance(player_sent, int) == False:\\n            for sentence in player_sent:\\n                ss = analyser.polarity_scores(sentence)\\n                ll.append(ss)\\n            sentiment[num] = ll\\n    return sentiment\\n\\n\\n#sentiment_original = sentiment_analysis(df[\"text\"])\\nsentiment_token = sentiment_analysis(sentence_tokenized)\\nsentiment_snowstemmed = sentiment_analysis(sentence_snowstemmeed)\\n\\n# Compound Score: \\n    # calculated by summing the valence scores of each word in the lexicon\\n    # normalized to be between -1 (maximum of the negative score) and +1 (maximum of the positive score)\\n    # ≥ 0.05 is a positive\\n    # ≤ -0.05 is a negative\\n    # ≥ 0.05 and ≤ -0.05 is neutral\\n    \\nnew_df = pd.DataFrame()\\ni = 0\\nfor senti_token, snow_stem in zip(sentiment_token, sentiment_snowstemmed):\\n    senti_token_compound = []\\n    senti_stem_compound = []\\n    sentiment_result_token = []\\n    sentiment_result_stem = []\\n\\n    if isinstance(senti_token, int) == False:\\n        for s1 in senti_token:\\n            senti_token_compound.append(s1[\"compound\"])\\n            if s1[\"compound\"] >= 0.05:\\n                sentiment_result_token.append(\"positive\")\\n            elif s1[\"compound\"] <= -0.05:\\n                sentiment_result_token.append(\"negative\")\\n            else:\\n                sentiment_result_token.append(\"neutral\")\\n                \\n    if isinstance(snow_stem, int) == False:        \\n        for s2 in snow_stem:\\n            senti_stem_compound.append(s2[\"compound\"])\\n            if s2[\"compound\"] >= 0.05:\\n                sentiment_result_stem.append(\"positive\")\\n            elif s2[\"compound\"] <= -0.05:\\n                sentiment_result_stem.append(\"negative\")\\n            else:\\n                sentiment_result_stem.append(\"neutral\")\\n    \\n    \\n    tw_df = pd.DataFrame.from_dict({\"text\":df[\"text\"][i],\\n                                \"sentiment_token_compound\":senti_token_compound,\\n                                \"sentiment_result_token\" :sentiment_result_token, \\n                                \"sentiment_stem_compound\":senti_stem_compound,\\n                                \"sentiment_result_stem\":sentiment_result_stem})\\n    new_df = new_df.append(tw_df)\\n    i += 1\\n    \\n    \\ndf_merged = new_df.merge(df, how=\\'inner\\', on=\\'text\\')\\ndf = df_merged\\n'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def text_cleaner(text):\n",
    "    semiclean_text = []\n",
    "    for body in text.split():\n",
    "        body = re.sub(r'@[A-Za-z0-9_]+','',body)\n",
    "        body = re.sub(r\"http\\S+\", \"\", body)\n",
    "        body = re.sub(r\"[0-9]*\", \"\", body)\n",
    "        body = re.sub(r\"(”|“|-|\\+|`|#|,|;|\\|)*\", \"\", body)\n",
    "        body = re.sub(r\"&amp\", \"\", body)\n",
    "        body = body.lower()\n",
    "        semiclean_text.append(body)\n",
    "        #print(semiclean_text)\n",
    "        \n",
    "    return semiclean_text\n",
    "\n",
    "def tokenization_and_stem(semiclean_text):\n",
    "    total_token_ls = []\n",
    "    total_snowballstemmer_token_ls = []\n",
    "    #semiclean_text = ''.join(semiclean_text)\n",
    "    #print(semiclean_text)\n",
    "    for sentence in semiclean_text:\n",
    "        #print(sentence)\n",
    "        #sentence = ''.join(sentence)\n",
    "        token_ls = []\n",
    "        snowballstemmer_token_ls = []\n",
    "        tokens = nltk.word_tokenize(sentence)\n",
    "        #print(tokens)\n",
    "        for token in tokens:\n",
    "            #print(token)\n",
    "            if token not in stopwords:\n",
    "                token_ls.append(token)\n",
    "                snowballstemmer_token_ls.append(snowballstemmer.stem(token))\n",
    "        total_token_ls.append(token_ls)\n",
    "        total_snowballstemmer_token_ls.append(snowballstemmer_token_ls)\n",
    "        #print(total_token_ls)\n",
    "   \n",
    "        return total_token_ls, total_snowballstemmer_token_ls\n",
    "\n",
    "# Create lists with same length as dataframe column\n",
    "df_text = [0]*len(df['text'])\n",
    "token_ls = [0]*len(df['text'])\n",
    "snowstemmer_token_ls = [0]*len(df['text'])\n",
    "\n",
    "for num, text in enumerate(df['text']):\n",
    "    result = tokenization_and_stem(text_cleaner(text))\n",
    "    if result is not None:\n",
    "        token_ls[num], snowstemmer_token_ls[num] = tokenization_and_stem(text_cleaner(text))\n",
    "#print(token_ls)\n",
    "\n",
    "def back_to_clean_sent(token_ls):\n",
    "    \"\"\"\n",
    "    In order to perform sentiment analysis,\n",
    "    here we put the words back into sentences. \n",
    "    \"\"\"\n",
    "    clean_sent_ls = []\n",
    "    for word_ls in token_ls:\n",
    "        clean_sent = \"\"\n",
    "        for word in word_ls:\n",
    "            clean_sent += (word + \" \")\n",
    "        clean_sent_ls.append(clean_sent)\n",
    "    return clean_sent_ls\n",
    "\n",
    "\n",
    "sentence_tokenized = [0]*len(df[\"text\"])\n",
    "for num, token in enumerate(token_ls):\n",
    "    if isinstance(token, int) == False:\n",
    "        sentence_tokenized[num] = back_to_clean_sent(token)\n",
    "#print(sentence_tokenized)\n",
    "    \n",
    "sentence_snowstemmeed = [0]*len(df[\"text\"])\n",
    "for num, token in enumerate(snowstemmer_token_ls):\n",
    "    if isinstance(token, int) == False:\n",
    "        sentence_snowstemmeed[num] = back_to_clean_sent(token)\n",
    "        \n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyser = SentimentIntensityAnalyzer()\n",
    "\n",
    "def sentiment_analysis(insert_processed_sentence):\n",
    "    \"\"\"\n",
    "    Do the sentiment analysis\n",
    "    \"\"\"\n",
    "    sentiment = [0]*len(df[\"text\"])\n",
    "    for num, player_sent in enumerate(insert_processed_sentence):\n",
    "        ll = []\n",
    "        if isinstance(player_sent, int) == False:\n",
    "            for sentence in player_sent:\n",
    "                ss = analyser.polarity_scores(sentence)\n",
    "                ll.append(ss)\n",
    "            sentiment[num] = ll\n",
    "    return sentiment\n",
    "\n",
    "\n",
    "#sentiment_original = sentiment_analysis(df[\"text\"])\n",
    "sentiment_token = sentiment_analysis(sentence_tokenized)\n",
    "sentiment_snowstemmed = sentiment_analysis(sentence_snowstemmeed)\n",
    "\n",
    "# Compound Score: \n",
    "    # calculated by summing the valence scores of each word in the lexicon\n",
    "    # normalized to be between -1 (maximum of the negative score) and +1 (maximum of the positive score)\n",
    "    # ≥ 0.05 is a positive\n",
    "    # ≤ -0.05 is a negative\n",
    "    # ≥ 0.05 and ≤ -0.05 is neutral\n",
    "    \n",
    "new_df = pd.DataFrame()\n",
    "i = 0\n",
    "for senti_token, snow_stem in zip(sentiment_token, sentiment_snowstemmed):\n",
    "    senti_token_compound = []\n",
    "    senti_stem_compound = []\n",
    "    sentiment_result_token = []\n",
    "    sentiment_result_stem = []\n",
    "\n",
    "    if isinstance(senti_token, int) == False:\n",
    "        for s1 in senti_token:\n",
    "            senti_token_compound.append(s1[\"compound\"])\n",
    "            if s1[\"compound\"] >= 0.05:\n",
    "                sentiment_result_token.append(\"positive\")\n",
    "            elif s1[\"compound\"] <= -0.05:\n",
    "                sentiment_result_token.append(\"negative\")\n",
    "            else:\n",
    "                sentiment_result_token.append(\"neutral\")\n",
    "                \n",
    "    if isinstance(snow_stem, int) == False:        \n",
    "        for s2 in snow_stem:\n",
    "            senti_stem_compound.append(s2[\"compound\"])\n",
    "            if s2[\"compound\"] >= 0.05:\n",
    "                sentiment_result_stem.append(\"positive\")\n",
    "            elif s2[\"compound\"] <= -0.05:\n",
    "                sentiment_result_stem.append(\"negative\")\n",
    "            else:\n",
    "                sentiment_result_stem.append(\"neutral\")\n",
    "    \n",
    "    \n",
    "    tw_df = pd.DataFrame.from_dict({\"text\":df[\"text\"][i],\n",
    "                                \"sentiment_token_compound\":senti_token_compound,\n",
    "                                \"sentiment_result_token\" :sentiment_result_token, \n",
    "                                \"sentiment_stem_compound\":senti_stem_compound,\n",
    "                                \"sentiment_result_stem\":sentiment_result_stem})\n",
    "    new_df = new_df.append(tw_df)\n",
    "    i += 1\n",
    "    \n",
    "    \n",
    "df_merged = new_df.merge(df, how='inner', on='text')\n",
    "df = df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv('liarcodeData.csv', index=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "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>text</th>\n",
       "      <th>sentiment_token_compound</th>\n",
       "      <th>sentiment_result_token</th>\n",
       "      <th>sentiment_stem_compound</th>\n",
       "      <th>sentiment_result_stem</th>\n",
       "      <th>label</th>\n",
       "      <th>topic</th>\n",
       "      <th>author</th>\n",
       "      <th>pos</th>\n",
       "      <th>location</th>\n",
       "      <th>party</th>\n",
       "      <th>false</th>\n",
       "      <th>barely</th>\n",
       "      <th>half</th>\n",
       "      <th>mostly</th>\n",
       "      <th>pantsonfire</th>\n",
       "      <th>context</th>\n",
       "      <th>word_count</th>\n",
       "      <th>truthval</th>\n",
       "      <th>textCluster</th>\n",
       "      <th>adjs</th>\n",
       "      <th>advbs</th>\n",
       "      <th>nouns</th>\n",
       "      <th>pnouns</th>\n",
       "      <th>nounsPercent</th>\n",
       "      <th>adjsPercent</th>\n",
       "      <th>advbsPercent</th>\n",
       "      <th>pnounsPercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Says the Annies List political group supports ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>FALSE</td>\n",
       "      <td>16</td>\n",
       "      <td>dwayne-bohac</td>\n",
       "      <td>State representative</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a mailer</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>When did the decline of coal start? It started...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>half-true</td>\n",
       "      <td>1792</td>\n",
       "      <td>scott-surovell</td>\n",
       "      <td>State delegate</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>a floor speech.</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hillary Clinton agrees with John McCain \"by vo...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>mostly-true</td>\n",
       "      <td>2001</td>\n",
       "      <td>barack-obama</td>\n",
       "      <td>President</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Denver</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Chicago Bears have had more starting quart...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>TRUE</td>\n",
       "      <td>1620</td>\n",
       "      <td>robin-vos</td>\n",
       "      <td>Wisconsin Assembly speaker</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>a an online opinion-piece</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I'm the only person on this stage who has work...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral</td>\n",
       "      <td>half-true</td>\n",
       "      <td>1850</td>\n",
       "      <td>barack-obama</td>\n",
       "      <td>President</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>a Democratic debate in Philadelphia, Pa.</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  Says the Annies List political group supports ...   \n",
       "1  When did the decline of coal start? It started...   \n",
       "2  Hillary Clinton agrees with John McCain \"by vo...   \n",
       "3  The Chicago Bears have had more starting quart...   \n",
       "4  I'm the only person on this stage who has work...   \n",
       "\n",
       "   sentiment_token_compound sentiment_result_token  sentiment_stem_compound  \\\n",
       "0                       0.0                neutral                      0.0   \n",
       "1                       0.0                neutral                      0.0   \n",
       "2                       0.0                neutral                      0.0   \n",
       "3                       0.0                neutral                      0.0   \n",
       "4                       0.0                neutral                      0.0   \n",
       "\n",
       "  sentiment_result_stem        label  topic          author  \\\n",
       "0               neutral        FALSE     16    dwayne-bohac   \n",
       "1               neutral    half-true   1792  scott-surovell   \n",
       "2               neutral  mostly-true   2001    barack-obama   \n",
       "3               neutral         TRUE   1620       robin-vos   \n",
       "4               neutral    half-true   1850    barack-obama   \n",
       "\n",
       "                          pos  location  party  false  barely   half  mostly  \\\n",
       "0        State representative        56      1    0.0     1.0    0.0     0.0   \n",
       "1              State delegate        63      1    0.0     0.0    1.0     1.0   \n",
       "2                   President        18      1   70.0    71.0  160.0   163.0   \n",
       "3  Wisconsin Assembly speaker        73      1    0.0     3.0    2.0     5.0   \n",
       "4                   President        18      1   70.0    71.0  160.0   163.0   \n",
       "\n",
       "   pantsonfire                                   context  word_count  \\\n",
       "0          0.0                                  a mailer          11   \n",
       "1          0.0                           a floor speech.          24   \n",
       "2          9.0                                    Denver          19   \n",
       "3          1.0                 a an online opinion-piece          27   \n",
       "4          9.0  a Democratic debate in Philadelphia, Pa.          27   \n",
       "\n",
       "   truthval  textCluster  adjs  advbs  nouns  pnouns  nounsPercent  \\\n",
       "0         0            4     2      0      4       2      0.363636   \n",
       "1         1            6     1      2      5       4      0.208333   \n",
       "2         1            6     0      0      2       7      0.105263   \n",
       "3         1            5     3      1      5       3      0.185185   \n",
       "4         1            6     3      2      5       3      0.185185   \n",
       "\n",
       "   adjsPercent  advbsPercent  pnounsPercent  \n",
       "0     0.181818      0.000000       0.181818  \n",
       "1     0.041667      0.083333       0.166667  \n",
       "2     0.000000      0.000000       0.368421  \n",
       "3     0.111111      0.037037       0.111111  \n",
       "4     0.111111      0.074074       0.111111  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_domain = df[['party','location','word_count','textCluster', \"sentiment_stem_compound\", 'word_count','textCluster' ,'nounsPercent', 'advbsPercent', 'pnounsPercent', 'adjsPercent' ]]\n",
    "\n",
    "\n",
    "X = df[[ 'topic', 'truthval','party'  ]]\n",
    "\n",
    "model = KMeans(n_clusters = 2)\n",
    "model.fit(X)\n",
    "modelPreds = model.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BIRCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "from sklearn.cluster import Birch \n",
    "\n",
    "#X, clusters = make_blobs(n_samples = 6724, centers = 2, cluster_std = 0.75, random_state = 0) \n",
    "  \n",
    "modelBirch = Birch(branching_factor = 40, n_clusters = 2, threshold = 0.75) \n",
    "modelBirch.fit(X)\n",
    "predsBirch = modelBirch.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predsBirch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "\n",
    "def create_cm(t1, t2, title):\n",
    "    cm = confusion_matrix(t1, t2)\n",
    "    print('precision score: ', precision_score(t1,t2))\n",
    "    print('recall score: ', recall_score(t1,t2))\n",
    "    plt.matshow(cm)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()\n",
    "    print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.26      0.34      0.29      1756\n",
      "           1       0.74      0.65      0.69      4994\n",
      "\n",
      "    accuracy                           0.57      6750\n",
      "   macro avg       0.50      0.50      0.49      6750\n",
      "weighted avg       0.61      0.57      0.59      6750\n",
      "\n",
      "precision score:  0.73806275579809\n",
      "recall score:  0.6499799759711654\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 604 1152]\n",
      " [1748 3246]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(df['truthval'],modelPreds))\n",
    "create_cm(df['truthval'],modelPreds,\"KMeans\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.38      0.30      1756\n",
      "           1       0.73      0.60      0.66      4994\n",
      "\n",
      "    accuracy                           0.54      6750\n",
      "   macro avg       0.49      0.49      0.48      6750\n",
      "weighted avg       0.61      0.54      0.56      6750\n",
      "\n",
      "precision score:  0.7317133038782523\n",
      "recall score:  0.5969162995594713\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 663 1093]\n",
      " [2013 2981]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(df['truthval'],predsBirch))\n",
    "create_cm(df['truthval'],predsBirch,\"BIRCH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}