nlp-lab/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import all usefull tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset 1 - fake_or_real_news.csv\n",
    "Read in File fake_or_real_news.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/fake_or_real_news.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ignores first column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.set_index('Unnamed: 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "quick view at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8476</th>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10294</th>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3608</th>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10142</th>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
       "      <td>It's primary day in New York and front-runners...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        title  \\\n",
       "Unnamed: 0                                                      \n",
       "8476                             You Can Smell Hillary’s Fear   \n",
       "10294       Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "3608              Kerry to go to Paris in gesture of sympathy   \n",
       "10142       Bernie supporters on Twitter erupt in anger ag...   \n",
       "875          The Battle of New York: Why This Primary Matters   \n",
       "\n",
       "                                                         text label  \n",
       "Unnamed: 0                                                           \n",
       "8476        Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
       "10294       Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
       "3608        U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
       "10142       — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
       "875         It's primary day in New York and front-runners...  REAL  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "store label from tob row of the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#store label before dropping it\n",
    "bin_y = df.label\n",
    "#y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cut of label column to get an unlabled array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('label', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from skikit learn the function: train_test_split\n",
    "* in the dataframe  get text column by df['text']\n",
    "* use stored y label df\n",
    "* use seed 42\n",
    "* determine split size: in this case 0.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_X_train, bin_X_test, bin_y_train, bin_y_test = train_test_split(df['text'], bin_y, test_size=0.25, random_state=4222)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset 2 - liar_dataset.zip\n",
    "Read in File liar_dataset.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#training data file\n",
    "df = pd.read_csv('/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/train.tsv', delimiter=\"\\t\", header=None, usecols=[1,2], names=['y', 'claim'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>false</td>\n",
       "      <td>Says the Annies List political group supports ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>half-true</td>\n",
       "      <td>When did the decline of coal start? It started...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mostly-true</td>\n",
       "      <td>Hillary Clinton agrees with John McCain \"by vo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>false</td>\n",
       "      <td>Health care reform legislation is likely to ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>half-true</td>\n",
       "      <td>The economic turnaround started at the end of ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             y                                              claim\n",
       "0        false  Says the Annies List political group supports ...\n",
       "1    half-true  When did the decline of coal start? It started...\n",
       "2  mostly-true  Hillary Clinton agrees with John McCain \"by vo...\n",
       "3        false  Health care reform legislation is likely to ma...\n",
       "4    half-true  The economic turnaround started at the end of ..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mul_X_train = df.claim\n",
    "mul_y_train = df.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test data file\n",
    "df = pd.read_csv('/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/test.tsv', delimiter=\"\\t\", header=None, usecols=[1,2], names=['y', 'claim'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr 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>y</th>\n",
       "      <th>claim</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>true</td>\n",
       "      <td>Building a wall on the U.S.-Mexico border will...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>false</td>\n",
       "      <td>Wisconsin is on pace to double the number of l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>false</td>\n",
       "      <td>Says John McCain has done nothing to help the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>half-true</td>\n",
       "      <td>Suzanne Bonamici supports a plan that will cut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pants-fire</td>\n",
       "      <td>When asked by a reporter whether hes at the ce...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            y                                              claim\n",
       "0        true  Building a wall on the U.S.-Mexico border will...\n",
       "1       false  Wisconsin is on pace to double the number of l...\n",
       "2       false  Says John McCain has done nothing to help the ...\n",
       "3   half-true  Suzanne Bonamici supports a plan that will cut...\n",
       "4  pants-fire  When asked by a reporter whether hes at the ce..."
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mul_X_test = df.claim\n",
    "mul_y_test = df.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#test data file\n",
    "df = pd.read_csv('/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/valid.tsv', delimiter=\"\\t\", header=None, usecols=[1,2], names=['y', 'claim'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mul_X_valid = df.claim\n",
    "mul_y_valid = df.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(mul_X_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Dataset 3\n",
    "* using code from Diego by copy paste with some small modifications\n",
    "* thanks for distributing this code @diego ;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- fake news\n",
      "Index(['y', 'claim'], dtype='object')\n",
      "3171\n",
      "3164\n",
      "6335\n",
      "-- liar liar\n",
      "Index(['y', 'claim'], dtype='object')\n",
      "{'mostly-true', 'pants-fire', 'true', 'half-true', 'false', 'barely-true'} 10240\n",
      "1676\n",
      "1995\n",
      "{'true', 'false'} 3671\n",
      "false    5159\n",
      "true     4847\n",
      "Name: y, dtype: int64\n",
      "done\n"
     ]
    }
   ],
   "source": [
    "'''import random\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "ds1 = sys.argv[1]\n",
    "ds2 = sys.argv[2]'''\n",
    "\n",
    "try:\n",
    "    print('-- fake news')\n",
    "    df1 = pd.read_csv('/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/fake_or_real_news.csv', sep=',', usecols=['title','text','label'])\n",
    "    df1['claim'] = df1[['title', 'text']].apply(lambda x: '. '.join(x), axis=1)\n",
    "    del df1['title']\n",
    "    del df1['text']\n",
    "    df1.rename(index=str, columns={'label': 'y'}, inplace=True)\n",
    "    print(df1.keys())\n",
    "    print(len(df1[df1['y']=='REAL']))\n",
    "    print(len(df1[df1['y']=='FAKE']))\n",
    "    df1['y'] = np.where(df1['y'] == 'FAKE', 'false', 'true')\n",
    "    print(len(df1))\n",
    "\n",
    "    print('-- liar liar')\n",
    "    df2 = pd.read_csv('/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/train.tsv', sep='\\t', header=None, usecols=[1,2], names=['y', 'claim'])\n",
    "    print(df2.keys())\n",
    "    print(set(df2.y), len(df2))\n",
    "    print(len(df2[df2['y'] == 'true']))\n",
    "    print(len(df2[df2['y'] == 'false']))\n",
    "    df2=df2[(df2['y'] == 'true') | (df2['y'] == 'false')]\n",
    "    print(set(df2.y), len(df2))\n",
    "\n",
    "    df3=pd.concat([df1, df2], ignore_index=True)\n",
    "\n",
    "    print(df3['y'].value_counts())\n",
    "    print('done')\n",
    "    concat_X_train, concat_X_test, concat_y_train, concat_y_test = train_test_split(df3['claim'], df3['y'], test_size=0.25, random_state=4222)\n",
    "    \n",
    "    \n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorizer Classifiers\n",
    "* tfids removes words in pregenerating the vectors by evaluating if this word appears more than 70% often in all articles\n",
    "* an immense naive approach would be to store a set of the over all occuring word in all the texts and for each text determining how often this word occurs.\n",
    "* also testing some min df thresholds as lower bound for regarded occurences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Vectorizer on Binary Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_count_vectorizer = CountVectorizer(stop_words='english')\n",
    "bin_count_train = bin_count_vectorizer.fit_transform(bin_X_train)\n",
    "bin_count_test = bin_count_vectorizer.transform(bin_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7, min_df=0.0005)\n",
    "bin_tfidf_train = bin_tfidf_vectorizer.fit_transform(bin_X_train)\n",
    "bin_tfidf_test = bin_tfidf_vectorizer.transform(bin_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a short look on the last 10 tokens for the vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zoomed',\n",
       " 'zooming',\n",
       " 'zor',\n",
       " 'zucker',\n",
       " 'zuckerberg',\n",
       " 'zuesse',\n",
       " 'zurich',\n",
       " 'zwick',\n",
       " 'état',\n",
       " 'œthe']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_tfidf_vectorizer.get_feature_names()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['تنجح', 'حلب', 'عن', 'لم', 'ما', 'محاولات', 'من', 'هذا', 'والمرضى', 'ยงade']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_count_vectorizer.get_feature_names()[-10:]#[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Vectorizer on Multilabel Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mul_count_vectorizer = CountVectorizer(stop_words='english')\n",
    "mul_count_train = mul_count_vectorizer.fit_transform(mul_X_train)\n",
    "mul_count_test = mul_count_vectorizer.transform(mul_X_test)\n",
    "mul_count_valid = mul_count_vectorizer.transform(mul_X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mul_tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)#, min_df=0.0005)\n",
    "mul_tfidf_train = mul_tfidf_vectorizer.fit_transform(mul_X_train)\n",
    "mul_tfidf_test = mul_tfidf_vectorizer.transform(mul_X_test)\n",
    "mul_tfidf_valid = mul_tfidf_vectorizer.transform(mul_X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zip',\n",
       " 'zippo',\n",
       " 'zombie',\n",
       " 'zombies',\n",
       " 'zone',\n",
       " 'zones',\n",
       " 'zoning',\n",
       " 'zoo',\n",
       " 'zuckerberg',\n",
       " 'zuckerbergs']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_tfidf_vectorizer.get_feature_names()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zip',\n",
       " 'zippo',\n",
       " 'zombie',\n",
       " 'zombies',\n",
       " 'zone',\n",
       " 'zones',\n",
       " 'zoning',\n",
       " 'zoo',\n",
       " 'zuckerberg',\n",
       " 'zuckerbergs']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_count_vectorizer.get_feature_names()[-10:]#[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix Code\n",
    "copy paste by distributed notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    See full source and example: \n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    \n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    #added after jonas hint\n",
    "    fig_1,ax_1 = plt.subplots()\n",
    "    \n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configurations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration 1\n",
    "* model a - train - [performance measures][0:4]\n",
    "* model a - test - [performance measures][0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* model a = MultinomialNB\n",
    "    * with tfidf vectorizer* dataset 1\n",
    "        * in contrast to the notebook with count vect.\n",
    "    * with dataset 1: fake_or_real_news.csv\n",
    "    * ** Take care with seeds for example split train test data function**\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.914\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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noL1f2jZO0tBi53ViNbPcyfAprdeTPB26qQsjone63ZucU1sAhwNfTd/zV0n1kuqBy4H+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\nWM3MMubEamaWMSdWM7OMObGamWXs/wP8oyQamEghrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b9b55f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(bin_tfidf_train, bin_y_train)\n",
    "pred = clf.predict(bin_tfidf_train)\n",
    "score = metrics.accuracy_score(bin_y_train, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(bin_y_train, pred, labels=['FAKE', 'REAL'])\n",
    "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.872\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114164898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(bin_tfidf_train, bin_y_train)\n",
    "pred = clf.predict(bin_tfidf_test)\n",
    "score = metrics.accuracy_score(bin_y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(bin_y_test, pred, labels=['FAKE', 'REAL'])\n",
    "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration 2\n",
    "* model b - train - [performance measures]\n",
    "* model b - validation - [performance measures]\n",
    "* model b - test - [performance measures]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.602\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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iuHc58FbEiZ0PKSZmALOBbwFU9ZSqlrXLF8BNIG0c/ZwHsolIZhHxxfpAUNUb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\nNskRpyU5E5/JNS9VPRlt6B7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+rB3SOKklxMr3gTHnd0tqLt++n6jtPcojwckV43gNBoPH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\n8AZBEBSZEN4gCIIi8x/FqW3KYadROgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141259e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(mul_tfidf_train, mul_y_train)\n",
    "pred = clf.predict(mul_tfidf_train)\n",
    "\n",
    "#print(pred[:20])\n",
    "\n",
    "score = metrics.accuracy_score(mul_y_train, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(mul_y_train, pred, labels=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])\n",
    "plot_confusion_matrix(cm, classes=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.238\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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QD0DRAnHsSIlMazovfpo0gVq1a1O9eg2/QzmMiOumFewRDfLSgt0v7uviTxG5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\nJElSEv8GmCutT8r42l8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147ef588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(mul_tfidf_train, mul_y_train)\n",
    "pred = clf.predict(mul_tfidf_test)\n",
    "score = metrics.accuracy_score(mul_y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(mul_y_test, pred, labels=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])\n",
    "plot_confusion_matrix(cm, classes=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.231\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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ikl9EBojIWhHZKCI/iEgB5xtE5E5gKPCQiCxJkVcRa8vvJ0Rkg4i0EJEJIvKe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114858fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(mul_tfidf_train, mul_y_train)\n",
    "pred = clf.predict(mul_tfidf_valid)\n",
    "score = metrics.accuracy_score(mul_y_valid, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(mul_y_valid, pred, labels=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])\n",
    "plot_confusion_matrix(cm, classes=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration 3 (!no train)\n",
    "* model a - test - dataset2 - [performance measures]\n",
    "* model b - test - dataset1 - [performance measures]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            true\n",
      "1           false\n",
      "2           false\n",
      "3       half-true\n",
      "4      pants-fire\n",
      "5            true\n",
      "6            true\n",
      "7     barely-true\n",
      "8            true\n",
      "9     barely-true\n",
      "10    barely-true\n",
      "11    barely-true\n",
      "12     pants-fire\n",
      "13          false\n",
      "14      half-true\n",
      "15           true\n",
      "16     pants-fire\n",
      "17      half-true\n",
      "18           true\n",
      "19          false\n",
      "20    mostly-true\n",
      "21      half-true\n",
      "22     pants-fire\n",
      "23    mostly-true\n",
      "24          false\n",
      "25           true\n",
      "26    barely-true\n",
      "27      half-true\n",
      "28           true\n",
      "29    mostly-true\n",
      "Name: y, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#print(type(bin_tfidf_train),type(mul_tfidf_test))\n",
    "\n",
    "tmp_mul_tfidf_test = bin_tfidf_vectorizer.transform(mul_X_test)\n",
    "\n",
    "print(mul_y_test[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf.fit(bin_tfidf_train, bin_y_train)\n",
    "pred = clf.predict(tmp_mul_tfidf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1267\n",
      "['true' 'true' 'true' 'true' 'true' 'true' 'true' 'true' 'true' 'true'\n",
      " 'true' 'true' 'true' 'true' 'true' 'true' 'false' 'true' 'true' 'true'\n",
      " 'true' 'true' 'true' 'false' 'false' 'true' 'false' 'true' 'true' 'false']\n"
     ]
    }
   ],
   "source": [
    "print(len(pred))\n",
    "pred = np.array(pred, dtype=object)\n",
    "\n",
    "pred[pred == \"FAKE\"] = \"false\"\n",
    "pred[pred == \"REAL\"] = \"true\"\n",
    "\n",
    "print(pred[:30])\n",
    "#pred[pred['FAKE']==false]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.169\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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l70EdGXn7nzY5f9M1f6R9k13TpG5TXp0wntYtm9GyeVNuv21EuuVsgmsrmbsGdeCzO/vy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\nzFriBGtm1hInWDOzlvwCAdo7IVKwydUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141d4b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score = metrics.accuracy_score(mul_y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(mul_y_test, pred, labels=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true' ])\n",
    "plot_confusion_matrix(cm, classes=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MultinomialNB()\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "clf = MLPClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0           true\n",
      "1          false\n",
      "2          false\n",
      "3      half-true\n",
      "4     pants-fire\n",
      "5           true\n",
      "6           true\n",
      "7    barely-true\n",
      "8           true\n",
      "9    barely-true\n",
      "Name: y, dtype: object\n"
     ]
    }
   ],
   "source": [
    "tmp_bin_tfidf_test = mul_tfidf_vectorizer.transform(bin_X_test)\n",
    "\n",
    "print(mul_y_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(mul_tfidf_train, mul_y_train)\n",
    "pred = clf.predict(tmp_bin_tfidf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pred[:20])\n",
    "\n",
    "bin_y_test = np.array(bin_y_test, dtype=object)\n",
    "\n",
    "bin_y_test[bin_y_test == \"FAKE\"] = \"false\"\n",
    "bin_y_test[bin_y_test == \"REAL\"] = \"true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = metrics.accuracy_score(bin_y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(bin_y_test, pred, labels=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])\n",
    "plot_confusion_matrix(cm, classes=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration 4\n",
    "* model c - train - [performance measures][0:4]\n",
    "* model c - test - [performance measures][0:4]"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}