{
 "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.neural_network import MLPClassifier\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#https://github.com/kjam/random_hackery/blob/master/Attempting%20to%20detect%20fake%20news.ipynb"
   ]
  },
  {
   "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": {
    "collapsed": true
   },
   "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": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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 column with the classification of each text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "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": {
    "collapsed": true
   },
   "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 4222\n",
    "* determine split size: in this case 0.25\n",
    "* **take care that these config is the same to get compareable results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "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\n",
    "* cause of three file read in each file on its own and assign it to train test vaidation \n",
    "\n",
    "#### So first train set\n",
    "\n",
    "* only use the text part and ignore the others\n",
    "* it would also be possible to for example to concatiate the other information ito the text part but youll lose the robustness againstother datasets without these coulums and you'll mix up information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "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": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</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",
       "    </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": "markdown",
   "metadata": {},
   "source": [
    "#### second dataset: test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "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": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</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",
       "    </tr>\n",
       "  </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",
       "    </tr>\n",
       "    <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..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": "markdown",
   "metadata": {},
   "source": [
    "#### as third dataset: validation set"
   ]
  },
  {
   "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",
      "{'true', 'pants-fire', 'false', 'barely-true', 'mostly-true', 'half-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": "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 (tfidf)\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 of words over all texts ... not easy to predict if this is a good idea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Vectorizer on Binary Classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "generate two different vectorizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#building the vectorspace\n",
    "bin_count_vectorizer = CountVectorizer(stop_words='english')\n",
    "bin_count_train = bin_count_vectorizer.fit_transform(bin_X_train)\n",
    "#transform  the other sets into same feature vector as trained on train data \n",
    "bin_count_test = bin_count_vectorizer.transform(bin_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#building the vectorspace\n",
    "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",
    "#transform  the other sets into same feature vector as trained on train data \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": [
       "['تنجح', 'حلب', 'عن', 'لم', 'ما', 'محاولات', 'من', 'هذا', 'والمرضى', 'ยงade']"
      ]
     },
     "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\n",
    "* same again\n",
    "* only additional transform for validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "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": 23,
   "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": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_tfidf_vectorizer.get_feature_names()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_count_vectorizer.get_feature_names()[-10:]#[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Vectors on merged Sets\n",
    "* again the same ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "concat_count_vectorizer = CountVectorizer(stop_words='english')\n",
    "concat_count_train = concat_count_vectorizer.fit_transform(concat_X_train)\n",
    "concat_count_test = concat_count_vectorizer.transform(concat_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "concat_tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)#, min_df=0.0005)\n",
    "concat_tfidf_train = concat_tfidf_vectorizer.fit_transform(concat_X_train)\n",
    "concat_tfidf_test = concat_tfidf_vectorizer.transform(concat_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['القادمون',\n",
       " 'ایران',\n",
       " 'جنگ',\n",
       " 'سال',\n",
       " 'عربي',\n",
       " 'علیه',\n",
       " 'مطالعاتی',\n",
       " 'مورد',\n",
       " 'کدآمایی',\n",
       " 'ยงade']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_tfidf_vectorizer.get_feature_names()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['القادمون',\n",
       " 'ایران',\n",
       " 'جنگ',\n",
       " 'سال',\n",
       " 'عربي',\n",
       " 'علیه',\n",
       " 'مطالعاتی',\n",
       " 'مورد',\n",
       " 'کدآمایی',\n",
       " 'ยงade']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_count_vectorizer.get_feature_names()[-10:]#[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix Code\n",
    "* copy paste by distributed notebook\n",
    "* thx for providing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Dictionary definition for export to rdf in the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "example_configuration = {\n",
    "    \"name\":\"experiment1\",\n",
    "    \n",
    "    \"model\":\"generic_model_1_name\",\n",
    "    \"model-name\":\"deepNN\",\n",
    "\n",
    "\n",
    "    \"dataset\":\"X1\",\n",
    "    \"dataset-name\":\"fake_or_real_news\",\n",
    "    \"dataset-link\":\"'/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/fake_or_real_news.csv'\",\n",
    "    \n",
    "    \"measurement-name\":\"measurement_configuration1\",\n",
    "        \n",
    "    \"execution-name\":\"execution_configuration1\",\n",
    "    \n",
    "    \"precision\":0.33,\n",
    "    \"recall\":0.73,\n",
    "    \"accuracy\":0.55\n",
    "}"
   ]
  },
  {
   "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": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_model_a = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.887\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZZa7K86oTq5nlj3usZmZZ8g0CZmbZElBX19qP48tWdUdvZm1SVk9plXStpGmSxhe0jZT0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1640c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_model_a.fit(bin_tfidf_train, bin_y_train)\n",
    "pred = clf_model_a.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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.826\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a162f7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clf_model_a.fit(bin_tfidf_train, bin_y_train)\n",
    "pred = clf_model_a.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'])\n",
    "\n",
    "precision = cm[1,1]/(cm[1,1]+cm[0,1])\n",
    "recall = cm[1,1]/(cm[1,1]+cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "configuration_1 = {\n",
    "    \"name\":\"configuration1\",\n",
    "    \n",
    "    \"model\":\"model_a\",\n",
    "    \"model-name\":\"multinomialNB\",\n",
    "\n",
    "\n",
    "    \"dataset\":\"X1\",\n",
    "    \"dataset-name\":\"fake_or_real_news\",\n",
    "    \"dataset-link\":\"unknown\",\n",
    "    \n",
    "    \"measurement-name\":\"measurement_config1\",\n",
    "        \n",
    "    \"execution-name\":\"execution_config1\",\n",
    "    \n",
    "    \"precision\":precision,\n",
    "    \"recall\":recall,\n",
    "    \"accuracy\":score\n",
    "}"
   ]
  },
  {
   "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": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_model_b = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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 0x1a14d84a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_model_b.fit(mul_tfidf_train, mul_y_train)\n",
    "pred = clf_model_b.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": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#clf = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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 0x1a162ee240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clf.fit(mul_tfidf_train, mul_y_train)\n",
    "pred = clf_model_b.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": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#clf_model_b = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.231\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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oCwCta8ZzJFmZsc7/BPvTz9MpVaoUW7dupXXLy6lWvTpNmzX3O6zjZLFEECci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\nYIuiKFrEvwEZTQgEsUeetgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16437780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clf_model_b.fit(mul_tfidf_train, mul_y_train)\n",
    "pred = clf_model_b.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": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "precision = cm[1,1]/(cm[1,1]+cm[0,1])\n",
    "recall = cm[1,1]/(cm[1,1]+cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "configuration_2 = {\n",
    "    \"name\":\"configuration2\",\n",
    "    \n",
    "    \"model\":\"model_a\",\n",
    "    \"model-name\":\"multinomialNB\",\n",
    "\n",
    "\n",
    "    \"dataset\":\"X2\",\n",
    "    \"dataset-name\":\"liar\",\n",
    "    \"dataset-link\":\"unknown\",\n",
    "    \n",
    "    \"measurement-name\":\"measurement_config2\",\n",
    "        \n",
    "    \"execution-name\":\"execution_config2\",\n",
    "    \n",
    "    \"precision\":precision,\n",
    "    \"recall\":recall,\n",
    "    \"accuracy\":score\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration 3\n",
    "**In contrast to the discussed true false FAKE REAL bin class approch, I just want to see the expected horible results :)**\n",
    "* model a - test - dataset2 - [performance measures]\n",
    "* model b - test - dataset1 - [performance measures]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_tmp = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
      "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": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_tmp.fit(bin_tfidf_train, bin_y_train)\n",
    "pred = clf_tmp.predict(tmp_mul_tfidf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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' 'true' '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": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.170\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
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qvi3p938f8MB/ZvLAf2YCcMTeu3DmYU1Y+ufacou1KGmSP0vkLdg0U2+XXdm7dVsAatSs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a164043c8>"
      ]
     },
     "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": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_tmp = MultinomialNB()\n",
    "#from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "#clf = MLPClassifier(hidden_layer_sizes=(40,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_tmp.fit(mul_tfidf_train, mul_y_train)\n",
    "pred = clf_tmp.predict(tmp_bin_tfidf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['half-true' 'false' 'false' 'half-true' 'half-true' 'barely-true' 'false'\n",
      " 'mostly-true' 'half-true' 'false' 'half-true' 'half-true' 'false' 'false'\n",
      " 'false' 'half-true' 'half-true' 'half-true' 'false' 'true']\n"
     ]
    }
   ],
   "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": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.220\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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VyaYgaXIpYYHpdeDLajz/InBgDOPauYL2Z4AmMfHDqcDXAGY2HrgEeE3S2Dh/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\nxMpH1DMCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15546a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": "markdown",
   "metadata": {},
   "source": [
    "As expected not these fancy results -.- "
   ]
  },
  {
   "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": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_model_c = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.860\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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GpJkfN9BZ5JOo9zcIfCCpY9pb7QjMSsunA4Xh0QWYkZbvskL507UdRBH186uH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\nMuZgNTPLmIPVzCxjDlYzs4z9P3Wjv5Jr3ijvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a155caf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_model_c.fit(concat_tfidf_train, concat_y_train)\n",
    "pred = clf_model_c.predict(concat_tfidf_train)\n",
    "score = metrics.accuracy_score(concat_y_train, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(concat_y_train, pred, labels=['true',  'false'])\n",
    "plot_confusion_matrix(cm, classes=['true',  'false'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#clf = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.749\n",
      "Confusion matrix, without normalization\n"
     ]
    },
    {
     "data": {
      "image/png": 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GLXavUk9gQ2AY0DUiJkASvkCXtFh34POCj41P1y0xX7wys9xYwjuvJkfEJrXu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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a16435278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clf.fit(concat_tfidf_train, concat_y_train)\n",
    "pred = clf_model_c.predict(concat_tfidf_test)\n",
    "score = metrics.accuracy_score(concat_y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)\n",
    "cm = metrics.confusion_matrix(concat_y_test, pred, labels=['true',  'false'])\n",
    "plot_confusion_matrix(cm, classes=['true',  'false'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "precision = cm[1,1]/(cm[1,1]+cm[0,1])\n",
    "recall = cm[1,1]/(cm[1,1]+cm[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "configuration_4 = {\n",
    "    \"name\":\"configuration4\",\n",
    "    \n",
    "    \"model\":\"model_c\",\n",
    "    \"model-name\":\"multinomialNB\",\n",
    "\n",
    "\n",
    "    \"dataset\":\"X3\",\n",
    "    \"dataset-name\":\"concat\",\n",
    "    \"dataset-link\":\"unknown\",\n",
    "    \n",
    "    \"measurement-name\":\"measurement_config4\",\n",
    "        \n",
    "    \"execution-name\":\"execution_config4\",\n",
    "    \n",
    "    \"precision\":precision,\n",
    "    \"recall\":recall,\n",
    "    \"accuracy\":score\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting results as RDF data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from rdflib import Namespace, Graph, Literal\n",
    "from rdflib.namespace import FOAF, OWL, XSD, RDFS, DCTERMS, DOAP, DC, RDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prov = Namespace('http://www.w3.org/ns/prov#')\n",
    "dcat = Namespace('http://www.w3.org/ns/dcat#')\n",
    "mexalgo = Namespace('http://mex.aksw.org/mex-algo#')\n",
    "mexperf = Namespace('http://mex.aksw.org/mex-perf#')\n",
    "mexcore = Namespace('http://mex.aksw.org/mex-core#')\n",
    "this = Namespace('http://mex.aksw.org/examples/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_configuration = {\n",
    "    \"name\":\"experiment1\",\n",
    "    \n",
    "    \"model\":\"generic_model_1_name\",\n",
    "    \"model-name\":\"deepNN\",\n",
    "\n",
    "\n",
    "    \"dataset\":\"X1\",\n",
    "    \"dataset-name\":\"Liar\",\n",
    "    \"dataset-link\":\"google.com\",\n",
    "    \n",
    "    \"measurement-name\":\"generic_measurement_hub\",\n",
    "        \n",
    "    \"execution-name\":\"genereric_execution_name\",\n",
    "    \n",
    "    \"precision\":0.33,\n",
    "    \"recall\":0.73,\n",
    "    \"accuracy\":0.55\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment_root_graph(root_node_name):\n",
    "    g = Graph()\n",
    "    g.add((this[root_node_name],RDF.type, mexcore.Experiment))\n",
    "    g.add((this[root_node_name],RDF.type, mexcore.ApplicationContext))\n",
    "    g.add((this[root_node_name],RDFS.label, Literal('2719095')))\n",
    "    g.add((this[root_node_name],DCTERMS.date, Literal('2018-05-22',datatype=XSD.date)))\n",
    "    g.add((this[root_node_name],FOAF.givenName, Literal('Carsten')))\n",
    "    g.add((this[root_node_name],FOAF.mbox, Literal('carsten.draschner@gmail.com')))\n",
    "    return g ,this[root_node_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'rdflib.term.URIRef'>\n"
     ]
    }
   ],
   "source": [
    "g, r = experiment_root_graph(\"first_test\")\n",
    "print(type(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#extend a rdf graph with a configuration\n",
    "def experiment_to_rdf(graph, experiment, root_node):\n",
    "    \n",
    "    g.add((this[experiment[\"name\"]],RDF.type,mexcore.ExperimentConfiguration))\n",
    "    g.add((this[experiment[\"name\"]],prov.used, this[experiment[\"model\"]]))\n",
    "    g.add((this[experiment[\"name\"]],prov.wasStartedBy, root_node))\n",
    "    \n",
    "    #dataset information\n",
    "    g.add((this[experiment[\"dataset\"]],RDF.type,mexcore.Dataset))\n",
    "    g.add((this.dataset2,RDFS.label,Literal(experiment[\"dataset-name\"])))\n",
    "    g.add((this.dataset2,DCTERMS.landingPage,Literal(experiment[\"dataset-link\"])))\n",
    "    \n",
    "    #model description\n",
    "    g.add((this[experiment[\"model\"]],RDF.type,mexalgo.Algorithm))\n",
    "    g.add((this[experiment[\"model\"]],RDFS.label,Literal(experiment[\"model-name\"])))\n",
    "    g.add((this[experiment[\"model\"]],DCTERMS.identifier,Literal(experiment[\"model-name\"])))\n",
    "    ###g.add((this.model1,mexalgo.hasHyperParameter,this.hyerparameter1))\n",
    "    \n",
    "    #execution\n",
    "    g.add((this[experiment[\"execution-name\"]],RDF.type,mexcore.ExecutionOverall))\n",
    "    g.add((this[experiment[\"execution-name\"]],prov.generated,this[experiment[\"measurement-name\"]]))\n",
    "    g.add((this[experiment[\"execution-name\"]],prov.used,this.test))\n",
    "    g.add((this[experiment[\"execution-name\"]],prov.used,this[experiment[\"model\"]]))\n",
    "    \n",
    "    #test\n",
    "    g.add((this.test,RDF.type,mexcore.Test))\n",
    "    g.add((this.test,RDFS.label,Literal('Test')))\n",
    "    \n",
    "    #evaluation information\n",
    "    g.add((this[experiment[\"measurement-name\"]],RDF.type,mexcore.PerformanceMeasure))\n",
    "    g.add((this[experiment[\"measurement-name\"]],mexperf.precision,Literal(experiment[\"precision\"],datatype=XSD.float)))\n",
    "    g.add((this[experiment[\"measurement-name\"]],mexperf.recall,Literal(experiment[\"recall\"],datatype=XSD.float)))\n",
    "    g.add((this[experiment[\"measurement-name\"]],mexperf.accuracy,Literal(experiment[\"accuracy\"],datatype=XSD.float)))\n",
    "    g.add((this[experiment[\"measurement-name\"]],prov.wasGeneratedBy,this[experiment[\"execution-name\"]]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_to_rdf(g,configuration_1,r)\n",
    "experiment_to_rdf(g,configuration_2,r)\n",
    "experiment_to_rdf(g,configuration_4,r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('task2_metadata.ttl','wb') as f:\n",
    "    f.write(g.serialize(format='turtle'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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