Prepare_Train_and_Validation_Datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The dataset I will use for my experiments is a medium sized Dutch dataset containing news articles from various Dutch newspapers. Each article is labelled as being written in a partisan or neutral style.\n",
    "\n",
    "The dataset was originally published at the following [url](https://github.com/dpgmedia/partisan-news2019). However not all files are available anymore (they didn't pay for the AWS storage bills??).\n",
    "\n",
    "The dataset is still available as a [Kaggle Dataset](https://www.kaggle.com/datasets/rsmits/dpgmedia2019). Use the Kaggle Dataset to download the files if you require those.\n",
    "\n",
    "The focus of this notebook is todo some brief EDA, some data pre-processing and to split the dataset into rather small Train and Validation dataset.\n",
    "\n",
    "The size of the datasets is chosen with the idea in mind that finetuning of GPT-3.5 doesn't need many samples to learn a classification. And also to make sure that my credit card bill won't explode ;-)\n",
    "\n",
    "The size is still acceptable to also train and validate some older Transformer models (Multi-lingual DistilBert and Bert). This allows us to make an interresting comparison between regular Transformer models and the latest available GPT-3.5 model. \n",
    "\n",
    "!! Note: At the time of writing I have not yet gained access to GPT-4...but when I do I will add it to this experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Modules\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "SEED = 42\n",
    "MAX_WORDS = 192"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the files as mentioned below from the Kaggle Dataset location and store them in a folder of your own choosing. Modify the code if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DPGNews2019 Dataframe Shape: (103870, 8)\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>mainSection</th>\n",
       "      <th>published_at</th>\n",
       "      <th>publisher</th>\n",
       "      <th>partisan</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10971898</td>\n",
       "      <td>Ons oordeel over de mooiste en lelijkste kerst...</td>\n",
       "      <td>Ons oordeel over de mooiste en lelijkste kerst...</td>\n",
       "      <td>/algemeen</td>\n",
       "      <td>2018-12-25</td>\n",
       "      <td>eindhovensdagblad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ed.nl/show/ons-oordeel-over-de-mooiste-en-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10976088</td>\n",
       "      <td>Alan Parsons Project speelt in april in Paradiso</td>\n",
       "      <td>Alan Parsons Project speelt in april in Paradi...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>parool</td>\n",
       "      <td>true</td>\n",
       "      <td>www.parool.nl/nieuws/alan-parsons-project-spee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9359799</td>\n",
       "      <td>'Geer, ga met je dikke kont van die struisvoge...</td>\n",
       "      <td>'Geer, ga met je dikke kont van die struisvoge...</td>\n",
       "      <td>/algemeen</td>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>eindhovensdagblad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ed.nl/show/-geer-ga-met-je-dikke-kont-van-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10237812</td>\n",
       "      <td>België onderzoekt video kindermishandeling</td>\n",
       "      <td>België onderzoekt video kindermishandelingDe B...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2018-07-22</td>\n",
       "      <td>ad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ad.nl/buitenland/belgie-onderzoekt-video-k...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7303393</td>\n",
       "      <td>Oud-medewerkers Turkse beurs opgepakt voor 'ba...</td>\n",
       "      <td>Oud-medewerkers Turkse beurs opgepakt voor 'ba...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2017-05-12</td>\n",
       "      <td>ad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ad.nl/buitenland/oud-medewerkers-turkse-be...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                              title  \\\n",
       "0  10971898  Ons oordeel over de mooiste en lelijkste kerst...   \n",
       "1  10976088   Alan Parsons Project speelt in april in Paradiso   \n",
       "2   9359799  'Geer, ga met je dikke kont van die struisvoge...   \n",
       "3  10237812         België onderzoekt video kindermishandeling   \n",
       "4   7303393  Oud-medewerkers Turkse beurs opgepakt voor 'ba...   \n",
       "\n",
       "                                                text mainSection published_at  \\\n",
       "0  Ons oordeel over de mooiste en lelijkste kerst...   /algemeen   2018-12-25   \n",
       "1  Alan Parsons Project speelt in april in Paradi...     /nieuws   2018-12-06   \n",
       "2  'Geer, ga met je dikke kont van die struisvoge...   /algemeen   2018-01-11   \n",
       "3  België onderzoekt video kindermishandelingDe B...     /nieuws   2018-07-22   \n",
       "4  Oud-medewerkers Turkse beurs opgepakt voor 'ba...     /nieuws   2017-05-12   \n",
       "\n",
       "           publisher partisan  \\\n",
       "0  eindhovensdagblad    false   \n",
       "1             parool     true   \n",
       "2  eindhovensdagblad    false   \n",
       "3                 ad    false   \n",
       "4                 ad    false   \n",
       "\n",
       "                                                 url  \n",
       "0  www.ed.nl/show/ons-oordeel-over-de-mooiste-en-...  \n",
       "1  www.parool.nl/nieuws/alan-parsons-project-spee...  \n",
       "2  www.ed.nl/show/-geer-ga-met-je-dikke-kont-van-...  \n",
       "3  www.ad.nl/buitenland/belgie-onderzoekt-video-k...  \n",
       "4  www.ad.nl/buitenland/oud-medewerkers-turkse-be...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_dpgnews_df(seed):\n",
    "    # Set 1: Articles\n",
    "    articles_df = pd.read_json('./dpgMedia2019-articles-bypublisher.jsonl', lines = True)\n",
    "    articles_df = articles_df.set_index('id')\n",
    "    \n",
    "    # Set 2: Labels\n",
    "    labels_df = pd.read_json('./dpgMedia2019-labels-bypublisher.jsonl', lines = True)\n",
    "    labels_df = labels_df.set_index('id')\n",
    "    \n",
    "    # Finalize Full Data\n",
    "    dpgnews_df = articles_df.join(labels_df, on = ['id'], how = 'inner')\n",
    "    \n",
    "    # Randomize all rows...\n",
    "    dpgnews_df = dpgnews_df.sample(frac = 1.0, random_state = seed)\n",
    "    dpgnews_df.reset_index(inplace = True)\n",
    "    print(f'DPGNews2019 Dataframe Shape: {dpgnews_df.shape}') \n",
    "\n",
    "    return dpgnews_df\n",
    "\n",
    "# Get DpgNews Dataframe\n",
    "dpgnews_df = get_dpgnews_df(SEED)\n",
    "\n",
    "# summary\n",
    "dpgnews_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see some samples.\n",
    "\n",
    "Looking at the value counts below for the partisan values we can see that the dataset is balanced enough to use for our binary classification experiment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "partisan\n",
       "true     52922\n",
       "false    50948\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partisan Label Count\n",
    "dpgnews_df.partisan.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will plot a quick overview of the article count per newspaper publisher and the partisan label distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Article count per Publisher')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "sns.set(rc= {\"figure.figsize\": (16, 8)}) \n",
    "sns.countplot(x = 'publisher', data = dpgnews_df, hue = 'partisan').set(title = 'Article count per Publisher')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The word count in each article can differ a lot. Let's determine the word count per article and look at the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Word count\n",
    "dpgnews_df['text_wordcount'] = dpgnews_df.text.str.split(' ').str.len()\n",
    "\n",
    "# Plot Words Count\n",
    "g = sns.displot(dpgnews_df, kind = 'kde', rug = True, x = 'text_wordcount', hue = 'partisan')\n",
    "g.set_axis_labels('Words Per Article Count', 'Density', labelpad = 10)\n",
    "g.fig.set_size_inches(16, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of articles has a word count of a few hundred words with some outliers having a word count of 5000 or higher words.\n",
    "\n",
    "To allow for easier comparison between GPT-3.5 and other Transformer models (which often have a limit of 512 input tokens) I will maximize each article at a maximum of 192 words.\n",
    "\n",
    "Even with different tokenization methods the amount of tokenized text is the same that way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>mainSection</th>\n",
       "      <th>published_at</th>\n",
       "      <th>publisher</th>\n",
       "      <th>partisan</th>\n",
       "      <th>url</th>\n",
       "      <th>text_wordcount</th>\n",
       "      <th>max_words_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10971898</td>\n",
       "      <td>Ons oordeel over de mooiste en lelijkste kerst...</td>\n",
       "      <td>Ons oordeel over de mooiste en lelijkste kerst...</td>\n",
       "      <td>/algemeen</td>\n",
       "      <td>2018-12-25</td>\n",
       "      <td>eindhovensdagblad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ed.nl/show/ons-oordeel-over-de-mooiste-en-...</td>\n",
       "      <td>1691</td>\n",
       "      <td>Ons oordeel over de mooiste en lelijkste kerst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10976088</td>\n",
       "      <td>Alan Parsons Project speelt in april in Paradiso</td>\n",
       "      <td>Alan Parsons Project speelt in april in Paradi...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2018-12-06</td>\n",
       "      <td>parool</td>\n",
       "      <td>true</td>\n",
       "      <td>www.parool.nl/nieuws/alan-parsons-project-spee...</td>\n",
       "      <td>157</td>\n",
       "      <td>Alan Parsons Project speelt in april in Paradi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9359799</td>\n",
       "      <td>'Geer, ga met je dikke kont van die struisvoge...</td>\n",
       "      <td>'Geer, ga met je dikke kont van die struisvoge...</td>\n",
       "      <td>/algemeen</td>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>eindhovensdagblad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ed.nl/show/-geer-ga-met-je-dikke-kont-van-...</td>\n",
       "      <td>280</td>\n",
       "      <td>'Geer, ga met je dikke kont van die struisvoge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10237812</td>\n",
       "      <td>België onderzoekt video kindermishandeling</td>\n",
       "      <td>België onderzoekt video kindermishandelingDe B...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2018-07-22</td>\n",
       "      <td>ad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ad.nl/buitenland/belgie-onderzoekt-video-k...</td>\n",
       "      <td>121</td>\n",
       "      <td>België onderzoekt video kindermishandelingDe B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7303393</td>\n",
       "      <td>Oud-medewerkers Turkse beurs opgepakt voor 'ba...</td>\n",
       "      <td>Oud-medewerkers Turkse beurs opgepakt voor 'ba...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2017-05-12</td>\n",
       "      <td>ad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ad.nl/buitenland/oud-medewerkers-turkse-be...</td>\n",
       "      <td>107</td>\n",
       "      <td>Oud-medewerkers Turkse beurs opgepakt voor 'ba...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                              title  \\\n",
       "0  10971898  Ons oordeel over de mooiste en lelijkste kerst...   \n",
       "1  10976088   Alan Parsons Project speelt in april in Paradiso   \n",
       "2   9359799  'Geer, ga met je dikke kont van die struisvoge...   \n",
       "3  10237812         België onderzoekt video kindermishandeling   \n",
       "4   7303393  Oud-medewerkers Turkse beurs opgepakt voor 'ba...   \n",
       "\n",
       "                                                text mainSection published_at  \\\n",
       "0  Ons oordeel over de mooiste en lelijkste kerst...   /algemeen   2018-12-25   \n",
       "1  Alan Parsons Project speelt in april in Paradi...     /nieuws   2018-12-06   \n",
       "2  'Geer, ga met je dikke kont van die struisvoge...   /algemeen   2018-01-11   \n",
       "3  België onderzoekt video kindermishandelingDe B...     /nieuws   2018-07-22   \n",
       "4  Oud-medewerkers Turkse beurs opgepakt voor 'ba...     /nieuws   2017-05-12   \n",
       "\n",
       "           publisher partisan  \\\n",
       "0  eindhovensdagblad    false   \n",
       "1             parool     true   \n",
       "2  eindhovensdagblad    false   \n",
       "3                 ad    false   \n",
       "4                 ad    false   \n",
       "\n",
       "                                                 url  text_wordcount  \\\n",
       "0  www.ed.nl/show/ons-oordeel-over-de-mooiste-en-...            1691   \n",
       "1  www.parool.nl/nieuws/alan-parsons-project-spee...             157   \n",
       "2  www.ed.nl/show/-geer-ga-met-je-dikke-kont-van-...             280   \n",
       "3  www.ad.nl/buitenland/belgie-onderzoekt-video-k...             121   \n",
       "4  www.ad.nl/buitenland/oud-medewerkers-turkse-be...             107   \n",
       "\n",
       "                                      max_words_text  \n",
       "0  Ons oordeel over de mooiste en lelijkste kerst...  \n",
       "1  Alan Parsons Project speelt in april in Paradi...  \n",
       "2  'Geer, ga met je dikke kont van die struisvoge...  \n",
       "3  België onderzoekt video kindermishandelingDe B...  \n",
       "4  Oud-medewerkers Turkse beurs opgepakt voor 'ba...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximize Text to number of words\n",
    "def maximize_word_count(text):\n",
    "    text_list = text.split(' ')\n",
    "    \n",
    "    if len(text_list) >= MAX_WORDS:\n",
    "        maximized_text = ' '.join(text_list[:MAX_WORDS])\n",
    "    else:\n",
    "        maximized_text = ' '.join(text_list)\n",
    "    return maximized_text\n",
    "\n",
    "# Map\n",
    "dpgnews_df['max_words_text'] =  dpgnews_df.apply(lambda x: maximize_word_count(x.text), axis = 1)   \n",
    "\n",
    "# Summary\n",
    "dpgnews_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will create binary labels and split the main dataset in smaller training and validation datasets.\n",
    "\n",
    "The training and validation .csv files are stored in Github in case you would like to reproduce these experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset Shape: (3069, 11)\n",
      "Validation Dataset Shape: (1559, 11)\n"
     ]
    }
   ],
   "source": [
    "# Partisan Modify\n",
    "labels = []\n",
    "\n",
    "# Tokenize\n",
    "for index, row in dpgnews_df.iterrows():\n",
    "    partisan = row['partisan']\n",
    "    labels.append(1 if partisan == 'true' else 0)\n",
    "dpgnews_df[\"labels\"] = labels\n",
    "\n",
    "# Train Test Split\n",
    "subset_df, val_df = train_test_split(dpgnews_df, \n",
    "                                    test_size = 0.015, \n",
    "                                    random_state = SEED,\n",
    "                                    stratify = dpgnews_df.partisan.values)\n",
    "\n",
    "# Train Test Split\n",
    "train_df, _ = train_test_split(subset_df, \n",
    "                               test_size = 0.97, \n",
    "                               random_state = SEED,\n",
    "                               stratify = subset_df.partisan.values)\n",
    "\n",
    "# Summary\n",
    "print(f'Training Dataset Shape: {train_df.shape}')\n",
    "print(f'Validation Dataset Shape: {val_df.shape}')\n",
    "\n",
    "# Save to Disk\n",
    "train_df.to_csv('./data/train_df.csv', index = False)\n",
    "val_df.to_csv('./data/val_df.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "labels\n",
       "1    1564\n",
       "0    1505\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value Counts\n",
    "train_df.labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>mainSection</th>\n",
       "      <th>published_at</th>\n",
       "      <th>publisher</th>\n",
       "      <th>partisan</th>\n",
       "      <th>url</th>\n",
       "      <th>text_wordcount</th>\n",
       "      <th>max_words_text</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25432</th>\n",
       "      <td>10706318</td>\n",
       "      <td>Ogen als schoteltjes bij de Tachtigjarige Oorlog</td>\n",
       "      <td>Ogen als schoteltjes bij de Tachtigjarige Oorl...</td>\n",
       "      <td>/home</td>\n",
       "      <td>2018-10-07</td>\n",
       "      <td>trouw</td>\n",
       "      <td>true</td>\n",
       "      <td>www.trouw.nl/home/ogen-als-schoteltjes-bij-de-...</td>\n",
       "      <td>539</td>\n",
       "      <td>Ogen als schoteltjes bij de Tachtigjarige Oorl...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31592</th>\n",
       "      <td>12633805</td>\n",
       "      <td>Geen beeld, maar een monument voor Mandela in ...</td>\n",
       "      <td>Geen beeld, maar een monument voor Mandela in ...</td>\n",
       "      <td>/amsterdam</td>\n",
       "      <td>2019-05-10</td>\n",
       "      <td>parool</td>\n",
       "      <td>true</td>\n",
       "      <td>www.parool.nl/amsterdam/geen-beeld-maar-een-mo...</td>\n",
       "      <td>662</td>\n",
       "      <td>Geen beeld, maar een monument voor Mandela in ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67378</th>\n",
       "      <td>7140125</td>\n",
       "      <td>Hoe ga je een onveilige arbeidscultuur zoals i...</td>\n",
       "      <td>Hoe ga je een onveilige arbeidscultuur zoals i...</td>\n",
       "      <td>/</td>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>trouw</td>\n",
       "      <td>true</td>\n",
       "      <td></td>\n",
       "      <td>494</td>\n",
       "      <td>Hoe ga je een onveilige arbeidscultuur zoals i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102377</th>\n",
       "      <td>4490774</td>\n",
       "      <td>Wetenschappers ontdekken lichtgevende discokikker</td>\n",
       "      <td>Wetenschappers ontdekken lichtgevende discokik...</td>\n",
       "      <td>/</td>\n",
       "      <td>2017-03-14</td>\n",
       "      <td>trouw</td>\n",
       "      <td>true</td>\n",
       "      <td></td>\n",
       "      <td>291</td>\n",
       "      <td>Wetenschappers ontdekken lichtgevende discokik...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78848</th>\n",
       "      <td>10592180</td>\n",
       "      <td>Meer fouten kabinet bij steun aan strijdgroepe...</td>\n",
       "      <td>Meer fouten kabinet bij steun aan strijdgroepe...</td>\n",
       "      <td>/home</td>\n",
       "      <td>2018-09-11</td>\n",
       "      <td>trouw</td>\n",
       "      <td>true</td>\n",
       "      <td>www.trouw.nl/home/meer-fouten-kabinet-bij-steu...</td>\n",
       "      <td>471</td>\n",
       "      <td>Meer fouten kabinet bij steun aan strijdgroepe...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                              title  \\\n",
       "25432   10706318   Ogen als schoteltjes bij de Tachtigjarige Oorlog   \n",
       "31592   12633805  Geen beeld, maar een monument voor Mandela in ...   \n",
       "67378    7140125  Hoe ga je een onveilige arbeidscultuur zoals i...   \n",
       "102377   4490774  Wetenschappers ontdekken lichtgevende discokikker   \n",
       "78848   10592180  Meer fouten kabinet bij steun aan strijdgroepe...   \n",
       "\n",
       "                                                     text mainSection  \\\n",
       "25432   Ogen als schoteltjes bij de Tachtigjarige Oorl...       /home   \n",
       "31592   Geen beeld, maar een monument voor Mandela in ...  /amsterdam   \n",
       "67378   Hoe ga je een onveilige arbeidscultuur zoals i...           /   \n",
       "102377  Wetenschappers ontdekken lichtgevende discokik...           /   \n",
       "78848   Meer fouten kabinet bij steun aan strijdgroepe...       /home   \n",
       "\n",
       "       published_at publisher partisan  \\\n",
       "25432    2018-10-07     trouw     true   \n",
       "31592    2019-05-10    parool     true   \n",
       "67378    2017-04-18     trouw     true   \n",
       "102377   2017-03-14     trouw     true   \n",
       "78848    2018-09-11     trouw     true   \n",
       "\n",
       "                                                      url  text_wordcount  \\\n",
       "25432   www.trouw.nl/home/ogen-als-schoteltjes-bij-de-...             539   \n",
       "31592   www.parool.nl/amsterdam/geen-beeld-maar-een-mo...             662   \n",
       "67378                                                                 494   \n",
       "102377                                                                291   \n",
       "78848   www.trouw.nl/home/meer-fouten-kabinet-bij-steu...             471   \n",
       "\n",
       "                                           max_words_text  labels  \n",
       "25432   Ogen als schoteltjes bij de Tachtigjarige Oorl...       1  \n",
       "31592   Geen beeld, maar een monument voor Mandela in ...       1  \n",
       "67378   Hoe ga je een onveilige arbeidscultuur zoals i...       1  \n",
       "102377  Wetenschappers ontdekken lichtgevende discokik...       1  \n",
       "78848   Meer fouten kabinet bij steun aan strijdgroepe...       1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Training Dataset - Article count per Publisher')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "sns.set(rc= {\"figure.figsize\": (16, 8)}) \n",
    "sns.countplot(x = 'publisher', data = train_df, hue = 'labels').set(title = 'Training Dataset - Article count per Publisher')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "labels\n",
       "1    794\n",
       "0    765\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value Counts\n",
    "val_df.labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>mainSection</th>\n",
       "      <th>published_at</th>\n",
       "      <th>publisher</th>\n",
       "      <th>partisan</th>\n",
       "      <th>url</th>\n",
       "      <th>text_wordcount</th>\n",
       "      <th>max_words_text</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24718</th>\n",
       "      <td>9266995</td>\n",
       "      <td>Verdachte dodelijke steekpartijen Maastricht l...</td>\n",
       "      <td>Verdachte dodelijke steekpartijen Maastricht l...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>ad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ad.nl/binnenland/verdachte-dodelijke-steek...</td>\n",
       "      <td>188</td>\n",
       "      <td>Verdachte dodelijke steekpartijen Maastricht l...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93241</th>\n",
       "      <td>4130077</td>\n",
       "      <td>Honderden arrestaties bij acties tegen mensen ...</td>\n",
       "      <td>Honderden arrestaties bij acties tegen mensen ...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2017-02-11</td>\n",
       "      <td>ad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ad.nl/buitenland/honderden-arrestaties-bij...</td>\n",
       "      <td>122</td>\n",
       "      <td>Honderden arrestaties bij acties tegen mensen ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85956</th>\n",
       "      <td>11147268</td>\n",
       "      <td>Waarom de 'oudejaarsbonus' voor de jongeren va...</td>\n",
       "      <td>Waarom de 'oudejaarsbonus' voor de jongeren va...</td>\n",
       "      <td>/home</td>\n",
       "      <td>2019-01-20</td>\n",
       "      <td>trouw</td>\n",
       "      <td>true</td>\n",
       "      <td>www.trouw.nl/home/waarom-de-oudejaarsbonus-voo...</td>\n",
       "      <td>262</td>\n",
       "      <td>Waarom de 'oudejaarsbonus' voor de jongeren va...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65239</th>\n",
       "      <td>10749100</td>\n",
       "      <td>Klaar voor de verdediging</td>\n",
       "      <td>Klaar voor de verdedigingOver ruim een week be...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2018-10-16</td>\n",
       "      <td>ad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ad.nl/binnenland/klaar-voor-de-verdediging...</td>\n",
       "      <td>411</td>\n",
       "      <td>Klaar voor de verdedigingOver ruim een week be...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9132</th>\n",
       "      <td>10700707</td>\n",
       "      <td>Windvlaag grijpt springmatras en doodt 2-jarig...</td>\n",
       "      <td>Windvlaag grijpt springmatras en doodt 2-jarig...</td>\n",
       "      <td>/nieuws</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>ad</td>\n",
       "      <td>false</td>\n",
       "      <td>www.ad.nl/buitenland/windvlaag-grijpt-springma...</td>\n",
       "      <td>286</td>\n",
       "      <td>Windvlaag grijpt springmatras en doodt 2-jarig...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                              title  \\\n",
       "24718   9266995  Verdachte dodelijke steekpartijen Maastricht l...   \n",
       "93241   4130077  Honderden arrestaties bij acties tegen mensen ...   \n",
       "85956  11147268  Waarom de 'oudejaarsbonus' voor de jongeren va...   \n",
       "65239  10749100                          Klaar voor de verdediging   \n",
       "9132   10700707  Windvlaag grijpt springmatras en doodt 2-jarig...   \n",
       "\n",
       "                                                    text mainSection  \\\n",
       "24718  Verdachte dodelijke steekpartijen Maastricht l...     /nieuws   \n",
       "93241  Honderden arrestaties bij acties tegen mensen ...     /nieuws   \n",
       "85956  Waarom de 'oudejaarsbonus' voor de jongeren va...       /home   \n",
       "65239  Klaar voor de verdedigingOver ruim een week be...     /nieuws   \n",
       "9132   Windvlaag grijpt springmatras en doodt 2-jarig...     /nieuws   \n",
       "\n",
       "      published_at publisher partisan  \\\n",
       "24718   2017-12-18        ad    false   \n",
       "93241   2017-02-11        ad    false   \n",
       "85956   2019-01-20     trouw     true   \n",
       "65239   2018-10-16        ad    false   \n",
       "9132    2018-10-05        ad    false   \n",
       "\n",
       "                                                     url  text_wordcount  \\\n",
       "24718  www.ad.nl/binnenland/verdachte-dodelijke-steek...             188   \n",
       "93241  www.ad.nl/buitenland/honderden-arrestaties-bij...             122   \n",
       "85956  www.trouw.nl/home/waarom-de-oudejaarsbonus-voo...             262   \n",
       "65239  www.ad.nl/binnenland/klaar-voor-de-verdediging...             411   \n",
       "9132   www.ad.nl/buitenland/windvlaag-grijpt-springma...             286   \n",
       "\n",
       "                                          max_words_text  labels  \n",
       "24718  Verdachte dodelijke steekpartijen Maastricht l...       0  \n",
       "93241  Honderden arrestaties bij acties tegen mensen ...       0  \n",
       "85956  Waarom de 'oudejaarsbonus' voor de jongeren va...       1  \n",
       "65239  Klaar voor de verdedigingOver ruim een week be...       0  \n",
       "9132   Windvlaag grijpt springmatras en doodt 2-jarig...       0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary\n",
    "val_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Validation Dataset - Article count per Publisher')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "sns.set(rc= {\"figure.figsize\": (16, 8)}) \n",
    "sns.countplot(x = 'publisher', data = val_df, hue = 'labels').set(title = 'Validation Dataset - Article count per Publisher')"
   ]
  }
 ],
 "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}