{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from datasets_common import write_dataset, train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_dir = Path('ru')\n",
    "parts_dir = dataset_dir / 'dataset_parts' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for dataset_path in parts_dir.glob(\"*.csv\"):\n",
    "    dfs.append(pd.read_csv(dataset_path))\n",
    "df = pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_name = 'category'\n",
    "df = df.rename(columns={'categories': label_name})\n",
    "for column in df.columns:\n",
    "    def transform_cell(value):\n",
    "        prefixes = [\"{'translation_text': '\", \"{\\'translation_text\\': \\'\", \"\\'translation_text\\':\", \"{'translation_text': \\\"\"]\n",
    "        suffix = \"\\'}\"\n",
    "        for prefix in prefixes:\n",
    "            if value.startswith(prefix):\n",
    "                value = value[len(prefix):]\n",
    "        if value.endswith(suffix):\n",
    "            value = value[:-len(suffix)]\n",
    "        return value\n",
    "    df[column] = df[column].apply(transform_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[label_name]\n",
    "X = df.drop(columns=label_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_filename = \"arxiv_train.csv\"\n",
    "test_filename = \"arxiv_test.csv\"\n",
    "write_dataset(dest_dir=dataset_dir, X=X_train, y=y_train, filename=train_filename, to_json=False)\n",
    "write_dataset(dest_dir=dataset_dir, X=X_test, y=y_test, filename=test_filename, to_json=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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",
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