00-03-split_NYT50.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_json(\"./data/nyt_corpus_LDC2008T19_50.json\")\n",
    "df = shuffle(df, random_state=0)\n",
    "\n",
    "df_test = df.iloc[0:3452]\n",
    "df_val = df.iloc[3452:7452]\n",
    "df_train = df.iloc[7452:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = \"./data/nyt_corpus_LDC2008T19_50\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(base_dir):\n",
    "  os.makedirs(base_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.to_json(base_dir + \"/train.json\")\n",
    "df_val.to_json(base_dir + \"/val.json\")\n",
    "df_test.to_json(base_dir + \"/test.json\")"
   ]
  }
 ],
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