{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "\n",
    "DATA_DIR = os.path.join(\"..\", \"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1335 entries, 0 to 1334\n",
      "Columns: 324 entries, Unnamed: 0 to LastD1Season tourney\n",
      "dtypes: float64(186), int64(131), object(7)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "all_teams_agg_df = pd.read_csv(os.path.join(DATA_DIR, \"AllTeamsAgg.csv\"))\n",
    "\n",
    "all_teams_agg_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_device() -> str:\n",
    "    if torch.cuda.is_available():\n",
    "        return \"cuda\"\n",
    "    return \"cpu\"\n",
    "\n",
    "DEVICE = get_device() "
   ]
  }
 ],
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