{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PyTorch train model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wczytanie niezbędnych bibliotek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wczytanie danych z pliku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../data/btc_train.csv')\n",
    "data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przygotowanie danych\n",
    "Powinienembył zrobić to w zadaniu 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "data['date'] = le.fit_transform(data['date'])\n",
    "data['hour'] = le.fit_transform(data['hour'])\n",
    "data['Volume BTC'] = data['Volume BTC']/10\n",
    "\n",
    "# Przekształć łańcuchy znaków na liczby aby zapobiec 'TypeError: can't convert np.ndarray of type numpy.object_.'\n",
    "for col in data.columns:\n",
    "    data[col] = pd.to_numeric(data[col], errors='coerce')\n",
    "\n",
    "# # Zamień brakujące wartości na 0 aby zapobiec 'IndexError: Target -9223372036854775808 is out of bounds.'\n",
    "data = data.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Przygotowanie inputs oraz targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Przekształć dane na tensory PyTorch\n",
    "inputs = torch.tensor(data[['date', 'hour', 'Volume BTC']].values, dtype=torch.float32)\n",
    "targets = torch.tensor(data['Volume USD'].values, dtype=torch.float32).view(-1, 1) # zmieniono z torch.float32 na torch.long aby zapobiec RuntimeError: expected scalar type Long but found Float\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utwórz DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_set = TensorDataset(inputs, targets)\n",
    "data_loader = DataLoader(data_set, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nn.Sequential(\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(inputs.shape[1], 64),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(64, 1),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funkcja straty i optymalizator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = nn.MSELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trenowanie modelu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model został wytrenowany.\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(10):\n",
    "    for X, y in data_loader:\n",
    "        pred = model(X)\n",
    "        loss = loss_fn(pred, y)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "print(\"Model został wytrenowany.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zapis modelu do pliku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model został zapisany do pliku 'model.pth'.\n"
     ]
    }
   ],
   "source": [
    "torch.save(model.state_dict(), \"model.pth\")\n",
    "print(\"Model został zapisany do pliku 'model.pth'.\")\n"
   ]
  }
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