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{
"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"
]
}
],
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PyTorch train model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wczytanie niezbędnych bibliotek"
]
},
{
"cell_type": "code",
"execution_count": 289,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import torch\n",
"from torch import nn\n",
"from sklearn.preprocessing import LabelEncoder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wczytanie danych z pliku"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv('../data/btc_test.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": 291,
"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": 292,
"metadata": {},
"outputs": [],
"source": [
"# Przekształć dane na tensory PyTorch\n",
"inputs = torch.tensor(data[['date', 'hour', 'Volume BTC']].values, dtype=torch.float32)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model"
]
},
{
"cell_type": "code",
"execution_count": 293,
"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": [
"### Wczytanie wytrenowanego modelu"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sequential(\n",
" (0): Flatten(start_dim=1, end_dim=-1)\n",
" (1): Linear(in_features=3, out_features=64, bias=True)\n",
" (2): ReLU()\n",
" (3): Linear(in_features=64, out_features=1, bias=True)\n",
")"
]
},
"execution_count": 294,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.load_state_dict(torch.load(\"model.pth\"))\n",
"model.eval()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predykcja modelu"
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[772837.5000],\n",
" [772837.5000],\n",
" [772837.5000],\n",
" ...,\n",
" [772837.5000],\n",
" [772837.5000],\n",
" [772837.5000]], grad_fn=<MulBackward0>)\n"
]
}
],
"source": [
"predictions = model(inputs)\n",
"predicted_data = (predictions.float() * 10)\n",
"print(predicted_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zapis danych do pliku csv"
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "detach() missing 1 required positional arguments: \"input\"",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[300], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predicted_data_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mnumpy(predicted_data))\n\u001b[0;32m 2\u001b[0m predicted_data_df\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredict_result.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
"\u001b[1;31mTypeError\u001b[0m: detach() missing 1 required positional arguments: \"input\""
]
}
],
"source": [
"predicted_data_df = pd.DataFrame(torch.detach(predicted_data).numpy())\n",
"predicted_data_df.to_csv(\"predict_result.csv\", index=False)"
]
}
],
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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