Dodanie plików które przypadkowo usunąłem
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ium_05/dockerfile
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ium_05/dockerfile
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# Nasz obraz będzie dziedziczył z obrazu Ubuntu w wersji latest
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FROM ubuntu:latest
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# Instalujemy niezbędne zależności. Zwróć uwagę na flagę "-y" (assume yes)
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RUN apt update && apt install -y figlet python3 python3-pip
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# Instalacja pakietów Pythona za pomocą PIP
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RUN pip3 install pandas kaggle torch sklearn
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# Dodajemy nasz skrypt Pythona do obrazu Docker
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COPY learning.ipynb /learning.ipynb
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# Ustawiamy domyślną komendę do uruchomienia naszego skryptu Pythona
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CMD ["python3", "/learning.ipynb"]
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228
ium_05/learning.ipynb
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228
ium_05/learning.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PyTorch train model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Wczytanie niezbędnych bibliotek"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 233,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from torch.utils.data import DataLoader, TensorDataset\n",
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"import pandas as pd\n",
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"from sklearn.preprocessing import LabelEncoder"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Wczytanie danych z pliku"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 234,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv('../data/btc_train.csv')\n",
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"data = pd.DataFrame(data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Przygotowanie danych\n",
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"Powinienembył zrobić to w zadaniu 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 235,
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"metadata": {},
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"outputs": [],
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"source": [
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"le = LabelEncoder()\n",
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"data['date'] = le.fit_transform(data['date'])\n",
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"data['hour'] = le.fit_transform(data['hour'])\n",
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"data['Volume BTC'] = data['Volume BTC']/10\n",
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"\n",
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"# Przekształć łańcuchy znaków na liczby aby zapobiec 'TypeError: can't convert np.ndarray of type numpy.object_.'\n",
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"for col in data.columns:\n",
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" data[col] = pd.to_numeric(data[col], errors='coerce')\n",
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"\n",
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"# # Zamień brakujące wartości na 0 aby zapobiec 'IndexError: Target -9223372036854775808 is out of bounds.'\n",
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"data = data.fillna(0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Przygotowanie inputs oraz targets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 236,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Przekształć dane na tensory PyTorch\n",
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"inputs = torch.tensor(data[['date', 'hour', 'Volume BTC']].values, dtype=torch.float32)\n",
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"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",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Utwórz DataLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 237,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_set = TensorDataset(inputs, targets)\n",
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"data_loader = DataLoader(data_set, batch_size=64)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 238,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = nn.Sequential(\n",
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" nn.Flatten(),\n",
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" nn.Linear(inputs.shape[1], 64),\n",
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" nn.ReLU(),\n",
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" nn.Linear(64, 1),\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Funkcja straty i optymalizator"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 239,
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"metadata": {},
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"outputs": [],
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"source": [
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"loss_fn = nn.MSELoss()\n",
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"optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Trenowanie modelu"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 240,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model został wytrenowany.\n"
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]
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}
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],
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"source": [
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"for epoch in range(10):\n",
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" for X, y in data_loader:\n",
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" pred = model(X)\n",
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" loss = loss_fn(pred, y)\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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"print(\"Model został wytrenowany.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Zapis modelu do pliku"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 241,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model został zapisany do pliku 'model.pth'.\n"
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]
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}
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],
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"source": [
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"torch.save(model.state_dict(), \"model.pth\")\n",
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"print(\"Model został zapisany do pliku 'model.pth'.\")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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BIN
ium_05/model.pth
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BIN
ium_05/model.pth
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227
ium_05/predict.ipynb
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ium_05/predict.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PyTorch train model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Wczytanie niezbędnych bibliotek"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 289,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import torch\n",
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"from torch import nn\n",
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"from sklearn.preprocessing import LabelEncoder"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Wczytanie danych z pliku"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 290,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv('../data/btc_test.csv')\n",
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"data = pd.DataFrame(data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Przygotowanie danych\n",
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"Powinienembył zrobić to w zadaniu 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 291,
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"metadata": {},
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"outputs": [],
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"source": [
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"le = LabelEncoder()\n",
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"data['date'] = le.fit_transform(data['date'])\n",
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"data['hour'] = le.fit_transform(data['hour'])\n",
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"data['Volume BTC'] = data['Volume BTC']/10\n",
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"\n",
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"# Przekształć łańcuchy znaków na liczby aby zapobiec 'TypeError: can't convert np.ndarray of type numpy.object_.'\n",
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"for col in data.columns:\n",
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" data[col] = pd.to_numeric(data[col], errors='coerce')\n",
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"\n",
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"# Zamień brakujące wartości na 0 aby zapobiec 'IndexError: Target -9223372036854775808 is out of bounds.'\n",
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"data = data.fillna(0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Przygotowanie inputs oraz targets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 292,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Przekształć dane na tensory PyTorch\n",
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"inputs = torch.tensor(data[['date', 'hour', 'Volume BTC']].values, dtype=torch.float32)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 293,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = nn.Sequential(\n",
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" nn.Flatten(),\n",
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" nn.Linear(inputs.shape[1], 64),\n",
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" nn.ReLU(),\n",
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" nn.Linear(64, 1),\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Wczytanie wytrenowanego modelu"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 294,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Sequential(\n",
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" (0): Flatten(start_dim=1, end_dim=-1)\n",
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" (1): Linear(in_features=3, out_features=64, bias=True)\n",
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" (2): ReLU()\n",
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" (3): Linear(in_features=64, out_features=1, bias=True)\n",
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")"
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]
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},
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"execution_count": 294,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model.load_state_dict(torch.load(\"model.pth\"))\n",
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"model.eval()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Predykcja modelu"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 298,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[772837.5000],\n",
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" [772837.5000],\n",
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" [772837.5000],\n",
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" ...,\n",
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" [772837.5000],\n",
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" [772837.5000],\n",
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" [772837.5000]], grad_fn=<MulBackward0>)\n"
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]
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}
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],
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"source": [
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"predictions = model(inputs)\n",
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"predicted_data = (predictions.float() * 10)\n",
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"print(predicted_data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Zapis danych do pliku csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 300,
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "detach() missing 1 required positional arguments: \"input\"",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
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"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",
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"\u001b[1;31mTypeError\u001b[0m: detach() missing 1 required positional arguments: \"input\""
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]
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}
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],
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"source": [
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"predicted_data_df = pd.DataFrame(torch.detach(predicted_data).numpy())\n",
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"predicted_data_df.to_csv(\"predict_result.csv\", index=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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429942
ium_05/predict_result.csv
Normal file
429942
ium_05/predict_result.csv
Normal file
File diff suppressed because it is too large
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