129 lines
3.8 KiB
Python
129 lines
3.8 KiB
Python
import pandas as pd
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import sklearn.model_selection
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import numpy as np
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import requests
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url = "https://huggingface.co/datasets/mstz/wine/raw/main/Wine_Quality_Data.csv"
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save_path = "Wine_Quality_Data.csv"
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response = requests.get(url)
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response.raise_for_status()
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with open(save_path, "wb") as f:
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f.write(response.content)
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wine_dataset = pd.read_csv("Wine_Quality_Data.csv")
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wine_dataset['color'] = wine_dataset['color'].replace({'red': 1, 'white': 0})
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for column in wine_dataset.columns:
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wine_dataset[column] = wine_dataset[column] / wine_dataset[column].abs().max() # normalizacja
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from sklearn.model_selection import train_test_split
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wine_train, wine_test = sklearn.model_selection.train_test_split(wine_dataset, test_size=0.1, random_state=1, stratify=wine_dataset["color"])
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wine_train["color"].value_counts()
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# podzielenie na train i test
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wine_test["color"].value_counts()
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wine_test, wine_val = sklearn.model_selection.train_test_split(wine_test, test_size=0.5, random_state=1, stratify=wine_test["color"]) # podzielenie na test i validation
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wine_test["color"].value_counts()
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wine_val["color"].value_counts()
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import seaborn as sns
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sns.set_theme()
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import torch
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from torch import nn
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from torch.utils.data import DataLoader, Dataset
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class TabularDataset(Dataset):
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def __init__(self, data):
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self.data = data.values.astype('float32')
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def __getitem__(self, index):
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x = torch.tensor(self.data[index, :-1])
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y = torch.tensor(self.data[index, -1])
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return x, y
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def __len__(self):
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return len(self.data)
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batch_size = 64
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train_dataset = TabularDataset(wine_train)
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train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_dataset = TabularDataset(wine_test)
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test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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class TabularModel(nn.Module):
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def __init__(self, input_dim, hidden_dim, output_dim):
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super(TabularModel, self).__init__()
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self.fc1 = nn.Linear(input_dim, hidden_dim)
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self.relu = nn.ReLU()
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self.fc2 = nn.Linear(hidden_dim, output_dim)
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self.softmax = nn.Softmax(dim=1)
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def forward(self, x):
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out = self.fc1(x)
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out = self.relu(out)
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out = self.fc2(out)
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out = self.softmax(out)
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return out
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def predict(self, x):
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with torch.no_grad():
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output = self.forward(x)
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_, predicted = torch.max(output, 1)
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return predicted
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input_dim = wine_train.shape[1] - 1
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hidden_dim = 32
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output_dim = 2
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model = TabularModel(input_dim, hidden_dim, output_dim)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters())
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num_epochs = 10
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lr = 0.001
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alpha = 0.001
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model = TabularModel(input_dim=len(wine_train.columns)-1, hidden_dim=hidden_dim, output_dim=output_dim)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=alpha)
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for epoch in range(num_epochs):
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running_loss = 0.0
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for i, data in enumerate(train_dataloader, 0):
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inputs, labels = data
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labels = labels.type(torch.LongTensor)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item()
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# Print the loss every 1000 mini-batches
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if (epoch%2) == 0:
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print(f'Epoch {epoch + 1}, loss: {running_loss / len(train_dataloader):.4f}')
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print('Finished Training')
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correct = 0
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total = 0
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with torch.no_grad():
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for data in test_dataloader:
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inputs, labels = data
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predicted = model.predict(inputs.float())
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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accuracy= 100 * correct / total
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print('Accuracy on test set: %d %%' % accuracy)
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