72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
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import torch
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import torch.nn as nn
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import pandas as pd
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from abc import ABC, abstractmethod
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from Qtorch.Functions import dsplit
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from Qtorch.Functions import save_to_xlsx as stx
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# from sklearn.metrics import confusion_matrix
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class Qnn(nn.Module, ABC):
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def __init__(self, data, labels=None, test_size=0.2, random_state=None):
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super(Qnn, self).__init__()
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.original_labels = labels
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# Split data
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self.X_train, self.X_test, self.y_train, self.y_test, self.labels = dsplit(
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data=data,
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labels=labels,
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test_size=test_size,
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random_state=random_state
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)
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self.train_loader, self.test_loader = self._prepare_data()
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self.result = {
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'acc_and_loss': {
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'epoch': [],
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'loss': [],
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'train_accuracy': [],
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'test_accuracy': [],
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},
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'confusion_matrix': None,
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}
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@abstractmethod
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def forward(self, x):
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pass
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@abstractmethod
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def train_model(self, epochs):
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pass
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def fit(self, epochs=100):
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self.train_model(epochs)
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def save(self, project_name):
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for filename, data in self.result.items():
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if filename == 'confusion_matrix':
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data = pd.DataFrame(data, columns=self.original_labels, index=self.original_labels)
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else:
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data = pd.DataFrame(data)
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stx(project_name, filename, data)
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def _prepare_data(self):
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X_train_tensor = torch.tensor(self.X_train, dtype=torch.float32)
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y_train_tensor = torch.tensor(self.y_train, dtype=torch.long)
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X_test_tensor = torch.tensor(self.X_test, dtype=torch.float32)
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y_test_tensor = torch.tensor(self.y_test, dtype=torch.long)
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train_dataset = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor)
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test_dataset = torch.utils.data.TensorDataset(X_test_tensor, y_test_tensor)
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train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
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test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)
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return train_loader, test_loader
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# def confusion_matrix(self, test_outputs):
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# predicted = torch.argmax(test_outputs, dim=1)
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# true_label = torch.argmax(self.y_test, dim=1)
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# return confusion_matrix(predicted.cpu(), true_label.cpu())
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