82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
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import torch
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import pandas as pd
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import numpy as np
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from tqdm import tqdm
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import matplotlib
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import matplotlib.pyplot as plt
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import seaborn as sns
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train_dataset = pd.read_csv('train_dataset.csv')
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test_dataset = pd.read_csv('test_dataset.csv')
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X_train = train_dataset.drop(columns=['No-show']).to_numpy()
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X_test = test_dataset.drop(columns=['No-show']).to_numpy()
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y_train = train_dataset['No-show'].to_numpy()
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y_test = test_dataset['No-show'].to_numpy()
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class LogisticRegression(torch.nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LogisticRegression, self).__init__()
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self.linear = torch.nn.Linear(input_dim, output_dim)
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def forward(self, x):
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outputs = torch.sigmoid(self.linear(x))
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return outputs
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epochs = 50_000
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input_dim = 9
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output_dim = 1
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learning_rate = 0.01
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model = LogisticRegression(input_dim, output_dim)
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criterion = torch.nn.BCELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
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X_train, X_test = torch.Tensor(X_train),torch.Tensor(X_test)
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y_train, y_test = torch.Tensor(y_train),torch.Tensor(y_test)
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losses = []
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losses_test = []
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Iterations = []
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iter = 0
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for epoch in tqdm(range(int(epochs)), desc='Training Epochs'):
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x = X_train
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labels = y_train
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optimizer.zero_grad() # Setting our stored gradients equal to zero
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outputs = model(X_train)
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loss = criterion(torch.squeeze(outputs), labels)
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loss.backward() # Computes the gradient of the given tensor w.r.t. the weights/bias
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optimizer.step() # Updates weights and biases with the optimizer (SGD)
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iter+=1
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if iter%10000==0:
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with torch.no_grad():
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# Calculating the loss and accuracy for the test dataset
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correct_test = 0
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total_test = 0
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outputs_test = torch.squeeze(model(X_test))
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loss_test = criterion(outputs_test, y_test)
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predicted_test = outputs_test.round().detach().numpy()
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total_test += y_test.size(0)
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correct_test += np.sum(predicted_test == y_test.detach().numpy())
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accuracy_test = 100 * correct_test/total_test
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losses_test.append(loss_test.item())
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# Calculating the loss and accuracy for the train dataset
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total = 0
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correct = 0
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total += y_train.size(0)
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correct += np.sum(torch.squeeze(outputs).round().detach().numpy() == y_train.detach().numpy())
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accuracy = 100 * correct/total
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losses.append(loss.item())
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Iterations.append(iter)
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print(f"Iteration: {iter}. \nTest - Loss: {loss_test.item()}. Accuracy: {accuracy_test}")
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print(f"Train - Loss: {loss.item()}. Accuracy: {accuracy}\n")
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with open("logs.txt", "a") as myfile:
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myfile.write(f"loss={loss.item()}, accuracy={accuracy}\n")
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