Update pages/4_LogisticRegressioin.py

pages/4_LogisticRegressioin.py

@@ -1 +1,74 @@
-#LogisticRegression
+#LogisticRegression
+
+import streamlit as st
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define the Logistic Regression Model
+class LogisticRegressionModel(nn.Module):
+    def __init__(self):
+        super(LogisticRegressionModel, self).__init__()
+        self.linear = nn.Linear(1, 1)
+    
+    def forward(self, x):
+        return torch.sigmoid(self.linear(x))
+
+# Generate synthetic data
+np.random.seed(0)
+torch.manual_seed(0)
+n_samples = 100
+X = np.random.rand(n_samples, 1) * 10  # Random hours between 0 and 10
+y = (X > 5).astype(int).flatten()  # Pass if study hours > 5, otherwise fail
+
+# Convert to torch tensors
+X_tensor = torch.tensor(X, dtype=torch.float32)
+y_tensor = torch.tensor(y, dtype=torch.float32)
+
+# Streamlit interface
+st.title('Logistic Regression with PyTorch')
+
+# User inputs
+num_epochs = st.number_input('Number of Epochs', min_value=100, max_value=5000, step=100, value=1000)
+learning_rate = st.number_input('Learning Rate', min_value=0.0001, max_value=0.1, step=0.0001, format="%.4f", value=0.01)
+test_hours = st.text_input('Test Study Hours (comma separated)', '4.0, 6.0, 9.0')
+
+# Initialize the model
+model = LogisticRegressionModel()
+
+# Binary Cross Entropy Loss
+criterion = nn.BCELoss()
+
+# Stochastic Gradient Descent Optimizer
+optimizer = optim.SGD(model.parameters(), lr=learning_rate)
+
+# Training the model
+loss_values = []
+for epoch in range(num_epochs):
+    outputs = model(X_tensor)
+    loss = criterion(outputs, y_tensor.view(-1, 1))
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    loss_values.append(loss.item())
+
+# Plot the loss curve
+fig, ax = plt.subplots()
+ax.plot(range(num_epochs), loss_values)
+ax.set_xlabel('Epoch')
+ax.set_ylabel('Loss')
+ax.set_title('Loss Curve')
+st.pyplot(fig)
+
+# Evaluation
+model.eval()
+test_hours = [float(hour.strip()) for hour in test_hours.split(',')]
+test_tensor = torch.tensor(test_hours, dtype=torch.float32).view(-1, 1)
+predictions = model(test_tensor).detach().numpy()
+
+# Display predictions
+st.write('## Predictions')
+for i, test_hour in enumerate(test_hours):
+    st.write(f"Study hours: {test_hour}, Predicted pass probability: {predictions[i][0]:.4f}")