Update pages/42_regression.py

pages/42_regression.py

@@ -1,79 +1,69 @@
+import streamlit as st
 import numpy as np
 import matplotlib.pyplot as plt
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from sklearn.model_selection import train_test_split
-
-# Step 1: Create Synthetic Data
-np.random.seed(42)
-X = np.linspace(-10, 10, 1000)
-y = 2.5 * X + np.random.normal(0, 2, X.shape)  # Linear relation with noise
-
-# Split into training and test sets
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Convert to PyTorch tensors
-X_train = torch.tensor(X_train, dtype=torch.float32).view(-1, 1)
-y_train = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)
-X_test = torch.tensor(X_test, dtype=torch.float32).view(-1, 1)
-y_test = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)
-
-# Step 2: Define and Train a Neural Network Model
-class SimpleNN(nn.Module):
-    def __init__(self):
-        super(SimpleNN, self).__init__()
-        self.fc1 = nn.Linear(1, 10)
-        self.fc2 = nn.Linear(10, 1)
-
-    def forward(self, x):
-        x = torch.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
-
-# Initialize model, loss function, and optimizer
-model = SimpleNN()
-criterion = nn.MSELoss()
-optimizer = optim.Adam(model.parameters(), lr=0.01)
-
-# Training loop
-epochs = 500
-losses = []
-
-for epoch in range(epochs):
-    model.train()
-    optimizer.zero_grad()
-    outputs = model(X_train)
-    loss = criterion(outputs, y_train)
-    loss.backward()
-    optimizer.step()
-    losses.append(loss.item())
-    if (epoch + 1) % 50 == 0:
-        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')
-
-# Step 3: Plot the Results
-# Plot the synthetic data and the model's predictions
-model.eval()
-with torch.no_grad():
-    predicted = model(X_test).numpy()
-
-plt.figure(figsize=(12, 6))
-
-# Plot data and predictions
-plt.subplot(1, 2, 1)
-plt.scatter(X_test, y_test, label='Original data', alpha=0.5)
-plt.scatter(X_test, predicted, label='Fitted line', alpha=0.5)
-plt.title('Regression Results')
-plt.xlabel('X')
-plt.ylabel('y')
-plt.legend()
-
-# Plot training loss
-plt.subplot(1, 2, 2)
-plt.plot(losses)
-plt.title('Training Loss')
-plt.xlabel('Epoch')
-plt.ylabel('Loss')
-
-plt.tight_layout()
-plt.show()
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+
+# Title
+st.title("Neural Network Line Fitting")
+
+# Sidebar sliders for generating synthetic data
+st.sidebar.header("Synthetic Data Controls")
+true_w = st.sidebar.slider('True W (slope)', min_value=-10.0, max_value=10.0, value=2.0, step=0.1)
+true_b = st.sidebar.slider('True B (intercept)', min_value=-10.0, max_value=10.0, value=1.0, step=0.1)
+num_points = st.sidebar.slider('Number of data points', min_value=10, max_value=1000, value=100, step=10)
+
+# Generate synthetic data
+np.random.seed(0)
+x_data = np.random.uniform(-100, 100, num_points)
+noise = np.random.normal(0, 10, num_points)
+y_data = true_w * x_data + true_b + noise
+
+# Neural network model
+model = Sequential([
+    Dense(1, input_dim=1)
+])
+
+model.compile(optimizer='adam', loss='mean_squared_error')
+
+# Train the model
+model.fit(x_data, y_data, epochs=100, verbose=0)
+
+# Get the trained parameters
+trained_w = model.layers[0].get_weights()[0][0][0]
+trained_b = model.layers[0].get_weights()[1][0]
+
+# Make predictions
+x_pred = np.linspace(-100, 100, 200)
+y_pred = model.predict(x_pred)
+
+# Plot the results
+fig, ax = plt.subplots(figsize=(10, 5))
+
+# Plot for the x-axis (bottom line)
+ax.hlines(-1, -100, 100, color='blue', linestyle='--')  # X-axis
+
+# Plot for the y-axis (top line)
+ax.hlines(1, -100, 100, color='blue', linestyle='--')  # Y-axis
+
+# Plot the synthetic data points
+ax.scatter(x_data, y_data, color='gray', alpha=0.5, label='Data points')
+
+# Plot the prediction line
+ax.plot(x_pred, y_pred, color='red', label=f'Fitted line: y = {trained_w:.2f}x + {trained_b:.2f}')
+
+# Update the layout
+ax.set_xlim(-100, 100)
+ax.set_ylim(-2, 2)
+ax.set_xlabel('X-axis and Y-axis')
+ax.set_yticks([])  # Hide y-axis ticks
+ax.set_title('Neural Network Line Fitting')
+ax.legend()
+ax.grid(True)
+
+# Display the plot in Streamlit
+st.pyplot(fig)
+
+# Display the trained parameters
+st.write(f'Trained parameters: w = {trained_w:.2f}, b = {trained_b:.2f}')