Huge Bump to Codebase added more features

app.py

@@ -10,19 +10,24 @@ np.random.seed(0)
 
 
 def plot_it(X_train_x, X_train_y, Y_train_x, Y_train_y, X_test_x, X_test_y, alpha):
+    # Prepare the training and test data
     X_train = np.array([[X_train_x, X_train_y]]).T
     y_train = [Y_train_x, Y_train_y]
     X_test = np.array([[X_test_x, X_test_y]]).T
 
+    # Define the classifiers for Ordinary Least Squares (OLS) and Ridge Regression
     classifiers = dict(
         ols=linear_model.LinearRegression(), ridge=linear_model.Ridge(alpha=alpha)
     )
 
-    fig, axs = plt.subplots(ncols=len(classifiers), figsize=(8, 4))
+    # Create a figure with subplots for each classifier
+    fig, axs = plt.subplots(ncols=len(classifiers), figsize=(8, 6))
 
+    # Iterate over the classifiers and plot the results
     for i, (name, clf) in enumerate(classifiers.items()):
         ax = axs[i]
 
+        # Generate and fit the data multiple times for visualization purposes
         for _ in range(6):
             this_X = 0.1 * np.random.normal(size=(2, 1)) + X_train
             clf.fit(this_X, y_train)
@@ -30,13 +35,31 @@ def plot_it(X_train_x, X_train_y, Y_train_x, Y_train_y, X_test_x, X_test_y, alph
             ax.plot(X_test, clf.predict(X_test), color="gray")
             ax.scatter(this_X, y_train, s=3, c="gray", marker="o", zorder=10)
 
+        # Fit the classifier to the original training data
         clf.fit(X_train, y_train)
+
+        # Plot the fitted line and the training data points
         ax.plot(X_test, clf.predict(X_test), linewidth=2, color="blue")
         ax.scatter(X_train, y_train, s=30, c="red", marker="+", zorder=10)
+        # Get the regression coefficients
+        coef = clf.coef_
+        intercept = clf.intercept_
+
+        # Create a text box with the regression coefficients
+        text_box = f"Intercept: {intercept:.2f}\nCoefficient: {coef[0]:.2f}"
+
+        # Add the text box to the plot
+        ax.text(
+            0.05,
+            0.95,
+            text_box,
+            transform=ax.transAxes,
+            fontsize=10,
+            verticalalignment="top",
+            bbox=dict(facecolor="white", alpha=0.8),
+        )
 
         ax.set_title(name)
-        ax.set_xlim(0, 2)
-        ax.set_ylim((0, 1.6))
         ax.set_xlabel("X")
         ax.set_ylabel("y")
 
@@ -44,45 +67,56 @@ def plot_it(X_train_x, X_train_y, Y_train_x, Y_train_y, X_test_x, X_test_y, alph
 
 
 with gr.Blocks() as demo:
+    # Introduction and explanation of the demo
     gr.Markdown("# Ordinary Least Squares and Ridge Regression Variance")
     gr.Markdown(
-        "This interactive demo is based on the [Ordinary Least Squares and Ridge Regression Variance](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge_variance.html) shows how to use linear regression with OLS and ridge regression, and compare the variance of the coefficients. It generates a synthetic dataset with a small number of features and a large number of samples, fits both models to the data, and plots the variance of the coefficients for each model. It demonstrates that ridge regression can reduce the variance of coefficients when there is multicollinearity between the features, making it a useful tool in certain regression scenarios."
+        "This interactive demo is based on the [Ordinary Least Squares and Ridge Regression Variance](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge_variance.html). It illustrates the concepts of Ordinary Least Squares (OLS) and Ridge Regression variance, demonstrates how to use linear regression with OLS and ridge regression, and compares the variance of the coefficients. You will have the opportunity to create your own data points, generate a synthetic dataset with a small number of features, fit both models to the data, and observe the variance of the coefficients for each model. This demo showcases how ridge regression can reduce the variance of coefficients when there is multicollinearity between the features, making it a valuable tool in certain regression scenarios."
     )
 
-    gr.Markdown("Select Training points For X_train and Y_train")
-
+    # Explanation of selecting training points for X_train and Y_train
+    gr.Markdown("## Select Training Points for X_train and Y_train")
+    gr.Markdown(
+        "In regression tasks, we split the available data into a training set and a test set. The training set is used to train the regression model, and the test set is used to evaluate its performance. Here, you can select the coordinates of the training points that form the training set."
+    )
     with gr.Row():
         with gr.Column():
+            gr.Markdown("X_train consists of training points (X_train_x, X_train_y)")
             X_train_x = gr.Slider(
-                value=0.5, minimum=0, maximum=100, step=0.1, label="X_train_x"
+                value=0.5, minimum=0, maximum=3, step=0.1, label="X_train_x"
             )
             X_train_y = gr.Slider(
-                value=1, minimum=0, maximum=100, step=0.1, label="X_train_y"
+                value=1, minimum=0, maximum=3, step=0.1, label="X_train_y"
             )
         with gr.Column():
+            gr.Markdown("Y_train consists of training points (Y_train_x, Y_train_y)")
             Y_train_x = gr.Slider(
-                value=0.5, minimum=0, maximum=100, step=0.1, label="Y_train_x"
+                value=0.5, minimum=0, maximum=3, step=0.1, label="Y_train_x"
             )
             Y_train_y = gr.Slider(
-                value=1, minimum=0, maximum=100, step=0.1, label="Y_train_y"
+                value=1, minimum=0, maximum=3, step=0.1, label="Y_train_y"
             )
-    gr.Markdown("X_test")
+
+    # Explanation of selecting X_test
+    gr.Markdown("## Select Test Point (X_test)")
+    gr.Markdown(
+        "To evaluate the trained regression model, we need a test point that is not part of the training set. Here, you can select the coordinates of the test point, which will be used to predict the target value based on the learned regression function."
+    )
     with gr.Row():
-        X_test_x = gr.Slider(
-            value=0, minimum=0, maximum=100, step=0.1, label="X_test_x"
-        )
-        X_test_y = gr.Slider(
-            value=2, minimum=0, maximum=100, step=0.1, label="X_test_y"
-        )
+        X_test_x = gr.Slider(value=0, minimum=0, maximum=3, step=0.1, label="X_test_x")
+        X_test_y = gr.Slider(value=2, minimum=0, maximum=3, step=0.1, label="X_test_y")
 
-    gr.Markdown("Select Classifier parameters")
-    alpha = gr.Slider(value=0.5, minimum=0, maximum=100, step=0.1, label="alpha")
+    # Explanation of selecting classifier parameters
+    gr.Markdown("## Select Classifier Parameters")
+    gr.Markdown(
+        "In this demo, we compare two regression models: Ordinary Least Squares (OLS) and Ridge Regression. You can adjust the 'alpha' parameter for the Ridge Regression model, which controls the amount of regularization. Higher values of alpha correspond to stronger regularization, reducing the variance of the coefficients."
+    )
+    alpha = gr.Slider(value=0.5, minimum=0, maximum=3, step=0.1, label="alpha")
 
+    # Button to trigger the plot
     gr.Button("Plot").click(
         plot_it,
         inputs=[X_train_x, X_train_y, Y_train_x, Y_train_y, X_test_x, X_test_y, alpha],
         outputs=gr.Plot(),
     )
 
-
 demo.launch()