Update app.py

app.py

@@ -74,6 +74,28 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
     plt.yticks(())
     return plt.gcf()
 
+# Function for Feature Scatter Plots
+def plot_feature_scatter(input_data, n_samples):
+    data, _ = make_moons(n_samples=n_samples, noise=0.05) if input_data == "Moons" else make_blobs(n_samples=n_samples, random_state=0)
+    plt.figure(figsize=(5, 5))
+    plt.scatter(data[:, 0], data[:, 1], alpha=0.8, c="blue", s=20, label="Features")
+    plt.title(f"Feature Scatter Plot - {input_data}")
+    plt.legend()
+    return plt.gcf()
+
+# Function for Anomaly Examples
+def plot_anomaly_examples(input_data, n_samples, outliers_fraction):
+    n_outliers = int(outliers_fraction * n_samples)
+    rng = np.random.RandomState(42)
+    data, _ = make_blobs(n_samples=n_samples, random_state=0)
+    outliers = rng.uniform(low=-6, high=6, size=(n_outliers, 2))
+    plt.figure(figsize=(5, 5))
+    plt.scatter(data[:, 0], data[:, 1], alpha=0.8, c="blue", s=20, label="Inliers")
+    plt.scatter(outliers[:, 0], outliers[:, 1], alpha=0.8, c="red", s=20, label="Outliers")
+    plt.title("Anomaly Examples")
+    plt.legend()
+    return plt.gcf()
+
 # Gradio Interface
 description = "Compare how different anomaly detection algorithms perform on various datasets."
 title = "🕵️‍♀️ Compare Anomaly Detection Algorithms 🕵️‍♂️"
@@ -88,8 +110,10 @@ with gr.Blocks() as demo:
         value="Moons",
         label="Dataset"
     )
-    n_samples = gr.Slider(minimum=10, maximum=10000, step=25, value=500, label="Number of Samples")
-    outliers_fraction = gr.Slider(minimum=0.001, maximum=0.999, step=0.1, value=0.2, label="Fraction of Outliers")
+    n_samples = gr.Slider(minimum=100, maximum=500, step=25, value=300, label="Number of Samples")
+    outliers_fraction = gr.Slider(
+        minimum=0.01, maximum=0.99, step=0.01, value=0.2, label="Fraction of Outliers"
+    )
     
     # Models and their plots in a row
     input_models = ["Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"]
@@ -100,17 +124,24 @@ with gr.Blocks() as demo:
             plot = gr.Plot(label=model_name)
             plots.append((model_name, plot))
 
+    # Additional Rows for Scatter and Anomaly Examples
+    with gr.Row():
+        feature_plot = gr.Plot(label="Feature Scatter Plot")
+        anomaly_plot = gr.Plot(label="Anomaly Examples")
+
     # Update function
     def update(input_data, outliers_fraction, n_samples):
-        results = []
+        model_results = []
         for clf_name, plot in plots:
             fig = train_models(input_data, outliers_fraction, n_samples, clf_name)
-            results.append(fig)
-        return results
+            model_results.append(fig)
+        feature_fig = plot_feature_scatter(input_data, n_samples)
+        anomaly_fig = plot_anomaly_examples(input_data, n_samples, outliers_fraction)
+        return model_results + [feature_fig, anomaly_fig]
 
     # Set change triggers
     inputs = [input_data, outliers_fraction, n_samples]
-    demo_outputs = [plot for _, plot in plots]
+    demo_outputs = [plot for _, plot in plots] + [feature_plot, anomaly_plot]
     input_data.change(fn=update, inputs=inputs, outputs=demo_outputs)
     n_samples.change(fn=update, inputs=inputs, outputs=demo_outputs)
     outliers_fraction.change(fn=update, inputs=inputs, outputs=demo_outputs)