Update app.py

app.py

@@ -33,6 +33,55 @@ def prepare_data(input_data, n_samples, outliers_fraction=0.0):
     labels[-len(outliers):] = "Anomaly"
     return X, labels
 
+# Function to train models and generate plots
+def train_models(input_data, outliers_fraction, n_samples, clf_name):
+    X, _ = prepare_data(input_data, n_samples, outliers_fraction)
+
+    # Define classifiers
+    NAME_CLF_MAPPING = {
+        "Robust covariance": EllipticEnvelope(contamination=outliers_fraction),
+        "One-Class SVM": svm.OneClassSVM(nu=outliers_fraction, kernel="rbf", gamma=0.1),
+        "One-Class SVM (SGD)": make_pipeline(
+            Nystroem(gamma=0.1, random_state=42, n_components=150),
+            SGDOneClassSVM(
+                nu=outliers_fraction,
+                shuffle=True,
+                fit_intercept=True,
+                random_state=42,
+                tol=1e-6,
+            ),
+        ),
+        "Isolation Forest": IsolationForest(contamination=outliers_fraction, random_state=42),
+        "Local Outlier Factor": LocalOutlierFactor(n_neighbors=35, contamination=outliers_fraction),
+    }
+
+    clf = NAME_CLF_MAPPING[clf_name]
+    xx, yy = np.meshgrid(np.linspace(-7, 7, 150), np.linspace(-7, 7, 150))
+
+    t0 = time.time()
+    if clf_name == "Local Outlier Factor":
+        y_pred = clf.fit_predict(X)
+    else:
+        clf.fit(X)
+        y_pred = clf.predict(X)
+    t1 = time.time()
+
+    # Plotting
+    plt.figure(figsize=(5, 5))
+    if clf_name != "Local Outlier Factor":
+        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
+        Z = Z.reshape(xx.shape)
+        plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors="black")
+
+    colors = np.array(["#377eb8", "#ff7f00"])
+    plt.scatter(X[:, 0], X[:, 1], s=30, color=colors[(y_pred + 1) // 2])
+    plt.title(f"{clf_name} ({t1 - t0:.2f}s)")
+    plt.xlim(-7, 7)
+    plt.ylim(-7, 7)
+    plt.xticks(())
+    plt.yticks(())
+    return plt.gcf()
+
 # Function to detect anomalies and generate anomaly records
 def detect_anomalies(input_data, n_samples, outliers_fraction, model_name):
     X, labels = prepare_data(input_data, n_samples, outliers_fraction)
@@ -99,79 +148,25 @@ def get_anomaly_samples(input_data, n_samples, outliers_fraction, model_name):
 
     return top_10, middle_10, bottom_10
 
-# Function to generate feature scatter plots
-def plot_interactive_feature_scatter(input_data, feature_x, feature_y, n_samples):
-    data, _ = prepare_data(input_data, n_samples)
-    x_data = data[:, 0] if feature_x == "Feature1" else data[:, 1]
-    y_data = data[:, 1] if feature_y == "Feature2" else data[:, 0]
-
-    # Generate scatter plot
-    plt.figure(figsize=(6, 6))
-    plt.scatter(x_data, y_data, alpha=0.8, c="blue", s=20, label="Features")
-    plt.title(f"Feature Interaction Scatter Plot - {feature_x} vs {feature_y}")
-    plt.xlabel(feature_x)
-    plt.ylabel(feature_y)
-    plt.legend()
-    return plt.gcf()
-
 # Gradio Interface
 with gr.Blocks() as demo:
-    # App Title and Description
     gr.Markdown("## 🕵️‍♀️ Anomaly Detection App 🕵️‍♂️")
-    gr.Markdown("Explore anomaly detection models, feature interactions, and anomaly examples.")
-
-    # Interactive Feature Scatter Plot
-    gr.Markdown("### 1. Interactive Feature Scatter Plot")
     input_data = gr.Radio(
         choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
         value="Moons",
         label="Dataset"
     )
-    feature_x = gr.Dropdown(choices=["Feature1", "Feature2"], value="Feature1", label="Feature 1")
-    feature_y = gr.Dropdown(choices=["Feature1", "Feature2"], value="Feature2", label="Feature 2")
     n_samples = gr.Slider(minimum=10, maximum=10000, step=25, value=500, label="Number of Samples")
-    scatter_plot_button = gr.Button("Generate Scatter Plot")
-    scatter_plot = gr.Plot(label="Feature Scatter Plot")
-
-    scatter_plot_button.click(
-        fn=plot_interactive_feature_scatter,
-        inputs=[input_data, feature_x, feature_y, n_samples],
-        outputs=scatter_plot,
-    )
-
-    # Compare Anomaly Detection Algorithms
-    gr.Markdown("### 2. Compare Anomaly Detection Algorithms")
     outliers_fraction = gr.Slider(minimum=0.001, maximum=0.999, step=0.1, value=0.2, label="Fraction of Outliers")
-    input_models = ["Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"]
-    plots = []
-    with gr.Row():
-        for model_name in input_models:
-            plot = gr.Plot(label=model_name)
-            plots.append((model_name, plot))
-
-    def update_anomaly_comparison(input_data, outliers_fraction, n_samples):
-        results = []
-        for clf_name, plot in plots:
-            fig = train_models(input_data, outliers_fraction, n_samples, clf_name)
-            results.append(fig)
-        return results
-
-    anomaly_inputs = [input_data, outliers_fraction, n_samples]
-    anomaly_outputs = [plot for _, plot in plots]
-    input_data.change(fn=update_anomaly_comparison, inputs=anomaly_inputs, outputs=anomaly_outputs)
-    n_samples.change(fn=update_anomaly_comparison, inputs=anomaly_inputs, outputs=anomaly_outputs)
-    outliers_fraction.change(fn=update_anomaly_comparison, inputs=anomaly_inputs, outputs=anomaly_outputs)
-
-    # Anomaly Samples Tab
-    gr.Markdown("### 3. Example Anomaly Records")
-    model_dropdown = gr.Dropdown(choices=input_models, value="Isolation Forest", label="Select Model")
+    model_dropdown = gr.Dropdown(choices=["Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"], label="Select Model")
+
+    # Anomaly Samples Output
     top_table = gr.Dataframe(label="Top 10 Anomalies")
     middle_table = gr.Dataframe(label="Middle 10 Records")
     bottom_table = gr.Dataframe(label="Bottom 10 Normals")
     anomaly_samples_button = gr.Button("Show Anomaly Samples")
-
     anomaly_samples_button.click(
-        fn=get_anomaly_samples, 
+        fn=get_anomaly_samples,
         inputs=[input_data, n_samples, outliers_fraction, model_dropdown],
         outputs=[top_table, middle_table, bottom_table],
     )