import numpy as np import pandas as pd from sklearn.datasets import make_classification from sklearn.ensemble import IsolationForest from sklearn.metrics import roc_curve, auc import shap import matplotlib.pyplot as plt import gradio as gr # Generate synthetic data with 20 features np.random.seed(42) X, _ = make_classification( n_samples=500, n_features=20, n_informative=10, n_redundant=5, n_clusters_per_class=1, random_state=42 ) outliers = np.random.uniform(low=-6, high=6, size=(50, 20)) # Add outliers X = np.vstack([X, outliers]) # Convert to DataFrame columns = [f"Feature{i+1}" for i in range(20)] df = pd.DataFrame(X, columns=columns) # Fit Isolation Forest iso_forest = IsolationForest( n_estimators=100, max_samples=256, contamination=0.1, random_state=42 ) iso_forest.fit(df) # Predict anomaly scores anomaly_scores = iso_forest.decision_function(df) # Negative values indicate anomalies anomaly_labels = iso_forest.predict(df) # -1 for anomaly, 1 for normal # Add results to DataFrame df["Anomaly_Score"] = anomaly_scores df["Anomaly_Label"] = np.where(anomaly_labels == -1, "Anomaly", "Normal") # Generate true labels (1 for anomaly, 0 for normal) for ROC curve true_labels = np.where(df["Anomaly_Label"] == "Anomaly", 1, 0) # SHAP Explainability explainer = shap.Explainer(iso_forest, df[columns]) shap_values = explainer(df[columns]) # Define functions for Gradio def get_shap_summary(): """Generates SHAP summary plot.""" plt.figure() shap.summary_plot(shap_values, df[columns], feature_names=columns, show=False) plt.savefig("shap_summary.png") return "shap_summary.png" def get_shap_waterfall(index): """Generates SHAP waterfall plot for a specific data point.""" specific_index = int(index) plt.figure() shap.waterfall_plot( shap.Explanation( values=shap_values.values[specific_index], base_values=shap_values.base_values[specific_index], data=df.iloc[specific_index], feature_names=columns ) ) plt.savefig("shap_waterfall.png") return "shap_waterfall.png" def get_scatter_plot(feature1, feature2): """Generates scatter plot for two features.""" plt.figure(figsize=(8, 6)) plt.scatter( df[feature1], df[feature2], c=(df["Anomaly_Label"] == "Anomaly"), cmap="coolwarm", edgecolor="k", alpha=0.7 ) plt.title(f"Isolation Forest - {feature1} vs {feature2}") plt.xlabel(feature1) plt.ylabel(feature2) plt.savefig("scatter_plot.png") return "scatter_plot.png" def get_roc_curve(): """Generates the ROC curve plot.""" fpr, tpr, _ = roc_curve(true_labels, -df["Anomaly_Score"]) # Use -scores as higher scores mean normal roc_auc = auc(fpr, tpr) plt.figure(figsize=(8, 6)) plt.plot(fpr, tpr, label=f"ROC Curve (AUC = {roc_auc:.2f})") plt.plot([0, 1], [0, 1], "k--", label="Random Guess") plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") plt.title("Receiver Operating Characteristic (ROC) Curve") plt.legend(loc="lower right") plt.grid() plt.savefig("roc_curve.png") return "roc_curve.png" def get_anomaly_samples(): """Returns formatted top, middle, and bottom 10 records based on anomaly score.""" sorted_df = df.sort_values("Anomaly_Score", ascending=False) # Top 10 anomalies top_10 = sorted_df[sorted_df["Anomaly_Label"] == "Anomaly"].head(10) # Middle 10 (mix of anomalies and normal) mid_start = len(sorted_df) // 2 - 50 # Get a broader middle slice middle_section = sorted_df.iloc[mid_start: mid_start + 100] # Consider a larger middle slice middle_anomalies = middle_section[middle_section["Anomaly_Label"] == "Anomaly"].sample(n=5, random_state=42) middle_normals = middle_section[middle_section["Anomaly_Label"] == "Normal"].sample(n=5, random_state=42) middle_10 = pd.concat([middle_anomalies, middle_normals]).sort_values("Anomaly_Score", ascending=False) # Bottom 10 normal records bottom_10 = sorted_df[sorted_df["Anomaly_Label"] == "Normal"].tail(10) return top_10, middle_10, bottom_10 # Create Gradio interface with gr.Blocks() as demo: gr.Markdown("# Isolation Forest Anomaly Detection") with gr.Tab("SHAP Summary"): gr.Markdown("### Global Explainability: SHAP Summary Plot") shap_button = gr.Button("Generate SHAP Summary Plot") shap_image = gr.Image() shap_button.click(get_shap_summary, outputs=shap_image) with gr.Tab("SHAP Waterfall"): gr.Markdown("### Local Explainability: SHAP Waterfall Plot") index_input = gr.Number(label="Data Point Index", value=0) shap_waterfall_button = gr.Button("Generate SHAP Waterfall Plot") shap_waterfall_image = gr.Image() shap_waterfall_button.click(get_shap_waterfall, inputs=index_input, outputs=shap_waterfall_image) with gr.Tab("Feature Scatter Plot"): gr.Markdown("### Feature Interaction: Scatter Plot") feature1_dropdown = gr.Dropdown(choices=columns, label="Feature 1") feature2_dropdown = gr.Dropdown(choices=columns, label="Feature 2") scatter_button = gr.Button("Generate Scatter Plot") scatter_image = gr.Image() scatter_button.click(get_scatter_plot, inputs=[feature1_dropdown, feature2_dropdown], outputs=scatter_image) with gr.Tab("Anomaly Samples"): gr.HTML("

Top 10 Records (Anomalies)

") top_table = gr.Dataframe(label="Top 10 Records") gr.HTML("

Middle 10 Records (Mixed)

") middle_table = gr.Dataframe(label="Middle 10 Records") gr.HTML("

Bottom 10 Records (Normal)

") bottom_table = gr.Dataframe(label="Bottom 10 Records") anomaly_samples_button = gr.Button("Show Anomaly Samples") anomaly_samples_button.click( get_anomaly_samples, outputs=[top_table, middle_table, bottom_table] ) with gr.Tab("ROC Curve"): gr.Markdown("### ROC Curve for Isolation Forest") roc_button = gr.Button("Generate ROC Curve") roc_image = gr.Image() roc_button.click(get_roc_curve, outputs=roc_image) # Launch the Gradio app demo.launch()