Create app.py

app.py

@@ -0,0 +1,151 @@
+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
+from sklearn import svm
+from sklearn.covariance import EllipticEnvelope
+from sklearn.neighbors import LocalOutlierFactor
+from sklearn.linear_model import SGDOneClassSVM
+from sklearn.kernel_approximation import Nystroem
+from sklearn.pipeline import make_pipeline
+import time
+from functools import partial
+
+
+# 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])
+
+
+# Functions for Anomaly Detection Algorithms tab
+def train_models(input_data, outliers_fraction, n_samples, clf_name):
+    """Train anomaly detection models and plot results."""
+    n_outliers = int(outliers_fraction * n_samples)
+    n_inliers = n_samples - n_outliers
+    blobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2)
+    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),
+    }
+    DATA_MAPPING = {
+        "Central Blob": make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5, **blobs_params)[0],
+        "Two Blobs": make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5], **blobs_params)[0],
+        "Blob with Noise": make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, 0.3], **blobs_params)[0],
+        "Moons": 4.0
+        * (make_moons(n_samples=n_samples, noise=0.05, random_state=0)[0] - np.array([0.5, 0.25])),
+        "Noise": 14.0 * (np.random.RandomState(42).rand(n_samples, 2) - 0.5),
+    }
+    xx, yy = np.meshgrid(np.linspace(-7, 7, 150), np.linspace(-7, 7, 150))
+    clf = NAME_CLF_MAPPING[clf_name]
+    plt.figure(figsize=(10, 8))
+    X = DATA_MAPPING[input_data]
+    rng = np.random.RandomState(42)
+    X = np.concatenate([X, rng.uniform(low=-6, high=6, size=(n_outliers, 2))], axis=0)
+    t0 = time.time()
+    clf.fit(X)
+    t1 = time.time()
+
+    if clf_name == "Local Outlier Factor":
+        y_pred = clf.fit_predict(X)
+    else:
+        y_pred = clf.fit(X).predict(X)
+
+    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.xlim(-7, 7)
+    plt.ylim(-7, 7)
+    plt.xticks(())
+    plt.yticks(())
+    plt.title(f"{clf_name} (time: {t1 - t0:.2f}s)")
+    return plt
+
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Isolation Forest Anomaly Detection")
+    
+    with gr.Tab("Anomaly Detection Algorithms"):
+        gr.Markdown("## Compare Anomaly Detection Algorithms")
+        input_models = [
+            "Robust covariance", "One-Class SVM", "One-Class SVM (SGD)", "Isolation Forest", "Local Outlier Factor"
+        ]
+        input_data = gr.Radio(
+            choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
+            value="Moons",
+            label="Dataset Type"
+        )
+        n_samples = gr.Slider(
+            minimum=100, maximum=500, step=25, value=300, label="Number of Samples"
+        )
+        outliers_fraction = gr.Slider(
+            minimum=0.1, maximum=0.9, step=0.1, value=0.2, label="Outlier Fraction"
+        )
+
+        for clf_name in input_models:
+            plot = gr.Plot(label=clf_name)
+            fn = partial(train_models, clf_name=clf_name)
+            input_data.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
+            n_samples.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
+            outliers_fraction.change(fn=fn, inputs=[input_data, outliers_fraction, n_samples], outputs=plot)
+
+# Launch the Gradio app
+demo.launch()