Update app.py

app.py

@@ -1,7 +1,120 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.datasets import make_moons, make_blobs
+from sklearn import svm
+from sklearn.covariance import EllipticEnvelope
+from sklearn.ensemble import IsolationForest
+from sklearn.neighbors import LocalOutlierFactor
+from sklearn.linear_model import SGDOneClassSVM
+from sklearn.kernel_approximation import Nystroem
+from sklearn.pipeline import make_pipeline
+from sklearn.datasets import make_blobs, make_moons
 import gradio as gr
+import time
+
+# Function to train models and generate plots
+def train_models(input_data, outliers_fraction, n_samples, clf_name):
+    # Prepare data
+    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)
+    
+    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),
+    }
+    
+    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),
+    }
+    
+    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)
+
+    xx, yy = np.meshgrid(np.linspace(-7, 7, 150), np.linspace(-7, 7, 150))
+    clf = NAME_CLF_MAPPING[clf_name]
+
+    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()
+
+    # Plot
+    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()
+
+# Gradio Interface
+description = "Compare how different anomaly detection algorithms perform on various datasets."
+title = "🕵️‍♀️ Compare Anomaly Detection Algorithms 🕵️‍♂️"
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
+    
+    # Inputs
+    input_data = gr.Radio(
+        choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
+        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")
+    
+    # Models and their plots in a row
+    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))
+
+    # Update function
+    def update(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
+
+    # Set change triggers
+    inputs = [input_data, outliers_fraction, n_samples]
+    demo_outputs = [plot for _, plot in plots]
+    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)
+
 
 
 # Function to generate interactive feature scatter plots