Update app.py

app.py

@@ -33,7 +33,56 @@ def prepare_data(input_data, n_samples, outliers_fraction=0.0):
     labels[-len(outliers):] = "Anomaly"
     return X, labels
 
-# Function to train and detect anomalies
+# 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)
 
@@ -81,7 +130,7 @@ def detect_anomalies(input_data, n_samples, outliers_fraction, model_name):
 
     return df
 
-# Function to fetch anomaly records based on the selected model
+# Function to get anomaly samples
 def get_anomaly_samples(input_data, n_samples, outliers_fraction, model_name):
     df = detect_anomalies(input_data, n_samples, outliers_fraction, model_name)