Update app.py

app.py

@@ -22,8 +22,8 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
         "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_inliers, noise=0.05, random_state=0)[0] - np.array([0.5, 0.25])),
-        "Noise": 14.0 * (np.random.RandomState(42).rand(n_inliers, 2) - 0.5),
+        "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 = {
@@ -45,8 +45,7 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
     
     X = DATA_MAPPING[input_data]
     rng = np.random.RandomState(42)
-    X_outliers = rng.uniform(low=-6, high=6, size=(n_outliers, 2))
-    X = np.concatenate([X, X_outliers], axis=0)
+    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]
@@ -54,30 +53,25 @@ def train_models(input_data, outliers_fraction, n_samples, clf_name):
     t0 = time.time()
     if clf_name == "Local Outlier Factor":
         y_pred = clf.fit_predict(X)
-        # The decision_function is inverse of the LocalOutlierFactor._score_samples
-        Z = clf._decision_function(np.c_[xx.ravel(), yy.ravel()])
     else:
         clf.fit(X)
         y_pred = clf.predict(X)
-        Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
     t1 = time.time()
 
     # Plot
-    plt.figure(figsize=(6, 6))
-    Z = Z.reshape(xx.shape)
-    plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.Blues_r)
-    a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors="red")
-    plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors="palevioletred")
-    
-    s = 20
-    b1 = plt.scatter(X[:-n_outliers, 0], X[:-n_outliers, 1], c="white", s=s, edgecolors="k")
-    b2 = plt.scatter(X[-n_outliers:, 0], X[-n_outliers:, 1], c="black", s=s, edgecolors="k")
-    plt.axis("tight")
-    plt.xlim((-7, 7))
-    plt.ylim((-7, 7))
+    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(())
-    plt.title(f"{clf_name} ({t1 - t0:.2f}s)")
     return plt.gcf()
 
 # Gradio Interface
@@ -88,32 +82,30 @@ with gr.Blocks() as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown(description)
     
-    with gr.Row():
-        with gr.Column(scale=1):
-            # Inputs
-            input_data = gr.Radio(
-                choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
-                value="Moons",
-                label="Dataset"
-            )
-            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="Fraction of Outliers")
-        with gr.Column(scale=3):
-            # Models and their plots
-            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))
+    # Inputs
+    input_data = gr.Radio(
+        choices=["Central Blob", "Two Blobs", "Blob with Noise", "Moons", "Noise"],
+        value="Moons",
+        label="Dataset"
+    )
+    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="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)
-            plt.close(fig)
         return results
 
     # Set change triggers
@@ -122,8 +114,5 @@ with gr.Blocks() as demo:
     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)
-    
-    # Initial update to display plots on load
-    demo.load(fn=update, inputs=inputs, outputs=demo_outputs)
 
 demo.launch(debug=True)