Update app.py

app.py

@@ -42,37 +42,35 @@ def create_plot(x1, y1, x2, y2, cov1, cov2, n1, n2, max_depth, n_estimators):
 
     clf.fit(X, y)
 
-    fig = plt.figure(figsize=(12, 5))
-    ax = fig.add_subplot(121)
+    fig = plt.figure(figsize=(4.5, 6.9))
+    ax = fig.add_subplot(211)
 
     xx, yy, Z = get_decision_surface(X, y, clf)
-    ax.contourf(xx, yy, Z, cmap=CMAP, alpha=0.65)
+    ax.contourf(xx, yy, Z, cmap=CMAP, alpha=0.4)
 
     X1, y1 = X[y==0], y[y==0]
     X2, y2 = X[y==1], y[y==1]
 
-    ax.scatter(X1[:, 0], X1[:, 1], c=C1, edgecolor='k', s=40, label='Class A')
-    ax.scatter(X2[:, 0], X2[:, 1], c=C2, edgecolor='k', s=40, label='Class B')
+    ax.scatter(X1[:, 0], X1[:, 1], c=C1, edgecolor='k', s=20, label='Class A')
+    ax.scatter(X2[:, 0], X2[:, 1], c=C2, edgecolor='k', s=20, label='Class B')
     
-    ax.set_xlabel('x'); ax.set_ylabel('y')
     ax.legend()
     ax.set_title(f'AdaBoostClassifier Decision Surface')
 
     scores = clf.decision_function(X)
 
-    ax = fig.add_subplot(122)
+    ax = fig.add_subplot(212)
     ax.hist(scores[y==0], bins=100, range=(scores.min(), scores.max()), facecolor=C1, label="Class A", alpha=0.5, edgecolor="k")
     ax.hist(scores[y==1], bins=100, range=(scores.min(), scores.max()), facecolor=C2, label="Class B", alpha=0.5, edgecolor="k")
 
     ax.set_xlabel('Score'); ax.set_ylabel('Frequency')
     ax.legend()
     ax.set_title('Decision Scores')
+    fig.set_tight_layout(True)
 
     return fig
 
 info = '''
-# AdaBoost Classifier Example on Gaussian Quantile Generated Data.
-
 This example fits an [AdaBoost classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier) on two non-linearly separable classes. The samples are generated using two [Gaussian quantiles](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_gaussian_quantiles.html#sklearn.datasets.make_gaussian_quantiles) of configurable mean and covariance (see the sliders below).
 
 For the first generated Gaussian, the inner half quantile is assigned to Class A and the outer half quantile is assigned to class B. For the second generated quantile, the opposite assignment happens (inner = Class B, outer = Class A).
@@ -84,43 +82,44 @@ Use the controls below to change the Gaussian distribution parameters, number of
 Created by [@huabdul](https://huggingface.co/huabdul) based on [Scikit-learn docs](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html).
 '''
 with gr.Blocks(analytics_enabled=False) as demo:
-    gr.Markdown(info)
-    
-    with gr.Row():
-        with gr.Column():
-            s_x1 = gr.Slider(-10, 10, value=0, step=0.1, label='Mean x1')
-        with gr.Column():
-            s_y1 = gr.Slider(-10, 10, value=0, step=0.1, label='Mean y1')
-    with gr.Row():
-        with gr.Column():
-            s_x2 = gr.Slider(-10, 10, value=2, step=0.1, label='Mean x2')
-        with gr.Column():
-            s_y2 = gr.Slider(-10, 10, value=2, step=0.1, label='Mean y2')
-
-    with gr.Row():
-        with gr.Column():
-            s_cov1 = gr.Slider(0.01, 5, value=1, step=0.01, label='Covariance 1')
-        with gr.Column():
-            s_cov2 = gr.Slider(0.01, 5, value=2, step=0.01, label='Covariance 2')
-        
     with gr.Row():
-        with gr.Column():
-            s_n_samples1 = gr.Slider(1, 1000, value=200, step=1, label='n_samples 1')
-        with gr.Column():
-            s_n_samples2 = gr.Slider(1, 1000, value=300, step=1, label='n_samples 2')
-
-    with gr.Row():
-        with gr.Column():
-            s_max_depth = gr.Slider(1, 50, value=1, step=1, label='AdaBoostClassifier max_depth')
-        with gr.Column():
-            s_n_estimators = gr.Slider(1, 500, value=300, step=1, label='AdaBoostClassifier n_estimators')
-    
-    btn = gr.Button('Submit')
-
-    plot = gr.Plot(label='Decision Surfaces & Histogram of Scores')
+        with gr.Column(scale=2):
+            gr.Markdown(info)
+            with gr.Row():
+                with gr.Column(min_width=100):
+                    s_x1 = gr.Slider(-10, 10, value=0, step=0.1, label='Mean x1')
+                with gr.Column(min_width=100):
+                    s_y1 = gr.Slider(-10, 10, value=0, step=0.1, label='Mean y1')
+            with gr.Row():
+                with gr.Column(min_width=100):
+                    s_x2 = gr.Slider(-10, 10, value=2, step=0.1, label='Mean x2')
+                with gr.Column(min_width=100):
+                    s_y2 = gr.Slider(-10, 10, value=2, step=0.1, label='Mean y2')
+
+            with gr.Row():
+                with gr.Column(min_width=100):
+                    s_cov1 = gr.Slider(0.01, 5, value=1, step=0.01, label='Covariance 1')
+                with gr.Column(min_width=100):
+                    s_cov2 = gr.Slider(0.01, 5, value=2, step=0.01, label='Covariance 2')
+                
+            with gr.Row():
+                with gr.Column(min_width=100):
+                    s_n_samples1 = gr.Slider(1, 1000, value=200, step=1, label='n_samples 1')
+                with gr.Column(min_width=100):
+                    s_n_samples2 = gr.Slider(1, 1000, value=300, step=1, label='n_samples 2')
+
+            with gr.Row():
+                with gr.Column(min_width=100):
+                    s_max_depth = gr.Slider(1, 50, value=1, step=1, label='AdaBoostClassifier max_depth')
+                with gr.Column(min_width=100):
+                    s_n_estimators = gr.Slider(1, 500, value=300, step=1, label='AdaBoostClassifier n_estimators')
+           
+            btn = gr.Button('Submit')
+        with gr.Column(scale=1.5):
+            plot = gr.Plot(show_label=False)
 
     btn.click(create_plot, inputs=[s_x1, s_y1, s_x2, s_y2, s_cov1, s_cov2, s_n_samples1, s_n_samples2, s_max_depth, s_n_estimators], outputs=[plot])
     demo.load(create_plot, inputs=[s_x1, s_y1, s_x2, s_y2, s_cov1, s_cov2, s_n_samples1, s_n_samples2, s_max_depth, s_n_estimators], outputs=[plot])
 
 demo.launch()
-#=======================================================
+#=======================================================