Update app.py

app.py

@@ -18,7 +18,6 @@ import gradio as gr
 C1, C2, C3 = '#ff0000', '#ffff00', '#0000ff'
 CMAP = ListedColormap([C1, C2, C3])
 GRANULARITY = 0.05
-SEED = 1
 
 FEATURE_NAMES = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"]
 TARGET_NAMES = ["Setosa", "Versicolour", "Virginica"]
@@ -39,9 +38,7 @@ def get_decision_surface(X, y, model):
     return xx, yy, Z
 
 def create_plot(feature_string, max_depth, n_neighbors, gamma, weight1, weight2, weight3):
-    
-    np.random.seed(SEED)
-    
+
     feature_list = feature_string.split(',')
     feature_list = [s.strip() for s in feature_list]
     idx_x = FEATURE_NAMES.index(feature_list[0])
@@ -68,12 +65,13 @@ def create_plot(feature_string, max_depth, n_neighbors, gamma, weight1, weight2,
     clf3.fit(X, y)
     eclf.fit(X, y)
 
-    fig = plt.figure(figsize=(12, 12))
+    fig, _ = plt.subplots(2, 2, figsize=(7, 7), sharex=True, sharey=True)
 
     for i, clf in enumerate([clf1, clf2, clf3, eclf]):
         xx, yy, Z = get_decision_surface(X, y, clf)
 
         ax = fig.add_subplot(2, 2, i+1)
+        ax.set_axis_off()
         ax.contourf(xx, yy, Z, cmap=CMAP, alpha=0.65)
 
         for j, label in enumerate(TARGET_NAMES):
@@ -81,47 +79,46 @@ def create_plot(feature_string, max_depth, n_neighbors, gamma, weight1, weight2,
             y_label = y[y==j]
             ax.scatter(X_label[:, 0], X_label[:, 1], c=[[C1], [C2], [C3]][j]*len(y_label), edgecolor='k', s=40, label=label)
         
-        ax.set_xlabel(feature_list[0]); ax.set_ylabel(feature_list[1])
         ax.legend()
         ax.set_title(f'{MODEL_NAMES[i]}')
-    
+        
+    fig.supxlabel(feature_list[0]); fig.supylabel(feature_list[1])
+    fig.set_tight_layout(True)
+    fig.set_constrained_layout(True)
     return fig
 
 info = '''
 # Voting Classifier Decision Surface
 
-This app plots the decision surface of four classifiers on two selected features of the Iris dataset:
-- DecisionTreeClassifier.
-- KNeighborsClassifier.
-- SupportVectorClassifier.
-- A VotingClassifier from all of the above.
+This app plots the decision surface of four classifiers on two selected features of the Iris dataset: DecisionTreeClassifier, KNeighborsClassifier, SupportVectorClassifier, and a VotingClassifier from all of them.
 
 Use the controls below to tune the parameters of the classifiers and the weights of each of them in the soft voting classifier and click submit. The more weight you assign to a classifier, the more importance will be assigned to its predictions compared to the other classifiers in the vote.
+
+Created by [@huabdul]() based on [scikit-learn docs]().
 '''
 
 with gr.Blocks(analytics_enabled=False) as demo:
-    gr.Markdown(info)
-
     selections = combinations(FEATURE_NAMES, 2)
     selections = [f'{s[0]}, {s[1]}' for s in selections]
 
-    dd = gr.Dropdown(selections, value=selections[0], interactive=True, label="Input features")
-
     with gr.Row():
         with gr.Column():
-            slider_max_depth = gr.Slider(1, 50, value=4, step=1, label='max_depth (for DecisionTreeClassifier)')
-            slider_n_neighbors = gr.Slider(1, 20, value=7, step=1, label='n_neighbors (for KNeighborsClassifier)')
-            slider_gamma = gr.Slider(0, 10, value=0.1, step=0.1, label='gamma (for SVC)')
-
-        with gr.Column():
-            slider_w1 = gr.Slider(0, 10, value=2, step=0.1, label='DecisionTreeClassifier weight')
-            slider_w2 = gr.Slider(0, 10, value=1, step=0.1, label='KNeighborsClassifier weight')
-            slider_w3 = gr.Slider(0, 10, value=2, step=0.1, label='SVC weight')
-    
+            gr.Markdown(info)
+            dd = gr.Dropdown(selections, value=selections[0], interactive=True, label="Input features")
+            with gr.Row(): 
+                with gr.Column():
+                    slider_max_depth = gr.Slider(1, 50, value=4, step=1, label='max_depth (for DecisionTreeClassifier)')
+                    slider_n_neighbors = gr.Slider(1, 20, value=7, step=1, label='n_neighbors (for KNeighborsClassifier)')
+                    slider_gamma = gr.Slider(0, 10, value=0.1, step=0.1, label='gamma (for SVC)')
+                with gr.Column():
+                    slider_w1 = gr.Slider(0, 10, value=2, step=0.1, label='DecisionTreeClassifier weight')
+                    slider_w2 = gr.Slider(0, 10, value=1, step=0.1, label='KNeighborsClassifier weight')
+                    slider_w3 = gr.Slider(0, 10, value=2, step=0.1, label='SVC weight')
     
-    btn = gr.Button(value='Submit')
-    
-    plot = gr.Plot(label='Decision Surfaces')
+            btn = gr.Button(value='Submit')
+        
+        with gr.Column():
+            plot = gr.Plot(show_label=False)
 
     btn.click(create_plot, inputs=[dd, slider_max_depth, slider_n_neighbors, slider_gamma, slider_w1, slider_w2, slider_w3], outputs=[plot])