Create app.py

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+plt.rcParams['figure.dpi'] = 100
+
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.datasets import make_gaussian_quantiles
+from sklearn.inspection import DecisionBoundaryDisplay
+
+import gradio as gr
+
+#=======================================================
+C1, C2 = '#ff0000', '#0000ff'
+CMAP = ListedColormap([C1, C2])
+GRANULARITY = 0.05
+#=======================================================
+def get_decision_surface(X, y, model):
+    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+    xrange = np.arange(x_min, x_max, GRANULARITY)
+    yrange = np.arange(y_min, y_max, GRANULARITY)
+    xx, yy = np.meshgrid(xrange, yrange)
+
+    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    return xx, yy, Z
+
+def create_plot(x1, y1, x2, y2, cov1, cov2, n1, n2, max_depth, n_estimators):
+    #Generate the dataset
+    X1, y1 = make_gaussian_quantiles(
+        mean=(x1, y1), cov=cov1, n_samples=n1, n_features=2, n_classes=2
+        )
+    X2, y2 = make_gaussian_quantiles(
+        mean=(x2, y2), cov=cov2, n_samples=n2, n_features=2, n_classes=2
+        )
+    X = np.concatenate((X1, X2))
+    y = np.concatenate((y1, -y2 + 1))
+
+    clf =  AdaBoostClassifier(DecisionTreeClassifier(max_depth=max_depth), algorithm="SAMME", n_estimators=n_estimators)
+
+    clf.fit(X, y)
+
+    fig = plt.figure(figsize=(12, 5))
+    ax = fig.add_subplot(121)
+
+    xx, yy, Z = get_decision_surface(X, y, clf)
+    ax.contourf(xx, yy, Z, cmap=CMAP, alpha=0.65)
+
+    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.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.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')
+
+    return fig
+
+info = '''
+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).
+
+A histogram of the decision scores of the AdaBoostClassifer is shown below. Values closer to -1 mean a high confidence that the sample belongs to Class A, and values closer to 1 mean a high confidence that the sample belongs to Class B.
+
+Use the controls below to change the Gaussian distribution parameters, number of generated samples in each Gaussian distribution, and the classifier's max_depth and n_estimators.
+'''
+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')
+
+    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(share=True, debug=True)
+#=======================================================