Initial Commit

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.tree import DecisionTreeRegressor
+import gradio as gr
+
+# Create a random dataset
+rng = np.random.RandomState(1)
+X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
+y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
+y[::5, :] += 0.5 - rng.rand(20, 2)
+
+
+def plot_multi_tree(d1,d2,d3):
+    # Fit regression model
+    regr_1 = DecisionTreeRegressor(max_depth=d1)
+    regr_2 = DecisionTreeRegressor(max_depth=d2)
+    regr_3 = DecisionTreeRegressor(max_depth=d3)
+    regr_1.fit(X, y)
+    regr_2.fit(X, y)
+    regr_3.fit(X, y)
+
+    # Predict
+    X_test = np.arange(-100.0, 100.0, 0.01)[:, np.newaxis]
+    y_1 = regr_1.predict(X_test)
+    y_2 = regr_2.predict(X_test)
+    y_3 = regr_3.predict(X_test)
+
+    # Plot the results
+    fig = plt.figure()
+    s = 25
+    plt.scatter(y[:, 0], y[:, 1], c="navy", s=s, edgecolor="black", label="data")
+    plt.scatter(
+        y_1[:, 0],
+        y_1[:, 1],
+        c="cornflowerblue",
+        s=s,
+        edgecolor="black",
+        label= f"max_depth={d1}",
+    )
+    plt.scatter(y_2[:, 0], y_2[:, 1], c="red", s=s, edgecolor="black", label= f"max_depth={d2}")
+    plt.scatter(
+        y_3[:, 0], y_3[:, 1], c="orange", s=s, edgecolor="black", label= f"max_depth={d3}"
+    )
+    plt.xlim([-6, 6])
+    plt.ylim([-6, 6])
+    plt.xlabel("target 1")
+    plt.ylabel("target 2")
+    plt.title("Multi-output Decision Tree Regression")
+    plt.legend(loc="best")
+    return fig
+
+
+
+
+title = " Illustration of multi-output regression with decision tree.🌲 "
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+
+    with gr.Row():
+        d1 = gr.Slider(minimum=0, maximum=20, step=1,  value = 2,
+                label = "Depth 1")
+        d2 = gr.Slider(minimum=0, maximum=20, step=1, value = 5,
+                label = "Depth 2")
+        d3 = gr.Slider(minimum=0, maximum=20, step=1, value = 8,
+                label = "Depth 3")
+    
+    btn = gr.Button(value="Submit")
+    btn.click(plot_multi_tree, inputs= [d1,d2,d3], outputs= gr.Plot(label='Multi-output regression with decision trees') ) # 
+    
+
+demo.launch()

requirements.txt

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+scikit-learn==1.2.1