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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() |