|
""" |
|
Demo is Derived from https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression_multioutput.html |
|
""" |
|
|
|
import numpy as np |
|
import matplotlib.pyplot as plt |
|
from sklearn.tree import DecisionTreeRegressor |
|
import gradio as gr |
|
|
|
|
|
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): |
|
|
|
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) |
|
|
|
|
|
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) |
|
|
|
|
|
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}") |
|
gr.Markdown(" This example shows how different max_depth of decision tree affect the predictions <br>" |
|
" Larger max_depth makes model learn the finner details resulting in **overfitting** <br>" |
|
" Play with the Depth parameter to see how it fits a noisy circle dataset.<br>") |
|
|
|
gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression_multioutput.html)** <br>") |
|
|
|
gr.Markdown(" **Dataset** : It is a toy dataset generated in shape of a circle with some small random noise added to it. <br>") |
|
gr.Markdown(" Different depths corresponds to different tree depth of DecisionTreeRegressor Models. <br>" |
|
" Larger Depth trying to overfit and learn even the finner details of the data.<br>" |
|
) |
|
|
|
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() |
