File size: 2,605 Bytes
c6aadc4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
import gradio as gr
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.multioutput import MultiOutputRegressor
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
def compare(max_depth,n_estimators):
rng = np.random.RandomState(1)
X = np.sort(200 * rng.rand(600, 1) - 100, axis=0)
y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
y += 0.5 - rng.rand(*y.shape)
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=400, test_size=200, random_state=4
)
regr_multirf = MultiOutputRegressor(
RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=0)
)
regr_multirf.fit(X_train, y_train)
regr_rf = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=2)
regr_rf.fit(X_train, y_train)
# Predict on new data
y_multirf = regr_multirf.predict(X_test)
y_rf = regr_rf.predict(X_test)
# Plot the results
fig, ax = plt.subplots()
s = 50
a = 0.4
ax.scatter(
y_test[:, 0],
y_test[:, 1],
edgecolor="k",
c="navy",
s=s,
marker="s",
alpha=a,
label="Data",
)
ax.scatter(
y_multirf[:, 0],
y_multirf[:, 1],
edgecolor="k",
c="cornflowerblue",
s=s,
alpha=a,
label="Multi RF score=%.2f" % regr_multirf.score(X_test, y_test),
)
ax.scatter(
y_rf[:, 0],
y_rf[:, 1],
edgecolor="k",
c="c",
s=s,
marker="^",
alpha=a,
label="RF score=%.2f" % regr_rf.score(X_test, y_test),
)
ax.set_xlim([-6, 6])
ax.set_ylim([-6, 6])
ax.set_xlabel("target 1")
ax.set_ylabel("target 2")
ax.set_title("Comparing random forests and the multi-output meta estimator")
ax.legend()
return fig
title = "Comparing random forests and the multi-output meta estimator"
with gr.Blocks(title=title) as demo:
gr.Markdown(f"## {title}")
gr.Markdown("This app demonstrates random forests and the multi-output meta estimator comparison")
max_depth = gr.Slider(minimum=10, maximum=50, step=1, label = "Maximum Depth")
n_estimators = gr.Slider(minimum=50, maximum=300, step=1, label = "Number of Estimators")
plot = gr.Plot(label=title)
n_estimators.change(fn=compare, inputs=[max_depth,n_estimators], outputs=[plot])
max_depth.change(fn=compare, inputs=[max_depth,n_estimators], outputs=[plot])
demo.launch()
|