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import gradio as gr
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import permutation_importance
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def create_dataset():
X, y = make_classification(
n_samples=1000,
n_features=10,
n_informative=3,
n_redundant=0,
n_repeated=0,
n_classes=2,
random_state=0,
shuffle=False,
)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
return X_train, X_test, y_train, y_test
def train_model():
X_train, X_test, y_train, y_test = create_dataset()
feature_names = [f"feature {i}" for i in range(X_train.shape[1])]
forest = RandomForestClassifier(random_state=0)
forest.fit(X_train, y_train)
return forest, feature_names, X_test, y_test
def plot_mean_decrease(clf, feature_names):
importances = clf.feature_importances_
std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)
forest_importances = pd.Series(importances, index=feature_names)
fig, ax = plt.subplots()
forest_importances.plot.bar(yerr=std, ax=ax)
ax.set_title("Feature importances using MDI")
ax.set_ylabel("Mean decrease in impurity")
fig.tight_layout()
return fig
def plot_feature_perm(clf, feature_names, X_test, y_test):
result = permutation_importance(
clf, X_test, y_test, n_repeats=10, random_state=42, n_jobs=2
)
forest_importances = pd.Series(result.importances_mean, index=feature_names)
fig, ax = plt.subplots()
forest_importances.plot.bar(yerr=result.importances_std, ax=ax)
ax.set_title("Feature importances using permutation on full model")
ax.set_ylabel("Mean accuracy decrease")
fig.tight_layout()
return fig
title = "Feature importances with a forest of trees 🌳"
description = """This example shows the use of a forest of trees to evaluate the importance of features on an artificial classification task.
The blue bars are the feature importances of the forest, along with their inter-trees variability represented by the error bars.
"""
with gr.Blocks() as demo:
gr.Markdown(f"## {title}")
gr.Markdown(description)
# with gr.Column():
clf, feature_names, X_test, y_test = train_model()
with gr.Row():
plot = gr.Plot(plot_mean_decrease(clf, feature_names))
plot2 = gr.Plot(plot_feature_perm(clf, feature_names, X_test, y_test))
# input_data = gr.Dropdown(choices=feature_names, label="Feature", value="body-mass index")
# coef = gr.Textbox(label="Coefficients")
# mse = gr.Textbox(label="Mean squared error (MSE)")
# r2 = gr.Textbox(label="R2 score")
# input_data.change(fn=train_model, inputs=[input_data], outputs=[plot, coef, mse, r2], queue=False)
demo.launch(enable_queue=True)
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