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import numpy as np
import gradio as gr
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.datasets import fetch_openml
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import utils
def app_fn(seed: int, n_cat: int, n_estimators: int, min_samples_leaf: int):
X, y = fetch_openml(
"titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
)
rng = np.random.RandomState(seed=seed)
X["random_cat"] = rng.randint(n_cat, size=X.shape[0])
X["random_num"] = rng.randn(X.shape[0])
categorical_columns = ["pclass", "sex", "embarked", "random_cat"]
numerical_columns = ["age", "sibsp", "parch", "fare", "random_num"]
X = X[categorical_columns + numerical_columns]
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=seed)
categorical_encoder = OrdinalEncoder(
handle_unknown="use_encoded_value", unknown_value=-1, encoded_missing_value=-1
)
numerical_pipe = SimpleImputer(strategy="mean")
preprocessing = ColumnTransformer(
[
("cat", categorical_encoder, categorical_columns),
("num", numerical_pipe, numerical_columns),
],
verbose_feature_names_out=False,
)
clf = Pipeline(
[
("preprocess", preprocessing),
("classifier", RandomForestClassifier(
random_state=seed,
n_estimators=n_estimators,
min_samples_leaf=min_samples_leaf
)
),
]
)
clf.fit(X_train, y_train)
fig_mdi = utils.plot_rf_importance(clf)
fig_perm_train = utils.plot_permutation_boxplot(clf, X_train, y_train, set_="train set")
fig_perm_test = utils.plot_permutation_boxplot(clf, X_test, y_test, set_="test set")
return fig_mdi, fig_perm_train, fig_perm_test
title = "Permutation Importance vs Random Forest Feature Importance (MDI)"
with gr.Blocks(title=title) as demo:
gr.Markdown(f"# {title}")
gr.Markdown(
"""
This demo compares the feature importances of a Random Forest classifier using the Mean Decrease Impurity (MDI) method and the Permutation Importance method. \
To showcase the difference between the two methods, we add two random features to the Titanic dataset. \
The first random feature is categorical and the second one is numerical. \
The categorical feature can have its number of categories changed \
and the numerical feature is sampled from a Standard Normal Distribution. \
Random Forest hyperparameters can also be changed to verify the impact of model complexity on the feature importances.
See the original scikit-learn example [here](https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py).
"""
)
with gr.Row():
seed = gr.inputs.Slider(0, 42, 1, default=42, label="Seed")
n_cat = gr.inputs.Slider(2, 30, 1, default=3, label="Number of categories in random_cat")
n_estimators = gr.inputs.Slider(5, 150, 5, default=100, label="Number of Trees")
min_samples_leaf = gr.inputs.Slider(1, 30, 5, default=1, label="Minimum number of samples to create a leaf")
fig_mdi = gr.Plot(label="Mean Decrease Impurity (MDI)")
with gr.Row():
fig_perm_train = gr.Plot(label="Permutation Importance (Train)")
fig_perm_test = gr.Plot(label="Permutation Importance (Test)")
seed.change(fn=app_fn, outputs=[fig_mdi, fig_perm_train, fig_perm_test], inputs=[seed, n_cat, n_estimators, min_samples_leaf])
n_cat.change(fn=app_fn, outputs=[fig_mdi, fig_perm_train, fig_perm_test], inputs=[seed, n_cat, n_estimators, min_samples_leaf])
n_estimators.change(fn=app_fn, outputs=[fig_mdi, fig_perm_train, fig_perm_test], inputs=[seed, n_cat, n_estimators, min_samples_leaf])
min_samples_leaf.change(fn=app_fn, outputs=[fig_mdi, fig_perm_train, fig_perm_test], inputs=[seed, n_cat, n_estimators, min_samples_leaf])
demo.load(fn=app_fn, outputs=[fig_mdi, fig_perm_train, fig_perm_test], inputs=[seed, n_cat, n_estimators, min_samples_leaf])
demo.launch() |