App itself

app.py

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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.
+
+        [Original Example](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="# Cats in random_cat")
+        n_estimators = gr.inputs.Slider(5, 150, 5, default=100, label="# Trees in the forest")
+        min_samples_leaf = gr.inputs.Slider(1, 30, 5, default=1, label="Minimum # samples required to be at a leaf node")
+
+    btn = gr.Button(label="Run")
+    
+    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)")
+    
+    btn.click(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()