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import numpy as np
import pandas as pd
import plotly.express as px
from sklearn.inspection import permutation_importance
def plot_rf_importance(clf):
feature_names = clf[:-1].get_feature_names_out()
mdi_importances = pd.Series(
clf[-1].feature_importances_, index=feature_names
).sort_values(ascending=True)
fig = px.bar(mdi_importances, orientation="h", title="Random Forest Feature Importances (MDI)")
fig.update_layout(showlegend=False, xaxis_title="Importance", yaxis_title="Feature")
return fig
def plot_permutation_boxplot(clf, X: np.ndarray, y: np.array, set_: str=None):
result = permutation_importance(
clf, X, y, n_repeats=10, random_state=42, n_jobs=2
)
sorted_importances_idx = result.importances_mean.argsort()
importances = pd.DataFrame(
result.importances[sorted_importances_idx].T,
columns=X.columns[sorted_importances_idx],
)
fig = px.box(
importances.melt(),
y="variable",
x="value"
)
# Add dashed vertical line
fig.add_shape(
type="line",
x0=0,
y0=-1,
x1=0,
y1=len(importances.columns),
opacity=0.5,
line=dict(
dash="dash"
),
)
# Adapt x-range
x_min = importances.min().min()
x_min = x_min - 0.005 if x_min < 0 else -0.005
x_max = importances.max().max() + 0.005
fig.update_xaxes(range=[x_min, x_max])
fig.update_layout(
title=f"Permutation Importances {set_ if set_ else ''}",
xaxis_title="Importance",
yaxis_title="Feature",
showlegend=False
)
return fig |