utilities

utils.py

@@ -0,0 +1,60 @@
+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