Explain batches

Generate attribution matrices and per-sample metadata with FPDEEngine

Use FPDEEngine.explain_batch when you need one attribution row per sample. Use FPDEEngine.explain_matrix when you only need the attribution matrix.

Explain a batch with details

attribution_matrix, details = engine.explain_batch(
    X_test,
    lambda_hyb=0.5,
    normalize="l1",
)

print(attribution_matrix.shape)
print(details[0]["target_label"])
print(details[0]["rival_label"])

The matrix has shape (n_samples, n_features). Each row is an attribution vector for the matching row in X_test.

Explain a batch without details

attribution_matrix = engine.explain_matrix(
    X_test,
    lambda_hyb=0.5,
    normalize="l1",
)

Use this when you plan to aggregate or save attribution rows and do not need per-sample labels or evidence values.

Keep feature order stable

FPDE assumes every matrix you pass to the engine uses the same feature order. Apply the same preprocessing pipeline to training, validation, and explanation data.

A feature dimension mismatch means the explanation data does not match the fitted prototype state. Check scaling, encoding, feature selection, and column order.

Summarize a batch

import numpy as np

mean_abs = np.mean(np.abs(attribution_matrix), axis=0)
top = np.argsort(mean_abs)[::-1][:10]

for index in top:
    print(feature_names[index], mean_abs[index])

This ranks features by average absolute attribution magnitude. It does not change the sign interpretation for each local explanation.

Save reproducible outputs

Save the attribution matrix with enough metadata to recreate it later:

• FPDE version
• lambda_hyb
• normalize
• anchor_strategy
• Feature names and feature order
• Target and rival labels from details
• Training data or prototype-state source