问如何在SpaCy模型中使用SHAP？

EN

import spacy
textcat_spacy = spacy.load("my-model")
tokenizer_spacy = spacy.tokenizer.Tokenizer(textcat_spacy.vocab)

# Run the spacy pipeline on some random text just to retrieve the classes
doc = textcat_spacy("hi")
classes = list(doc.cats.keys())

# Define a function to predict
def predict(texts):
    # convert texts to bare strings
    texts = [str(text) for text in texts]
    results = []
    for doc in textcat_spacy.pipe(texts):
        # results.append([{'label': cat, 'score': doc.cats[cat]} for cat in doc.cats])
        results.append([doc.cats[cat] for cat in classes])
    return results

# Create a function to create a transformers-like tokenizer to match shap's expectations
def tok_adapter(text, return_offsets_mapping=False):
    doc = tokenizer_spacy(text)
    out = {"input_ids": [tok.norm for tok in doc]}
    if return_offsets_mapping:
        out["offset_mapping"] = [(tok.idx, tok.idx + len(tok)) for tok in doc]
    return out

import shap
# Create the Shap Explainer
# - predict is the "model" function, adapted to a transformers-like model
# - masker is the masker used by shap, which relies on a transformers-like tokenizer
# - algorithm is set to permuation, which is the one used for transformers models
# - output_names are the classes (altough it is not propagated to the permutation explainer currently, which is why plots do not have the labels)
# - max_evals is set to a high number to reduce the probability of cases where the explainer fails because there are too many tokens
explainer = shap.Explainer(predict, masker=shap.maskers.Text(tok_adapter), algorithm="permutation", output_names=classes, max_evals=1500)

sample = "Some text to classify"
# Process the text using SpaCy
doc = textcat_spacy(sample)
# Get the shap values
shap_values = explainer([sample])