I am trying to do some bad case analysis on my product categorization model using SHAP. My data looks something like this:
corpus_train, corpus_test, y_train, y_test = train_test_split(data['Name_Description'],
data['Category_Target'],
test_size = 0.2,
random_state=8)
vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1, 3), min_df=3, analyzer='word')
X_train = vectorizer.fit_transform(corpus_train)
X_test = vectorizer.transform(corpus_test)
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)
X_train_sample = shap.sample(X_train, 100)
X_test_sample = shap.sample(X_test, 20)
masker = shap.maskers.Independent(data=X_test_sample)
explainer = shap.LinearExplainer(model, masker=masker)
shap_values = explainer.shap_values(X_test_sample)
X_test_array = X_test_sample.toarray()
shap.summary_plot(shap_values, X_test_array, feature_names=vectorizer.get_feature_names(), class_names=data['Category'].unique())
Now to save space I didn’t include the actual summary plot, but it looks fine. My issue is that I want to be able to analyze a single prediction and get something more along these lines:
In other words, I want to know which specific words contribute the most to the prediction. But when I run the code in cell 36 in the image above I get an
AttributeError: 'numpy.ndarray' object has no attribute 'output_names'
I’m still confused on the indexing of shap_values. How can I solve this?
Answer
I was unable to find a solution with SHAP, but I found a solution using LIME. The following code displays a very similar output where its easy to see how the model made its prediction and how much certain words contributed.
c = make_pipeline(vectorizer, classifier) # saving a list of strings version of the X_test object ls_X_test= list(corpus_test) # saving the class names in a dictionary to increase interpretability class_names = list(data.Category.unique()) # Create the LIME explainer # add the class names for interpretability LIME_explainer = LimeTextExplainer(class_names=class_names) # explain the chosen prediction # use the probability results of the logistic regression # can also add num_features parameter to reduce the number of features explained LIME_exp = LIME_explainer.explain_instance(ls_X_test[idx], c.predict_proba) LIME_exp.show_in_notebook(text=True, predict_proba=True)