def predict_fn()

in sagemaker_notebook_instance/containers/model/entry_point.py [0:0]

• 32 lines of code
• 1 McCabe index (conditional complexity)

def predict_fn(request, model_assets):
    print('making batch')
    request = [request]
    print('extracting features')
    numerical_features, categorical_features, textual_features = extract_features(
        request,
        model_assets['numerical_feature_names'],
        model_assets['categorical_feature_names'],
        model_assets['textual_feature_names']
    )
    
    print('transforming numerical_features')
    numerical_features = model_assets['numerical_transformer'].transform(numerical_features)
    print('transforming categorical_features')
    categorical_features = model_assets['categorical_transformer'].transform(categorical_features)
    print('transforming textual_features')
    textual_features = model_assets['textual_transformer'].transform(textual_features)
    
    # concat features
    print('concatenating features')
    categorical_features = categorical_features.toarray()
    textual_features = np.array(textual_features)
    textual_features = textual_features.reshape(textual_features.shape[0], -1)
    features = np.concatenate([
        numerical_features,
        categorical_features,
        textual_features
    ], axis=1)
    
    print('predicting using model')
    prediction = model_assets['classifier'].predict_proba(features)
    probability = prediction[0][1].tolist()
    output = {
        'probability': probability
    }
    return output