utils_models.py 文件源码

def load_ml_model(file_name):

    with open(file_name, 'rb') as read_file:
        base_pipeline = dill.load(read_file)

    if isinstance(base_pipeline, utils_categorical_ensembling.CategoricalEnsembler):
        for step in base_pipeline.transformation_pipeline.named_steps:
            pipeline_step = base_pipeline.transformation_pipeline.named_steps[step]

            try:
                if pipeline_step.get('model_name', 'reallylongnonsensicalstring')[:12] == 'DeepLearning':
                    pipeline_step.model = insert_deep_learning_model(pipeline_step, file_name)
            except AttributeError:
                pass

        for step in base_pipeline.trained_models:
            pipeline_step = base_pipeline.trained_models[step]

            try:
                if pipeline_step.get('model_name', 'reallylongnonsensicalstring')[:12] == 'DeepLearning':
                    pipeline_step.model = insert_deep_learning_model(pipeline_step, file_name)
            except AttributeError:
                pass

    else:

        for step in base_pipeline.named_steps:
            pipeline_step = base_pipeline.named_steps[step]
            try:
                if pipeline_step.get('model_name', 'reallylongnonsensicalstring')[:12] == 'DeepLearning':
                    pipeline_step.model = insert_deep_learning_model(pipeline_step, file_name)
            except AttributeError:
                pass

    return base_pipeline

# Keeping this here for legacy support