zoo.automl.common package

Submodules

zoo.automl.common.metrics module

zoo.automl.common.parameters module

zoo.automl.common.util module

class zoo.automl.common.util.NumpyEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)

Bases: json.encoder.JSONEncoder

convert numpy array to list for JSON serialize

default(obj)

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return JSONEncoder.default(self, o)

zoo.automl.common.util.convert_bayes_configs(config)

zoo.automl.common.util.get_remote_list(dir_in)

zoo.automl.common.util.load_config(file_path)

zoo.automl.common.util.process(cmd)

zoo.automl.common.util.restore(file, feature_transformers=None, model=None, config=None)

zoo.automl.common.util.restore_hdfs(model_path, remote_dir, feature_transformers=None, model=None, config=None)

zoo.automl.common.util.restore_zip(file, feature_transformers=None, model=None, config=None)

zoo.automl.common.util.save(file_path, feature_transformers=None, model=None, config=None)

zoo.automl.common.util.save_config(file_path, config, replace=False)

Parameters:

• file_path – the file path of config to be saved.
• config – dict. The config to be saved
• replace – whether to replace if the config file already existed.

Returns:

zoo.automl.common.util.save_zip(file, feature_transformers=None, model=None, config=None)

zoo.automl.common.util.train_val_test_split(df, val_ratio=0, test_ratio=0.1, look_back=0, horizon=1)

split input dataframe into train_df, val_df and test_df according to split ratio. The dataframe is splitted in its originally order in timeline. e.g. |......... train_df(80%) ........ | ... val_df(10%) ...| …test_df(10%)…| :param df: dataframe to be splitted :param val_ratio: validation ratio :param test_ratio: test ratio :param look_back: the length to look back :param horizon: num of steps to look forward :return:

zoo.automl.common.util.upload_ppl_hdfs(upload_dir, ckpt_name)

Module contents