Source code for watson_machine_learning_client.helpers.helpers

from typing import Union
import json
from configparser import ConfigParser

__all__ = [

[docs]def get_credentials_from_config(env_name, credentials_name, config_path="./config.ini"): """Load credentials from config file. [DEV_LC] wml_credentials = { } cos_credentials = { } :param env_name: the name of [ENV] defined in config file :type env_name: str :param credentials_name: name of credentials :type credentials_name: str :param config_path: path to the config file :type config_path: str :return: dict >>> get_credentials_from_config(env_name='DEV_LC', credentials_name='wml_credentials') """ config = ConfigParser() return json.loads(config.get(env_name, credentials_name))
def pipeline_to_script(pipeline) -> Union['str', 'HTML']: """ Create a python script based on a passed pipeline model. (Pythone code representation of pipeline model) Parameters ---------- pipeline: Union[Pipeline, TrainedPipeline], required Example ------- >>> pipeline_to_script(pipeline=best_pipeline) >>> """ from lale.helpers import import_from_sklearn_pipeline from sklearn.pipeline import Pipeline from watson_machine_learning_client.utils.autoai.utils import is_ipython from watson_machine_learning_client.utils import create_download_link import os script_name = "" if isinstance(pipeline, Pipeline): pipeline = import_from_sklearn_pipeline(pipeline) script = pipeline.pretty_print() with open(script_name, 'w') as f: f.write(script) script_location = f"{os.path.abspath('.')}/{script_name}" if is_ipython(): return create_download_link(script_location) else: return f"Pipeline python script location: {script_location}"