Source code for evalml.tuners.skopt_tuner

import warnings

import pandas as pd
from skopt import Optimizer

from .tuner import Tuner
from .tuner_exceptions import ParameterError

from evalml.utils.logger import get_logger

logger = get_logger(__file__)


[docs]class SKOptTuner(Tuner): """Bayesian Optimizer. Arguments: pipeline_hyperparameter_ranges (dict): A set of hyperparameter ranges corresponding to a pipeline's parameters. random_seed (int): The seed for the random number generator. Defaults to 0. """ def __init__(self, pipeline_hyperparameter_ranges, random_seed=0): super().__init__(pipeline_hyperparameter_ranges, random_seed=random_seed) self.opt = Optimizer( self._search_space_ranges, "ET", acq_optimizer="sampling", random_state=random_seed, )
[docs] def add(self, pipeline_parameters, score): """Add score to sample. Arguments: pipeline_parameters (dict): A dict of the parameters used to evaluate a pipeline score (float): The score obtained by evaluating the pipeline with the provided parameters Returns: None """ # skip adding nan scores if pd.isnull(score): return flat_parameter_values = self._convert_to_flat_parameters(pipeline_parameters) try: self.opt.tell(flat_parameter_values, score) except Exception as e: logger.debug( "SKOpt tuner received error during add. Score: {}\nParameters: {}\nFlat parameter values: {}\nError: {}".format( pipeline_parameters, score, flat_parameter_values, e ) ) if str(e) == "'<=' not supported between instances of 'int' and 'NoneType'": msg = "Invalid parameters specified to SKOptTuner.add: parameters {} error {}".format( pipeline_parameters, str(e) ) logger.error(msg) raise ParameterError(msg) raise (e)
[docs] def propose(self): """Returns a suggested set of parameters to train and score a pipeline with, based off the search space dimensions and prior samples. Returns: dict: Proposed pipeline parameters """ with warnings.catch_warnings(): warnings.simplefilter("ignore") if not len(self._search_space_ranges): return self._convert_to_pipeline_parameters({}) flat_parameters = self.opt.ask() return self._convert_to_pipeline_parameters(flat_parameters)