Source code for evalml.tuners.skopt_tuner

import pandas as pd
from skopt import Optimizer


[docs]class SKOptTuner:
    """Bayesian Optimizer"""

[docs]    def __init__(self, space, random_state=0):
        self.opt = Optimizer(space, "ET", acq_optimizer="sampling", random_state=random_state)

[docs]    def add(self, parameters, score):
        # skip adding nan scores for
        if not pd.isnull(score):
            return self.opt.tell(list(parameters), score)

[docs]    def propose(self):
        return self.opt.ask()