for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
"""Experiment adapter for sklearn cross-validation experiments."""
from sklearn import clone
from sklearn.model_selection import cross_validate
from sklearn.utils.validation import _num_samples
from hyperactive.base import BaseExperiment
class SklearnCvExperiment(BaseExperiment):
def __init__(self, estimator, scoring, cv, X, y):
self.estimator = estimator
self.X = X
self.y = y
self.scoring = scoring
self.cv = cv
def _paramnames(self):
"""Return the parameter names of the search.
Returns
-------
list of str
The parameter names of the search parameters.
"""
return list(self.estimator.get_params().keys())
def _score(self, **params):
"""Score the parameters.
Parameters
----------
params : dict with string keys
Parameters to score.
float
The score of the parameters.
dict
Additional metadata about the search.
estimator = clone(self.estimator)
estimator.set_params(**params)
cv_results = cross_validate(
self.estimator,
self.X,
self.y,
cv=self.cv,
)
add_info_d = {
"score_time": cv_results["score_time"],
"fit_time": cv_results["fit_time"],
"n_test_samples": _num_samples(self.X),
}
return cv_results["test_score"].mean(), add_info_d
SimonBlanke /
Hyperactive
Loading history...