def _check_fit_params(X, fit_params, indices=None):
"""Check and validate the parameters passed during `fit`.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data array.
fit_params : dict
Dictionary containing the parameters passed at fit.
indices : array-like of shape (n_samples,), default=None
Indices to be selected if the parameter has the same size as
`X`.
Returns
-------
fit_params_validated : dict
Validated parameters. We ensure that the values support
indexing.
"""
try:
from sklearn.utils.validation import \
_check_fit_params as _sklearn_check_fit_params
return _sklearn_check_fit_params(X, fit_params, indices)
except ImportError:
from sklearn.model_selection import _validation
fit_params_validated = \
{k: _validation._index_param_value(X, v, indices)
for k, v in fit_params.items()}
return fit_params_validated
def _check_fit_params(
X, # type: TwoDimArrayLikeType
fit_params, # type: Dict
indices # type: OneDimArrayLikeType
):
# type: (...) -> Dict
if _sklearn_version >= '0.22.1':
return _sklearn_check_fit_params(X, fit_params, indices)
else: # '_sklearn_version < 0.22.1'
return {
key: _sklearn_index_param_value(X, value, indices)
for key, value in fit_params.items()
}
