Run fit on the estimator with parameters chosen sequentially by SigOpt.
        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            Training vector, where n_samples in the number of samples and
            n_features is the number of features.
        y : array-like, shape = [n_samples] or [n_samples, n_output], optional
            Target relative to X for classification or regression;
            None for unsupervised learning.

def fit(self, X, y=None, groups=None, **fit_params):
        """
        Run fit on the estimator with parameters chosen sequentially by SigOpt.
        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            Training vector, where n_samples in the number of samples and
            n_features is the number of features.
        y : array-like, shape = [n_samples] or [n_samples, n_output], optional
            Target relative to X for classification or regression;
            None for unsupervised learning.
        """
        return self._fit(X, y=y, groups=groups, **fit_params)