from sklearn.multioutput import MultiOutputRegressor, _fit_estimator from sklearn.utils.parallel import Parallel, delayed from sklearn.utils.validation import has_fit_parameter class FreqaiMultiOutputRegressor(MultiOutputRegressor): def fit(self, X, y, sample_weight=None, fit_params=None): """Fit the model to data, separately for each output variable. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. y : {array-like, sparse matrix} of shape (n_samples, n_outputs) Multi-output targets. An indicator matrix turns on multilabel estimation. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If `None`, then samples are equally weighted. Only supported if the underlying regressor supports sample weights. fit_params : A list of dicts for the fit_params Parameters passed to the ``estimator.fit`` method of each step. Each dict may contain same or different values (e.g. different eval_sets or init_models) .. versionadded:: 0.23 Returns ------- self : object Returns a fitted instance. """ if not hasattr(self.estimator, "fit"): raise ValueError("The base estimator should implement a fit method") y = self._validate_data(X="no_validation", y=y, multi_output=True) if y.ndim == 1: raise ValueError( "y must have at least two dimensions for " "multi-output regression but has only one." ) if sample_weight is not None and not has_fit_parameter( self.estimator, "sample_weight" ): raise ValueError("Underlying estimator does not support sample weights.") if not fit_params: fit_params = [None] * y.shape[1] self.estimators_ = Parallel(n_jobs=self.n_jobs)( delayed(_fit_estimator)( self.estimator, X, y[:, i], sample_weight, **fit_params[i] ) for i in range(y.shape[1]) ) if hasattr(self.estimators_[0], "n_features_in_"): self.n_features_in_ = self.estimators_[0].n_features_in_ if hasattr(self.estimators_[0], "feature_names_in_"): self.feature_names_in_ = self.estimators_[0].feature_names_in_ return