ee62faa244ca20b05d2ec0d907ea6379de18e0aa,dask_ml/wrappers.py,Incremental,fit,#Incremental#Any#Any#,343

Before Change

    def fit(self, X, y=None, **fit_kwargs):
        check_scoring(self.estimator, self.scoring)
        if not dask.is_dask_collection(X) and not dask.is_dask_collection(y):
            result = self.estimator.partial_fit(X=X, y=y, **fit_kwargs)
        else:
            result = fit(self.estimator, X, y, **fit_kwargs)
            copy_learned_attributes(result, self.estimator)
        copy_learned_attributes(result, self)
        return self

    def __repr__(self):

After Change

    def fit(self, X, y=None, **fit_kwargs):
        estimator = sklearn.base.clone(self.estimator)
        return self._fit_for_estimator(estimator, X, y, **fit_kwargs)

    def partial_fit(self, X, y=None, **fit_kwargs):
        Fit the underlying estimator.

Instances