splearn/linear_model/base.py
def _spark_fit(self, cls, Z, *args, **kwargs):
        """Wraps a Scikit-learn Linear model's fit method to use with RDD
        input.

        Parameters
        ----------
        cls : class object
            The sklearn linear model's class to wrap.
        Z : TupleRDD or DictRDD
            The distributed train data in a DictRDD.

        Returns
        -------
        self: the wrapped class
        """
        mapper = lambda X_y: super(cls, self).fit(
            X_y[0], X_y[1], *args, **kwargs
        )
        models = Z.map(mapper)
        avg = models.reduce(operator.add) / models.count()
        self.__dict__.update(avg.__dict__)
        return self
def _spark_fit(self, cls, Z, *args, **kwargs):
        mapper = lambda X_y: super(cls, self).fit(
            X_y[0], X_y[1], *args, **kwargs
        )
        models = Z.map(mapper)
        avg = models.reduce(operator.add) / models.count()
        self.__dict__.update(avg.__dict__)
        return self
def _spark_fit ( self , cls , Z , * args , * * kwargs ) : mapper = lambda X_y : super ( cls , self ) . fit ( X_y [ 0 ] , X_y [ 1 ] , * args , * * kwargs ) models = Z . map ( mapper ) avg = models . reduce ( operator . add ) / models . count ( ) self . __dict__ . update ( avg . __dict__ ) return self
Wraps a Scikit-learn Linear model's fit method to use with RDD
        input.

        Parameters
        ----------
        cls : class object
            The sklearn linear model's class to wrap.
        Z : TupleRDD or DictRDD
            The distributed train data in a DictRDD.

        Returns
        -------
        self: the wrapped class
Wraps a Scikit - learn Linear model s fit method to use with RDD input .