testing

                               frac_valid=.1, frac_test=.1, seed=None,

                               log_every_n=1000):

        # Obtain original x, y, and w arrays

        numpyArrayList = dataset.to_numpy();

        numpyArrayList = dataset.to_numpy()

        numpyArrayList = self.__randomize_arrays(numpyArrayList)

        X = numpyArrayList[0]

        y = numpyArrayList[1]

        w = numpyArrayList[2]

        ids = numpyArrayList[3]

        print("this is y!")

        print(y)

        """

        frac_train identifies percentage of datapoints that need to be present in split -- so 80% training data may actually be 90% of data (but 80% of actual datapoints, not NaN, will be present in split)

        """