fixing tests

        index_list = self.__generate_required_index(w, required_hits_list)

        X_train = X_test = X

        y_train = y_test = y

        w_train = w_test = np.zeroes(w.shape)

        w_train = w_test = np.zeros(w.shape)

        ids_train = ids_test = ids

        #chunk appropriate values into weights matrices

          w_test[index:, colIndex] = w[index:, colIndex]

        #check out if any rows in either w_train or w_test are just zeros

        rowsToKeepTrain = np.where(w_train.any(axis=1))[0]

        rowsToKeepTest = np.where(w_test.any(axis=1))[0]

        rowsToKeepTrain = w_train.any(axis=1)

        rowsToKeepTest = w_test.any(axis=1)

        #prune train sets

        w_train = w_train[rowsToKeepTrain]

        datasets = [train_data, valid_data, test_data]

        datasetIndex = 0

        for dataset in datasets:

            np_list = dataset.to_numpy()

            y = np_list[1]

            X, y, w, ids = dataset.to_numpy()

            # verify that each task in the train dataset has some hits

            y_df = pd.DataFrame(data=y)

            totalRows = len(y_df.index)

            for col in y_df:

                column = y_df[col]

                NaN_count = column.isnull().sum()

                if NaN_count == totalRows:

            for col in w.T:

                if not np.any(col):

                    print("fail -- one column doesn't have results")

                    if datasetIndex == 0:

                        print("train_data failed")