def get_low_selective_features(csr_matrix, label, num_classes_feature_can_appear): transposed = csr_matrix.transpose().tocsr() results = parallel.rowwise( transposed, is_low_selective_, min(8, transposed.shape[0]), {"num_classes_feature_can_appear": num_classes_feature_can_appear, "label": label}, ) superset = set() for r in results: superset = superset.union(r) return superset
def compute_row_sum_parallel(csr_matrix, parallelism): results = parallel.rowwise(csr_matrix, row_sum_, parallelism, {}) superdict = dict() for r in results: superdict = dict(superdict.items() + r.items()) return superdict