def _load_featureset(dir_path, feat_files, suffix, id_col='id', label_col='y',
                     ids_to_floats=False, quiet=False, class_map=None,
                     feature_hasher=False, num_features=None):
    # if the training file is specified via train_file, then dir_path
    # actually contains the entire file name
    if isfile(dir_path):
        return Reader.for_path(dir_path, label_col=label_col, id_col=id_col,
                               ids_to_floats=ids_to_floats, quiet=quiet,
                               class_map=class_map,
                               feature_hasher=feature_hasher,
                               num_features=num_features).read()
    else:
        merged_set = None
        for file_name in sorted(join(dir_path, featfile + suffix) for
                                featfile in feat_files):
            fs = Reader.for_path(file_name, label_col=label_col, id_col=id_col,
                                 ids_to_floats=ids_to_floats, quiet=quiet,
                                 class_map=class_map,
                                 feature_hasher=feature_hasher,
                                 num_features=num_features).read()
            if merged_set is None:
                merged_set = fs
            else:
                merged_set += fs
        return merged_set
Load a list of feature files and merge them.
dataset/ETHPy150Open EducationalTestingService/skll/skll/experiments.py/_load_featureset