msaf/input_output.py
def get_configuration(feature, annot_beats, framesync, boundaries_id,
                      labels_id):
    """Gets the configuration dictionary from the current parameters of the
    algorithms to be evaluated."""
    config = {}
    config["annot_beats"] = annot_beats
    config["feature"] = feature
    config["framesync"] = framesync
    bound_config = {}
    if boundaries_id != "gt":
        bound_config = \
            eval(msaf.algorithms.__name__ + "." + boundaries_id).config
        config.update(bound_config)
    if labels_id is not None:
        label_config = \
            eval(msaf.algorithms.__name__ + "." + labels_id).config

        # Make sure we don't have parameter name duplicates
        if labels_id != boundaries_id:
            overlap = set(bound_config.keys()). \
                intersection(set(label_config.keys()))
            assert len(overlap) == 0, \
                "Parameter %s must not exist both in %s and %s algorithms" % \
                (overlap, boundaries_id, labels_id)
        config.update(label_config)
    return config
def get_configuration(feature, annot_beats, framesync, boundaries_id,
                      labels_id):
    """Gets the configuration dictionary from the current parameters of the
    algorithms to be evaluated."""
    config = {}
    config["annot_beats"] = annot_beats
    config["feature"] = feature
    config["framesync"] = framesync
    bound_config = {}
    if boundaries_id != "gt":
        bound_config = \
            eval(msaf.algorithms.__name__ + "." + boundaries_id).config
        config.update(bound_config)
    if labels_id is not None:
        label_config = \
            eval(msaf.algorithms.__name__ + "." + labels_id).config

        # Make sure we don't have parameter name duplicates
        if labels_id != boundaries_id:
            overlap = set(bound_config.keys()). \
                intersection(set(label_config.keys()))
            assert len(overlap) == 0, \
                "Parameter %s must not exist both in %s and %s algorithms" % \
                (overlap, boundaries_id, labels_id)
        config.update(label_config)
    return config
Gets the configuration dictionary from the current parameters of the
    algorithms to be evaluated.