3e34690bca279c672505a58212746c8053595412,mir_eval/io.py,,load_delimited,#Any#Any#Any#,13

Before Change

    //
    //   2. numpy"s text loader does not handle non-numeric data
    //
    with open(filename, "r") as input_file:
        for row, line in enumerate(input_file, 1):
            // Split each line using the supplied delimiter
            data = splitter.split(line.strip(), n_columns - 1)

            // Throw a helpful error if we got an unexpected // of columns
            if n_columns != len(data):
                raise ValueError("Expected {} columns, got {} at "
                                 "{}:{:d}:\n\t{}".format(n_columns, len(data),
                                                         filename, row, line))

            for value, column, converter in zip(data, columns, converters):
                // Try converting the value, throw a helpful error on failure
                try:
                    converted_value = converter(value)
                except:
                    raise ValueError("Couldn"t convert value {} using {} "
                                     "found at {}:{:d}:\n\t{}".format(
                                         value, converter.__name__, filename,
                                         row, line))
                column.append(converted_value)

    // Sane output
    if n_columns == 1:
        return columns[0]
    else:
        return columns

After Change

        // Open the file for reading
        input_file = open(filename, "r")
    // If the provided has a read attribute, we can use it as a file handle
    elif hasattr(filename, "read"):
        input_file = filename
    // Raise error otherwise
    else:
        raise ValueError("filename must be a string or file handle")

    // Note: we do io manually here for two reasons.
    //   1. The csv module has difficulties with unicode, which may lead
    //      to failures on certain annotation strings

Instances