2137c84c95286a1c888140cf425c27ff087271bb,solutionbox/structured_data/mltoolbox/_structured_data/preprocess/local_preprocess.py,,run_numerical_categorical_analysis,#Any#Any#,70

Before Change

    // label2,count
    // ...
    // where label1 is the most frequent label, and label2 is the 2nd most, etc.
    labels = "\n".join(["%s,%d" % (label, count)
                        for label, count in sorted(six.iteritems(label_count),
                                                   key=lambda x: x[1],
                                                   reverse=True)])
    file_io.write_string_to_file(
        os.path.join(args.output_dir, CATEGORICAL_ANALYSIS_FILE % name),
        labels)

After Change

  // Write the vocab files. Each label is on its own line.
  for name, unique_labels in six.iteritems(categorical_results):
    labels = "\n".join(list(unique_labels))
    file_io.write_string_to_file(
        os.path.join(args.output_dir, CATEGORICAL_ANALYSIS_FILE % name),
        labels)

Instances