b8b9b6d34d35671fb8651f40503d51fc72477128,scripts/2015_nips_paper/run_with_metalearning.py,,main,#Any#Any#Any#Any#Any#,37

Before Change

    // We need to get task type, metric, is_sparse_or_dense information to
    // construct the path to the specific metadata directory. For details see
    // get_metalearning_suggestion() in smbo.py.
    TASK_TYPES_TO_STRING = {  // Mimic the same dict in autosklearn.constants
        "binary": "binary.classification",
        "multiclass": "multiclass.classification",
    }
    task_type = type_of_target(y_train)
    metadata_for_this_task = os.path.abspath(
        os.path.join(working_directory,
                     "metadata_%i/balanced_accuracy_%s_%s" % (task_id,
                                                              TASK_TYPES_TO_STRING[task_type],
                                                              sparse_or_dense)))

    configuration_output_dir = os.path.join(working_directory, str(seed))
    tmp_dir = os.path.join(configuration_output_dir, str(task_id))
    try:

After Change

                    )

    with open(os.path.join(tmp_dir, "score_metalearning.csv"), "w") as fh:
        T = 0
        fh.write("Time,Train Performance,Test Performance\n")
        // Add start time:0, Train Performance:1, Test Performance: 1
        fh.write("{0},{1},{2}\n".format(T, 1, 1))
        for t, dummy, s in zip(automl.cv_results_["mean_fit_time"],
                               [1 for i in range(len(automl.cv_results_["mean_fit_time"]))],
                               1 - automl.cv_results_["mean_test_score"]):  // We compute rank based on error.
            T += t
            fh.write("{0},{1},{2}\n".format(T, dummy, s))


if __name__=="__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--working-directory", type=str, required=True)
    parser.add_argument("--time-limit", type=int, required=True)

Instances