01_create_commands.py

import argparse
import itertools
import os
import sys

import openml

sys.path.append(".")
from update_metadata_util import classification_tasks, regression_tasks


parser = argparse.ArgumentParser()
parser.add_argument("--working-directory", type=str, required=True)
parser.add_argument("--test", action="store_true")
args = parser.parse_args()
working_directory = args.working_directory
test = args.test

command_file_name = os.path.join(working_directory, "metadata_commands.txt")

this_directory = os.path.dirname(os.path.abspath(__file__))
script_name = "run_auto-sklearn_for_metadata_generation.py"
absolute_script_name = os.path.join(this_directory, script_name)

commands = []
for task_id in classification_tasks if not test else (233, 245, 258):
    for metric in ("accuracy", "balanced_accuracy", "roc_auc", "logloss"):

        if (
            len(openml.tasks.get_task(task_id, download_data=False).class_labels) > 2
            and metric == "roc_auc"
        ):
            continue

        command = (
            "python3 %s --working-directory %s --time-limit 86400 "
            "--per-run-time-limit 1800 --task-id %d -s 1 --metric %s"
            % (absolute_script_name, working_directory, task_id, metric)
        )
        commands.append(command)
for task_id in regression_tasks if not test else (360029, 360033):
    for metric in ("r2", "root_mean_squared_error", "mean_absolute_error"):
        command = (
            "python3 %s --working-directory %s --time-limit 86400 "
            "--per-run-time-limit 1800 --task-id %d -s 1 --metric %s"
            % (absolute_script_name, working_directory, task_id, metric)
        )
        commands.append(command)

with open(command_file_name, "w") as fh:
    for command in commands:
        fh.writelines(command)
        fh.write("\n")