def METHOD_NAME(self, *args, **options):
    public_dataset = MlInputFromPublicDataset(options["comparisons_url"])
    nb_comparisons = 0
    with transaction.atomic():
        poll = Poll.default_poll()
        poll.algorithm = ALGORITHM_MEHESTAN
        poll.save()
        usernames = public_dataset.users.public_username.unique()
        comparisons = public_dataset.comparisons
        if options["user_sampling"]:
            usernames = set(
                pd.Series(usernames)
                .sample(frac=options["user_sampling"], random_state=RANDOM_SEED)
                .values
            ).union(SEED_USERS)
            comparisons = comparisons[comparisons.public_username.isin(usernames)]
        EmailDomain.objects.create(
            domain="@trusted.example", status=EmailDomain.STATUS_ACCEPTED
        )
        users = {
            username: self.create_user(username, public_dataset) for username in usernames
        }
        print(f"Created {len(users)} users")
        videos = self.create_videos(set(comparisons.entity_a) | set(comparisons.entity_b))
        print(f"Created {len(videos)} video entities")
        for ((username, video_a, video_b), rows) in comparisons.groupby(
            ["public_username", "entity_a", "entity_b"]
        ):
            comparison = Comparison.objects.create(
                user=users[username],
                poll=poll,
                entity_1=videos[video_a],
                entity_2=videos[video_b],
            )
            for _, values in rows.iterrows():
                ComparisonCriteriaScore.objects.create(
                    comparison=comparison,
                    criteria=values["criteria"],
                    score=values["score"],
                )
            nb_comparisons += 1
        print(f"Created {nb_comparisons} comparisons")
        for entity in Entity.objects.iterator():
            entity.update_n_ratings()
            entity.update_entity_poll_rating(poll=poll)
        self.create_test_user()
        ContributorRating.objects.update(is_public=True)
    if settings.YOUTUBE_API_KEY:
        print("Fetching video metadata from Youtube...")
        self.fetch_video_metadata(videos)
        print("Done.")
    print("Running ml-train...")
    call_command("ml_train", "--no-trust-algo")