[Question] Where does the Bayesian Optimisation is working for Hyperparameter search?

[sayannath]

Short Question Description

I just want to know where does the Bayesian Optimisation is working for Hyperparameter search? I am currently working on Fairness so I have a query on that.

Thanks

[eddiebergman]

We use SMAC as the bayesian optimization library. You can find it here although it's quite convoluted considering it inherits andd overrides some of SMAC's functionality.

https://github.com/automl/auto-sklearn/blob/673211252ca508b6f5bb92cf5fa87c6455bbad99/autosklearn/smbo.py#L16

[sayannath]

Hey @eddiebergman! Thanks for the reply. I actually want to log all the model and hyperparameters used by the autosklearn model.

PS: Not about the ensemble models. I want the models which used while getting trained before getting the best model.

[eddiebergman]

Hiyo, unfortunatly the easy ways are not the most informative:

• You can use leaderboard(detailed=True, ensemble_only=False)
  • This has the downside you won't really see the configurations as a whole
• You can use show_models() which will give you the actual models that are actually used in the final ensemble.
  • However it's not really the best for visual output as you can't directly see the hyperparameters, you would have to interogate the actual objects returned.
• You can directly access askl.automl_.runhistory_.items() which is generated by the underlying Bayesian Optimization tool SMAC.
  • This again, does not directly give hyperparameters but instead an iterator of (trial_key, trial_value): tuple[TrialKey, TrialValue] which you will have to use with askl.automl_.runhistory_.get_config(trial_key.config_id) to get the actual configuration. Ref: (TrialKey, TrailValue)

[sayannath]

I am getting the cost and the configuration like this:

data_for_json = []
for run_key, run_value in run_history.data.items():
    config_id = run_key.config_id
    config = run_history.ids_config[config_id]

    # Convert configuration to a serializable format (dictionary with primitives)
    config_dict = config.get_dictionary()

    # Append configuration and cost to the list
    data_for_json.append({
        "configuration": config_dict,
        "cost": run_value.cost,
        # "run_value": run_value,
        # If you need to convert cost to a score, adjust accordingly
        # Example for accuracy: "score": 1 - run_value.cost
    })

And letting you know that I am using bi-objective function and in that I am returning a combined score.

So is that the correct way to do so. I am also dumbing all the info in a JSON file.

[eddiebergman]

Seems correct to me :)

[sayannath]

While training I said that I am using the bi-objective function in autosklearn. Like this:

def bi_objective_fn(solution, prediction):
    """
    Calculate a combined score of accuracy and fairness.

    :param solution: True labels.
    :param prediction: Predicted labels.
    :return: Combined score.
    """
    protected_attr = "Sex"
    metric_id = 2

    split = generate_train_subset("test_split.txt")
    subset_data_orig_train = data_orig_train.subset(split)

    if os.stat("beta.txt").st_size == 0:
        default = RandomForestClassifier(
            n_estimators=1750,
            criterion="gini",
            max_features=0.5,
            min_samples_split=6,
            min_samples_leaf=6,
            min_weight_fraction_leaf=0.0,
            max_leaf_nodes=None,
            min_impurity_decrease=0.0,
            bootstrap=True,
            max_depth=None,
        )
        degrees = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
        mutation_strategies = {"0": [1, 0], "1": [0, 1]}
        dataset_orig = subset_data_orig_train
        res = create_baseline(
            default,
            dataset_orig,
            privileged_groups,
            unprivileged_groups,
            data_splits=10,
            repetitions=10,
            odds=mutation_strategies,
            options=[0, 1],
            degrees=degrees,
        )
        acc0 = np.array(
            [np.mean([row[0] for row in res["0"][degree]]) for degree in degrees]
        )
        acc1 = np.array(
            [np.mean([row[0] for row in res["1"][degree]]) for degree in degrees]
        )
        fair0 = np.array(
            [
                np.mean([row[metric_id] for row in res["0"][degree]])
                for degree in degrees
            ]
        )
        fair1 = np.array(
            [
                np.mean([row[metric_id] for row in res["1"][degree]])
                for degree in degrees
            ]
        )

        if min(acc0) > min(acc1):
            beta = (max(acc0) - min(acc0)) / (max(acc0) - min(acc0) + max(fair0))
        else:
            beta = (max(acc1) - min(acc1)) / (max(acc1) - min(acc1) + max(fair1))

        f = open("beta.txt", "w")
        f.write(str(beta))
        f.close()
    else:
        f = open("beta.txt", "r")
        beta = float(f.read())
        f.close()
    beta += 0.2
    if beta > 1.0:
        beta = 1.0
    try:
        num_keys = sum(1 for line in open("num_keys.txt"))
        print(num_keys)
        beta -= 0.050 * int(int(num_keys) / 10)
        if int(num_keys) % 10 == 0:
            os.remove(temp_path + "/.auto-sklearn/ensemble_read_losses.pkl")
        f.close()
    except FileNotFoundError:
        pass
    fairness_metrics = [
        1 - np.mean(solution == prediction),
        disparate_impact(subset_data_orig_train, prediction, protected_attr),
        statistical_parity_difference(
            subset_data_orig_train, prediction, protected_attr
        ),
        equal_opportunity_difference(
            subset_data_orig_train, prediction, solution, protected_attr
        ),
        average_odds_difference(
            subset_data_orig_train, prediction, solution, protected_attr
        ),
    ]

    print(
        fairness_metrics[metric_id],
        1 - np.mean(solution == prediction),
        fairness_metrics[metric_id] * beta
        + (1 - np.mean(solution == prediction)) * (1 - beta),
        beta,
    )

    combined_score = fairness_metrics[metric_id] * beta + (
            1 - np.mean(solution == prediction)
    ) * (1 - beta)

    print(
        f"Beta: {beta}, Combined Score: {combined_score}, Fairness Metric: {fairness_metrics}, Accuracy: {np.mean(solution == prediction)}"
    )
    write_file(
        "./titanic_rf_spd_results/titanic_rf_score.txt",
        str(
            f"Combined Score: {combined_score}, Fairness Metric: {fairness_metrics}, Accuracy: {np.mean(solution == prediction)}\n"
        ),
        mode="a",
    )
    return combined_score


# Create a custom metric object (bi-objective function)
accuracy_scorer = autosklearn.metrics.make_scorer(
    name="accu",
    score_func=bi_objective_fn,
    optimum=1,
    greater_is_better=False,
    needs_proba=False,
    needs_threshold=False,
)

automl = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=60 * 60,
    memory_limit=10000000,
    include_estimators=["CustomRandomForest"],
    ensemble_size=1,
    initial_configurations_via_metalearning=25,
    include_preprocessors=[
        "kernel_pca",
        "select_percentile_classification",
        "select_rates_classification",
    ],
    tmp_folder=temp_path,
    delete_tmp_folder_after_terminate=False,
    metric=accuracy_scorer,
)

So I am unable to get what actually the run_value.cost signifies.

As in most of the cost is 0.0. Can you help me with this?

[eddiebergman]

I can't really tell you why it's 0.0 all the time but one thing that might help to know about is the worst_possible_result of make_scorer which it seems to be returning.

You're sure that your metric is able to return a result? It seems like it's just constantly setting the worst_possible_result

[sayannath]

Thanks for the help! Can you help me out with initial_configurations_via_metalearning. What actually does 25 signify?

And also what does this file autosklearn/metalearning/optimizers/metalearn_optimizer/metalearn_optimizerDefault.cfg actually do? Is it something we need to change to improve performance of the model?

[eddiebergman]

I would advise not touching the config files and honestly they're quite outdated given the version of sklearn they ran on.

You can read more in the autosklearn paper but essentially the 25 signifies that it should use this metadata to decide on 25 initial candidates to evaluate, where these are the 25 configurations that give the best "coverage" across the metadatasets, i.e. on average, one of these 25 would have been the best choice for each and every dataset in the metadataset collection. There are some potential issues, notably, does your dataset "look like" one of these metadatasets? If so, then great, you'll have a good estimator in the first 25 evaluations. If not, damn, you'll have to wait 25 evaluations before the BO loop kicks in to start searching. By default, the BO algorithm in autosklearn will just use 25 random samples if there are no initial metalearning configurations, i.e. you set it to 0.

Therefore the choice comes down to, do you think your data is suitably unique such that the metalearning configurations are all going to perform worse than a random set of configurations? Sometimes the answer is yes, but without proof of such, it's usually no.

Longer story, I'm still in the process of slowly building a revamped AutoSklearn and there we hope to include user provided metadata. Part of this will also be to provide an updated metadataset that solves some issues in the current set of configurations from metalearning. Feel free to check out AutoML Toolkit (amltk) which it is based on ;)

[sayannath]

Thanks for the detailed info. Is there any type of caching happens when we run the same model on the same dataset for couple of times?

[eddiebergman]

Nope, AutoSklearn doesn't cache between calls. In fact there's almost no caching that happens at all other than dumping models and predictions to disk to use later for predict()

[sayannath]

@eddiebergman

Can I get intermediate results, of the models which are ensembling, and apply any technique to make the models better by keeping mutation based OR out-of-automl meta-learning based idea in mind?

[eddiebergman]

Intermediate results of models, while it's running...not easily at all. Intermediate results in terms of post-analysis, yes, although models which are not in the top 50 (default) are pruned to save disk space.

Whether you can improve these models further, yup absolutely. We are revisiting the pipelines in the newer version

[sayannath]

Hey! Can you tell me what the cost means when we get the run history?

[eddiebergman]

It's just the metric value converted in some manner such that it's something to be minimized which is what SMAC needs. For bounded metrics, this also means it's min-max normalized betwwen (0, 1) where 0 means optimal and 1 means worst. For unbounded metrics, this really just means sign flipping the value.

[sayannath]

Hey @eddiebergman

Can we add custom meta-feature in autokslearn metalearning?