Add initial anomaly_detection task with IsolationForest

[Muhammad Rashid (PhD) (rashidrao-pk)]

Closes #413

This draft PR adds initial support for anomaly detection in FLAML using IsolationForest.

Scope:

• Add anomaly_detection task type
• Add is_anomaly_detection()
• Register isolation_forest
• Add IsolationForestEstimator subclassing SKLearnEstimator
• Expose predict, score_samples, and decision_function
• Keep sklearn convention: 1 = normal, -1 = anomaly
• Add unit test for synthetic point anomalies

Notes:

• This first PR intentionally keeps the scope minimal to lock the API.
• Full label-free AutoML.fit(X_train=..., task="anomaly_detection") integration can be completed after maintainer feedback.

cc Li Jiang (@thinkall)

[Muhammad Rashid (PhD) (rashidrao-pk)]

@microsoft-github-policy-service agree

[Muhammad Rashid (PhD) (rashidrao-pk)]

cc Kevin Chen (@int-chaos) for review, as discussed in #413.

[Copilot]

Pull request overview

This PR introduces an initial anomaly detection task type to FLAML, centered around a first estimator implementation based on scikit-learn’s IsolationForest, plus a basic unit test to validate anomaly scoring behavior.

Changes:

• Adds a new task identifier anomaly_detection and a corresponding is_anomaly_detection() helper on Task.
• Registers a new estimator name isolation_forest and adds an IsolationForestEstimator (subclassing SKLearnEstimator).
• Adds a unit test using synthetic point anomalies to validate prediction output shape and anomaly ranking quality.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 4 comments.

File	Description
test/automl/test_anomaly_detection.py	Adds a unit test validating IsolationForest-based anomaly scoring on synthetic data.
flaml/automl/task/task.py	Introduces the ANOMALY_DETECTION task constant and Task.is_anomaly_detection().
flaml/automl/task/generic_task.py	Registers the isolation_forest estimator and wires anomaly detection defaults (estimator + metric).
flaml/automl/model.py	Adds IsolationForestEstimator implementation and exposes score_samples / decision_function.

[Muhammad Rashid (PhD) (rashidrao-pk)]

Thanks for the review. I will fix the search-space init value, formatting, and Spark-dataframe error path.

For the ap metric comment: I agree this needs proper handling through anomaly scores (score_samples) in the AutoML evaluation path. Since this draft PR intentionally does not yet implement full label-free AutoML.fit(...) integration, I propose to address the metric evaluation path in the next PR together with the AutoML integration.

[Li Jiang (thinkall)]

Hi Muhammad Rashid (PhD) (@rashidrao-pk) , could you add an e2e test for anomaly_detection? Thanks a lot!

[Muhammad Rashid (PhD) (rashidrao-pk)]

Hi Li Jiang (@thinkall),

Thanks for the suggestion.

I've added an end-to-end test for anomaly_detection and pushed the updates to this PR.

The test now exercises the AutoML.fit(...) workflow using the new anomaly_detection task and verifies that the estimator is correctly integrated into the AutoML pipeline, rather than only testing IsolationForestEstimator in isolation.

In addition, I reran the test suite and the project's pre-commit checks locally:

• ✅ pytest test/automl/test_anomaly_detection.py
• ✅ pre-commit run --all-files

Please let me know if you'd like the e2e test to cover any additional scenarios or edge cases.

[Copilot]

Pull request overview

Copilot reviewed 4 out of 4 changed files in this pull request and generated 3 comments.

[Copilot]

Pull request overview

Copilot reviewed 4 out of 4 changed files in this pull request and generated no new comments.

Suppressed comments (5)

flaml/automl/task/generic_task.py:1096

• The new split type is accepted, but holdout preparation only creates a validation split for classification or regression (prepare_data() lines 969–1016). For anomaly detection without an explicit X_val, state.X_val remains None, and evaluation subsequently calls predict(None). Extend the uniform holdout branch to split anomaly-detection data as well.

        elif self.is_anomaly_detection():
            assert split_type in ["auto", "uniform", "time", "group"]
            return split_type if split_type != "auto" else "uniform"

flaml/automl/task/generic_task.py:1378

• ap does not currently evaluate anomaly scores for this task. get_y_pred() only uses probability scores for binary tasks, so anomaly detection falls through to predict() and supplies hard labels where 1 means normal and -1 means anomalous. With the 0/1 anomaly labels used by the added test, average precision therefore ranks normals as positives. Add anomaly-specific prediction/label polarity handling (for example, using negated score_samples) before making ap the default.

        elif self.is_anomaly_detection():
            return "ap"

test/automl/test_anomaly_detection.py:6

• This import is unused and will be flagged by the repository's Ruff F401 check. Remove it unless the test directly instantiates the estimator.

from flaml.automl.model import IsolationForestEstimator

flaml/automl/model.py:1531

• IsolationForest supports n_jobs, so removing it forces every forest fit to run serially and ignores FLAML's configured worker count. Preserve this parameter as the other sklearn forest estimators do.

        params.pop("n_jobs", None)

test/automl/test_anomaly_detection.py:64

• The PR adds decision_function() as part of the public anomaly-detection API, but this end-to-end test only exercises predict() and score_samples(). Add a call and shape assertion so regressions in the third exposed method are covered.

    preds = automl.predict(X_val)
    scores = automl.model.score_samples(X_val)

    assert preds.shape == y_val.shape
    assert scores.shape == y_val.shape