lof_anomaly_detector — Logtalk 3.100.1

# `lof_anomaly_detector`

Local Outlier Factor anomaly detector supporting multiple distance metrics, mixed continuous and categorical features, and missing values. The detector memorizes the training instances and computes Local Outlier Factor values by comparing the local reachability density of a query to the densities of its neighbors.

The library implements the `anomaly_detector_protocol` defined in the `anomaly_detection_protocols` library. It learns a compact detector from a dataset by selecting baseline training examples from the declared class labels, computes normalized anomaly scores for new instances, predicts `normal` or `anomaly`, and exports learned detectors as clauses or files.

Datasets are represented as objects implementing the `anomaly_dataset_protocol` protocol from the `anomaly_detection_protocols` library. See the `anomaly_detection_protocols/test_datasets` directory for examples.

## API documentation

Open the ../../apis/library_index.html#lof_anomaly_detector link in a web browser.

## Loading

To load this library, load the `loader.lgt` file:

    | ?- logtalk_load(lof_anomaly_detector(loader)).

## Testing

To test this library predicates, load the `tester.lgt` file:

    | ?- logtalk_load(lof_anomaly_detector(tester)).

## Features

- Density-based anomaly scoring: computes Local Outlier Factor scores from local reachability densities.

- Normalized scores: Raw LOF values are normalized to the interval `[0.0, 1.0]` by mapping the ideal baseline value `1.0` to `0.0` and scaling larger values against the largest training raw score.

- Mixed features: automatically handles continuous and categorical features declared by the dataset.

- Missing values: ignores missing dimensions while normalizing distances (distances are normalized by the number of comparable dimensions).

- Baseline training selection: `baseline_class_values/1` declares which class labels are admissible for fitting the detector, while `baseline_selection_policy/1` controls whether non-baseline examples are rejected (default) or filtered before training.

- Multiple metrics: supports Euclidean, Manhattan, Chebyshev, and Minkowski distance metrics.

- Detector export: learned detectors can be exported as predicate clauses.

- Dataset validation: learning rejects empty datasets with a `domain_error(non_empty_dataset, Dataset)` exception.

## Options

The following options can be passed to the `learn/3` and `predict/4` predicates:

- `k(K)`: Number of neighbors to consider (default: 5)

- `distance_metric(Metric)`: Distance metric to use. Options: `euclidean` (default), `manhattan`, `chebyshev`, `minkowski`

- `anomaly_threshold(Threshold)`: Threshold for `predict/3-4` (default: `0.4`)

- `baseline_class_values(Classes)`: Learn-time list of admissible baseline class labels (default: `[normal]`)

- `baseline_selection_policy(Policy)`: Learn-time handling of non-baseline examples. Supported values are `reject` (default) and `filter`

## Detector representation

The learned detector is represented by default as:

    lof_detector(TrainingDataset, AttributeNames, FeatureTypes, AttributeScales, Instances, ReferenceScores, Diagnostics)

Where:

- `AttributeNames`: List of attribute names in order

- `FeatureTypes`: List of feature types (`numeric` or `categorical`)

- `AttributeScales`: Normalization scales for numeric features

- `Instances`: List of retained baseline training `Id-Class-Values` triples

- `ReferenceScores`: Cached leave-one-out raw training scores for the retained baseline training instances

- `Diagnostics`: Learned metadata terms including `model/1`, `training_dataset/1`, `attribute_names/1`, `feature_types/1`, `example_count/1`, `reference_score_count/1`, and `options/1`

The `score/3` predicate always treats its input as a fresh query. Only `score_all/3` on the original training dataset with the `reject` baseline selection policy reuses the cached leave-one-out `ReferenceScores` for all examples. With the `filter` policy, retained baseline training examples reuse the cached leave-one-out scores while excluded examples are scored as fresh queries against the learned baseline detector.

When exported using `export_to_clauses/4` or `export_to_file/4`, this detector term is serialized directly as the single argument of the generated predicate clause so that the exported model can be loaded and reused as-is.

## References

1. Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J. (2000). “LOF: Identifying density-based local outliers”. SIGMOD.