________________________________________________________________________

This file is part of Logtalk <https://logtalk.org/>
SPDX-FileCopyrightText: 1998-2026 Paulo Moura <pmoura@logtalk.org>
SPDX-License-Identifier: Apache-2.0

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
________________________________________________________________________


`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](../../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.
