schulze_ranker — Logtalk 3.100.1

# `schulze_ranker`

Schulze pairwise preference ranker. It builds the direct pairwise strength graph from aggregated matchups and applies the Schulze strongest-path dynamic program to derive the final pairwise preference relation.

The library implements the `ranker_protocol` defined in the `ranking_protocols` library. It provides predicates for learning a ranker from pairwise preferences, using it to order candidate items, and exporting it as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the `pairwise_ranking_dataset_protocol` protocol from the `ranking_protocols` library. See the `test_datasets` directory for examples. The current implementation requires a well-formed connected pairwise dataset so that all ranked items remain comparable in the aggregated matchup graph.

## API documentation

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

## Loading

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

    | ?- logtalk_load(schulze_ranker(loader)).

## Testing

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

    | ?- logtalk_load(schulze_ranker(tester)).

## Features

- Pairwise Preference Learning: Learns one deterministic score per item from aggregated pairwise outcomes.

- Schulze Strongest Paths: Computes the final ranking relation using the Schulze strongest-path dynamic program over aggregated head-to-head data.

- Configurable Direct Edge Semantics: Supports both winning-votes and victory-margins victory-strength modes. The `victory_strength/1` option selects whether direct edges use winning votes or victory margins before strongest-path propagation.

- Strongest-Path Access: Exposes the labeled strongest-path relation for ordered item pairs using the `strongest_paths/2` predicate.

- Deterministic Ranking: Orders candidate items by final Schulze relation win counts with deterministic tie-breaking.

- Tie-breaking: Items tied in the final Schulze relation receive the same learned score, and ranking ties are then broken deterministically using the standard term order of the item identifiers.

- Strict Dataset Validation: Rejects duplicate items, undeclared items, self-preferences, non-positive weights, and disconnected comparison graphs.

- Training Diagnostics: Learned rankers include dataset summary metadata and the effective victory-strength mode.

- Ranker Export: Learned rankers can be exported as self-contained terms.

## Options

The following options can be passed to the `learn/3` predicate:

- `victory_strength(winning_votes)`: Use the winning side’s aggregated vote total as the direct edge strength.

- `victory_strength(margins)`: Use the victory margin as the direct edge strength.

The default is `victory_strength(winning_votes)`.

## Strongest paths

The `strongest_paths/2` predicate returns the learned strongest-path matrix as an ordered list of `path(Item1, Item2, Strength)` terms for all ordered pairs of distinct learned items.

This predicate complements `scores/2` and `rank/3` without changing their semantics. The `scores/2` predicate still returns the number of opponents that each item beats in the final Schulze relation, while `strongest_paths/2` exposes the underlying pairwise path strengths used to derive that relation.

## Diagnostics syntax

The `diagnostics/2` predicate returns a list of metadata terms with the form:

    [
        model(schulze_ranker),
        options(Options),
        strongest_paths(StrongestPaths),
        dataset_summary(DatasetSummary)
    ]

## Ranker representation

The learned ranker is represented by a compound term of the form:

    schulze_ranker(Items, Scores, Diagnostics)

Where:

- `Items`: List of ranked items.

- `Scores`: List of `Item-Score` pairs.

- `Diagnostics`: List of metadata terms, including the effective `victory_strength/1` option, the labeled strongest paths, and the dataset summary.