valanga 0.1.7

Shared types and utilities for evaluation

valanga

Shared Python types and lightweight utilities for describing turn-based games and their evaluations. The package exposes protocols for representing states, outcomes, and state representations that other libraries can build on.

Key concepts

• Game primitives: Color, BranchKey, State, and related protocols describe whose turn it is, how to enumerate legal branches, and how to copy or advance a state. 【F:src/valanga/game.py†L14-L95】
• Evaluations: FloatyStateEvaluation captures heuristic scores while ForcedOutcome records a definitive result plus the line of play that forces it. Both are grouped under BoardEvaluation. 【F:src/valanga/evaluations.py†L13-L43】
• Game termination: OverEvent combines how a game ended with who won, backed by HowOver and Winner enums. Utility helpers like is_over() and get_over_tag() simplify downstream checks. 【F:src/valanga/over_event.py†L8-L115】
• State representations: ContentRepresentation defines how to turn a State into evaluator input, and RepresentationFactory builds or updates those representations from states and modifications. 【F:src/valanga/represention_for_evaluation.py†L9-L22】【F:src/valanga/representation_factory.py†L7-L55】
• Progress reporting: PlayerProgressMessage communicates per-player progress percentages during long-running work. 【F:src/valanga/progress_messsage.py†L9-L18】

Installation

pip install .

The project targets Python 3.13 and has no required runtime dependencies.

Quick start

Below is a minimal example that marks a finished game and obtains an evaluation structure:

from valanga import ForcedOutcome, OverEvent
from valanga.over_event import HowOver, Winner

# A checkmate where white wins.
over_event = OverEvent(how_over=HowOver.WIN, who_is_winner=Winner.WHITE)

# Record the forced outcome and the line of optimal moves that lead to it.
forced = ForcedOutcome(outcome=over_event, line=["e2e4", "e7e5"])
print(forced.outcome.get_over_tag())  # -> OverTags.TAG_WIN_WHITE

To wire in a custom state representation, supply callables to RepresentationFactory that know how to build representations from a State and its modifications.

Development

Install the development dependencies and run the test suite:

pip install -e '.[dev]'
pytest