databank/scripts/run_analyzers.py

"""Run selected analyzers over existing DB data or last run results.

This is a scaffold to wire analyzers end-to-end once implementations are ready.
"""

from __future__ import annotations

import os
import sys
import pathlib
from typing import Iterable, List
import importlib

# Ensure 'src' is on sys.path when running from repo root or scripts dir
_SCRIPT_DIR = pathlib.Path(__file__).resolve().parent
_ROOT = _SCRIPT_DIR.parent
_SRC = _ROOT / "src"
if _SRC.exists() and str(_SRC) not in sys.path:
    sys.path.insert(0, str(_SRC))


def _ensure_src_on_path() -> None:
    """Put repository src on sys.path if available (no-op if already present)."""
    script_dir = pathlib.Path(__file__).resolve().parent
    root = script_dir.parent
    src = root / "src"
    if src.exists():
        sys.path.insert(0, str(src))


def load_data(db, limit: int | None = None) -> List[dict]:
    """Load match documents from DB as analyzer inputs (scaffold)."""
    # NOTE: Adjust projection to include what analyzers need.
    return db.find("match", projection={"_id": 0}, limit=limit)


def _safe_count(db, kind: str) -> int:
    """Best-effort count using find; avoids driver-specific count API."""
    try:
        docs = db.find(kind, projection={"_id": 1}, limit=None)
        return len(list(docs)) if docs is not None else 0
    except (RuntimeError, ValueError, TypeError):  # diagnostics only
        return 0


def main() -> None:
    """Build analyzers and run them on existing data (skeleton)."""
    # Ensure imports resolve regardless of cwd, then import dynamically
    _ensure_src_on_path()

    db_mod = importlib.import_module("databank.db")
    teams_mod = importlib.import_module("databank.analytics.teams")
    elo_mod = importlib.import_module("databank.analytics.elo")
    dc_mod = importlib.import_module("databank.analytics.dixon_coles")
    mc_mod = importlib.import_module("databank.analytics.markov_chain")
    h2h_mod = importlib.import_module("databank.analytics.h2h")
    calib_mod = importlib.import_module("databank.analytics.calibration")
    season_mc_mod = importlib.import_module("databank.analytics.monte_carlo")
    sos_mod = importlib.import_module("databank.analytics.sos")

    mongodb_cls = getattr(db_mod, "MongoDB")
    team_extractor_cls = getattr(teams_mod, "TeamExtractorAnalyzer")
    elo_analyzer_cls = getattr(elo_mod, "EloAnalyzer")
    dixon_coles_cls = getattr(dc_mod, "DixonColesAnalyzer")
    markov_chain_cls = getattr(mc_mod, "MarkovChainAnalyzer")
    h2h_cls = getattr(h2h_mod, "H2HAnalyzer")
    calibration_cls = getattr(calib_mod, "CalibrationAnalyzer")
    season_mc_cls = getattr(season_mc_mod, "SeasonMonteCarloAnalyzer")
    sos_cls = getattr(sos_mod, "StrengthOfScheduleAnalyzer")

    uri = os.getenv("DATABANK_DB_URI", "mongodb://localhost:27017")
    name = os.getenv("DATABANK_DB_NAME", "databank")

    db = mongodb_cls(uri=uri, name=name)
    db.connect()

    data = load_data(db)
    print(f"Loaded matches: {len(data) if data is not None else 0}")

    analyzers: Iterable = [
        team_extractor_cls(),
        elo_analyzer_cls(),
        dixon_coles_cls(),
        markov_chain_cls(),
        h2h_cls(),
        calibration_cls(),
        sos_cls(),
        season_mc_cls(),
    ]

    for analyzer in analyzers:
        print(f"Running analyzer: {analyzer.__class__.__name__}")
        try:
            analyzer.prepare(data)
            analyzer.validate(data)
            transformed = analyzer.transform(data)
            result = analyzer.compute(transformed, db=db, persist=True)
            analyzer.finalize(result)
            print(f" -> Done: {analyzer.__class__.__name__}")

            # Diagnostics: show where data is persisted for Elo
            if isinstance(analyzer, elo_analyzer_cls):
                ratings_cnt = _safe_count(db, "elo_ratings")
                history_cnt = _safe_count(db, "ratings_history")
                print(
                    "    Elo persisted to collections:",
                    f"elo_ratings={ratings_cnt}",
                    f"ratings_history={history_cnt}",
                )
                try:
                    processed = (
                        result.get("processed") if isinstance(result, dict) else None
                    )
                    print(f"    Elo processed matches: {processed}")
                except (RuntimeError, ValueError, TypeError):  # diagnostics only
                    pass
        except NotImplementedError as exc:
            print(f" -> Skipped (not implemented): {exc}")
        except (RuntimeError, ValueError) as exc:  # pragma: no cover - diagnostics only
            print(f" -> Error: {type(exc).__name__}: {exc}")


if __name__ == "__main__":
    main()