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(),
    ]

    # Prepare optional DC config from environment
    def _env_float(name: str, default: float) -> float:
        try:
            return (
                float(os.getenv(name, "")) if os.getenv(name) is not None else default
            )
        except ValueError:
            return default

    def _env_int(name: str, default: int) -> int:
        try:
            return int(os.getenv(name, "")) if os.getenv(name) is not None else default
        except ValueError:
            return default

    def _env_rho_range(name: str, default: tuple[float, float]) -> tuple[float, float]:
        s = os.getenv(name)
        if not s:
            return default
        try:
            lo_str, hi_str = s.split(",", 1)
            return float(lo_str), float(hi_str)
        except (ValueError, TypeError):
            return default

    def _env_int_optional(name: str) -> int | None:
        s = os.getenv(name)
        if not s:
            return None
        try:
            return int(s)
        except ValueError:
            return None

    dc_kwargs = {
        "halflife_days": _env_float("DATABANK_DC_HALFLIFE_DAYS", 180.0),
        "rho_range": _env_rho_range("DATABANK_DC_RHO_RANGE", (-0.3, 0.3)),
        "rho_step": _env_float("DATABANK_DC_RHO_STEP", 0.01),
        "max_iters": _env_int("DATABANK_DC_MAX_ITERS", 20),
        "tol": _env_float("DATABANK_DC_TOL", 1e-4),
    }

    # Optional history configuration for DC
    history_mode = os.getenv("DATABANK_DC_HISTORY", "both").strip().lower()
    if history_mode in {"none", "predictions", "snapshots", "both"}:
        dc_kwargs["history"] = history_mode
    dc_kwargs["snapshot_every"] = _env_int("DATABANK_DC_SNAPSHOT_EVERY", 10)
    dc_kwargs["max_iters_history"] = _env_int("DATABANK_DC_MAX_ITERS_HISTORY", 10)
    dc_kwargs["max_goals"] = _env_int("DATABANK_DC_MAX_GOALS", 8)
    # Progress controls
    dc_kwargs["verbose"] = os.getenv("DATABANK_DC_VERBOSE", "1").strip() not in {
        "0",
        "false",
        "False",
    }
    dc_kwargs["progress_every"] = _env_int("DATABANK_DC_PROGRESS_EVERY", 100)
    dc_kwargs["flush_every"] = _env_int("DATABANK_DC_FLUSH_EVERY", 1000)
    dc_kwargs["skip_if_exists"] = os.getenv(
        "DATABANK_DC_SKIP_IF_EXISTS", "0"
    ).strip() not in {
        "0",
        "false",
        "False",
    }
    # Safety clamps/tuning knobs
    dc_kwargs["param_min"] = _env_float("DATABANK_DC_PARAM_MIN", 0.3)
    dc_kwargs["param_max"] = _env_float("DATABANK_DC_PARAM_MAX", 3.0)
    dc_kwargs["base_min"] = _env_float("DATABANK_DC_BASE_MIN", 0.3)
    dc_kwargs["base_max"] = _env_float("DATABANK_DC_BASE_MAX", 3.0)
    dc_kwargs["mu_max"] = _env_float("DATABANK_DC_MU_MAX", 6.0)
    dc_kwargs["min_history_matches"] = _env_int("DATABANK_DC_MIN_HISTORY_MATCHES", 3)

    for analyzer in analyzers:
        print(f"Running analyzer: {analyzer.__class__.__name__}")
        try:
            analyzer.prepare(data)
            analyzer.validate(data)
            transformed = analyzer.transform(data)
            if isinstance(analyzer, dixon_coles_cls):
                # Pass DC-specific kwargs from environment
                result = analyzer.compute(transformed, db=db, persist=True, **dc_kwargs)
                print("    DC config:", dc_kwargs)
            elif isinstance(analyzer, calibration_cls):
                # Calibration env knobs
                cal_kwargs = {
                    "source_kind": os.getenv(
                        "DATABANK_CAL_SOURCE_KIND", "dc_predictions"
                    ),
                    "bins": _env_int("DATABANK_CAL_BINS", 10),
                    # User asked higher min sample threshold, default 50
                    "min_per_bin": _env_int("DATABANK_CAL_MIN_PER_BIN", 50),
                    "verbose": os.getenv("DATABANK_CAL_VERBOSE", "1").strip()
                    not in {"0", "false", "False"},
                }
                since_ts = _env_int_optional("DATABANK_CAL_SINCE_TS")
                until_ts = _env_int_optional("DATABANK_CAL_UNTIL_TS")
                if since_ts is not None:
                    cal_kwargs["since_ts"] = since_ts
                if until_ts is not None:
                    cal_kwargs["until_ts"] = until_ts
                season = os.getenv("DATABANK_CAL_SEASON")
                if season:
                    cal_kwargs["season_filter"] = season
                groups_inc = os.getenv("DATABANK_CAL_GROUPS_INCLUDE")
                if groups_inc:
                    cal_kwargs["groups_include"] = groups_inc

                result = analyzer.compute(
                    transformed, db=db, persist=True, **cal_kwargs
                )
                print("    CAL config:", cal_kwargs)
                # Console summary of calibration metrics per group
                if isinstance(result, dict) and isinstance(result.get("metrics"), dict):
                    metrics = result["metrics"]
                    print("    CAL summary (per group):")
                    for gk, m in metrics.items():
                        try:
                            b = float(m.get("brier", float("nan")))
                            ll = float(m.get("logloss", float("nan")))
                            ece = float(m.get("ece", float("nan")))
                            n = int(m.get("n", 0))
                            print(
                                (
                                    f"      - {gk}: n={n} | Brier={b:.4f}"
                                    f" | LogLoss={ll:.4f} | ECE={ece:.4f}"
                                )
                            )
                        except (ValueError, TypeError, KeyError):
                            print(f"      - {gk}: (metrics unavailable)")
            else:
                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
            # Diagnostics: DC persistence and counts
            if isinstance(analyzer, dixon_coles_cls):
                dc_cnt = _safe_count(db, "dc_params")
                print("    DC persisted to collection:", f"dc_params={dc_cnt}")
                if isinstance(result, dict):
                    mu = result.get("matches_used")
                    persisted = result.get("persisted")
                    print(
                        "    DC matches_used: "
                        f"{mu}; persisted docs in this run: {persisted}"
                    )
                    gs = result.get("groups_skipped")
                    if isinstance(gs, int) and gs > 0:
                        print(f"    DC groups skipped (skip_if_exists): {gs}")
                # Optional extra collections
                preds_cnt = _safe_count(db, "dc_predictions")
                snaps_cnt = _safe_count(db, "dc_params_history")
                if preds_cnt:
                    print("    DC predictions count:", preds_cnt)
                if snaps_cnt:
                    print("    DC params history count:", snaps_cnt)
                if isinstance(result, dict):
                    pw = result.get("predictions_written")
                    sw = result.get("snapshots_written")
                    if pw is not None or sw is not None:
                        print(
                            f"    DC written (streaming): predictions={pw}, snapshots={sw}"
                        )
        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()