donnees_meteo/docs/11 - Modèle Chronos/scripts/run_chronos_tuned.py

# scripts/run_chronos_tuned.py
from __future__ import annotations

from pathlib import Path
import sys

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch

PROJECT_ROOT = Path(__file__).resolve().parents[3]
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

from meteo.dataset import load_raw_csv

try:
    from chronos import ChronosPipeline
except ImportError as exc:
    raise SystemExit("chronos-forecasting manquant : pip install -r requirements.txt") from exc

CSV_PATH = PROJECT_ROOT / "data" / "weather_minutely.csv"
DOC_DIR = Path(__file__).resolve().parent.parent
DATA_DIR = DOC_DIR / "data"
FIG_DIR = DOC_DIR / "figures"

# Réglages plus prudents : contexte raccourci, horizon 64 max, plus d'échantillons
MODEL_ID = "amazon/chronos-t5-small"
RESAMPLE_RULE = "1h"
CONTEXT_H = 288  # 12 jours
MAX_HORIZON_H = 64  # <=64 conseillé
HORIZONS_H = (1, 6, 24, 48)  # on reste cohérents avec nos jalons
NUM_SAMPLES = 100

TARGETS = {
    "temperature": {"kind": "reg"},
    "wind_speed": {"kind": "reg"},
    "rain_rate": {"kind": "rain"},
}


def _load_series(target: str) -> pd.Series:
    df = load_raw_csv(CSV_PATH)
    if target not in df.columns:
        raise SystemExit(f"Colonne absente : {target}")
    s = df[target].resample(RESAMPLE_RULE).mean().interpolate(limit_direction="both")
    return s.dropna()


def _prepare_window(series: pd.Series, context_h: int, horizon_h: int) -> tuple[np.ndarray, pd.Series]:
    needed = context_h + horizon_h
    if len(series) < needed:
        raise SystemExit(f"Pas assez de données pour {needed} heures.")
    window = series.iloc[-needed:]
    context = window.iloc[:context_h]
    target = window.iloc[context_h:]
    return context.to_numpy(dtype=float), target


def _plot_metrics(df: pd.DataFrame, target: str, output_path: Path) -> None:
    output_path.parent.mkdir(parents=True, exist_ok=True)
    fig, ax = plt.subplots(figsize=(6, 4))
    if df["kind"].iloc[0] == "reg":
        ax.plot(df["horizon_h"], df["mae"], marker="o", label="MAE")
        ax.plot(df["horizon_h"], df["rmse"], marker="o", label="RMSE")
        ax.set_ylabel("Erreur (°C)" if target == "temperature" else "Erreur (unité)")
    else:
        ax.plot(df["horizon_h"], df["f1"], marker="o", label="F1")
        ax.plot(df["horizon_h"], df["brier"], marker="o", label="Brier")
        ax.set_ylabel("Score")
    ax.set_xlabel("Horizon (heures)")
    ax.set_title(f"Chronos (small, réglages prudents) – {target}")
    ax.grid(True, linestyle=":", alpha=0.4)
    ax.legend()
    fig.tight_layout()
    fig.savefig(output_path, dpi=150)
    plt.close(fig)


def main() -> None:
    pipeline = ChronosPipeline.from_pretrained(
        MODEL_ID,
        device_map="auto",
        dtype="auto",
    )

    rows: list[dict[str, object]] = []

    for target, meta in TARGETS.items():
        series = _load_series(target)
        context_arr, target_series = _prepare_window(series, CONTEXT_H, MAX_HORIZON_H)

        context_tensor = torch.tensor(context_arr, dtype=torch.float32)
        forecasts = pipeline.predict(
            [context_tensor],
            prediction_length=MAX_HORIZON_H,
            num_samples=NUM_SAMPLES,
        )
        forecast_mean = forecasts.mean(0)
        if forecast_mean.ndim == 2:
            forecast_mean = forecast_mean[0]
        forecast_series = pd.Series(
            np.asarray(forecast_mean).ravel(),
            index=pd.date_range(target_series.index[0], periods=MAX_HORIZON_H, freq=RESAMPLE_RULE),
        )

        for h in HORIZONS_H:
            y_true = target_series.iloc[:h]
            y_pred = forecast_series.iloc[:h]
            if meta["kind"] == "reg":
                mae = float((y_pred - y_true).abs().mean())
                rmse = float(np.sqrt(((y_pred - y_true) ** 2).mean()))
                rows.append(
                    {"target": target, "kind": "reg", "horizon_h": h, "mae": mae, "rmse": rmse}
                )
            else:
                y_true_bin = (y_true > 0).astype(int)
                y_pred_bin = (y_pred > 0).astype(int)
                if y_true_bin.sum() == 0:
                    f1 = 0.0
                else:
                    tp = ((y_true_bin == 1) & (y_pred_bin == 1)).sum()
                    fp = ((y_true_bin == 0) & (y_pred_bin == 1)).sum()
                    fn = ((y_true_bin == 1) & (y_pred_bin == 0)).sum()
                    prec = tp / (tp + fp) if tp + fp > 0 else 0.0
                    rec = tp / (tp + fn) if tp + fn > 0 else 0.0
                    f1 = 2 * prec * rec / (prec + rec) if prec + rec > 0 else 0.0
                proba = y_pred.clip(0, 1)
                brier = float(((proba - y_true_bin) ** 2).mean())
                rows.append(
                    {"target": target, "kind": "cls", "horizon_h": h, "f1": float(f1), "brier": brier}
                )

        out_prefix = f"{target}_{MODEL_ID.replace('/', '__')}_tuned"
        DATA_DIR.mkdir(parents=True, exist_ok=True)
        pd.concat([forecast_series.rename("y_pred"), target_series.rename("y_true")], axis=1).to_csv(
            DATA_DIR / f"chronos_tuned_forecast_{out_prefix}.csv",
            index=True,
        )

    df_metrics = pd.DataFrame(rows)
    DATA_DIR.mkdir(parents=True, exist_ok=True)
    metrics_path = DATA_DIR / "chronos_tuned_metrics.csv"
    df_metrics.to_csv(metrics_path, index=False)

    for target in df_metrics["target"].unique():
        sub = df_metrics[df_metrics["target"] == target].sort_values("horizon_h")
        _plot_metrics(sub, target, FIG_DIR / f"chronos_tuned_{target}.png")

    print(df_metrics.to_string(index=False, float_format=lambda x: f"{x:.3f}"))
    print(f"✔ Sauvegardé : {metrics_path}")
    print("✔ Figures par cible (réglages prudents) dans figures/chronos_tuned_<target>.png")


if __name__ == "__main__":
    main()