88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
import datetime
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from enum import StrEnum
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from pathlib import Path
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from typing import Literal
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# Racine du depot de travail (/home/user/tp sur la VM) : lab/constants.py -> parents[1]
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REPO_ROOT = Path(__file__).resolve().parents[1]
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# Donnees source, deja preparees (hors git, volumineuses) : voir /data sur la VM
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SOURCE_DIR = Path("/data/modelling")
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# Sorties de split versionnees par DVC dans le depot
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DATASET_DIR = REPO_ROOT / "data"
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FEATURE_FILENAME = "features.parquet"
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TARGET_FILENAME = "target.parquet"
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class SplitStrategy(StrEnum):
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FULL_HISTORY = "full_history"
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RECENT_HISTORY = "recent_history"
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# Strategie de split active : pilote a la fois le decoupage produit par split/cli.py
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# et le parametre "split_strategy" logge dans MLflow. On la modifie (et on committe)
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# a chaque changement de version de dataset pour synchroniser DVC et Git.
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CHOSEN_SPLIT_STRATEGY = SplitStrategy.RECENT_HISTORY
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DatasetPart = Literal["train", "test", "validation"]
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DATASET_SPLIT_DATES: dict[SplitStrategy, dict[DatasetPart, tuple[datetime.date, datetime.date]]] = {
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# Partie 1 : tout l'historique disponible pour l'entrainement
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SplitStrategy.FULL_HISTORY: {
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"train": (datetime.date(2011, 1, 1), datetime.date(2012, 12, 31)),
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"validation": (datetime.date(2013, 1, 1), datetime.date(2013, 12, 31)),
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"test": (datetime.date(2014, 1, 1), datetime.date(2014, 12, 31)),
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},
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# Partie 2 : donnees plus recentes uniquement
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SplitStrategy.RECENT_HISTORY: {
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"train": (datetime.date(2013, 1, 1), datetime.date(2013, 12, 31)),
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"validation": (datetime.date(2014, 1, 1), datetime.date(2014, 5, 31)),
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"test": (datetime.date(2014, 6, 1), datetime.date(2014, 12, 31)),
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},
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}
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class ModellingStrategy(StrEnum):
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SHORT_MEMORY = "short_memory"
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SEASONALITY = "seasonality"
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TENDENCY = "tendency"
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MIXED = "mixed"
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FULL = "full"
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Features = Literal["lag_1d", "lag_7d", "lag_30d", "lag_365d", "rolling_mean_7d", "rolling_mean_30d"]
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TARGET = "consumption_kwh"
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MODELLING_FEATURES: dict[ModellingStrategy, list] = {
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# La conso depend surtout de la veille
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ModellingStrategy.SHORT_MEMORY: ["lag_1d"],
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# La conso est plus saisonniere que journaliere
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ModellingStrategy.SEASONALITY: ["lag_7d", "lag_30d"],
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# La conso suit surtout une tendance
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ModellingStrategy.TENDENCY: ["rolling_mean_7d", "rolling_mean_30d"],
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# Melange lags + tendance
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ModellingStrategy.MIXED: ["lag_1d", "lag_7d", "lag_30d", "rolling_mean_30d"],
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# Toutes les features disponibles (ajoutee en Partie 2 Etape 3)
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ModellingStrategy.FULL: ["lag_1d", "lag_7d", "lag_30d", "lag_365d", "rolling_mean_7d", "rolling_mean_30d"],
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}
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# Valeurs d'alpha demandees par l'enonce (Partie 3)
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RIDGE_ALPHAS = [1, 1e3, 1e9]
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# --- TP03 : Model Registry + service de prediction ---
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# Nom sous lequel les modeles sont enregistres dans le MLflow Model Registry.
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# On garde un nom stable pour retrouver le modele et empiler ses versions.
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REGISTERED_MODEL_NAME = "electricity-consumption"
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# Alias pointant vers la version promue (chargee par l'API). On identifie le modele
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# a servir par son alias (mobile) plutot que par un numero de version (fige).
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MODEL_ALIAS = "champion"
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# Strategie de features du modele expose par l'API : "full" (les 6 features).
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# L'ordre des colonnes servies doit correspondre a celui de l'entrainement.
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SERVING_STRATEGY = ModellingStrategy.FULL
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SERVING_FEATURES = MODELLING_FEATURES[SERVING_STRATEGY]
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