"""Helpers de merge pour feature engineering - ISO 5055. Ce module contient les fonctions de merge extraites de compute_features_for_split pour réduire la complexité cyclomatique. Conformité ISO/IEC 5055 (Code Quality). """ from __future__ import annotations import pandas as pd def merge_player_features( result: pd.DataFrame, feature_df: pd.DataFrame, feature_cols: list[str], prefix: str = "", ) -> pd.DataFrame: """Merge features joueur pour blanc et noir. Args: ---- result: DataFrame cible feature_df: DataFrame source avec joueur_nom feature_cols: Colonnes à merger prefix: Préfixe optionnel pour les colonnes Returns: ------- DataFrame avec features mergées """ if feature_df.empty: return result for color in ["blanc", "noir"]: nom_col = f"{color}_nom" if nom_col not in result.columns: continue cols_exist = [c for c in feature_cols if c in feature_df.columns] if not cols_exist: continue col_mapping = {c: f"{prefix}{c}_{color}" for c in cols_exist} merge_df = feature_df.rename(columns=col_mapping) result = result.merge( merge_df[["joueur_nom"] + list(col_mapping.values())], left_on=nom_col, right_on="joueur_nom", how="left", ) if "joueur_nom" in result.columns: result = result.drop(columns=["joueur_nom"]) return result def merge_club_reliability( result: pd.DataFrame, club_reliability: pd.DataFrame, ) -> pd.DataFrame: """Merge features de fiabilité club. Args: ---- result: DataFrame cible club_reliability: DataFrame fiabilité clubs Returns: ------- DataFrame avec features mergées """ if club_reliability.empty: return result club_cols = ["taux_forfait", "taux_non_joue", "fiabilite_score"] for suffix, col in [("dom", "equipe_dom"), ("ext", "equipe_ext")]: if col not in result.columns: continue merge_df = club_reliability.rename(columns={c: f"{c}_{suffix}" for c in club_cols}) result = result.merge( merge_df[["equipe"] + [f"{c}_{suffix}" for c in club_cols]], left_on=col, right_on="equipe", how="left", ) if "equipe" in result.columns: result = result.drop(columns=["equipe"]) return result def merge_team_enjeu( result: pd.DataFrame, team_enjeu: pd.DataFrame, ) -> pd.DataFrame: """Merge features d'enjeu équipe. Args: ---- result: DataFrame cible team_enjeu: DataFrame enjeu équipes Returns: ------- DataFrame avec features mergées """ if team_enjeu.empty or "ronde" not in team_enjeu.columns: return result for suffix, col in [("dom", "equipe_dom"), ("ext", "equipe_ext")]: result = _merge_single_team_enjeu(result, team_enjeu, suffix, col) return result def _merge_single_team_enjeu( result: pd.DataFrame, team_enjeu: pd.DataFrame, suffix: str, col: str, ) -> pd.DataFrame: """Merge enjeu pour une equipe (dom ou ext).""" cols_exist = _get_enjeu_cols(result, team_enjeu, col) if not cols_exist: return result merge_df = team_enjeu.rename(columns={c: f"{c}_{suffix}" for c in cols_exist}) return _execute_enjeu_merge(result, merge_df, suffix, col, cols_exist) def _get_enjeu_cols(result: pd.DataFrame, team_enjeu: pd.DataFrame, col: str) -> list[str]: """Recupere les colonnes d'enjeu disponibles.""" if col not in result.columns or "saison" not in result.columns: return [] merge_cols = ["zone_enjeu", "niveau_hierarchique", "position"] return [c for c in merge_cols if c in team_enjeu.columns] def _execute_enjeu_merge( result: pd.DataFrame, merge_df: pd.DataFrame, suffix: str, col: str, cols_exist: list[str], ) -> pd.DataFrame: """Execute le merge d'enjeu.""" merge_keys = _get_merge_keys(result, merge_df) left_keys = [col, "saison"] + (["ronde"] if "ronde" in result.columns else []) result = result.merge( merge_df[merge_keys + [f"{c}_{suffix}" for c in cols_exist]].drop_duplicates(), left_on=left_keys, right_on=merge_keys, how="left", ) if "equipe" in result.columns: result = result.drop(columns=["equipe"]) return result def _get_merge_keys(result: pd.DataFrame, merge_df: pd.DataFrame) -> list[str]: """Determine les cles de merge.""" merge_keys = ["equipe", "saison"] if "ronde" in result.columns and "ronde" in merge_df.columns: merge_keys.append("ronde") return merge_keys def merge_noyau_features( result: pd.DataFrame, noyau_df: pd.DataFrame, ) -> pd.DataFrame: """Merge les features noyau par (joueur, equipe, saison, ronde). Produit les colonnes: - est_dans_noyau_blanc / est_dans_noyau_noir - pct_noyau_equipe_dom / pct_noyau_equipe_ext Args: ---- result: DataFrame cible avec blanc_nom, noir_nom, equipe_dom, equipe_ext, saison, ronde noyau_df: DataFrame depuis extract_noyau_features() Returns: ------- DataFrame avec features noyau mergees """ if noyau_df.empty: return result required = {"joueur_nom", "equipe", "saison", "ronde", "est_dans_noyau", "pct_noyau_match"} if not required.issubset(noyau_df.columns): return result for color, equipe_col in [("blanc", "equipe_dom"), ("noir", "equipe_ext")]: nom_col = f"{color}_nom" if nom_col not in result.columns or equipe_col not in result.columns: continue rename_map = { "est_dans_noyau": f"est_dans_noyau_{color}", "pct_noyau_match": f"pct_noyau_equipe_{'dom' if color == 'blanc' else 'ext'}", } sub = noyau_df.rename(columns=rename_map)[ ["joueur_nom", "equipe", "saison", "ronde"] + list(rename_map.values()) ].drop_duplicates() result = result.merge( sub, left_on=[nom_col, equipe_col, "saison", "ronde"], right_on=["joueur_nom", "equipe", "saison", "ronde"], how="left", ) result = result.drop(columns=["joueur_nom", "equipe"], errors="ignore") return result def merge_h2h_features( result: pd.DataFrame, h2h: pd.DataFrame, ) -> pd.DataFrame: """Merge features head-to-head. Args: ---- result: DataFrame cible h2h: DataFrame confrontations directes Returns: ------- DataFrame avec features mergées """ if h2h.empty: return result if "blanc_nom" not in result.columns or "noir_nom" not in result.columns: return result def get_h2h_key(row: pd.Series) -> tuple[str, str]: b, n = str(row["blanc_nom"]), str(row["noir_nom"]) return (b, n) if b < n else (n, b) result["_h2h_key"] = result.apply(get_h2h_key, axis=1) h2h["_h2h_key"] = list(zip(h2h["joueur_a"], h2h["joueur_b"], strict=False)) h2h_merge = h2h[["_h2h_key", "nb_confrontations", "avantage_a"]].copy() result = result.merge(h2h_merge, on="_h2h_key", how="left") def adjust_h2h(row: pd.Series) -> float: if pd.isna(row.get("avantage_a")): return float("nan") b, n = str(row["blanc_nom"]), str(row["noir_nom"]) return row["avantage_a"] if b < n else -row["avantage_a"] result["h2h_avantage_blanc"] = result.apply(adjust_h2h, axis=1) result["h2h_nb_confrontations"] = result["nb_confrontations"] result = result.drop(columns=["_h2h_key", "nb_confrontations", "avantage_a"], errors="ignore") return result