Upload soccer_feature_engineering.py

soccer_feature_engineering.py

@@ -1 +1,157 @@
-See /app/soccer_feature_engineering.py
+"""
+Soccer Feature Engineering Pipeline
+====================================
+Engineers 33 match-level, team-level features from SkillCorner dynamic_events.csv.
+Based on the Kaggle Soccer Feature Engineering Hackathon requirements.
+
+Input:  SkillCorner opendata repository (or any folder with *_dynamic_events.csv)
+Output: features.csv — one row per team per match, 35 columns (match_id, team_id + 33 features)
+
+All features are raw aggregated counts or cumulative distances — no percentages,
+no ratios, fully competition-compliant.
+"""
+
+import glob
+import json
+import os
+
+import numpy as np
+import pandas as pd
+
+
+def discover_dynamic_events_files(data_root="/app/opendata/data/matches"):
+    """Dynamically discover all *_dynamic_events.csv files via glob."""
+    pattern = os.path.join(data_root, "*", "*_dynamic_events.csv")
+    files = glob.glob(pattern, recursive=True)
+    files.sort()
+    return files
+
+
+def load_matches_metadata(data_root="/app/opendata/data"):
+    """Load matches.json to get home/away team mapping."""
+    path = os.path.join(data_root, "matches.json")
+    with open(path, "r") as f:
+        matches = json.load(f)
+    mapping = {}
+    for m in matches:
+        mapping[str(m["id"])] = {
+            "home_team_id": m["home_team"]["id"],
+            "away_team_id": m["away_team"]["id"],
+            "home_team_name": m["home_team"]["short_name"],
+            "away_team_name": m["away_team"]["short_name"],
+        }
+    return mapping
+
+
+def compute_features_for_match(dynamic_events_path, match_meta=None):
+    """
+    Compute all 33 features for a single match's dynamic_events.csv.
+    Returns a DataFrame with one row per team.
+    """
+    df = pd.read_csv(dynamic_events_path, low_memory=False)
+    match_id = df["match_id"].iloc[0]
+    team_ids = sorted(df["team_id"].unique().tolist())
+
+    records = []
+    for team_id in team_ids:
+        rec = {"match_id": match_id, "team_id": int(team_id)}
+
+        pp = df[(df["event_type"] == "player_possession") & (df["team_id"] == team_id)].copy()
+        obe = df[(df["event_type"] == "on_ball_engagement") & (df["team_id"] == team_id)].copy()
+        obr = df[(df["event_type"] == "off_ball_run") & (df["team_id"] == team_id)].copy()
+
+        # DIMENSION 1 — ATTACKING STRUCTURE (att1–att5)
+        passes = pp[pp["pass_outcome"].notna()]
+        rec["att1"] = int((passes["third_end"] == "attacking_third").sum())
+        rec["att2"] = int(((pp["carry"] == True) & (pp["third_end"] == "attacking_third")).sum())
+        pass_opp_bypassed = passes["n_opponents_bypassed"].fillna(0).clip(lower=0)
+        rec["att3"] = float(pass_opp_bypassed.sum())
+        rec["att4"] = int((passes["last_line_break"] == True).sum())
+        rec["att5"] = int((passes["third_start"] == "attacking_third").sum())
+
+        # DIMENSION 2 — BUILD-UP PROFILE (att6–att10)
+        phase_counts = pp["team_in_possession_phase_type"].value_counts()
+        rec["att6"] = int(phase_counts.get("build_up", 0))
+        rec["att7"] = int(phase_counts.get("direct", 0))
+        rec["att8"] = int(phase_counts.get("set_play", 0))
+        rec["att9"] = int(phase_counts.get("quick_break", 0))
+        rec["att10"] = int(phase_counts.get("transition", 0))
+
+        # DIMENSION 3 — POSSESSION QUALITY (att11–att20)
+        rec["att11"] = int((pp["one_touch"] == True).sum())
+        rec["att12"] = int((pp["quick_pass"] == True).sum())
+        rec["att13"] = int(pp["lead_to_shot"].sum())
+        rec["att14"] = int(pp["lead_to_goal"].sum())
+        rec["att15"] = float(pp["delta_to_last_defensive_line_gain"].fillna(0).clip(lower=0).sum())
+        rec["att16"] = float(pp["last_defensive_line_height_gain"].fillna(0).clip(lower=0).sum())
+        rec["att17"] = int((pp["forward_momentum"] == True).sum())
+        rec["att18"] = float(pp["n_passing_options"].fillna(0).sum())
+        rec["att19"] = float(pp["n_passing_options_dangerous_difficult"].fillna(0).sum())
+        rec["att20"] = int((obr["event_subtype"] == "run_ahead_of_the_ball").sum())
+
+        # DIMENSION 4 — PRESSING & DEFENSIVE SHAPE (def1–def7)
+        rec["def1"] = int(len(obe))
+        rec["def2"] = int((obe["event_subtype"] == "counter_press").sum())
+        rec["def3"] = int((obe["event_subtype"] == "recovery_press").sum())
+        chain_starts = obe[obe["index_in_pressing_chain"] == 1.0]
+        rec["def4"] = float(chain_starts["pressing_chain_length"].fillna(0).sum())
+        rec["def5"] = int(len(chain_starts))
+        rec["def6"] = float(obe["pressing_chain_length"].max() if len(obe) > 0 else 0)
+        rec["def7"] = int((obe["stop_possession_danger"] == True).sum())
+
+        # DIMENSION 5 — OFF-BALL MOVEMENT INTELLIGENCE (run1–run5 + gng)
+        rec["run1"] = int((obr["break_defensive_line"] == True).sum())
+        rec["run2"] = int((obr["push_defensive_line"] == True).sum())
+        rec["run3"] = int((obr["event_subtype"] == "behind").sum())
+        rec["run4"] = int((obr["event_subtype"] == "overlap").sum())
+        rec["run5"] = int((obr["third_start"] == "attacking_third").sum())
+        rec["att_give_and_go_initiated"] = int((pp["initiate_give_and_go"] == True).sum())
+
+        records.append(rec)
+
+    return pd.DataFrame(records)
+
+
+def run_pipeline(data_root="/app/opendata/data/matches", output_path="/app/features.csv"):
+    files = discover_dynamic_events_files(data_root)
+    print(f"Discovered {len(files)} dynamic_events.csv files")
+
+    all_dfs = []
+    for f in files:
+        try:
+            match_df = compute_features_for_match(f)
+            all_dfs.append(match_df)
+            print(f"  ✓ {os.path.basename(f)} -> {len(match_df)} rows")
+        except Exception as e:
+            print(f"  ✗ {os.path.basename(f)} -> ERROR: {e}")
+
+    if not all_dfs:
+        raise ValueError("No valid match files processed.")
+
+    features_df = pd.concat(all_dfs, ignore_index=True)
+
+    feature_cols = (
+        [f"att{i}" for i in range(1, 21)] +
+        [f"def{i}" for i in range(1, 8)] +
+        [f"run{i}" for i in range(1, 6)] +
+        ["att_give_and_go_initiated"]
+    )
+    ordered_cols = ["match_id", "team_id"] + feature_cols
+    features_df = features_df[[c for c in ordered_cols if c in features_df.columns]]
+
+    for col in features_df.columns:
+        if features_df[col].dtype == float:
+            rounded = features_df[col].round(2)
+            if (rounded == rounded.astype(int)).all():
+                features_df[col] = rounded.astype(int)
+            else:
+                features_df[col] = rounded
+
+    features_df.to_csv(output_path, index=False)
+    print(f"\nWrote {len(features_df)} rows × {len(features_df.columns)} columns to {output_path}")
+    print(f"Shape: {features_df.shape}")
+    return features_df
+
+
+if __name__ == "__main__":
+    run_pipeline()