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9da4e9c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | """Verifica el contrato del modelo de ataque rediseñado (matchup GNN) tal como lo
consume la app: importa el módulo vendorizado, carga el bundle y predice una
distribución válida de ataque sobre las 10 zonas (suma ~1).
Se saltea si los artifacts NN no están disponibles localmente (cold CI sin Azure).
"""
from __future__ import annotations
import numpy as np
import pytest
matplotlib = pytest.importorskip("matplotlib")
matplotlib.use("Agg")
torch = pytest.importorskip("torch")
from racing_reports import vendor_env # noqa: E402
ATTACK_BUNDLE = vendor_env.DATA_DIR / "modeling" / "attack_matchup_gnn_bundle.pt"
MODELING_DATASET = vendor_env.DATA_DIR / "modeling" / "attack_prediction_dataset.parquet"
pytestmark = pytest.mark.skipif(
not (ATTACK_BUNDLE.exists() and MODELING_DATASET.exists()),
reason="Artifacts NN (bundle/dataset) no disponibles localmente.",
)
def test_app_imports_matchup_model() -> None:
pro = vendor_env.import_script("build_head_to_head_report_pro")
assert pro.attack_model_mod.__name__ == "train_attack_matchup_gnn"
assert pro.ATTACK_MODEL_PATH.name == "attack_matchup_gnn_bundle.pt"
def test_bundle_loads_without_key_mismatch() -> None:
attack_mod = vendor_env.import_script("train_attack_matchup_gnn")
bundle = torch.load(ATTACK_BUNDLE, map_location="cpu", weights_only=False)
global_dim = len(bundle["global_bundle"]["global_feature_columns"])
node_dim = len(bundle["node_bundle"]["means"][0])
model = attack_mod.ResidualAttackGNN(node_dim=node_dim, global_dim=global_dim)
missing, unexpected = model.load_state_dict(bundle["model_state_dict"], strict=False)
assert not missing, f"faltan claves: {missing}"
assert not unexpected, f"claves inesperadas: {unexpected}"
assert bundle["zone_order"] == attack_mod.ZONE_ORDER
def test_prediction_is_a_valid_distribution() -> None:
pro = vendor_env.import_script("build_head_to_head_report_pro")
df_model = pro._load_modeling_dataset()
racing = "bzkwzatvwahmbzok1ymm5vqa1"
sub = df_model[df_model["teamId"] == racing]
if sub.empty:
pytest.skip("Racing no presente en el dataset local.")
row = sub.iloc[-1]
opp_id = (
row["opponent_team_id"]
if "opponent_team_id" in sub.columns and row.get("opponent_team_id")
else df_model[df_model["team_name"] == row["opponent_name"]]["teamId"].iloc[-1]
)
pred = pro._predict_single_team_future(
df_model, racing, opp_id, "Racing de Santander", str(row["opponent_name"]),
row["league"], row["season"], is_home=True,
)
zones = list(pred.attack.keys())
assert len(zones) == 10
total = sum(pred.attack.values())
assert total == pytest.approx(1.0, abs=1e-4), f"la distribución no suma 1: {total}"
assert all(0.0 <= v <= 1.0 for v in pred.attack.values())
assert len(pred.pv) == 10
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