lint

bpl/_util.py

@@ -1,4 +1,5 @@
 """Private utility functions."""
+
 from typing import Iterable, Optional, Tuple, Union
 
 import jax

bpl/base.py

@@ -1,4 +1,5 @@
 """Implementation of the probabilistic model for soccer matches."""
+
 from __future__ import annotations
 
 from abc import abstractmethod

bpl/dixon_coles.py

@@ -1,4 +1,5 @@
 """Implementation of a simple team level model."""
+
 from __future__ import annotations
 
 from typing import Any, Dict, Iterable, Optional, Tuple, Union

bpl/dynamic_dixon_coles.py

@@ -1,4 +1,5 @@
 """Implementation of the neutral model with dynamic parameters in the current version of bpl."""
+
 from __future__ import annotations
 
 import warnings

bpl/extended_dixon_coles.py

@@ -1,4 +1,5 @@
 """Implementation of the model in the current version of bpl."""
+
 from __future__ import annotations
 
 import warnings

bpl/neutral_dixon_coles.py

@@ -1,4 +1,5 @@
 """Implementation of the neutral model for predicting the World Cup."""
+
 from __future__ import annotations
 
 import warnings

bpl/neutral_dixon_coles_WC.py

@@ -1,4 +1,5 @@
 """Implementation of the neutral model for predicting the World Cup."""
+
 from __future__ import annotations
 
 import warnings

tests/test_all_models.py

@@ -1,4 +1,5 @@
 """Shared tests across all models, e.g. checking probabilities are valid."""
+
 import jax.numpy as jnp
 import pytest
 

tests/test_neutral_dixon_coles.py

@@ -6,6 +6,7 @@
 
 TOL = 1e-02
 
+
 @pytest.fixture
 def model(neutral_dummy_data):
     return NeutralDixonColesMatchPredictor().fit(neutral_dummy_data)

tests/test_neutral_dixon_coles_WC.py

@@ -6,6 +6,7 @@
 
 TOL = 5e-02
 
+
 @pytest.fixture
 def model(neutral_dummy_data):
     return NeutralDixonColesMatchPredictorWC().fit(neutral_dummy_data)
@@ -97,7 +98,9 @@ def test_predict_concede_n_proba(model):
     assert len(proba_team_concede) == 1
     assert (proba_team_concede[0] >= 0) and (proba_team_concede[0] <= 1)
 
-    proba_opponent_score = model.predict_score_n_proba(1, "1", "0", "0", "1", home=False)
+    proba_opponent_score = model.predict_score_n_proba(
+        1, "1", "0", "0", "1", home=False
+    )
     assert proba_team_concede.tolist() == pytest.approx(
         proba_opponent_score.tolist(), abs=TOL
     )