Fix CI ruff lint and format issues

Co-authored-by: m9h <28337207+m9h@users.noreply.github.com>

Author: google-labs-jules[bot]

.github/workflows/ci.yml

@@ -23,14 +23,14 @@ jobs:
       - name: Install dependencies
         run: uv sync --group dev 2>/dev/null || uv pip install -e ".[dev]"
       - name: Ruff check
-        run: uv run ruff check src/ tests/
+        run: uv run ruff check alf/
       - name: Ruff format check
-        run: uv run ruff format --check src/ tests/
+        run: uv run ruff format --check alf/
       - name: No-mock check
         run: |
-          if grep -rn "unittest\.mock\|MagicMock\|from mock import\|@patch" tests/ --include="*.py" | grep -v "^#"; then
+          if grep -rn "unittest\.mock\|MagicMock\|from mock import\|@patch" alf/tests/ --include="*.py" | grep -v "^#"; then
             echo "::error::unittest.mock usage detected in tests"
             exit 1
           fi
       - name: Pytest
-        run: uv run pytest tests/ -v --tb=short
+        run: uv run pytest alf/tests/ -v --tb=short

alf/__pycache__/__init__.cpython-312.pyc

@@ -27,7 +27,7 @@
         Inference. Journal of Mathematical Psychology.
 """
 
-from typing import Any, Optional
+from typing import Any
 
 import numpy as np
 
@@ -108,7 +108,10 @@ def step(
 
         # 3. Action selection
         policy_idx, policy_probs = alf_policy.select_action(
-            G, self.E, self.gamma, rng=self.rng,
+            G,
+            self.E,
+            self.gamma,
+            rng=self.rng,
         )
         self.policy_prob_history.append(policy_probs.copy())
 
@@ -133,19 +136,20 @@ def learn(self, outcome_valence: float) -> None:
         """
         if self.action_history:
             last_policy_idx = (
-                self.policy_prob_history[-1].argmax()
-                if self.policy_prob_history
-                else 0
+                self.policy_prob_history[-1].argmax() if self.policy_prob_history else 0
             )
             self.E = alf_policy.update_habits(
-                self.E, last_policy_idx, outcome_valence,
+                self.E,
+                last_policy_idx,
+                outcome_valence,
                 learning_rate=self.learning_rate,
             )
 
     def update_precision(self, prediction_error: float) -> None:
         """Adapt policy precision based on prediction error."""
         self.gamma = alf_policy.update_precision(
-            self.gamma, prediction_error,
+            self.gamma,
+            prediction_error,
         )
 
     def reset(self) -> None: