add tests

tests/test_metrics.py

+from lm_eval.api.task import ConfigurableTask, TaskConfig
+
+
+class MockConfigurableTask(ConfigurableTask):
+    """Mock task for testing metrics"""
+
+    def __init__(self):
+        # Create a minimal config
+        config = {
+            "task": "test_acc_mutual_info",
+            "output_type": "multiple_choice",
+            "metric_list": [{"metric": "acc"}, {"metric": "acc_mutual_info"}],
+            "doc_to_choice": ["A", "B", "C"],
+            "doc_to_target": 1,  # Correct answer is index 1 (choice "B")
+            "target_delimiter": " ",
+        }
+
+        # Initialize with minimal setup
+        self._config = TaskConfig(**config)
+        self.OUTPUT_TYPE = "multiple_choice"
+
+        # Set up required attributes
+        self.multiple_input = 0
+        self.multiple_target = 0
+
+        # Set up metrics
+        self._metric_fn_list = {"acc": None, "acc_mutual_info": None}
+        self._metric_fn_kwargs = {"acc": {}, "acc_mutual_info": {}}
+        self._aggregation_list = {}
+        self._higher_is_better = {}
+
+    def doc_to_choice(self, doc):
+        return ["A", "B", "C"]
+
+    def doc_to_target(self, doc):
+        return 1  # Choice "B" is correct
+
+    # Required abstract methods (minimal implementations)
+    def has_training_docs(self):
+        return False
+
+    def has_validation_docs(self):
+        return False
+
+    def has_test_docs(self):
+        return True
+
+    def download(self, **kwargs):
+        pass
+
+
+def test_acc_mutual_info_slicing():
+    """Test that acc_mutual_info correctly slices conditional and unconditional loglikelihoods"""
+
+    task = MockConfigurableTask()
+
+    # Simulate loglikelihood results for 3 choices
+    # Format: [(loglikelihood, is_greedy), ...]
+    # First 3 are conditional P(choice|context), next 3 are unconditional P(choice)
+
+    # Combined results as they would come from the model
+    # Order: conditional_1, conditional_2, conditional_3, unconditional_1, unconditional_2, unconditional_3
+    # Conditional: [-2.0, -1.0, -3.0] - Choice B (index 1) has highest prob
+    # Unconditional: [-2.5, -2.0, -2.5] - Choice B has higher unconditional prob too
+    results = [
+        (-2.0, False),
+        (-1.0, True),
+        (-3.0, False),  # Conditional
+        (-2.5, False),
+        (-2.0, False),
+        (-2.5, False),
+    ]  # Unconditional
+
+    # Test the process_results method
+    doc = {}  # Mock document
+    result_dict = task.process_results(doc, results)
+
+    # Verify that both acc and acc_mutual_info are calculated
+    assert "acc" in result_dict
+    assert "acc_mutual_info" in result_dict
+
+    # Both should be 1.0 since choice B (index 1) is correct and has highest probability
+    assert result_dict["acc"] == 1.0, f"Expected acc=1.0, got {result_dict['acc']}"
+    assert result_dict["acc_mutual_info"] == 1.0, (
+        f"Expected acc_mutual_info=1.0, got {result_dict['acc_mutual_info']}"
+    )
+
+
+def test_acc_mutual_info_different_predictions():
+    """Test case where conditional and mutual info predictions differ"""
+
+    task = MockConfigurableTask()
+
+    # Mutual info calculation:
+    # Conditional:   A=-1.0, B=-2.0, C=-3.0 (A wins conditionally)
+    # Unconditional: A=-0.5, B=-2.0, C=-3.0 (A has much higher unconditional prob)
+    # Mutual info = conditional - unconditional:
+    # A: -1.0 - (-0.5) = -0.5
+    # B: -2.0 - (-2.0) = 0.0    <- B wins with mutual info!
+    # C: -3.0 - (-3.0) = 0.0
+
+    results = [
+        (-1.0, True),
+        (-2.0, False),
+        (-3.0, False),  # Conditional (A wins)
+        (-0.5, False),
+        (-2.0, False),
+        (-3.0, False),
+    ]  # Unconditional
+
+    doc = {}
+    result_dict = task.process_results(doc, results)
+
+    # Regular acc should be 0.0 (A predicted, but B is correct)
+    assert result_dict["acc"] == 0.0, f"Expected acc=0.0, got {result_dict['acc']}"
+
+    # Mutual info should be 1.0 (B predicted with mutual info, and B is correct)
+    assert result_dict["acc_mutual_info"] == 1.0, (
+        f"Expected acc_mutual_info=1.0, got {result_dict['acc_mutual_info']}"
+    )
+
+
+def test_acc_mutual_info_without_metric():
+    """Test that normal behavior works when acc_mutual_info is not in metric list"""
+
+    # Create task without acc_mutual_info
+    config = {
+        "task": "test_normal",
+        "output_type": "multiple_choice",
+        "metric_list": [{"metric": "acc"}],  # Only acc, no acc_mutual_info
+        "doc_to_choice": ["A", "B", "C"],
+        "doc_to_target": 1,
+        "target_delimiter": " ",
+    }
+
+    task = MockConfigurableTask()
+    task._config = TaskConfig(**config)
+    task._metric_fn_list = {"acc": None}  # Only acc
+
+    # Only conditional loglikelihoods (no unconditional since acc_mutual_info not requested)
+    results = [(-2.0, False), (-1.0, True), (-3.0, False)]  # 3 choices, B wins
+
+    doc = {}
+    result_dict = task.process_results(doc, results)
+
+    # Should only have acc, not acc_mutual_info
+    assert "acc" in result_dict
+    assert "acc_mutual_info" not in result_dict
+    assert result_dict["acc"] == 1.0
+
+
+if __name__ == "__main__":
+    test_acc_mutual_info_slicing()
+    test_acc_mutual_info_different_predictions()
+    test_acc_mutual_info_without_metric()
+    print("All tests passed!")