Add minimal unit tests for 'FairnessAccuracyComputer'.

test/fairness/api/test_accuracy_computer.py

+# coding: utf-8
+
+# Copyright 2023 Inria (Institut National de Recherche en Informatique
+# et Automatique)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Unit tests for 'declearn.fairness.api.FairnessAccuracyComputer'."""
+
+from typing import Optional
+from unittest import mock
+
+import pytest
+
+from declearn.dataset import Dataset
+from declearn.fairness.api import FairnessAccuracyComputer, FairnessDataset
+from declearn.metrics import MetricSet
+from declearn.model.api import Model
+
+
+N_BATCHES = 8
+
+
+@pytest.fixture(name="dataset")
+def dataset_fixture() -> FairnessDataset:
+    """Mock FairnessDataset providing fixture."""
+    # Set up a mock FairnessDataset.
+    groups = [(0, 0), (0, 1), (1, 0), (1, 1)]
+    dataset = mock.create_autospec(FairnessDataset, instance=True)
+    dataset.get_sensitive_group_definitions.return_value = groups
+    # Set up a mock Dataset.
+    subdataset = mock.create_autospec(Dataset, instance=True)
+    batches = [mock.MagicMock() for _ in range(N_BATCHES)]
+    subdataset.generate_batches.return_value = iter(batches)
+    # Have the FairnessDataset return the Dataset for any group.
+    dataset.get_sensitive_group_subset.return_value = subdataset
+    return dataset
+
+
+class TestFairnessAccuracyComputer:
+    """Unit tests for 'declearn.fairness.api.FairnessAccuracyComputer'."""
+
+    @pytest.mark.parametrize("n_batch", [None, 4, 12])
+    def test_compute_metrics_over_sensitive_groups(
+        self,
+        dataset: FairnessDataset,
+        n_batch: Optional[int],
+    ) -> None:
+        """Test the 'compute_metrics_over_sensitive_groups' method."""
+        # Set up mock objects and run (mocked) computations.
+        computer = FairnessAccuracyComputer(dataset)
+        metrics = mock.create_autospec(MetricSet, instance=True)
+        model = mock.create_autospec(Model, instance=True)
+        mock_pred = (mock.MagicMock(), mock.MagicMock(), None)
+        model.compute_batch_predictions.return_value = mock_pred
+        results = computer.compute_metrics_over_sensitive_group(
+            group=(0, 0),
+            metrics=metrics,
+            model=model,
+            batch_size=8,
+            n_batch=n_batch,
+        )
+        # Verify that expected (mocked) computations happened.
+        expected_nbatches = min(n_batch or N_BATCHES, N_BATCHES)
+        assert results is metrics.get_result.return_value
+        metrics.reset.assert_called_once()
+        assert metrics.update.call_count == expected_nbatches
+        assert model.compute_batch_predictions.call_count == expected_nbatches
+        subset = computer.g_data[(0, 0)]
+        subset.generate_batches.assert_called_once_with(  # type: ignore
+            batch_size=8, shuffle=n_batch is not None, drop_remainder=False
+        )