def test_predict(self):
model = MultitaskClassifier([self.task1])
results = model.predict(self.dataloader)
self.assertEqual(sorted(list(results.keys())), ["golds", "probs"])
np.testing.assert_array_equal(
results["golds"]["task1"],
self.dataloader.dataset.Y_dict["task1"].numpy())
np.testing.assert_array_equal(results["probs"]["task1"],
np.ones((NUM_EXAMPLES, 2)) * 0.5)
results = model.predict(self.dataloader, return_preds=True)
self.assertEqual(sorted(list(results.keys())),
["golds", "preds", "probs"])
# deterministic random tie breaking alternates predicted labels
np.testing.assert_array_equal(
results["preds"]["task1"],
np.array([0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0]),
)
def test_remapped_labels(self):
# Test additional label keys in the Y_dict
# Without remapping, model should ignore them
task_name = self.task1.name
X = torch.FloatTensor([[i, i] for i in range(NUM_EXAMPLES)])
Y = torch.ones(NUM_EXAMPLES).long()
Y_dict = {task_name: Y, "other_task": Y}
dataset = DictDataset(name="dataset",
split="train",
X_dict={"data": X},
Y_dict=Y_dict)
dataloader = DictDataLoader(dataset, batch_size=BATCH_SIZE)
model = MultitaskClassifier([self.task1])
loss_dict, count_dict = model.calculate_loss(dataset.X_dict,
dataset.Y_dict)
self.assertIn("task1", loss_dict)
# Test setting without remapping
results = model.predict(dataloader)
self.assertIn("task1", results["golds"])
self.assertNotIn("other_task", results["golds"])
scores = model.score([dataloader])
self.assertIn("task1/dataset/train/accuracy", scores)
self.assertNotIn("other_task/dataset/train/accuracy", scores)
# Test remapped labelsets
results = model.predict(dataloader,
remap_labels={"other_task": task_name})
self.assertIn("task1", results["golds"])
self.assertIn("other_task", results["golds"])
results = model.score([dataloader],
remap_labels={"other_task": task_name})
self.assertIn("task1/dataset/train/accuracy", results)
self.assertIn("other_task/dataset/train/accuracy", results)