created metrax_test which tests metrax metrics are jittable

Author: jshin1394

src/metrax/metrax_test.py

@@ -0,0 +1,128 @@
+# Copyright 2024 Google LLC
+#
+# 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.
+
+"""Tests for metrax NNX metrics."""
+from absl.testing import absltest
+from absl.testing import parameterized
+import jax
+import metrax
+import metrax.nnx
+import numpy as np
+
+np.random.seed(42)
+BATCHES = 1
+BATCH_SIZE = 8
+OUTPUT_LABELS = np.random.randint(
+    0,
+    2,
+    size=(BATCHES, BATCH_SIZE),
+).astype(np.float32)
+OUTPUT_PREDS = np.random.uniform(size=(BATCHES, BATCH_SIZE))
+
+STRING_PREDS = [
+    'the cat sat on the mat',
+    'a quick brown fox jumps over the lazy dog',
+    'hello world',
+]
+STRING_REFS = [
+    'the cat sat on the hat',
+    'the quick brown fox jumps over the lazy dog',
+    'hello beautiful world',
+]
+TOKENIZED_PREDS = [sentence.split() for sentence in STRING_PREDS]
+TOKENIZED_REFS = [sentence.split() for sentence in STRING_REFS]
+
+
+class MetraxTest(parameterized.TestCase):
+
+  @parameterized.named_parameters(
+      (
+          'aucpr',
+          metrax.AUCPR,
+          {'predictions': OUTPUT_LABELS, 'labels': OUTPUT_PREDS},
+      ),
+      (
+          'aucroc',
+          metrax.AUCROC,
+          {'predictions': OUTPUT_LABELS, 'labels': OUTPUT_PREDS},
+      ),
+      (
+          'average',
+          metrax.Average,
+          {'values': OUTPUT_PREDS},
+      ),
+      (
+          'averageprecisionatk',
+          metrax.AveragePrecisionAtK,
+          {
+              'predictions': OUTPUT_LABELS,
+              'labels': OUTPUT_PREDS,
+              'ks': np.array([3]),
+          },
+      ),
+      (
+          'mse',
+          metrax.MSE,
+          {'predictions': OUTPUT_LABELS, 'labels': OUTPUT_PREDS},
+      ),
+      (
+          'perplexity',
+          metrax.Perplexity,
+          {'predictions': OUTPUT_LABELS, 'labels': OUTPUT_PREDS},
+      ),
+      (
+          'precision',
+          metrax.Precision,
+          {'predictions': OUTPUT_LABELS, 'labels': OUTPUT_PREDS},
+      ),
+      (
+          'rmse',
+          metrax.RMSE,
+          {'predictions': OUTPUT_LABELS, 'labels': OUTPUT_PREDS},
+      ),
+      (
+          'rsquared',
+          metrax.RSQUARED,
+          {'predictions': OUTPUT_LABELS, 'labels': OUTPUT_PREDS},
+      ),
+      (
+          'recall',
+          metrax.Recall,
+          {'predictions': OUTPUT_LABELS, 'labels': OUTPUT_PREDS},
+      ),
+  )
+  def test_metrics_jittable(self, metric, kwargs):
+    """Tests that jitted metrax metric yields the same result as non-jitted metric."""
+    computed_metric = metric.from_model_output(**kwargs)
+    jitted_metric = jax.jit(metric.from_model_output)(**kwargs)
+    np.testing.assert_allclose(
+        computed_metric.compute(), jitted_metric.compute()
+    )
+
+  @parameterized.named_parameters(
+      (
+          'wer',
+          metrax.WER,
+          {'predictions': TOKENIZED_PREDS, 'references': TOKENIZED_REFS},
+      ),
+  )
+  def test_metrics_not_jittable(self, metric, kwargs):
+    """Tests that attempting to jit and call a known non-jittable metric raises an error."""
+    np.testing.assert_raises(
+        TypeError, lambda: jax.jit(metric.from_model_output)(**kwargs)
+    )
+
+
+if __name__ == '__main__':
+  absltest.main()