def testEvaluationJob(self):
model_version = 1
total_tasks = 5
latest_chkp_version = 2
job = EvaluationJob(_eval_metrics_fn(), model_version, total_tasks)
self.assertEqual(0, job._completed_tasks)
self.assertFalse(job.finished())
self.assertFalse(self.ok_to_new_job(job, latest_chkp_version))
# Now make 4 tasks finished
for i in range(4):
job.complete_task()
self.assertEqual(4, job._completed_tasks)
self.assertFalse(job.finished())
self.assertFalse(self.ok_to_new_job(job, latest_chkp_version))
# One more task finishes
job.complete_task()
self.assertEqual(5, job._completed_tasks)
self.assertTrue(job.finished())
self.assertTrue(self.ok_to_new_job(job, latest_chkp_version))
# No new model checkpoint
latest_chkp_version = job.model_version
self.assertFalse(self.ok_to_new_job(job, latest_chkp_version))
latest_chkp_version = job.model_version + 1
self.assertTrue(self.ok_to_new_job(job, latest_chkp_version))
model_outputs = {}
model_outputs[MetricsDictKey.MODEL_OUTPUT] = ndarray_to_pb(
np.array([[1], [6], [3]], np.float32)
)
labels = ndarray_to_pb(np.array([[1], [0], [3]], np.float32))
job.report_evaluation_metrics(model_outputs, labels)
job.report_evaluation_metrics(
{
MetricsDictKey.MODEL_OUTPUT: ndarray_to_pb(
np.array([[4], [5], [6], [7], [8]], np.float32)
)
},
ndarray_to_pb(np.array([[7], [8], [9], [10], [11]], np.float32)),
)
expected_acc = 0.25
evaluation_metrics = job.evaluation_metrics.get_evaluation_summary()
self.assertAlmostEqual(expected_acc, evaluation_metrics.get("acc"))
self.assertAlmostEqual(expected_acc, evaluation_metrics.get("acc_fn"))
self.assertAlmostEqual(10.125, evaluation_metrics.get("mse"))
def verify(array):
pb = ndarray_to_pb(array)
new_array = pb_to_ndarray(pb)
np.testing.assert_array_equal(array, new_array)
