def _log_end_of_run_report(self):
missing_models = missing_model_hashes(self.experiment_hash,
self.db_engine)
if len(missing_models) > 0:
logging.info(
"Found %s missing model hashes."
"This means that they were supposed to either be trained or reused"
"by this experiment but are not present in the models table."
"Inspect the logs for any training errors. Full list: %s",
len(missing_models), missing_models)
else:
logging.info(
"All models that were supposed to be trained were trained. Awesome!"
)
missing_matrices = missing_matrix_uuids(self.experiment_hash,
self.db_engine)
if len(missing_matrices) > 0:
logging.info(
"Found %s missing matrix uuids."
"This means that they were supposed to either be build or reused"
"by this experiment but are not present in the matrices table."
"Inspect the logs for any matrix building errors. Full list: %s",
len(missing_matrices), missing_matrices)
else:
logging.info(
"All matrices that were supposed to be build were built. Awesome!"
)
def test_missing_matrix_uuids():
with testing.postgresql.Postgresql() as postgresql:
db_engine = create_engine(postgresql.url())
ensure_db(db_engine)
experiment_hash = save_experiment_and_get_hash({}, 1234, db_engine)
matrix_uuids = ['abcd', 'bcde', 'cdef']
# if we associate matrix uuids with an experiment but don't actually build the matrices
# they should show up as missing
associate_matrices_with_experiment(experiment_hash, matrix_uuids, db_engine)
assert missing_matrix_uuids(experiment_hash, db_engine) == matrix_uuids
# if we insert a matrix row they should no longer be considered missing
db_engine.execute(
f"insert into {Matrix.__table__.fullname} (matrix_uuid) values (%s)",
matrix_uuids[0]
)
assert missing_matrix_uuids(experiment_hash, db_engine) == matrix_uuids[1:]