def check_for_update(self, predictor_id: UUID) -> Optional[Predictor]:
"""
Check if there are updates available for a predictor.
Typically these are updates to the training data. For example, a GEM Table may have
been re-built to include additional rows.
This check does not update the predictor; it just returns the update that is available.
To perform the update, the response should then be used to call PredictorCollection.update
Parameters
----------
predictor_id: UUID
Unique identifier of the predictor to check
Returns
-------
Optional[Predictor]
The update, if an update is available; None otherwise.
"""
path = "/projects/{}/predictors/{}/check-for-update".format(
self.project_id, predictor_id)
data = self.session.get_resource(path)
if data["updatable"]:
enveloped = GraphPredictor.stuff_predictor_into_envelope(
data["update"])
built: Predictor = Predictor.build(enveloped)
built.uid = predictor_id
return built
else:
return None
def from_predictor_responses(self, predictor: Predictor,
inputs: List[Descriptor]) -> List[Descriptor]:
"""
[ALPHA] Get responses for a predictor, given an input space.
Parameters
----------
predictor : Predictor
The predictor whose available responses are to be computed.
inputs : List[Descriptor]
The input space to the predictor.
Returns
-------
List[Descriptor]
The computable responses of the predictor given the provided input space (as
descriptors).
"""
response = self.session.post_resource(
path='/projects/{}/material-descriptors/predictor-responses'.format(self.project_id),
json={
'predictor': predictor.dump()['config'],
'inputs': [i.dump() for i in inputs]
}
)
return [Descriptor.build(r) for r in response['responses']]
def test_polymorphic_legacy_deserialization(valid_simple_ml_data):
"""Ensure that a polymorphically deserialized SimplePredictor looks sane."""
predictor: SimpleMLPredictor = Predictor.build(valid_simple_ml_data)
assert predictor.name == 'ML predictor'
assert predictor.description == 'Predicts z from input x and latent variable y'
assert len(predictor.inputs) == 1
assert predictor.inputs[0] == x
assert len(predictor.outputs) == 1
assert predictor.outputs[0] == z
assert len(predictor.latent_variables) == 1
assert predictor.latent_variables[0] == y
assert predictor.training_data == 'training_data_key'
def test_graph_default_training_data():
"""Test that default training data list isn't shared."""
# create two serialized graph predictors with no defined training data
gp1raw = {'config': {'name': 'one', 'description': '', 'predictors': [], 'type': 'Graph'},
'archived': False, 'module_type': 'PREDICTOR', 'display_name': 'one'}
gp2raw = {'config': {'name': 'two', 'description': '', 'predictors': [], 'type': 'Graph'},
'archived': False, 'module_type': 'PREDICTOR', 'display_name': 'two'}
# build them, populating the default empty list of training data
gp1: GraphPredictor = Predictor.build(gp1raw)
gp2: GraphPredictor = Predictor.build(gp2raw)
# check it is empty
assert len(gp1.training_data) == 0
assert len(gp2.training_data) == 0
# add training data to one of them
gp1.training_data.append(GemTableDataSource(uuid.uuid4(), 1))
# check that the training data doesn't bleed into both
assert len(gp1.training_data) == 1
assert len(gp2.training_data) == 0
def test_polymorphic_legacy_deserialization(valid_simple_ml_predictor_data):
"""Ensure that a polymorphically deserialized SimplePredictor looks sane."""
predictor: SimpleMLPredictor = Predictor.build(
valid_simple_ml_predictor_data)
assert predictor.name == 'ML predictor'
assert predictor.description == 'Predicts z from input x and latent variable y'
assert len(predictor.inputs) == 1
assert predictor.inputs[0] == RealDescriptor("x", 0, 100, "")
assert len(predictor.outputs) == 1
assert predictor.outputs[0] == RealDescriptor("z", 0, 100, "")
assert len(predictor.latent_variables) == 1
assert predictor.latent_variables[0] == RealDescriptor("y", 0, 100, "")
assert len(predictor.training_data) == 1
assert predictor.training_data[0].table_id == UUID(
'e5c51369-8e71-4ec6-b027-1f92bdc14762')
def test_invalid_predictor_type(invalid_predictor_data):
"""Ensures we raise proper exception when an invalid type is used."""
with pytest.raises(ValueError):
Predictor.build(invalid_predictor_data)
def build(self, data: dict) -> Predictor:
"""Build an individual Predictor."""
predictor: Predictor = Predictor.build(data)
predictor._session = self.session
predictor._project_id = self.project_id
return predictor