def testDoWithTunerFn(self):
self._exec_properties['tuner_fn'] = '%s.%s' % (
tuner_module.tuner_fn.__module__, tuner_module.tuner_fn.__name__)
ps_type = ps_pb2.Type(
binary_classification=ps_pb2.BinaryClassification(label='class'))
ps = ps_pb2.ProblemStatement(
owner=['nitroml'],
tasks=[ps_pb2.Task(
name='mockdata_1',
type=ps_type,
)])
self._exec_properties['custom_config'] = json_utils.dumps({
'problem_statement':
text_format.MessageToString(message=ps, as_utf8=True),
})
tuner = executor.Executor(self._context)
tuner.Do(input_dict=self._input_dict,
output_dict=self._output_dict,
exec_properties=self._exec_properties)
self._verify_output()
def testDoWithMajoritVoting(self):
exec_properties = self._exec_properties.copy()
exec_properties['tuner_fn'] = '%s.%s' % (
tuner_module.tuner_fn.__module__, tuner_module.tuner_fn.__name__)
exec_properties['metalearning_algorithm'] = 'majority_voting'
input_dict = self._input_dict.copy()
ps_type = ps_pb2.Type(
binary_classification=ps_pb2.BinaryClassification(label='class'))
ps = ps_pb2.ProblemStatement(
owner=['nitroml'],
tasks=[ps_pb2.Task(
name='mockdata_1',
type=ps_type,
)])
exec_properties['custom_config'] = json_utils.dumps({
'problem_statement':
text_format.MessageToString(message=ps, as_utf8=True),
})
hps_artifact = artifacts.KCandidateHyperParameters()
hps_artifact.uri = os.path.join(self._testdata_dir,
'MetaLearner.majority_voting',
'hparams_out')
input_dict['warmup_hyperparameters'] = [hps_artifact]
tuner = executor.Executor(self._context)
tuner.Do(input_dict=input_dict,
output_dict=self._output_dict,
exec_properties=exec_properties)
self._verify_output()
def _get_task_type(self):
"""Creates a `ps_pb2.Type` from the number of classes."""
if self.num_classes == 0:
return ps_pb2.Type(
one_dimensional_regression=ps_pb2.OneDimensionalRegression(
label=self._label_key))
if self.num_classes == 2:
return ps_pb2.Type(
binary_classification=ps_pb2.BinaryClassification(
label=self._label_key))
return ps_pb2.Type(
multi_class_classification=ps_pb2.MultiClassClassification(
label=self._label_key))
def problem_statement(self) -> ps_pb2.ProblemStatement:
"""Returns the ProblemStatement associated with this Task."""
# Supervised keys is a two-tuple.
_, target_key = self._dataset_builder.info.supervised_keys
return ps_pb2.ProblemStatement(
owner=['nitroml'],
tasks=[
ps_pb2.Task(
name=self.name,
type=ps_pb2.Type(
binary_classification=ps_pb2.BinaryClassification(
label=target_key)),
)
])