def test_apiclient_search_solutions(search_solutions_request_mock,
logger_mock):
instance = TA3APIClient(port=9999)
dataset = 'test_dataset'
expected_value = 'response'
# instance mocks
instance._get_dataset_doc_path = MagicMock(return_value='dataset-doc-path')
instance._build_problem = MagicMock(return_value='build-problem')
instance.stub.SearchSolutions = MagicMock(return_value=expected_value)
value = instance.search_solutions(dataset)
assert value == expected_value
search_solutions_request_mock.assert_called_once_with(
user_agent='ta3_api_test.py',
version='2019.6.11',
time_bound_search=1.,
priority=0.,
allowed_value_types=[
ValueType.Value('RAW'),
ValueType.Value('DATASET_URI'),
ValueType.Value('CSV_URI'),
],
inputs=[Value(dataset_uri='dataset-doc-path')],
problem='build-problem')
assert logger_mock.call_count == 2
def fit_solution(self, solution_id, dataset):
request = core_pb2.FitSolutionRequest(
solution_id=solution_id,
inputs=[Value(dataset_uri=self._get_dataset_doc_path(dataset))],
expose_outputs=[
'outputs.0'
# 'steps.0.produce'
],
expose_value_types=[ValueType.Value('CSV_URI')],
# users=[
# core_pb2.SolutionRunUser(
# id='dummy',
# choosen=True,
# reason='dummy'
# )
# ]
)
LOGGER.debug("%s: %s", request.__class__.__name__, request)
response = self.stub.FitSolution(request)
LOGGER.debug("%s: %s", response.__class__.__name__, response)
return response
def test_apiclient_fit_solution(fit_solution_request_mock, logger_mock):
instance = TA3APIClient(port=9999)
solution_id = 'solution-id'
dataset = 'test-dataset'
expected_response = 'response'
# mocks
fit_solution_request_mock.return_value = 'request'
instance._get_dataset_doc_path = MagicMock(return_value='dataset-doc-path')
instance.stub.FitSolution = MagicMock(return_value=expected_response)
return_value = instance.fit_solution(solution_id, dataset)
assert return_value == expected_response
assert logger_mock.call_count == 2
fit_solution_request_mock.assert_called_once_with(
solution_id=solution_id,
inputs=[Value(dataset_uri='dataset-doc-path')],
expose_outputs=['outputs.0'],
expose_value_types=[ValueType.Value('CSV_URI')])
instance.stub.FitSolution.called_once_with('request')
def search_solutions(self, dataset, time_bound_search=1.):
created_at = Timestamp()
created_at.FromDatetime(datetime.utcnow())
request = core_pb2.SearchSolutionsRequest(
user_agent='ta3_api_test.py',
version='2019.7.9',
time_bound_search=time_bound_search,
priority=0.,
allowed_value_types=[
ValueType.Value('RAW'),
ValueType.Value('DATASET_URI'),
ValueType.Value('CSV_URI'),
],
inputs=[Value(dataset_uri=self._get_dataset_doc_path(dataset))],
problem=self._build_problem(dataset)
# template=pipeline_pb2.PipelineDescription(
# id='dummy',
# source=pipeline_pb2.PipelineSource(
# name='dummy',
# contact='dummy',
# pipelines=['dummy'],
# ),
# created=created_at,
# context=pipeline_pb2.PipelineContext.Value('PIPELINE_CONTEXT_UNKNOWN'),
# name='dummy',
# description='dummy',
# users=[
# pipeline_pb2.PipelineDescriptionUser(
# id='dummy',
# reason='dummy',
# rationale='dummy'
# )
# ],
# inputs=[
# pipeline_pb2.PipelineDescriptionInput(
# name='dummy'
# )
# ],
# outputs=[
# pipeline_pb2.PipelineDescriptionOutput(
# name='dummy',
# data='dummy'
# )
# ],
# steps=[
# pipeline_pb2.PipelineDescriptionStep(
# primitive=pipeline_pb2.PrimitivePipelineDescriptionStep(
# primitive=Primitive(
# id='dummy',
# version='dummy',
# python_path='dummy',
# name='dummy',
# digest='dummy'
# ),
# arguments={
# 'dummy': pipeline_pb2.PrimitiveStepArgument(
# data=pipeline_pb2.DataArgument(
# data='dummy'
# )
# )
# },
# outputs=[
# pipeline_pb2.StepOutput(
# id='dummy'
# )
# ],
# hyperparams={
# 'dummy': pipeline_pb2.PrimitiveStepHyperparameter(
# data=pipeline_pb2.DataArgument(
# data='dummy'
# )
# )
# },
# users=[
# pipeline_pb2.PipelineDescriptionUser(
# id='dummy',
# reason='dummy',
# rationale='dummy'
# )
# ],
# )
# )
# ]
# ),
)
LOGGER.debug("%s: %s", request.__class__.__name__, request)
response = self.stub.SearchSolutions(request)
LOGGER.debug("%s: %s", response.__class__.__name__, response)
return response
