def search_solutions_request(test_paths, specified_template=None):
user_agent = "test_agent"
version = core_pb2.DESCRIPTOR.GetOptions().Extensions[
core_pb2.protocol_version]
time_bound = 0.5
priority = 10
# allowed_value_types = [value_pb2.ValueType.Value(value) for value in ALLOWED_VALUE_TYPES]
problem_description = utils.encode_problem_description(
problem_module.Problem.load(test_paths['TRAIN']['problem']))
template = None
if specified_template == 'FULL':
with d3m_utils.silence():
pipeline = pipeline_utils.load_pipeline(
FULL_SPECIFIED_PIPELINE_PATH)
template = utils.encode_pipeline_description(
pipeline, ALLOWED_VALUE_TYPES, constants.Path.TEMP_STORAGE_ROOT)
elif specified_template == 'PRE': # PRE for PREPROCESSING
pipeline = runtime_module.get_pipeline(PRE_SPECIFIED_PIPELINE_PATH,
load_all_primitives=False)
template = utils.encode_pipeline_description(
pipeline, ALLOWED_VALUE_TYPES, constants.Path.TEMP_STORAGE_ROOT)
inputs = [value_pb2.Value(dataset_uri=test_paths['TRAIN']['dataset'])]
request = core_pb2.SearchSolutionsRequest(
user_agent=user_agent,
version=version,
time_bound_search=time_bound,
priority=priority,
allowed_value_types=ALLOWED_VALUE_TYPES,
problem=problem_description,
template=template,
inputs=inputs)
return request
def load_default_pipeline():
from axolotl.utils import pipeline as pipeline_utils
pipeline = pipeline_utils.load_pipeline(DEFAULT_PIPELINE_DIR)
return pipeline
def get_classification_pipeline():
with open(schemas_utils.PIPELINES_DB_DIR) as file:
default_pipelines = json.load(file)
return pipeline_utils.load_pipeline(default_pipelines['CLASSIFICATION'][0])
def test_fit_lr(self):
pipeline_info = os.path.join(os.path.dirname(__file__), 'resources', 'logistic_regeression.json')
pipeline = pipeline_utils.load_pipeline(pipeline_info)
_, pipeline_result = self.tuner_base.search_fit(input_data=[self.dataset], time_limit=60,
pipeline_candidates=[pipeline])
self.assertEqual(pipeline_result.error, None)