def test_score_sklearn(self):
from sasctl.services import microanalytic_score as mas
m = mas.get_module(SCIKIT_MODEL_NAME.replace(' ', ''))
m = mas.define_steps(m)
r = m.predict(sepalwidth=1, sepallength=2, petallength=3, petalwidth=4)
assert r == 'virginica'
def test_score_mas(self, iris_dataset, request):
from sasctl.services import microanalytic_score as mas
module_name = request.config.cache.get('MAS_MODULE_NAME', None)
assert module_name is not None
# Retrieve the module from MAS
module = mas.get_module(module_name)
assert module.name == module_name
# Create Python methods for the model steps
module = mas.define_steps(module)
# Call .predict()
x = iris_dataset.iloc[0, :]
assert hasattr(module, 'predict')
result = module.predict(x)
assert isinstance(result, six.string_types)
assert result in ('setosa', 'virginica', 'versicolor')
# Call .predict_proba()
assert hasattr(module, 'predict_proba')
probs = module.predict_proba(x)
assert len(probs) == 3
assert all(isinstance(p, float) for p in probs)
assert round(sum(probs), 5) == 1.0
def test_score_sklearn(self):
from sasctl.services import microanalytic_score as mas
m = mas.get_module(SCIKIT_MODEL_NAME.replace(' ', ''))
m = mas.define_steps(m)
r = m.score(sepalwidth=1, sepallength=2, petallength=3, petalwidth=4)
assert isinstance(r, tuple)
pytest.xfail('PyMAS integration not yet working.')
assert r['rc'] is None
def test_score_mas(self, boston_dataset, request):
from sasctl.services import microanalytic_score as mas
module_name = request.config.cache.get('MAS_MODULE_NAME', None)
assert module_name is not None
# Retrieve the module from MAS
module = mas.get_module(module_name)
assert module.name == module_name
# Create Python methods for the model steps
module = mas.define_steps(module)
assert hasattr(module, 'score')
result = module.score(boston_dataset.iloc[0, :])
assert round(result, 4) == 30.0038
def run_inference_for_base64(base64img):
host = os.getenv('OPENMM_HOST', '')
user = os.getenv('OPENMM_USER', '')
password = os.getenv('OPENMM_PASSWORD', '')
model = os.getenv('OPENMM_MODEL', '')
try:
img = np.array(
Image.open(BytesIO(base64.decodebytes(base64img.encode()))))
with Session(host, user, password, verify_ssl=False, protocol='http'):
mod = mas.get_module(model)
response = mas.execute_module_step(mod,
'score',
image_names='test_image',
image_strings=base64img)
return transform_openmm_detections(response, img=img)
except:
e = sys.exc_info()[0]
print(e)
def test_score_mas(self, boston_dataset, request):
from sasctl.services import microanalytic_score as mas
module_name = request.config.cache.get('MAS_MODULE_NAME', None)
assert module_name is not None
# Retrieve the module from MAS
module = mas.get_module(module_name)
assert module.name == module_name
# Create Python methods for the model steps
module = mas.define_steps(module)
assert hasattr(module, 'predict')
result = module.predict(boston_dataset.iloc[0, :])
# Don't think order of rows is guaranteed.
assert isinstance(result, float)
assert result > 1
project = 'iris_os' user = '******' password = '******' #astore_table = 'gb_astore' #astore_caslib = 'public' ################################### ####### Getting astore table ###### s = Session(host, user, password, verify_ssl=False) module = mas.get_module(modelname) module = mas.define_steps(module) steps = mas.list_module_steps(module) steps[0]['id'] steps[0]['links'] print(help(module.predict)) res = module.predict(5.0, 2.0, 3.5, 1.0) res2 = module.predict_proba(5.0, 2.0, 3.5, 1.0) print(res, res2) """
def test_delete_module(self):
assert mas.get_module(self.MODULE_NAME) is not None
mas.delete_module(self.MODULE_NAME)
assert mas.get_module(self.MODULE_NAME) is None
def test_get_module(self):
module = mas.get_module(self.MODULE_NAME)
assert module.id == self.MODULE_NAME
