def test_get_model_contents(self):
# Resolves https://github.com/sassoftware/python-sasctl/issues/33
content = mr.get_model_contents(self.MODEL_NAME)
assert isinstance(content, list)
assert 'AstoreMetadata.json' in [str(c) for c in content]
assert 'ModelProperties.json' in [str(c) for c in content]
assert 'inputVar.json' in [str(c) for c in content]
assert 'outputVar.json' in [str(c) for c in content]
def test_register_model(self, sklearn_linear_model):
from sasctl.tasks import register_model
from sasctl import RestObj
sk_model, X, _ = sklearn_linear_model
# Register model and ensure attributes are set correctly
model = register_model(sk_model,
self.MODEL_NAME,
project=self.PROJECT_NAME,
input=X,
force=True)
assert isinstance(model, RestObj)
assert self.MODEL_NAME == model.name
assert 'prediction' == model.function.lower()
assert 'Linear regression' == model.algorithm
assert 'Python' == model.trainCodeType
assert 'ds2MultiType' == model.scoreCodeType
assert len(model.inputVariables) == 13
assert len(model.outputVariables) == 1
# Don't compare to sys.version since cassettes used may have been
# created by a different version
assert re.match(r'Python \d\.\d', model.tool)
# Ensure input & output metadata was set
for col in X.columns:
assert 1 == len([
v for v in model.inputVariables + model.outputVariables
if v.get('name') == col
])
# Ensure model files were created
from sasctl.services import model_repository as mr
files = mr.get_model_contents(model)
filenames = [f.name for f in files]
assert 'model.pkl' in filenames
assert 'dmcas_epscorecode.sas' in filenames
assert 'dmcas_packagescorecode.sas' in filenames
def test_register_sklearn(self, sklearn_model):
from sasctl.tasks import register_model
from sasctl import RestObj
sk_model, train_df = sklearn_model
# Register model and ensure attributes are set correctly
model = register_model(sk_model,
SCIKIT_MODEL_NAME,
project=PROJECT_NAME,
input=train_df,
force=True)
assert isinstance(model, RestObj)
assert SCIKIT_MODEL_NAME == model.name
assert 'Classification' == model.function
assert 'Logistic regression' == model.algorithm
assert 'Python' == model.trainCodeType
assert 'ds2MultiType' == model.scoreCodeType
# Don't compare to sys.version since cassettes used may have been created by a different version
assert re.match('Python \d\.\d', model.tool)
# Ensure input & output metadata was set
for col in train_df.columns:
assert 1 == len([
v for v in model.inputVariables + model.outputVariables
if v.get('name') == col
])
# Ensure model files were created
from sasctl.services.model_repository import get_model_contents
files = get_model_contents(model)
filenames = [f.name for f in files]
assert 'model.pkl' in filenames
assert 'dmcas_espscorecode.sas' in filenames
assert 'dmcas_packagescorecode.sas' in filenames
project= project,
force=True,
version = 'latest')
path = Path.cwd()
##############################
######## adding files ########
#3THIS SESSION IS BUGGED, CANNOT REPLACE FILE
filenames = {'file':['training_code.py'],
'role':['train']}
#### uploading files
model_repository.get_model_contents(modelname)
for i in range(len(filenames['file'])):
with open(path / filenames['file'][i], "rb") as _file:
model_repository.add_model_content(
model = modelname,
file = _file,
name = filenames['file'][i],
role= filenames['role'][i],
)
print('uploaded ' + filenames['file'][i] + ' as ' + filenames['role'][i])
_file.close()
#### Publish model