def test_create_performance_definition(self):
from sasctl.services import model_repository as mr
from sasctl.services import model_management as mm
project = mr.get_project(self.PROJECT_NAME)
# Update project properties
project['function'] = 'prediction'
project['targetLevel'] = 'interval'
project['targetVariable'] = 'Price'
project['predictionVariable'] = 'var1'
project = mr.update_project(project)
mm.create_performance_definition(self.MODEL_NAME, 'Public', 'boston')
def test_table_prefix_format():
with pytest.raises(ValueError):
# Underscores should not be allowed
_ = mm.create_performance_definition('model', 'TestLibrary',
'invalid_name')
def test_create_performance_definition():
import copy
from sasctl import current_session
PROJECT = RestObj({'name': 'Test Project', 'id': '98765'})
MODEL = RestObj({
'name': 'Test Model',
'id': '12345',
'projectId': PROJECT['id']
})
USER = '******'
with mock.patch('sasctl.core.requests.Session.request'):
current_session('example.com', USER, 'password')
with mock.patch('sasctl._services.model_repository.ModelRepository'
'.get_model') as get_model:
with mock.patch('sasctl._services.model_repository.ModelRepository'
'.get_project') as get_project:
with mock.patch('sasctl._services.model_management.ModelManagement'
'.post') as post:
get_model.return_value = MODEL
with pytest.raises(ValueError):
# Project missing all required properties
get_project.return_value = copy.deepcopy(PROJECT)
_ = mm.create_performance_definition(
'model', 'TestLibrary', 'TestData')
with pytest.raises(ValueError):
# Project missing some required properties
get_project.return_value = copy.deepcopy(PROJECT)
get_project.return_value['targetVariable'] = 'target'
_ = mm.create_performance_definition(
'model', 'TestLibrary', 'TestData')
with pytest.raises(ValueError):
# Project missing some required properties
get_project.return_value = copy.deepcopy(PROJECT)
get_project.return_value['targetLevel'] = 'interval'
_ = mm.create_performance_definition(
'model', 'TestLibrary', 'TestData')
with pytest.raises(ValueError):
# Project missing some required properties
get_project.return_value = copy.deepcopy(PROJECT)
get_project.return_value[
'predictionVariable'] = 'predicted'
_ = mm.create_performance_definition(
'model', 'TestLibrary', 'TestData')
get_project.return_value = copy.deepcopy(PROJECT)
get_project.return_value['targetVariable'] = 'target'
get_project.return_value['targetLevel'] = 'interval'
get_project.return_value['predictionVariable'] = 'predicted'
_ = mm.create_performance_definition('model',
'TestLibrary',
'TestData',
max_bins=3,
monitor_challenger=True,
monitor_champion=True)
assert post.call_count == 1
url, data = post.call_args
assert PROJECT['id'] == data['json']['projectId']
assert MODEL['id'] in data['json']['modelIds']
assert 'TestLibrary' == data['json']['dataLibrary']
assert 'TestData' == data['json']['dataPrefix']
assert 'cas-shared-default' == data['json']['casServerId']
assert data['json']['name'] is not None
assert data['json']['description'] is not None
assert data['json']['maxBins'] == 3
assert data['json']['championMonitored'] == True
assert data['json']['challengerMonitored'] == True
def test_table_prefix_format():
with pytest.raises(ValueError):
# Underscores should not be allowed
_ = mm.create_performance_definition('model', 'TestLibrary',
'invalid_name')
lm,
model_name,
input=X_train, # Use X to determine model inputs
project=project, # Register in "Iris" project
force=True) # Create project if it doesn't exist
# Update project properties. Target variable must be set before performance
# definitions can be created.
project = mr.get_project(project)
project['targetVariable'] = 'Price'
project = mr.update_project(project)
# Instruct the project to look for tables in the "Public" CAS library with
# names starting with "boston_" and use these tables to track model
# performance over time.
mm.create_performance_definition(model_name, 'Public', 'boston')
# Publish the model to the real-time scoring engine
module_lm = publish_model(model_name, 'maslocal')
# Select the first row of testing data
x = X_test.iloc[0, :]
# Call the published module and score the record
result = module_lm.score(**x)
print(result)
# Build a second model
dt = DecisionTreeRegressor()
dt.fit(X_train, y_train)