def test_publish_and_execute(tmpdir):
import pickle
from sasctl.utils.pymas import from_pickle
from sasctl.services import microanalytic_score as mas
sklearn = pytest.importorskip('sklearn')
from sklearn import datasets
from sklearn.linear_model import LinearRegression
pd = pytest.importorskip('pandas')
data = sklearn.datasets.load_boston()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.DataFrame(data.target, columns=['Price'])
lm = LinearRegression()
lm.fit(X, y)
pkl = pickle.dumps(lm)
p = from_pickle(pkl, 'predict', X, array_input=True)
mas.create_module('sasctl_test', source=p.score_code(), language='ds2')
x1 = {k.lower(): v for k, v in X.iloc[0, :].items()}
result = mas.execute_module_step('sasctl_test', 'score', **x1)
assert result['rc'] == 0
assert result['var1'] == 24
assert result['msg'] is None
def test_create_python_module():
with mock.patch('sasctl.services.microanalytic_score.post') as post:
with pytest.raises(ValueError):
mas.create_module() # Source code is required
with mock.patch('sasctl.services.microanalytic_score.post') as post:
source = '\n'.join(
("def testMethod(var1, var2):", " 'Output: out1, out2'",
" out1 = var1 + 5", " out2 = var2.upper()",
" return out1, out2"))
mas.create_module(source=source)
assert post.call_count == 1
json = post.call_args[1].get('json', {})
assert 'text/x-python' == json['type']
assert 'public' == json['scope']
def test_publish_python_module(caplog):
from sasctl.services.microanalytic_score import create_module
import logging
caplog.set_level(logging.DEBUG, logger='sasctl.core')
source = '\n'.join(("def myfunction(var1, var2):",
" 'Output: out1, out2'", " out1 = var1 + 5",
" out2 = var2.upper()", " return out1, out2"))
r = create_module(source=source, name='sasctl_testmethod')
assert 'sasctl_testmethod' == r.id
assert 'public' == r.scope
def test_create_python_module(self):
source = '\n'.join(("def myfunction(var1, var2):",
" 'Output: out1, out2'",
" out1 = var1 + 5",
" out2 = var2.upper()",
" return out1, out2",
"def myfunction2(var1, var2):",
" 'Output: out1'",
" return var1 + var2"
))
r = mas.create_module(source=source, name=self.MODULE_NAME)
assert self.MODULE_NAME == r.id
assert 'public' == r.scope
def test_publish_and_execute(tmpdir, boston_dataset):
import pickle
from sasctl.utils.pymas import from_pickle
from sasctl.services import microanalytic_score as mas
from sklearn.linear_model import LinearRegression
X = boston_dataset[boston_dataset.columns[:-1]]
y = boston_dataset[boston_dataset.columns[-1]]
lm = LinearRegression()
lm.fit(X, y)
pkl = pickle.dumps(lm)
p = from_pickle(pkl, 'predict', X, array_input=True)
# Generate the score code & publish as a model
code = p.score_code()
mas.create_module('sasctl_test', source=code, language='ds2')
x1 = {k.lower(): v for k, v in X.iloc[0, :].items()}
result = mas.execute_module_step('sasctl_test', 'predict', **x1)
assert round(result['var1'], 3) == 30.004
mas.delete_module('sasctl_test')