def test_bugfix_27():
"""NaN values should be converted to null before being sent to MAS
https://github.com/sassoftware/python-sasctl/issues/27
"""
import io
from sasctl.core import RestObj
from sasctl.services import microanalytic_score as mas
pd = pytest.importorskip('pandas')
df = pd.read_csv(io.StringIO('\n'.join([
u'BAD,LOAN,MORTDUE,VALUE,REASON,JOB,YOJ,DEROG,DELINQ,CLAGE,NINQ,CLNO,DEBTINC',
u'0,1.0,1100.0,25860.0,39025.0,HomeImp,Other,10.5,0.0,0.0,94.36666667,1.0,9.0,',
u'1,1.0,1300.0,70053.0,68400.0,HomeImp,Other,7.0,0.0,2.0,121.8333333,0.0,14.0,'
])))
with mock.patch('sasctl._services.microanalytic_score.MicroAnalyticScore.get_module') as get_module:
get_module.return_value = RestObj({
'name': 'Mock Module',
'id': 'mockmodule'
})
with mock.patch('sasctl._services.microanalytic_score.MicroAnalyticScore.post') as post:
x = df.iloc[0, :]
mas.execute_module_step('module', 'step', **x)
# Ensure we're passing NaN to execute_module_step
assert pd.isna(x['DEBTINC'])
# Make sure the value has been converted to None before being serialized to JSON.
# This ensures that the JSON value will be null.
json = post.call_args[1]['json']
inputs = json['inputs']
debtinc = [i for i in inputs if i['name'] == 'DEBTINC'].pop()
assert debtinc['value'] is None
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_module_execute(self, request, iris_dataset):
# Store module name so we can retrieve it in later tests
module_name = request.config.cache.get('CAS_MODULE_NAME', None)
x = iris_dataset.drop('Species', axis=1).iloc[0, :]
response = microanalytic_score.execute_module_step(
module_name, 'score', **x.to_dict())
assert all(k in response for k in ('P_Species0', 'P_Species1',
'P_Species2', 'I_Species'))
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_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')
def test_module_execute(self):
response = microanalytic_score.execute_module_step(self.module_name,
'score',
Income=1e4,
Credit_Limit=4000)
print('done')
# encoding: utf-8
#
# Copyright © 2019, SAS Institute Inc., Cary, NC, USA. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import swat
from sasctl import Session
from sasctl.tasks import register_model, publish_model
from sasctl.services import microanalytic_score as mas
s = swat.CAS('hostname', 'username', 'password')
s.loadactionset('decisionTree')
tbl = s.CASTable('iris')
tbl.decisiontree.gbtreetrain(target='Species',
inputs=['SepalLength', 'SepalWidth',
'PetalLength', 'PetalWidth'],
savestate='gradboost_astore')
astore = s.CASTable('gradboost_astore')
with Session(s):
model = register_model(astore, 'Gradient Boosting', 'Iris')
module = publish_model(model, 'maslocal')
response = mas.execute_module_step(module, 'score',
SepalLength=5.1,
SepalWidth=3.5,
PetalLength=1.4,
PetalWidth=0.2)