def test_model_joblib():
sklearn_model, data = _train_sample_model()
model_file = os.path.join(JOBLIB_FILE[0], JOBLIB_FILE[1])
joblib.dump(value=sklearn_model, filename=model_file)
model = SKLearnModel("model", JOBLIB_FILE[0])
model.load()
request = data[0:1].tolist()
response = model.predict({"instances": request})
assert response["predictions"] == [0]
def _run_pickle_model(model_dir, model_name):
sklearn_model, data = _train_sample_model()
model_file = os.path.join(model_dir, model_name)
pickle.dump(sklearn_model, open(model_file, 'wb'))
model = SKLearnModel("model", model_dir)
model.load()
request = data[0:1].tolist()
response = model.predict({"instances": request})
assert response["predictions"] == [0]
def test_model():
iris = datasets.load_iris()
X, y = iris.data, iris.target
sklearn_model = svm.SVC(gamma='scale')
sklearn_model.fit(X, y)
model_file = os.path.join((model_dir), JOBLIB_FILE)
joblib.dump(value=sklearn_model, filename=model_file)
server = SKLearnModel("sklearnmodel", model_dir)
server.load()
request = X[0:1].tolist()
response = server.predict(request)
assert response == [0]
def test_mixedtype_model_joblib():
model = SKLearnModel("model", MIXEDTYPE_DIR)
model.load()
request = [{
'MSZoning': 'RL',
'LotArea': 8450,
'LotShape': 'Reg',
'Utilities': 'AllPub',
'YrSold': 2008,
'Neighborhood': 'CollgCr',
'OverallQual': 7,
'YearBuilt': 2003,
'SaleType': 'WD',
'GarageArea': 548
}]
response = model.predict({"instances": request})
assert response["predictions"] == [12.202832815138274]