def test_client():
testing_client = app.test_client()
initialize_app(testing_client)
testing_client.testing = True
# This code generates a h2o model to be used for running predictions
# against the iris dataset
h2o.init()
training_frame = h2o.import_file(os.path.join(model_cache_dir, 'iris.csv'))
estimator = H2OGradientBoostingEstimator()
estimator.train(
training_frame=training_frame,
x=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
y='species')
model_file = estimator.download_mojo(path=model_cache_dir,
get_genmodel_jar=True,
genmodel_name='h2o-genmodel.jar')
model_dir = os.path.join(model_cache_dir, '1_0')
with zipfile.ZipFile(os.path.join(model_dir, 'h2o'), 'w') as zip:
zip.write(model_file, os.path.basename(model_file))
zip.write(os.path.join(model_cache_dir, 'h2o-genmodel.jar'),
'h2o-genmodel.jar')
return testing_client
import sys
from os.path import dirname, realpath, join
from flask import Flask
from microservice_flask import initialize_app
from unittest.mock import patch
parentddir = dirname(dirname(dirname(realpath(__file__))))
sys.path.append(join(parentddir, 'acumosintegrationservice'))
BASE_URL = 'http://127.0.0.1:5000/v2/'
app = Flask(__name__)
initialize_app(app)
'''
@pytest.fixture(scope='session')
def test_client():
testing_client = app.test_client()
initialize_app(testing_client)
testing_client.testing = True
return testing_client
'''
@patch('acumosintegrationservice.api.acumos_e5_api.AcumosE5API.get_model_list')
def test_get_solutions(get_model_list):
get_model_list.return_value = {
'solutions': [