def test_model_drift():
"""
catch model drift. meant to show when test fails and returns error msg, but could be set to break instead
:return: error message, or success message
"""
error_msg = 'Successfully ran model drift-volume anomaly tests.'
target_feature = conf_object.project_conf["target_feature"]
# fake data of the "previous" data
data_previous_dummy = preprocess_data()
# fake data of the incoming "new" data that has drifted
data_drifted_dummy = deepcopy(data_previous_dummy)
data_drifted_dummy[target_feature] = np.zeros(len(data_previous_dummy))
mu1 = data_previous_dummy[target_feature].mean()
mu2 = data_drifted_dummy[target_feature].mean()
std = data_previous_dummy[target_feature].std()
drift_threshold = std * 3
try:
assert abs(mu1 - mu2) < drift_threshold, "Model drift detected!"
except Exception as ex:
error_msg = "This is supposed to break since dummy data mean is 0 BTC. " \
"Returning error message for testing purposes."
return error_msg
def process_request(observation: dict):
"""
format request from API into form taken by model
:param observation: request from API
:return: formatted request
"""
observation = pd.DataFrame(observation["bitcoin_last_minute"], index=[0])
observation = preprocess_data(observation)
return observation
def test_preprocessing_train():
"""
make sure preprocessing and training steps work
:return:
"""
data = preprocess_data()
data = {
'bitcoin_last_minute': data.to_dict()
} # convert to "API" request format
assert data is not None, "Error in preprocess_data()."
mae = train_model(request_dict=data)
assert mae is not None, "Error in train_model()."
def test_post(self, mock_post):
self.data = preprocess_data()
self.data = {
'bitcoin_last_minute': self.data.to_dict()
} # convert to "API" request format
info = self.data
url = 'http://0.0.0.0:5000/train'
resp = requests.post(url,
data=json.dumps(info),
headers={'Content-Type': 'application/json'})
mock_post.assert_called_with(
url,
data=json.dumps(info),
headers={'Content-Type': 'application/json'})
def __init__(self):
try:
self.data = pd.read_pickle(
os.path.join(os.path.dirname(__file__), "data.pkl"))
except Exception as ex:
logging.info(
"Couldn't load data.pkl, so preprocessing data to generate.")
self.data = preprocess_data()
self.project_conf = conf_object.project_conf
self.model = None
self.target_feature = self.project_conf["target_feature"]
self.features = list(self.data.columns)
self.features.remove(self.target_feature)
self.actuals = self.data[self.target_feature].values
def setUp(self) -> None:
self.data = preprocess_data()
self.data = {
'bitcoin_last_minute': self.data.to_dict()
} # convert to "API" request format
from data.data_preprocessing import preprocess_data
from data.data_lookback import lookback
from conf.conf_loader import conf_object
import logging
data = preprocess_data()
data = lookback(dataset=data,
features=conf_object.project_conf["lookback_features"],
timesteps=conf_object.project_conf["time_window"])
data.to_pickle('data.pkl')
logging.info("Finished preprocessing.")