def test_y_None():
path = os.path.join(THIS_DIR, os.pardir, 'tests/data/dummy.txt')
df = read_data(path)
X = df[['value']]
y = None
model = MockModel()
models = [model]
with pytest.raises(TypeError):
eval_models(X, y, models, label_col_name='is_anomaly')
def test_X_None():
X = None
y = None
model = MockModel()
models = [model]
try:
res = eval_models(X, y, models, label_col_name='is_anomaly')
except TypeError:
assert True
return
assert False
def test_real_model():
path = os.path.join(THIS_DIR, os.pardir, 'tests/data/dummy.txt')
df = read_data(path)
model = MovingAverageSeasonalTrendinessDetector(is_multicategory=True,
freq='12H')
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models(X, y, models, label_col_name='is_anomaly')
print(res)
def test_eval_models_all_false():
path = os.path.join(THIS_DIR, os.pardir, 'tests/data/dummy.txt')
df = read_data(path)
df['is_anomaly'] = 0
model = MockModel()
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models(X, y, models, label_col_name='is_anomaly')
assert math.isnan(res['MockModel']['f1'])
assert res['MockModel']['precision'] == 0
assert math.isnan(res['MockModel']['recall'])
def test_eval_models_half_false():
path = os.path.join(THIS_DIR, os.pardir, 'tests/data/dummy2.txt')
df = read_data(path)
df['is_anomaly'] = 0
df.iloc[-1]['is_anomaly'] = 1
df.iloc[-2]['is_anomaly'] = 1
model = MockModel()
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models(X,
y,
models,
label_col_name='is_anomaly',
window_size_for_metrics=0)
assert res['MockModel']['precision'] == 0.5
assert res['MockModel']['recall'] == 1.0