def test_read_data():
path = os.path.join(THIS_DIR, os.pardir, 'test/dummy.txt')
df = read_data(path)
index = df.index
# assert index is pd.MultiIndex
assert len(df) == 16
assert isinstance(df.index, pd.MultiIndex)
def test_y_None():
path = os.path.join(THIS_DIR, os.pardir, 'test/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_real_model():
path = os.path.join(THIS_DIR, os.pardir, 'test/dummy.txt')
df = read_data(path)
model = StlTrendinessDetector(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_n_splits_big():
path = os.path.join(THIS_DIR, os.pardir, 'test/dummy2.txt')
df = read_data(path)
model = MockModel()
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models_CV(X, y, models, n_splits=40000,verbose=True ,label_col_name='is_anomaly')
assert res['MockModel']['f1'] == 1.0
assert res['MockModel']['precision'] == 1.0
assert res['MockModel']['recall'] == 1.0
def test_eval_models_all_false():
path = os.path.join(THIS_DIR, os.pardir, 'test/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, 'test/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