def test_X_None():
X = None
y = None
from tests.mock_model import MockModel
model = MockModel()
models = [model]
with pytest.raises(TypeError):
eval_models(X, y, models)
def test_y_None():
path = os.path.join(THIS_DIR, os.pardir, 'tests/dummy.txt')
df = read_data(path)
X = df[['value']]
y = None
from tests.mock_model import MockModel
model = MockModel()
models = [model]
with pytest.raises(TypeError):
eval_models(X, y, models)
def test_real_model():
path = os.path.join(THIS_DIR, os.pardir, 'tests/dummy.txt')
df = read_data(path)
model = LatestWindowAnomalyDetector(alpha=0.05)
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models(X, y, models)
print(res)
def test_eval_models_all_false():
path = os.path.join(THIS_DIR, os.pardir, 'tests/dummy.txt')
df = read_data(path)
df['is_anomaly'] = 0
from tests.mock_model import MockModel
model = MockModel(prediction=0)
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models(X, y, models)
assert math.isnan(res['MockModel(prediction=0)']['f1'])
assert math.isnan(res['MockModel(prediction=0)']['precision'])
assert math.isnan(res['MockModel(prediction=0)']['recall'])
def test_n_splits_big():
path = os.path.join(THIS_DIR, os.pardir, 'tests/dummy2.txt')
df = read_data(path)
from tests.mock_model import MockModel
model = MockModel()
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models(X, y, models, n_splits=40000, verbose=True)
assert res['MockModel(prediction=1)']['f1'] == 1.0
assert res['MockModel(prediction=1)']['precision'] == 1.0
assert res['MockModel(prediction=1)']['recall'] == 1.0
def test_eval_models_half_false():
path = os.path.join(THIS_DIR, os.pardir, 'tests/dummy2.txt')
df = read_data(path)
df['is_anomaly'] = 0
df.iloc[-1]['is_anomaly'] = 1
df.iloc[-2]['is_anomaly'] = 1
from tests.mock_model import MockModel
model = MockModel()
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models(X, y, models)
assert res['MockModel(prediction=1)']['precision'] == 0.5
assert res['MockModel(prediction=1)']['recall'] == 1.0
def test_eval_models_all_true():
path = os.path.join(THIS_DIR, os.pardir, 'tests/dummy2.txt')
df = read_data(path)
from tests.mock_model import MockModel
model = MockModel()
models = [model]
X = df[['value']]
y = df[['is_anomaly']]
res = eval_models(X, y, models)
print(res)
assert res['MockModel(prediction=1)']['f1'] == 1.0
assert res['MockModel(prediction=1)']['precision'] == 1.0
assert res['MockModel(prediction=1)']['recall'] == 1.0