def test_nonunique_output():
with pytest.raises(ValueError, match="ambiguous"):
Pipenet([
{
"name": "deseasonal_residual",
"model": (transformer.NaiveSeasonalDecomposition(freq=6)),
"input": "original",
},
{
"name": "abs_residual",
"model":
transformer.CustomizedTransformer1D(transform_func=abs),
"input": "deseasonal_residual",
},
{
"name": "iqr_ad",
"model": detector.InterQuartileRangeAD(c=(None, 3)),
"input": "abs_residual",
},
{
"name": "sign_check",
"model": detector.ThresholdAD(high=0.0, low=-float("inf")),
"input": "deseasonal_residual",
},
])
def test_pipeline():
s = pd.Series(
[0, 1, 2, 3, 2, 1] * 10,
index=pd.date_range(start="2017-1-1", periods=60, freq="D"),
)
my_pipe = Pipeline([
(
"deseasonal_residual",
transformer.NaiveSeasonalDecomposition(freq=6),
),
(
"abs_residual",
transformer.CustomizedTransformer1D(transform_func=abs),
),
("iqr_ad", detector.InterQuartileRangeAD(c=(None, 3))),
])
my_pipe.fit_detect(s)
def test_transformer_pipe():
s = pd.Series(
[0, 1, 2, 3, 2, 1] * 10,
index=pd.date_range(start="2017-1-1", periods=60, freq="D"),
)
my_pipe = Pipenet([
{
"name": "deseasonal_residual",
"model": transformer.NaiveSeasonalDecomposition(freq=6),
"input": "original",
},
{
"name": "abs_residual",
"model": transformer.CustomizedTransformer1D(transform_func=abs),
"input": "deseasonal_residual",
},
])
with pytest.raises(RuntimeError, match="`fit_transform`"):
my_pipe.fit_detect(s)
my_pipe.fit_transform(s)
def test_detector_return_intermediate():
s = pd.Series(
[0, 1, 2, 3, 2, 1] * 10,
index=pd.date_range(start="2017-1-1", periods=60, freq="D"),
)
my_pipe = Pipenet([
{
"name": "deseasonal_residual",
"model": (transformer.NaiveSeasonalDecomposition(freq=6)),
"input": "original",
},
{
"name": "abs_residual",
"model": transformer.CustomizedTransformer1D(transform_func=abs),
"input": "deseasonal_residual",
},
{
"name": "iqr_ad",
"model": detector.InterQuartileRangeAD(c=(None, 3)),
"input": "abs_residual",
},
{
"name": "sign_check",
"model": detector.ThresholdAD(high=0.0, low=-float("inf")),
"input": "deseasonal_residual",
},
{
"name": "and",
"model": aggregator.AndAggregator(),
"input": ["iqr_ad", "sign_check"],
},
])
result = my_pipe.fit_detect(s, return_intermediate=True)
assert set(result.keys()) == {
"original",
"deseasonal_residual",
"abs_residual",
"iqr_ad",
"sign_check",
"and",
}
def test_skip_fit():
s = pd.Series(
[0, 1, 2, 3, 2, 1] * 10,
index=pd.date_range(start="2017-1-1", periods=60, freq="D"),
)
deseasonal_residual = transformer.NaiveSeasonalDecomposition(freq=6)
my_pipe = Pipenet([
{
"name": "deseasonal_residual",
"model": deseasonal_residual,
"input": "original",
},
{
"name": "abs_residual",
"model": transformer.CustomizedTransformer1D(transform_func=abs),
"input": "deseasonal_residual",
},
{
"name": "iqr_ad",
"model": detector.InterQuartileRangeAD(c=(None, 3)),
"input": "abs_residual",
},
{
"name": "sign_check",
"model": detector.ThresholdAD(high=0.0, low=-float("inf")),
"input": "deseasonal_residual",
},
{
"name": "and",
"model": aggregator.AndAggregator(),
"input": ["iqr_ad", "sign_check"],
},
])
with pytest.raises(RuntimeError):
my_pipe.fit_detect(s, skip_fit=["deseasonal_residual"])
my_pipe.fit_detect(s)
detector.InterQuartileRangeAD(),
detector.GeneralizedESDTestAD(),
detector.PersistAD(),
detector.LevelShiftAD(),
detector.VolatilityShiftAD(),
detector.AutoregressionAD(),
detector.SeasonalAD(freq=2),
transformer.RollingAggregate(agg="median"),
transformer.RollingAggregate(agg="quantile", agg_params={"q": 0.5}),
transformer.DoubleRollingAggregate(agg="median"),
transformer.DoubleRollingAggregate(agg="quantile",
agg_params={"q": [0.1, 0.5, 0.9]}),
transformer.DoubleRollingAggregate(agg="hist",
agg_params={"bins": [30, 50, 70]}),
transformer.StandardScale(),
transformer.NaiveSeasonalDecomposition(freq=2),
transformer.STLDecomposition(freq=2),
]
one2many_models = [
transformer.RollingAggregate(agg="quantile",
agg_params={"q": [0.1, 0.5, 0.9]}),
transformer.RollingAggregate(agg="hist", agg_params={"bins": [20, 50,
80]}),
transformer.Retrospect(n_steps=3),
]
many2one_models = [
detector.MinClusterDetector(KMeans(n_clusters=2)),
detector.OutlierDetector(
LocalOutlierFactor(n_neighbors=20, contamination=0.1)),