def test_nonunique_output():
with pytest.raises(ValueError, match="ambiguous"):
Pipenet(
{
"deseasonal_residual": {
"model": (
transformer.ClassicSeasonalDecomposition(freq=6)
),
"input": "original",
},
"abs_residual": {
"model": transformer.CustomizedTransformer1D(
transform_func=abs
),
"input": "deseasonal_residual",
},
"iqr_ad": {
"model": detector.InterQuartileRangeAD(c=(None, 3)),
"input": "abs_residual",
},
"sign_check": {
"model": detector.ThresholdAD(high=0.0, low=-float("inf")),
"input": "deseasonal_residual",
},
}
)
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(
{
"deseasonal_residual": {
"model": transformer.ClassicSeasonalDecomposition(freq=6),
"input": "original",
},
"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(s)
my_pipe.transform(s)
my_pipe.fit_transform(s)
my_pipe = Pipeline(
[
(
"deseasonal_residual",
transformer.ClassicSeasonalDecomposition(freq=6),
),
(
"abs_residual",
transformer.CustomizedTransformer1D(transform_func=abs),
),
]
)
with pytest.raises(RuntimeError, match="`fit_transform`"):
my_pipe.fit_detect(s)
my_pipe.fit(s)
my_pipe.transform(s)
my_pipe.fit_transform(s)
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_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)
def test_pipeline():
s = pd.Series(
[0, 1, 2, 3, 2, 1] * 5 + [0, 1, 2, 4, 2, 1] + [0, 1, 2, 3, 2, 1] * 4,
index=pd.date_range(start="2017-1-1", periods=60, freq="D"),
)
my_pipe = Pipeline(
[
(
"deseasonal_residual",
transformer.ClassicSeasonalDecomposition(freq=6),
),
(
"abs_residual",
transformer.CustomizedTransformer1D(transform_func=abs),
),
("ad", detector.QuantileAD(high=0.99)),
]
)
my_pipe.fit_detect(s)
assert (
my_pipe.score(
s,
pd.Series([0] * 33 + [1] + [0] * 26, index=s.index),
scoring="recall",
)
== 1
)
assert (
my_pipe.score(
s,
pd.Series([0] * 33 + [1] + [0] * 26, index=s.index),
scoring="precision",
)
== 1
)
assert (
my_pipe.score(
s,
pd.Series([0] * 33 + [1] + [0] * 26, index=s.index),
scoring="f1",
)
== 1
)
assert (
my_pipe.score(
s,
pd.Series([0] * 33 + [1] + [0] * 26, index=s.index),
scoring="iou",
)
== 1
)
assert my_pipe.get_params() == {
"deseasonal_residual": {"freq": 6, "trend": False},
"abs_residual": {
"fit_func": None,
"fit_func_params": None,
"transform_func": abs,
"transform_func_params": None,
},
"ad": {"high": 0.99, "low": None},
}