AnomalySine¶
Simulate a stream with anomalies in sine waves
The data generated corresponds to sine (attribute 1
) and cosine (attribute 2
) functions. Anomalies are induced by replacing values from attribute 2
with values from a sine function different to the one used in attribute 1
. The contextual
flag can be used to introduce contextual anomalies which are values in the normal global range, but abnormal compared to the seasonal pattern. Contextual attributes are introduced by replacing values in attribute 2
with values from attribute 1
.
Parameters¶
-
n_samples (int) – defaults to
10000
Number of samples
-
n_anomalies (int) – defaults to
2500
Number of anomalies. Can't be larger than
n_samples
. -
contextual (bool) – defaults to
False
If True, will add contextual anomalies
-
n_contextual (int) – defaults to
2500
Number of contextual anomalies. Can't be larger than
n_samples
. -
shift (int) – defaults to
4
Shift in number of samples applied when retrieving contextual anomalies
-
noise (float) – defaults to
0.5
Amount of noise
-
replace (bool) – defaults to
True
If True, anomalies are randomly sampled with replacement
-
seed (int) – defaults to
None
If int,
seed
is used to seed the random number generator; If RandomState instance,seed
is the random number generator; If None, the random number generator is theRandomState
instance used bynp.random
.
Attributes¶
-
desc
Return the description from the docstring.
Examples¶
>>> from river import synth
>>> dataset = synth.AnomalySine(seed=12345,
... n_samples=100,
... n_anomalies=25,
... contextual=True,
... n_contextual=10)
>>> for x, y in dataset.take(5):
... print(x, y)
{'sine': -0.1023, 'cosine': 0.2171} 0.0
{'sine': 0.4868, 'cosine': 0.6876} 0.0
{'sine': 0.2197, 'cosine': 0.8612} 0.0
{'sine': 0.4037, 'cosine': 0.2671} 0.0
{'sine': 1.8243, 'cosine': 1.8268} 1.0
Methods¶
take
Iterate over the k samples.
Parameters
- k (int)