find anomalies¶

path: orion.primitives.timeseries_anomalies.find_anomalies

description: this primitive extracts anomalies from sequences of errors following the approach explained in the related paper.

see json.

argument	type	description
parameters
`errors`	`numpy.ndarray`	array of errors
`index`	`numpy.ndarray`	array of indices of errors
hyperparameters
`z_range`	`tuple`	list of two values denoting the range out of which the start points for the `scipy.fmin` function are chosen
`window_size`	`int`	size of the window for which a threshold is calculated
`window_step_size`	`int`	number of steps the window is moved before another threshold is calculated for the new window
`lower_threshold`	`bool`	indicates whether to apply a lower threshold to find unusually low errors
`fixes_threshold`	`bool`	indicates whether to use fixed or dynamic thresholding
`min_percent`	`float`	percentage of separation the anomalies need to meet between themselves and the highest non-anomalous error in the window sequence
`anomaly_padding`	`int`	number of errors before and after a found anomaly that are added to the anomalous sequence
output
`y`	`numpy.ndarray`	array containing start-index, end-index, score for each anomalous sequence that was found

In [1]: import numpy as np

In [2]: from mlstars import load_primitive

In [3]: primitive = load_primitive('orion.primitives.timeseries_anomalies.find_anomalies',
   ...:     arguments={"anomaly_padding": 1})
   ...: 

In [4]: errors = np.array([0.01] * 45 + [1] * 10 + [0.01] * 45)

In [5]: index = np.array(range(100))

In [6]: primitive.produce(errors=errors, index=index)
Out[6]: array([[44.        , 55.        ,  2.19458128]])

regression errors Pipelines