regression errors

path orion.primitives.timeseries_errors.regression_errors

description this primitive computes an array of absolute errors comparing predictions and expected output. Optionally smooth them using EWMA.

see json.

argument	type	description
parameters
y	numpy.ndarray	ground truth
y_hat	numpy.ndarray	predicted values
hyperparameters
smooth	bool	indicates whether the returned errors should be smoothed with EWMA
smoothing_window	float	size of the smoothing window, expressed as a proportion of the total
mask	bool	indicates whether the returned errors should be masked with the minimum error value
masking_window	float	size of the masking window, expressed as a proportion of the total
output
errors	numpy.ndarray	array of errors

In [1]: import numpy as np

In [2]: from mlstars import load_primitive

In [3]: primitive = load_primitive('orion.primitives.timeseries_errors.regression_errors')

In [4]: y = np.array([[1]] * 100)

In [5]: y_hat = np.array([[.99]] * 100)

In [6]: errors = primitive.produce(y=y, y_hat=y_hat)

In [7]: print("average error value: {:.2f}".format(errors.mean()))
average error value: 0.01

reconstuction errors find anomalies