skmultiflow.drift_detection.HDDM_A

class skmultiflow.drift_detection.HDDM_A(drift_confidence=0.001, warning_confidence=0.005, two_side_option=True)

Drift Detection Method based on Hoeffding’s bounds with moving average-test.

Parameters

drift_confidencefloat (default=0.001)

Confidence to the drift

warning_confidencefloat (default=0.005)

Confidence to the warning

two_side_optionbool (default=True)

Option to monitor error increments and decrements (two-sided) or only increments (one-sided)

Notes

HDDM_A [1] is a drift detection method based on the Hoeffding’s inequality. HDDM_A uses the average as estimator. It receives as input a stream of real values and returns the estimated status of the stream: STABLE, WARNING or DRIFT.

Implementation based on MOA [2].

References

1

Frías-Blanco I, del Campo-Ávila J, Ramos-Jimenez G, et al. Online and non-parametric drift detection methods based on Hoeffding’s bounds. IEEE Transactions on Knowledge and Data Engineering, 2014, 27(3): 810-823.

2

Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer. MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604, 2010.

Examples

>>> # Imports
>>> import numpy as np
>>> from skmultiflow.drift_detection.hddm_a import HDDM_A
>>> hddm_a = HDDM_A()
>>> # Simulating a data stream as a normal distribution of 1's and 0's
>>> data_stream = np.random.randint(2, size=2000)
>>> # Changing the data concept from index 999 to 1500, simulating an
>>> # increase in error rate
>>> for i in range(999, 1500):
...     data_stream[i] = 0
>>> # Adding stream elements to HDDM_A and verifying if drift occurred
>>> for i in range(2000):
...     hddm_a.add_element(data_stream[i])
...     if hddm_a.detected_warning_zone():
...         print('Warning zone has been detected in data: ' + str(data_stream[i]) + ' - of index: ' + str(i))
...     if hddm_a.detected_change():
...         print('Change has been detected in data: ' + str(data_stream[i]) + ' - of index: ' + str(i))

Methods

add_element(self, prediction)	Add a new element to the statistics
detected_change(self)	This function returns whether concept drift was detected or not.
detected_warning_zone(self)	If the change detector supports the warning zone, this function will return whether it’s inside the warning zone or not.
get_info(self)	Collects and returns the information about the configuration of the estimator
get_length_estimation(self)	Returns the length estimation.
get_params(self[, deep])	Get parameters for this estimator.
reset(self)	Resets the change detector parameters.
set_params(self, **params)	Set the parameters of this estimator.

Attributes

estimator_type

add_element(self, prediction)

Add a new element to the statistics

Parameters

prediction: int (either 0 or 1)

This parameter indicates whether the last sample analyzed was correctly classified or not. 1 indicates an error (miss-classification).

Notes

After calling this method, to verify if change was detected or if the learner is in the warning zone, one should call the super method detected_change, which returns True if concept drift was detected and False otherwise.

detected_change(self)

This function returns whether concept drift was detected or not.

Returns

bool

Whether concept drift was detected or not.

detected_warning_zone(self)

If the change detector supports the warning zone, this function will return whether it’s inside the warning zone or not.

Returns

bool

Whether the change detector is in the warning zone or not.

get_info(self)

Collects and returns the information about the configuration of the estimator

Returns

string

Configuration of the estimator.

get_length_estimation(self)

Returns the length estimation.

Returns

int

The length estimation

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

reset(self)

Resets the change detector parameters.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns

self