SRPClassifier

Streaming Random Patches ensemble classifier.

The Streaming Random Patches (SRP) ¹ is an ensemble method that simulates bagging or random subspaces. The default algorithm uses both bagging and random subspaces, namely Random Patches. The default base estimator is a Hoeffding Tree, but other base estimators can be used (differently from random forest variations).

Parameters

• model (base.Estimator) – defaults to None

  The base estimator.

• n_models (int) – defaults to 10

  Number of members in the ensemble.

• subspace_size (Union[int, float, str]) – defaults to 0.6

  Number of features per subset for each classifier where M is the total number of features.
  A negative value means M - subspace_size.
  Only applies when using random subspaces or random patches.
  * If int indicates the number of features to use. Valid range [2, M].
  * If float indicates the percentage of features to use, Valid range (0., 1.].
  * 'sqrt' - sqrt(M)+1
  * 'rmsqrt' - Residual from M-(sqrt(M)+1)

• training_method (str) – defaults to patches

  The training method to use.
  * 'subspaces' - Random subspaces.
  * 'resampling' - Resampling.
  * 'patches' - Random patches.

• lam (int) – defaults to 6

  Lambda value for resampling.

• drift_detector (base.DriftDetector) – defaults to None

  Drift detector.

• warning_detector (base.DriftDetector) – defaults to None

  Warning detector.

• disable_detector (str) – defaults to off

  Option to disable drift detectors:
  * If 'off', detectors are enabled.
  * If 'drift', disables concept drift detection and the background learner.
  * If 'warning', disables the background learner and ensemble members are reset if drift is detected.
  

• disable_weighted_vote (bool) – defaults to False

  If True, disables weighted voting.

• seed (int) – defaults to None

  Random number generator seed for reproducibility.

• metric (Optional[river.metrics.base.ClassificationMetric]) – defaults to None

  The metric to track members performance within the ensemble. This implementation assumes that larger values are better when using weighted votes.

Attributes

• models

Examples

>>> from river import ensemble
>>> from river import evaluate
>>> from river import metrics
>>> from river.datasets import synth
>>> from river import tree

>>> dataset = synth.ConceptDriftStream(
...     seed=42,
...     position=500,
...     width=50
... ).take(1000)

>>> base_model = tree.HoeffdingTreeClassifier(
...     grace_period=50, delta=0.01,
...     nominal_attributes=['age', 'car', 'zipcode']
... )
>>> model = ensemble.SRPClassifier(
...     model=base_model, n_models=3, seed=42,
... )

>>> metric = metrics.Accuracy()

>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 73.27%

Methods

append

S.append(value) -- append value to the end of the sequence

Parameters

• item

clear

S.clear() -> None -- remove all items from S

copy

count

S.count(value) -> integer -- return number of occurrences of value

Parameters

• item

extend

S.extend(iterable) -- extend sequence by appending elements from the iterable

Parameters

• other

index

S.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

Parameters

• item
• args

insert

S.insert(index, value) -- insert value before index

Parameters

• i
• item

learn_one

pop

S.pop([index]) -> item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.

Parameters

• i – defaults to -1

predict_one

Predict the label of a set of features x.

Parameters

• x (dict)

Returns

typing.Union[bool, str, int, NoneType]: The predicted label.

predict_proba_one

Predict the probability of each label for a dictionary of features x.

Parameters

• x

Returns

A dictionary that associates a probability which each label.

remove

S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.

Parameters

• item

reset

reverse

S.reverse() -- reverse IN PLACE

sort

Notes

This implementation uses n_models=10 as default given the impact on processing time. The optimal number of models depends on the data and resources available.

References

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1. Heitor Murilo Gomes, Jesse Read, Albert Bifet. Streaming Random Patches for Evolving Data Stream Classification. IEEE International Conference on Data Mining (ICDM), 2019. ↩