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 (Union[river.metrics.base.ClassificationMetric, NoneType]) – 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 import synth
>>> from river import tree

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

>>> base_model = tree.HoeffdingTreeClassifier(
...     grace_period=50, split_confidence=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

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

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]: 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. ↩