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

  Type → base.Estimator | None

  Default → None

  The base estimator.

• n_models

  Type → int

  Default → 10

  Number of members in the ensemble.

• subspace_size

  Type → int | float | str

  Default → 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

  Type → str

  Default → patches

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

• lam

  Type → int

  Default → 6

  Lambda value for resampling.

• drift_detector

  Type → base.DriftDetector | None

  Default → None

  Drift detector.

• warning_detector

  Type → base.DriftDetector | None

  Default → None

  Warning detector.

• disable_detector

  Type → str

  Default → 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

  Type → bool

  Default → False

  If True, disables weighted voting.

• seed

  Type → int | None

  Default → None

  Random number generator seed for reproducibility.

• metric

  Type → ClassificationMetric | None

  Default → 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: 72.17%

Methods

learn_one

predict_one

Predict the label of a set of features x.

Parameters

• x — 'dict'
• kwargs

Returns

base.typing.ClfTarget | None: The predicted label.

predict_proba_one

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

Parameters

• x
• kwargs

Returns

A dictionary that associates a probability which each label.

reset

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.

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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. ↩