SRPRegressor

Streaming Random Patches ensemble regressor.

The Streaming Random Patches ¹ ensemble method for regression trains each base learner on a subset of features and instances from the original data, namely a random patch. This strategy to enforce diverse base models is similar to the one in the random forest, yet it is not restricted to using decision trees as base learner.

This method is an adaptation of ² for regression.

Parameters

• model

  Type → base.Regressor | 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 bagging.

• 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 → True

  If True, disables weighted voting.

• drift_detection_criteria

  Type → str

  Default → error

  The criteria used to track drifts.
  * 'error' - absolute error.
  * 'prediction' - predicted target values.

• aggregation_method

  Type → str

  Default → mean

  The method to use to aggregate predictions in the ensemble.
  * 'mean'
  * 'median'

• seed

  Default → None

  Random number generator seed for reproducibility.

• metric

  Type → RegressionMetric | None

  Default → None

  The metric to track members performance within the ensemble.

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.FriedmanDrift(
    drift_type='gsg',
    position=(350, 750),
    transition_window=200,
    seed=42
).take(1000)

base_model = tree.HoeffdingTreeRegressor(grace_period=50)
model = ensemble.SRPRegressor(
    model=base_model,
    training_method="patches",
    n_models=3,
    seed=42
)

metric = metrics.R2()

evaluate.progressive_val_score(dataset, model, metric)

R2: 0.571117

Methods

learn_one

predict_one

Predict the output of features x.

Parameters

• x
• kwargs

Returns

The prediction.

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.

---

1. Heitor Gomes, Jacob Montiel, Saulo Martiello Mastelini, Bernhard Pfahringer, and Albert Bifet. On Ensemble Techniques for Data Stream Regression. IJCNN'20. International Joint Conference on Neural Networks. 2020. ↩

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