AdaptiveRandomForestClassifier

Adaptive Random Forest classifier.

The 3 most important aspects of Adaptive Random Forest ¹ are:

1. inducing diversity through re-sampling

2. inducing diversity through randomly selecting subsets of features for node splits

3. drift detectors per base tree, which cause selective resets in response to drifts

It also allows training background trees, which start training if a warning is detected and replace the active tree if the warning escalates to a drift.

Parameters

• n_models (int) – defaults to 10

  Number of trees in the ensemble.

• max_features (Union[bool, str, int]) – defaults to sqrt

  Max number of attributes for each node split.
  - If int, then consider max_features at each split.
  - If float, then max_features is a percentage and int(max_features * n_features) features are considered per split.
  - If "sqrt", then max_features=sqrt(n_features).
  - If "log2", then max_features=log2(n_features).
  - If None, then max_features=n_features.

• lambda_value (int) – defaults to 6

  The lambda value for bagging (lambda=6 corresponds to Leveraging Bagging).

• metric (river.metrics.base.MultiClassMetric) – defaults to Accuracy: 0.00%

  Metric used to track trees performance within the ensemble.

• disable_weighted_vote – defaults to False

  If True, disables the weighted vote prediction.

• drift_detector (Union[base.DriftDetector, NoneType]) – defaults to ADWIN

  Drift Detection method. Set to None to disable Drift detection.

• warning_detector (Union[base.DriftDetector, NoneType]) – defaults to ADWIN

  Warning Detection method. Set to None to disable warning detection.

• grace_period (int) – defaults to 50

  [Tree parameter] Number of instances a leaf should observe between split attempts.

• max_depth (int) – defaults to None

  [Tree parameter] The maximum depth a tree can reach. If None, the tree will grow indefinitely.

• split_criterion (str) – defaults to info_gain

  [Tree parameter] Split criterion to use.
  - 'gini' - Gini
  - 'info_gain' - Information Gain
  - 'hellinger' - Hellinger Distance

• split_confidence (float) – defaults to 0.01

  [Tree parameter] Allowed error in split decision, a value closer to 0 takes longer to decide.

• tie_threshold (float) – defaults to 0.05

  [Tree parameter] Threshold below which a split will be forced to break ties.

• leaf_prediction (str) – defaults to nba

  [Tree parameter] Prediction mechanism used at leafs.
  - 'mc' - Majority Class
  - 'nb' - Naive Bayes
  - 'nba' - Naive Bayes Adaptive

• nb_threshold (int) – defaults to 0

  [Tree parameter] Number of instances a leaf should observe before allowing Naive Bayes.

• nominal_attributes (list) – defaults to None

  [Tree parameter] List of Nominal attributes. If empty, then assume that all attributes are numerical.

• splitter (river.tree.splitter.base_splitter.Splitter) – defaults to None

  [Tree parameter] The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the tree.splitter module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property is_target_class. This is an advanced option. Special care must be taken when choosing different splitters. By default, tree.splitter.GaussianSplitter is used if splitter is None.

• max_size (int) – defaults to 32

  [Tree parameter] Maximum memory (MB) consumed by the tree.

• memory_estimate_period (int) – defaults to 2000000

  [Tree parameter] Number of instances between memory consumption checks.

• seed (int) – defaults to None

  If int, seed is used to seed the random number generator; If RandomState, seed is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

• kwargs

  Other parameters passed to tree.HoeffdingTree. Check the tree module documentation for more information.

Examples

>>> from river import synth
>>> from river import ensemble
>>> from river import evaluate
>>> from river import metrics

>>> dataset = synth.ConceptDriftStream(seed=42, position=500,
...                                    width=40).take(1000)

>>> model = ensemble.AdaptiveRandomForestClassifier(
...     n_models=3,
...     seed=42
... )

>>> metric = metrics.Accuracy()

>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 70.47%

Methods

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.

learn_one

predict_many

Predict the labels of a DataFrame X.

Parameters

• X (pandas.core.frame.DataFrame)

Returns

Series: Series of predicted labels.

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_many

Predict the labels of a DataFrame X.

Parameters

• X (pandas.core.frame.DataFrame)

Returns

DataFrame: DataFrame that associate probabilities which each label as columns.

predict_proba_one

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

Parameters

• x (dict)

Returns

typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.

reset

Reset the forest.

References

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1. Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabricio Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem. Adaptive random forests for evolving data stream classification. In Machine Learning, DOI: 10.1007/s10994-017-5642-8, Springer, 2017. ↩