ARFClassifier

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 None

  Metric used to track trees performance within the ensemble. Defaults to metrics.Accuracy().

• disable_weighted_vote – defaults to False

  If True, disables the weighted vote prediction.

• drift_detector (Optional[base.DriftDetector]) – defaults to None

  Drift Detection method. Set to None to disable Drift detection. Defaults to drift.ADWIN(delta=0.001).

• warning_detector (Optional[base.DriftDetector]) – defaults to None

  Warning Detection method. Set to None to disable warning detection. Defaults to drift.ADWIN(delta=0.01).

• 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

• delta (float) – defaults to 0.01

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

• tau (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) – 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.

• binary_split (bool) – defaults to False

  [Tree parameter] If True, only allow binary splits.

• max_size (float) – defaults to 100.0

  [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.

• stop_mem_management (bool) – defaults to False

  [Tree parameter] If True, stop growing as soon as memory limit is hit.

• remove_poor_attrs (bool) – defaults to False

  [Tree parameter] If True, disable poor attributes to reduce memory usage.

• merit_preprune (bool) – defaults to True

  [Tree parameter] If True, enable merit-based tree pre-pruning.

• seed (int) – defaults to None

  Random seed for reproducibility.

Attributes

• models

Examples

>>> from river import evaluate
>>> from river import forest
>>> from river import metrics
>>> from river.datasets import synth

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

>>> model = forest.ARFClassifier(seed=8, leaf_prediction="mc")

>>> metric = metrics.Accuracy()

>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 71.07%

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)
• kwargs

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 (dict)

Returns

typing.Dict[typing.Union[bool, str, int], float]: 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

reverse

S.reverse() -- reverse IN PLACE

sort

References

---

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. ↩