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ARFRegressor

Adaptive Random Forest regressor.

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

Notice that this implementation is slightly different from the original algorithm proposed in 2. The HoeffdingTreeRegressor is used as base learner, instead of FIMT-DD. It also adds a new strategy to monitor the predictions and check for concept drifts. The deviations of the predictions to the target are monitored and normalized in the [0, 1] range to fulfill ADWIN's requirements. We assume that the data subjected to the normalization follows a normal distribution, and thus, lies within the interval of the mean \(\pm3\sigma\).

Parameters

  • n_models

    Type → int

    Default → 10

    Number of trees in the ensemble.

  • max_features

    Default → 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.

  • aggregation_method

    Type → str

    Default → median

    The method to use to aggregate predictions in the ensemble.
    - 'mean'
    - 'median' - If selected will disable the weighted vote.

  • lambda_value

    Type → int

    Default → 6

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

  • metric

    Type → metrics.base.RegressionMetric | None

    Default → None

    Metric used to track trees performance within the ensemble. Depending, on the configuration, this metric is also used to weight predictions from the members of the ensemble. Defaults to metrics.MSE()`.

  • disable_weighted_vote

    Default → True

    If True, disables the weighted vote prediction, i.e. does not assign weights to individual tree's predictions and uses the arithmetic mean instead. Otherwise will use the metric value to weight predictions.

  • drift_detector

    Type → base.DriftDetector | None

    Default → None

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

  • warning_detector

    Type → base.DriftDetector | None

    Default → None

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

  • grace_period

    Type → int

    Default → 50

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

  • max_depth

    Type → int | None

    Default → None

    [Tree parameter] The maximum depth a tree can reach. If None, the tree will grow until the system recursion limit.

  • delta

    Type → float

    Default → 0.01

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

  • tau

    Type → float

    Default → 0.05

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

  • leaf_prediction

    Type → str

    Default → adaptive

    [Tree parameter] Prediction mechanism used at leaves.
    - 'mean' - Target mean
    - 'model' - Uses the model defined in leaf_model
    - 'adaptive' - Chooses between 'mean' and 'model' dynamically

  • leaf_model

    Type → base.Regressor | None

    Default → None

    [Tree parameter] The regression model used to provide responses if leaf_prediction='model'. If not provided, an instance of linear_model.LinearRegression with the default hyperparameters is used.

  • model_selector_decay

    Type → float

    Default → 0.95

    [Tree parameter] The exponential decaying factor applied to the learning models' squared errors, that are monitored if leaf_prediction='adaptive'. Must be between 0 and 1. The closer to 1, the more importance is going to be given to past observations. On the other hand, if its value approaches 0, the recent observed errors are going to have more influence on the final decision.

  • nominal_attributes

    Type → list | None

    Default → None

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

  • splitter

    Type → Splitter | None

    Default → 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.EBSTSplitter is used if splitter is None.

  • min_samples_split

    Type → int

    Default → 5

    [Tree parameter] The minimum number of samples every branch resulting from a split candidate must have to be considered valid.

  • binary_split

    Type → bool

    Default → False

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

  • max_size

    Type → float

    Default → 500.0

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

  • memory_estimate_period

    Type → int

    Default → 2000000

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

  • stop_mem_management

    Type → bool

    Default → False

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

  • remove_poor_attrs

    Type → bool

    Default → False

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

  • merit_preprune

    Type → bool

    Default → True

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

  • seed

    Type → int | None

    Default → None

    Random seed for reproducibility.

Attributes

  • models

  • valid_aggregation_method

    Valid aggregation_method values.

Examples

from river import datasets
from river import evaluate
from river import forest
from river import metrics
from river import preprocessing

dataset = datasets.TrumpApproval()

model = (
    preprocessing.StandardScaler() |
    forest.ARFRegressor(seed=42)
)

metric = metrics.MAE()

evaluate.progressive_val_score(dataset, model, metric)

MAE: 0.772113

Methods

learn_one
n_drifts_detected

Get the total number of concept drifts detected, or such number on an individual tree basis (optionally).

Parameters

  • tree_id — 'int | None' — defaults to None

Returns

int: The number of concept drifts detected.

n_warnings_detected

Get the total number of concept drift warnings detected, or the number on an individual tree basis (optionally).

Parameters

  • tree_id — 'int | None' — defaults to None

Returns

int: The number of concept drift warnings detected.

predict_one

Predict the output of features x.

Parameters

  • x — 'dict'

Returns

base.typing.RegTarget: The prediction.

  1. Gomes, H.M., Bifet, A., Read, J., Barddal, J.P., Enembreck, F., Pfharinger, B., Holmes, G. and Abdessalem, T., 2017. Adaptive random forests for evolving data stream classification. Machine Learning, 106(9-10), pp.1469-1495. ↩

  2. Gomes, H.M., Barddal, J.P., Boiko, L.E., Bifet, A., 2018. Adaptive random forests for data stream regression. ESANN 2018. ↩