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ExtremelyFastDecisionTreeClassifier

Extremely Fast Decision Tree classifier.

Also referred to as Hoeffding AnyTime Tree (HATT) classifier.

Parameters

  • grace_period (int) – defaults to 200

    Number of instances a leaf should observe between split attempts.

  • max_depth (int) – defaults to None

    The maximum depth a tree can reach. If None, the tree will grow indefinitely.

  • min_samples_reevaluate (int) – defaults to 20

    Number of instances a node should observe before reevaluating the best split.

  • split_criterion (str) – defaults to info_gain

    Split criterion to use.
    - 'gini' - Gini
    - 'info_gain' - Information Gain
    - 'hellinger' - Helinger Distance

  • split_confidence (float) – defaults to 1e-07

    Allowed error in split decision, a value closer to 0 takes longer to decide.

  • tie_threshold (float) – defaults to 0.05

    Threshold below which a split will be forced to break ties.

  • leaf_prediction (str) – defaults to nba

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

  • nb_threshold (int) – defaults to 0

    Number of instances a leaf should observe before allowing Naive Bayes.

  • nominal_attributes (list) – defaults to None

    List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.

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

    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

    If True, only allow binary splits.

  • max_size (float) – defaults to 100.0

    The max size of the tree, in Megabytes (MB).

  • memory_estimate_period (int) – defaults to 1000000

    Interval (number of processed instances) between memory consumption checks.

  • stop_mem_management (bool) – defaults to False

    If True, stop growing as soon as memory limit is hit.

  • remove_poor_attrs (bool) – defaults to False

    If True, disable poor attributes to reduce memory usage.

  • merit_preprune (bool) – defaults to True

    If True, enable merit-based tree pre-pruning.

Attributes

  • height

  • leaf_prediction

    Return the prediction strategy used by the tree at its leaves.

  • max_size

    Max allowed size tree can reach (in MB).

  • n_active_leaves

  • n_branches

  • n_inactive_leaves

  • n_leaves

  • n_nodes

  • split_criterion

    Return a string with the name of the split criterion being used by the tree.

  • summary

    Collect metrics corresponding to the current status of the tree in a string buffer.

Examples

>>> from river import synth
>>> from river import evaluate
>>> from river import metrics
>>> from river import tree

>>> gen = synth.Agrawal(classification_function=0, seed=42)
>>> # Take 1000 instances from the infinite data generator
>>> dataset = iter(gen.take(1000))

>>> model = tree.ExtremelyFastDecisionTreeClassifier(
...     grace_period=100,
...     split_confidence=1e-5,
...     nominal_attributes=['elevel', 'car', 'zipcode'],
...     min_samples_reevaluate=100
... )

>>> metric = metrics.Accuracy()

>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 87.89%

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.

debug_one

Print an explanation of how x is predicted.

Parameters

  • x (dict)

Returns

typing.Union[str, NoneType]: A representation of the path followed by the tree to predict x; None if

draw

Draw the tree using the graphviz library.

Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.

Parameters

  • max_depth (int) – defaults to None
    The maximum depth a tree can reach. If None, the tree will grow indefinitely.
learn_one

Incrementally train the model

Parameters

  • x
  • y
  • sample_weight – defaults to 1.0

Returns

self

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_one

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

Parameters

  • x

Returns

A dictionary that associates a probability which each label.

to_dataframe

Return a representation of the current tree structure organized in a pandas.DataFrame object.

In case the tree is empty or it only contains a single node (a leaf), None is returned.

Returns

df

Notes

The Extremely Fast Decision Tree (EFDT) 1 constructs a tree incrementally. The EFDT seeks to select and deploy a split as soon as it is confident the split is useful, and then revisits that decision, replacing the split if it subsequently becomes evident that a better split is available. The EFDT learns rapidly from a stationary distribution and eventually it learns the asymptotic batch tree if the distribution from which the data are drawn is stationary.

References

  1. C. Manapragada, G. Webb, and M. Salehi. Extremely Fast Decision Tree. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). ACM, New York, NY, USA, 1953-1962. DOI: https://doi.org/10.1145/3219819.3220005 ↩