LabelCombinationHoeffdingTreeClassifier

Label Combination Hoeffding Tree for multi-label classification.

Label combination transforms the problem from multi-label to multi-class. For each unique combination of labels it assigns a class and proceeds with training the hoeffding tree normally.

The transformation is done by changing the label set which could be seen as a binary number to an int which will represent the class, and after the prediction the int is converted back to a binary number which is the predicted label-set.

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.

• 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 datasets
>>> from river import evaluate
>>> from river import metrics
>>> from river import tree

>>> dataset = iter(datasets.Music().take(200))
>>> model = tree.LabelCombinationHoeffdingTreeClassifier(
...     split_confidence=1e-5,
...     grace_period=50
... )

>>> metric = metrics.multioutput.MicroAverage(metrics.Accuracy())

>>> evaluate.progressive_val_score(dataset, model, metric)
MicroAverage(Accuracy): 71.11%

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

Update the Multi-label Hoeffding Tree Classifier.

Parameters

• x
• y
• sample_weight – defaults to 1.0

Returns

self

predict_one

Predict the labels of an instance.

Parameters

• x (dict)

Returns

typing.Union[bool, str, int]: Predicted labels.

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