RuleTree.base.RuleTreeBaseGainSplitter.RuleTreeBaseGainSplitter

RuleTree.base.RuleTreeBaseGainSplitter.RuleTreeBaseGainSplitter#

class RuleTreeBaseGainSplitter[source]#

Bases: RuleTreeBaseSplitter, ABC

Base class of the splitters that test every candidate split and keep the one with the highest gain: a new splitter only defines the task (task) and the gain of a split (gain()).

The candidate splits are the thresholds halfway between consecutive distinct values of each numerical feature (x <= threshold goes left) and the values of each categorical feature (x == value goes left). Splits with fewer than min_samples_leaf instances on a side are skipped. The search is exhaustive and not optimized: gain is called once per candidate.

Variables:: min_samples_leaf (int) – Minimum number of instances on each side of a split. 1 unless the subclass sets it (e.g. as a parameter of __init__).

See also

RuleTreeBaseSplitter: the attributes set by fit.

Examples

>>> import numpy as np
>>> from RuleTree.base.RuleTreeBaseGainSplitter import RuleTreeBaseGainSplitter
>>> from RuleTree.utils.define import MODEL_TYPE_CLF
>>> class MisclassificationSplitter(RuleTreeBaseGainSplitter):
...     task = MODEL_TYPE_CLF
...
...     def gain(self, y, left, sample_weight=None):
...         # decrease of the number of errors when each side predicts its majority class
...         errors = lambda labels: len(labels) - np.unique(labels, return_counts=True)[1].max()
...         return errors(y) - errors(y[left]) - errors(y[~left])
>>> X = np.array([[1.0], [2.0], [3.0], [4.0]])
>>> splitter = MisclassificationSplitter().fit(X, np.array([0, 0, 1, 1]), numerical=[0], categorical=[])
>>> splitter.feature_, splitter.threshold_
(0, 2.5)

__init__()#

Methods

`__init__`()
`apply`(X)	Apply the split to a feature matrix.
`candidate_thresholds`(x)	Thresholds tested on a numerical feature: halfway between its consecutive distinct values.
`fit`(X, y, numerical, categorical[, ...])	Test every candidate split and keep the one with the highest gain.
`gain`(y, left[, sample_weight])	Gain of a candidate split: the higher, the better.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`get_state`()	Fitted state of the splitter: the attributes listed in `_state`.
`set_fit_request`(*[, categorical, ...])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_state`(state)	Restore the fitted state saved by `get_state()`.
`update_statistics`(X, y[, sample_weight])	Recompute the statistics of the split (e.g. impurity) on new data, without changing the split.

Attributes

`min_samples_leaf`
`task`

min_samples_leaf = 1#

abstractmethod gain(y: ndarray, left: ndarray, sample_weight=None) → float[source]#

Gain of a candidate split: the higher, the better.

Parameters:

y (numpy.ndarray of shape (n_samples,)) – Target of the instances in the node.
left (numpy.ndarray of bool of shape (n_samples,)) – Instances that go to the left child.
sample_weight (numpy.ndarray of shape (n_samples,), default=None) – Sample weights of the instances in the node.

Returns:

gain

Return type:

float

candidate_thresholds(x: ndarray) → ndarray[source]#

Thresholds tested on a numerical feature: halfway between its consecutive distinct values.

Parameters:: x (numpy.ndarray of shape (n_samples,)) – Values of the feature.
Returns:: thresholds
Return type:: numpy.ndarray

fit(X, y, numerical, categorical, sample_weight=None, check_input=True)[source]#

Test every candidate split and keep the one with the highest gain.

Parameters:

X (numpy.ndarray of shape (n_samples, n_features)) – Feature matrix of the instances in the node.
y (numpy.ndarray of shape (n_samples,)) – Target of the instances in the node.
numerical (list of int) – Indices of the numerical columns of X.
categorical (list of int) – Indices of the categorical columns of X.
sample_weight (numpy.ndarray of shape (n_samples,), default=None) – Sample weights of the instances in the node, passed to gain.
check_input (bool, default=True) – Not used.

Returns:

self – The fitted splitter.

Return type:

RuleTreeBaseGainSplitter

Raises:

NoSplitFoundWarning – If no candidate split has at least min_samples_leaf instances on each side.

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

categorical (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for categorical parameter in fit.
check_input (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for check_input parameter in fit.
numerical (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for numerical parameter in fit.
sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

RuleTree.base.RuleTreeBaseGainSplitter.RuleTreeBaseGainSplitter

Contents

RuleTree.base.RuleTreeBaseGainSplitter.RuleTreeBaseGainSplitter#