ProximityForest¶
-
class
sktime.classification.distance_based.
ProximityForest
(random_state=None, n_estimators=100, distance_measure=None, get_distance_measure=None, get_exemplars=<function get_one_exemplar_per_class_proximity>, get_gain=<function gini_gain>, verbosity=0, max_depth=inf, is_leaf=<function pure>, n_jobs=1, n_stump_evaluations=5, find_stump=None, setup_distance_measure_getter=<function setup_all_distance_measure_getter>)[source]¶ Proximity Forest class to model a decision tree forest which uses distance measures to partition data, see [1].
- Parameters
random_state (random, default = None) – seed for reproducibility
n_estimators (int, default=100) – The number of trees in the forest.
distance_measure (default = None) –
get_distance_measure (default=None,) – distance measure getters
get_exemplars (default=get_one_exemplar_per_class_proximity,) –
get_gain (default=gini_gain,) – function to score the quality of a split
verbosity (default=0,) – logging verbosity
max_depth (default=np.math.inf,) –
is_leaf (default=pure,) –
n_jobs (default=int, 1,) – number of jobs to run in parallel *across threads”
n_stump_evaluations (int, default=5,) –
find_stump (default=None,) – function to find the best split of data
setup_distance_measure_getter=setup_all_distance_measure_getter –
:param : :param setup_distance_measure_getter: :type setup_distance_measure_getter: function to setup the distance
Notes
- ..[1] Ben Lucas et al., “Proximity Forest: an effective and scalable distance-based
classifier for time series”,Data Mining and Knowledge Discovery, 33(3): 607-635, 2019 https://arxiv.org/abs/1808.10594
Java wrapper of authors original https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/ classifiers/distance_based/ProximityForestWrapper.java Java version https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/ classifiers/distance_based/proximity/ProximityForest.java
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__init__
(random_state=None, n_estimators=100, distance_measure=None, get_distance_measure=None, get_exemplars=<function get_one_exemplar_per_class_proximity>, get_gain=<function gini_gain>, verbosity=0, max_depth=inf, is_leaf=<function pure>, n_jobs=1, n_stump_evaluations=5, find_stump=None, setup_distance_measure_getter=<function setup_all_distance_measure_getter>)[source]¶ build a Proximity Forest object :param random_state: the random state :param get_exemplars: get the exemplars from a given dataframe and list of class labels :param distance_measure: distance measure to use :param get_distance_measure: method to get the distance measure if no already set :param setup_distance_measure_getter: method to setup the distance measures based upon the dataset given :param get_gain: method to find the gain of a data split :param max_depth: maximum depth of the tree :param is_leaf: function to decide when to mark a node as a leaf node :param verbosity: number reflecting the verbosity of logging :param n_jobs: number of parallel threads to use while building :param find_stump: method to find the best split of data / stump at a node :param n_stump_evaluations: number of stump evaluations to do if find_stump method is None :param n_estimators: number of trees to construct