Lifelong Learning Forest

Lifelong Classification Forest

class proglearn.forest.LifelongClassificationForest(default_n_estimators=100, default_tree_construction_proportion=0.67, default_kappa=inf, default_max_depth=30)

A class used to represent a lifelong classification forest.

Parameters

default_n_estimators : int, default=100

The number of trees used in the Lifelong Classification Forest used if 'n_estimators' is not fed to add_{task, transformer}.

default_tree_construction_proportion : int, default=0.67

The proportions of the input data set aside to train each decision tree. The remainder of the data is used to fill in voting posteriors. This is used if 'tree_construction_proportion' is not fed to add_task.

default_kappa : float, default=np.inf

The coefficient for finite sample correction. This is used if 'kappa' is not fed to add_task.

default_max_depth : int, default=30

The maximum depth of a tree in the Lifelong Classification Forest. This is used if 'max_depth' is not fed to add_task.

Attributes

task_id_to_X	(dict) A dictionary with keys of type obj corresponding to task ids and values of type ndarray corresponding to the input data matrix X. This dictionary thus maps input data matrix to the task where posteriors are to be estimated.
task_id_to_y	(dict) A dictionary with keys of type obj corresponding to task ids and values of type ndarray corresponding to output data matrix y. This dictionary thus maps output data matrix to the task where posteriors are to be estimated.
transformer_id_to_X	(dict) A dictionary with keys of type obj corresponding to transformer ids and values of type ndarray corresponding to the output data matrix X. This dictionary thus maps input data matrix to a particular transformer.
transformer_id_to_y	(dict) A dictionary with keys of type obj corresponding to transformer ids and values of type ndarray corresponding to the output data matrix y. This dictionary thus maps output data matrix to a particular transformer.
transformer_id_to_transformers	(dict) A dictionary with keys of type obj corresponding to transformer ids and values of type obj corresponding to a transformer. This dictionary thus maps transformer ids to the corresponding transformers.
task_id_to_trasnformer_id_to_voters	(dict) A nested dictionary with outer key of type obj, corresponding to task ids inner key of type obj, corresponding to transformer ids, and values of type obj, corresponding to a voter. This dictionary thus maps voters to a corresponding transformer assigned to a particular task.
task_id_to_decider	(dict) A dictionary with keys of type obj, corresponding to task ids, and values of type obj corresponding to a decider. This dictionary thus maps deciders to a particular task.
task_id_to_decider_class	(dict) A dictionary with keys of type obj corresponding to task ids and values of type obj corresponding to a decider class. This dictionary thus maps decider classes to a particular task id.
task_id_to_voter_class	(dict) A dictionary with keys of type obj corresponding to task ids and values of type obj corresponding to a voter class. This dictionary thus maps voter classes to a particular task id.
task_id_to_voter_kwargs	(dict) A dictionary with keys of type obj corresponding to task ids and values of type obj corresponding to a voter kwargs. This dictionary thus maps voter kwargs to a particular task id.
task_id_to_decider_kwargs	(dict) A dictionary with keys of type obj corresponding to task ids and values of type obj corresponding to a decider kwargs. This dictionary thus maps decider kwargs to a particular task id.
task_id_to_bag_id_to_voter_data_idx	(dict) A nested dictionary with outer keys of type obj corresponding to task ids inner keys of type obj corresponding to bag ids and values of type obj corresponding to voter data indices. This dictionary thus maps voter data indices to particular bags for particular tasks.
task_id_to_decider_idx	(dict) A dictionary with keys of type obj corresponding to task ids and values of type obj corresponding to decider indices. This dictionary thus maps decider indices to particular tasks.
default_transformer_class	(TreeClassificationTransformer) The class of transformer to which the forest defaults if None is provided in any of the functions which add or set transformers.
default_transformer_kwargs	(dict) A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines to which type of transformer the forest defaults if None is provided in any of the functions which add or set transformers.
default_voter_class	(TreeClassificationVoter) The class of voter to which the forest defaults if None is provided in any of the functions which add or set voters.
default_voter_kwargs	(dict) A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines to which type of voter the forest defaults if None is provided in any of the functions which add or set voters.
default_decider_class	(SimpleArgmaxAverage) The class of decider to which the forest defaults if None is provided in any of the functions which add or set deciders.
default_decider_kwargs	(dict) A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines to which type of decider the forest defaults if None is provided in any of the functions which add or set deciders.

Uncertainty Forest

class proglearn.forest.UncertaintyForest(n_estimators=100, kappa=inf, max_depth=30, tree_construction_proportion=0.67)

A class used to represent an uncertainty forest.

Parameters

n_estimators : int, default=100

The number of trees in the UncertaintyForest

kappa : float, default=np.inf

The coefficient for finite sample correction. If set to the default value, finite sample correction is not performed.

max_depth : int, default=30

The maximum depth of a tree in the UncertaintyForest

tree_construction_proportion : float, default = 0.67

The proportions of the input data set aside to train each decision tree. The remainder of the data is used to fill in voting posteriors.

Attributes

default_transformer_class	(TreeClassificationTransformer) The class of transformer to which the forest defaults if None is provided in any of the functions which add or set transformers.
default_transformer_kwargs	(dict) A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines to which type of transformer the forest defaults if None is provided in any of the functions which add or set transformers.
default_voter_class	(TreeClassificationVoter) The class of voter to which the forest defaults if None is provided in any of the functions which add or set voters.
default_voter_kwargs	(dict) A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines to which type of voter the forest defaults if None is provided in any of the functions which add or set voters.
default_decider_class	(SimpleArgmaxAverage) The class of decider to which the forest defaults if None is provided in any of the functions which add or set deciders.
default_decider_kwargs	(dict) A dictionary with keys of type string and values of type obj corresponding to the given string kwarg. This determines to which type of decider the forest defaults if None is provided in any of the functions which add or set deciders.