LifelongClassificationForest

class proglearn.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.

Methods Summary

LifelongClassificationForest.add_task(X, y)	adds a task with id task_id, max tree depth max_depth, given input data matrix X and output data matrix y, to the Lifelong Classification Forest.
LifelongClassificationForest.add_transformer(X, y)	adds a transformer with id transformer_id and max tree depth max_depth, trained on given input data matrix, X, and output data matrix, y, to the Lifelong Classification Forest.
LifelongClassificationForest.get_task_ids()
LifelongClassificationForest.get_transformer_ids()
LifelongClassificationForest.predict(X, task_id)	predicts class labels under task_id for each example in input data X.
LifelongClassificationForest.predict_proba(X, ...)	estimates class posteriors under task_id for each example in input data X.
LifelongClassificationForest.set_decider(...)
LifelongClassificationForest.set_transformer([...])
LifelongClassificationForest.set_voter(...)

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LifelongClassificationForest.add_task(X, y, task_id=None, n_estimators='default', tree_construction_proportion='default', kappa='default', max_depth='default')

adds a task with id task_id, max tree depth max_depth, given input data matrix X and output data matrix y, to the Lifelong Classification Forest. Also splits data for training and voting based on tree_construction_proportion and uses the value of kappa to determine whether the learner will have finite sample correction.

Parameters

X : ndarray

The input data matrix.

y : ndarray

The output (response) data matrix.

task_id : obj, default=None

The id corresponding to the task being added.

n_estimators : int or str, default='default'

The number of trees used for the given task.

tree_construction_proportion : int or str, default='default'

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. The default is used if 'default' is provided.

kappa : float or str, default='default'

The coefficient for finite sample correction. The default is used if 'default' is provided.

max_depth : int or str, default='default'

The maximum depth of a tree in the Lifelong Classification Forest. The default is used if 'default' is provided.

Returns

self : LifelongClassificationForest

The object itself.

LifelongClassificationForest.add_transformer(X, y, transformer_id=None, n_estimators='default', max_depth='default')

adds a transformer with id transformer_id and max tree depth max_depth, trained on given input data matrix, X, and output data matrix, y, to the Lifelong Classification Forest. Also trains the voters and deciders from new transformer to previous tasks, and will train voters and deciders from this transformer to all new tasks.

Parameters

X : ndarray

The input data matrix.

y : ndarray

The output (response) data matrix.

transformer_id : obj, default=None

The id corresponding to the transformer being added.

n_estimators : int or str, default='default'

The number of trees used for the given task.

max_depth : int or str, default='default'

The maximum depth of a tree in the Lifelong Classification Forest. The default is used if 'default' is provided.

Returns

self : LifelongClassificationForest

The object itself.

LifelongClassificationForest.get_task_ids()

LifelongClassificationForest.get_transformer_ids()

LifelongClassificationForest.predict(X, task_id)

predicts class labels under task_id for each example in input data X.

Parameters

X : ndarray

The input data matrix.

task_id : obj

The id corresponding to the task being mapped to.

Returns

y_hat : ndarray of shape [n_samples]

predicted class label per example

LifelongClassificationForest.predict_proba(X, task_id)

estimates class posteriors under task_id for each example in input data X.

Parameters

X : ndarray

The input data matrix.

task_id:

The id corresponding to the task being mapped to.

Returns

y_proba_hat : ndarray of shape [n_samples, n_classes]

posteriors per example

LifelongClassificationForest.set_decider(task_id, transformer_ids, decider_class=None, decider_kwargs=None)

LifelongClassificationForest.set_transformer(transformer_id=None, transformer=None, transformer_data_idx=None, transformer_class=None, transformer_kwargs=None)

LifelongClassificationForest.set_voter(transformer_id, task_id=None, voter_class=None, voter_kwargs=None, bag_id=None)