Source code for adanet.ensemble.strategy

# Copyright 2019 The AdaNet Authors. All Rights Reserved.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     https://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Search strategy algorithms."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import abc
import collections


[docs]class Candidate( collections.namedtuple("Candidate", [ "name", "subnetwork_builders", "previous_ensemble_subnetwork_builders" ])): """An ensemble candidate found during the search phase. Args: name: String name of this ensemble candidate. subnetwork_builders: Candidate :class:`adanet.subnetwork.Builder` instances to include in the ensemble. previous_ensemble_subnetwork_builders: :class:`adanet.subnetwork.Builder` instances to include from the previous ensemble. """ def __new__(cls, name, subnetwork_builders, previous_ensemble_subnetwork_builders): return super(Candidate, cls).__new__( cls, name=name, subnetwork_builders=tuple(subnetwork_builders), previous_ensemble_subnetwork_builders=tuple( previous_ensemble_subnetwork_builders or []))
[docs]class Strategy(object): """An abstract ensemble strategy.""" __metaclass__ = abc.ABCMeta
[docs] @abc.abstractmethod def generate_ensemble_candidates(self, subnetwork_builders, previous_ensemble_subnetwork_builders): """Generates ensemble candidates to search over this iteration. Args: subnetwork_builders: Candidate :class:`adanet.subnetwork.Builder` instances for this iteration. previous_ensemble_subnetwork_builders: :class:`adanet.subnetwork.Builder` instances from the previous ensemble. Including only a subset of these in a returned :class:`adanet.ensemble.Candidate` is equivalent to pruning the previous ensemble. Returns: An iterable of :class:`adanet.ensemble.Candidate` instances to train and consider this iteration. """
# TODO: Pruning the previous subnetwork may require more metadata # such as `subnetwork.Reports` and `ensemble.Reports` to make smart # decisions.
[docs]class SoloStrategy(Strategy): """Produces a model composed of a single subnetwork. *An ensemble of one.* This is effectively the same as pruning all previous ensemble subnetworks, and only adding one subnetwork candidate to the ensemble. """
[docs] def generate_ensemble_candidates(self, subnetwork_builders, previous_ensemble_subnetwork_builders): return [ Candidate("{}_solo".format(subnetwork_builder.name), [subnetwork_builder], None) for subnetwork_builder in subnetwork_builders ]
[docs]class GrowStrategy(Strategy): """Greedily grows an ensemble, one subnetwork at a time."""
[docs] def generate_ensemble_candidates(self, subnetwork_builders, previous_ensemble_subnetwork_builders): return [ Candidate("{}_grow".format(subnetwork_builder.name), [subnetwork_builder], previous_ensemble_subnetwork_builders) for subnetwork_builder in subnetwork_builders ]
[docs]class AllStrategy(Strategy): """Ensembles all subnetworks from the current iteration."""
[docs] def generate_ensemble_candidates(self, subnetwork_builders, previous_ensemble_subnetwork_builders): return [ Candidate("all", subnetwork_builders, previous_ensemble_subnetwork_builders) ]