import tensorflow as tf
from gmmvi.models.gmm_wrapper import GmmWrapper
[docs]class ComponentStepsizeAdaptation:
""" This class provides a common interface for adapting the individual stepsizes for the component updates.
There are currently three options for component stepsize adpatation:
1. The :py:class:`FixedComponentStepsizeAdaptation` is a dummy-class, that does not do anything.
2. The :py:class:`DecayingComponentStepsizeAdaptation` uses exponential decay.
3. The :py:class:`ImprovementBasedComponentStepsizeAdaptation` uses the procedure of VIPS :cite:p:`Arenz2020` to
increase the stepsize if a component improved during the last updates, and to decrease it otherwise.
Parameters:
gmm_wrapper: :py:class:`GmmWrapper<gmmvi.models.gmm_wrapper.GmmWrapper>`
The wrapped model where we want to adapt the number of components.
initial_stepsize: float
The stepsize used when the component receives its first update
"""
def __init__(self, gmm_wrapper: GmmWrapper, initial_stepsize: float):
self.gmm_wrapper = gmm_wrapper
self.initial_stepsize = initial_stepsize
tf.assert_equal(gmm_wrapper.stepsizes, initial_stepsize)
[docs] @staticmethod
def build_from_config(config, gmm_wrapper):
"""This static method provides a convenient way to create a :py:class:`FixedComponentStepsizeAdaptation`,
:py:class:`DecayingComponentStepsizeAdaptation` or :py:class:`ImprovementBasedComponentStepsizeAdaptation`
depending on the provided config.
Parameters:
config: dict
The dictionary is typically read from YAML a file, and holds all hyperparameters.
gmm_wrapper: :py:class:`GmmWrapper<gmmvi.models.gmm_wrapper.GmmWrapper>`
The wrapped model.
"""
if config["component_stepsize_adapter_type"] == "improvement-based":
return ImprovementBasedComponentStepsizeAdaptation(gmm_wrapper, **config["component_stepsize_adapter_config"])
elif config["component_stepsize_adapter_type"] == "decaying":
return DecayingComponentStepsizeAdaptation(gmm_wrapper, **config["component_stepsize_adapter_config"])
elif config["component_stepsize_adapter_type"] == "fixed":
return FixedComponentStepsizeAdaptation(gmm_wrapper, **config["component_stepsize_adapter_config"])
else:
raise ValueError(
f"config['component_stepsize_adapter_type'] is '{config['component_stepsize_adapter_type']}' "
f"which is an unknown type")
[docs] def update_stepsize(self, current_stepsizes: tf.Tensor) -> tf.Tensor:
"""
Update the stepsizes, according to the chosen procedure.
Parameters:
current_stepsizes: tf.Tensor
A tensor that contains the stepsize of each component
Returns:
tf.Tensor: a tensor of same size as *current_stepsizes* that contains the updates stepsizes.
"""
raise NotImplementedError
[docs]class FixedComponentStepsizeAdaptation(ComponentStepsizeAdaptation):
""" This class is a dummy class, that can be used when we want to keep the stepsizes constant.
Parameters:
gmm_wrapper: :py:class:`GmmWrapper<gmmvi.models.gmm_wrapper.GmmWrapper>`
The wrapped model where we want to adapt the number of components.
initial_stepsize: float
The stepsize used for all component updates
"""
def __init__(self, gmm_wrapper: GmmWrapper, initial_stepsize: float):
super(FixedComponentStepsizeAdaptation, self).__init__(gmm_wrapper, initial_stepsize)
[docs] def update_stepsize(self, current_stepsizes: tf.Tensor) -> tf.Tensor:
""" This dummy function does nothing.
Parameters:
current_stepsizes: tf.Tensor
A tensor that contains the stepsize of each component
Returns:
tf.Tensor: the same *current_stepsizes* tensor, that it was called with.
"""
return current_stepsizes
[docs]class DecayingComponentStepsizeAdaptation(ComponentStepsizeAdaptation):
""" This class implements an exponentially decaying stepsize schedule.
See :cite:p:`Khan2018a`.
Parameters:
gmm_wrapper: :py:class:`GmmWrapper<gmmvi.models.gmm_wrapper.GmmWrapper>`
The wrapped model.
annealing_exponent: float
controls how fast the stepsize decays
initial_stepsize: float
The stepsize used for all component updates
"""
def __init__(self, gmm_wrapper: GmmWrapper, annealing_exponent: float, initial_stepsize: float):
super(DecayingComponentStepsizeAdaptation, self).__init__(gmm_wrapper, initial_stepsize)
self.annealing_exponent = annealing_exponent
self.num_received_updates = gmm_wrapper.num_received_updates
[docs] def update_stepsize(self, current_stepsizes: tf.Tensor) -> tf.Tensor:
""" Updates the stepsize using exponential decay. More specifially, the new stepsize is given by
:math:`\\frac{\\text{initial\\_stepsize}}{1 + \\text{num\\_received\\_updates}^\\text{annealing\\_exponent}}`.
Parameters:
current_stepsizes: tf.Tensor
A tensor that contains the stepsize of each component
Returns:
tf.Tensor: a tensor of same size as *current_stepsizes* that contains the updates stepsizes.
"""
new_stepsizes = tf.TensorArray(tf.float32, size=0, dynamic_size=True)
for i in range(tf.shape(current_stepsizes)[0]):
new_stepsize = self.initial_stepsize \
/ (1 + tf.math.pow(float(self.num_received_updates[i]), self.annealing_exponent))
new_stepsizes = new_stepsizes.write(i, new_stepsize)
return new_stepsizes.stack()
[docs]class ImprovementBasedComponentStepsizeAdaptation(ComponentStepsizeAdaptation):
""" Increases the stepsize if the last component update increased its reward, decreases it otherwise.
See :cite:p:`Arenz2020`.
Parameters:
gmm_wrapper: :py:class:`GmmWrapper<gmmvi.models.gmm_wrapper.GmmWrapper>`
The wrapped model where we want to adapt the number of components.
initial_stepsize: float
The stepsize used for all component updates
min_stepsize: float
Do not not decrease the stepsize below this point
max_stepsize: float
Do not increase the stepsize above this point
stepsize_inc_factor: float
Factor (>1) for increasing the stepsize
stepsize_dec_factor: float
Factor in the range [0, 1] for decreasing the stepsize
"""
def __init__(self, gmm_wrapper: GmmWrapper, initial_stepsize: float, min_stepsize: float, max_stepsize: float,
stepsize_inc_factor: float, stepsize_dec_factor: float):
super(ImprovementBasedComponentStepsizeAdaptation, self).__init__(gmm_wrapper, initial_stepsize)
self.reward_history = gmm_wrapper.reward_history
self.min_stepsize = min_stepsize
self.max_stepsize = max_stepsize
self.stepsize_inc_factor = stepsize_inc_factor
self.stepsize_dec_factor = stepsize_dec_factor
[docs] def update_stepsize(self, current_stepsizes: tf.Tensor) -> tf.Tensor:
""" Updates the stepsize of each component based on previous reward improvements :cite:p:`Arenz2020`.
Parameters:
current_stepsizes: tf.Tensor
A tensor that contains the stepsize of each component
Returns:
tf.Tensor: a tensor of same size as *current_stepsizes* that contains the updates stepsizes.
"""
new_stepsizes = tf.TensorArray(tf.float32, size=0, dynamic_size=True)
for i in range(tf.shape(current_stepsizes)[0]):
if self.reward_history[i][-2] >= self.reward_history[i][-1]:
new_stepsize = tf.math.maximum(
self.stepsize_dec_factor * current_stepsizes[i],
self.min_stepsize,
)
else:
new_stepsize = tf.math.minimum(
self.stepsize_inc_factor * current_stepsizes[i],
self.max_stepsize,
)
new_stepsizes = new_stepsizes.write(i, new_stepsize)
return new_stepsizes.stack()