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# -*- coding: utf-8 -*-
#========================================================================
# Copyright 2019 Science Technology Facilities Council
# Copyright 2019 University of Manchester
#
# This work is part of the Core Imaging Library developed by Science Technology
# Facilities Council and University of Manchester
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#=========================================================================
import time, functools
from numbers import Integral
class Algorithm(object):
'''Base class for iterative algorithms
provides the minimal infrastructure.
Algorithms are iterables so can be easily run in a for loop. They will
stop as soon as the stop cryterion is met.
The user is required to implement the set_up, __init__, update and
and update_objective methods
A courtesy method run is available to run n iterations. The method accepts
a callback function that receives the current iteration number and the actual objective
value and can be used to trigger print to screens and other user interactions. The run
method will stop when the stopping cryterion is met.
'''
def __init__(self, **kwargs):
'''Constructor
Set the minimal number of parameters:
iteration: current iteration number
max_iteration: maximum number of iterations
memopt: whether to use memory optimisation ()
timing: list to hold the times it took to run each iteration
update_objectice_interval: the interval every which we would save the current
objective. 1 means every iteration, 2 every 2 iteration
and so forth. This is by default 1 and should be increased
when evaluating the objective is computationally expensive.
'''
self.iteration = 0
self.__max_iteration = kwargs.get('max_iteration', 0)
self.__loss = []
self.memopt = False
self.configured = False
self.timing = []
self._iteration = []
self.update_objective_interval = kwargs.get('update_objective_interval', 1)
def set_up(self, *args, **kwargs):
'''Set up the algorithm'''
raise NotImplementedError()
def update(self):
'''A single iteration of the algorithm'''
raise NotImplementedError()
def should_stop(self):
'''default stopping cryterion: number of iterations
The user can change this in concrete implementatition of iterative algorithms.'''
return self.max_iteration_stop_cryterion()
def max_iteration_stop_cryterion(self):
'''default stop cryterion for iterative algorithm: max_iteration reached'''
return self.iteration >= self.max_iteration
def __iter__(self):
'''Algorithm is an iterable'''
return self
def next(self):
'''Algorithm is an iterable
python2 backwards compatibility'''
return self.__next__()
def __next__(self):
'''Algorithm is an iterable
calling this method triggers update and update_objective
'''
if self.should_stop():
raise StopIteration()
else:
time0 = time.time()
if not self.configured:
raise ValueError('Algorithm not configured correctly. Please run set_up.')
if self.iteration == 0:
self.update_objective()
self._iteration.append(self.iteration)
self.update()
self.timing.append( time.time() - time0 )
if self.iteration % self.update_objective_interval == 0:
self.update_objective()
self.iteration += 1
def get_output(self):
'''Returns the solution found'''
return self.x
def get_last_loss(self):
'''Returns the last stored value of the loss function
if update_objective_interval is 1 it is the value of the objective at the current
iteration. If update_objective_interval > 1 it is the last stored value.
'''
return self.__loss[-1]
def get_last_objective(self):
'''alias to get_last_loss'''
return self.get_last_loss()
def update_objective(self):
'''calculates the objective with the current solution'''
raise NotImplementedError()
@property
def loss(self):
'''returns the list of the values of the objective during the iteration
The length of this list may be shorter than the number of iterations run when
the update_objective_interval > 1
'''
return self.__loss
@property
def objective(self):
'''alias of loss'''
return self.loss
@property
def max_iteration(self):
'''gets the maximum number of iterations'''
return self.__max_iteration
@max_iteration.setter
def max_iteration(self, value):
'''sets the maximum number of iterations'''
assert isinstance(value, int)
self.__max_iteration = value
@property
def update_objective_interval(self):
return self.__update_objective_interval
@update_objective_interval.setter
def update_objective_interval(self, value):
if isinstance(value, Integral):
if value >= 1:
self.__update_objective_interval = value
else:
raise ValueError('Update objective interval must be an integer >= 1')
else:
raise ValueError('Update objective interval must be an integer >= 1')
def run(self, iterations, verbose=True, callback=None):
'''run n iterations and update the user with the callback if specified'''
if self.should_stop():
print ("Stop cryterion has been reached.")
i = 0
if verbose:
print (self.verbose_header())
if self.iteration == 0:
if verbose:
print(self.verbose_output())
for _ in self:
if (self.iteration) % self.update_objective_interval == 0:
if verbose:
print (self.verbose_output())
if callback is not None:
callback(self.iteration, self.get_last_objective(), self.x)
i += 1
if i == iterations:
if self.iteration != self._iteration[-1]:
self.update_objective()
if verbose:
print (self.verbose_output())
break
def verbose_output(self):
'''Creates a nice tabulated output'''
timing = self.timing[-self.update_objective_interval-1:-1]
self._iteration.append(self.iteration)
if len (timing) == 0:
t = 0
else:
t = sum(timing)/len(timing)
out = "{:>9} {:>10} {:>13} {}".format(
self.iteration,
self.max_iteration,
"{:.3f}".format(t),
self.objective_to_string()
)
return out
def objective_to_string(self):
el = self.get_last_objective()
if type(el) == list:
string = functools.reduce(lambda x,y: x+' {:>13.5e}'.format(y), el[:-1],'')
string += '{:>15.5e}'.format(el[-1])
else:
string = "{:>20.5e}".format(el)
return string
def verbose_header(self):
el = self.get_last_objective()
if type(el) == list:
out = "{:>9} {:>10} {:>13} {:>13} {:>13} {:>15}\n".format('Iter',
'Max Iter',
'Time/Iter',
'Primal' , 'Dual', 'Primal-Dual')
out += "{:>9} {:>10} {:>13} {:>13} {:>13} {:>15}".format('',
'',
'[s]',
'Objective' , 'Objective', 'Gap')
else:
out = "{:>9} {:>10} {:>13} {:>20}\n".format('Iter',
'Max Iter',
'Time/Iter',
'Objective')
out += "{:>9} {:>10} {:>13} {:>20}".format('',
'',
'[s]',
'')
return out
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