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optimizer.py

Super class for optimization algorithms

class Optimizer(object):

Parent class for all heuristic optimization algorithms.

Parameters:

  • ...

def __init__(self, logger, logname, optlogger, taskdef, simdbin, opdbin, simdbout, opdbout, **keywords):

Initialize optimizer:

>>> epsilon = 0.00001
>>> from simo.optimization.optimizer import Optimizer
>>> execfile('optimization/test/mocks4optimizer.py')
>>> chart_path = 'optimization/test'
>>> opt = Optimizer(logger, logname, taskdef, simdbin, simdbout,
...                 False, False, False, chart_path, True, 'test', True,
...                 keyword1='test-kw')

def _add_error(self, msg):

Log error message:

>>> opt._add_error('Test error message')  
Called Logger.log_message('optimization-test', 'ERROR',
                          'Test error message')

def _add_info(self, msg):

Log info message

>>> opt._add_info('Test info message')  
Called Logger.log_message('optimization-test', 'INFO',
                          'Test info message')

def set_data(self, iteration):

Parameters

iteration -- current simulation iteration indice as int

Set optimization data: collect values from data and operation result databases into data structure:

>>> opt._data = omatrix1  # replace data handler with a mock
>>> opt.set_data(0)  
Called OMatrix.construct_data(0)
Called Logger.log_message(
    'optimization-test',
    'ERROR',
    'Could not construct optimization data matrix')
False
>>> opt._data = omatrix2  # replace data handler with another mock
>>> opt.set_data(0)  
Called OMatrix.construct_data(0)
True

>>> opt._global_best_U
>>> opt._global_best_sol
array([0, 0, 0, 0, 0])

def _lambda_opt_move(self, solution, mylambda):

Do a random lambda opt move, ie. change the treatment of lambda units randomly in the current solution

Parameters:

solution -- current solution as numpy array
mylambda -- the number of units to be changed as int
>>> import numpy
>>> S = numpy.zeros(5, dtype=int)
>>> S_new = numpy.copy(S)
>>> opt._lambda_opt_move(S_new, 1)
True
>>> sum(S != S_new)
1
>>> S_new = numpy.copy(S)
>>> opt._lambda_opt_move(S_new, 2)
True
>>> sum(S != S_new)
2
>>> S_new = numpy.copy(S)
>>> opt._lambda_opt_move(S_new, 3)
True
>>> sum(S != S_new)
3
>>> S_new = numpy.copy(S)
>>> opt._lambda_opt_move(S_new, 4)
True
>>> sum(S != S_new)
4

Try doing N-opt move when number of units equals N and some of the units have only a single branch (impossible lambda-opt move):

>>> S_new = numpy.copy(S)
>>> opt._lambda_opt_move(S_new, 5)
Called Logger.log_message(
    'optimization-test',
    'ERROR',
    'Could not make 5-opt move!')
False

def _generate_random_solution(self, num_of_solutions=100):

Choose a feasible random solution by randomly choosing a branch for each unit in the data.

Parameters:

num_of_solutions -- number of initial solutions as int
>>> opt._generate_random_solution(2) 
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  finding random initial solution')
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  feasible initial solution found in ... secs')
True
>>> opt._curr_sol
array([0, 0, 0, 0, 0])
>>> opt._best_sol
array([0, 0, 0, 0, 0])

def _store_solution(self, limit=None):

Store the optimal solution of a single iteration

Parameters:

limit -- maximum unit number as int
>>> opt._best_U = 0.8
>>> opt._best_sol[:] = [2,9,5,3,7]
>>> opt._store_solution() 
Called OMatrix.compare_utilities(None, 0.8...)
Called OMatrix.get_status(array([2, 9, 5, 3, 7]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  utility: 0.800\tbest so far: 0.800')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
>>> opt._best_sol[:] = [3,4,6,0,1]
>>> opt._store_solution() 
Called OMatrix.compare_utilities(0.8..., 0.8...)
Called OMatrix.get_status(array([3, 4, 6, 0, 1]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  utility: 0.800\tbest so far: 0.800')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
>>> opt._global_best_sol
array([3, 4, 6, 0, 1])

def _store_results(self, repeats, iteration, log_util=True, skip_weight_splits=None):

Parameters:

iteration -- current simulated iteration indice as int

Store the optimum treatment schedules to result databases

>>> opt._store_results(3, 0)  
Called OMatrix.get_status(array([3, 4, 6, 0, 1]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Best utility out of 3 repeats: 0.800')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Writing optimized treatment schedule to result database(s)...')
Called SimInputDB.store_solution(
    [...],
    'test',
    False)
Called SimInputDB.copy_to_db(
    [...],
    'comp_unit',
    <Mock ... SimOutputDB>,
    0,
    skip_weight_splits=None)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Writing completed in ... min')

def _run_algorithm(self, runf, repeats, initial_solutions):

Run an optimization algorithm

Parameters:

runf – algorithm specific runner method repeats – number of repeats as int initial_solutions – number of initial random solutions as int
>>> from minimock import Mock
>>> runf = Mock('run_my_algorithm')
>>> opt._stat_logger = Mock('StatLogger')
>>> opt._run_algorithm(runf, 3, 2)  
Called OMatrix.analyze_data(
    <Mock ... SimInputDB>,
    <bound method Optimizer._add_info of <simo.optimization.optimizer.Optimizer object at ...>>)
Called OMatrix.construct_data(0)
Called StatLogger.start(
    0,
    {0: [0, 1], 1: [0, 2, 3, 4, 5], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0]},
    'max')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  finding random initial solution')
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  feasible initial solution found in ... secs')
Called run_my_algorithm()
Called StatLogger.add_repeat()
Called OMatrix.compare_utilities(None, 1.0)
Called OMatrix.get_status(array([0, 0, 0, 0, 0]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  utility: 1.000\tbest so far: 1.000')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  finding random initial solution')
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  feasible initial solution found in ... secs')
Called run_my_algorithm()
Called StatLogger.add_repeat()
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_status(array([0, 0, 0, 0, 0]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  utility: 1.000\tbest so far: 1.000')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  finding random initial solution')
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  feasible initial solution found in ... secs')
Called run_my_algorithm()
Called StatLogger.add_repeat()
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_status(array([0, 0, 0, 0, 0]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  utility: 1.000\tbest so far: 1.000')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called StatLogger.stop(0)
Called OMatrix.get_status(array([0, 0, 0, 0, 0]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Best utility out of 3 repeats: 1.000')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Writing optimized treatment schedule to result database(s)...')
Called SimInputDB.store_solution(
    [(0, 'UNIT1', 'UNIT1', 0, 1.0, False), (0, 'UNIT2', 'UNIT2', 0, 1.0, False), (0, 'UNIT3', 'UNIT3', 0, 1.0, False), (0, 'UNIT4', 'UNIT4', 0, 1.0, False), (0, 'UNIT5', 'UNIT5', 0, 1.0, False)],
    'test',
    False)
Called SimInputDB.copy_to_db(
    [(0, 'UNIT1', 'UNIT1', 0, 1.0, False), (0, 'UNIT2', 'UNIT2', 0, 1.0, False), (0, 'UNIT3', 'UNIT3', 0, 1.0, False), (0, 'UNIT4', 'UNIT4', 0, 1.0, False), (0, 'UNIT5', 'UNIT5', 0, 1.0, False)],
    'comp_unit',
    <Mock ... SimOutputDB>,
    0,
    skip_weight_splits=None)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Writing completed in ... min')
Called OMatrix.construct_data(1)
Called StatLogger.start(
    1,
    {0: [0, 1], 1: [0, 2, 3, 4, 5], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0]},
    'max')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  finding random initial solution')
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  feasible initial solution found in ... secs')
Called run_my_algorithm()
Called StatLogger.add_repeat()
Called OMatrix.compare_utilities(None, 1.0)
Called OMatrix.get_status(array([0, 0, 0, 0, 0]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  utility: 1.000\tbest so far: 1.000')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  finding random initial solution')
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  feasible initial solution found in ... secs')
Called run_my_algorithm()
Called StatLogger.add_repeat()
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_status(array([0, 0, 0, 0, 0]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  utility: 1.000\tbest so far: 1.000')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  finding random initial solution')
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_random_solution()
Called OMatrix.solution_feasibility(array([0, 0, 0, 0, 0]), first_seek=True)
Called OMatrix.solution_utility(array([0, 0, 0, 0, 0]))
Called OMatrix.compare_utilities(1.0, 1.0)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  feasible initial solution found in ... secs')
Called run_my_algorithm()
Called StatLogger.add_repeat()
Called OMatrix.compare_utilities(1.0, 1.0)
Called OMatrix.get_status(array([0, 0, 0, 0, 0]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  utility: 1.000\tbest so far: 1.000')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called StatLogger.stop(1)
Called OMatrix.get_status(array([0, 0, 0, 0, 0]))
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Best utility out of 3 repeats: 1.000')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  1. subobjective: 12345.679')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    '  2. subobjective: 234.568')
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Writing optimized treatment schedule to result database(s)...')
Called SimInputDB.store_solution(
    [(1, 'UNIT1', 'UNIT1', 0, 1.0, False), (1, 'UNIT2', 'UNIT2', 0, 1.0, False), (1, 'UNIT3', 'UNIT3', 0, 1.0, False), (1, 'UNIT4', 'UNIT4', 0, 1.0, False), (1, 'UNIT5', 'UNIT5', 0, 1.0, False)],
    'test',
    False)
Called SimInputDB.copy_to_db(
    [(1, 'UNIT1', 'UNIT1', 0, 1.0, False), (1, 'UNIT2', 'UNIT2', 0, 1.0, False), (1, 'UNIT3', 'UNIT3', 0, 1.0, False), (1, 'UNIT4', 'UNIT4', 0, 1.0, False), (1, 'UNIT5', 'UNIT5', 0, 1.0, False)],
    'comp_unit',
    <Mock ... SimOutputDB>,
    1,
    skip_weight_splits=None)
Called Logger.log_message(
    'optimization-test',
    'INFO',
    'Writing completed in ... min')

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