Source code for macop.algorithms.Algorithm

"""Abstract Algorithm class used as basic algorithm implementation with some specific initialization
"""

# main imports
import logging
import pkgutil
import sys
from ..utils.color import macop_text, macop_line, macop_progress


# Generic algorithm class
[docs]class Algorithm(): """Algorithm class used as basic algorithm Attributes: initalizer: {function} -- basic function strategy to initialize solution evaluator: {function} -- basic function in order to obtained fitness (mono or multiple objectives) operators: {[Operator]} -- list of operator to use when launching algorithm policy: {Policy} -- Policy class implementation strategy to select operators validator: {function} -- basic function to check if solution is valid or not under some constraints maximise: {bool} -- specify kind of optimization problem currentSolution: {Solution} -- current solution managed for current evaluation bestSolution: {Solution} -- best solution found so far during running algorithm callbacks: {[Callback]} -- list of Callback class implementation to do some instructions every number of evaluations and `load` when initializing algorithm parent: {Algorithm} -- parent algorithm reference in case of inner Algorithm instance (optional) """ def __init__(self, _initalizer, _evaluator, _operators, _policy, _validator, _maximise=True, _parent=None): self.initializer = _initalizer self.evaluator = _evaluator self.operators = _operators self.policy = _policy self.validator = _validator self.callbacks = [] self.bestSolution = None self.currentSolution = None # by default self.numberOfEvaluations = 0 self.maxEvaluations = 0 # other parameters self.parent = _parent # parent algorithm if it's sub algorithm #self.maxEvaluations = 0 # by default self.maximise = _maximise # track reference of algo into operator (keep an eye into best solution) for operator in self.operators: operator.setAlgo(self) # also track reference for policy self.policy.setAlgo(self)
[docs] def addCallback(self, _callback): """Add new callback to algorithm specifying usefull parameters Args: _callback: {Callback} -- specific Callback instance """ # specify current main algorithm reference _callback.setAlgo(self) # set as new self.callbacks.append(_callback)
[docs] def resume(self): """Resume algorithm using Callback instances """ # load every callback if many things are necessary to do before running algorithm for callback in self.callbacks: callback.load()
[docs] def initRun(self): """ Initialize the current solution and best solution """ self.currentSolution = self.initializer() # evaluate current solution self.currentSolution.evaluate(self.evaluator) # keep in memory best known solution (current solution) if self.bestSolution is None: self.bestSolution = self.currentSolution
[docs] def increaseEvaluation(self): """ Increase number of evaluation once a solution is evaluated """ self.numberOfEvaluations += 1 if self.parent is not None: self.parent.numberOfEvaluations += 1
[docs] def getGlobalEvaluation(self): """Get the global number of evaluation (if inner algorithm) Returns: {int} -- current global number of evaluation """ if self.parent is not None: return self.parent.numberOfEvaluations return self.numberOfEvaluations
[docs] def getGlobalMaxEvaluation(self): """Get the global max number of evaluation (if inner algorithm) Returns: {int} -- current global max number of evaluation """ if self.parent is not None: return self.parent.maxEvaluations return self.maxEvaluations
[docs] def stop(self): """ Global stopping criteria (check for inner algorithm too) """ if self.parent is not None: return self.parent.numberOfEvaluations >= self.parent.maxEvaluations or self.numberOfEvaluations >= self.maxEvaluations return self.numberOfEvaluations >= self.maxEvaluations
[docs] def evaluate(self, _solution): """ Evaluate a solution using evaluator passed when intialize algorithm Args: solution: {Solution} -- solution to evaluate Returns: fitness score of solution which is not already evaluated or changed Note: if multi-objective problem this method can be updated using array of `evaluator` """ return _solution.evaluate(self.evaluator)
[docs] def update(self, _solution): """ Apply update function to solution using specific `policy` Check if solution is valid after modification and returns it Args: solution: {Solution} -- solution to update using current policy Returns: {Solution} -- updated solution obtained by the selected operator """ # two parameters are sent if specific crossover solution are wished sol = self.policy.apply(_solution) if (sol.isValid(self.validator)): return sol else: logging.info("-- New solution is not valid %s" % sol) return _solution
[docs] def isBetter(self, _solution): """ Check if solution is better than best found Args: solution: {Solution} -- solution to compare with best one Returns: {bool} -- `True` if better """ # depending of problem to solve (maximizing or minimizing) if self.maximise: if _solution.fitness() > self.bestSolution.fitness(): return True else: if _solution.fitness() < self.bestSolution.fitness(): return True # by default return False
[docs] def run(self, _evaluations): """ Run the specific algorithm following number of evaluations to find optima """ # append number of max evaluation if multiple run called self.maxEvaluations += _evaluations # check if global evaluation is used or not if self.parent is not None and self.getGlobalEvaluation() != 0: # init number evaluations of inner algorithm depending of globalEvaluation # allows to restart from `checkpoint` last evaluation into inner algorithm rest = self.getGlobalEvaluation() % self.maxEvaluations self.numberOfEvaluations = rest else: self.numberOfEvaluations = 0 logging.info("Run %s with %s evaluations" % (self.__str__(), _evaluations))
[docs] def progress(self): """ Log progress and apply callbacks if necessary """ if len(self.callbacks) > 0: for callback in self.callbacks: callback.run() macop_progress(self.getGlobalEvaluation(), self.getGlobalMaxEvaluation()) logging.info("-- %s evaluation %s of %s (%s%%) - BEST SCORE %s" % (type(self).__name__, self.numberOfEvaluations, self.maxEvaluations, "{0:.2f}".format( (self.numberOfEvaluations) / self.maxEvaluations * 100.), self.bestSolution.fitness()))
[docs] def end(self): """Display end message into `run` method """ print( macop_text('({}) Found after {} evaluations \n - {}'.format( type(self).__name__, self.numberOfEvaluations, self.bestSolution))) print(macop_line())
def information(self): logging.info("-- Best %s - SCORE %s" % (self.bestSolution, self.bestSolution.fitness())) def __str__(self): return "%s using %s" % (type(self).__name__, type( self.bestSolution).__name__)