def main():
"""
This function is an entry point of the application.
"""
# reading command-line argumets and options
parser = optparse.OptionParser()
parser.set_defaults(debug=False, xls=False)
parser.add_option('--debug', action='store_true', dest='debug')
parser.add_option('--randomized', action='store_true', dest='randomized')
(options, args) = parser.parse_args()
# obtaining execution paramters
parameters = get_execution_parameters(options, args)
if (options.debug):
print("Execution parameters: ", parameters)
# seeding random process
if (not options.randomized):
random.seed(parameters['RandomSeed'])
# reading read elements from input file
(labels, reads) = read_labels_reads(options, parameters)
if (options.debug):
print("Mutatuion labels:", labels)
print("Reads (from input):")
for x in reads:
print(x)
# creating fitness function
creator.create("FitnessMax", base.Fitness, weights=(1.0, ))
# creating strucute of the individual
creator.create("Individual", GaNode, fitness=creator.FitnessMax)
# creating toolbox for execution of the genetic algorithm
toolbox = base.Toolbox()
# registering bolean attribute to toolbbox
toolbox.register("attr_bool", random.randint, 0, 1)
# registering individual creation to toolbbox
toolbox.register("individual",
init_ga_node_individual,
creator.Individual,
labels=labels,
size=3 * len(labels))
# registering mutation operator to toolbbox
toolbox.register("mutate", mutation_ga_node)
# registering population to toolbbox
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", evaluation_ga_node, reads)
# creating one individual via toolbox
test_ind = toolbox.individual()
# printing test individual
print(test_ind)
# testing if created individual is inherited from GaMode
# and printing output
if (issubclass(type(test_ind), GaNode)):
print("Class Individual is sublass of class GaNode")
else:
print("Class Individual is NOT sublass of class GaNode")
# setting fitness of the individual
test_ind.fitness.values = (12, 0)
# executiong mutation on the individual
toolbox.mutate(test_ind)
# assign reads to nodes and calculate total distance
(assignment, diff) = assign_reads_to_tree(test_ind, reads)
print(assignment)
print(diff)
return
def main():
"""
This function is an entry point of the application.
"""
# reading command-line argumets and options
parser = optparse.OptionParser()
parser.set_defaults(debug=False, xls=False)
parser.add_option('--debug', action='store_true', dest='debug')
parser.add_option('--verbose', action='store_true', dest='verbose')
parser.add_option('--randomized', action='store_true', dest='randomized')
(options, args) = parser.parse_args()
# obtaining execution paramters
parameters = get_execution_parameters(options, args)
if (options.debug or options.verbose):
print("Execution parameters: ", parameters)
# seeding random process
if (not options.randomized):
random.seed(parameters['RandomSeed'])
# reading read elements from input file
(labels, reads) = read_labels_reads(options, parameters)
if (options.debug or options.verbose):
print("Mutation labels:", labels)
print("Reads (from input):")
for x in reads:
print(x)
# create fitness function
creator.create("FitnessMin", base.Fitness, weights=(-1.0, ))
# create strucute of the individual
creator.create("Individual", GaNode, fitness=creator.FitnessMin)
# create toolbox for execution of the genetic algorithm
toolbox = base.Toolbox()
# register bolean attribute to toolbbox
toolbox.register("attr_bool", random.randint, 0, 1)
# register individual creation to toolbbox
toolbox.register("individual",
init_ga_node_individual,
creator.Individual,
labels=labels,
size=2 * len(labels))
# register population to toolbbox
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# register evaluation function
toolbox.register("evaluate", evaluation_ga_node, reads)
# register the crossover operator
toolbox.register("mate", crossover_ga_node)
# register a mutation operator
toolbox.register("mutate", mutation_ga_node)
# operator for selecting individuals for breeding the next
# generation: each individual of the current generation
# is replaced by the 'fittest' (best) of three individuals
# drawn randomly from the current generation.
toolbox.register("select", tools.selTournament, tournsize=3)
# create an initial population, where each individual is a GaTree
population_size = 5
pop = toolbox.population(n=population_size)
if (options.verbose):
print("Population (size %d) - initial\n" % len(pop))
print(pop)
# Probability with which two individuals are crossed
crossover_probability = 0.5
# Probability for mutating an individual
mutation_probability = 0.2
if (options.debug or options.verbose):
print("Start of evolution")
# Evaluate the entire population
fitnesses = list(map(toolbox.evaluate, pop))
if (options.debug):
print("Fitnesses of individuals in population - initial")
print(fitnesses)
# Assign fitness to individuals in population
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
# Variable keeping track of the number of generations
generation = 0
# Begin the evolution
while True:
if (options.debug or options.verbose):
print("-- Generation %i --" % generation)
if (options.debug or options.verbose):
fits = [ind.fitness.values[0] for ind in pop]
length = len(pop)
mean = sum(fits) / length
sum2 = sum(x * x for x in fits)
std = abs(sum2 / length - mean**2)**0.5
print(" Fitness: ", fits)
print(" Min %s" % min(fits))
print(" Max %s" % max(fits))
print(" Avg %s" % mean)
print(" Std %s" % std)
best_in_generation = tools.selBest(pop, 1)[0]
print(" Best individual: \n %s", best_in_generation)
# A new generation
generation += 1
# Select the next generation individuals
offspring = toolbox.select(pop, len(pop))
# Clone the selected individuals
offspring = list(map(toolbox.clone, offspring))
# Apply crossover on the offspring
for child1, child2 in zip(offspring[::2], offspring[1::2]):
# cross two individuals with previously determined probability
if random.random() < crossover_probability:
toolbox.mate(child1, child2)
# fitness values of the children
# must be recalculated later
del child1.fitness.values
del child2.fitness.values
# Apply mutation on the offspring
for mutant in offspring:
# mutate an individual with previously determined probability
if random.random() < mutation_probability:
toolbox.mutate(mutant)
# fitness values of the mutant
# must be recalculated later
del mutant.fitness.values
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# The population is entirely replaced by the offspring
pop[:] = offspring
# Gather all the fitnesses in one list and print the stats
# Check if any of finishing criteria is meet
# Criteria based on number of generations
if (generation > 10):
break
# Criteria based on standard deviation of fitness in population
fits = [ind.fitness.values[0] for ind in pop]
sum2 = sum(x * x for x in fits)
std = abs(sum2 / length - mean**2)**0.5
if (std <= 0):
break
if (options.debug or options.verbose):
print("-- End of evolution --")
if (options.verbose):
print("Population (size %d) - at end\n" % len(pop))
print(pop)
best_ind = tools.selBest(pop, 1)[0]
print("Best individual is\n%s\n, with fitness %s" %
(best_ind, best_ind.fitness.values))
return