def run_deap(X, y):
popsize = 500
mutRate = 0.4
crRate = 0.6
GenMax = 250
# concatenate selected features with their target values
dataset = np.column_stack((X, y))
evo = Evolution(
dataset=dataset.tolist(), # data samples
popsize=popsize, # initial population size
hofsize=25, # the number of best individual to track
cx=crRate, # crossover rate
mut=mutRate, # mutation rate
maxgen=GenMax, # max number of generations
)
pop, logbook, hof = evo.run(verbose=1)
return evo, pop, logbook, hof
evo = Evolution(size=i,
fitness=fitness,
combine_params=0.5,
mutate_params={
"std": 0.5,
"dim": 1,
"min": 0,
"max": 5
},
pop_params={
"min": 0,
"max": 5,
"dim": 1
},
method="combine")
evo.run(epochs)
results.append(evo.result)
for i in labels:
evo = Evolution(size=i,
fitness=fitness,
combine_params=0.1,
mutate_params={
"std": 0.5,
"dim": 1,
"min": 0,
"max": 5
},
pop_params={
"min": 0,
"max": 5,
"dim": 1
mut = mutRate[6], # mutation rate
maxgen = GenMax[2], # max number of generations
)
logs = pd.DataFrame()
gen = np.zeros((reps, GenMax[2]+1))
nevals = np.zeros((reps, GenMax[2]+1))
avg = np.zeros((reps, GenMax[2]+1))
mini = np.zeros((reps, GenMax[2]+1))
maxi = np.zeros((reps, GenMax[2]+1))
for l in range(reps):
np.random.seed(reps)
pop, logbook, hof= evo.run()
gen[l][:] = logbook.select('gen')
nevals[l][:] = logbook.select('nevals')
avg[l][:] = logbook.select('avg')
mini[l][:] = logbook.select('min')
maxi[l][:] = logbook.select('max')
AvgEval = []
Avg = []
AvgMin = []
AvgMax = []
for n in range(GenMax[2]+1):
totalEval = 0
totalAvg = 0
totalMin = 0
