def do_fit(system, verbose): try: degree = system.model['d'] except TypeError: msg = red('Error: ') + 'Need to run mod before fit. ' clogger.error(msg) return with warnings.catch_warnings(record=True) as w: p = polyfit(system.time, system.vrad, degree) if len(w): msg = yellow('Warning: ') + 'Polyfit may be poorly conditioned. ' \ + 'Maybe try a lower degree drift?' clogger.info(msg) return p
def do_genetic(system, just_gen=False): """ Carry out the fit using a genetic algorithm and if just_gen=False try to improve it with a run of the LM algorithm """ try: degree = system.model['d'] keplerians = system.model['k'] except TypeError: msg = red('Error: ') + 'Need to run mod before gen. ' clogger.error(msg) return maxP = system.per.get_peaks(output_period=True)[1] size_maxP = 10**(len(str(int(maxP)))-1) system.fit = {} msg = blue('INFO: ') + 'Initializing genetic algorithm...' clogger.info(msg) msg = blue(' : ') + 'Model is: %d keplerians + %d drift' % (keplerians, degree) clogger.info(msg) vel = zeros_like(system.time) def chi2_1(individual): """ Fitness function for 1 planet model """ P, K, ecc, omega, T0, gam = individual get_rvn(system.time, P, K, ecc, omega, T0, gam, vel) chi2 = sum(((system.vrad - vel)/system.error)**2) #print chi2 return chi2, def chi2_n(individual): """ Fitness function for N planet model """ P, K, ecc, omega, T0, gam = [individual[i::6] for i in range(6)] #print ecc get_rvn(system.time, P, K, ecc, omega, T0, gam[0], vel) #print 'out of get_rvn' chi2 = sum(((system.vrad - vel)/system.error)**2) #print chi2 return chi2, ## create the required types -- the fitness and the individual. creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) # minimization of a single objective creator.create("Individual", list, fitness=creator.FitnessMin) ## create parameters by sampling from their priors def P_prior(): return random.uniform(5, 1000) # return random.gauss(maxP, size_maxP) def K_prior(): return random.uniform(0, 150) def ecc_prior(): return random.uniform(0, 0.9) def om_prior(): return random.uniform(0, 360) def t0_prior(): return random.uniform(2350000, 2550000) def gamma_prior(): return random.uniform(-100, 100) priors = [P_prior, K_prior, ecc_prior, om_prior, t0_prior, gamma_prior] toolbox = base.Toolbox() toolbox.register("individual", tools.initCycle, creator.Individual, priors, n=keplerians) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def mutPrior(individual, indpb): for i, fcn in enumerate(zip(individual, priors)): if random.random() < indpb: individual[i] = fcn[1]() return individual, toolbox.register("evaluate", chi2_n) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", mutPrior, indpb=0.10) toolbox.register("select", tools.selTournament, tournsize=3) npop = 500 ngen = 150 npar = 5*keplerians+1 ## build the population pop = toolbox.population(n=npop) ## helper functions hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("avg", nanmean) # stats.register("std", nanstd) stats.register("min", np.nanmin) # stats.register("max", np.nanmax) # stats.register("total", sigma3) stats.register("red", lambda v: min(v)/(len(system.time)-npar) ) msg = blue('INFO: ') + 'Created population with N=%d. Going to evolve for %d generations...' % (npop,ngen) clogger.info(msg) ## run the genetic algorithm algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=ngen, stats=stats, halloffame=hof, verbose=True) ## output results information msg = yellow('RESULT: ') + 'Best individual is' clogger.info(msg) ## loop over planets print("%3s %12s %10s %10s %10s %15s %9s" % \ ('', 'P[days]', 'K[km/s]', 'e', unichr(0x3c9).encode('utf-8')+'[deg]', 'T0[days]', 'gam') ) for i, planet in enumerate(list(ascii_lowercase)[:keplerians]): P, K, ecc, omega, T0, gam = [hof[0][j::6] for j in range(6)] print("%3s %12.1f %10.2f %10.2f %10.2f %15.2f %9.2f" % (planet, P[i], K[i], ecc[i], omega[i], T0[i], gam[i]) ) msg = yellow('RESULT: ') + 'Best fitness value: %s\n' % (hof[0].fitness) clogger.info(msg) if just_gen: # save fit in the system and return, no need for LM system.fit['params'] = hof[0] system.fit['chi2'] = hof[0].fitness/(len(system.time)-npar) return msg = blue('INFO: ') + 'Calling LM to improve result...' clogger.info(msg) ## call levenberg markardt fit lm = do_lm(system, [hof[0][j::6] for j in range(6)]) lm_par = lm[0] ## loop over planets msg = yellow('RESULT: ') + 'Best fit is' clogger.info(msg) print("%3s %12s %10s %10s %10s %15s %9s" % \ ('', 'P[days]', 'K[km/s]', 'e', unichr(0x3c9).encode('utf-8')+'[deg]', 'T0[days]', 'gam') ) for i, planet in enumerate(list(ascii_lowercase)[:keplerians]): P, K, ecc, omega, T0, gam = [lm_par[j::6] for j in range(6)] print("%3s %12.1f %10.2f %10.2f %10.2f %15.2f %9.2f" % (planet, P[i], K[i], ecc[i], omega[i], T0[i], gam[i]) ) chi2 = chi2_n(lm_par)[0] msg = yellow('RESULT: ') + 'Best fitness value: %f, %f' % (chi2, chi2/(len(system.time)-npar)) clogger.info(msg) # save fit in the system system.fit['params'] = lm_par system.fit['chi2'] = chi2 # # print p.minFitness, p.maxFitness, p.avgFitness, p.sumFitness # print 'Genetic:', p.bestFitIndividual, p.bestFitIndividual.fitness # lm = do_lm(system, p.bestFitIndividual.genes) # lm_par = lm[0] # print 'LM:', lm_par # # get best solution curve # new_time = system.get_time_to_plot() # vel = zeros_like(new_time) # P, K, ecc, omega, t0 = p.bestFitIndividual.genes # get_rv(new_time, P, K, ecc, omega, t0, vel) # # plot RV with time # plot(system.time, system.vrad, 'o') # plot(new_time, vel, '-') # P, K, ecc, omega, t0 = lm_par # get_rv(new_time, P, K, ecc, omega, t0, vel) # plot(new_time, vel, 'r-') # show() return