下面是一个可用于特征选择的简单 GA 代码。它使用了功能强大的 deap 库，但学习曲线可能比较陡峭。不过，这个简单的版本应该足够清晰。
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# to maximize the objective# fitness_weights = 1.0# to minimize the objective
fitness_weights =-1.0# copy the original dataframes into local copies, once
X_ga = X.copy()
y_ga = y.copy()# 'const' (the first column) is not an actual feature, do not include it
X_features = X_ga.columns.to_list()[1:]
try:
del creator.FitnessMax
del creator.Individual
except Exception as e:
pass
creator.create("FitnessMax", base.Fitness, weights=(fitness_weights,))
creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax
)
try:
del toolbox
except Exception as e:
pass
toolbox = base.Toolbox()# Attribute generator
toolbox.register("attr_bool", random.randint,0,1)# Structure initializers
toolbox.register("individual",
tools.initRepeat,
creator.Individual,
toolbox.attr_bool,len(X_features),)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
def evalOneMax(individual):
# objective function# create True/False selector list for features# and add True at the start for 'const'
cols_select =[True]+[i ==1for i in list(individual)]# fit model using the features selected from the individual
lin_mod = sm.OLS(y_ga, X_ga.loc[:, cols_select], hascnotallow=True).fit()return(lin_mod.bic,)
toolbox.register("evaluate", evalOneMax)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
random.seed(0)
pop = toolbox.population(n=300)
hof = tools.HallOfFame(1)
pop, log = algorithms.eaSimple(
pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=10, halloffame=hof, verbose=True)
best_individual_ga_small = list(hof[0])
best_features_ga_small =[
X_features[i]for i, val in enumerate(best_individual_ga_small)if val ==1]
best_objective_ga_small =(
sm.OLS(y_ga, X_ga[['const']+ best_features_ga_small], hascnotallow=True).fit().bic
)print(f'best objective: {best_objective_ga_small}')print(f'best features: {best_features_ga_small}')
这个简单的代码很容易理解，但效率很低。下面有更复杂版本的 GA 代码，更复杂、更优化的代码 1000 代后。
# to maximize the objective# fitness_weights = 1.0# to minimize the objective
fitness_weights =-1.0# copy the original dataframes into local copies, once
X_ga = X.astype(float).copy()
y_ga = y.astype(float).copy()# 'const' (the first column) is not an actual feature, do not include it
X_features = X_ga.columns.to_list()[1:]
try:
del creator.FitnessMax
del creator.Individual
except Exception as e:
pass
creator.create("FitnessMax", base.Fitness, weights=(fitness_weights,))
creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax
)
try:
del toolbox
except Exception as e:
pass
toolbox = base.Toolbox()# Attribute generator
toolbox.register("attr_bool", random.randint,0,1)# Structure initializers
toolbox.register("individual",
tools.initRepeat,
creator.Individual,
toolbox.attr_bool,len(X_features),)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
def evalMany(individuals):
# individuals is an array of shape (n_individuals, n_features)# transform it into a list of lists:# - a list of individuals# - each individual is a list of feature selectors
ind_list =[list(i)for i in list(individuals)]
ret =[]for ind in ind_list:
# create a list of True/False feature selectors from each individual# pre-pend True to always select the 'const' feature
cols_select =[True]+[i ==1for i in list(ind)]# fit model using the features selected from the individual
lin_mod = sm.OLS(y_ga, X_ga.loc[:, cols_select], hascnotallow=True).fit()
ret.append((lin_mod.bic,))return ret
# multiprocess pool to evaluate individuals
def joblib_map(f, njobs,*iters):
return Parallel(n_jobs=njobs)(delayed(f)(*args)for args in zip(*iters))
def selElitistAndTournament(
individuals, k_tournament, k_elitist=0, tournsize=3):
# elitist tournament# in addition to the regular tournament, ensure the top #k_elistist individuals are preservedreturn tools.selBest(individuals, k_elitist)+ tools.selTournament(
individuals, k_tournament, tournsize
)# Hyperparameters
population_size =1000
crossover_probability =0.5
individual_mutation_probability =0.2
gene_mutation_probability =0.1
tournament_size =3
elite_size =0
n_jobs = os.cpu_count()# register the pool as the mapper# and the custom function as the evaluator
toolbox.register("map_multi", joblib_map)
toolbox.register("evaluate", evalMany)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=gene_mutation_probability)# toolbox.register("select", tools.selTournament, tournsize=tournament_size)# selection with tournament and optional elitism
toolbox.register("select",
selElitistAndTournament,
k_tournament=population_size - elite_size,
k_elitist=elite_size,
tournsize=tournament_size,)
random.seed(0)
population = toolbox.population(n=population_size)
hall_of_fame = tools.HallOfFame(1)
objective_runs_ga =0# Evaluate the individuals with an invalid fitness
invalid_ind =[ind for ind in population ifnot ind.fitness.valid]# split the population in a list with n_jobs elements# each element is an array containing multiple individuals# send individuals to the evaluator
fitnesses_nested = toolbox.map_multi(
toolbox.evaluate, n_jobs, np.array_split(invalid_ind, n_jobs))
objective_runs_ga +=len(invalid_ind)
fitnesses =[]for l in fitnesses_nested:
fitnesses += l
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values= fit
hall_of_fame.update(population)
n_gen =1000
best_objective_per_gen_ga = np.full(n_gen, np.nan)
best_objective_ga = np.nan
best_generation_ga =0
gene_values_mean = np.zeros((n_gen,len(X_features)))
gene_maes = np.full(n_gen, np.nan)
time_to_best_ga = np.inf
# Begin the generational process
iterator = tqdm(range(1, n_gen +1),desc='generation')
t_start =time.time()for gen in iterator:
t_start_loop =time.time()# Select the next generation of individuals
offspring = toolbox.select(population)# Vary the pool of individuals via cross-over and mutation
offspring = algorithms.varAnd(
offspring,
toolbox,
crossover_probability,
individual_mutation_probability,)# Evaluate the individuals with an invalid fitness
invalid_ind =[ind for ind in offspring ifnot ind.fitness.valid]# split the population in a list with n_jobs elements# each element is an array containing multiple individuals# send list to the evaluator pool
fitnesses_nested = toolbox.map_multi(
toolbox.evaluate, n_jobs, np.array_split(invalid_ind, n_jobs))
objective_runs_ga +=len(invalid_ind)
fitnesses =[]for l in fitnesses_nested:
fitnesses += l
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values= fit
# Update the hall of fame with the generated individuals
hall_of_fame.update(offspring)# Replace the current population by the offspring
population[:]= offspring
t_end_loop =time.time()# record the mean gene values across the population
gene_values_mean[gen -1, :]= np.array(population).mean(axis=0)if gen >=2:
gene_maes[gen -1]= mean_absolute_error(
gene_values_mean[gen -2, :], gene_values_mean[gen -1, :])# pick best individual for stats recording
best_individual_ga = tools.selBest(population,1)[0]
best_objective_per_gen_ga[gen -1]= best_individual_ga.fitness.values[0]if(
best_objective_ga is np.nan
or fitness_weights * best_individual_ga.fitness.values[0]> fitness_weights * best_objective_ga
):
best_objective_ga = best_individual_ga.fitness.values[0]
best_generation_ga = gen
time_to_best_ga = t_end_loop - t_start
print(
f'gen: {gen:4n}, curr/prev gene MAE: {gene_maes[gen - 1]:.4f}, new best objective: {best_objective_ga:.4f}, time to best: {time_to_best_ga:.4f}')if os.path.isfile('break'):
# to gracefully break the loop, manually create a file called 'break'print(f'Found break file, stopping now.')
iterator.close()break
g_completed_ga = gen
best_individual_ga = list(hall_of_fame[0])
best_features_ga =[
X_features[i]for i, val in enumerate(best_individual_ga)if val ==1]
best_objective_ga =(
sm.OLS(y_ga, X_ga[['const']+ best_features_ga], hascnotallow=True).fit().bic
)## 强烈建议关注#公众号：数据STUDIO 更多优质内容每日更新！
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best objective: 33705.569572544795
best generation: 787
objective runs: 600525timeto best: 158.027 sec
遗传算法-种群
遗传算法-交叉
遗传算法-变异
