def table_best():
stats_cx = open_pickle("data/cx_compared_result")
stats_no_cx = open_pickle("data/no_cx_compared_result")
# hardcoded!!!
cx_hg = np.mean(stats_cx["fitnesses"][6], axis=0)
cx_mw = np.mean(stats_cx["fitnesses"][4], axis=0)
no_cx_hg = np.mean(stats_no_cx["fitnesses"][2], axis=0)
no_cx_mw = np.mean(stats_no_cx["fitnesses"][2], axis=0)
print("CX highest gain: " + str(cx_hg))
print("CX most wins: " + str(cx_mw))
print("No CX highest gain: " + str(no_cx_hg))
print("No CX most wins: " + str(no_cx_mw))
def get_best(save=False):
algs = ["cx", "no_cx"]
for alg in algs:
all_gains = []
all_wins = []
print(alg)
stats = open_pickle("data/" + alg + "_compared_result")
for i, s in enumerate(stats["fitnesses"]):
avg_fitness = np.mean(s, axis=0)
gain = np.sum(avg_fitness)
wins = np.sum(avg_fitness >= 0)
all_gains.append(gain)
all_wins.append(wins)
print(i, avg_fitness, gain, wins)
highest_gain = np.argmax(all_gains)
most_wins = np.argmax(all_wins)
print("Highest gain: run " + str(highest_gain) + ", most wins: run " +
str(most_wins))
if save:
np.savetxt("data/" + alg + "_highest_gain.txt", highest_gain)
np.savetxt("data/" + alg + "_most_wins.txt", most_wins)
def evol_time():
algs = ["cx", "no_cx"]
fig, ax = plt.subplots()
for alg in algs:
c = next(color_cycle)
stats = open_pickle("data/" + alg + "_merged_stats")
plt.plot(stats["gen"],
np.amax(stats["max"], axis=0),
".",
color=c,
alpha=0.8)
plt.plot(stats["gen"][np.argmax(np.amax(stats["max"], axis=0))],
np.max(np.amax(stats["max"], axis=0)),
"x",
markersize=20,
color=c)
plt.errorbar(stats["gen"],
np.mean(stats["avg"], axis=0),
yerr=np.std(stats["avg"], axis=0),
label=alg,
color=c)
fig.tight_layout()
plt.xlabel("Generation")
plt.ylabel("Gain")
plt.legend()
plt.savefig("evolution-over-time.pdf")
plt.show()
def merge_runs(ea_type):
n = 10
population = []
for i in range(n):
individual = open_pickle("data/" + ea_type + "_run_" + str(i) +
"_individual")[0]
population.append(individual)
save_pickle(population, "data/" + ea_type + "_merged_individuals")
def best_fitnesses_barplot():
stats_cx = open_pickle("data/cx_compared_result")
stats_no_cx = open_pickle("data/no_cx_compared_result")
# hardcoded!!!
cx_hg = np.mean(stats_cx["fitnesses"][6], axis=0)
cx_mw = np.mean(stats_cx["fitnesses"][4], axis=0)
no_cx_hg = np.mean(stats_no_cx["fitnesses"][2], axis=0)
no_cx_mw = np.mean(stats_no_cx["fitnesses"][2], axis=0)
best = [cx_hg, cx_mw, no_cx_hg, no_cx_mw]
df_all = pd.DataFrame()
for i, b in enumerate(best):
df_tmp = pd.DataFrame()
df_tmp["Fitness"] = b
df_tmp["Enemy"] = np.arange(8) + 1
df_tmp["Enemy_name"] = df_tmp["Enemy"].map(ENEMY_MAP)
if i == 0:
alg_type = "CX (highest_gain)"
elif i == 1:
alg_type = "CX (most wins)"
elif i == 2:
alg_type = "No CX (highest gain)"
elif i == 3:
alg_type = "No CX (most wins)"
df_tmp["Alg_type"] = alg_type
df_all = pd.concat([df_all, df_tmp])
print(df_all)
fig, ax = plt.subplots()
sns.barplot(x="Enemy",
y="Fitness",
hue="Alg_type",
palette=["C0", "C4", "C1", "C3"],
data=df_all)
fig.tight_layout()
plt.savefig("bar-plot.pdf")
plt.show()
def merge_stats(ea_type):
n = 10
avgs = []
maxs = []
for i in range(n):
stats = open_pickle("data/" + ea_type + "_run_" + str(i) + "_stats")
avgs.append(stats["avg"][0])
maxs.append(stats["max"][0])
sts = {"gen": stats["gen"], "avg": avgs, "max": maxs}
save_pickle(sts, "data/" + ea_type + "_merged_stats")
def main(ea_type):
fittest_individuals = open_pickle("data/" + ea_type +
"_merged_individuals")
pop_fitnesses = []
for fi in fittest_individuals:
ind_fitnesses = []
for _ in range(0, 5):
f = run(fi)
ind_fitnesses.append(f)
pop_fitnesses.append(ind_fitnesses)
print(ind_fitnesses)
stats = {"individuals": fittest_individuals, "fitnesses": pop_fitnesses}
save_pickle(stats, "data/" + ea_type + "_compared_result")
def boxplot():
algs = ["cx", "no_cx"]
df_all = pd.DataFrame()
for alg in algs:
color = next(color_cycle)
stats = open_pickle("data/" + alg + "_compared_result")
df_tmp = pd.DataFrame()
df_tmp["Gain"] = np.sum(np.mean(stats["fitnesses"], axis=1), axis=1)
df_tmp["Alg"] = alg
df_all = pd.concat([df_all, df_tmp])
fig, ax = plt.subplots()
ax = sns.boxplot(x="Alg", y="Gain", data=df_all)
fig.tight_layout()
plt.savefig("box-plot.pdf")
plt.show()