def evaluation(
method,
path,
all_number_Para,
target_list,
real_graph_path,
is_discrete,
filenumber=10,
alaph=0.01,
):
# pre_set variables is zero
length_target_list = len(target_list)
real_p, real_c, real_s = real_p_c_s(all_number_Para, real_graph_path)
num_re, num_undirect = 0, 0
num_miss, num_extra = 0, 0
for m in range(filenumber):
completePath = path + str(m + 1) + ".csv"
data = pd.read_csv(completePath)
get_p, get_c, undirected = [[]] * length_target_list, [[]] * \
length_target_list, [[]] * length_target_list
print(get_p)
for i, target in enumerate(target_list):
if method == "PCDbyPCD":
P, c, undirected[i] = PCDbyPCD(data, target, alaph,
is_discrete)
elif method == "MBbyMB":
P, c, undirected[i] = MBbyMB(data, target, alaph, is_discrete)
elif method == "CMB":
P, c, undirected[i] = CMB(data, target, alaph, is_discrete)
else:
raise Exception("method input error!")
get_p[i] = P
get_c[i] = c
for n, target in enumerate(target_list):
reverse_direction = list(
(set(real_p[target]).intersection(set(get_c[n]))).union(
set(real_c[target]).intersection(set(get_p[n]))))
num_re += len(reverse_direction)
print(undirected)
undirected_direction = list(undirected[n])
num_undirect += len(undirected_direction)
miss_direction = list(
(set(real_p[target]).difference(set(get_p[n]))).union(
set(real_c[target]).difference(set(get_c[n]))))
num_miss += len(miss_direction)
extra_direction = list(
((set(get_p[n]).difference(real_p[target])).union(
set(get_c[n]).difference(set(real_c[target])))))
num_extra += len(extra_direction)
commonDivisor = length_target_list * filenumber
return num_undirect / commonDivisor, num_re / commonDivisor, num_miss / commonDivisor, num_extra / commonDivisor
def example(method, data, list_target, alpha, is_discrete):
file = open("../output/outputLSL.txt", "w+")
if method == "PCDbyPCD":
start_time = time.process_time()
for target in list_target:
parents, children, undirected = PCDbyPCD(data, target, alpha,
is_discrete)
file.write(
str(target) + " parents: " + str(parents) + " ,children: " +
str(children) + " ,undirected: " + str(undirected) + ".\n")
print(
str(target) + " parents: " + str(parents) + " ,children: " +
str(children) + " ,undirected: " + str(undirected))
end_time = time.process_time()
elif method == "MBbyMB":
start_time = time.process_time()
for target in list_target:
parents, children, undirected = MB_by_MB(data, target, alpha,
is_discrete)
file.write(
str(target) + " parents: " + str(parents) + " ,children: " +
str(children) + " ,undirected: " + str(undirected) + ".\n")
print(
str(target) + " parents: " + str(parents) + " ,children: " +
str(children) + " ,undirected: " + str(undirected))
end_time = time.process_time()
elif method == "CMB":
start_time = time.process_time()
for target in list_target:
parents, children, undirected = CMB(data, target, alpha,
is_discrete)
file.write(
str(target) + " parents: " + str(parents) + " ,children: " +
str(children) + " ,undirected: " + str(undirected) + ".\n")
print(
str(target) + " parents: " + str(parents) + " ,children: " +
str(children) + " ,undirected: " + str(undirected))
end_time = time.process_time()
else:
raise Exception("method input error!")
print("the Running time is: " + str(end_time - start_time))
file.write("the Running time is: " + str(end_time - start_time) + "\n")
file.close()
def evaluation_nocache(
method,
path,
all_number_Para,
target_list,
real_graph_path,
is_discrete,
filenumber=10,
alaph=0.01,
):
# pre_set variables is zero
length_target_list = len(target_list)
real_p, real_c, real_s = real_p_c_s(all_number_Para, real_graph_path)
num_re, num_undirect = 0, 0
num_miss, num_extra = 0, 0
num_true = 0
all_time = 0
num_ci = 0
for m in range(filenumber):
completePath = path + str(m + 1) + ".csv"
data = pd.read_csv(completePath)
get_p, get_c, get_un = [[]] * length_target_list, [[]] * \
length_target_list, [[]] * length_target_list
for i, target in enumerate(target_list):
if method == "PCDbyPCD":
start_time = time.process_time()
parents, children, PC, undirected, n_c = PCDbyPCD(
data, target, alaph, is_discrete)
end_time = time.process_time()
elif method == "MBbyMB":
start_time = time.process_time()
parents, children, PC, undirected, n_c = MBbyMB(
data, target, alaph, is_discrete)
end_time = time.process_time()
elif method == "CMB":
start_time = time.process_time()
parents, children, PC, undirected, n_c = CMB(
data, target, alaph, is_discrete)
end_time = time.process_time()
else:
raise Exception("method input error!")
get_p[i] = parents
get_c[i] = children
get_un[i] = undirected
all_time += end_time - start_time
num_ci += n_c
print("use time:", all_time)
for n, target in enumerate(target_list):
true_diection = list(
(set(real_p[target]).intersection(set(get_p[n]))).union(
set(real_c[target]).intersection(set(get_c[n]))))
num_true += len(true_diection)
reverse_direction = list(
(set(real_p[target]).intersection(set(get_c[n]))).union(
set(real_c[target]).intersection(set(get_p[n]))))
num_re += len(reverse_direction)
undirected_direction = list(get_un[n])
num_undirect += len(undirected_direction)
miss_direction = list(
((set(real_p[target]).difference(set(get_p[n]))).union(
set(real_c[target]).difference(set(get_c[n])))).difference(
set(reverse_direction).union(undirected_direction)))
num_miss += len(miss_direction)
extra_direction = list(
((set(get_p[n]).difference(real_p[target])).union(
set(get_c[n]).difference(set(real_c[target])))))
num_extra += len(extra_direction)
commonDivisor = length_target_list * filenumber
return num_true / commonDivisor, num_re / commonDivisor, num_miss / commonDivisor, num_extra / commonDivisor, num_undirect / commonDivisor, num_ci / commonDivisor, all_time / commonDivisor