def FinalProcessData(AVY_dprove_path, pdr_IC3_path, iimc_path, data_path):
method_list = utils.method_list
AVY_dprove_dic = utils.ReadJson(AVY_dprove_path)
pdr_IC3_dic = utils.ReadJson(pdr_IC3_path)
pdr_IC3_name_list = list(pdr_IC3_dic.keys())
iimc_dic = utils.ReadJson(iimc_path)
data_list = []
for name in pdr_IC3_name_list:
temp_data = [name]
temp_data.append(AVY_dprove_dic[name]["dprove"])
temp_data.append(pdr_IC3_dic[name]["pdr"])
temp_data.append(iimc_dic[name]["iimc"])
temp_data.append(pdr_IC3_dic[name]["IC3"])
mark = False
for time in temp_data[1:]:
if time != "timeout" and time != "failed" and time != "0.0":
mark = True
if mark == True:
data_list.append(temp_data)
title = "filename"
for method in method_list:
title = title + "," + method
with open(data_path, "w") as writer:
writer.write(title + "\n")
for line in data_list:
writer.write(",".join(line) + "\n")
def FormerProcessData(others_path, data_path, AVY_dprove_path=""):
method_list = utils.method_list
others_list = utils.ReadJson(others_path)
others_dic = {}
for i in range(len(others_list)):
aig = others_list[i]
aig_name = list(aig.keys())[0]
run_times = aig[aig_name]
temp_dic = {}
for method_time_pair in run_times:
method = list(method_time_pair.keys())[0]
time = str(method_time_pair[method])
temp_dic[method] = time
others_dic[aig_name] = temp_dic
others_name_list = list(others_dic.keys())
# print(len(others_name_list))
AVY_dprove_name_list = []
AVY_dprove_dic = {}
if AVY_dprove_path != "":
AVY_dprove_dic = utils.ReadJson(AVY_dprove_path)
AVY_dprove_name_list = list(AVY_dprove_dic.keys())
# print(len(AVY_dprove_name_list))
data_list = []
if AVY_dprove_path != "":
others_method_list = copy.deepcopy(method_list)
others_method_list.remove("dprove")
for name in AVY_dprove_name_list:
if all(keyword in others_dic[name].keys() for keyword in others_method_list):
temp_data = [name, AVY_dprove_dic[name]["dprove"]]
for method in others_method_list:
temp_data.append(others_dic[name][method])
mark = False
for time in temp_data[1:]:
if time != "timeout" and time != "failed" and time != "0.0":
mark = True
if mark == True:
data_list.append(temp_data)
else:
for name in others_name_list:
if all(keyword in others_dic[name].keys() for keyword in method_list):
temp_data = [name]
for method in method_list:
temp_data.append(others_dic[name][method])
mark = False
for time in temp_data[1:]:
if time != "timeout" and time != "failed" and time != "0.0":
mark = True
if mark == True:
data_list.append(temp_data)
title = "filename"
for method in method_list:
title = title + "," + method
with open(data_path, "w") as writer:
writer.write(title + "\n")
for line in data_list:
writer.write(",".join(line) + "\n")
def ProcessNameList(train_name_list_path, test_name_list_path):
train_name_list = utils.ReadJson(train_name_list_path)
test_name_list = utils.ReadJson(test_name_list_path)
total_name_list = list(set(train_name_list) | set(test_name_list))
processed_name_list = []
for name in total_name_list:
processed_name_list.append(name.split(".aig")[0])
return total_name_list, processed_name_list
def Statistic(train_label_dic_path, test_label_dic_path):
train_label_dic = utils.ReadJson(train_label_dic_path)
test_label_dic = utils.ReadJson(test_label_dic_path)
statistic_dic = utils.Statistic([train_label_dic])
print("train_data")
print(statistic_dic)
statistic_dic = utils.Statistic([test_label_dic])
print("test_data")
print(statistic_dic)
statistic_dic = utils.Statistic([train_label_dic, test_label_dic])
print("all_data")
print(statistic_dic)
def ClassifyAddCol(data, name_list, classify_predict_path, choose_top_method_number):
method_list = utils.method_list
predict = utils.ReadJson(classify_predict_path)
statistic_dic = utils.InitialDic()
for index in range(len(data)):
target_method_list = predict[name_list[index]]
predict_time = "None"
point = 0
for i in range(choose_top_method_number):
target_method = target_method_list[i]
assert(target_method in method_list)
temp_point = method_list.index(target_method) + 1
temp_time = data[index][temp_point]
if predict_time == "None":
predict_time = temp_time
point = temp_point
elif temp_time != "timeout" and temp_time != "failed" and temp_time != "0.0" and temp_time != "0":
if predict_time == "timeout" or predict_time == "failed" or predict_time == "0.0" or predict_time == "0":
predict_time = temp_time
point = temp_point
elif float(temp_time) < float(predict_time):
predict_time = temp_time
point = temp_point
data[index].append(predict_time)
statistic_dic[method_list[point - 1]] += 1
return data
def ProcessiimcData(iimc_path, data_path):
method_list = utils.method_list
iimc_dic = utils.ReadJson(iimc_path)
iimc_name_list = list(iimc_dic.keys())
# print(len(iimc_name_list))
data_list = []
for name in iimc_name_list:
if all(keyword in iimc_dic[name].keys() for keyword in method_list):
time_list = []
for method in method_list:
time_list.append(iimc_dic[name][method])
temp_data = [name]
for method in method_list:
temp_data.append(iimc_dic[name][method])
data_list.append(temp_data)
title = "filename"
for method in method_list:
title = title + "," + method
with open(data_path, "w") as writer:
writer.write(title + ",DeepChecker\n")
for line in data_list:
writer.write(",".join(line) + "\n")
def SplitData(data_path,
train_data_path,
test_data_path,
new_format_json_path,
remove_mark=False):
with open(data_path, "r") as csvfile:
data = list(csv.reader(csvfile))
print(len(data))
new_format = utils.ReadJson(new_format_json_path)
new_data = []
if remove_mark:
for item in data[1:]:
if item[0] not in new_format:
new_data.append(item)
else:
new_data = data[1:]
train_data, test_data = train_test_split(new_data, test_size=0.2)
print(len(train_data))
print(len(test_data))
with open(train_data_path, 'w') as writer:
for line in train_data:
writer.write(",".join(line) + "\n")
with open(test_data_path, 'w') as writer:
for line in test_data:
writer.write(",".join(line) + "\n")
def ClassifyAddPredictionWithEncoding(predict_data_path,
predict_data_with_encoding_path,
encoding_time_path_0,
encoding_time_path_1,
encoding_time_path_2):
with open(predict_data_path, newline='') as csvfile:
data = list(csv.reader(csvfile))
title_list = data[0]
data = data[1:]
encoding_time_path_list = [
encoding_time_path_0, encoding_time_path_1, encoding_time_path_2
]
for i in range(len(encoding_time_path_list)):
encoding_time_path = encoding_time_path_list[i]
encoding_time_dic = utils.ReadJson(encoding_time_path)
for index in range(len(data)):
name = data[index][0].split(".aig")[0]
encoding_time = encoding_time_dic[name]
predict_time = data[index][i + 5]
if predict_time != "timeout" and predict_time != "failed" and predict_time != "0.0" and predict_time != "0":
total_time = str(encoding_time + float(predict_time))
else:
total_time = "timeout"
data[index].append(total_time)
title = ",".join(title_list)
for encoding_layer in utils.encoding_layer_list:
title = title + "," + "AddEncoding_" + encoding_layer
with open(predict_data_with_encoding_path, "w") as writer:
writer.write(title + "\n")
for line in data:
writer.write(",".join(line) + "\n")
def StatisticSamples(test_label_dic_path, statistic_name_dic_path):
test_label_dic = utils.ReadJson(test_label_dic_path)
statistic_name_dic = {}
for method in utils.method_list:
statistic_name_dic[method] = []
for name in test_label_dic.keys():
statistic_name_dic[test_label_dic[name]].append(name)
utils.WriteJson(statistic_name_dic, statistic_name_dic_path)
def ProcessData(AVY_dprove_path, pdr_IC3_path, others_path, data_path):
method_list = utils.method_list
AVY_dprove_dic = utils.ReadJson(AVY_dprove_path)
pdr_IC3_dic = utils.ReadJson(pdr_IC3_path)
pdr_IC3_name_list = list(pdr_IC3_dic.keys())
others_list = utils.ReadJson(others_path)
others_dic = {}
for i in range(len(others_list)):
aig = others_list[i]
aig_name = list(aig.keys())[0]
run_times = aig[aig_name]
temp_dic = {}
for method_time_pair in run_times:
method = list(method_time_pair.keys())[0]
time = str(method_time_pair[method])
temp_dic[method] = time
others_dic[aig_name] = temp_dic
data_list = []
for name in pdr_IC3_name_list:
temp_data = [name]
temp_data.append(AVY_dprove_dic[name]["dprove"])
temp_data.append(pdr_IC3_dic[name]["pdr"])
temp_data.append(others_dic[name]["iimc"])
temp_data.append(pdr_IC3_dic[name]["IC3"])
mark = False
for time in temp_data[1:]:
if time != "timeout" and time != "failed" and time != "0.0" and time != "0":
mark = True
if mark == True:
data_list.append(temp_data)
title = "filename"
for method in method_list:
title = title + "," + method
with open(data_path, "w") as writer:
writer.write(title + "\n")
for line in data_list:
writer.write(",".join(line) + "\n")
def GeneratePrediction(name_list_path, classify_predict, layer):
name_list = utils.ReadJson(name_list_path)
title = "filename,predict"
data = []
for name in name_list:
line = []
line.append(name)
line.append(classify_predict[name][0])
data.append(line)
with open(utils.classify_predict_path + "predict_" + str(layer) + ".csv",
"w") as writer:
writer.write(title + "\n")
for line in data:
writer.write(",".join(line) + "\n")
def Predict(name_list_path, model_path, layer, method):
name_list = utils.ReadJson(name_list_path)
if layer == 0:
encoding_dic_dir = utils.encoding_dic_dir_0
elif layer == 1:
encoding_dic_dir = utils.encoding_dic_dir_1
elif layer == 2:
encoding_dic_dir = utils.encoding_dic_dir_2
vec_list = utils.GetVecListFromDic(encoding_dic_dir, name_list)
model = utils.Load_pkl(model_path)
predict_time_list = model.predict(vec_list)
time_predict = predict_time_list.tolist()
time_predict_path = utils.time_predict_path + "predict_" + method + "_" + str(layer) + ".json"
utils.WriteJson(time_predict, time_predict_path)
return time_predict
def Predict(name_list_path, model_path, layer):
name_list = utils.ReadJson(name_list_path)
if layer == 0:
encoding_dic_dir = utils.encoding_dic_dir_0
elif layer == 1:
encoding_dic_dir = utils.encoding_dic_dir_1
elif layer == 2:
encoding_dic_dir = utils.encoding_dic_dir_2
vec_list = utils.GetVecListFromDic(encoding_dic_dir, name_list)
model = utils.Load_pkl(model_path)
predictions = model.predict_proba(vec_list)
predict_label_list = np.argsort(-predictions, axis=1)
classify_predict_path = utils.classify_predict_path + "predict_" + str(layer) + ".json"
classify_predict = GeneratePredictResult(name_list, predict_label_list, classify_predict_path)
return classify_predict
def GeneratePrediction(name_list_path, time_predict, layer, method):
name_list = utils.ReadJson(name_list_path)
title = "filename,predict"
data = []
for name in name_list:
line = []
line.append(name)
line.append(str(time_predict[name_list.index(name)]))
data.append(line)
with open(
utils.time_predict_path + "predict_" + method + "_" + str(layer) +
".csv", "w") as writer:
writer.write(title + "\n")
for line in data:
writer.write(",".join(line) + "\n")
def ChangeData(data):
if data != "timeout" and data != "failed" and data != "0.0" and data != "0":
changed_data = float(data)
else:
changed_data = 3600.00
return changed_data
if __name__ == '__main__':
time_basic_data_path = utils.time_basic_data_path
for method in utils.method_list:
save_path = utils.time_result_path + "2-depth_Encoding_Sort_" + method + ".pdf"
plt.figure()
predict_name_sort = utils.ReadJson(time_basic_data_path + method +
"_name_sort_2.json")
truth_name_sort = utils.ReadJson(time_basic_data_path + method +
"_name_sort_truth.json")
yaxis = list(range(len(truth_name_sort)))
xaxis = []
for name in truth_name_sort:
xaxis.append(predict_name_sort.index(name))
plt.plot([0, 824], [0, 824], color="k", linewidth=2)
plt.scatter(xaxis, yaxis, s=7, color="k")
# temp_ax.set_xscale('linear')
# temp_ax.set_yscale('linear')
plt.title(utils.NameMap(method), size=30)
plt.xlim(0, 824)
plt.ylim(0, 824)
plt.xticks(range(0, len(yaxis), 200),
import utils
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
classify_basic_data_path = utils.classify_basic_data_path
embedded_dir_0 = utils.embedded_dir_0
embedded_dir_1 = utils.embedded_dir_1
embedded_dir_2 = utils.embedded_dir_2
statistic_name_dic_path = classify_basic_data_path + "statistic_name_dic.json"
statistic_name_dic = utils.ReadJson(statistic_name_dic_path)
statistic_sample_distribution_path = utils.statistic_sample_distribution_path
for i in range(len(utils.encoding_layer_list)):
dir = [embedded_dir_0, embedded_dir_1, embedded_dir_2][i]
for method in utils.method_list:
temp_path = statistic_sample_distribution_path + method + "_distribution_" + str(i) + ".pdf"
plt.figure()
index = utils.method_list.index(method)
name_list = statistic_name_dic[method]
vec_list = utils.GetVecList(dir, name_list)
statistic_vec = np.log(np.array(vec_list).sum(axis=0) + 1.0).tolist()
# print(statistic_vec)
# plt.subplot(2,2,index + 1)
if i == 0:
width = 0.3
x = range(0,len(vec_list[0]),1)
elif i == 1:
if __name__ == '__main__':
use_all_methods = utils.use_all_methods
embedded_dir_0 = utils.embedded_dir_0
embedded_dir_1 = utils.embedded_dir_1
embedded_dir_2 = utils.embedded_dir_2
time_basic_data_path = utils.time_basic_data_path
train_name_list_path = time_basic_data_path + "train_name_list.json"
train_time_message_path = time_basic_data_path + "train_time_message.json"
test_name_list_path = time_basic_data_path + "test_name_list.json"
test_time_message_path = time_basic_data_path + "test_time_message.json"
train_name_list = utils.ReadJson(train_name_list_path)
train_time_message = utils.ReadJson(train_time_message_path)
test_name_list = utils.ReadJson(test_name_list_path)
test_time_message = utils.ReadJson(test_time_message_path)
time_predict_path = utils.time_predict_path
time_predict_path_0 = time_predict_path + "time_predict_0.json"
time_predict_path_1 = time_predict_path + "time_predict_1.json"
time_predict_path_2 = time_predict_path + "time_predict_2.json"
layer_0 = "0"
layer_1 = "1"
layer_2 = "2"
if use_all_methods:
print("0")
def ClassifyAddPrediction(data_path, name_list_path, predict_data_path,
classify_predict_path_0, classify_predict_path_1, classify_predict_path_2):
with open(data_path, newline='') as csvfile:
data = list(csv.reader(csvfile))
method_list = utils.method_list
name_list = utils.ReadJson(name_list_path)
for set_predict_path in [classify_predict_path_0, classify_predict_path_1, classify_predict_path_2]:
data = ClassifyAddCol(data, name_list, set_predict_path, utils.choose_top_method_number_1)
for set_predict_path in [classify_predict_path_0, classify_predict_path_1, classify_predict_path_2]:
data = ClassifyAddCol(data, name_list, set_predict_path, utils.choose_top_method_number_2)
for index in range(len(data)):
predict_time = "timeout"
for method in method_list:
temp_point = method_list.index(method) + 1
temp_time = data[index][temp_point]
if temp_time != "timeout" and temp_time != "failed" and temp_time != "0.0" and temp_time != "0":
if predict_time == "timeout":
predict_time = temp_time
elif float(temp_time) < float(predict_time):
predict_time = temp_time
data[index].append(predict_time)
correct_num = 0
for index in range(len(data)):
random_point = random.randint(1, len(utils.method_list))
random_predict = data[index][random_point]
if random_predict == data[index][-1]:
correct_num += 1
data[index].append(random_predict)
print("Random Acc top1")
print(correct_num / len(data))
correct_num = 0
for index in range(len(data)):
point_candidate = [1, 2, 3, 4]
random_point_list = random.sample(point_candidate, utils.choose_top_method_number_2)
random_predict = "timeout"
for point in random_point_list:
temp_time = data[index][point]
if temp_time != "timeout" and temp_time != "failed" and temp_time != "0.0" and temp_time != "0":
if random_predict == "timeout":
random_predict = temp_time
elif float(temp_time) < float(random_predict):
random_predict = temp_time
if random_predict == data[index][-2]:
correct_num += 1
data[index].append(random_predict)
print("Random Acc top2")
print(correct_num / len(data))
title = "filename"
for method in method_list:
title = title + "," + method
for encoding_layer in utils.encoding_layer_list:
title = title + "," + "top1_" + encoding_layer
for encoding_layer in utils.encoding_layer_list:
title = title + "," + "top2_" + encoding_layer
title = title + ",Ground Truth,Random Top1,Random Top2"
with open(predict_data_path, "w") as writer:
writer.write(title + "\n")
for line in data:
writer.write(",".join(line) + "\n")
import utils
if __name__ == '__main__':
directory_path = "/mnt/hd0/DeepChecker/StatisticAvgEncodingTime/old_directory_3D.json"
new_directory_path = "/mnt/hd0/DeepChecker/StatisticAvgEncodingTime/old_directory.json"
latex_format_path = "/mnt/hd0/DeepChecker/StatisticAvgEncodingTime/latex_format.txt"
directory = utils.ReadJson(directory_path)
new_directory = []
latex_format = ""
for index in range(len(directory)):
item = directory[index]
reverse_item = item[::-1]
new_directory.append(reverse_item)
lettermark = False
firstmark = True
for i in range(len(reverse_item)):
if reverse_item[i] != "-":
if lettermark == False:
lettermark = True
if firstmark == False:
latex_format += "|"
else:
firstmark = False
latex_format += "\\mathtt{"
latex_format += reverse_item[i]
else:
if lettermark == True:
lettermark = False
latex_format += "}\\verb|"
latex_format += "-"
if lettermark == True:
def JudgeSituation15(test_name_list_path):
test_name_list = utils.ReadJson(test_name_list_path)
for name in test_name_list:
vec = utils.GetVec(utils.embedded_dir_1, name)
if vec[14] != 0:
print(name)
if __name__ == '__main__':
use_all_methods = utils.use_all_methods
embedded_dir_0 = utils.embedded_dir_0
embedded_dir_1 = utils.embedded_dir_1
embedded_dir_2 = utils.embedded_dir_2
embedded_dir_list = [embedded_dir_0, embedded_dir_1, embedded_dir_2]
classify_basic_data_path = utils.classify_basic_data_path
train_name_list_path = classify_basic_data_path + "train_name_list.json"
train_label_dic_path = classify_basic_data_path + "train_label_dic.json"
test_name_list_path = classify_basic_data_path + "test_name_list.json"
test_label_dic_path = classify_basic_data_path + "test_label_dic.json"
train_name_list = utils.ReadJson(train_name_list_path)
train_label_dic = utils.ReadJson(train_label_dic_path)
test_name_list = utils.ReadJson(test_name_list_path)
test_label_dic = utils.ReadJson(test_label_dic_path)
train_label_list = utils.GetLabelList(train_name_list, train_label_dic)
test_label_list = utils.GetLabelList(test_name_list, test_label_dic)
classify_predict_path = utils.classify_predict_path
classify_predict_path_0 = classify_predict_path + "DNN_predict_0.json"
classify_predict_path_1 = classify_predict_path + "DNN_predict_1.json"
classify_predict_path_2 = classify_predict_path + "DNN_predict_2.json"
classify_model_path = utils.classify_model_path
classify_model_path_0 = classify_model_path + "DNN_model_0.pkl"
classify_model_path_1 = classify_model_path + "DNN_model_1.pkl"
if __name__ == '__main__':
time_predict_path = utils.time_predict_path
time_predict_path_0 = time_predict_path + "time_predict_0.json"
time_predict_path_1 = time_predict_path + "time_predict_1.json"
time_predict_path_2 = time_predict_path + "time_predict_2.json"
time_basic_data_path = utils.time_basic_data_path
test_name_list_path = time_basic_data_path + "test_name_list.json"
test_data_path = time_basic_data_path + "test_data.csv"
predict_data_path = time_basic_data_path + "time_predict_data.csv"
with open(test_data_path, newline='') as csvfile:
data = list(csv.reader(csvfile))
test_name_list = utils.ReadJson(test_name_list_path)
method_list = utils.method_list
for test_set_predict_path in [
time_predict_path_0, time_predict_path_1, time_predict_path_2
]:
predict = utils.ReadJson(test_set_predict_path)
for method in method_list:
time_list = predict[method]
for index in range(len(data)):
data[index].append(str(time_list[index]))
for index in range(len(data)):
predict_time = "timeout"
for i in range(len(utils.method_list)):
temp_point = i + 1
temp_time = data[index][temp_point]
sum_time_list.append(sum_time)
solved_number_list.append(solved_number)
else:
sum_time += temp_time
sum_time_list.append(sum_time)
solved_number += 1
solved_number_list.append(solved_number)
return sum_time_list, solved_number_list
if __name__ == '__main__':
time_predict_path = utils.time_predict_path
time_predict_path_0 = time_predict_path + "time_predict_0.json"
time_predict_path_1 = time_predict_path + "time_predict_1.json"
time_predict_path_2 = time_predict_path + "time_predict_2.json"
time_predict_0 = utils.ReadJson(time_predict_path_0)
time_predict_1 = utils.ReadJson(time_predict_path_1)
time_predict_2 = utils.ReadJson(time_predict_path_2)
time_predict_list = [time_predict_2, time_predict_1, time_predict_0]
time_predict_label_list = [
"2-depth Encoding", "1-depth Encoding", "0-depth Encoding"
]
time_basic_data_path = utils.time_basic_data_path
test_name_list_path = time_basic_data_path + "test_name_list.json"
test_time_message_path = time_basic_data_path + "test_time_message.json"
test_timeout_message_path = time_basic_data_path + "test_timeout_message.json"
test_name_list = utils.ReadJson(test_name_list_path)
test_time_message = utils.ReadJson(test_time_message_path)
test_timeout_message = utils.ReadJson(test_timeout_message_path)
def ProcessDataForBenchmark(AVY_dprove_path, pdr_IC3_path, others_path,
hwmcc_clean_path, train_path, test_path):
AVY_dprove_dic = utils.ReadJson(AVY_dprove_path)
pdr_IC3_dic = utils.ReadJson(pdr_IC3_path)
train_name_list = list(pdr_IC3_dic.keys())
others_list = utils.ReadJson(others_path)
others_dic = {}
for i in range(len(others_list)):
aig = others_list[i]
aig_name = list(aig.keys())[0]
run_times = aig[aig_name]
temp_dic = {}
for method_time_pair in run_times:
method = list(method_time_pair.keys())[0]
time = str(method_time_pair[method])
temp_dic[method] = time
others_dic[aig_name] = temp_dic
hwmcc_dic = utils.ReadJson(hwmcc_clean_path)
test_name_list = list(hwmcc_dic.keys())
print(len(train_name_list))
print(len(test_name_list))
train_name_list = list(set(train_name_list) - set(test_name_list))
print(len(train_name_list))
data_list = []
for name in train_name_list:
temp_data = [name]
temp_data.append(AVY_dprove_dic[name]["dprove"])
temp_data.append(pdr_IC3_dic[name]["pdr"])
temp_data.append(others_dic[name]["iimc"])
temp_data.append(pdr_IC3_dic[name]["IC3"])
mark = False
for time in temp_data[1:]:
if time != "timeout" and time != "failed" and time != "0.0" and time != "0":
mark = True
if mark == True:
data_list.append(temp_data)
with open(train_path, "w") as writer:
for line in data_list:
writer.write(",".join(line) + "\n")
data_list = []
for name in test_name_list:
temp_data = [name]
temp_data.append(hwmcc_dic[name]["dprove"])
temp_data.append(hwmcc_dic[name]["pdr"])
temp_data.append(hwmcc_dic[name]["iimc"])
temp_data.append(hwmcc_dic[name]["IC3"])
mark = False
for time in temp_data[1:]:
if time != "timeout" and time != "failed" and time != "0.0" and time != "0":
mark = True
if mark == True:
data_list.append(temp_data)
with open(test_path, "w") as writer:
for line in data_list:
writer.write(",".join(line) + "\n")
import utils
import matplotlib.pyplot as plt
if __name__ == '__main__':
importance_message_path = utils.importance_message_path
importance_path_0 = importance_message_path + "importance_0.json"
importance_path_1 = importance_message_path + "importance_1.json"
importance_path_2 = importance_message_path + "importance_2.json"
importance_0 = utils.ReadJson(importance_path_0)
importance_1 = utils.ReadJson(importance_path_1)
importance_2 = utils.ReadJson(importance_path_2)
importance_fig_path = utils.importance_fig_path
importance_0_save_path = importance_fig_path + "importance_0.pdf"
importance_1_save_path = importance_fig_path + "importance_1.pdf"
importance_2_save_path = importance_fig_path + "importance_2.pdf"
plt.figure()
#plt.title("Importance of 0-depth Encoding")
plt.bar(range(len(importance_0)), importance_0, width=0.3, color="k")
#plt.xlabel('Features')
#plt.ylabel('Importance')
x = range(0, 4, 1)
y = [0, 0.1, 0.2, 0.3, 0.4]
plt.xticks(x, size=25)
plt.yticks(y, size=25)
plt.subplots_adjust(left=0.12, right=0.99, top=0.96, bottom=0.1)
plt.savefig(importance_0_save_path)
plt.show()
import utils
import numpy as np
if __name__ == '__main__':
embedded_dir_2 = utils.embedded_dir_2
train_name_list_path = utils.classify_basic_data_path + "train_name_list.json"
test_name_list_path = utils.classify_basic_data_path + "test_name_list.json"
train_name_list = utils.ReadJson(train_name_list_path)
test_name_list = utils.ReadJson(test_name_list_path)
train_vec_list = utils.GetVecList(embedded_dir_2, train_name_list)
test_vec_list = utils.GetVecList(embedded_dir_2, test_name_list)
train_statistic_vec = np.array(train_vec_list).sum(axis=0)
test_statistic_vec = np.array(test_vec_list).sum(axis=0)
print(train_statistic_vec)
print(test_statistic_vec)
for index in range(133, 161):
if train_statistic_vec[index] != 0:
print("train")
print(index)
if test_statistic_vec[index] != 0:
print("test")
print(index)
