def auto_DNN(prex=None,
graph_name=None,
emb_method_name1=None,
emb_method_name2=None,
model_name=None,
DNN_binNum=None):
print('----------------------------------------------------------')
print("dataset: " + graph_name + '\n' + "baselines:" + emb_method_name1 +
"," + emb_method_name2)
results_base_dir = 'D:\hybridrec//results//'
all_file_dir = 'D:\hybridrec\dataset\split_train_test//' + prex
results_dir = 'D:\hybridrec/results//' + prex
graph_results_dir = results_dir + graph_name + '//'
# （facebook_combined的规律：ratio越小则正负样本的预测准确率越高，花的时间也越少）
ratio = 1 # 负样本的总数是正样 本的ratio倍 # 改这里
path_scores_method1 = results_base_dir + prex + graph_name + "//" + graph_name + "_" + emb_method_name1 + "_scores.mat"
path_scores_method2 = results_base_dir + prex + graph_name + "//" + graph_name + "_" + emb_method_name2 + "_scores.mat"
# Initialize the model，改这里
# hidden_layer_sizes=(10, 20, 10)：三个隐藏层，分别10、20、10个神经元
if model_name == "mlp":
model = MLPClassifier(hidden_layer_sizes=(10, 20),
activation='relu',
solver='adam',
max_iter=200,
alpha=0.01,
batch_size=256,
learning_rate='constant',
learning_rate_init=0.001,
shuffle=False,
random_state=2020,
early_stopping=True,
validation_fraction=0.2,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
n_iter_no_change=10)
pass
if model_name == "svm":
model = SVC(C=5, random_state=42) # 出问题了
pass
if model_name == "lr":
model = LogisticRegression(C=5,
penalty='l1',
tol=1e-6,
random_state=42) # penalty 有l1和l2
pass
if model_name == "lgbm":
model = LGBMClassifier(num_leaves=31,
learning_rate=0.1,
n_estimators=64,
random_state=42,
n_jobs=-1)
pass
if model_name == "xgb":
model = XGBClassifier(max_depth=5,
learning_rate=0.1,
n_jobs=-1,
nthread=-1,
gamma=0.06,
min_child_weight=5,
subsample=1,
colsample_bytree=0.9,
reg_alpha=0,
reg_lambda=0.5,
random_state=42)
pass
if model_name == "ld":
model = LinearDiscriminantAnalysis(solver='lsqr')
pass
if model_name == "rf":
model = RandomForestClassifier(n_estimators=50,
max_depth=20,
min_samples_split=2,
min_samples_leaf=5,
max_features="log2",
random_state=12)
pass
if not (os.path.exists(path_scores_method1)
and os.path.exists(path_scores_method2)):
print("dataset: " + graph_name + '----' + "baselines:" +
emb_method_name1 + "," + emb_method_name2 + ': 分数未完全计算')
if os.path.exists(path_scores_method1) and os.path.exists(
path_scores_method2):
# 获取归一化分数
scores_matrix_one_dict = (loadmat(path_scores_method1))
scores_matrix_two_dict = (loadmat(path_scores_method2))
scores_matrix_one = scores_matrix_one_dict['scores']
scores_matrix_two = scores_matrix_two_dict['scores']
if emb_method_name1 not in all_embedding_methods:
scores_matrix_one = csr_matrix(np.triu(scores_matrix_one.A,
k=1)) # k=1表示不包括对角线
if emb_method_name2 not in all_embedding_methods:
scores_matrix_two = csr_matrix(np.triu(scores_matrix_two.A, k=1))
scores_matrix_one_norm = normalize_matrix(
csr_matrix1=csr_matrix(scores_matrix_one))
scores_matrix_two_norm = normalize_matrix(
csr_matrix1=csr_matrix(scores_matrix_two))
# 获取train_binary和test_binary
graph_train_path = get_trainset_path(base_dir=all_file_dir,
graph_name=graph_name,
connected_pattern='undirected',
from_zeros_one='0')
graph_test_path = get_testset_path(base_dir=all_file_dir,
graph_name=graph_name)
G = read_graph(weighted=0, input=graph_train_path, directed=0)
train_binary = csr_matrix(nx.convert_matrix.to_scipy_sparse_matrix(G))
train_binary = csr_matrix(np.triu(train_binary.A, k=1))
test_binary = get_test_matrix_binary(graph_test_path=graph_test_path,
N=train_binary.shape[0])
del scores_matrix_one, scores_matrix_two
gc.collect()
# 获取正样本的分数
exist_binary = csr_matrix(np.triu(train_binary.A, k=1)) # k=1表示不包括对角线
exist_scores_one_list = (np.array(
scores_matrix_one_norm[exist_binary > 0], dtype=float))[0]
exist_scores_two_list = (np.array(
scores_matrix_two_norm[exist_binary > 0], dtype=float))[0]
# 构建测试样本（正样本+负样本）
X_train_1 = (np.array([exist_scores_one_list,
exist_scores_two_list])).T
X_train_0 = negative_samples(
train_binary=train_binary,
test_binary=test_binary,
scores_matrix_one_norm=scores_matrix_one_norm,
scores_matrix_two_norm=scores_matrix_two_norm,
ratio=ratio)
Y_train_1 = np.random.randint(1, 2, X_train_1.shape[0])
Y_train_0 = np.random.randint(0, 1, X_train_0.shape[0])
X_train = np.vstack((np.array(X_train_1), np.array(X_train_0)))
Y_train = (np.hstack((np.array(Y_train_1), np.array(Y_train_0)))).T
time_start = time.time()
# 模型训练
model.fit(X_train, Y_train)
# 模型预测
preds_0 = model.predict(X_train_0)
preds_1 = model.predict(X_train_1)
print(np.sum(preds_0))
print(np.sum(preds_1))
preds_0_proba = model.predict_proba(X_train_0)
preds_1_proba = model.predict_proba(X_train_1)
# 模型预测
scores_matrix_DNN = predicted_scores_DNN(
model=model,
train_binary=train_binary,
test_binary=test_binary,
scores_matrix_one_norm=scores_matrix_one_norm,
scores_matrix_two_norm=scores_matrix_two_norm)
save_DNN_hybrid_scores(scores_matrix_DNN=scores_matrix_DNN,
method1=emb_method_name1,
method2=emb_method_name2,
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
model_name=model_name)
scores_matrix_DNN_norm = normalize_matrix(
csr_matrix1=scores_matrix_DNN)
# 计算DNN的rasterization grids
DNN_raster_grids = rasterization_grids(
binNum=DNN_binNum,
train_binary=train_binary,
scores_matrix_DNN=scores_matrix_DNN_norm,
scores_matrix_one_norm=scores_matrix_one_norm,
scores_matrix_two_norm=scores_matrix_two_norm)
# DNN_raster_grids = np.log10(DNN_raster_grids) # 出现-inf而报错
DNN_raster_grids = normalize_matrix_full(
csr_matrix1=csr_matrix(DNN_raster_grids))
DNN_raster_grids = better_show_grids(csr_matrix1=DNN_raster_grids)
save_DNN_raster_scores(rastser_grids=DNN_raster_grids,
method1=emb_method_name1,
method2=emb_method_name2,
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
model_name=model_name,
DNN_binNum=DNN_binNum)
source = np.float32(DNN_raster_grids.A)
result = cv2.GaussianBlur(source, (5, 5), 0)
title = graph_name + '-' + model_name + '-' + emb_method_name1 + '-' + emb_method_name2
plot_contourf(result=result, title=title, binNum=10)
# 读取PNR grids
PNR_path = results_base_dir + prex + graph_name + "//" + "PNR1_" + graph_name + "_" + emb_method_name1 + "_" + emb_method_name2 + "_50_count.mat"
if is_excel_file_exist(PNR_path):
PNR_dict = (loadmat(PNR_path))
PNR_matrix = PNR_dict["count"]
PNR_matrix = better_show_grids(csr_matrix1=PNR_matrix)
source = np.float32(PNR_matrix.A)
result = cv2.GaussianBlur(source, (5, 5),
0) #(5, 5)表示高斯矩阵的长与宽都是5，标准差取0
title = graph_name + '-PNR-' + emb_method_name1 + '-' + emb_method_name2
plot_contourf(result=result, title=title, binNum=10)
# 评估DNN
exist_binary = csr_matrix(np.triu(train_binary.A, k=1)) # k=1表示不包括对角线
nonexist_binary = csr_matrix(
np.triu(np.ones(exist_binary.shape), k=1) - exist_binary.A)
nonexist_scores_DNN_list = (np.array(
scores_matrix_DNN[nonexist_binary > 0], dtype=float))[0]
L_full = int(np.sum(test_binary))
L_array = np.array([
int(L_full / 20),
int(L_full / 10),
int(L_full / 5),
int(L_full / 2), L_full
])
AP_DNN, AUC_DNN, Precision_DNN, Recall_DNN, F1score_DNN = \
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_DNN_list,
L_array=L_array)
# print('AP_DNN: ' + str(AP_DNN))
# print('\n')
# print('AUC_DNN: ' + str(AUC_DNN))
# print('\n')
# print('Precision_DNN: ' + str(Precision_DNN))
# print('\n')
# print('Recall_DNN: ' + str(Recall_DNN))
# print('\n')
# print('F1score_DNN: ' + str(F1score_DNN))
# print('\n')
# 把precision、recall、F1score、AP写入excel文件
DNN_write_to_excel(DL_name=model_name,
dataset_name=graph_name,
method1=emb_method_name1,
method2=emb_method_name2,
precision_DL=Precision_DNN,
recall_DL=Recall_DNN,
F1score_DL=F1score_DNN,
AP_DL=AP_DNN)
time_end = time.time()
print("It takes : " + str((time_end - time_start) / 60.0) + " mins.")
pass
# 评估evaluation
graph_test_path=get_testset_path(base_dir=all_file_dir, graph_name=graph_name)
test_binary=get_test_matrix_binary(graph_test_path=graph_test_path, N=N)
L_full = int(np.sum(test_binary))
L_array = np.array([int(L_full/20),int(L_full/10),
int(L_full/5), int(L_full/2),
L_full])
del scores_matrix_one_norm, scores_matrix_two_norm, exist_scores_one_list, exist_scores_two_list, scores_matrix_hybrid_norm
gc.collect()
AP_PNR, AUC_PNR, Precision_PNR, Recall_PNR, F1score_PNR=\
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_PNR_list,
L_array=L_array)
AP_method1, AUC_method1, Precision_method1, Recall_method1, F1score_method1=\
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_one_list,
L_array=L_array)
AP_method2, AUC_method2, Precision_method2, Recall_method2, F1score_method2=\
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_two_list,
L_array=L_array)
AP_weighted, AUC_weighted, Precision_weighted, Recall_weighted, F1score_weighted=\
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_hybrid_list,
def auto_PNR(prex=None,
graph_name=None,
emb_method_name1=None,
emb_method_name2=None):
print('----------------------------------------------------------')
time_start = time.time()
# 初始化训练集和测试集的路径
# prex = 'preprocessing_code2//' # 改这里
all_file_dir = 'D:\hybridrec\dataset\split_train_test//' + prex
binNum = 50 # 改这里
emb_method_name1 = emb_method_name1.lower() # 改这里
emb_method_name2 = emb_method_name2.lower() # 改这里
print("dataset: " + graph_name + '\n' + "baselines:" + emb_method_name1 +
"," + emb_method_name2)
conf_method1 = None
conf_method2 = None
if emb_method_name1 in all_embedding_methods:
config_path_method1 = 'conf/' + emb_method_name1 + '.properties'
config_method1 = configparser.ConfigParser()
config_method1.read(config_path_method1)
conf_method1 = dict(config_method1.items("hyperparameters"))
if emb_method_name2 in all_embedding_methods:
config_path_method2 = 'conf/' + emb_method_name2 + '.properties'
config_method2 = configparser.ConfigParser()
config_method2.read(config_path_method2)
conf_method2 = dict(config_method2.items("hyperparameters"))
# 初始化embedding和scores的路径
results_dir = 'D:\hybridrec/results//' + prex
graph_results_dir = results_dir + graph_name + '//'
# 计算emb method 1
if not ((emb_method_name1 == 'arope') or
(emb_method_name1 == 'graph2gauss') or
(is_heuristic_method(emb_method_name1) == True)):
graph_train_path = get_trainset_path(
base_dir=all_file_dir,
graph_name=graph_name,
connected_pattern=get_connp(emb_method_name1),
from_zeros_one=get_from_zeros_one(emb_method_name1))
graph_results_path = graph_results_dir + graph_name + '_' + emb_method_name1 + '.emb'
if not os.path.isfile(graph_results_path):
run_emb_method(input=graph_train_path,
output=graph_results_path,
emb_method_name=emb_method_name1)
# 计算emb method 2
if not ((emb_method_name2 == 'arope') or
(emb_method_name2 == 'graph2gauss') or
(is_heuristic_method(emb_method_name2) == True)):
graph_train_path = get_trainset_path(
base_dir=all_file_dir,
graph_name=graph_name,
connected_pattern=get_connp(emb_method_name2),
from_zeros_one=get_from_zeros_one(emb_method_name2))
graph_results_path = graph_results_dir + graph_name + '_' + emb_method_name2 + '.emb'
if not os.path.isfile(graph_results_path):
run_emb_method(input=graph_train_path,
output=graph_results_path,
emb_method_name=emb_method_name2)
# 计算scores1
if conf_method1 != None:
embedding_size_method1 = int(conf_method1['embedding_size'])
if emb_method_name1 == 'splitter':
scores_matrix_one = inner_product_scores_splitter(
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
emb_method_name=emb_method_name1,
col_start=0,
col_end=embedding_size_method1 + 1,
skiprows=1,
delimiter=',')
elif (emb_method_name1 == 'attentionwalk') or (emb_method_name1
== 'grarep'):
scores_matrix_one = inner_product_scores(
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
emb_method_name=emb_method_name1,
col_start=0,
col_end=embedding_size_method1 + 1,
skiprows=1,
delimiter=',')
elif (emb_method_name1 == 'drne') or (emb_method_name1 == 'prune'):
scores_matrix_one = inner_product_scores(
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
emb_method_name=emb_method_name1,
col_start=0,
col_end=embedding_size_method1,
skiprows=0,
delimiter=' ') # embedding_size_method有一些是要+1有一些不需要的
elif (emb_method_name1 == 'arope'):
scores_matrix_one = inner_product_scores_arope(
all_file_dir=all_file_dir,
graph_name=graph_name,
graph_results_dir=graph_results_dir)
elif (emb_method_name1 == 'graph2gauss'):
scores_matrix_one = energy_kl_scores_graph2gauss(
all_file_dir=all_file_dir,
graph_name=graph_name,
graph_results_dir=graph_results_dir)
elif is_heuristic_method(emb_method_name1):
scores_matrix_one = heuristic_scores(
all_file_dir=all_file_dir,
graph_name=graph_name,
graph_results_dir=graph_results_dir,
heuristic_method=emb_method_name1)
else:
scores_matrix_one = inner_product_scores(
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
emb_method_name=emb_method_name1,
col_start=0,
col_end=embedding_size_method1 + 1,
skiprows=1,
delimiter=' ')
# 计算scores2
if conf_method2 != None:
embedding_size_method2 = int(conf_method2['embedding_size'])
if emb_method_name2 == 'splitter':
scores_matrix_two = inner_product_scores_splitter(
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
emb_method_name=emb_method_name2,
col_start=0,
col_end=embedding_size_method2 + 1,
skiprows=1,
delimiter=',')
elif (emb_method_name2 == 'attentionwalk') or (emb_method_name2
== 'grarep'):
scores_matrix_two = inner_product_scores(
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
emb_method_name=emb_method_name2,
col_start=0,
col_end=embedding_size_method2 + 1,
skiprows=1,
delimiter=',')
elif (emb_method_name2 == 'drne') or (emb_method_name2 == 'prune'):
scores_matrix_two = inner_product_scores(
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
emb_method_name=emb_method_name2,
col_start=0,
col_end=embedding_size_method2,
skiprows=0,
delimiter=' ')
elif (emb_method_name2 == 'arope'):
scores_matrix_two = inner_product_scores_arope(
all_file_dir=all_file_dir,
graph_name=graph_name,
graph_results_dir=graph_results_dir)
elif (emb_method_name2 == 'graph2gauss'):
scores_matrix_two = energy_kl_scores_graph2gauss(
all_file_dir=all_file_dir,
graph_name=graph_name,
graph_results_dir=graph_results_dir)
elif is_heuristic_method(emb_method_name2):
scores_matrix_two = heuristic_scores(
all_file_dir=all_file_dir,
graph_name=graph_name,
graph_results_dir=graph_results_dir,
heuristic_method=emb_method_name2)
else:
scores_matrix_two = inner_product_scores(
graph_results_dir=graph_results_dir,
dataset_name=graph_name,
emb_method_name=emb_method_name2,
col_start=0,
col_end=embedding_size_method2 + 1,
skiprows=1,
delimiter=' ')
# scores取上三角（注意:1、前面需要保证所有的分数在右上角或占满整个矩阵。2、前面有些是右上角，有些是占满整个矩阵）
# scores_matrix_one_full = scores_matrix_one.A
# scores_matrix_two_full = scores_matrix_two.A
# plot_matrix(matrix = scores_matrix_one_full)
# plot_matrix(matrix = scores_matrix_two_full)
scores_matrix_one = sp.csr_matrix(np.triu(scores_matrix_one.A,
k=1)) # k=1表示不包括对角线
scores_matrix_two = sp.csr_matrix(np.triu(scores_matrix_two.A, k=1))
# 读入train的binary数据
graph_train_path = get_trainset_path(base_dir=all_file_dir,
graph_name=graph_name,
connected_pattern='undirected',
from_zeros_one='0')
G = read_graph(weighted=0, input=graph_train_path, directed=0)
train_binary = sp.csr_matrix(nx.convert_matrix.to_scipy_sparse_matrix(G))
train_binary = sp.csr_matrix(np.triu(train_binary.A, k=1))
# train_binary_full = train_binary.A
# 或 train_binary = sp.csr_matrix(np.array(nx.to_numpy_matrix(G)))
# 构建exist和nonexist的binary
exist_binary = sp.csr_matrix(np.triu(train_binary.A, k=1)) # k=1表示不包括对角线
nonexist_binary = sp.csr_matrix(
np.triu(np.ones(exist_binary.shape), k=1) - exist_binary.A)
# 分数归一化到[0.0, 1.0]
scores_matrix_one_norm = normalize_matrix(csr_matrix1=scores_matrix_one)
scores_matrix_two_norm = normalize_matrix(csr_matrix1=scores_matrix_two)
# plot_matrix(scores_matrix_one_norm.A)
# plot_matrix(scores_matrix_two_norm.A)
del scores_matrix_one, scores_matrix_two
gc.collect()
# 划分bin
val_max = 1.0
val_min = 0.0
# bin_array = sorted(divide_bin(val_max = val_max, val_min = val_min, binNum = binNum))
interval = float((val_max - val_min) / binNum)
# 获取exist_binary和nonexist_binary的分数
exist_scores_one_list = (np.array(scores_matrix_one_norm[exist_binary > 0],
dtype=float))[0]
nonexist_scores_one_list = (np.array(
scores_matrix_one_norm[nonexist_binary > 0], dtype=float))[0]
exist_scores_two_list = (np.array(scores_matrix_two_norm[exist_binary > 0],
dtype=float))[0]
nonexist_scores_two_list = (np.array(
scores_matrix_two_norm[nonexist_binary > 0], dtype=float))[0]
# # 变为稀疏矩阵
# exist_scores_one_list_csr = sp.csr_matrix(exist_scores_one_list)
# nonexist_scores_one_list_csr = sp.csr_matrix(nonexist_scores_one_list)
# exist_scores_two_list_csr = sp.csr_matrix(exist_scores_two_list)
# nonexist_scores_two_list_csr = sp.csr_matrix(nonexist_scores_two_list)
# temp = scores_matrix_one_norm[exist_binary > 0][0] # 我怕在把分数变为list的时候出问题
# 初始化两个大小为binNum* bnNum的二维栅格
exist_raster_grids = np.zeros((binNum, binNum))
nonexist_raster_grids = np.zeros((binNum, binNum))
# 计算落在exist_raster_grids栅格的existing links的数量
exist_links_num = len(exist_scores_one_list)
exist_row_col_zero_num = 0 # 那些两个矩阵的分数都是0的不作统计
for i in range(exist_links_num):
# row_index和col_index的范围从0-->binNum-1
if (exist_scores_one_list[i] == 0.0) & (exist_scores_two_list[i]
== 0.0):
exist_row_col_zero_num = exist_row_col_zero_num + 1
continue
row_index = int(
get_row_col_index(score=exist_scores_one_list[i],
interval=interval,
binNum=binNum))
col_index = int(
get_row_col_index(score=exist_scores_two_list[i],
interval=interval,
binNum=binNum))
exist_raster_grids[row_index,
col_index] = exist_raster_grids[row_index,
col_index] + 1
print("exist_row_col_zero_num:" + str(exist_row_col_zero_num))
print('sum exist_raster_grids:' + str(np.sum(exist_raster_grids)))
# 计算落在nonexist_raster_grids栅格的nonexisting links的数量
nonexist_links_num = len(nonexist_scores_one_list)
nonexist_row_col_zero_num = 0 # 那些两个矩阵的分数都是0的不作统计
for i in range(nonexist_links_num):
# row_index和col_index的范围从0-->binNum-1
if (nonexist_scores_one_list[i] <= 0.0) & (nonexist_scores_two_list[i]
<= 0.0):
nonexist_row_col_zero_num = nonexist_row_col_zero_num + 1
continue
row_index = int(
get_row_col_index(score=nonexist_scores_one_list[i],
interval=interval,
binNum=binNum))
col_index = int(
get_row_col_index(score=nonexist_scores_two_list[i],
interval=interval,
binNum=binNum))
nonexist_raster_grids[row_index,
col_index] = nonexist_raster_grids[row_index,
col_index] + 1
print("nonexist_row_col_zero_num:" + str(nonexist_row_col_zero_num))
print('sum nonexist_raster_grids:' + str(np.sum(nonexist_raster_grids)))
# 计算PNR分数
N = train_binary.shape[0]
print("Graph size：" + str(N) + '\n')
L_T = np.sum(train_binary.A)
O = N * (N - 1) / 2
coefficient = (O - L_T) / L_T
PNR1 = coefficient * (exist_raster_grids / (nonexist_raster_grids + 1)
) # 分母加1避免出现inf或nan，不影响evaluation但是可能好看
PNR2 = (exist_raster_grids / nonexist_raster_grids) # inf和nan置为0
PNR2[np.isnan(PNR2)] = 0
PNR2[np.isinf(PNR2)] = 0
PNR2 = coefficient * PNR2
# 画图（注意：图的横纵坐标是从左上角开始的而不是想象中的左上角）
# sns.heatmap(PNR1, cmap='Reds')
# plt.savefig(graph_results_dir + emb_method_name1 +'_'+ emb_method_name2 + '_' +'bin_' + str(binNum) + "_PNR1.jpg")
# plt.show()
# sns.heatmap(PNR2, cmap='Reds')
# plt.savefig(graph_results_dir + emb_method_name1 +'_'+ emb_method_name2 + '_'+ 'bin_' + str(binNum) + "_PNR2.jpg")
# plt.show()
# plt.matshow(PNR1) # 好丑
# plt.show()
# 保存（exist_raster_grids、nonexist_raster_grids、PNR1、PNR2）
save_ndarray_to_mat(exist_raster_grids, 'exist_raster_grids',
graph_results_dir, graph_name, emb_method_name1,
emb_method_name2, binNum)
save_ndarray_to_mat(nonexist_raster_grids, 'nonexist_raster_grids',
graph_results_dir, graph_name, emb_method_name1,
emb_method_name2, binNum)
save_ndarray_to_mat(PNR1, 'PNR1', graph_results_dir, graph_name,
emb_method_name1, emb_method_name2, binNum)
save_ndarray_to_mat(PNR2, 'PNR2', graph_results_dir, graph_name,
emb_method_name1, emb_method_name2, binNum)
# PNR调整分数(只调整non-existing link的部分)
nonexist_scores_PNR_list = transfer_scores_PNR(
scores_matrix_one_norm=scores_matrix_one_norm,
scores_matrix_two_norm=scores_matrix_two_norm,
train_binary=train_binary,
PNR=PNR2,
interval=interval,
binNum=binNum)
# weighted hybird方法的分数，0.5均权直接相加
scores_matrix_hybrid_norm = 0.5 * scores_matrix_one_norm + 0.5 * scores_matrix_two_norm
nonexist_scores_hybrid_list = (np.array(
scores_matrix_hybrid_norm[nonexist_binary > 0], dtype=float))[0]
# 评估evaluation
graph_test_path = get_testset_path(base_dir=all_file_dir,
graph_name=graph_name)
test_binary = get_test_matrix_binary(graph_test_path=graph_test_path, N=N)
L_full = int(np.sum(test_binary))
L_array = np.array([
int(L_full / 20),
int(L_full / 10),
int(L_full / 5),
int(L_full / 2), L_full
])
del scores_matrix_one_norm, scores_matrix_two_norm, exist_scores_one_list, exist_scores_two_list, scores_matrix_hybrid_norm
gc.collect()
AP_PNR, AUC_PNR, Precision_PNR, Recall_PNR, F1score_PNR=\
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_PNR_list,
L_array=L_array)
AP_method1, AUC_method1, Precision_method1, Recall_method1, F1score_method1=\
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_one_list,
L_array=L_array)
AP_method2, AUC_method2, Precision_method2, Recall_method2, F1score_method2=\
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_two_list,
L_array=L_array)
AP_weighted, AUC_weighted, Precision_weighted, Recall_weighted, F1score_weighted=\
evaluators(train_binary=train_binary,
test_binary=test_binary,
scores_list=nonexist_scores_hybrid_list,
L_array=L_array)
print('AP_PNR: ' + str(AP_PNR))
print('AP_method1: ' + str(AP_method1))
print('AP_method2: ' + str(AP_method2))
print('AP_weighted: ' + str(AP_weighted))
print('\n')
print('AUC_PNR: ' + str(AUC_PNR))
print('AUC_method1: ' + str(AUC_method1))
print('AUC_method2: ' + str(AUC_method2))
print('AUC_weighted: ' + str(AUC_weighted))
print('\n')
print('Precision_PNR: ' + str(Precision_PNR))
print('Precision_method1: ' + str(Precision_method1))
print('Precision_method2: ' + str(Precision_method2))
print('Precision_weighted: ' + str(Precision_weighted))
print('\n')
print('Recall_PNR: ' + str(Recall_PNR))
print('Recall_method1: ' + str(Recall_method1))
print('Recall_method2: ' + str(Recall_method2))
print('Recall_weighted: ' + str(Recall_weighted))
print('\n')
print('F1score_PNR: ' + str(F1score_PNR))
print('F1score_method1: ' + str(F1score_method1))
print('F1score_method2: ' + str(F1score_method2))
print('F1score_weighted: ' + str(F1score_weighted))
print('\n')
write_to_excel(graph_name, emb_method_name1, emb_method_name2,
Precision_PNR, Precision_method1, Precision_method2,
Precision_weighted, Recall_PNR, Recall_method1,
Recall_method2, Recall_weighted, F1score_PNR,
F1score_method1, F1score_method2, F1score_weighted, AP_PNR,
AP_method1, AP_method2, AP_weighted, AUC_PNR, AUC_method1,
AUC_method2, AUC_weighted)
time_end = time.time()
print("time span: " + str((time_end - time_start) / 60.00) + " mins")
# facebook_combined：bin=5, 1.5分钟
# facebook_combined：cn和pearson\aa和cn花了3.5分钟
# facebook_combined：graphdistance和cn花了11分钟
# facebook_combined: graphdistance和cn的PNE矩阵为全0
# facebooke_combined: attentionwalk和prone花了7.5分钟
# facebooke_combined: 有rootedpagerank的效果都很差;
# arope比PNR好一点，SDNE和PRUE很差很差；drne和graph2gauss也是极差的但是PNR融合后表现极好；
# blogcatalog:aa和ja花了3小时
# （path based--katz和graphdistance都十分慢，neighbor based和rank based很快）
# google 15000 nodes: 2.5小时
print(
'--------------------------------------------------------------------------------'
)
pass