def plot_contourf_overlap(result=None, title=None, color=None):
result = normalize_matrix_full(csr_matrix1=csr_matrix(result))
# 准备画图数据
result = csr_matrix(result)
x = np.linspace(0, 1, result.shape[0])
y = np.linspace(0, 1, result.shape[0])
[X, Y] = np.meshgrid(x, y)
Z = result.A
# 为等高线图填充颜色, 16指定将等高线分为几部分
colorslist = ['white']
colorslist.append(color)
# 将颜色条命名为mylist，一共插值颜色条50个
cmap = col.LinearSegmentedColormap.from_list('mylist',
colorslist,
N=len(colorslist) * 1)
temp = plt.contourf(X, Y, Z, 1, alpha=1.0, cmap=cmap) # 使用颜色映射来区分不同高度的区域
plt.contour(X,
Y,
Z, [temp._A[-2]],
linewidths=1.0,
alpha=1.0,
colors='black') # 使用颜色映射来区分不同高度的区域
# ax = plt.axes()
# ax.set_title(title, fontsize=18, position=(0.5, 1.05))
foo_fig = plt.gcf() # 'get current figure'
foo_fig.savefig('D:\第二个工作-实验数据\overlap//' + title + '.eps', format='eps')
plt.show()
def plot_contourf(result=None, title=None, binNum=10):
result = normalize_matrix_full(csr_matrix1=csr_matrix(result))
# 设置颜色映射cmap
cmap = sns.diverging_palette(50, 20, sep=16, as_cmap=True, n=1)
cmap = sns.light_palette((260, 75, 60),
input="husl") # input: {'rgb'，'hls'，'husl'，xkcd'}
cmap = sns.dark_palette((260, 75, 60), input="husl", reverse=True)
# startcolor = '#ffffff' #红色，读者可以自行修改 #ff0000
# midcolor = '#0000ff' #绿色，读者可以自行修改 #00ff00
# endcolor = '#ff0000' #蓝色，读者可以自行修改 #0000ff
# cmap = col.LinearSegmentedColormap.from_list('own2',[startcolor,midcolor,endcolor])
# 准备画图数据
result = csr_matrix(result)
x = np.linspace(0, 1, result.shape[0])
y = np.linspace(0, 1, result.shape[0])
[X, Y] = np.meshgrid(x, y)
Z = result.A
# 为等高线图填充颜色, 16指定将等高线分为几部分
# colorslist = ['GhostWhite', 'LightGray', 'LightBLue', 'SkyBlue', 'LightGoldenrodYellow', 'OrangeRed', 'DarkMagenta']
colorslist = [
'GhostWhite', 'LightGray', 'LightBLue', 'SkyBlue',
'LightGoldenrodYellow', 'OrangeRed'
]
# 将颜色条命名为mylist，一共插值颜色条50个
cmap = col.LinearSegmentedColormap.from_list('mylist',
colorslist,
N=len(colorslist) * 50)
temp = plt.contourf(X, Y, Z, binNum, alpha=1.0,
cmap=cmap) # 使用颜色映射来区分不同高度的区域
plt.colorbar()
C = plt.contour(X,
Y,
Z, [temp._A[binNum - 2]],
linewidths=1.0,
alpha=1.0,
colors='black') # 使用颜色映射来区分不同高度的区域
# plt.clabel(C, inline = True, fontsize = 10)
ax = plt.axes()
ax.set_title(title, fontsize=18, position=(0.5, 1.05))
foo_fig = plt.gcf() # 'get current figure'
foo_fig.savefig('./figures//' + title + '.png', format='png', dpi=600)
plt.show()
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