def GCN_tol_embedding(i):
for idx in range(0, subSum):
print(idx)
tolFeature = load_pickle(tolFeaturePathName, "/tolFeature_%d.pkl" % idx)
tolFeature_df = pd.DataFrame(
tolFeature, columns=['label', 'AF1', 'AF2', 'AF3', 'AF4', 'AF5', 'AF6', 'AF7', 'AF8'])
sp_muldG = load_pickle(muldigPathName, "/G_%d.pkl" % idx)
y_cols_name = ['label']
x_cols_name = [x for x in tolFeature_df.columns if x not in y_cols_name]
global scipy_adj_matrix,train_x,train_y
train_x = dcopy(tolFeature_df[x_cols_name])
train_y = dcopy(tolFeature_df[y_cols_name])
pos_cnt, neg_cnt = int(train_y.sum()), int(len(train_y) - train_y.sum())
scipy_adj_matrix = get_scipy_adj_matrix(sp_muldG)
print('pos node cnts:', pos_cnt)
print('neg node cnts:', neg_cnt, 'pos/all ratio:',
pos_cnt / (pos_cnt + neg_cnt))
embSize = 8
fGCNembedding = get_GCN_embedding(epoch=6, lr=0.005, weight_decay=1e-6,
esize=embSize, random_seed=7+i)
print("finish calculate embedding data!")
save_pickle(fGCNembedding, GCNPathName+"%d_%d" %
(embSize,testNum), "/fGCNembedding_%d.pkl" % idx)
def GCN_classfication(emsize):
gcn_res, rcnt = [0, 0, 0, 0], 5
rs = 7
for i in range(rcnt):
for idx in range(subSum - 1, subSum):
#print(idx)
#print(tolFeaturePathName+"/tolFeature_%d.pkl" % idx)
tolFeature = load_pickle(tolFeaturePathName,
"/tolFeature_%d.pkl" % idx)
tolFeature_df = pd.DataFrame(tolFeature,
columns=[
'label', 'AF1', 'AF2', 'AF3',
'AF4', 'AF5', 'AF6', 'AF7', 'AF8'
])
sp_muldG = load_pickle(muldigPathName, "/G_%d.pkl" % idx)
#sp_mulG = load_pickle(mulgPathName+"/G_%d.pkl" % idx)
#print(mulgPathName+"/G_%d.pkl" % idx)
y_cols_name = ['label']
x_cols_name = [
x for x in tolFeature_df.columns if x not in y_cols_name
]
global scipy_adj_matrix, train_x, train_y
train_x = dcopy(tolFeature_df[x_cols_name])
train_y = dcopy(tolFeature_df[y_cols_name])
pos_cnt, neg_cnt = int(
train_y.sum()), int(len(train_y) - train_y.sum())
scipy_adj_matrix = get_scipy_adj_matrix(sp_muldG)
#print('pos node cnts:', pos_cnt)
#print('neg node cnts:', neg_cnt, 'pos/all ratio:',
# pos_cnt / (pos_cnt + neg_cnt))
fGCNembedding = get_GCN_embedding(epoch=6,
lr=0.005,
weight_decay=1e-6,
esize=emsize,
random_seed=rs + i)
print("finish calculate embedding data!")
save_pickle(fGCNembedding, GCNPathName + "%d" % testNum,
"/fGCNembedding_%d.pkl" % idx)
trainX, trainY, testX, testY = get_input_data(
GCNPathName + "%d" % testNum, "/fGCNembedding", esize, True,
False, True)
#ftrainX, ftrainY, ftestX, ftestY = get_pure_feature(False)
#print(ftrainX[:,subSum-1,:].size)
#print(trainX[:, subSum-1, :])
inputX = trainX[:, subSum - 1, :]
#inputX=np.concatenate((ftrainX[:,subSum-1,:], trainX[:, subSum-1, :]), axis=1)
#inputX=trainX
trainX_2D, trainY_1D = inputX, trainY[:, subSum - 1, 0]
lgb_res = lgb_train_model_with_split(pd.DataFrame(trainX_2D),
pd.DataFrame(trainY_1D), 2011)
#print_res(lgb_res)
for j in range(len(gcn_res)):
gcn_res[j] += lgb_res[j]
gcn_res = [i / rcnt for i in gcn_res]
print(gcn_res)
gc.collect()
return gcn_res
