def n2v_classification():
global train_x, train_y
n2v_res, rcnt = [0, 0, 0, 0], 5
for i in tqdm(range(rcnt)):
node_feas, labels = dcopy(train_x.values), dcopy(train_y.values)
embe_feas = read_embeds(N2VPathName_new + '/embeds_n2v_%d.dat' % i)
np_fealab = np.hstack((node_feas, embe_feas, labels))
columns_name = ['f%02d' % i
for i in range(np_fealab.shape[1] - 1)] + ['label']
df_fealab = pd.DataFrame(data=np_fealab,
columns=columns_name,
dtype=float)
df_fealab['label'] = df_fealab['label'].astype(int)
y_cols_name = ['label']
x_cols_name = [x for x in df_fealab.columns if x not in y_cols_name]
n2vdf_x = df_fealab[x_cols_name]
n2vdf_y = df_fealab[y_cols_name]
print(n2vdf_x.shape, n2vdf_y.shape)
lgb_res = lgb_train_model_with_split(n2vdf_x, n2vdf_y, RANDOM_SEED)
for i in range(len(n2v_res)):
n2v_res[i] += lgb_res[i]
n2v_res = [i / rcnt for i in n2v_res]
gc.collect()
return n2v_res
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
def pure_feature_classification():
trainX, trainY, testX, testY = get_pure_feature(False)
trainX_2D, trainY_1D = trainX[:, subSum - 1, :], trainY[:, subSum - 1, 0]
result = lgb_train_model_with_split(pd.DataFrame(trainX_2D),
pd.DataFrame(trainY_1D), 2011)
print_res(result)
return result
def GCN_classfication():
trainX, trainY, testX, testY = get_input_data(
GCNPathName+"%d_%d" %(embSize,testNum), "/fGCNembedding",True, False)
trainX_2D, trainY_1D = trainX[:, subSum-1, :], trainY[:, subSum-1, 0]
ftrainX, ftrainY, ftestX, ftestY = get_pure_feature(False)
inputX=np.concatenate((ftrainX[:,subSum-1,:], trainX[:, subSum-1, :]), axis=1)
result = lgb_train_model_with_split(
pd.DataFrame(inputX), pd.DataFrame(trainY_1D), 2011)
print_res(result)
def LSTM_classification(modelFileName):
# 不切分训练测试集
new_model = load_RNNmodel(modelFileName)
trainX, trainY, testX, testY = get_pure_feature(False)
print(trainX.shape,trainY.shape)
trainX_emb, testX_emb = get_autoEncoder_Embedding_Layer(
trainX, trainX, new_model)
#实际上传入的test也是trainX 因为在这里不切分数据集
trainX_2D = trainX_emb[:, subSum-1, :]
trainY_1D = trainY[:, subSum-1, 0]
result = lgb_train_model_with_split(
pd.DataFrame(trainX_2D), pd.DataFrame(trainY_1D), 2011)
print_res(result)
def feature_classification():
global train_x, train_y
res, rcnt = [0, 0, 0, 0], 5
for i in tqdm(range((rcnt))):
trainX, trainY, testX, testY = get_pure_feature(False)
trainX_2D, trainY_1D = trainX[:, subSum - 1, :], trainY[:,
subSum - 1, :]
print(trainX_2D.shape, trainY_1D.shape)
f_res = lgb_train_model_with_split(pd.DataFrame(trainX_2D),
pd.DataFrame(trainY_1D),
RANDOM_SEED + i)
print(f_res)
for j in range(len(f_res)):
res[j] += f_res[j]
res = [j / rcnt for j in res]
gc.collect()
return res
def GCN_LSTM_classification(modelFileName):
new_model = load_RNNmodel(modelFileName)
trainX, trainY, testX, testY = get_input_data(
GCNPathName+"%d_%d" %(embSize,testNum), "/fGCNembedding", True, False)
#trainX= trainX[:, :, :8]
ftrainX, ftrainY, ftestX, ftestY = get_pure_feature(False)
print(trainX.shape,trainY.shape)
trainX_emb, testX_emb = get_autoEncoder_Embedding_Layer(
trainX, trainX, new_model)
print(trainX_emb.shape)
for i in range(subSum-1,subSum):
#print(str(i)+":------")
inputX=np.concatenate((ftrainX[:,i,:], trainX_emb[:, i, :]), axis=1)
#inputX=trainX_emb[:, i, :]
print(inputX.shape)
trainX_2D, trainY_1D =inputX, trainY[:,subSum-1, 0]
result = lgb_train_model_with_split(
pd.DataFrame(trainX_2D), pd.DataFrame(trainY_1D), 2011)
print_res(result)