def runAlg(self, dataType):
'''
运行算法的函数，并且画图
:param dataType: gsm / lte
:return:
'''
if dataType != "gsm" and dataType != "lte":
raise Exception("未知数据类型")
#分类器和回归器的结果
regRes = []
claRes = []
predorCla = Predictor("cla", dataType)
predorReg = Predictor("reg", dataType)
for time in range(10):
print str(time) + " -- begin"
#跑算法
claRes.append(predorCla.fit())
regRes.append(predorReg.fit())
print str(time) + " -- change data"
# 重新生成一个数据的划分
predorReg.changeData()
predorCla.changeData()
#排序
regRes.sort()
claRes.sort()
folder = "gsmResult/" if "gsm" == dataType else "lteResult/"
#画图
self.draw(regRes, folder + "regImg")
self.draw(claRes, folder + "claImg")
#输出结果和中位结果
resFile = open(folder + "res", 'w')
resFile.write("reg result:\n")
resFile.write(str(regRes))
resFile.write("\nreg mid:\n")
resFile.write(str((regRes[4] + regRes[5]) / 2))
resFile.write("\ncla result:\n")
resFile.write(str(claRes))
resFile.write("\ncla mid:\n")
resFile.write(str((claRes[4] + claRes[5]) / 2))
flags.DEFINE_boolean('use_embedding', True, 'Use embedding or not')
flags.DEFINE_integer('feat_dim', -1, None)
flags.DEFINE_list(
'node_dim', [256],
'Dimension of hidden layers between feature and node embedding')
flags.DEFINE_list(
'instance_h_dim', [256],
'Dimension of hidden layers between node embedding and instance embedding, last element is the dimension of instance embedding'
)
flags.DEFINE_list(
'graph_h_dim', [128],
'Dimension of hidden layers between instance embedding and subgraph embedding, last element is the dimension of subgraph embedding'
)
flags.DEFINE_float('keep_prob', 0.6, 'Used for dropout')
flags.DEFINE_list('kernel_sizes', [1], 'List of number of nodes in kernel')
flags.DEFINE_string('pooling', 'max', '[max, average, sum]')
flags.DEFINE_integer('epoch', 4, None)
flags.DEFINE_float('learning_rate', 1e-4, None)
flags.DEFINE_float('lambda_2', 1e-2, 'Coefficient of l2 regularization loss')
flags.DEFINE_float('memory_fraction', 0.5, None)
FLAGS = flags.FLAGS
if __name__ == '__main__':
predictor = Predictor(FLAGS)
train_accuracy, test_accuracy = predictor.fit()
print('Training Accuracy: %f', train_accuracy)
print('Testing Accuracy: %f', test_accuracy)
