結果輸出到文件

import os# 定義打印類 class Logging():def __init__(self, filename):self.filename = filenamedef record(self, str_log):filename = self.filenameprint(str_log)with open(filename, 'a') as f:f.write("%s\r" % str_log)f.flush()# 使用 log_dir = os.path.join(os.getcwd(), 'log') if not os.path.exists(log_dir): os.makedirs(log_dir) log_path = os.path.join(log_dir, 'baseline.log') log = Logging(log_path)log.record('epoch[%d],TrainLoss[%.2f],TestLoss[%.2f],AUC[%.4f],KS[%.4f],time[%.1f + %.1f]' % (epoch, train_loss,test_loss, auc, ks, t1 - t0, t2 - t1))

List相同規律打亂(可以2個以上數組）

from random import shufflea=[1,2,3,4]b=[11,22,33,44]c=list(zip(a,b))shuffle(c)a,b = zip(*c)# a (2, 4, 3, 1)# b (22, 44, 33, 11)

random相關
random.sample(population,k): 不放回抽取，從集群population中選取k個元素，返回一個列表，集群可以是list、tuple、str、set。

pickle 一次存取多個數據

a = {"a":1,"b":2} b = {"a":2,"b":3,"c":4} # 存數據 with open("data/dataset.pickle",'wb') as f:pickle.dump((a,b),f) # 取數據 with open("data/dataset.pickle",'rb') as f:a,b = = pickle.load(f)

mat 格式 轉 txt格式

import pandas as pd from scipy.io import loadmatm_rating = loadmat("rating_with_timestamp.mat") m_trust = loadmat("trust_with_timestamp.mat") # print(m_trust) # 通過打印，查看數據的KEY df_rating = pd.DataFrame(m_rating['rating']) df_trust = pd.DataFrame(m_trust['trust']) # print(df_trust.head(5)) df_rating.to_csv("rating.txt", header=None, index=False, sep=" ") df_trust.to_csv("trust.txt", header=None, index=False, sep=" ")

6.隨機數種子

import os import torch import random import numpy as np# 定義 def set_seed(seed):torch.manual_seed(seed)torch.cuda.manual_seed(seed)torch.cuda.manual_seed_all(seed)torch.backends.cudnn.deterministic = Truerandom.seed(seed)np.random.seed(seed)os.environ['PYTHONHASHSEED'] = str(seed)# 使用 set_seed(2021)

總結

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