update projects

projects/codes/common/utils.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-12 16:02:24
+LastEditor: John
+LastEditTime: 2022-07-31 23:18:04
+Discription: 
+Environment: 
+'''
+import os
+import numpy as np
+from pathlib import Path
+import matplotlib.pyplot as plt
+import seaborn as sns
+import json
+
+from matplotlib.font_manager import FontProperties  # 导入字体模块
+
+def chinese_font():
+    ''' 设置中文字体，注意需要根据自己电脑情况更改字体路径，否则还是默认的字体
+    '''
+    try:
+        font = FontProperties(
+        fname='/System/Library/Fonts/STHeiti Light.ttc', size=15) # fname系统字体路径，此处是mac的
+    except:
+        font = None
+    return font
+
+def plot_rewards_cn(rewards, ma_rewards, cfg, tag='train'):
+    ''' 中文画图
+    '''
+    sns.set()
+    plt.figure()
+    plt.title(u"{}环境下{}算法的学习曲线".format(cfg.env_name,
+              cfg.algo_name), fontproperties=chinese_font())
+    plt.xlabel(u'回合数', fontproperties=chinese_font())
+    plt.plot(rewards)
+    plt.plot(ma_rewards)
+    plt.legend((u'奖励', u'滑动平均奖励',), loc="best", prop=chinese_font())
+    if cfg.save:
+        plt.savefig(cfg.result_path+f"{tag}_rewards_curve_cn")
+    # plt.show()
+
+
+def plot_rewards(rewards, ma_rewards, cfg, tag='train'):
+    sns.set()
+    plt.figure()  # 创建一个图形实例，方便同时多画几个图
+    plt.title("learning curve on {} of {} for {}".format(
+        cfg.device, cfg.algo_name, cfg.env_name))
+    plt.xlabel('epsiodes')
+    plt.plot(rewards, label='rewards')
+    plt.plot(ma_rewards, label='ma rewards')
+    plt.legend()
+    if cfg.save_fig:
+        plt.savefig(cfg.result_path+"{}_rewards_curve".format(tag))
+    plt.show()
+
+
+def plot_losses(losses, algo="DQN", save=True, path='./'):
+    sns.set()
+    plt.figure()
+    plt.title("loss curve of {}".format(algo))
+    plt.xlabel('epsiodes')
+    plt.plot(losses, label='rewards')
+    plt.legend()
+    if save:
+        plt.savefig(path+"losses_curve")
+    plt.show()
+
+def save_results(dic, tag='train', path='./results'):
+    ''' 保存奖励
+    '''
+    for key,value in dic.items():
+        np.save(path+'{}_{}.npy'.format(tag,key),value)
+    print('Results saved！')
+    
+# def save_results(rewards, ma_rewards, tag='train', path='./results'):
+#     ''' 保存奖励
+#     '''
+#     np.save(path+'{}_rewards.npy'.format(tag), rewards)
+#     np.save(path+'{}_ma_rewards.npy'.format(tag), ma_rewards)
+#     print('Result saved!')
+
+
+def make_dir(*paths):
+    ''' 创建文件夹
+    '''
+    for path in paths:
+        Path(path).mkdir(parents=True, exist_ok=True)
+
+
+def del_empty_dir(*paths):
+    ''' 删除目录下所有空文件夹
+    '''
+    for path in paths:
+        dirs = os.listdir(path)
+        for dir in dirs:
+            if not os.listdir(os.path.join(path, dir)):
+                os.removedirs(os.path.join(path, dir))
+
+def save_args(args):
+    # save parameters    
+    args_dict = vars(args)   
+    with open(args.result_path+'params.json', 'w') as fp:
+        json.dump(args_dict, fp)   
+    print("Parameters saved!")
+def smooth(data, weight=0.9):  
+    '''_summary_
+
+    Args:
+        data (List):输入数据
+        weight (Float): 平滑权重，处于0-1之间，数值越高说明越平滑，一般取0.9
+
+    Returns:
+        smoothed (List): 平滑后的数据
+    '''
+    last = data[0]  # First value in the plot (first timestep)
+    smoothed = list()
+    for point in data:
+        smoothed_val = last * weight + (1 - weight) * point  # 计算平滑值
+        smoothed.append(smoothed_val)                    
+        last = smoothed_val                                
+
+    return smoothed