| from numpy import load | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| # import matplotlib.axes | |
| filepath = "agents/dqn_v2-7/evaluations.npz" | |
| data = load(filepath) | |
| lst = data.files # data.files lists the keys that are available for data | |
| # print('ep_lengths: \n', data['ep_lengths']) | |
| # results and ep_lengths are 2d arrays, because each evaluation is 5 episodes long. | |
| # I want to plot the average of each evaluation. | |
| print(data["results"]) | |
| print() | |
| print(np.delete(np.sort(data["results"]), 0, 1)) | |
| # for i in range(len(data["results"])): | |
| # print(np.average(data["results"][i])) | |
| ''' | |
| # for each item in results, loop through the array and save the average | |
| avg_ep_result_arr = [] | |
| for eval in data['results']: | |
| result_sum = 0 | |
| for result in eval: | |
| result_sum = result_sum + result | |
| avg_ep_result = result_sum / len(eval) | |
| avg_ep_result_arr.append(avg_ep_result) | |
| avg_ep_len_arr = [] | |
| for eval in data['ep_lengths']: | |
| max_len = 0 | |
| y_limit = 0 | |
| ep_len_sum = 0 | |
| for ep_length in eval: | |
| ep_len_sum = ep_len_sum + ep_length | |
| if ep_length > max_len: | |
| max_len = ep_length | |
| if ep_length > y_limit and y_limit < max_len: | |
| y_limit = ep_length | |
| avg_ep_len = ep_len_sum / len(eval) | |
| avg_ep_len_arr.append(avg_ep_len) | |
| y_limit = y_limit * 1.9 | |
| x = plt.plot(data['timesteps'], avg_ep_result_arr) | |
| # plt.bar(data['timesteps'], avg_ep_len_arr, width=10000) | |
| y = plt.plot(data['timesteps'], avg_ep_len_arr) | |
| plt.ylim(top=y_limit) | |
| # plt.ylabel("Avg ep score") | |
| # lineObjects = plt.plot(x, y) | |
| plt.legend(["avg ep result", "avg ep length"]) | |
| plt.title("result and length over steps\nfilepath: " + filepath) | |
| plt.show() | |
| ''' |