check.py

import pickle
import matplotlib.pyplot as plt
from statistics import median, mean

with open('final_score', 'rb') as f:
    score = pickle.load(f)

with open('final_score_segmentation', 'rb') as f:
    score_segmentation = pickle.load(f)

with open('supervised_loss', 'rb') as f:
    loss = pickle.load(f)

plt.figure()
plt.title('Supervised Loss')
plt.plot(loss)
plt.savefig('save/supervised', dpi=300)
plt.show()


with open('test_score', 'rb') as f:
    test_score = pickle.load(f)

# test_score['policy'] = test_score['policy'][:10]

print(mean(test_score['policy']))
print(mean(test_score['random']))

plt.figure()
plt.title('Final Score')
plt.plot(test_score['policy'], label = 'Trained Model')
plt.plot(test_score['random'], label = 'Random Policy')
plt.legend()
plt.savefig('save/test', dpi=300)
plt.show()


# score_avg_100 = list()

# for i in range(len(score)-100):
#     score_avg_100.append(sum(score[i:i+100])/100)

# score_avg_1000 = list()
# xlabel = range(900,len(score)-100)

# for i in range(len(score)-1000):
#     score_avg_1000.append(sum(score[i:i+1000])/1000)

# plt.figure()
# plt.title("Final Score")    
# plt.plot(score_avg_100, label = 'Rolling Window = 100')
# plt.plot(xlabel, score_avg_1000, label = 'Rolling Window = 1000')
# plt.legend()
# plt.savefig('save/score', dpi=300)
# plt.show()



score_avg_100_segmentation = list()

for i in range(len(score_segmentation)-100):
    score_avg_100_segmentation.append(sum(score_segmentation[i:i+100])/100)

score_avg_1000_segmentation = list()
xlabel = range(900,len(score_segmentation)-100)

for i in range(len(score_segmentation)-1000):
    score_avg_1000_segmentation.append(sum(score_segmentation[i:i+1000])/1000)

plt.figure()
plt.title("Final Score")    
#plt.plot(score_segmentation)
plt.plot(score_avg_100_segmentation, label = 'Rolling Window = 100')
plt.plot(xlabel, score_avg_1000_segmentation, label = 'Rolling Window = 1000')
plt.legend()
plt.savefig('save/score_segmentation', dpi=300)
plt.show()