

In [257]:

    
from generate_samples import load_rnn_samples, load_human_samples



In [258]:

    
rnn_poems = load_rnn_samples()



In [259]:

    
human_poems = load_human_samples()

Evaluation



In [273]:

    
from evaluate import eval_poems
from rhyme import RhymeEvaluator
import pandas as pd
%matplotlib inline
%autoreload 2
%reload_ext autoreload



In [274]:

    
evaluator = RhymeEvaluator()



In [263]:

    
rnn_scores, rnn_mean, rnn_std = eval_poems(evaluator, rnn_poems)









    



Mean score = 0.766685714286, standard deviation = 0.308113787539



In [264]:

    
human_scores, human_mean, human_std = eval_poems(evaluator, human_poems)









    



Mean score = 0.894175, standard deviation = 0.184304499285



In [284]:

    
rnn_df = pd.DataFrame(rnn_scores, columns=['Combined Score'])



In [334]:

    
ax = rnn_df.plot(kind='hist', bins=100, figsize=(10, 10), legend=False, fontsize=14)
ax.set_xlabel('Combined Score', fontsize=20)
ax.set_ylabel('Frequency', fontsize=20)
ax.set_title('Score Distribution for Generated Sample', fontsize=24)









    Out[334]:





<matplotlib.text.Text at 0x12e8f2dd0>



In [309]:

    
human_df = pd.DataFrame(human_scores, columns=['Combined Score'])



In [335]:

    
ax = human_df.plot(kind='hist', bins=100, figsize=(10, 10), legend=False, fontsize=14)
ax.set_xlabel('Combined Score', fontsize=20)
ax.set_ylabel('Frequency', fontsize=20)
ax.set_title('Score Distribution for Training Data Sample', fontsize=24)









    Out[335]:





<matplotlib.text.Text at 0x12ed6d450>

Individual



In [265]:

    
poem = human_poems[0]
poem









    Out[265]:





[u'\u60b2\u6b4c\u5f53\u54ed\u6709\u4f59\u60b2',
 u'\u4eca\u5915\u540c\u541b\u9189\u59cb\u77e5',
 u'\u5374\u501a\u80e1\u5e8a\u7981\u4e0d\u5f97',
 u'\u4e00\u65f6\u53cc\u6cea\u5815\u91d1\u536e']



In [275]:

    
evaluator.rhyme_util.get_poem_tone_types(poem)









    Out[275]:





['pp*pz*p', 'p**pzz*', 'z*pp***', '*ppz**p']



In [246]:

    
(max_combined, max_tone, max_rhyme), scores = evaluator.eval(poem, output_all_scores=True, output_split=True)



In [249]:

    
(max_combined, max_tone, max_rhyme)









    Out[249]:





(0.9464285714285714, 0.9642857142857143, 1.0)



In [251]:

    
scores









    Out[251]:





[(0.9464285714285714, 0.8928571428571429, 1.0),
 (0.48214285714285715, 0.9642857142857143, 0.0),
 (0.8571428571428572, 0.7142857142857143, 1.0),
 (0.39285714285714285, 0.7857142857142857, 0.0)]