import json
import numpy as np
import pylab as P
from pprint import pprint
%matplotlib inline

with open('./default.json') as f:
    data = json.load(f)

pprint(data.keys())

P.close()
P.figure()

fig, axarr = P.subplots(4, sharex=True, figsize=(14,12))
axarr[0].set_title("Training history at epoch %s with embedding dim=%s, lr=%s, batch_size=%s, num_corrupted=%s" % (data['max_epoch'], data['emb_dim'], data['lr'], data['batch_size'], data['num_cor']))
axarr[0].plot(data['loss'])
axarr[0].set_ylabel('average batch loss')
axarr[1].plot(data['num_bad'])
axarr[1].set_ylabel('average batch # examples exceeding margin')
axarr[2].plot(data['mean_rank'])
axarr[2].set_ylabel('mean rank on validation set')
axarr[3].plot(data['hit_rate'])
axarr[3].set_ylabel('hit rate@10 on validation set')
axarr[3].set_xlabel('epoch')
for i in range(4):
    axarr[i].set_yscale('log')
fig.tight_layout()