In [1]:

    

import json
from pprint import pprint
import numpy as np
import copy

%matplotlib inline

# Import the data
import WTBLoad
wtb = WTBLoad.load()


    

In [2]:

    

from sklearn import manifold
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['figure.figsize'] = (15,10)

tsne = manifold.TSNE(n_components=2, random_state=10)
trans_data = tsne.fit_transform(wtb).T
#pprint(trans_data)

additions = wtb.index.values
plt.scatter(trans_data[0], trans_data[1])
for label, x, y in zip(additions,trans_data[0], trans_data[1]):
  plt.annotate(
      label, 
      xy = (x, y), xytext = (0,0),
      textcoords = 'offset points', ha = 'right', va = 'bottom'
      )
plt.axis('tight')


plt.show()