import numpy as np
import gzip
import pickle
from sklearn.manifold.t_sne import TSNE

import os, os.path
fn = "mnist.pkl.gz"
if not os.path.exists(fn):
    def download_file(url):
        import requests
        local_filename = url.split('/')[-1]
        r = requests.get(url, stream=True)
        with open(local_filename, 'wb') as f:
            for chunk in r.iter_content(chunk_size=1024): 
                if chunk:
                    f.write(chunk)
        return local_filename

    url = "http://deeplearning.net/data/mnist/"
    download_file(url + fn)

with gzip.open(fn, "rb") as f:
    train, val, test = pickle.load(f, encoding='latin1')

X = np.vstack((train[0], val[0], test[0]))
y = np.hstack((train[1], val[1], test[1]))

sz=3000
np.random.seed(112017)
pos = np.random.choice(len(y), sz, replace=False)

def _embed(X):
    return TSNE(init='pca').fit_transform(X)

import timeit
t0 = timeit.default_timer()
X2d = _embed(X[pos])
t1 = timeit.default_timer()

print(t1-t0)