In [1]:

    

import glob
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import scipy

from matplotlib.ticker import NullFormatter
from sklearn import manifold
from time import time

from tensorflow.examples.tutorials.mnist import input_data
from tsne import bh_sne

from models import FullyConnectedDiscriminator, ConvDiscriminator, FullyConnectedVAE, ConvVAE

result_dir = 'aws-results'

results = [('Conv 2', os.path.join(result_dir, 'Conv2Discriminator/*')),
           ('Conv 5', os.path.join(result_dir, 'Conv5Discriminator/*')),
           ('Conv 8', os.path.join(result_dir, 'Conv8Discriminator/*')),
           ('FC 100 Relu', os.path.join(result_dir, 'FullyConnected100ReluDiscriminator/*')),
           ('FC 100 Sigmoid', os.path.join(result_dir, 'FullyConnected100SigmoidDiscriminator/*')),
           ('FC 10 Relu', os.path.join(result_dir, 'FullyConnected10ReluDiscriminator/*')),
           ('FC 10 Sigmoid', os.path.join(result_dir, 'FullyConnected10SigmoidDiscriminator/*')),
           ('FC 200 Relu', os.path.join(result_dir, 'FullyConnected200ReluDiscriminator/*')),
           ('FC 200 Sigmoid', os.path.join(result_dir, 'FullyConnected200SigmoidDiscriminator/*')),
           ('FC 20 Relu', os.path.join(result_dir, 'FullyConnected20ReluDiscriminator/*')),
           ('FC 40 Relu', os.path.join(result_dir, 'FullyConnected40ReluDiscriminator/*')),
           ('FC 80 Relu', os.path.join(result_dir, 'FullyConnected80ReluDiscriminator/*')),
           ('FC 20 Sigmoid', os.path.join(result_dir, 'FullyConnected20SigmoidDiscriminator/*')),
           ('FC 40 Sigmoid', os.path.join(result_dir, 'FullyConnected40SigmoidDiscriminator/*')),
           ('FC 80 Sigmoid', os.path.join(result_dir, 'FullyConnected80SigmoidDiscriminator/*')),
           ('FC 160 Sigmoid', os.path.join(result_dir, 'FullyConnected160SigmoidDiscriminator/*')),
           ]

# seed = 0
batch_size = 2500

mnist = input_data.read_data_sets('data/', validation_size=batch_size)
images, labels = mnist.validation.next_batch(batch_size)
# bh_sne needs data as float64
images = np.asarray(images).astype('float64')

distance_matrices = []

for i, architecture in enumerate(results):
    name = architecture[0]
    architecture_results = architecture[1]
    for result in glob.glob(architecture_results):
        with open(os.path.join(result, 'config.json'), 'r') as config_file:
            config = json.load(config_file)
            print(config['result_dir'])
            # TODO: the result_dir in the config file should use a relative path
            config['result_dir'] = config['result_dir'].replace('/home/ubuntu/space-of-representations/results', result_dir)
        if config['vae']:
            if config['conv']:
                print('Loading a convolutional vae')
                model = ConvVAE(config)
            else:
                print('Loading a fully connected vae')
                model = FullyConnectedVAE(config)
        else:
            if config['conv']:
                print('Loading a convolutional discriminator')
                model = ConvDiscriminator(config)
            else:
                print('Loading a fully connected discriminator')
                model = FullyConnectedDiscriminator(config)
        sess = model.sess
        representation = sess.run(model.representation, {model.x: images})
        # bh_sne needs data as float64
        representation = np.asarray(representation).astype('float64')
        min_ = np.min(representation)
        max_ = np.max(representation)
        representation = (representation - min_) / (max_ - min_)

        distance_matrices.append(scipy.spatial.distance.pdist(representation, metric='euclidean'))


    

    




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In [2]:

    

distance_matrices = np.matrix(distance_matrices)
print('Distance matrices shape: {}'.format(distance_matrices.shape))
# bh_sne needs data as float64
distance_matrices = np.asarray(distance_matrices).astype('float64')


    

    




Distance matrices shape: (52, 3123750)

In [3]:

    

n_neighbors = 4
n_components = 2


    

In [4]:

    

# t-SNE
perplexity = n_neighbors
t0 = time()
meta_sne = bh_sne(distance_matrices, perplexity=perplexity)
t1 = time()
print("t-SNE: %.2g sec" % (t1 - t0))
sne_x = meta_sne[:, 0]
sne_y = meta_sne[:, 1]

fig, ax = plt.subplots(figsize=(20,20))
ax.scatter(sne_x, sne_y)
names = []
for x in results:
    for i in range(len(glob.glob(x[1]))):
        names.append(x[0])
for i, name in enumerate(names):
    ax.annotate(name, (sne_x[i], sne_y[i]))
plt.title('t-SNE')
plt.show()
#fig.savefig('perplexity {}.png'.format(perplexity))


    

    




t-SNE: 20 sec






    

In [5]:

    

# Isomap
t0 = time()
Y = manifold.Isomap(n_neighbors, n_components).fit_transform(distance_matrices)
t1 = time()
print("Isomap: %.2g sec" % (t1 - t0))

fig, ax = plt.subplots(figsize=(20,20))
ax.scatter(Y[:, 0], Y[:, 1])
for i, name in enumerate(names):
    ax.annotate(name, (Y[i, 0], Y[i, 1]))
plt.title("Isomap (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')
plt.show()


    

    




Isomap: 12 sec






    

In [6]:

    

# MDS
t0 = time()
mds = manifold.MDS(n_components, max_iter=100, n_init=1)
Y = mds.fit_transform(distance_matrices)
t1 = time()
print("MDS: %.2g sec" % (t1 - t0))
fig, ax = plt.subplots(figsize=(20,20))
ax.scatter(Y[:, 0], Y[:, 1])
for i, name in enumerate(names):
    ax.annotate(name, (Y[i, 0], Y[i, 1]))
plt.title("MDS (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')
plt.show()


    

    




MDS: 0.63 sec






    

In [7]:

    

methods = ['standard', 'ltsa', 'modified'] # Removed hessian
labels = ['LLE', 'LTSA', 'Modified LLE']

for index, method in enumerate(methods):
    t0 = time()
    Y = manifold.LocallyLinearEmbedding(n_neighbors, n_components,
                                        eigen_solver='auto',
                                        method=method).fit_transform(distance_matrices)
    t1 = time()
    print("%s: %.2g sec" % (methods[index], t1 - t0))

    fig, ax = plt.subplots(figsize=(20,20))
    ax.scatter(Y[:, 0], Y[:, 1])
    for i, name in enumerate(names):
        ax.annotate(name, (Y[i, 0], Y[i, 1]))
    plt.title("%s (%.2g sec)" % (labels[index], t1 - t0))
    ax.xaxis.set_major_formatter(NullFormatter())
    ax.yaxis.set_major_formatter(NullFormatter())
    plt.axis('tight')
    plt.show()


    

    




standard: 16 sec






    













    




ltsa: 15 sec






    













    




modified: 15 sec






    

In [8]:

    

t0 = time()
se = manifold.SpectralEmbedding(n_components=n_components,
                                n_neighbors=n_neighbors)
Y = se.fit_transform(distance_matrices)
t1 = time()
print("SpectralEmbedding: %.2g sec" % (t1 - t0))
fig, ax = plt.subplots(figsize=(20,20))
ax.scatter(Y[:, 0], Y[:, 1])
for i, name in enumerate(names):
    ax.annotate(name, (Y[i, 0], Y[i, 1]))
plt.title("Spectral Embedding (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
plt.axis('tight')
plt.show()


    

    




/home/psoulos/bin/anaconda2/envs/sor/lib/python2.7/site-packages/sklearn/manifold/spectral_embedding_.py:229: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.
  warnings.warn("Graph is not fully connected, spectral embedding"






    




SpectralEmbedding: 13 sec