t-SNE可视化(MNIST例子)

如下所示：

import pickle as pkl
import numpy as np
from matplotlib import pyplot as plt
from tsne import bh_sne
import sys 

with open("data", 'rb') as f:
            if sys.version_info > (3, 0):
                data = pkl.load(f, encoding='latin1')
            else:
                data = pkl.load(f)

data =data.astype('float64')

with open("label", 'rb') as f:
            if sys.version_info > (3, 0):
                y_data = pkl.load(f, encoding='latin1')
            else:
                y_data = pkl.load(f)
classNum = 6
y_data = np.where(y_data==1)[1]*(9.0/classNum)

vis_data = bh_sne(data)

# plot the result
vis_x = vis_data[:, 0]
vis_y = vis_data[:, 1]

fig = plt.figure()
plt.scatter(vis_x, vis_y, c=y_data, s=1, cmap=plt.cm.get_cmap("jet", 10))
plt.colorbar(ticks=range(10))
plt.clim(-0.5, 9.5)
plt.show()
fig.savefig('test.png')

结果：

以MNIST为例，先做PCA降到50维，再做t-sne：

from time import time
from tsne import bh_sne
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
from matplotlib import offsetbox
from sklearn import (manifold, datasets, decomposition, ensemble,
                     discriminant_analysis, random_projection)
from sklearn import decomposition

mnist = input_data.read_data_sets('./input_data', one_hot=False)
sub_sample = 5000
y = mnist.train.labels[0:sub_sample]
X = mnist.train.images[0:sub_sample]

n_samples, n_features = X.shape
n_neighbors = 30

#----------------------------------------------------------------------
# Scale and visualize the embedding vectors
def plot_embedding(X_emb, title=None):
    x_min, x_max = np.min(X_emb, 0), np.max(X_emb, 0)
    X_emb = (X_emb - x_min) / (x_max - x_min)

    plt.figure()
    ax = plt.subplot(111)
    for i in range(X_emb.shape[0]):
        plt.text(X_emb[i, 0], X_emb[i, 1], str(y[i]),
                 color=plt.cm.Set1(y[i] / 10.),
                 fontdict={'weight': 'bold', 'size': 9})

    if hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = np.array([[1., 1.]])  # just something big
        for i in range(sub_sample):
            dist = np.sum((X_emb[i] - shown_images) ** 2, 1)
            if np.min(dist) < 8e-3:
                # don't show points that are too close
                continue
            shown_images = np.r_[shown_images, [X_emb[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(X[i].reshape(28,28)[::2,::2], cmap=plt.cm.gray_r),
                X_emb[i])
            ax.add_artist(imagebox)
    plt.xticks([]), plt.yticks([])
    if title is not None:
        plt.title(title)

#----------------------------------------------------------------------
# Plot images of the digits
n_img_per_row = 20
img = np.zeros((30 * n_img_per_row, 30 * n_img_per_row))
for i in range(n_img_per_row):
    ix = 30 * i + 1
    for j in range(n_img_per_row):
        iy = 30 * j + 1
        img[ix:ix + 28, iy:iy + 28] = X[i * n_img_per_row + j].reshape((28, 28))

plt.imshow(img, cmap=plt.cm.binary)
plt.xticks([])
plt.yticks([])
plt.title('A selection from the 64-dimensional digits dataset')

# t-SNE embedding of the digits dataset
print("Computing t-SNE embedding")
t0 = time()
X_pca = decomposition.TruncatedSVD(n_components=50).fit_transform(X)
# data =X.astype('float64')
X_tsne  = bh_sne(X_pca)

plot_embedding(X_tsne,
               "t-SNE embedding of the digits (time %.2fs)" %
               (time() - t0))

plt.show()

结果如下：

更多降维的可视化参考：http://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py