import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.manifold import TSNE
class FeatureVisualize(object):
'''
Visualize features by TSNE
'''
def __init__(self, features, labels):
'''
features: (m,n)
labels: (m,)
'''
self.features = features
self.labels = labels
def plot_tsne(self, save_eps=False):
''' Plot TSNE figure. Set save_eps=True if you want to save a .eps file.
'''
tsne = TSNE(n_components=2, init='pca', random_state=0)
features = tsne.fit_transform(self.features)
x_min, x_max = np.min(features, 0), np.max(features, 0)
data = (features - x_min) / (x_max - x_min)
del features
for i in range(data.shape[0]):
plt.text(data[i, 0], data[i, 1], str(self.labels[i]),
color=plt.cm.Set1(self.labels[i] / 10.),
fontdict={'weight': 'bold', 'size': 9})
plt.xticks([])
plt.yticks([])
plt.title('T-SNE')
if save_eps:
plt.savefig('tsne.eps', dpi=600, format='eps')
plt.show()
if __name__ == '__main__':
digits = datasets.load_digits(n_class=5)
features, labels = digits.data, digits.target
print(features.shape)
print(labels.shape)
vis = FeatureVisualize(features, labels)
vis.plot_tsne(save_eps=True)
