Quickly visualize your data in 2d and 3d with PCA and TSNE (t-sne)

visualizing data in 2d and 3d.py

# imports from matplotlib import pyplot as plt
from matplotlib import pyplot as plt
import pylab
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
%matplotlib inline
%pylab inline

from sklearn.manifold import TSNE
from sklear.decomposition import PCA

#TSNE(2)
tsne = TSNE(n_components=2, random_state=0).fit_transform(X_train)
x, y = list(zip(*X_2d))

plt.figure(figsize=(9, 6))
plt.scatter(x, y)
plt.legend()
plt.show()

#TSNE(3)
data = TSNE(n_components=3, random_state=0).fit_transform(X_train)
x, y, z = list(zip(*data))

fig = pylab.figure()
ax = fig.add_subplot(111, projection = '3d')
sc = ax.scatter(x,y,z)

# PCA(2)
x, y = list(zip(*MinMaxScaler().fit_transform(PCA(2).fit_transform(X_train[:]))))

plt.figure(figsize=(9, 6))
plt.scatter(x, y)
plt.legend()
plt.show()

# PCA(3)
x, y, z = list(zip(*MinMaxScaler().fit_transform(PCA(3).fit_transform(X_train[:]))))

fig = pylab.figure()
ax = fig.add_subplot(111, projection = '3d')
sc = ax.scatter(x,y,z)