t-SNE for 2-dimensional maps

Unsupervised Learning in Python

Benjamin Wilson

Director of Research at lateral.io

t-SNE for 2-dimensional maps

t-SNE = "t-distributed stochastic neighbor embedding"
Maps samples to 2D space (or 3D)
Map approximately preserves nearness of samples
Great for inspecting datasets

t-SNE on the iris dataset

Iris dataset has 4 measurements, so samples are 4-dimensional
t-SNE maps samples to 2D space
t-SNE didn't know that there were different species
... yet kept the species mostly separate

scatter plot of t-SNE performed on Iris dataset

Interpreting t-SNE scatter plots

"versicolor" and "virginica" harder to distinguish from one another
Consistent with k-means inertia plot: could argue for 2 clusters, or for 3

scatter plot of t-SNE performed on Iris dataset

t-SNE in sklearn

2D NumPy array samples

print(samples)

[[ 5.   3.3  1.4  0.2]
 [ 5.   3.5  1.3  0.3]
 [ 4.9  2.4  3.3  1. ]
 [ 6.3  2.8  5.1  1.5]
 ...
 [ 4.9  3.1  1.5  0.1]]

List species giving species of labels as number (0, 1, or 2)

print(species)

[0, 0, 1, 2, ..., 0]

t-SNE in sklearn

import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
model = TSNE(learning_rate=100)

transformed = model.fit_transform(samples)
xs = transformed[:,0]
ys = transformed[:,1]
plt.scatter(xs, ys, c=species)
plt.show()

scatter plot of t-SNE performed on Iris dataset

t-SNE has only fit_transform()

Has a fit_transform() method
Simultaneously fits the model and transforms the data
Has no separate fit() or transform() methods
Can't extend the map to include new data samples
Must start over each time!

t-SNE learning rate

Choose learning rate for the dataset
Wrong choice: points bunch together
Try values between 50 and 200

Different every time

t-SNE features are different every time
Piedmont wines, 3 runs, 3 different scatter plots!
... however: The wine varieties (=colors) have same position relative to one another

3 scatter plots of t-SNE performed on wines dataset 3 different times

Let's practice!

Unsupervised Learning in Python