t-SNE visualization of high-dimensional data

Riduzione della dimensionalità in Python

Jeroen Boeye

Head of Machine Learning, Faktion

t-SNE on IRIS dataset

iris clusters

t-SNE on IRIS dataset

iris clusters 1 annotated

t-SNE on IRIS dataset

iris clusters 3 annotated

t-SNE on female ANSUR dataset

df.shape

(1986, 99)

non_numeric = ['BMI_class', 'Height_class', 
               'Gender',  'Component', 'Branch']

df_numeric = df.drop(non_numeric, axis=1)

df_numeric.shape

(1986, 94)

Fitting t-SNE

from sklearn.manifold import TSNE

m = TSNE(learning_rate=50)

tsne_features = m.fit_transform(df_numeric)

tsne_features[1:4,:]

array([[-37.962185,  15.066088],
       [-21.873512,  26.334448],
       [ 13.97476 ,  22.590828]], dtype=float32)

Assigning t-SNE features to our dataset

tsne_features[1:4,:]

array([[-37.962185,  15.066088],
       [-21.873512,  26.334448],
       [ 13.97476 ,  22.590828]], dtype=float32)

df['x'] = tsne_features[:,0]

df['y'] = tsne_features[:,1]

Plotting t-SNE

import seaborn as sns

sns.scatterplot(x="x", y="y", data=df)

plt.show()

Plotting t-SNE

ansur point cloud

Coloring points according to BMI category

import seaborn as sns
import matplotlib.pyplot as plt

sns.scatterplot(x="x", y="y", hue='BMI_class', data=df)

plt.show()

Coloring points according to BMI category

ansur point cloud BMI

Coloring points according to BMI category

ansur point cloud BMI annotated

Coloring points according to height category

import seaborn as sns

import matplotlib.pyplot as plt

sns.scatterplot(x="x", y="y", hue='Height_class', data=df)

plt.show()

Coloring points according to height category

ansur point cloud height

Coloring points according to height category

ansur point cloud double annotated

Let's practice!

Riduzione della dimensionalità in Python