dimensionality_reduction_mds.md

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Dimensionality Reduction with MDS

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

from sklearn import manifold, datasets
from matplotlib.colors import ListedColormap

iris = datasets.load_iris()
mds = manifold.MDS(n_components=2)
new_dim = mds.fit_transform(iris.data)

df = pd.DataFrame(new_dim, columns=['X', 'Y'])
df['label'] = iris.target
df.head()

	X	Y	label
0	2.103263	1.708053	0
1	2.407857	1.286837	0
2	2.516857	1.451582	0
3	2.496596	1.206902	0
4	2.130423	1.757476	0

fig = plt.figure()
fig.suptitle('MDS', fontsize=14, fontweight='bold')
ax = fig.add_subplot(111)

plt.scatter(df[df.label == 0].X, df[df.label == 0].Y, color='red', label=iris.target_names[0])
plt.scatter(df[df.label == 1].X, df[df.label == 1].Y, color='blue', label=iris.target_names[1])
plt.scatter(df[df.label == 2].X, df[df.label == 2].Y, color='green', label=iris.target_names[2])

_ = plt.legend(bbox_to_anchor=(1.25, 1))