dimensionality_reduction_isomap.md

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Dimensionality Reduction with Isomap

%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()
isomap = manifold.Isomap(n_components=2)
new_dim = isomap.fit_transform(iris.data)

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

	X	Y	label
0	0.118155	0.381038	0
1	0.113729	0.323243	0
2	0.113922	0.325955	0
3	0.113904	0.325207	0
4	0.117613	0.372992	0

fig = plt.figure()
fig.suptitle('Isomap', 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))