# 14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction

# Loading the Digits Dataset
from sklearn.datasets import load_digits

digits = load_digits()

# Creating a TSNE Estimator for Dimensionality Reduction
from sklearn.manifold import TSNE

tsne = TSNE(n_components=2, random_state=11)

# Transforming the Digits Dataset’s Features into Two Dimensions
reduced_data = tsne.fit_transform(digits.data)

reduced_data.shape

# Visualizing the Reduced Data
import matplotlib.pyplot as plt

dots = plt.scatter(reduced_data[:, 0], reduced_data[:, 1],
                    c='black')

# Visualizing the Reduced Data with Different Colors for Each Digit
dots = plt.scatter(reduced_data[:, 0], reduced_data[:, 1],
     c=digits.target, cmap=plt.cm.get_cmap('nipy_spectral_r', 10))

colorbar = plt.colorbar(dots)

# code for visualizing the Digits dataset in 3D
tsne3 = TSNE(n_components=3, random_state=11)
reduced_data3 = tsne3.fit_transform(digits.data) 
figure = plt.figure(figsize=(7, 5))
import mpl_toolkits.mplot3d.axes3d as axes3d
axes = axes3d.Axes3D(figure)
dots = axes.scatter(reduced_data3[:, 0], reduced_data3[:, 1], reduced_data3[:, 2], 
    c=digits.target, cmap=plt.cm.get_cmap('nipy_spectral_r', 10))
colorbar = plt.colorbar(dots)


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