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Machine Learning for Beginners in Python: Dimensionality Reduction On Sparse Feature Matrix

By on Monday, May 24, 2021

Dimensionality Reduction On Sparse Feature Matrix

Preliminaries

/* Load libraries */
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import TruncatedSVD
from scipy.sparse import csr_matrix
from sklearn import datasets
import numpy as np

Load Digits Data And Make Sparse

/* Load the data */
digits = datasets.load_digits()

/* Standardize the feature matrix */
X = StandardScaler().fit_transform(digits.data)

/* Make sparse matrix */
X_sparse = csr_matrix(X)

Create Truncated Singular Value Decomposition

/* Create a TSVD */
tsvd = TruncatedSVD(n_components=10)

Run Truncated Singular Value Decomposition

/* Conduct TSVD on sparse matrix */
X_sparse_tsvd = tsvd.fit(X_sparse).transform(X_sparse)

View Results

/* Show results */
print('Original number of features:', X_sparse.shape[1])
print('Reduced number of features:', X_sparse_tsvd.shape[1])
Original number of features: 64
Reduced number of features: 10

View Percent Of Variance Explained By New Features

/* Sum of first three components' explained variance ratios */
tsvd.explained_variance_ratio_[0:3].sum()
0.30039385372588506

 

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