Machine Learning for Beginners in Python: How to Select The Best Number Of Components For TSVD

Selecting The Best Number Of Components For TSVD


/* 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(

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

Run Truncated Singular Value Decomposition

/* Create and run an TSVD with one less than number of features */
tsvd = TruncatedSVD(n_components=X_sparse.shape[1]-1)
X_tsvd =

Create List Of Explained Variances

/* List of explained variances */
tsvd_var_ratios = tsvd.explained_variance_ratio_

Create Function Calculating Number Of Components Required To Pass Threshold

/* Create a function */
def select_n_components(var_ratio, goal_var: float) -> int:

    /* Set initial variance explained so far /*
    total_variance = 0.0
    /* Set initial number of features */
    n_components = 0
    /* For the explained variance of each feature: /*
    for explained_variance in var_ratio:
        /* Add the explained variance to the total */
        total_variance += explained_variance
        /* Add one to the number of components */
        n_components += 1
        /* If we reach our goal level of explained variance */
        if total_variance >= goal_var:
            /* End the loop */
    /* Return the number of components */
    return n_components

Run Function

/* Run function */
select_n_components(tsvd_var_ratios, 0.95)


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