# Selecting The Best Number Of Components For TSVD

## 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 */

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

/* 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 = tsvd.fit(X)``````

## 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 */
break

/* Return the number of components */
return n_components``````

## Run Function

``````
/* Run function */
select_n_components(tsvd_var_ratios, 0.95)``````
``40``

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