# 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 */
digits = datasets.load_digits()
/* 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`

# Python Example for Beginners

## Two Machine Learning Fields

There are two sides to machine learning:

**Practical Machine Learning:**This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.**Theoretical Machine Learning**: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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