Normalizer

Normalizer is a Transformer which transforms a dataset of Vector rows, normalizing each Vector to have unit norm.

Input

It takes in a DataFrame and transforms it to another DataFrame

Output

It adds a new column containing the normalized value of the input column, to the incoming DataFrame.

Type

ml-transformer

Class

fire.nodes.ml.NodeNormalizer

Fields

Name	Title	Description
inputCol	Input Column	The input column name
outputCol	Output Column	The output column name
p	P	Normalization in L^p space. Must be >= 1. (default: p = 2)

Details

Normalizer Node Details

The Normalizer node is a Transformer which transforms a dataset of Vector rows, normalizing each Vector to have unit norm. It takes parameter p, which specifies the p-norm used for normalization. (p=2 by default.)

This normalization can help standardize your input data and improve the behavior of learning algorithms.

When you don’t know the distribution of your data or when you know it’s not Gaussian, normalization is a smart approach to apply. Normalization is useful when your data has variable scales and the technique you’re employing, such as k-nearest neighbors and artificial neural networks, doesn’t make assumptions about the distribution of your data.

Input Parameters

• OUTPUT STORAGE LEVEL : Keep this as DEFAULT.

• Input Column : Select the column in the input schema that contains the features.

• Output Column : Set the name of the field in the output schema that contains the normalized features.

• P : The p-norm to use for normalization. Supported values are ‘1’ and ‘2’. Default is 2.

Examples

Normalizer Node Example

Consider the below dataset which contains two columns, id & features.

| id  |   features    |
|:---:|:--------------|
| 0   |[1.0,0.5,-1.0] |
| 1   |[2.0,1.0,1.0]  |
| 2   |[4.0,10.0,2.0] |

Applying Normalizer node with features as the input column and normFeatures as the output column, we should get the following dataframe as output:

| id  |   features    |   normFeatures    |
|:---:|:--------------|:------------------|
| 0   |[1.0,0.5,-1.0] | [1.0,0.5,-1.0]    |
| 1   | [2.0,1.0,1.0] | [1.0,0.5,0.5]     |
| 2   |[4.0,10.0,2.0] | [0.4,1.0,0.2]     |
|:---:|:--------------|:------------------|