Polynominal Expansion

Perform feature expansion in a polynomial space

Input

It takes in a DataFrame and transforms it to another DataFrame

Output

The output DataFrame contains a new column of type vector, Expanding your features into a polynomial space, which is formulated by an n-degree combination of original dimensions.

Type

ml-transformer

Class

fire.nodes.ml.NodePolynominalExpansion

Fields

Name	Title	Description
inputCol	Input Column	The input column name
outputCol	Output Column	The output column name
degree	Degree	The polynomial degree to expand, which should be >= 1. A value of 1 means no expansion.

Details

Polynominal Expansion Node Details

The Polynomial expansion Node helps in the process of expanding your features into a polynomial space, which is formulated by an n-degree combination of original dimensions.

For example, if a dataset had one input feature X, then a polynomial feature would be the addition of a new feature (column) where values were calculated by squaring the values in X, e.g. X^2. This process can be repeated for each input variable in the dataset, creating a transformed version of each.

Input Parameters

• OUTPUT STORAGE LEVEL : Keep this as DEFAULT.

• INPUT COLUMN : Select the required vector column from the list of input schema fields.

• OUTPUT COLUMN : Set a name for the output column.

• DEGREE : The polynomial degree to expand, which should be greater than or equal to 1. A value of 1 means no expansion. Default : 2.

Examples

Polynominal Expansion Node Example

Consider the below dataset which contains a vector field features

|features  |
|:--------:|
|[2.0,1.0] |
|[3.0,-1.0]|

On applying the Polynominal Expansion Node to the input column of features with a degree of 3, we get the below output dataframe with the output vector column of polyFeatures.

|features  |          polyFeatures                    |
|:--------:|:----------------------------------------:|
|[2.0,1.0] |[2.0,4.0,8.0,1.0,2.0,4.0,1.0,2.0,1.0]     |
|[3.0,-1.0]|[3.0,9.0,27.0,-1.0,-3.0,-9.0,1.0,3.0,-1.0]|