Independent Component Analysis

This transform is used to extract a specified number of independent components from a dataset and return the new dataset with these components along with the target column.

tags: [“Data Preparation”]

Parameters

The table gives a brief description about each parameter in Independent Component Analysis transform.

Name:

By default, the transform name is populated. You can also add a custom name for the transform.

Input Dataset:

The file name of the input dataset. You can select the dataset that was uploaded from the drop-down list.(Required: True, Multiple: False)

Output Dataset:

The file name with which the output dataset is created. (Required: True, Multiple: False)

Number of Components:

The number of independent components to be created. (Required: True, Multiple: False, Datatypes: [“LONG”], Options: [‘CONSTANT’])

Target Feature:

The target column or feature in the dataset. (Required: True, Multiple: False, Datatypes: [“STRING”], Options: [‘FIELDS’], Datasets: [‘df’])

The sample input for this transform looks as shown in the screenshot.

The output after running the Independent Component Analysis transform on the dataset appears as below:

How to use it in Notebook

The following is the code snippet you must use in the Jupyter Notebook editor to run the Independent Component Analysis transform:

template=TemplateV2.get_template_by('Independent Component Analysis')

recipe_Independent_Component_Analysis= project.addRecipe([car_data, employee_data, temperature_data, only_numeric], name='Independent Component Analysis')

transform=Transform()
transform.templateId = template.id
transform.name='Independent Component Analysis'
transform.variables = {
'input_dataset':'only_numeric',
'output_dataset':'independent_comp',
'n':1,
'target':"Age"}
recipe_Independent_Component_Analysis.add_transform(transform)
recipe_Independent_Component_Analysis.run()

Requirements

scikit-learn pandas