Missing Value Imputation

Version: 1.0.0

Commit Hash: 41c11455badc6f3048edf6611d038e40bc8f2bfd

Author: CauldronGO Team

Category: preprocessing

Impute missing values using various methods

Install with Cauldron get_app
README

Missing Value Imputation

ID: imputation
Version: 1.0.0
Category: preprocessing
Author: CauldronGO Team

Description

Impute missing values using various methods

Runtime

  • Type: python
  • Script: imputation.py

Inputs

Name Label Type Required Default Visibility
input_file Input File file Yes - Always visible
columns Columns to Impute column-selector (multiple) No - Always visible
method Imputation Method select (knn, simple, iterative, constant) Yes knn Always visible
k Number of Neighbors (KNN) number (min: 1, max: 20, step: 1) No 5 Visible when method = knn
strategy Simple Strategy select (mean, median, most_frequent) No mean Visible when method = simple
fillValue Fill Value (Constant) number No 0 Visible when method = constant
iterations Max Iterations (Iterative) number (min: 1, max: 100, step: 1) No 10 Visible when method = iterative

Input Details

Input File (input_file)

Data file with missing values

Columns to Impute (columns)

Select columns to impute (empty = all columns)

  • Column Source: input_file

Imputation Method (method)

Method to use for imputation

  • Options: knn, simple, iterative, constant

Number of Neighbors (KNN) (k)

Number of neighbors for KNN imputation

Simple Strategy (strategy)

Strategy for simple imputation

  • Options: mean, median, most_frequent

Fill Value (Constant) (fillValue)

Value to use for constant imputation

Max Iterations (Iterative) (iterations)

Maximum iterations for iterative imputation

Outputs

Name File Type Format Description
imputed_data imputed.data.txt data tsv Data with imputed values

Requirements

  • Python: >=3.11
  • Packages:
  • numpy>=1.24.0
  • pandas>=2.0.0
  • scikit-learn>=1.3.0

Example Data

This plugin includes example data for testing:

  input_file: diann/imputed.data.txt
  columns_source: diann/imputed.data.txt
  columns: [C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-IP_01.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-IP_02.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-IP_03.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-MockIP_01.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-MockIP_02.raw C:\Raja\DIA-NN searches\June 2022\LT-CBQCA-Test_DIA\RN-DS_220106_BCA_LT-MockIP_03.raw]
  method: knn
  k: 5

Load example data by clicking the Load Example button in the UI.

Usage

Via UI

  1. Navigate to preprocessing → Missing Value Imputation
  2. Fill in the required inputs
  3. Click Run Analysis

Via Plugin System

const jobId = await pluginService.executePlugin('imputation', {
  // Add parameters here
});