mc4 2.2.0

A python package for implementing Markov Chain Type 4 rank aggregation

Markov Chain Type 4 Rank Aggregation

implementation of MC4 Rank Aggregation algorithm using Python

Description

This project is all about implementing one of the most popular rank aggregation algorithms Markov Chain Type 4 or MC4. In the field of Machine Learning and many other scientific problems, several items are often needed to be ranked based on some criterion. However, different ranking schemes order the items based on different preference criteria. Hence the rankings produced by them may differ greatly.

Therefore a rank aggregation technique is often used for combining the individual rank lists into a single aggregated ranking. Though there are many rank aggregation algorithms, MC4 is one of the most renowned ones.

Resource

Links to the original contents

• Rank Aggregation methods for Web: PDF
• Rank Aggregation methods for Web: web version

Installation

For the latest release, pip install mc4

For a specific release, pip install mc4=={version} such as pip install mc4==1.0.0

General Usage

Using this package is very easy. You just need the following three lines of code to use the package.

from mc4.algorithm import mc4_aggregator

aggregated_ranks = mc4_aggregator('dataset.csv') 

# or 

aggregated_ranks = mc4_aggregator(df) 

print(aggregated_ranks)

here dataset.csv or df are lists of ranks provided by different ranking algorithms or rank lists. You can refer here for more info and some test datasets.

mc4_aggregator takes some additional arguments as well.

• order (string): order of the dataset, default is 'row'. More on this, here.
• header_row (int or None): row number of the dataset containing the header, default is None
• index_col (int or None): column number of the dataset containing the index, default is None
• precision (float): acceptable error margin for convergence, default is 1e-07
• iterations (int): number of iterations to reach stationary distribution, default is 200
• erg_number (float): small, positive number used to calculate ergodic transition matrix, default is 0.15

Command Line Usage

• To get help and usage details,

  ~$ mc4_aggregator -h or --help
  

• Use with default settings,

  ~$ mc4_aggregator <data source> e.g. mc4_aggregator dataset.csv
  

• Specify order using -oor --order, default is row

  ~$ mc4_aggregator <data source> -o <order> e.g. mc4_aggregator dataset.csv -o column
  

• Specify header row using -hr or --header_row, default is None

  ~$ mc4_aggregator <data source> -hr <header row> e.g. mc4_aggregator dataset.csv -hr 1
  

• Specify index column using -ic or --index_col, default is None

  ~$ mc4_aggregator <data source> -ic <index column> e.g. mc4_aggregator dataset.csv -ic 1
  

• Specify precision using -p or --precision, default is 1e-07

  ~$ mc4_aggregator <data source> -p <precision> e.g. mc4_aggregator dataset.csv -p 0.000001
  

• Specify iterations using -i or --iterations, default is 200

  ~$ mc4_aggregator <data source> -i <iterations> e.g. mc4_aggregator dataset.csv -i 300
  

• Specify ergodic number using -e or --erg_number, default is 0.15

  ~$ mc4_aggregator <data source> -p <precision> e.g. mc4_aggregator dataset.csv -e 0.20
  

• All together,

  ~$ mc4_aggregator dataset.csv -o column -hr 1 -ic 1 -p 0.000001 -i 300 -e 0.20
  

Output

Output of mc4_aggregator will be a dictionary containing itemwise ranks. In absence of item names, items will be represented using integers.

Important Links

• For full documentation, please visit Documentation Wiki
• To report issues or request a feature, please visit Issues
• Follow author, Ayan Kumar Saha - GitHub | LinkedIn