ranky 0.0.3

Compute rankings in Python.

Ranky

Compute rankings in Python.

Get started

pip install ranky

import ranky as rk

Read the documentation.

Main functions

The main functionalities include scoring metrics (e.g. accuracy, roc auc), rank metrics (e.g. Kendall Tau, Spearman correlation), ranking systems (e.g. Majority judgement, Kemeny-Young method) and some measurements (e.g. Kendall's W coefficient of concordance).

Most functions takes as input 2-dimensional numpy.array or pandas.DataFrame objects. DataFrame are the best to keep track of the names of each data point.

Let's consider the following preference matrix:

Each row is a candidate and each column is a judge. Here is the results of rk.borda(matrix), computing the mean rank of each candidate:

We can see that candidate2 has the best average ranking among the four judges.

Let's display it using rk.show(rk.borda(matrix)):

Ranking systems

• Random Dictator. rk.dictator(m)

• Score Voting. rk.score(m)

• Borda Count. rk.borda(m)

• Majority Judgement. rk.majority(m)

• Condorcet, p-value Condorcet. rk.condorcet(m), rk.condorcet(m, wins=rk.p_wins)

• Center. rk.center(m), rk.center(m, method='swap'), etc.

• Kemeny.

Metrics

• Scoring metrics. rk.metric(y_true, y_pred, method='accuracy')

• Rank correlation coefficients. rk.corr(r1, r2, method='spearman')

• Rank distances. rk.dist(r1, r2, method='levenshtein')

Visualizations

• rk.show, 1D or 2D
• rk.tsne, 2D or 3D

Other

• Consensus
• Concordane
• Centrality
• Kendall's W