ranky 0.0.4

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

Rank aggregation methods available:

• 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)
• Optimal rank aggregation using any rank metric: rk.center(m), rk.center(m, method='kendalltau'). Solver used [1].
• (Kemeny-Young method is optimal rank aggregation using Kemeny distance as metric.)
• (Optimal rank aggregation using Spearman correlation as metric is equivalent to Borda count.)

Metrics

Use any_metric(a, b, method) to call a metric from any of the three categories below.

• Scoring metrics: rk.metric(y_true, y_pred, method='accuracy'). Methods include: ['accuracy', 'balanced_accuracy', 'precision', 'average_precision', 'brier', 'f1_score', 'mxe', 'recall', 'jaccard', 'roc_auc', 'mse', 'rmse', 'sar']

• Rank correlation coefficients: rk.corr(r1, r2, method='spearman'). Methods include: ['kendalltau', 'spearman', 'pearson']

• Rank distances: rk.dist(r1, r2, method='levenshtein'). Methods include: ['hamming', 'levenshtein', 'winner', 'euclidean']

To add: general edit distances, kemeny distance, regression metrics...

Visualizations

• Use rk.show to visualize preference matrix (2D) or ranking ballots (1D).

>>> rk.show(m)

>>> rk.show(m['judge1'])

• Use rk.mds, to visualize (in 2D or 3D) the points in a given metric space.

>>> rk.mds(m, method='euclidean')

>>> rk.mds(m, method='spearman', axis=1)

• You can use rk.tsne similarly to rk.mds.

Other

• Rank, rk.rank, convert a 1D score ballot to ranking.
• Bootstrap, rk.bootstrap, sample a given axis.
• Consensus, rk.consensus, check if ranking exactly agree.
• Concordance, ,rk.concordance, mean rank distance between all judges of a preference matrix.
• Centrality, rk.centrality, mean rank distance between a ranking and a preference matrix.
• Kendall's W, rk.kendall_w, coefficient of concordance.

[1] Storn R. and Price K., Differential Evolution - a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization, 1997, 11, 341 - 359.