Evalica, your favourite evaluation toolkit

Evalica is a Python library that transforms pairwise comparisons into ranked lists of items. It offers convenient high-performant Rust implementations of the corresponding methods via PyO3, and additionally provides naïve Python code for most of them. Evalica is fully compatible with NumPy arrays and pandas data frames.

• Tutorial (and Tutorial.ipynb)
• Chatbot-Arena.ipynb
• Pair2Rank

The logo was created using Recraft.

Installation

• pip: pip install evalica
• Anaconda: conda install conda-forge::evalica

Usage

Imagine that we would like to rank the different meals and have the following dataset of three comparisons produced by food experts.

Item X	Item Y	Winner
pizza	burger	x
burger	sushi	y
pizza	sushi	tie

Given this hypothetical example, Evalica takes these three columns and computes the outcome of the given pairwise comparison according to the chosen model. Note that the first argument is the column Item X, the second argument is the column Item Y, and the third argument corresponds to the column Winner.

>>> from evalica import elo, Winner
>>> result = elo(
...     ['pizza', 'burger', 'pizza'],
...     ['burger', 'sushi', 'sushi'],
...     [Winner.X, Winner.Y, Winner.Draw],
... )
>>> result.scores
pizza     1014.972058
burger     970.647200
sushi     1014.380742
Name: elo, dtype: float64

As a result, we obtain Elo scores of our items. In this example, pizza was the most favoured item, sushi was the runner-up, and burger was the least preferred item.

Item	Score
pizza	1014.97
burger	970.65
sushi	1014.38

Command-Line Interface

Evalica also provides a simple command-line interface, allowing the use of these methods in shell scripts and for prototyping.

$ evalica -i food.csv bradley-terry
item,score,rank
Tacos,0.43428827947351706,1
Sushi,0.19060105855071743,2
Burger,0.14797720376982199,3
Pasta,0.12815347866987045,4
Pizza,0.0989799795360731,5

Refer to the food.csv file as an input example.

Crowd-Kit

Users of the Crowd-Kit library can easily switch to Evalica by replacing their label item references with the corresponding Winner values, enjoying the faster and cleaner code.

>>> import pandas as pd
>>> from crowdkit.aggregation import BradleyTerry
>>> df = pd.DataFrame(
...     [
...         ['item1', 'item2', 'item1'],
...         ['item3', 'item2', 'item2']
...     ],
...     columns=['left', 'right', 'label']
... )
>>> agg_bt = BradleyTerry(n_iter=100).fit_predict(df)

Evalica is not bound to the specific column names, reducing the potentially expensive operation of building a data frame, while remaining fully compatible with NumPy and pandas.

>>> import pandas as pd
>>> from evalica import bradley_terry, Winner
>>> df = pd.DataFrame(
...     [
...         ['item1', 'item2', Winner.X],
...         ['item2', 'item3', Winner.Y]
...     ],
...     columns=['left', 'right', 'label']
... )
>>> scores = bradley_terry(df['left'], df['right'], df['item'], limit=100)

Implemented Methods

Method	In Python	In Rust
Counting	✅	✅
Average Win Rate	✅	✅
Bradley–Terry	✅	✅
Elo	✅	✅
Eigenvalue	✅	✅
PageRank	✅	✅
Newman	✅	✅

Copyright

Copyright (c) 2024 Dmitry Ustalov. See LICENSE for details.