app.py

# Copyright 2023 Dmitry Ustalov
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

__author__ = 'Dmitry Ustalov'
__license__ = 'Apache 2.0'

from typing import IO, Tuple

import gradio as gr
import numpy as np
import numpy.typing as npt
import pandas as pd
import plotly.express as px
from plotly.graph_objects import Figure


def visualize(df_pairwise: pd.DataFrame) -> Figure:
    fig = px.imshow(df_pairwise, color_continuous_scale='RdBu', text_auto='.2f')
    fig.update_layout(xaxis_title='Loser', yaxis_title='Winner', xaxis_side='top')
    fig.update_traces(hovertemplate='Winner: %{y}<br>Loser: %{x}<br>Fraction of Wins: %{z}<extra></extra>')
    return fig


# https://gist.github.com/dustalov/41678b70c40ba5a55430fa5e77b121d9#file-newman-py
def aggregate(wins: npt.NDArray[np.int64], ties: npt.NDArray[np.int64],
              seed: int = 0, tolerance: float = 10e-6, limit: int = 20) -> npt.NDArray[np.float64]:
    assert wins.shape == ties.shape, 'wins and ties shapes are different'

    rng = np.random.default_rng(seed)

    pi, v = rng.random(wins.shape[0]), rng.random()

    converged, iterations = False, 0

    while not converged:
        iterations += 1

        v_numerator = np.sum(
            ties * (pi[:, np.newaxis] + pi) /
            (pi[:, np.newaxis] + pi + 2 * v * np.sqrt(pi[:, np.newaxis] * pi))
        ) / 2

        v_denominator = np.sum(
            wins * 2 * np.sqrt(pi[:, np.newaxis] * pi) /
            (pi[:, np.newaxis] + pi + 2 * v * np.sqrt(pi[:, np.newaxis] * pi))
        )

        v = v_numerator / v_denominator
        v = np.nan_to_num(v, copy=False, nan=tolerance)

        pi_old = pi.copy()

        pi_numerator = np.sum(
            (wins + ties / 2) * (pi + v * np.sqrt(pi[:, np.newaxis] * pi)) /
            (pi[:, np.newaxis] + pi + 2 + v * np.sqrt(pi[:, np.newaxis] * pi)),
            axis=1
        )

        pi_denominator = np.sum(
            (wins + ties / 2) * (1 + v * np.sqrt(pi[:, np.newaxis] * pi)) /
            (pi[:, np.newaxis] + pi + 2 + v * np.sqrt(pi[:, np.newaxis] * pi)),
            axis=0
        )

        pi = pi_numerator / pi_denominator
        pi = np.nan_to_num(pi, copy=False, nan=tolerance)

        converged = np.allclose(pi / (pi + 1), pi_old / (pi_old + 1),
                                rtol=tolerance, atol=tolerance) or (iterations >= limit)

    return pi


def handler(file: IO[bytes], seed: int) -> Tuple[pd.DataFrame, Figure]:
    if file is None:
        raise gr.Error('File must be uploaded')

    try:
        df = pd.read_csv(file.name, dtype=str)
    except ValueError as e:
        raise gr.Error(f'Parsing error: {e}')

    if not pd.Series(['left', 'right', 'winner']).isin(df.columns).all():
        raise gr.Error('Columns must exist: left, right, winner')

    if not df['winner'].isin(pd.Series(['left', 'right', 'tie'])).all():
        raise gr.Error('Allowed winner values: left, right, tie')

    df = df[['left', 'right', 'winner']]

    df.dropna(axis='rows', inplace=True)

    index = pd.Index(np.unique(df[['left', 'right']].values), name='item')

    df_wins = pd.pivot_table(df[df['winner'].isin(['left', 'right'])],
                             index='left', columns='right', values='winner',
                             aggfunc='count', fill_value=0)
    df_wins = df_wins.reindex(labels=index, columns=index, fill_value=0, copy=False)

    df_ties = pd.pivot_table(df[df['winner'] == 'tie'],
                             index='left', columns='right', values='winner', aggfunc='count',
                             fill_value=0)
    df_ties = df_ties.reindex(labels=index, columns=index, fill_value=0, copy=False)

    wins = df_wins.to_numpy(dtype=np.int64)
    ties = df_ties.to_numpy(dtype=np.int64)
    ties += ties.T

    scores = aggregate(wins, ties, seed=seed)

    df_result = pd.DataFrame(data={'score': scores}, index=index)
    df_result['rank'] = df_result['score'].rank(na_option='bottom', ascending=False).astype(int)
    df_result.fillna(np.NINF, inplace=True)
    df_result.sort_values(by=['rank', 'score'], ascending=[True, False], inplace=True)
    df_result.reset_index(inplace=True)

    df_pairwise = pd.DataFrame(data=scores[:, np.newaxis] / (scores + scores[:, np.newaxis]),
                               index=index, columns=index)
    df_pairwise = df_pairwise.reindex(labels=df_result['item'], columns=df_result['item'], copy=False)

    fig = visualize(df_pairwise)

    return df_result, fig


def main() -> None:
    iface = gr.Interface(
        fn=handler,
        inputs=[
            gr.File(
                file_types=['.tsv', '.csv'],
                label='Comparisons'
            ),
            gr.Number(
                label='Seed',
                precision=0
            )
        ],
        outputs=[
            gr.Dataframe(
                headers=['item', 'score', 'rank'],
                label='Ranking'
            ),
            gr.Plot(
                label='Pairwise Chances of Winning the Comparison'
            )
        ],
        examples=[
            ['food.csv', 0]
        ],
        title='Pair2Rank: Turn Your Side-by-Side Comparisons into Ranking!',
        description='''
This easy-to-use tool transforms pairwise comparisons (aka side-by-side) to a meaningful ranking of items.

As an input, it expects a comma-separated (CSV) file with a header containing the following columns:

- `left`: the first compared item
- `right`: the second compared item
- `winner`: the label indicating the winning item

Possible values for `winner` are `left`, `right`, or `tie`.
The provided example might be a good starting point.

As the output, this tool provides a table with items, their estimated scores, and ranks.
        ''',
        article='''
This tool attempts to implement the tie-aware ranking aggregation algorithm as described in
[Efficient Computation of Rankings from Pairwise Comparisons](https://www.jmlr.org/papers/v24/22-1086.html).
        ''',
        allow_flagging='never'
    )

    iface.launch()


if __name__ == '__main__':
    main()