app.py

#!/usr/bin/env python3

# 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 collections.abc import Callable
from typing import BinaryIO, cast

import evalica
import gradio as gr
import networkx as nx
import numpy as np
import pandas as pd
import plotly.express as px
from evalica import Winner
from plotly.graph_objects import Figure

TOLERANCE, LIMIT = 1e-6, 100


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


def counting(xs: "pd.Series[str]", ys: "pd.Series[str]",
             ws: "pd.Series[Winner]", index: dict[str, int]) -> "pd.Series[float]":  # type: ignore[type-var]
    result = evalica.counting(xs, ys, ws, index=index)
    return result.scores


def average_win_rate(xs: "pd.Series[str]", ys: "pd.Series[str]",
                     ws: "pd.Series[Winner]", index: dict[str, int]) -> "pd.Series[float]":  # type: ignore[type-var]
    result = evalica.counting(xs, ys, ws, index=index)
    return result.scores


def bradley_terry(xs: "pd.Series[str]", ys: "pd.Series[str]",
                  ws: "pd.Series[Winner]", index: dict[str, int]) -> "pd.Series[float]":  # type: ignore[type-var]
    result = evalica.bradley_terry(xs, ys, ws, index=index, tolerance=TOLERANCE, limit=LIMIT)
    return result.scores


def elo(xs: "pd.Series[str]", ys: "pd.Series[str]",
        ws: "pd.Series[Winner]", index: dict[str, int]) -> "pd.Series[float]":  # type: ignore[type-var]
    result = evalica.elo(xs, ys, ws, index=index)
    return result.scores


def eigen(xs: "pd.Series[str]", ys: "pd.Series[str]",
          ws: "pd.Series[Winner]", index: dict[str, int]) -> "pd.Series[float]":  # type: ignore[type-var]
    result = evalica.eigen(xs, ys, ws, index=index, tolerance=TOLERANCE, limit=LIMIT)
    return result.scores


def pagerank(xs: "pd.Series[str]", ys: "pd.Series[str]",
             ws: "pd.Series[Winner]", index: dict[str, int]) -> "pd.Series[float]":  # type: ignore[type-var]
    result = evalica.pagerank(xs, ys, ws, index=index, tolerance=TOLERANCE, limit=LIMIT)
    return result.scores


def newman(xs: "pd.Series[str]", ys: "pd.Series[str]",
           ws: "pd.Series[Winner]", index: dict[str, int]) -> "pd.Series[float]":  # type: ignore[type-var]
    result = evalica.newman(xs, ys, ws, index=index, tolerance=TOLERANCE, limit=LIMIT)
    return result.scores


ALGORITHMS = {
    "Counting": counting,
    "Average Win Rate": average_win_rate,
    "Bradley-Terry (1952)": bradley_terry,
    "Elo (1960)": elo,
    "Eigenvector (1987)": eigen,
    "PageRank (1998)": pagerank,
    "Newman (2023)": newman,
}


def largest_strongly_connected_component(df_pairs: pd.DataFrame) -> set[str]:
    G = nx.from_pandas_edgelist(df_pairs, source="left", target="right", create_using=nx.DiGraph)
    H = nx.from_pandas_edgelist(df_pairs[df_pairs["winner"] == "tie"], source="right", target="left",
                                create_using=nx.DiGraph)
    F = nx.compose(G, H)
    largest = max(nx.strongly_connected_components(F), key=len)
    return cast(set[str], largest)


def estimate(df_pairs: pd.DataFrame,
             algorithm: Callable[[  # type: ignore[type-var]
                 "pd.Series[str]", "pd.Series[str]", "pd.Series[Winner]", dict[str, int]],
             "pd.Series[float]",
             ],
             index: dict[str, int]) -> pd.DataFrame:
    scores = algorithm(df_pairs["left"], df_pairs["right"], df_pairs["winner"], index)

    df_result = pd.DataFrame(data={"score": scores}, index=index)
    df_result.index.name = "item"

    return df_result


def bootstrap(df_pairs: pd.DataFrame,
              algorithm: Callable[[  # type: ignore[type-var]
                  "pd.Series[str]", "pd.Series[str]", "pd.Series[Winner]", dict[str, int]],
              "pd.Series[float]",
              ],
              index: dict[str, int],
              rounds: int) -> pd.DataFrame:
    scores: list[pd.Series[float]] = []  # assuming model names are strings

    for r in range(rounds):
        df_sample = df_pairs.sample(frac=1.0, replace=True, random_state=r)

        sample_scores = algorithm(df_sample["left"], df_sample["right"], df_sample["winner"], index)

        scores.append(sample_scores)

    df_bootstrap = pd.DataFrame(scores, columns=index)

    ratings = df_bootstrap.quantile(.5)

    ci = df_bootstrap.apply(lambda row: (
        row.quantile(.025).item(), row.quantile(.975).item(),
    ), axis=0, result_type="reduce")

    df_result = pd.DataFrame({"score": ratings, "ci": ci})
    df_result.index.name = "item"

    return df_result


def handler(
        file: BinaryIO,
        algorithm: str,
        filtered: bool,
        truncated: bool,
        rounds: int,
) -> tuple[pd.DataFrame, Figure]:
    if file is None:
        raise gr.Error("File must be uploaded")

    if algorithm not in ALGORITHMS:
        raise gr.Error(f"Unknown algorithm: {algorithm}")

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

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

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

    df_pairs = df_pairs[["left", "right", "winner"]]
    df_pairs["winner"] = df_pairs["winner"].map(
        {"left": Winner.X, "right": Winner.Y, "tie": Winner.Draw},
    )

    df_pairs = df_pairs.dropna(axis=0)

    if filtered:
        largest = largest_strongly_connected_component(df_pairs)

        df_pairs = df_pairs.drop(df_pairs[~(df_pairs["left"].isin(largest) & df_pairs["right"].isin(largest))].index)

    *_, index = evalica.indexing(xs=df_pairs["left"], ys=df_pairs["right"])

    if rounds:
        df_result = bootstrap(df_pairs, ALGORITHMS[algorithm], index, rounds)
    else:
        df_result = estimate(df_pairs, ALGORITHMS[algorithm], index)

    df_result["pairs"] = pd.Series(0, dtype=int, index=index).add(
        df_pairs.groupby("left")["left"].count(), fill_value=0,
    ).add(
        df_pairs.groupby("right")["right"].count(), fill_value=0,
    ).astype(int)

    df_result["rank"] = df_result["score"].rank(na_option="bottom", ascending=False).astype(int)

    df_result = df_result.fillna(-np.inf)
    df_result = df_result.sort_values(by=["rank", "score"], ascending=[True, False])
    df_result = df_result.reset_index()

    if truncated:
        df_result = pd.concat((df_result.head(5), df_result.tail(5)), copy=False)
        df_result = df_result[~df_result.index.duplicated(keep="last")]

    pairwise = evalica.pairwise_scores(df_result["score"].to_numpy())

    df_pairwise = pd.DataFrame(data=pairwise, index=df_result["item"], columns=df_result["item"])

    fig = visualize(df_pairwise)

    if "ci" in df_result.columns:
        df_result["ci"] = df_result.apply(
            lambda row: f"({row['score'] - row['ci'][0]:.03f}; {row['ci'][1] - row['score']:.03f})",
            axis=1,
        )

    df_result["score"] = df_result["score"].apply(lambda x: f"{x:.03f}")

    return df_result, fig


def main() -> None:
    iface = gr.Interface(
        fn=handler,
        inputs=[
            gr.File(
                file_types=[".tsv", ".csv"],
                label="Comparisons",
            ),
            gr.Dropdown(
                choices=cast(list[str], ALGORITHMS),
                value="Bradley-Terry (1952)",
                label="Algorithm",
            ),
            gr.Checkbox(
                value=False,
                label="Largest SCC",
                info="Bradley-Terry, Eigenvector, and Newman algorithms require the comparison graph "
                     "to be strongly-connected. "
                     "This option keeps only the largest strongly-connected component (SCC) of the input graph. "
                     "Some items might be missing as a result of this filtering.",
            ),
            gr.Checkbox(
                value=False,
                label="Truncate Output",
                info="Perform the entire computation but output only five head and five tail items, "
                     "avoiding overlap.",
            ),
            gr.Number(
                value=0,
                minimum=0,
                maximum=10000,
                label="Bootstrap Rounds",
                info="Number of bootstrap rounds to perform for estimating the confidence interval.",
            ),
        ],
        outputs=[
            gr.Dataframe(
                headers=["item", "score", "ci", "pairs", "rank"],
                label="Ranking",
            ),
            gr.Plot(
                label="Pairwise Chances of Winning the Comparison",
            ),
        ],
        examples=[
            ["food.csv", "Counting", False, False, 0],
            ["food.csv", "Bradley-Terry (1952)", False, False, 1000],
            ["food.csv", "Eigenvector (1987)", False, False, 1000],
            ["food.csv", "PageRank (1998)", False, False, 1000],
            ["food.csv", "Newman (2023)", False, False, 1000],
            ["llmfao.csv", "Average Win Rate", False, True, 100],
            ["llmfao.csv", "Bradley-Terry (1952)", False, True, 100],
            ["llmfao.csv", "Elo (1960)", False, True, 100],
        ],
        title="Evalica: Turn Your Side-by-Side Comparisons into Ranking!",
        description="""
        """.strip(),
        article="""
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 examples might be a good starting point.

As the output, this tool provides a table with items, their estimated scores, and ranks.

**More Evalica:**

- Paper: TBD ([arXiv](https://arxiv.org/abs/2412.11314))
- GitHub: <https://github.com/dustalov/evalica>
- PyPI: <https://pypi.org/project/evalica/>
- conda-forge: <https://anaconda.org/conda-forge/evalica>
- LLMFAO: <https://evalovernite.substack.com/p/llmfao-human-ranking>
        """.strip(),
        flagging_mode="never",
    )

    iface.launch()


if __name__ == "__main__":
    main()