__all__ = ["plot_metrics_boxplot"]

from collections.abc import Sequence

import matplotlib

import matplotlib.figure

import matplotlib.pyplot as plt

from move.core.typing import FloatArray

from move.visualization.style import (

    DEFAULT_PLOT_STYLE,

    DEFAULT_QUALITATIVE_PALETTE,

    color_cycle,

    style_settings,

)

def plot_metrics_boxplot(

    scores: Sequence[FloatArray],

    labels: Sequence[str],

    style: str = DEFAULT_PLOT_STYLE,

    colormap: str = DEFAULT_QUALITATIVE_PALETTE,

) -> matplotlib.figure.Figure:

    """Plot a box plot, showing the distribution of metrics per dataset. Each

    score corresponds (for example) to a sample.

    Args:

        scores: List of dataset metrics

        labels: List of dataset names

        style: Name of style to apply to the plot

        colormap: Name of colormap to use for the boxes

    Returns:

        Figure

    """

    with style_settings(style), color_cycle(colormap):

        labelcolor = matplotlib.rcParams["axes.labelcolor"]

        fig, ax = plt.subplots()

        comps = ax.boxplot(

            scores[::-1],

            labels=labels[::-1],

            patch_artist=True,

            vert=False,

            capprops=dict(color=labelcolor),

            flierprops=dict(

                marker="d",

                markersize=5,

                markerfacecolor=labelcolor,

                markeredgecolor=labelcolor,

            ),

            medianprops=dict(color=labelcolor),

            whiskerprops=dict(color=labelcolor),

        )

        prop_cycle = matplotlib.rcParams["axes.prop_cycle"]

        for box, prop in zip(comps["boxes"], prop_cycle()):

            box.update(dict(facecolor=prop["color"], edgecolor=labelcolor))

        ax.set(xlim=(-0.05, 1.05), xlabel="Score", ylabel="Dataset")

    return fig