# Sebastian Raschka 2014-2024
# mlxtend Machine Learning Library Extensions
#
# Algorithm for plotting sequential feature selection.
# Author: Sebastian Raschka <sebastianraschka.com>
#
# License: BSD 3 clause

import matplotlib.pyplot as plt


def plot_sequential_feature_selection(
    metric_dict,
    figsize=None,
    kind="std_dev",
    color="blue",
    bcolor="steelblue",
    marker="o",
    alpha=0.2,
    ylabel="Performance",
    confidence_interval=0.95,
):
    """Plot feature selection results.

    Parameters
    ----------
    metric_dict : mlxtend.SequentialFeatureSelector.get_metric_dict() object
    figsize : tuple (default: None)
        Height and width of the figure
    kind : str (default: "std_dev")
        The kind of error bar or confidence interval in
        {'std_dev', 'std_err', 'ci', None}.
    color : str (default: "blue")
        Color of the lineplot (accepts any matplotlib color name)
    bcolor : str (default: "steelblue").
        Color of the error bars / confidence intervals
        (accepts any matplotlib color name).
    marker : str (default: "o")
        Marker of the line plot
        (accepts any matplotlib marker name).
    alpha : float in [0, 1] (default: 0.2)
        Transparency of the error bars / confidence intervals.
    ylabel : str (default: "Performance")
        Y-axis label.
    confidence_interval : float (default: 0.95)
        Confidence level if `kind='ci'`.

    Returns
    ----------
    fig : matplotlib.pyplot.figure() object

    Examples
    -----------
    For usage examples, please see
    https://rasbt.github.io/mlxtend/user_guide/plotting/plot_sequential_feature_selection/

    """

    allowed = {"std_dev", "std_err", "ci", None}
    if kind not in allowed:
        raise AttributeError("kind not in %s" % allowed)

    # fig = plt.figure()
    if figsize is not None:
        fig = plt.subplots(figsize=figsize)
    else:
        fig = plt.subplots()

    k_feat = sorted(metric_dict.keys())
    avg = [metric_dict[k]["avg_score"] for k in k_feat]

    if kind:
        upper, lower = [], []
        if kind == "ci":
            kind = "ci_bound"

        for k in k_feat:
            upper.append(metric_dict[k]["avg_score"] + metric_dict[k][kind])
            lower.append(metric_dict[k]["avg_score"] - metric_dict[k][kind])

        plt.fill_between(k_feat, upper, lower, alpha=alpha, color=bcolor, lw=1)

        if kind == "ci_bound":
            kind = "Confidence Interval (%d%%)" % (confidence_interval * 100)

    plt.plot(k_feat, avg, color=color, marker=marker)
    plt.ylabel(ylabel)
    plt.xlabel("Number of Features")
    feature_min = len(metric_dict[k_feat[0]]["feature_idx"])
    feature_max = len(metric_dict[k_feat[-1]]["feature_idx"])
    plt.xticks(range(feature_min, feature_max + 1), range(feature_min, feature_max + 1))
    return fig