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# coding: utf-8
# pylint: disable=too-many-locals, too-many-arguments, invalid-name,
# pylint: disable=too-many-branches
"""Plotting Library."""
from __future__ import absolute_import

import re
from io import BytesIO
import numpy as np
from .core import Booster
from .sklearn import XGBModel


def plot_importance(booster, ax=None, height=0.2,
                    xlim=None, ylim=None, title='Feature importance',
                    xlabel='F score', ylabel='Features',
                    importance_type='weight', max_num_features=None,
                    grid=True, **kwargs):

    """Plot importance based on fitted trees.

    Parameters
    ----------
    booster : Booster, XGBModel or dict
        Booster or XGBModel instance, or dict taken by Booster.get_fscore()
    ax : matplotlib Axes, default None
        Target axes instance. If None, new figure and axes will be created.
    importance_type : str, default "weight"
        How the importance is calculated: either "weight", "gain", or "cover"
        "weight" is the number of times a feature appears in a tree
        "gain" is the average gain of splits which use the feature
        "cover" is the average coverage of splits which use the feature
            where coverage is defined as the number of samples affected by the split
    max_num_features : int, default None
        Maximum number of top features displayed on plot. If None, all features will be displayed.
    height : float, default 0.2
        Bar height, passed to ax.barh()
    xlim : tuple, default None
        Tuple passed to axes.xlim()
    ylim : tuple, default None
        Tuple passed to axes.ylim()
    title : str, default "Feature importance"
        Axes title. To disable, pass None.
    xlabel : str, default "F score"
        X axis title label. To disable, pass None.
    ylabel : str, default "Features"
        Y axis title label. To disable, pass None.
    kwargs :
        Other keywords passed to ax.barh()

    Returns
    -------
    ax : matplotlib Axes
    """
    # TODO: move this to compat.py
    try:
        import matplotlib.pyplot as plt
    except ImportError:
        raise ImportError('You must install matplotlib to plot importance')

    if isinstance(booster, XGBModel):