plot_importance
1、plot_importance方法的解释
作用：基于拟合树的重要性可视化。
参数
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.
grid : bool, Turn the axes grids on or off. Default is True (On).
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，在树中出现的次数。
* "gain" is the average gain of splits which use the feature，使用该特性的分割的平均增益。
* "cover" is the average coverage of splits which use the feature, where coverage is defined as the number of samples affected by the split. 分割的平均覆盖率，其中覆盖率定义为受分割影响的样本数。
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.
show_values : bool, default True. Show values on plot. To disable, pass False.
kwargs : Other keywords passed to ax.barh()
返回
ax : matplotlib Axes
2、XGBModel之plot_importance的原生代码
# 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, show_values=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.
show_values : bool, default True
Show values on plot. To disable, pass False.
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):
importance = booster.get_booster().get_score(importance_type=importance_type)
elif isinstance(booster, Booster):
importance = booster.get_score(importance_type=importance_type)
elif isinstance(booster, dict):
importance = booster
else:
raise ValueError('tree must be Booster, XGBModel or dict instance')
if len(importance) == 0:
raise ValueError('Booster.get_score() results in empty')
tuples = [(k, importance[k]) for k in importance]
if max_num_features is not None:
tuples = sorted(tuples, key=lambda x: x[1])[-max_num_features:]
tuples = sorted(tuples, key=lambda x: x[1])
labels, values = zip(*tuples)
if ax is None:
_, ax = plt.subplots(1, 1)
ylocs = np.arange(len(values))
ax.barh(ylocs, values, align='center', height=height, **kwargs)
if show_values is True:
for x, y in zip(values, ylocs):
ax.text(x + 1, y, x, va='center')
ax.set_yticks(ylocs)
ax.set_yticklabels(labels)
if xlim is not None:
if not isinstance(xlim, tuple) or len(xlim) != 2:
raise ValueError('xlim must be a tuple of 2 elements')
xlim = (0, max(values) * 1.1)
ax.set_xlim(xlim)
if ylim is not None:
if not isinstance(ylim, tuple) or len(ylim) != 2:
raise ValueError('ylim must be a tuple of 2 elements')
ylim = (-1, len(values))
ax.set_ylim(ylim)
if title is not None:
ax.set_title(title)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
ax.grid(grid)
return ax
_NODEPAT = re.compile(r'(\d+):\[(.+)\]')
_LEAFPAT = re.compile(r'(\d+):(leaf=.+)')
_EDGEPAT = re.compile(r'yes=(\d+),no=(\d+),missing=(\d+)')
_EDGEPAT2 = re.compile(r'yes=(\d+),no=(\d+)')
def _parse_node(graph, text, condition_node_params, leaf_node_params):
"""parse dumped node"""
match = _NODEPAT.match(text)
if match is not None:
node = match.group(1)
graph.node(node, label=match.group(2), **condition_node_params)
return node
match = _LEAFPAT.match(text)
graph.node(node, label=match.group(2), **leaf_node_params)
raise ValueError('Unable to parse node: {0}'.format(text))
def _parse_edge(graph, node, text, yes_color='#0000FF', no_color='#FF0000'):
"""parse dumped edge"""
match = _EDGEPAT.match(text)
if match is not None:
yes, no, missing = match.groups()
if yes == missing:
graph.edge(node, yes, label='yes, missing', color=yes_color)
graph.edge(node, no, label='no', color=no_color)
else:
graph.edge(node, yes, label='yes', color=yes_color)
graph.edge(node, no, label='no, missing', color=no_color)
return
except ValueError:
pass
match = _EDGEPAT2.match(text)
yes, no = match.groups()
graph.edge(node, yes, label='yes', color=yes_color)
graph.edge(node, no, label='no', color=no_color)
return
raise ValueError('Unable to parse edge: {0}'.format(text))
def to_graphviz(booster, fmap='', num_trees=0, rankdir='UT',
yes_color='#0000FF', no_color='#FF0000',
condition_node_params=None, leaf_node_params=None, **kwargs):
"""Convert specified tree to graphviz instance. IPython can automatically plot the
returned graphiz instance. Otherwise, you should call .render() method
of the returned graphiz instance.
booster : Booster, XGBModel
Booster or XGBModel instance
fmap: str (optional)
The name of feature map file
num_trees : int, default 0
Specify the ordinal number of target tree
rankdir : str, default "UT"
Passed to graphiz via graph_attr
yes_color : str, default '#0000FF'
Edge color when meets the node condition.
no_color : str, default '#FF0000'
Edge color when doesn't meet the node condition.
condition_node_params : dict (optional)
condition node configuration,
{'shape':'box',
'style':'filled,rounded',
'fillcolor':'#78bceb'
}
leaf_node_params : dict (optional)
leaf node configuration
'style':'filled',
'fillcolor':'#e48038'
Other keywords passed to graphviz graph_attr
if condition_node_params is None:
condition_node_params = {}
if leaf_node_params is None:
leaf_node_params = {}
from graphviz import Digraph
raise ImportError('You must install graphviz to plot tree')
if not isinstance(booster, (Booster, XGBModel)):
raise ValueError('booster must be Booster or XGBModel instance')
booster = booster.get_booster()
tree = booster.get_dump(fmap=fmap)[num_trees]
tree = tree.split()
kwargs = kwargs.copy()
kwargs.update({'rankdir': rankdir})
graph = Digraph(graph_attr=kwargs)
for i, text in enumerate(tree):
if text[0].isdigit():
node = _parse_node(
graph, text, condition_node_params=condition_node_params,
leaf_node_params=leaf_node_params)
else:
if i == 0:
# 1st string must be node
raise ValueError('Unable to parse given string as tree')
_parse_edge(graph, node, text, yes_color=yes_color,
no_color=no_color)
return graph
def plot_tree(booster, fmap='', num_trees=0, rankdir='UT', ax=None, **kwargs):
"""Plot specified tree.
Other keywords passed to to_graphviz
import matplotlib.image as image
raise ImportError('You must install matplotlib to plot tree')
g = to_graphviz(booster, fmap=fmap, num_trees=num_trees,
rankdir=rankdir, **kwargs)
s = BytesIO()
s.write(g.pipe(format='png'))
s.seek(0)
img = image.imread(s)
ax.imshow(img)
ax.axis('off')
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