Raiders detailed method of introduction plot_importance of XGBModel of use: sklearn of XGBModel
table of Contents
About plot_importance of the XGBModel
plot_importance the use of XGBModel
About plot_importance of the XGBModel
Role : The importance of fitting tree-based visualization.
parameter
- 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).
- . either "weight", "GAIN", or "Cover": importance_type: How The Importance IS Calculated STR, default "weight"
* "weight" IS The Number of Times A Feature the Appears in A Tree, the number of occurrences in the tree .
* "Gain" is the average gain of splits which use the feature, using the average gain characteristic segmentation.
* "Cover" is the average coverage of splits which use the feature, where coverage is defined as the number of samples affected by the split. The average coverage of division, of which coverage is defined as the number of samples affected by the division. - 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()
return
- ax : matplotlib Axes
plot_importance the use of XGBModel
# 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.
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
* "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:]
else:
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')
else:
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')
else:
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)
if match is not None:
node = match.group(1)
graph.node(node, label=match.group(2), **leaf_node_params)
return node
raise ValueError('Unable to parse node: {0}'.format(text))
def _parse_edge(graph, node, text, yes_color='#0000FF', no_color='#FF0000'):
"""parse dumped edge"""
try:
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)
if match is not None:
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.
Parameters
----------
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
{'shape':'box',
'style':'filled',
'fillcolor':'#e48038'
}
kwargs :
Other keywords passed to graphviz graph_attr
Returns
-------
ax : matplotlib Axes
"""
if condition_node_params is None:
condition_node_params = {}
if leaf_node_params is None:
leaf_node_params = {}
try:
from graphviz import Digraph
except ImportError:
raise ImportError('You must install graphviz to plot tree')
if not isinstance(booster, (Booster, XGBModel)):
raise ValueError('booster must be Booster or XGBModel instance')
if isinstance(booster, XGBModel):
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.
Parameters
----------
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
ax : matplotlib Axes, default None
Target axes instance. If None, new figure and axes will be created.
kwargs :
Other keywords passed to to_graphviz
Returns
-------
ax : matplotlib Axes
"""
try:
import matplotlib.pyplot as plt
import matplotlib.image as image
except ImportError:
raise ImportError('You must install matplotlib to plot tree')
if ax is None:
_, ax = plt.subplots(1, 1)
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')
return ax
