xgboost.plotting Namespace Reference

Functions
Axes	plot_importance (Union[XGBModel, Booster, dict] booster, Optional[Axes] ax=None, float height=0.2, Optional[tuple] xlim=None, Optional[tuple] ylim=None, str title="Feature importance", str xlabel="F score", str ylabel="Features", PathLike fmap="", str importance_type="weight", Optional[int] max_num_features=None, bool grid=True, bool show_values=True, str values_format="{v}", **Any kwargs)
GraphvizSource	to_graphviz (Union[Booster, XGBModel] booster, PathLike fmap="", int num_trees=0, Optional[str] rankdir=None, Optional[str] yes_color=None, Optional[str] no_color=None, Optional[dict] condition_node_params=None, Optional[dict] leaf_node_params=None, **Any kwargs)
Axes	plot_tree (Booster booster, PathLike fmap="", int num_trees=0, Optional[str] rankdir=None, Optional[Axes] ax=None, **Any kwargs)

Variables
	Axes = Any
	GraphvizSource = Any

Detailed Description

Plotting Library.

Function Documentation

plot_importance()

Axes xgboost.plotting.plot_importance	(	Union[XGBModel, Booster, dict]	booster,
		Optional[Axes]	ax = None,
		float	height = 0.2,
		Optional[tuple]	xlim = None,
		Optional[tuple]	ylim = None,
		str	title = "Feature importance",
		str	xlabel = "F score",
		str	ylabel = "Features",
		PathLike	fmap = "",
		str	importance_type = "weight",
		Optional[int]	max_num_features = None,
		bool	grid = True,
		bool	show_values = True,
		str	values_format = "{v}",
		**Any	kwargs
	)

Plot importance based on fitted trees.

Parameters
----------
booster :
    Booster or XGBModel instance, or dict taken by Booster.get_fscore()
ax : matplotlib Axes
    Target axes instance. If None, new figure and axes will be created.
grid :
    Turn the axes grids on or off.  Default is True (On).
importance_type :
    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 :
    Maximum number of top features displayed on plot. If None, all features will be
    displayed.
height :
    Bar height, passed to ax.barh()
xlim :
    Tuple passed to axes.xlim()
ylim :
    Tuple passed to axes.ylim()
title :
    Axes title. To disable, pass None.
xlabel :
    X axis title label. To disable, pass None.
ylabel :
    Y axis title label. To disable, pass None.
fmap :
    The name of feature map file.
show_values :
    Show values on plot. To disable, pass False.
values_format :
    Format string for values. "v" will be replaced by the value of the feature
    importance.  e.g. Pass "{v:.2f}" in order to limit the number of digits after
    the decimal point to two, for each value printed on the graph.
kwargs :
    Other keywords passed to ax.barh()

Returns
-------
ax : matplotlib Axes

plot_tree()

Axes xgboost.plotting.plot_tree	(	Booster	booster,
		PathLike	fmap = "",
		int	num_trees = 0,
		Optional[str]	rankdir = None,
		Optional[Axes]	ax = None,
		**Any	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 "TB"
    Passed to graphviz 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

to_graphviz()

GraphvizSource xgboost.plotting.to_graphviz	(	Union[Booster, XGBModel]	booster,
		PathLike	fmap = "",
		int	num_trees = 0,
		Optional[str]	rankdir = None,
		Optional[str]	yes_color = None,
		Optional[str]	no_color = None,
		Optional[dict]	condition_node_params = None,
		Optional[dict]	leaf_node_params = None,
		**Any	kwargs
	)

Convert specified tree to graphviz instance. IPython can automatically plot
the returned graphviz instance. Otherwise, you should call .render() method
of the returned graphviz instance.

Parameters
----------
booster :
    Booster or XGBModel instance
fmap :
   The name of feature map file
num_trees :
    Specify the ordinal number of target tree
rankdir :
    Passed to graphviz via graph_attr
yes_color :
    Edge color when meets the node condition.
no_color :
    Edge color when doesn't meet the node condition.
condition_node_params :
    Condition node configuration for for graphviz.  Example:

    .. code-block:: python

        {'shape': 'box',
         'style': 'filled,rounded',
         'fillcolor': '#78bceb'}

leaf_node_params :
    Leaf node configuration for graphviz. Example:

    .. code-block:: python

        {'shape': 'box',
         'style': 'filled',
         'fillcolor': '#e48038'}

kwargs :
    Other keywords passed to graphviz graph_attr, e.g. ``graph [ {key} = {value} ]``

Returns
-------
graph: graphviz.Source