a5cd2412de
* Create pyproject.toml * Implement a custom build backend (see below) in packager directory. Build logic from setup.py has been refactored and migrated into the new backend. * Tested: pip wheel . (build wheel), python -m build --sdist . (source distribution)
296 lines
8.6 KiB
Python
296 lines
8.6 KiB
Python
# pylint: disable=too-many-locals, too-many-arguments, invalid-name,
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# pylint: disable=too-many-branches
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"""Plotting Library."""
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import json
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from io import BytesIO
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from typing import Any, Optional, Union
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import numpy as np
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from ._typing import PathLike
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from .core import Booster
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from .sklearn import XGBModel
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Axes = Any # real type is matplotlib.axes.Axes
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GraphvizSource = Any # real type is graphviz.Source
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def plot_importance(
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booster: Union[XGBModel, Booster, dict],
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ax: Optional[Axes] = None,
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height: float = 0.2,
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xlim: Optional[tuple] = None,
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ylim: Optional[tuple] = None,
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title: str = "Feature importance",
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xlabel: str = "F score",
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ylabel: str = "Features",
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fmap: PathLike = "",
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importance_type: str = "weight",
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max_num_features: Optional[int] = None,
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grid: bool = True,
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show_values: bool = True,
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values_format: str = "{v}",
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**kwargs: Any,
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) -> Axes:
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"""Plot importance based on fitted trees.
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Parameters
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----------
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booster :
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Booster or XGBModel instance, or dict taken by Booster.get_fscore()
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ax : matplotlib Axes
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Target axes instance. If None, new figure and axes will be created.
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grid :
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Turn the axes grids on or off. Default is True (On).
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importance_type :
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How the importance is calculated: either "weight", "gain", or "cover"
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* "weight" is the number of times a feature appears in a tree
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* "gain" is the average gain of splits which use the feature
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* "cover" is the average coverage of splits which use the feature
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where coverage is defined as the number of samples affected by the split
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max_num_features :
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Maximum number of top features displayed on plot. If None, all features will be
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displayed.
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height :
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Bar height, passed to ax.barh()
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xlim :
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Tuple passed to axes.xlim()
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ylim :
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Tuple passed to axes.ylim()
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title :
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Axes title. To disable, pass None.
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xlabel :
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X axis title label. To disable, pass None.
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ylabel :
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Y axis title label. To disable, pass None.
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fmap :
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The name of feature map file.
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show_values :
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Show values on plot. To disable, pass False.
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values_format :
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Format string for values. "v" will be replaced by the value of the feature
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importance. e.g. Pass "{v:.2f}" in order to limit the number of digits after
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the decimal point to two, for each value printed on the graph.
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kwargs :
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Other keywords passed to ax.barh()
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Returns
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-------
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ax : matplotlib Axes
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"""
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try:
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import matplotlib.pyplot as plt
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except ImportError as e:
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raise ImportError("You must install matplotlib to plot importance") from e
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if isinstance(booster, XGBModel):
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importance = booster.get_booster().get_score(
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importance_type=importance_type, fmap=fmap
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)
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elif isinstance(booster, Booster):
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importance = booster.get_score(importance_type=importance_type, fmap=fmap)
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elif isinstance(booster, dict):
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importance = booster
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else:
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raise ValueError("tree must be Booster, XGBModel or dict instance")
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if not importance:
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raise ValueError(
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"Booster.get_score() results in empty. "
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+ "This maybe caused by having all trees as decision dumps."
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)
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tuples = [(k, importance[k]) for k in importance]
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if max_num_features is not None:
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# pylint: disable=invalid-unary-operand-type
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tuples = sorted(tuples, key=lambda _x: _x[1])[-max_num_features:]
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else:
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tuples = sorted(tuples, key=lambda _x: _x[1])
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labels, values = zip(*tuples)
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if ax is None:
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_, ax = plt.subplots(1, 1)
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ylocs = np.arange(len(values))
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ax.barh(ylocs, values, align="center", height=height, **kwargs)
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if show_values is True:
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for x, y in zip(values, ylocs):
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ax.text(x + 1, y, values_format.format(v=x), va="center")
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ax.set_yticks(ylocs)
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ax.set_yticklabels(labels)
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if xlim is not None:
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if not isinstance(xlim, tuple) or len(xlim) != 2:
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raise ValueError("xlim must be a tuple of 2 elements")
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else:
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xlim = (0, max(values) * 1.1)
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ax.set_xlim(xlim)
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if ylim is not None:
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if not isinstance(ylim, tuple) or len(ylim) != 2:
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raise ValueError("ylim must be a tuple of 2 elements")
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else:
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ylim = (-1, len(values))
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ax.set_ylim(ylim)
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if title is not None:
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ax.set_title(title)
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if xlabel is not None:
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ax.set_xlabel(xlabel)
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if ylabel is not None:
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ax.set_ylabel(ylabel)
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ax.grid(grid)
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return ax
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def to_graphviz(
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booster: Union[Booster, XGBModel],
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fmap: PathLike = "",
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num_trees: int = 0,
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rankdir: Optional[str] = None,
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yes_color: Optional[str] = None,
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no_color: Optional[str] = None,
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condition_node_params: Optional[dict] = None,
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leaf_node_params: Optional[dict] = None,
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**kwargs: Any,
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) -> GraphvizSource:
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"""Convert specified tree to graphviz instance. IPython can automatically plot
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the returned graphviz instance. Otherwise, you should call .render() method
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of the returned graphviz instance.
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Parameters
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----------
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booster :
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Booster or XGBModel instance
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fmap :
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The name of feature map file
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num_trees :
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Specify the ordinal number of target tree
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rankdir :
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Passed to graphviz via graph_attr
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yes_color :
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Edge color when meets the node condition.
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no_color :
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Edge color when doesn't meet the node condition.
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condition_node_params :
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Condition node configuration for for graphviz. Example:
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.. code-block:: python
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{'shape': 'box',
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'style': 'filled,rounded',
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'fillcolor': '#78bceb'}
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leaf_node_params :
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Leaf node configuration for graphviz. Example:
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.. code-block:: python
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{'shape': 'box',
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'style': 'filled',
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'fillcolor': '#e48038'}
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kwargs :
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Other keywords passed to graphviz graph_attr, e.g. ``graph [ {key} = {value} ]``
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Returns
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-------
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graph: graphviz.Source
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"""
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try:
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from graphviz import Source
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except ImportError as e:
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raise ImportError("You must install graphviz to plot tree") from e
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if isinstance(booster, XGBModel):
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booster = booster.get_booster()
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# squash everything back into kwargs again for compatibility
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parameters = "dot"
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extra = {}
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for key, value in kwargs.items():
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extra[key] = value
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if rankdir is not None:
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kwargs["graph_attrs"] = {}
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kwargs["graph_attrs"]["rankdir"] = rankdir
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for key, value in extra.items():
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if kwargs.get("graph_attrs", None) is not None:
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kwargs["graph_attrs"][key] = value
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else:
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kwargs["graph_attrs"] = {}
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del kwargs[key]
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if yes_color is not None or no_color is not None:
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kwargs["edge"] = {}
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if yes_color is not None:
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kwargs["edge"]["yes_color"] = yes_color
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if no_color is not None:
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kwargs["edge"]["no_color"] = no_color
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if condition_node_params is not None:
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kwargs["condition_node_params"] = condition_node_params
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if leaf_node_params is not None:
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kwargs["leaf_node_params"] = leaf_node_params
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if kwargs:
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parameters += ":"
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parameters += json.dumps(kwargs)
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tree = booster.get_dump(fmap=fmap, dump_format=parameters)[num_trees]
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g = Source(tree)
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return g
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def plot_tree(
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booster: Booster,
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fmap: PathLike = "",
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num_trees: int = 0,
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rankdir: Optional[str] = None,
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ax: Optional[Axes] = None,
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**kwargs: Any,
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) -> Axes:
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"""Plot specified tree.
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Parameters
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----------
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booster : Booster, XGBModel
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Booster or XGBModel instance
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fmap: str (optional)
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The name of feature map file
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num_trees : int, default 0
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Specify the ordinal number of target tree
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rankdir : str, default "TB"
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Passed to graphviz via graph_attr
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ax : matplotlib Axes, default None
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Target axes instance. If None, new figure and axes will be created.
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kwargs :
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Other keywords passed to to_graphviz
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Returns
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-------
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ax : matplotlib Axes
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"""
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try:
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from matplotlib import image
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from matplotlib import pyplot as plt
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except ImportError as e:
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raise ImportError("You must install matplotlib to plot tree") from e
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if ax is None:
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_, ax = plt.subplots(1, 1)
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g = to_graphviz(booster, fmap=fmap, num_trees=num_trees, rankdir=rankdir, **kwargs)
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s = BytesIO()
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s.write(g.pipe(format="png"))
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s.seek(0)
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img = image.imread(s)
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ax.imshow(img)
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ax.axis("off")
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return ax
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