8bdf15120a
* Implement tree model dump with a code generator. * Split up generators. * Implement graphviz generator. * Use pattern matching. * [Breaking] Return a Source in `to_graphviz` instead of Digraph in Python package. Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
253 lines
7.9 KiB
Python
253 lines
7.9 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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# coding: utf-8
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"""Plotting Library."""
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from io import BytesIO
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import numpy as np
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from .core import Booster
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from .sklearn import XGBModel
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def plot_importance(booster, ax=None, height=0.2,
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xlim=None, ylim=None, title='Feature importance',
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xlabel='F score', ylabel='Features',
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importance_type='weight', max_num_features=None,
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grid=True, show_values=True, **kwargs):
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"""Plot importance based on fitted trees.
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Parameters
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----------
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booster : Booster, XGBModel or dict
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Booster or XGBModel instance, or dict taken by Booster.get_fscore()
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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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grid : bool, Turn the axes grids on or off. Default is True (On).
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importance_type : str, default "weight"
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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 : int, default None
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Maximum number of top features displayed on plot. If None, all features will be displayed.
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height : float, default 0.2
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Bar height, passed to ax.barh()
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xlim : tuple, default None
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Tuple passed to axes.xlim()
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ylim : tuple, default None
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Tuple passed to axes.ylim()
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title : str, default "Feature importance"
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Axes title. To disable, pass None.
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xlabel : str, default "F score"
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X axis title label. To disable, pass None.
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ylabel : str, default "Features"
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Y axis title label. To disable, pass None.
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show_values : bool, default True
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Show values on plot. To disable, pass False.
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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:
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raise ImportError('You must install matplotlib to plot importance')
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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)
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elif isinstance(booster, Booster):
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importance = booster.get_score(importance_type=importance_type)
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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('Booster.get_score() results in empty')
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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, 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(booster, fmap='', num_trees=0, rankdir=None,
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yes_color=None, no_color=None,
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condition_node_params=None, leaf_node_params=None, **kwargs):
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"""Convert specified tree to graphviz instance. IPython can automatically plot
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the returned graphiz instance. Otherwise, you should call .render() method
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of the returned graphiz instance.
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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 "UT"
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Passed to graphiz via graph_attr
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yes_color : str, default '#0000FF'
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Edge color when meets the node condition.
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no_color : str, default '#FF0000'
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Edge color when doesn't meet the node condition.
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condition_node_params : dict (optional)
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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 : dict (optional)
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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 : Other keywords passed to graphviz graph_attr, E.g.:
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``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:
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raise ImportError('You must install graphviz to plot tree')
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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 'graph_attrs' in kwargs.keys():
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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 += str(kwargs)
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tree = booster.get_dump(
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fmap=fmap,
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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(booster, fmap='', num_trees=0, rankdir=None, ax=None, **kwargs):
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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 graphiz 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 pyplot as plt
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from matplotlib import image
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except ImportError:
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raise ImportError('You must install matplotlib to plot tree')
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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,
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**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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