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>
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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import numpy as np
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import xgboost as xgb
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import testing as tm
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import unittest
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import pytest
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try:
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import matplotlib
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matplotlib.use('Agg')
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from matplotlib.axes import Axes
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from graphviz import Source
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except ImportError:
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pass
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pytestmark = pytest.mark.skipif(**tm.no_matplotlib())
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dpath = 'demo/data/'
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rng = np.random.RandomState(1994)
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class TestPlotting(unittest.TestCase):
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def test_plotting(self):
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bst2 = xgb.Booster(model_file='xgb.model')
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ax = xgb.plot_importance(bst2)
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assert isinstance(ax, Axes)
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assert ax.get_title() == 'Feature importance'
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assert ax.get_xlabel() == 'F score'
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assert ax.get_ylabel() == 'Features'
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assert len(ax.patches) == 4
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ax = xgb.plot_importance(bst2, color='r',
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title='t', xlabel='x', ylabel='y')
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assert isinstance(ax, Axes)
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assert ax.get_title() == 't'
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assert ax.get_xlabel() == 'x'
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assert ax.get_ylabel() == 'y'
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assert len(ax.patches) == 4
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for p in ax.patches:
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assert p.get_facecolor() == (1.0, 0, 0, 1.0) # red
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ax = xgb.plot_importance(bst2, color=['r', 'r', 'b', 'b'],
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title=None, xlabel=None, ylabel=None)
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assert isinstance(ax, Axes)
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assert ax.get_title() == ''
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assert ax.get_xlabel() == ''
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assert ax.get_ylabel() == ''
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assert len(ax.patches) == 4
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assert ax.patches[0].get_facecolor() == (1.0, 0, 0, 1.0) # red
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assert ax.patches[1].get_facecolor() == (1.0, 0, 0, 1.0) # red
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assert ax.patches[2].get_facecolor() == (0, 0, 1.0, 1.0) # blue
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assert ax.patches[3].get_facecolor() == (0, 0, 1.0, 1.0) # blue
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g = xgb.to_graphviz(bst2, num_trees=0)
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assert isinstance(g, Source)
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ax = xgb.plot_tree(bst2, num_trees=0)
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assert isinstance(ax, Axes)
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def test_importance_plot_lim(self):
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np.random.seed(1)
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dm = xgb.DMatrix(np.random.randn(100, 100), label=[0, 1] * 50)
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bst = xgb.train({}, dm)
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assert len(bst.get_fscore()) == 71
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ax = xgb.plot_importance(bst)
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assert ax.get_xlim() == (0., 11.)
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assert ax.get_ylim() == (-1., 71.)
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ax = xgb.plot_importance(bst, xlim=(0, 5), ylim=(10, 71))
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assert ax.get_xlim() == (0., 5.)
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assert ax.get_ylim() == (10., 71.)
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