xgboost/tests/python/test_plotting.py
Jiaming Yuan b9ebbffc57
Fix plotting test. (#6040)
Previously the test loads a model generated by `test_basic.py`, now we generate
the model explicitly.

* Cleanup saved files for basic tests.
2020-08-22 13:18:48 +08:00

77 lines
2.6 KiB
Python

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