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)
iris = load_iris()
tr_d, te_d, tr_l, te_l = train_test_split(iris.data,
iris.target,
train_size=120)
classifier = xgb.XGBClassifier(booster='gblinear', n_estimators=100)
classifier.fit(tr_d, tr_l)
preds = classifier.predict(te_d)
labels = te_l
err = sum([1 for p, l in zip(preds, labels) if p != l]) * 1.0 / len(te_l)
assert err < 0.5
@pytest.mark.skipif(**tm.no_matplotlib())
@pytest.mark.skipif(**tm.no_graphviz())
def test_sklearn_plotting():
from sklearn.datasets import load_iris
iris = load_iris()
classifier = xgb.XGBClassifier()
classifier.fit(iris.data, iris.target)
import matplotlib
matplotlib.use('Agg')
from matplotlib.axes import Axes
from graphviz import Source
ax = xgb.plot_importance(classifier)
import numpy as np
import xgboost as xgb
import testing as tm
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:
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'
