Use pytest conventions consistently (#6337)
* Do not derive from unittest.TestCase (not needed for pytest) * assertRaises -> pytest.raises * Simplify test_empty_dmatrix with test parametrization * setUpClass -> setup_class, tearDownClass -> teardown_class * Don't import unittest; import pytest * Use plain assert * Use parametrized tests in more places * Fix test_gpu_with_sklearn.py * Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls * Fix test_eta_decay_gpu_hist * Add parametrized tests for monotone constraints * Fix test names * Remove test parametrization * Revise test_slice to be not flaky
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Philip Hyunsu Cho
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GitHub
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@@ -1,7 +1,6 @@
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# -*- coding: utf-8 -*-
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import numpy as np
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import xgboost as xgb
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import unittest
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import scipy.sparse
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import pytest
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from scipy.sparse import rand, csr_matrix
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@@ -12,7 +11,7 @@ dpath = 'demo/data/'
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rng = np.random.RandomState(1994)
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class TestDMatrix(unittest.TestCase):
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class TestDMatrix:
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def test_warn_missing(self):
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from xgboost import data
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with pytest.warns(UserWarning):
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@@ -48,15 +47,19 @@ class TestDMatrix(unittest.TestCase):
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assert dm.num_col() == 2
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# 0d array
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self.assertRaises(ValueError, xgb.DMatrix, np.array(1))
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with pytest.raises(ValueError):
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xgb.DMatrix(np.array(1))
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# 1d array
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self.assertRaises(ValueError, xgb.DMatrix, np.array([1, 2, 3]))
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with pytest.raises(ValueError):
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xgb.DMatrix(np.array([1, 2, 3]))
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# 3d array
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data = np.random.randn(5, 5, 5)
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self.assertRaises(ValueError, xgb.DMatrix, data)
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with pytest.raises(ValueError):
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xgb.DMatrix(data)
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# object dtype
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data = np.array([['a', 'b'], ['c', 'd']])
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self.assertRaises(ValueError, xgb.DMatrix, data)
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with pytest.raises(ValueError):
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xgb.DMatrix(data)
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def test_csr(self):
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indptr = np.array([0, 2, 3, 6])
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@@ -106,56 +109,50 @@ class TestDMatrix(unittest.TestCase):
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def test_slice(self):
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X = rng.randn(100, 100)
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y = rng.randint(low=0, high=3, size=100)
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y = rng.randint(low=0, high=3, size=100).astype(np.float32)
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d = xgb.DMatrix(X, y)
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np.testing.assert_equal(d.get_label(), y.astype(np.float32))
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np.testing.assert_equal(d.get_label(), y)
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fw = rng.uniform(size=100).astype(np.float32)
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d.set_info(feature_weights=fw)
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eval_res_0 = {}
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booster = xgb.train(
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{'num_class': 3, 'objective': 'multi:softprob',
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'eval_metric': 'merror'},
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d,
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num_boost_round=2, evals=[(d, 'd')], evals_result=eval_res_0)
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predt = booster.predict(d)
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predt = predt.reshape(100 * 3, 1)
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d.set_base_margin(predt)
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# base margin is per-class in multi-class classifier
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base_margin = rng.randn(100, 3).astype(np.float32)
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d.set_base_margin(base_margin.flatten())
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ridxs = [1, 2, 3, 4, 5, 6]
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sliced = d.slice(ridxs)
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sliced_margin = sliced.get_float_info('base_margin')
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assert sliced_margin.shape[0] == len(ridxs) * 3
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# Slicing works with label and other meta info fields
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np.testing.assert_equal(sliced.get_label(), y[1:7])
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np.testing.assert_equal(sliced.get_float_info('feature_weights'), fw)
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np.testing.assert_equal(sliced.get_base_margin(), base_margin[1:7, :].flatten())
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np.testing.assert_equal(sliced.get_base_margin(), sliced.get_float_info('base_margin'))
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eval_res_1 = {}
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xgb.train(
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# Slicing a DMatrix results into a DMatrix that's equivalent to a DMatrix that's
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# constructed from the corresponding NumPy slice
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d2 = xgb.DMatrix(X[1:7, :], y[1:7])
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d2.set_base_margin(base_margin[1:7, :].flatten())
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eval_res = {}
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_ = xgb.train(
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{'num_class': 3, 'objective': 'multi:softprob',
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'eval_metric': 'merror'},
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sliced,
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num_boost_round=2, evals=[(sliced, 'd')], evals_result=eval_res_1)
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eval_res_0 = eval_res_0['d']['merror']
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eval_res_1 = eval_res_1['d']['merror']
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for i in range(len(eval_res_0)):
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assert abs(eval_res_0[i] - eval_res_1[i]) < 0.02
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'eval_metric': 'mlogloss'},
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d,
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num_boost_round=2, evals=[(d2, 'd2'), (sliced, 'sliced')], evals_result=eval_res)
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np.testing.assert_equal(eval_res['d2']['mlogloss'], eval_res['sliced']['mlogloss'])
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def test_feature_names_slice(self):
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data = np.random.randn(5, 5)
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# different length
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self.assertRaises(ValueError, xgb.DMatrix, data,
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feature_names=list('abcdef'))
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with pytest.raises(ValueError):
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xgb.DMatrix(data, feature_names=list('abcdef'))
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# contains duplicates
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self.assertRaises(ValueError, xgb.DMatrix, data,
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feature_names=['a', 'b', 'c', 'd', 'd'])
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with pytest.raises(ValueError):
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xgb.DMatrix(data, feature_names=['a', 'b', 'c', 'd', 'd'])
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# contains symbol
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self.assertRaises(ValueError, xgb.DMatrix, data,
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feature_names=['a', 'b', 'c', 'd', 'e<1'])
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with pytest.raises(ValueError):
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xgb.DMatrix(data, feature_names=['a', 'b', 'c', 'd', 'e<1'])
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dm = xgb.DMatrix(data)
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dm.feature_names = list('abcde')
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@@ -170,14 +167,12 @@ class TestDMatrix(unittest.TestCase):
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dm.feature_types = list('qiqiq')
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assert dm.feature_types == list('qiqiq')
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def incorrect_type_set():
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with pytest.raises(ValueError):
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dm.feature_types = list('abcde')
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self.assertRaises(ValueError, incorrect_type_set)
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# reset
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dm.feature_names = None
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self.assertEqual(dm.feature_names, ['f0', 'f1', 'f2', 'f3', 'f4'])
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assert dm.feature_names == ['f0', 'f1', 'f2', 'f3', 'f4']
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assert dm.feature_types is None
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def test_feature_names(self):
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@@ -209,7 +204,8 @@ class TestDMatrix(unittest.TestCase):
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# different feature name must raises error
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dm = xgb.DMatrix(dummy, feature_names=list('abcde'))
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self.assertRaises(ValueError, bst.predict, dm)
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with pytest.raises(ValueError):
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bst.predict(dm)
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def test_get_info(self):
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dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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@@ -234,9 +230,8 @@ class TestDMatrix(unittest.TestCase):
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fw -= 1
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def assign_weight():
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with pytest.raises(ValueError):
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m.set_info(feature_weights=fw)
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self.assertRaises(ValueError, assign_weight)
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def test_sparse_dmatrix_csr(self):
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nrow = 100
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