9c9070aea2
* 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
55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
# -*- coding: utf-8 -*-
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import pytest
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import numpy as np
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import testing as tm
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import xgboost as xgb
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try:
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import datatable as dt
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import pandas as pd
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except ImportError:
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pass
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pytestmark = pytest.mark.skipif(
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tm.no_dt()['condition'] or tm.no_pandas()['condition'],
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reason=tm.no_dt()['reason'] + ' or ' + tm.no_pandas()['reason'])
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class TestDataTable:
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def test_dt(self):
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df = pd.DataFrame([[1, 2., True], [2, 3., False]],
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columns=['a', 'b', 'c'])
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dtable = dt.Frame(df)
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labels = dt.Frame([1, 2])
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dm = xgb.DMatrix(dtable, label=labels)
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assert dm.feature_names == ['a', 'b', 'c']
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assert dm.feature_types == ['int', 'float', 'i']
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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np.testing.assert_array_equal(np.array([1, 2]), dm.get_label())
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# overwrite feature_names
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dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]),
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feature_names=['x', 'y', 'z'])
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assert dm.feature_names == ['x', 'y', 'z']
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assert dm.num_row() == 2
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assert dm.num_col() == 3
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# incorrect dtypes
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df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
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columns=['a', 'b', 'c'])
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dtable = dt.Frame(df)
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with pytest.raises(ValueError):
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xgb.DMatrix(dtable)
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df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
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dtable = dt.Frame(df)
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dm = xgb.DMatrix(dtable)
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assert dm.feature_names == ['A=1', 'A=2']
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assert dm.feature_types == ['int', 'int']
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assert dm.num_row() == 3
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assert dm.num_col() == 2
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