5374f52531
* Handles missing value. * Accept all floating point and integer types. * Move to cudf 9.0 API. * Remove requirement on `null_count`. * Arbitrary column types support.
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
import numpy as np
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import xgboost as xgb
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import sys
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import pytest
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sys.path.append("tests/python")
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import testing as tm
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def dmatrix_from_cudf(input_type, missing=np.NAN):
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'''Test constructing DMatrix from cudf'''
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import cudf
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import pandas as pd
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kRows = 80
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kCols = 3
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na = np.random.randn(kRows, kCols)
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na[:, 0:2] = na[:, 0:2].astype(input_type)
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na[5, 0] = missing
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na[3, 1] = missing
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pa = pd.DataFrame({'0': na[:, 0],
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'1': na[:, 1],
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'2': na[:, 2].astype(np.int32)})
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np_label = np.random.randn(kRows).astype(input_type)
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pa_label = pd.DataFrame(np_label)
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cd: cudf.DataFrame = cudf.from_pandas(pa)
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cd_label: cudf.DataFrame = cudf.from_pandas(pa_label)
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dtrain = xgb.DMatrix(cd, missing=missing, label=cd_label)
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assert dtrain.num_col() == kCols
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assert dtrain.num_row() == kRows
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class TestFromColumnar:
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'''Tests for constructing DMatrix from data structure conforming Apache
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Arrow specification.'''
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@pytest.mark.skipif(**tm.no_cudf())
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def test_from_cudf(self):
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'''Test constructing DMatrix from cudf'''
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dmatrix_from_cudf(np.float32, np.NAN)
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dmatrix_from_cudf(np.float64, np.NAN)
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dmatrix_from_cudf(np.uint8, 2)
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dmatrix_from_cudf(np.uint32, 3)
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dmatrix_from_cudf(np.uint64, 4)
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dmatrix_from_cudf(np.int8, 2)
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dmatrix_from_cudf(np.int32, -2)
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dmatrix_from_cudf(np.int64, -3)
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