173 lines
6.2 KiB
Python
173 lines
6.2 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, DMatrixT, 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.from_pandas(pa)
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cd_label = cudf.from_pandas(pa_label).iloc[:, 0]
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dtrain = DMatrixT(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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def _test_from_cudf(DMatrixT):
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'''Test constructing DMatrix from cudf'''
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import cudf
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dmatrix_from_cudf(np.float32, DMatrixT, np.NAN)
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dmatrix_from_cudf(np.float64, DMatrixT, np.NAN)
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dmatrix_from_cudf(np.int8, DMatrixT, 2)
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dmatrix_from_cudf(np.int32, DMatrixT, -2)
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dmatrix_from_cudf(np.int64, DMatrixT, -3)
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cd = cudf.DataFrame({'x': [1, 2, 3], 'y': [0.1, 0.2, 0.3]})
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dtrain = DMatrixT(cd)
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assert dtrain.feature_names == ['x', 'y']
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assert dtrain.feature_types == ['int', 'float']
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series = cudf.DataFrame({'x': [1, 2, 3]}).iloc[:, 0]
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assert isinstance(series, cudf.Series)
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dtrain = DMatrixT(series)
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assert dtrain.feature_names == ['x']
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assert dtrain.feature_types == ['int']
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with pytest.raises(Exception):
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dtrain = DMatrixT(cd, label=cd)
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# Test when number of elements is less than 8
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X = cudf.DataFrame({'x': cudf.Series([0, 1, 2, np.NAN, 4],
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dtype=np.int32)})
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dtrain = DMatrixT(X)
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assert dtrain.num_col() == 1
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assert dtrain.num_row() == 5
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# Boolean is not supported.
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X_boolean = cudf.DataFrame({'x': cudf.Series([True, False])})
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with pytest.raises(Exception):
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dtrain = DMatrixT(X_boolean)
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y_boolean = cudf.DataFrame({
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'x': cudf.Series([True, False, True, True, True])})
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with pytest.raises(Exception):
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dtrain = DMatrixT(X_boolean, label=y_boolean)
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def _test_cudf_training(DMatrixT):
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from cudf import DataFrame as df
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import pandas as pd
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np.random.seed(1)
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X = pd.DataFrame(np.random.randn(50, 10))
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y = pd.DataFrame(np.random.randn(50))
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weights = np.random.random(50) + 1.0
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cudf_weights = df.from_pandas(pd.DataFrame(weights))
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base_margin = np.random.random(50)
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cudf_base_margin = df.from_pandas(pd.DataFrame(base_margin))
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evals_result_cudf = {}
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dtrain_cudf = DMatrixT(df.from_pandas(X), df.from_pandas(y), weight=cudf_weights,
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base_margin=cudf_base_margin)
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params = {'gpu_id': 0, 'tree_method': 'gpu_hist'}
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xgb.train(params, dtrain_cudf, evals=[(dtrain_cudf, "train")],
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evals_result=evals_result_cudf)
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evals_result_np = {}
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dtrain_np = xgb.DMatrix(X, y, weight=weights, base_margin=base_margin)
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xgb.train(params, dtrain_np, evals=[(dtrain_np, "train")],
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evals_result=evals_result_np)
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assert np.array_equal(evals_result_cudf["train"]["rmse"], evals_result_np["train"]["rmse"])
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def _test_cudf_metainfo(DMatrixT):
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from cudf import DataFrame as df
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import pandas as pd
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n = 100
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X = np.random.random((n, 2))
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dmat_cudf = DMatrixT(df.from_pandas(pd.DataFrame(X)))
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dmat = xgb.DMatrix(X)
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floats = np.random.random(n)
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uints = np.array([4, 2, 8]).astype("uint32")
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cudf_floats = df.from_pandas(pd.DataFrame(floats))
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cudf_uints = df.from_pandas(pd.DataFrame(uints))
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dmat.set_float_info('weight', floats)
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dmat.set_float_info('label', floats)
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dmat.set_float_info('base_margin', floats)
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dmat.set_uint_info('group', uints)
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dmat_cudf.set_interface_info('weight', cudf_floats)
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dmat_cudf.set_interface_info('label', cudf_floats)
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dmat_cudf.set_interface_info('base_margin', cudf_floats)
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dmat_cudf.set_interface_info('group', cudf_uints)
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# Test setting info with cudf DataFrame
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assert np.array_equal(dmat.get_float_info('weight'), dmat_cudf.get_float_info('weight'))
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assert np.array_equal(dmat.get_float_info('label'), dmat_cudf.get_float_info('label'))
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assert np.array_equal(dmat.get_float_info('base_margin'),
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dmat_cudf.get_float_info('base_margin'))
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assert np.array_equal(dmat.get_uint_info('group_ptr'), dmat_cudf.get_uint_info('group_ptr'))
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# Test setting info with cudf Series
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dmat_cudf.set_interface_info('weight', cudf_floats[cudf_floats.columns[0]])
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dmat_cudf.set_interface_info('label', cudf_floats[cudf_floats.columns[0]])
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dmat_cudf.set_interface_info('base_margin', cudf_floats[cudf_floats.columns[0]])
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dmat_cudf.set_interface_info('group', cudf_uints[cudf_uints.columns[0]])
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assert np.array_equal(dmat.get_float_info('weight'), dmat_cudf.get_float_info('weight'))
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assert np.array_equal(dmat.get_float_info('label'), dmat_cudf.get_float_info('label'))
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assert np.array_equal(dmat.get_float_info('base_margin'),
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dmat_cudf.get_float_info('base_margin'))
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assert np.array_equal(dmat.get_uint_info('group_ptr'), dmat_cudf.get_uint_info('group_ptr'))
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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_simple_dmatrix_from_cudf(self):
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_test_from_cudf(xgb.DMatrix)
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@pytest.mark.skipif(**tm.no_cudf())
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def test_device_dmatrix_from_cudf(self):
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_test_from_cudf(xgb.DeviceQuantileDMatrix)
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@pytest.mark.skipif(**tm.no_cudf())
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def test_cudf_training_simple_dmatrix(self):
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_test_cudf_training(xgb.DMatrix)
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@pytest.mark.skipif(**tm.no_cudf())
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def test_cudf_training_device_dmatrix(self):
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_test_cudf_training(xgb.DeviceQuantileDMatrix)
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@pytest.mark.skipif(**tm.no_cudf())
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def test_cudf_metainfo_simple_dmatrix(self):
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_test_cudf_metainfo(xgb.DMatrix)
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@pytest.mark.skipif(**tm.no_cudf())
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def test_cudf_metainfo_device_dmatrix(self):
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_test_cudf_metainfo(xgb.DeviceQuantileDMatrix)
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