101 lines
3.6 KiB
Python
101 lines
3.6 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_cupy(input_type, missing=np.NAN):
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'''Test constructing DMatrix from cupy'''
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import cupy as cp
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kRows = 80
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kCols = 3
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np_X = np.random.randn(kRows, kCols).astype(dtype=input_type)
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X = cp.array(np_X)
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X[5, 0] = missing
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X[3, 1] = missing
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y = cp.random.randn(kRows).astype(dtype=input_type)
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dtrain = xgb.DMatrix(X, missing=missing, label=y)
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assert dtrain.num_col() == kCols
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assert dtrain.num_row() == kRows
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return dtrain
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class TestFromArrayInterface:
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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_cupy())
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def test_from_cupy(self):
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'''Test constructing DMatrix from cupy'''
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import cupy as cp
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dmatrix_from_cupy(np.float32, np.NAN)
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dmatrix_from_cupy(np.float64, np.NAN)
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dmatrix_from_cupy(np.uint8, 2)
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dmatrix_from_cupy(np.uint32, 3)
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dmatrix_from_cupy(np.uint64, 4)
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dmatrix_from_cupy(np.int8, 2)
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dmatrix_from_cupy(np.int32, -2)
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dmatrix_from_cupy(np.int64, -3)
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with pytest.raises(Exception):
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X = cp.random.randn(2, 2, dtype="float32")
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dtrain = xgb.DMatrix(X, label=X)
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@pytest.mark.skipif(**tm.no_cupy())
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def test_cupy_training(self):
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import cupy as cp
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np.random.seed(1)
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cp.random.seed(1)
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X = cp.random.randn(50, 10, dtype="float32")
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y = cp.random.randn(50, dtype="float32")
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weights = np.random.random(50) + 1
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cupy_weights = cp.array(weights)
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base_margin = np.random.random(50)
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cupy_base_margin = cp.array(base_margin)
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evals_result_cupy = {}
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dtrain_cp = xgb.DMatrix(X, y, weight=cupy_weights, base_margin=cupy_base_margin)
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params = {'gpu_id': 0, 'nthread': 1}
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xgb.train(params, dtrain_cp, evals=[(dtrain_cp, "train")],
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evals_result=evals_result_cupy)
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evals_result_np = {}
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dtrain_np = xgb.DMatrix(cp.asnumpy(X), cp.asnumpy(y), weight=weights,
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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_cupy["train"]["rmse"], evals_result_np["train"]["rmse"])
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@pytest.mark.skipif(**tm.no_cupy())
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def test_cupy_metainfo(self):
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import cupy as cp
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n = 100
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X = np.random.random((n, 2))
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dmat_cupy = xgb.DMatrix(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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cupy_floats = cp.array(floats)
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cupy_uints = cp.array(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_cupy.set_interface_info('weight', cupy_floats)
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dmat_cupy.set_interface_info('label', cupy_floats)
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dmat_cupy.set_interface_info('base_margin', cupy_floats)
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dmat_cupy.set_interface_info('group', cupy_uints)
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# Test setting info with cupy
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assert np.array_equal(dmat.get_float_info('weight'), dmat_cupy.get_float_info('weight'))
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assert np.array_equal(dmat.get_float_info('label'), dmat_cupy.get_float_info('label'))
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assert np.array_equal(dmat.get_float_info('base_margin'),
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dmat_cupy.get_float_info('base_margin'))
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assert np.array_equal(dmat.get_uint_info('group_ptr'), dmat_cupy.get_uint_info('group_ptr'))
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