58a6723eb1
* Add num target model parameter, which is configured from input labels. * Change elementwise metric and indexing for weights. * Add demo. * Add tests.
342 lines
12 KiB
Python
342 lines
12 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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from test_dmatrix import set_base_margin_info
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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(ValueError, match=r".*multi.*"):
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dtrain = DMatrixT(cd, label=cd)
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xgb.train({"tree_method": "gpu_hist", "objective": "multi:softprob"}, dtrain)
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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_info(weight=cudf_floats)
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dmat_cudf.set_info(label=cudf_floats)
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dmat_cudf.set_info(base_margin=cudf_floats)
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dmat_cudf.set_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_info(weight=cudf_floats[cudf_floats.columns[0]])
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dmat_cudf.set_info(label=cudf_floats[cudf_floats.columns[0]])
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dmat_cudf.set_info(base_margin=cudf_floats[cudf_floats.columns[0]])
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dmat_cudf.set_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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set_base_margin_info(df, DMatrixT, "gpu_hist")
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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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@pytest.mark.skipif(**tm.no_cudf())
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def test_cudf_categorical(self):
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import cudf
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_X, _y = tm.make_categorical(100, 30, 17, False)
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X = cudf.from_pandas(_X)
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y = cudf.from_pandas(_y)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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assert len(Xy.feature_types) == X.shape[1]
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assert all(t == "c" for t in Xy.feature_types)
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Xy = xgb.DeviceQuantileDMatrix(X, y, enable_categorical=True)
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assert len(Xy.feature_types) == X.shape[1]
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assert all(t == "c" for t in Xy.feature_types)
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# test missing value
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X = cudf.DataFrame({"f0": ["a", "b", np.NaN]})
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X["f0"] = X["f0"].astype("category")
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df, cat_codes, _, _ = xgb.data._transform_cudf_df(
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X, None, None, enable_categorical=True
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)
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for col in cat_codes:
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assert col.has_nulls
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y = [0, 1, 2]
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with pytest.raises(ValueError):
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xgb.DMatrix(X, y)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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assert Xy.num_row() == 3
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assert Xy.num_col() == 1
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with pytest.raises(ValueError):
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xgb.DeviceQuantileDMatrix(X, y)
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Xy = xgb.DeviceQuantileDMatrix(X, y, enable_categorical=True)
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assert Xy.num_row() == 3
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assert Xy.num_col() == 1
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X = X["f0"]
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with pytest.raises(ValueError):
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xgb.DMatrix(X, y)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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assert Xy.num_row() == 3
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assert Xy.num_col() == 1
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@pytest.mark.skipif(**tm.no_cudf())
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@pytest.mark.skipif(**tm.no_cupy())
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@pytest.mark.skipif(**tm.no_sklearn())
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@pytest.mark.skipif(**tm.no_pandas())
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def test_cudf_training_with_sklearn():
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from cudf import DataFrame as df
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from cudf import Series as ss
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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) > 0).astype(np.int8))
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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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X_cudf = df.from_pandas(X)
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y_cudf = df.from_pandas(y)
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y_cudf_series = ss(data=y.iloc[:, 0])
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for y_obj in [y_cudf, y_cudf_series]:
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clf = xgb.XGBClassifier(gpu_id=0, tree_method='gpu_hist')
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clf.fit(X_cudf, y_obj, sample_weight=cudf_weights, base_margin=cudf_base_margin,
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eval_set=[(X_cudf, y_obj)])
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pred = clf.predict(X_cudf)
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assert np.array_equal(np.unique(pred), np.array([0, 1]))
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class IterForDMatrixTest(xgb.core.DataIter):
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'''A data iterator for XGBoost DMatrix.
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`reset` and `next` are required for any data iterator, other functions here
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are utilites for demonstration's purpose.
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'''
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ROWS_PER_BATCH = 100 # data is splited by rows
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BATCHES = 16
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def __init__(self, categorical):
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'''Generate some random data for demostration.
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Actual data can be anything that is currently supported by XGBoost.
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'''
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import cudf
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self.rows = self.ROWS_PER_BATCH
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if categorical:
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self._data = []
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self._labels = []
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for i in range(self.BATCHES):
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X, y = tm.make_categorical(self.ROWS_PER_BATCH, 4, 13, False)
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self._data.append(cudf.from_pandas(X))
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self._labels.append(y)
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else:
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rng = np.random.RandomState(1994)
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self._data = [
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cudf.DataFrame(
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{'a': rng.randn(self.ROWS_PER_BATCH),
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'b': rng.randn(self.ROWS_PER_BATCH)})] * self.BATCHES
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self._labels = [rng.randn(self.rows)] * self.BATCHES
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self.it = 0 # set iterator to 0
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super().__init__()
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def as_array(self):
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import cudf
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return cudf.concat(self._data)
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def as_array_labels(self):
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return np.concatenate(self._labels)
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def data(self):
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'''Utility function for obtaining current batch of data.'''
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return self._data[self.it]
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def labels(self):
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'''Utility function for obtaining current batch of label.'''
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return self._labels[self.it]
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def reset(self):
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'''Reset the iterator'''
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self.it = 0
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def next(self, input_data):
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'''Yield next batch of data'''
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if self.it == len(self._data):
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# Return 0 when there's no more batch.
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return 0
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input_data(data=self.data(), label=self.labels())
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self.it += 1
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return 1
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@pytest.mark.skipif(**tm.no_cudf())
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@pytest.mark.parametrize("enable_categorical", [True, False])
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def test_from_cudf_iter(enable_categorical):
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rounds = 100
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it = IterForDMatrixTest(enable_categorical)
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params = {"tree_method": "gpu_hist"}
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# Use iterator
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m_it = xgb.DeviceQuantileDMatrix(it, enable_categorical=enable_categorical)
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reg_with_it = xgb.train(params, m_it, num_boost_round=rounds)
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X = it.as_array()
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y = it.as_array_labels()
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m = xgb.DMatrix(X, y, enable_categorical=enable_categorical)
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assert m_it.num_col() == m.num_col()
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assert m_it.num_row() == m.num_row()
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reg = xgb.train(params, m, num_boost_round=rounds)
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predict = reg.predict(m)
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predict_with_it = reg_with_it.predict(m_it)
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np.testing.assert_allclose(predict_with_it, predict)
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