566 lines
21 KiB
Python
566 lines
21 KiB
Python
import sys
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from typing import Type
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import numpy as np
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import pytest
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from test_dmatrix import set_base_margin_info
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import xgboost as xgb
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from xgboost import testing as tm
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from xgboost.core import DataSplitMode
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from xgboost.testing.data import pd_arrow_dtypes, pd_dtypes
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try:
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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(**tm.no_pandas())
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dpath = "demo/data/"
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rng = np.random.RandomState(1994)
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class TestPandas:
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def test_pandas(self, data_split_mode=DataSplitMode.ROW):
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world_size = xgb.collective.get_world_size()
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df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]], columns=["a", "b", "c"])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]), data_split_mode=data_split_mode)
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assert dm.num_row() == 2
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if data_split_mode == DataSplitMode.ROW:
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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_col() == 3
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["a", "b", "c"], world_size
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)
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assert dm.feature_types == ["int", "float", "i"] * world_size
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assert dm.num_col() == 3 * world_size
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np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
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# overwrite feature_names and feature_types
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dm = xgb.DMatrix(
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df,
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label=pd.Series([1, 2]),
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feature_names=["x", "y", "z"],
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feature_types=["q", "q", "q"],
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data_split_mode=data_split_mode,
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)
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assert dm.num_row() == 2
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["x", "y", "z"]
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assert dm.feature_types == ["q", "q", "q"]
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assert dm.num_col() == 3
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["x", "y", "z"], world_size
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)
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assert dm.feature_types == ["q", "q", "q"] * world_size
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assert dm.num_col() == 3 * world_size
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# incorrect dtypes
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df = pd.DataFrame([[1, 2.0, "x"], [2, 3.0, "y"]], columns=["a", "b", "c"])
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with pytest.raises(ValueError):
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xgb.DMatrix(df, data_split_mode=data_split_mode)
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# numeric columns
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df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]), data_split_mode=data_split_mode)
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assert dm.num_row() == 2
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["0", "1", "2"]
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assert dm.feature_types == ["int", "float", "i"]
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assert dm.num_col() == 3
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["0", "1", "2"], world_size
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)
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assert dm.feature_types == ["int", "float", "i"] * world_size
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assert dm.num_col() == 3 * world_size
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np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))
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df = pd.DataFrame([[1, 2.0, 1], [2, 3.0, 1]], columns=[4, 5, 6])
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dm = xgb.DMatrix(df, label=pd.Series([1, 2]), data_split_mode=data_split_mode)
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assert dm.num_row() == 2
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["4", "5", "6"]
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assert dm.feature_types == ["int", "float", "int"]
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assert dm.num_col() == 3
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["4", "5", "6"], world_size
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)
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assert dm.feature_types == ["int", "float", "int"] * world_size
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assert dm.num_col() == 3 * world_size
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df = pd.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
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dummies = pd.get_dummies(df)
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# B A_X A_Y A_Z
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# 0 1 1 0 0
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# 1 2 0 1 0
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# 2 3 0 0 1
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result, _, _ = xgb.data._transform_pandas_df(dummies, enable_categorical=False)
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exp = np.array(
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[[1.0, 1.0, 0.0, 0.0], [2.0, 0.0, 1.0, 0.0], [3.0, 0.0, 0.0, 1.0]]
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)
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np.testing.assert_array_equal(result, exp)
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dm = xgb.DMatrix(dummies, data_split_mode=data_split_mode)
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assert dm.num_row() == 3
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["B", "A_X", "A_Y", "A_Z"]
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if int(pd.__version__[0]) >= 2:
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assert dm.feature_types == ["int", "i", "i", "i"]
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else:
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assert dm.feature_types == ["int", "int", "int", "int"]
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assert dm.num_col() == 4
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["B", "A_X", "A_Y", "A_Z"], world_size
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)
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if int(pd.__version__[0]) >= 2:
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assert dm.feature_types == ["int", "i", "i", "i"] * world_size
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else:
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assert dm.feature_types == ["int", "int", "int", "int"] * world_size
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assert dm.num_col() == 4 * world_size
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df = pd.DataFrame({"A=1": [1, 2, 3], "A=2": [4, 5, 6]})
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dm = xgb.DMatrix(df, data_split_mode=data_split_mode)
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assert dm.num_row() == 3
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if data_split_mode == DataSplitMode.ROW:
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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_col() == 2
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["A=1", "A=2"], world_size
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)
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assert dm.feature_types == ["int", "int"] * world_size
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assert dm.num_col() == 2 * world_size
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df_int = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=[9, 10])
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dm_int = xgb.DMatrix(df_int, data_split_mode=data_split_mode)
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df_range = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=range(9, 11, 1))
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dm_range = xgb.DMatrix(df_range, data_split_mode=data_split_mode)
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if data_split_mode == DataSplitMode.ROW:
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assert dm_int.feature_names == ["9", "10"] # assert not "9 "
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else:
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assert dm_int.feature_names == tm.column_split_feature_names(
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["9", "10"], world_size
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)
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assert dm_int.feature_names == dm_range.feature_names
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# test MultiIndex as columns
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df = pd.DataFrame(
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[(1, 2, 3, 4, 5, 6), (6, 5, 4, 3, 2, 1)],
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columns=pd.MultiIndex.from_tuples(
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(
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("a", 1),
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("a", 2),
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("a", 3),
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("b", 1),
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("b", 2),
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("b", 3),
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)
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),
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)
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dm = xgb.DMatrix(df, data_split_mode=data_split_mode)
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assert dm.num_row() == 2
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if data_split_mode == DataSplitMode.ROW:
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assert dm.feature_names == ["a 1", "a 2", "a 3", "b 1", "b 2", "b 3"]
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assert dm.feature_types == ["int", "int", "int", "int", "int", "int"]
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assert dm.num_col() == 6
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else:
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assert dm.feature_names == tm.column_split_feature_names(
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["a 1", "a 2", "a 3", "b 1", "b 2", "b 3"], world_size
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)
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assert (
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dm.feature_types
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== ["int", "int", "int", "int", "int", "int"] * world_size
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)
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assert dm.num_col() == 6 * world_size
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# test Index as columns
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df = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=pd.Index([1, 2]))
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Xy = xgb.DMatrix(df, data_split_mode=data_split_mode)
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if data_split_mode == DataSplitMode.ROW:
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np.testing.assert_equal(np.array(Xy.feature_names), np.array(["1", "2"]))
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else:
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np.testing.assert_equal(
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np.array(Xy.feature_names),
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np.array(tm.column_split_feature_names(["1", "2"], world_size)),
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)
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# test pandas series
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data_series = pd.Series([1, 2, 3, 4, 5])
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dm = xgb.DMatrix(data_series, data_split_mode=data_split_mode)
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assert dm.num_row() == 5
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if data_split_mode == DataSplitMode.ROW:
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assert dm.num_col() == 1
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else:
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assert dm.num_col() == 1 * world_size
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def test_slice(self):
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rng = np.random.RandomState(1994)
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rows = 100
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X = rng.randint(3, 7, size=rows)
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X = pd.DataFrame({"f0": X})
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y = rng.randn(rows)
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ridxs = [1, 2, 3, 4, 5, 6]
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m = xgb.DMatrix(X, y)
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sliced = m.slice(ridxs)
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assert m.feature_types == sliced.feature_types
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def test_pandas_categorical(self, data_split_mode=DataSplitMode.ROW):
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world_size = xgb.collective.get_world_size()
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rng = np.random.RandomState(1994)
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rows = 100
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X = rng.randint(3, 7, size=rows)
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X = pd.Series(X, dtype="category")
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X = pd.DataFrame({"f0": X})
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y = rng.randn(rows)
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m = xgb.DMatrix(X, y, enable_categorical=True, data_split_mode=data_split_mode)
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assert m.feature_types[0] == "c"
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X_0 = ["f", "o", "o"]
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X_1 = [4, 3, 2]
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X = pd.DataFrame({"feat_0": X_0, "feat_1": X_1})
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X["feat_0"] = X["feat_0"].astype("category")
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transformed, _, feature_types = xgb.data._transform_pandas_df(
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X, enable_categorical=True
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)
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assert transformed[:, 0].min() == 0
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# test missing value
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X = pd.DataFrame({"f0": ["a", "b", np.NaN]})
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X["f0"] = X["f0"].astype("category")
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arr, _, _ = xgb.data._transform_pandas_df(X, enable_categorical=True)
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assert not np.any(arr == -1.0)
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X = X["f0"]
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y = y[: X.shape[0]]
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with pytest.raises(ValueError, match=r".*enable_categorical.*"):
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xgb.DMatrix(X, y, data_split_mode=data_split_mode)
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Xy = xgb.DMatrix(X, y, enable_categorical=True, data_split_mode=data_split_mode)
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assert Xy.num_row() == 3
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if data_split_mode == DataSplitMode.ROW:
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assert Xy.num_col() == 1
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else:
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assert Xy.num_col() == 1 * world_size
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def test_pandas_sparse(self):
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import pandas as pd
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rows = 100
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X = pd.DataFrame(
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{
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"A": pd.arrays.SparseArray(np.random.randint(0, 10, size=rows)),
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"B": pd.arrays.SparseArray(np.random.randn(rows)),
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"C": pd.arrays.SparseArray(
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np.random.permutation([True, False] * (rows // 2))
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),
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}
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)
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y = pd.Series(pd.arrays.SparseArray(np.random.randn(rows)))
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dtrain = xgb.DMatrix(X, y)
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booster = xgb.train({}, dtrain, num_boost_round=4)
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predt_sparse = booster.predict(xgb.DMatrix(X))
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predt_dense = booster.predict(xgb.DMatrix(X.sparse.to_dense()))
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np.testing.assert_allclose(predt_sparse, predt_dense)
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def test_pandas_label(self, data_split_mode=DataSplitMode.ROW):
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world_size = xgb.collective.get_world_size()
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# label must be a single column
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df = pd.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
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with pytest.raises(ValueError):
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xgb.data._transform_pandas_df(df, False, None, None, "label", "float")
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# label must be supported dtype
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df = pd.DataFrame({"A": np.array(["a", "b", "c"], dtype=object)})
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with pytest.raises(ValueError):
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xgb.data._transform_pandas_df(df, False, None, None, "label", "float")
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df = pd.DataFrame({"A": np.array([1, 2, 3], dtype=int)})
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result, _, _ = xgb.data._transform_pandas_df(
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df, False, None, None, "label", "float"
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)
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np.testing.assert_array_equal(
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result, np.array([[1.0], [2.0], [3.0]], dtype=float)
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)
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dm = xgb.DMatrix(
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np.random.randn(3, 2), label=df, data_split_mode=data_split_mode
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)
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assert dm.num_row() == 3
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if data_split_mode == DataSplitMode.ROW:
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assert dm.num_col() == 2
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else:
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assert dm.num_col() == 2 * world_size
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def test_pandas_weight(self, data_split_mode=DataSplitMode.ROW):
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world_size = xgb.collective.get_world_size()
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kRows = 32
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kCols = 8
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X = np.random.randn(kRows, kCols)
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y = np.random.randn(kRows)
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w = np.random.uniform(size=kRows).astype(np.float32)
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w_pd = pd.DataFrame(w)
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data = xgb.DMatrix(X, y, weight=w_pd, data_split_mode=data_split_mode)
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assert data.num_row() == kRows
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if data_split_mode == DataSplitMode.ROW:
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assert data.num_col() == kCols
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else:
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assert data.num_col() == kCols * world_size
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np.testing.assert_array_equal(data.get_weight(), w)
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def test_base_margin(self):
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set_base_margin_info(pd.DataFrame, xgb.DMatrix, "hist")
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def test_cv_as_pandas(self):
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dm, _ = tm.load_agaricus(__file__)
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params = {
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"max_depth": 2,
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"eta": 1,
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"verbosity": 0,
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"objective": "binary:logistic",
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"eval_metric": "error",
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}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10)
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assert isinstance(cv, pd.DataFrame)
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exp = pd.Index(
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["test-error-mean", "test-error-std", "train-error-mean", "train-error-std"]
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)
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assert len(cv.columns.intersection(exp)) == 4
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# show progress log (result is the same as above)
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, verbose_eval=True)
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assert isinstance(cv, pd.DataFrame)
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exp = pd.Index(
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["test-error-mean", "test-error-std", "train-error-mean", "train-error-std"]
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)
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assert len(cv.columns.intersection(exp)) == 4
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cv = xgb.cv(
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params, dm, num_boost_round=10, nfold=10, verbose_eval=True, show_stdv=False
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)
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assert isinstance(cv, pd.DataFrame)
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exp = pd.Index(
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["test-error-mean", "test-error-std", "train-error-mean", "train-error-std"]
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)
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assert len(cv.columns.intersection(exp)) == 4
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params = {
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"max_depth": 2,
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"eta": 1,
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"verbosity": 0,
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"objective": "binary:logistic",
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"eval_metric": "auc",
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}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
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assert "eval_metric" in params
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assert "auc" in cv.columns[0]
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params = {
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"max_depth": 2,
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"eta": 1,
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"verbosity": 0,
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"objective": "binary:logistic",
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"eval_metric": ["auc"],
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}
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cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=True)
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assert "eval_metric" in params
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assert "auc" in cv.columns[0]
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params = {
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"max_depth": 2,
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"eta": 1,
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"verbosity": 0,
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"objective": "binary:logistic",
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"eval_metric": ["auc"],
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}
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cv = xgb.cv(
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params,
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dm,
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num_boost_round=10,
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nfold=10,
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as_pandas=True,
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early_stopping_rounds=1,
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)
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assert "eval_metric" in params
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assert "auc" in cv.columns[0]
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assert cv.shape[0] < 10
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params = {
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"max_depth": 2,
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"eta": 1,
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"verbosity": 0,
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"objective": "binary:logistic",
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}
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cv = xgb.cv(
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params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics="auc"
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)
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assert "auc" in cv.columns[0]
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params = {
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"max_depth": 2,
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"eta": 1,
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"verbosity": 0,
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"objective": "binary:logistic",
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}
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cv = xgb.cv(
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params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=["auc"]
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)
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assert "auc" in cv.columns[0]
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|
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params = {
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"max_depth": 2,
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"eta": 1,
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"verbosity": 0,
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"objective": "binary:logistic",
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"eval_metric": ["auc"],
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}
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cv = xgb.cv(
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params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics="error"
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)
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assert "eval_metric" in params
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assert "auc" not in cv.columns[0]
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assert "error" in cv.columns[0]
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cv = xgb.cv(
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params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=["error"]
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)
|
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assert "eval_metric" in params
|
|
assert "auc" not in cv.columns[0]
|
|
assert "error" in cv.columns[0]
|
|
|
|
params = list(params.items())
|
|
cv = xgb.cv(
|
|
params, dm, num_boost_round=10, nfold=10, as_pandas=True, metrics=["error"]
|
|
)
|
|
assert isinstance(params, list)
|
|
assert "auc" not in cv.columns[0]
|
|
assert "error" in cv.columns[0]
|
|
|
|
@pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix])
|
|
def test_nullable_type(self, DMatrixT) -> None:
|
|
from xgboost.data import is_pd_cat_dtype
|
|
|
|
for orig, df in pd_dtypes():
|
|
if hasattr(df.dtypes, "__iter__"):
|
|
enable_categorical = any(is_pd_cat_dtype(dtype) for dtype in df.dtypes)
|
|
else:
|
|
# series
|
|
enable_categorical = is_pd_cat_dtype(df.dtype)
|
|
|
|
f0_orig = orig[orig.columns[0]] if isinstance(orig, pd.DataFrame) else orig
|
|
f0 = df[df.columns[0]] if isinstance(df, pd.DataFrame) else df
|
|
y_orig = f0_orig.astype(pd.Float32Dtype()).fillna(0)
|
|
y = f0.astype(pd.Float32Dtype()).fillna(0)
|
|
|
|
m_orig = DMatrixT(orig, enable_categorical=enable_categorical, label=y_orig)
|
|
# extension types
|
|
copy = df.copy()
|
|
m_etype = DMatrixT(df, enable_categorical=enable_categorical, label=y)
|
|
# no mutation
|
|
assert df.equals(copy)
|
|
# different from pd.BooleanDtype(), None is converted to False with bool
|
|
if hasattr(orig.dtypes, "__iter__") and any(
|
|
dtype == "bool" for dtype in orig.dtypes
|
|
):
|
|
assert not tm.predictor_equal(m_orig, m_etype)
|
|
else:
|
|
assert tm.predictor_equal(m_orig, m_etype)
|
|
|
|
np.testing.assert_allclose(m_orig.get_label(), m_etype.get_label())
|
|
np.testing.assert_allclose(m_etype.get_label(), y.values.astype(np.float32))
|
|
|
|
if isinstance(df, pd.DataFrame):
|
|
f0 = df["f0"]
|
|
with pytest.raises(ValueError, match="Label contains NaN"):
|
|
xgb.DMatrix(df, f0, enable_categorical=enable_categorical)
|
|
|
|
@pytest.mark.skipif(**tm.no_arrow())
|
|
@pytest.mark.parametrize("DMatrixT", [xgb.DMatrix, xgb.QuantileDMatrix])
|
|
def test_pyarrow_type(self, DMatrixT: Type[xgb.DMatrix]) -> None:
|
|
for orig, df in pd_arrow_dtypes():
|
|
f0_orig: pd.Series = orig["f0"]
|
|
f0 = df["f0"]
|
|
|
|
if f0.dtype.name.startswith("bool"):
|
|
y = None
|
|
y_orig = None
|
|
else:
|
|
y_orig = f0_orig.fillna(0, inplace=False)
|
|
y = f0.fillna(0, inplace=False)
|
|
|
|
m_orig = DMatrixT(orig, enable_categorical=True, label=y_orig)
|
|
m_etype = DMatrixT(df, enable_categorical=True, label=y)
|
|
|
|
assert tm.predictor_equal(m_orig, m_etype)
|
|
if y is not None:
|
|
np.testing.assert_allclose(m_orig.get_label(), m_etype.get_label())
|
|
np.testing.assert_allclose(m_etype.get_label(), y.values)
|
|
|
|
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
|
def test_pandas_column_split(self):
|
|
tm.run_with_rabit(
|
|
world_size=3, test_fn=self.test_pandas, data_split_mode=DataSplitMode.COL
|
|
)
|
|
|
|
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
|
def test_pandas_categorical_column_split(self):
|
|
tm.run_with_rabit(
|
|
world_size=3,
|
|
test_fn=self.test_pandas_categorical,
|
|
data_split_mode=DataSplitMode.COL,
|
|
)
|
|
|
|
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
|
def test_pandas_sparse_column_split(self):
|
|
rows = 100
|
|
X = pd.DataFrame(
|
|
{
|
|
"A": pd.arrays.SparseArray(np.random.randint(0, 10, size=rows)),
|
|
"B": pd.arrays.SparseArray(np.random.randn(rows)),
|
|
"C": pd.arrays.SparseArray(
|
|
np.random.permutation([True, False] * (rows // 2))
|
|
),
|
|
}
|
|
)
|
|
y = pd.Series(pd.arrays.SparseArray(np.random.randn(rows)))
|
|
|
|
def verify_pandas_sparse():
|
|
dtrain = xgb.DMatrix(X, y, data_split_mode=DataSplitMode.COL)
|
|
booster = xgb.train({}, dtrain, num_boost_round=4)
|
|
predt_sparse = booster.predict(
|
|
xgb.DMatrix(X, data_split_mode=DataSplitMode.COL)
|
|
)
|
|
predt_dense = booster.predict(
|
|
xgb.DMatrix(X.sparse.to_dense(), data_split_mode=DataSplitMode.COL)
|
|
)
|
|
np.testing.assert_allclose(predt_sparse, predt_dense)
|
|
|
|
tm.run_with_rabit(world_size=3, test_fn=verify_pandas_sparse)
|
|
|
|
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
|
def test_pandas_label_column_split(self):
|
|
tm.run_with_rabit(
|
|
world_size=3,
|
|
test_fn=self.test_pandas_label,
|
|
data_split_mode=DataSplitMode.COL,
|
|
)
|
|
|
|
@pytest.mark.skipif(tm.is_windows(), reason="Rabit does not run on windows")
|
|
def test_pandas_weight_column_split(self):
|
|
tm.run_with_rabit(
|
|
world_size=3,
|
|
test_fn=self.test_pandas_weight,
|
|
data_split_mode=DataSplitMode.COL,
|
|
)
|