1f9a57d17b
Previously, we use `libsvm` as default when format is not specified. However, the dmlc data parser is not particularly robust against errors, and the most common type of error is undefined format. Along with which, we will recommend users to use other data loader instead. We will continue the maintenance of the parsers as it's currently used for many internal tests including federated learning.
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
import numpy as np
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import pytest
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import xgboost as xgb
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from xgboost import testing as tm
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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 TestTreesToDataFrame:
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def build_model(self, max_depth, num_round):
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dtrain, _ = tm.load_agaricus(__file__)
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param = {'max_depth': max_depth, 'objective': 'binary:logistic',
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'verbosity': 1}
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num_round = num_round
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bst = xgb.train(param, dtrain, num_round)
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return bst
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def parse_dumped_model(self, booster, item_to_get, splitter):
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item_to_get += '='
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txt_dump = booster.get_dump(with_stats=True)
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tree_list = [tree.split('/n') for tree in txt_dump]
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split_trees = [tree[0].split(item_to_get)[1:] for tree in tree_list]
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res = sum([float(line.split(splitter)[0])
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for tree in split_trees for line in tree])
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return res
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def test_trees_to_dataframe(self):
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bst = self.build_model(max_depth=5, num_round=10)
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gain_from_dump = self.parse_dumped_model(booster=bst,
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item_to_get='gain',
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splitter=',')
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cover_from_dump = self.parse_dumped_model(booster=bst,
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item_to_get='cover',
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splitter='\n')
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# method being tested
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df = bst.trees_to_dataframe()
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# test for equality of gains
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gain_from_df = df[df.Feature != 'Leaf'][['Gain']].sum()
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assert np.allclose(gain_from_dump, gain_from_df)
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# test for equality of covers
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cover_from_df = df.Cover.sum()
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assert np.allclose(cover_from_dump, cover_from_df)
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def run_tree_to_df_categorical(self, tree_method: str) -> None:
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X, y = tm.make_categorical(100, 10, 31, False)
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Xy = xgb.DMatrix(X, y, enable_categorical=True)
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booster = xgb.train({"tree_method": tree_method}, Xy, num_boost_round=10)
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df = booster.trees_to_dataframe()
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for _, x in df.iterrows():
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if x["Feature"] != "Leaf":
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assert len(x["Category"]) >= 1
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def test_tree_to_df_categorical(self) -> None:
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self.run_tree_to_df_categorical("approx")
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def run_split_value_histograms(self, tree_method) -> None:
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X, y = tm.make_categorical(1000, 10, 13, False)
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reg = xgb.XGBRegressor(tree_method=tree_method, enable_categorical=True)
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reg.fit(X, y)
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with pytest.raises(ValueError, match="doesn't"):
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reg.get_booster().get_split_value_histogram("3", bins=5)
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def test_split_value_histograms(self):
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self.run_split_value_histograms("approx")
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