Make test for categorical data deterministic. (#8080)
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@ -87,6 +87,7 @@ class TestGPUUpdaters:
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def test_categorical_missing(self, rows, cols, cats):
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def test_categorical_missing(self, rows, cols, cats):
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self.cputest.run_categorical_missing(rows, cols, cats, "gpu_hist")
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self.cputest.run_categorical_missing(rows, cols, cats, "gpu_hist")
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@pytest.mark.skipif(**tm.no_pandas())
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def test_max_cat(self) -> None:
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def test_max_cat(self) -> None:
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self.cputest.run_max_cat("gpu_hist")
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self.cputest.run_max_cat("gpu_hist")
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@ -205,10 +205,13 @@ class TestTreeMethod:
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def run_max_cat(self, tree_method: str) -> None:
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def run_max_cat(self, tree_method: str) -> None:
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"""Test data with size smaller than number of categories."""
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"""Test data with size smaller than number of categories."""
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import pandas as pd
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import pandas as pd
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rng = np.random.default_rng(0)
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n_cat = 100
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n_cat = 100
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n = 5
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n = 5
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X = pd.Series(
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X = pd.Series(
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["".join(choice(ascii_lowercase) for i in range(3)) for i in range(n_cat)],
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["".join(rng.choice(list(ascii_lowercase), size=3)) for i in range(n_cat)],
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dtype="category",
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dtype="category",
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)[:n].to_frame()
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)[:n].to_frame()
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@ -222,6 +225,7 @@ class TestTreeMethod:
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assert tm.non_increasing(reg.evals_result()["validation_0"]["rmse"])
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assert tm.non_increasing(reg.evals_result()["validation_0"]["rmse"])
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@pytest.mark.parametrize("tree_method", ["hist", "approx"])
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@pytest.mark.parametrize("tree_method", ["hist", "approx"])
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@pytest.mark.skipif(**tm.no_pandas())
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def test_max_cat(self, tree_method) -> None:
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def test_max_cat(self, tree_method) -> None:
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self.run_max_cat(tree_method)
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self.run_max_cat(tree_method)
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