cfd2a9f872
- Move test files. - Run spark and dask separately to prevent conflicts. - Gather common code into the testing module.
50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
"""Strategies for updater tests."""
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from typing import cast
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import pytest
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hypothesis = pytest.importorskip("hypothesis")
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from hypothesis import strategies # pylint:disable=wrong-import-position
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exact_parameter_strategy = strategies.fixed_dictionaries(
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{
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"nthread": strategies.integers(1, 4),
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"max_depth": strategies.integers(1, 11),
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"min_child_weight": strategies.floats(0.5, 2.0),
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"alpha": strategies.floats(1e-5, 2.0),
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"lambda": strategies.floats(1e-5, 2.0),
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"eta": strategies.floats(0.01, 0.5),
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"gamma": strategies.floats(1e-5, 2.0),
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"seed": strategies.integers(0, 10),
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# We cannot enable subsampling as the training loss can increase
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# 'subsample': strategies.floats(0.5, 1.0),
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"colsample_bytree": strategies.floats(0.5, 1.0),
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"colsample_bylevel": strategies.floats(0.5, 1.0),
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}
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)
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hist_parameter_strategy = strategies.fixed_dictionaries(
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{
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"max_depth": strategies.integers(1, 11),
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"max_leaves": strategies.integers(0, 1024),
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"max_bin": strategies.integers(2, 512),
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"grow_policy": strategies.sampled_from(["lossguide", "depthwise"]),
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"min_child_weight": strategies.floats(0.5, 2.0),
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# We cannot enable subsampling as the training loss can increase
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# 'subsample': strategies.floats(0.5, 1.0),
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"colsample_bytree": strategies.floats(0.5, 1.0),
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"colsample_bylevel": strategies.floats(0.5, 1.0),
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}
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).filter(
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lambda x: (cast(int, x["max_depth"]) > 0 or cast(int, x["max_leaves"]) > 0)
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and (cast(int, x["max_depth"]) > 0 or x["grow_policy"] == "lossguide")
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)
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cat_parameter_strategy = strategies.fixed_dictionaries(
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{
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"max_cat_to_onehot": strategies.integers(1, 128),
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"max_cat_threshold": strategies.integers(1, 128),
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}
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)
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