wehub-resource-sync
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
from dataclasses import asdict
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from typing import get_type_hints
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import numpy as np
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import pytest
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from shap.plots import _style
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from shap.plots._style import get_style
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from shap.utils._exceptions import InvalidStyleOptionError
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# TODO: when the API is finalised, these functions will probably be
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# exposed in shap.plots, not shap.plots._style
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def test_default_style():
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default_stype = _style.load_default_style()
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assert configs_are_equal(get_style(), default_stype)
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def test_set_style():
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prev_style = get_style()
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_style.set_style(text_color="green")
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assert get_style().text_color == "green"
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assert not configs_are_equal(get_style(), prev_style)
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def test_style_context():
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original_text_color = get_style().text_color
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with _style.style_context(text_color="green"):
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assert get_style().text_color == "green"
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assert get_style().text_color == original_text_color
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def test_set_style_raises_on_invalid_options():
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with pytest.raises(InvalidStyleOptionError, match="Invalid style config option"):
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_style.set_style(foo="bar") # type: ignore
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def test_style_context_raises_on_invalid_options():
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with pytest.raises(InvalidStyleOptionError, match="Invalid style config option"):
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with _style.style_context(foo="bar"): # type: ignore
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pass
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def test_consistent_style_config_and_style_options():
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# The StyleConfig dataclass should have the same keys and Types as the StyleOptions TypedDict
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style_config_types = get_type_hints(_style.StyleConfig)
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style_options_types = get_type_hints(_style.StyleOptions)
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assert style_config_types == style_options_types
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# Helper functions to compare equality of config dataclasses
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def configs_are_equal(config1: _style.StyleConfig, config2: _style.StyleConfig):
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d1 = asdict(config1)
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d2 = asdict(config2)
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assert d1.keys() == d2.keys()
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return all(_values_are_equivalent(d1[key], d2[key]) for key in d1.keys())
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def _values_are_equivalent(value1, value2):
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value1_is_arraylike = isinstance(value1, (np.ndarray, list, tuple))
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value2_is_arraylike = isinstance(value2, (np.ndarray, list, tuple))
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if value1_is_arraylike and value2_is_arraylike:
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return np.allclose(value1, value2)
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elif value1_is_arraylike != value2_is_arraylike:
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return False
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return value1 == value2
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