56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
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from contextlib import suppress
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from docutils import nodes
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from docutils.parsers.rst import Directive
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from sklearn.utils import all_estimators
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from sklearn.utils._testing import SkipTest
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from sklearn.utils.estimator_checks import _construct_instance
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class AllowNanEstimators(Directive):
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@staticmethod
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def make_paragraph_for_estimator_type(estimator_type):
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intro = nodes.list_item()
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intro += nodes.strong(text="Estimators that allow NaN values for type ")
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intro += nodes.literal(text=f"{estimator_type}")
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intro += nodes.strong(text=":\n")
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exists = False
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lst = nodes.bullet_list()
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for name, est_class in all_estimators(type_filter=estimator_type):
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with suppress(SkipTest):
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est = _construct_instance(est_class)
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if est._get_tags().get("allow_nan"):
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module_name = ".".join(est_class.__module__.split(".")[:2])
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class_title = f"{est_class.__name__}"
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class_url = f"./generated/{module_name}.{class_title}.html"
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item = nodes.list_item()
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para = nodes.paragraph()
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para += nodes.reference(
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class_title, text=class_title, internal=False, refuri=class_url
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)
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exists = True
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item += para
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lst += item
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intro += lst
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return [intro] if exists else None
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def run(self):
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lst = nodes.bullet_list()
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for i in ["cluster", "regressor", "classifier", "transformer"]:
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item = self.make_paragraph_for_estimator_type(i)
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if item is not None:
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lst += item
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return [lst]
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def setup(app):
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app.add_directive("allow_nan_estimators", AllowNanEstimators)
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return {
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"version": "0.1",
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"parallel_read_safe": True,
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"parallel_write_safe": True,
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}
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