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from contextlib import suppress
from docutils import nodes
from docutils.parsers.rst import Directive
from sklearn.utils import all_estimators
from sklearn.utils._test_common.instance_generator import _construct_instances
from sklearn.utils._testing import SkipTest
class AllowNanEstimators(Directive):
@staticmethod
def make_paragraph_for_estimator_type(estimator_type):
intro = nodes.list_item()
intro += nodes.strong(text="Estimators that allow NaN values for type ")
intro += nodes.literal(text=f"{estimator_type}")
intro += nodes.strong(text=":\n")
exists = False
lst = nodes.bullet_list()
for name, est_class in all_estimators(type_filter=estimator_type):
with suppress(SkipTest):
# Here we generate the text only for one instance. This directive
# should not be used for meta-estimators where tags depend on the
# sub-estimator.
est = next(_construct_instances(est_class))
if est.__sklearn_tags__().input_tags.allow_nan:
module_name = ".".join(est_class.__module__.split(".")[:2])
class_title = f"{est_class.__name__}"
class_url = f"./generated/{module_name}.{class_title}.html"
item = nodes.list_item()
para = nodes.paragraph()
para += nodes.reference(
class_title, text=class_title, internal=False, refuri=class_url
)
exists = True
item += para
lst += item
intro += lst
return [intro] if exists else None
def run(self):
lst = nodes.bullet_list()
for i in ["cluster", "regressor", "classifier", "transformer"]:
item = self.make_paragraph_for_estimator_type(i)
if item is not None:
lst += item
return [lst]
def setup(app):
app.add_directive("allow_nan_estimators", AllowNanEstimators)
return {
"version": "0.1",
"parallel_read_safe": True,
"parallel_write_safe": True,
}
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