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ENH: Display parameters in HTML representation (#30763)
Co-authored-by: Guillaume Lemaitre <guillaume@probabl.ai>
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‎doc/whats_new/upcoming_changes/sklearn.base/30763.enhancement.rst

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@@ -0,0 +1,4 @@
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- :class:`base.BaseEstimator` now has a parameter table added to the
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estimators HTML representation that can be visualized with jupyter.
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By :user:`Guillaume Lemaitre <glemaitre>` and
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:user:`Dea María Léon <DeaMariaLeon>`

‎sklearn/base.py

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@@ -16,9 +16,12 @@
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from . import __version__
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from ._config import config_context, get_config
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from .exceptions import InconsistentVersionWarning
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from .utils._estimator_html_repr import _HTMLDocumentationLinkMixin, estimator_html_repr
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from .utils._metadata_requests import _MetadataRequester, _routing_enabled
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from .utils._missing import is_scalar_nan
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from .utils._param_validation import validate_parameter_constraints
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from .utils._repr_html.base import ReprHTMLMixin, _HTMLDocumentationLinkMixin
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from .utils._repr_html.estimator import estimator_html_repr
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from .utils._repr_html.params import ParamsDict
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from .utils._set_output import _SetOutputMixin
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from .utils._tags import (
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ClassifierTags,
@@ -150,7 +153,7 @@ def _clone_parametrized(estimator, *, safe=True):
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return new_object
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class BaseEstimator(_HTMLDocumentationLinkMixin, _MetadataRequester):
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class BaseEstimator(ReprHTMLMixin, _HTMLDocumentationLinkMixin, _MetadataRequester):
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"""Base class for all estimators in scikit-learn.
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Inheriting from this class provides default implementations of:
@@ -194,6 +197,8 @@ class BaseEstimator(_HTMLDocumentationLinkMixin, _MetadataRequester):
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array([3, 3, 3])
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"""
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_html_repr = estimator_html_repr
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@classmethod
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def _get_param_names(cls):
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"""Get parameter names for the estimator"""
@@ -249,6 +254,64 @@ def get_params(self, deep=True):
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out[key] = value
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return out
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def _get_params_html(self, deep=True):
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"""
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Get parameters for this estimator with a specific HTML representation.
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Parameters
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----------
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deep : bool, default=True
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If True, will return the parameters for this estimator and
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contained subobjects that are estimators.
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Returns
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-------
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params : ParamsDict
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Parameter names mapped to their values. We return a `ParamsDict`
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dictionary, which renders a specific HTML representation in table
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form.
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"""
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out = self.get_params(deep=deep)
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init_func = getattr(self.__init__, "deprecated_original", self.__init__)
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init_default_params = inspect.signature(init_func).parameters
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init_default_params = {
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name: param.default for name, param in init_default_params.items()
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}
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def is_non_default(param_name, param_value):
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"""Finds the parameters that have been set by the user."""
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if param_name not in init_default_params:
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# happens if k is part of a **kwargs
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return True
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if init_default_params[param_name] == inspect._empty:
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# k has no default value
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return True
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# avoid calling repr on nested estimators
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if isinstance(param_value, BaseEstimator) and type(param_value) is not type(
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init_default_params[param_name]
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):
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return True
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if param_value != init_default_params[param_name] and not (
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is_scalar_nan(init_default_params[param_name])
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and is_scalar_nan(param_value)
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):
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return True
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return False
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# reorder the parameters from `self.get_params` using the `__init__`
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# signature
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remaining_params = [name for name in out if name not in init_default_params]
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ordered_out = {name: out[name] for name in init_default_params if name in out}
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ordered_out.update({name: out[name] for name in remaining_params})
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non_default_ls = tuple(
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[name for name, value in ordered_out.items() if is_non_default(name, value)]
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)
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return ParamsDict(ordered_out, non_default=non_default_ls)
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def set_params(self, **params):
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"""Set the parameters of this estimator.
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@@ -409,36 +472,6 @@ class attribute, which is a dictionary `param_name: list of constraints`. See
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caller_name=self.__class__.__name__,
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)
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@property
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def _repr_html_(self):
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"""HTML representation of estimator.
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This is redundant with the logic of `_repr_mimebundle_`. The latter
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should be favored in the long term, `_repr_html_` is only
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implemented for consumers who do not interpret `_repr_mimbundle_`.
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"""
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if get_config()["display"] != "diagram":
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raise AttributeError(
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"_repr_html_ is only defined when the "
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"'display' configuration option is set to "
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"'diagram'"
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)
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return self._repr_html_inner
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def _repr_html_inner(self):
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"""This function is returned by the @property `_repr_html_` to make
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`hasattr(estimator, "_repr_html_") return `True` or `False` depending
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on `get_config()["display"]`.
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"""
433-
return estimator_html_repr(self)
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def _repr_mimebundle_(self, **kwargs):
436-
"""Mime bundle used by jupyter kernels to display estimator"""
437-
output = {"text/plain": repr(self)}
438-
if get_config()["display"] == "diagram":
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output["text/html"] = estimator_html_repr(self)
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return output
441-
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class ClassifierMixin:
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"""Mixin class for all classifiers in scikit-learn.

‎sklearn/compose/_column_transformer.py

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@@ -20,10 +20,10 @@
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from ..pipeline import _fit_transform_one, _name_estimators, _transform_one
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from ..preprocessing import FunctionTransformer
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from ..utils import Bunch
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from ..utils._estimator_html_repr import _VisualBlock
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from ..utils._indexing import _determine_key_type, _get_column_indices, _safe_indexing
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from ..utils._metadata_requests import METHODS
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from ..utils._param_validation import HasMethods, Hidden, Interval, StrOptions
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from ..utils._repr_html.estimator import _VisualBlock
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from ..utils._set_output import (
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_get_container_adapter,
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_get_output_config,

‎sklearn/ensemble/_stacking.py

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@@ -24,8 +24,8 @@
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from ..model_selection import check_cv, cross_val_predict
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from ..preprocessing import LabelEncoder
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from ..utils import Bunch
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from ..utils._estimator_html_repr import _VisualBlock
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from ..utils._param_validation import HasMethods, StrOptions
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from ..utils._repr_html.estimator import _VisualBlock
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from ..utils.metadata_routing import (
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MetadataRouter,
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MethodMapping,

‎sklearn/ensemble/_voting.py

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from ..exceptions import NotFittedError
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from ..preprocessing import LabelEncoder
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from ..utils import Bunch
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from ..utils._estimator_html_repr import _VisualBlock
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from ..utils._param_validation import StrOptions
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from ..utils._repr_html.estimator import _VisualBlock
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from ..utils.metadata_routing import (
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MetadataRouter,
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MethodMapping,

‎sklearn/model_selection/_search.py

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get_scorer_names,
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)
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from ..utils import Bunch, check_random_state
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from ..utils._estimator_html_repr import _VisualBlock
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from ..utils._param_validation import HasMethods, Interval, StrOptions
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from ..utils._repr_html.estimator import _VisualBlock
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from ..utils._tags import get_tags
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from ..utils.metadata_routing import (
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MetadataRouter,

‎sklearn/model_selection/tests/test_search.py

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@@ -2662,21 +2662,21 @@ def test_search_html_repr():
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search_cv = GridSearchCV(pipeline, param_grid=param_grid, refit=False)
26632663
with config_context(display="diagram"):
26642664
repr_html = search_cv._repr_html_()
2665-
assert "<pre>DummyClassifier()</pre>" in repr_html
2665+
assert "<div>DummyClassifier</div>" in repr_html
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26672667
# Fitted with `refit=False` shows the original pipeline
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search_cv.fit(X, y)
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with config_context(display="diagram"):
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repr_html = search_cv._repr_html_()
2671-
assert "<pre>DummyClassifier()</pre>" in repr_html
2671+
assert "<div>DummyClassifier</div>" in repr_html
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26732673
# Fitted with `refit=True` shows the best estimator
26742674
search_cv = GridSearchCV(pipeline, param_grid=param_grid, refit=True)
26752675
search_cv.fit(X, y)
26762676
with config_context(display="diagram"):
26772677
repr_html = search_cv._repr_html_()
2678-
assert "<pre>DummyClassifier()</pre>" not in repr_html
2679-
assert "<pre>LogisticRegression()</pre>" in repr_html
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assert "<div>DummyClassifier</div>" not in repr_html
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assert "<div>LogisticRegression</div>" in repr_html
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26812681

26822682
# Metadata Routing Tests

‎sklearn/pipeline.py

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from .exceptions import NotFittedError
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from .preprocessing import FunctionTransformer
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from .utils import Bunch
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from .utils._estimator_html_repr import _VisualBlock
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from .utils._metadata_requests import METHODS
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from .utils._param_validation import HasMethods, Hidden
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from .utils._repr_html.estimator import _VisualBlock
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from .utils._set_output import (
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_get_container_adapter,
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_safe_set_output,

‎sklearn/preprocessing/_function_transformer.py

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import numpy as np
88

99
from ..base import BaseEstimator, TransformerMixin, _fit_context
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from ..utils._estimator_html_repr import _VisualBlock
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from ..utils._param_validation import StrOptions
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from ..utils._repr_html.estimator import _VisualBlock
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from ..utils._set_output import (
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_get_adapter_from_container,
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_get_output_config,

‎sklearn/tests/test_base.py

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@@ -992,3 +992,11 @@ def predict(self, X, prop=None):
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with warnings.catch_warnings(record=True) as record:
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CustomOutlierDetector().set_predict_request(prop=True).fit_predict([[1]], [1])
994994
assert len(record) == 0
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996+
997+
def test_get_params_html():
998+
"""Check the behaviour of the `_get_params_html` method."""
999+
est = MyEstimator(empty="test")
1000+
1001+
assert est._get_params_html() == {"l1": 0, "empty": "test"}
1002+
assert est._get_params_html().non_default == ("empty",)

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