MNT Switch to absolute imports enforced by ruff (scikit-learn#31847)

Author: lesteve

doc/developers/contributing.rst

@@ -1271,7 +1271,7 @@ Suppose the function ``zero_one`` is renamed to ``zero_one_loss``, we add the de
 :class:`utils.deprecated` to ``zero_one`` and call ``zero_one_loss`` from that
 function::
 
-    from ..utils import deprecated
+    from sklearn.utils import deprecated
 
     def zero_one_loss(y_true, y_pred, normalize=True):
         # actual implementation

doc/developers/develop.rst

@@ -660,13 +660,11 @@ In addition, we add the following guidelines:
 * Avoid multiple statements on one line. Prefer a line return after
   a control flow statement (``if``/``for``).
 
-* Use relative imports for references inside scikit-learn.
+* Use absolute imports
 
-* Unit tests are an exception to the previous rule;
-  they should use absolute imports, exactly as client code would.
-  A corollary is that, if ``sklearn.foo`` exports a class or function
-  that is implemented in ``sklearn.foo.bar.baz``,
-  the test should import it from ``sklearn.foo``.
+* Unit tests should use imports exactly as client code would.
+  If ``sklearn.foo`` exports a class or function that is implemented in
+  ``sklearn.foo.bar.baz``, the test should import it from ``sklearn.foo``.
 
 * **Please don't use** ``import *`` **in any case**. It is considered harmful
   by the `official Python recommendations

pyproject.toml

@@ -137,7 +137,7 @@ preview = true
 # This enables us to use the explicit preview rules that we want only
 explicit-preview-rules = true
 # all rules can be found here: https://docs.astral.sh/ruff/rules/
-extend-select = ["E501", "W", "I", "CPY001", "PGH", "RUF"]
+extend-select = ["E501", "W", "I", "CPY001", "PGH", "RUF", "TID252"]
 ignore=[
     # do not assign a lambda expression, use a def
     "E731",
@@ -175,13 +175,16 @@ ignore=[
 [tool.ruff.lint.flake8-copyright]
 notice-rgx = "\\#\\ Authors:\\ The\\ scikit\\-learn\\ developers\\\r?\\\n\\#\\ SPDX\\-License\\-Identifier:\\ BSD\\-3\\-Clause"
 
+[tool.ruff.lint.flake8-tidy-imports]
+ban-relative-imports = "all"
+
 [tool.ruff.lint.per-file-ignores]
 # It's fine not to put the import at the top of the file in the examples
 # folder.
 "examples/*"=["E402"]
 "doc/conf.py"=["E402"]
 "**/tests/*"=["CPY001"]
-"asv_benchmarks/*"=["CPY001"]
+"asv_benchmarks/*"=["CPY001", "TID252"]
 "benchmarks/*"=["CPY001"]
 "doc/*"=["CPY001"]
 "build_tools/*"=["CPY001"]

sklearn/__check_build/__init__.py

@@ -49,6 +49,6 @@ def raise_build_error(e):
 
 
 try:
-    from ._check_build import check_build  # noqa: F401
+    from sklearn.__check_build._check_build import check_build  # noqa: F401
 except ImportError as e:
     raise_build_error(e)

sklearn/__init__.py

@@ -21,7 +21,7 @@
 import os
 import random
 
-from ._config import config_context, get_config, set_config
+from sklearn._config import config_context, get_config, set_config
 
 logger = logging.getLogger(__name__)
 
@@ -66,12 +66,9 @@
 # It is necessary to do this prior to importing show_versions as the
 # later is linked to the OpenMP runtime to make it possible to introspect
 # it and importing it first would fail if the OpenMP dll cannot be found.
-from . import (  # noqa: F401 E402
-    __check_build,
-    _distributor_init,
-)
-from .base import clone  # noqa: E402
-from .utils._show_versions import show_versions  # noqa: E402
+from sklearn import __check_build, _distributor_init  # noqa: E402 F401
+from sklearn.base import clone  # noqa: E402
+from sklearn.utils._show_versions import show_versions  # noqa: E402
 
 _submodules = [
     "calibration",

sklearn/_config.py

@@ -218,7 +218,7 @@ def set_config(
     if enable_cython_pairwise_dist is not None:
         local_config["enable_cython_pairwise_dist"] = enable_cython_pairwise_dist
     if array_api_dispatch is not None:
-        from .utils._array_api import _check_array_api_dispatch
+        from sklearn.utils._array_api import _check_array_api_dispatch
 
         _check_array_api_dispatch(array_api_dispatch)
         local_config["array_api_dispatch"] = array_api_dispatch

sklearn/_loss/__init__.py

@@ -6,7 +6,7 @@
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-from .loss import (
+from sklearn._loss.loss import (
     AbsoluteError,
     HalfBinomialLoss,
     HalfGammaLoss,

sklearn/_loss/link.py

@@ -12,7 +12,7 @@
 from scipy.special import expit, logit
 from scipy.stats import gmean
 
-from ..utils.extmath import softmax
+from sklearn.utils.extmath import softmax
 
 
 @dataclass

sklearn/_loss/loss.py

@@ -24,9 +24,7 @@
 import numpy as np
 from scipy.special import xlogy
 
-from ..utils import check_scalar
-from ..utils.stats import _weighted_percentile
-from ._loss import (
+from sklearn._loss._loss import (
     CyAbsoluteError,
     CyExponentialLoss,
     CyHalfBinomialLoss,
@@ -39,14 +37,16 @@
     CyHuberLoss,
     CyPinballLoss,
 )
-from .link import (
+from sklearn._loss.link import (
     HalfLogitLink,
     IdentityLink,
     Interval,
     LogitLink,
     LogLink,
     MultinomialLogit,
 )
+from sklearn.utils import check_scalar
+from sklearn.utils.stats import _weighted_percentile
 
 
 # Note: The shape of raw_prediction for multiclass classifications are

sklearn/base.py

@@ -13,26 +13,26 @@
 
 import numpy as np
 
-from . import __version__
-from ._config import config_context, get_config
-from .exceptions import InconsistentVersionWarning
-from .utils._metadata_requests import _MetadataRequester, _routing_enabled
-from .utils._missing import is_scalar_nan
-from .utils._param_validation import validate_parameter_constraints
-from .utils._repr_html.base import ReprHTMLMixin, _HTMLDocumentationLinkMixin
-from .utils._repr_html.estimator import estimator_html_repr
-from .utils._repr_html.params import ParamsDict
-from .utils._set_output import _SetOutputMixin
-from .utils._tags import (
+from sklearn import __version__
+from sklearn._config import config_context, get_config
+from sklearn.exceptions import InconsistentVersionWarning
+from sklearn.utils._metadata_requests import _MetadataRequester, _routing_enabled
+from sklearn.utils._missing import is_scalar_nan
+from sklearn.utils._param_validation import validate_parameter_constraints
+from sklearn.utils._repr_html.base import ReprHTMLMixin, _HTMLDocumentationLinkMixin
+from sklearn.utils._repr_html.estimator import estimator_html_repr
+from sklearn.utils._repr_html.params import ParamsDict
+from sklearn.utils._set_output import _SetOutputMixin
+from sklearn.utils._tags import (
     ClassifierTags,
     RegressorTags,
     Tags,
     TargetTags,
     TransformerTags,
     get_tags,
 )
-from .utils.fixes import _IS_32BIT
-from .utils.validation import (
+from sklearn.utils.fixes import _IS_32BIT
+from sklearn.utils.validation import (
     _check_feature_names_in,
     _generate_get_feature_names_out,
     _is_fitted,
@@ -366,7 +366,7 @@ def __repr__(self, N_CHAR_MAX=700):
         # characters to render. We pass it as an optional parameter to ease
         # the tests.
 
-        from .utils._pprint import _EstimatorPrettyPrinter
+        from sklearn.utils._pprint import _EstimatorPrettyPrinter
 
         N_MAX_ELEMENTS_TO_SHOW = 30  # number of elements to show in sequences
 
@@ -543,7 +543,7 @@ def score(self, X, y, sample_weight=None):
         score : float
             Mean accuracy of ``self.predict(X)`` w.r.t. `y`.
         """
-        from .metrics import accuracy_score
+        from sklearn.metrics import accuracy_score
 
         return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
 
@@ -633,7 +633,7 @@ def score(self, X, y, sample_weight=None):
         :class:`~sklearn.multioutput.MultiOutputRegressor`).
         """
 
-        from .metrics import r2_score
+        from sklearn.metrics import r2_score
 
         y_pred = self.predict(X)
         return r2_score(y, y_pred, sample_weight=sample_weight)