reconnect moved files to git repo

venv/lib/python3.11/site-packages/sklearn/utils/discovery.py

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+"""Utilities to discover scikit-learn objects."""
+
+import inspect
+import pkgutil
+from importlib import import_module
+from operator import itemgetter
+from pathlib import Path
+
+_MODULE_TO_IGNORE = {
+    "tests",
+    "externals",
+    "setup",
+    "conftest",
+    "experimental",
+    "estimator_checks",
+}
+
+
+def all_estimators(type_filter=None):
+    """Get a list of all estimators from `sklearn`.
+
+    This function crawls the module and gets all classes that inherit
+    from BaseEstimator. Classes that are defined in test-modules are not
+    included.
+
+    Parameters
+    ----------
+    type_filter : {"classifier", "regressor", "cluster", "transformer"} \
+            or list of such str, default=None
+        Which kind of estimators should be returned. If None, no filter is
+        applied and all estimators are returned.  Possible values are
+        'classifier', 'regressor', 'cluster' and 'transformer' to get
+        estimators only of these specific types, or a list of these to
+        get the estimators that fit at least one of the types.
+
+    Returns
+    -------
+    estimators : list of tuples
+        List of (name, class), where ``name`` is the class name as string
+        and ``class`` is the actual type of the class.
+
+    Examples
+    --------
+    >>> from sklearn.utils.discovery import all_estimators
+    >>> estimators = all_estimators()
+    >>> type(estimators)
+    <class 'list'>
+    >>> type(estimators[0])
+    <class 'tuple'>
+    >>> estimators[:2]
+    [('ARDRegression', <class 'sklearn.linear_model._bayes.ARDRegression'>),
+     ('AdaBoostClassifier',
+      <class 'sklearn.ensemble._weight_boosting.AdaBoostClassifier'>)]
+    >>> classifiers = all_estimators(type_filter="classifier")
+    >>> classifiers[:2]
+    [('AdaBoostClassifier',
+      <class 'sklearn.ensemble._weight_boosting.AdaBoostClassifier'>),
+     ('BaggingClassifier', <class 'sklearn.ensemble._bagging.BaggingClassifier'>)]
+    >>> regressors = all_estimators(type_filter="regressor")
+    >>> regressors[:2]
+    [('ARDRegression', <class 'sklearn.linear_model._bayes.ARDRegression'>),
+     ('AdaBoostRegressor',
+      <class 'sklearn.ensemble._weight_boosting.AdaBoostRegressor'>)]
+    >>> both = all_estimators(type_filter=["classifier", "regressor"])
+    >>> both[:2]
+    [('ARDRegression', <class 'sklearn.linear_model._bayes.ARDRegression'>),
+     ('AdaBoostClassifier',
+      <class 'sklearn.ensemble._weight_boosting.AdaBoostClassifier'>)]
+    """
+    # lazy import to avoid circular imports from sklearn.base
+    from ..base import (
+        BaseEstimator,
+        ClassifierMixin,
+        ClusterMixin,
+        RegressorMixin,
+        TransformerMixin,
+    )
+    from ._testing import ignore_warnings
+    from .fixes import _IS_PYPY
+
+    def is_abstract(c):
+        if not (hasattr(c, "__abstractmethods__")):
+            return False
+        if not len(c.__abstractmethods__):
+            return False
+        return True
+
+    all_classes = []
+    root = str(Path(__file__).parent.parent)  # sklearn package
+    # Ignore deprecation warnings triggered at import time and from walking
+    # packages
+    with ignore_warnings(category=FutureWarning):
+        for _, module_name, _ in pkgutil.walk_packages(path=[root], prefix="sklearn."):
+            module_parts = module_name.split(".")
+            if (
+                any(part in _MODULE_TO_IGNORE for part in module_parts)
+                or "._" in module_name
+            ):
+                continue
+            module = import_module(module_name)
+            classes = inspect.getmembers(module, inspect.isclass)
+            classes = [
+                (name, est_cls) for name, est_cls in classes if not name.startswith("_")
+            ]
+
+            # TODO: Remove when FeatureHasher is implemented in PYPY
+            # Skips FeatureHasher for PYPY
+            if _IS_PYPY and "feature_extraction" in module_name:
+                classes = [
+                    (name, est_cls)
+                    for name, est_cls in classes
+                    if name == "FeatureHasher"
+                ]
+
+            all_classes.extend(classes)
+
+    all_classes = set(all_classes)
+
+    estimators = [
+        c
+        for c in all_classes
+        if (issubclass(c[1], BaseEstimator) and c[0] != "BaseEstimator")
+    ]
+    # get rid of abstract base classes
+    estimators = [c for c in estimators if not is_abstract(c[1])]
+
+    if type_filter is not None:
+        if not isinstance(type_filter, list):
+            type_filter = [type_filter]
+        else:
+            type_filter = list(type_filter)  # copy
+        filtered_estimators = []
+        filters = {
+            "classifier": ClassifierMixin,
+            "regressor": RegressorMixin,
+            "transformer": TransformerMixin,
+            "cluster": ClusterMixin,
+        }
+        for name, mixin in filters.items():
+            if name in type_filter:
+                type_filter.remove(name)
+                filtered_estimators.extend(
+                    [est for est in estimators if issubclass(est[1], mixin)]
+                )
+        estimators = filtered_estimators
+        if type_filter:
+            raise ValueError(
+                "Parameter type_filter must be 'classifier', "
+                "'regressor', 'transformer', 'cluster' or "
+                "None, got"
+                f" {repr(type_filter)}."
+            )
+
+    # drop duplicates, sort for reproducibility
+    # itemgetter is used to ensure the sort does not extend to the 2nd item of
+    # the tuple
+    return sorted(set(estimators), key=itemgetter(0))
+
+
+def all_displays():
+    """Get a list of all displays from `sklearn`.
+
+    Returns
+    -------
+    displays : list of tuples
+        List of (name, class), where ``name`` is the display class name as
+        string and ``class`` is the actual type of the class.
+
+    Examples
+    --------
+    >>> from sklearn.utils.discovery import all_displays
+    >>> displays = all_displays()
+    >>> displays[0]
+    ('CalibrationDisplay', <class 'sklearn.calibration.CalibrationDisplay'>)
+    """
+    # lazy import to avoid circular imports from sklearn.base
+    from ._testing import ignore_warnings
+
+    all_classes = []
+    root = str(Path(__file__).parent.parent)  # sklearn package
+    # Ignore deprecation warnings triggered at import time and from walking
+    # packages
+    with ignore_warnings(category=FutureWarning):
+        for _, module_name, _ in pkgutil.walk_packages(path=[root], prefix="sklearn."):
+            module_parts = module_name.split(".")
+            if (
+                any(part in _MODULE_TO_IGNORE for part in module_parts)
+                or "._" in module_name
+            ):
+                continue
+            module = import_module(module_name)
+            classes = inspect.getmembers(module, inspect.isclass)
+            classes = [
+                (name, display_class)
+                for name, display_class in classes
+                if not name.startswith("_") and name.endswith("Display")
+            ]
+            all_classes.extend(classes)
+
+    return sorted(set(all_classes), key=itemgetter(0))
+
+
+def _is_checked_function(item):
+    if not inspect.isfunction(item):
+        return False
+
+    if item.__name__.startswith("_"):
+        return False
+
+    mod = item.__module__
+    if not mod.startswith("sklearn.") or mod.endswith("estimator_checks"):
+        return False
+
+    return True
+
+
+def all_functions():
+    """Get a list of all functions from `sklearn`.
+
+    Returns
+    -------
+    functions : list of tuples
+        List of (name, function), where ``name`` is the function name as
+        string and ``function`` is the actual function.
+
+    Examples
+    --------
+    >>> from sklearn.utils.discovery import all_functions
+    >>> functions = all_functions()
+    >>> name, function = functions[0]
+    >>> name
+    'accuracy_score'
+    """
+    # lazy import to avoid circular imports from sklearn.base
+    from ._testing import ignore_warnings
+
+    all_functions = []
+    root = str(Path(__file__).parent.parent)  # sklearn package
+    # Ignore deprecation warnings triggered at import time and from walking
+    # packages
+    with ignore_warnings(category=FutureWarning):
+        for _, module_name, _ in pkgutil.walk_packages(path=[root], prefix="sklearn."):
+            module_parts = module_name.split(".")
+            if (
+                any(part in _MODULE_TO_IGNORE for part in module_parts)
+                or "._" in module_name
+            ):
+                continue
+
+            module = import_module(module_name)
+            functions = inspect.getmembers(module, _is_checked_function)
+            functions = [
+                (func.__name__, func)
+                for name, func in functions
+                if not name.startswith("_")
+            ]
+            all_functions.extend(functions)
+
+    # drop duplicates, sort for reproducibility
+    # itemgetter is used to ensure the sort does not extend to the 2nd item of
+    # the tuple
+    return sorted(set(all_functions), key=itemgetter(0))