318 lines
11 KiB
Python
318 lines
11 KiB
Python
"""Utilities for meta-estimators"""
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# Author: Joel Nothman
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# Andreas Mueller
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# License: BSD
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from typing import List, Any
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import warnings
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from abc import ABCMeta, abstractmethod
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from operator import attrgetter
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from functools import update_wrapper
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import numpy as np
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from contextlib import suppress
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from ..utils import _safe_indexing
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from ..utils._tags import _safe_tags
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from ..base import BaseEstimator
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__all__ = ["available_if", "if_delegate_has_method"]
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class _BaseComposition(BaseEstimator, metaclass=ABCMeta):
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"""Handles parameter management for classifiers composed of named estimators."""
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steps: List[Any]
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@abstractmethod
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def __init__(self):
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pass
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def _get_params(self, attr, deep=True):
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out = super().get_params(deep=deep)
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if not deep:
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return out
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estimators = getattr(self, attr)
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try:
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out.update(estimators)
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except (TypeError, ValueError):
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# Ignore TypeError for cases where estimators is not a list of
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# (name, estimator) and ignore ValueError when the list is not
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# formatted correctly. This is to prevent errors when calling
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# `set_params`. `BaseEstimator.set_params` calls `get_params` which
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# can error for invalid values for `estimators`.
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return out
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for name, estimator in estimators:
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if hasattr(estimator, "get_params"):
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for key, value in estimator.get_params(deep=True).items():
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out["%s__%s" % (name, key)] = value
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return out
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def _set_params(self, attr, **params):
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# Ensure strict ordering of parameter setting:
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# 1. All steps
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if attr in params:
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setattr(self, attr, params.pop(attr))
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# 2. Replace items with estimators in params
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items = getattr(self, attr)
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if isinstance(items, list) and items:
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# Get item names used to identify valid names in params
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# `zip` raises a TypeError when `items` does not contains
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# elements of length 2
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with suppress(TypeError):
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item_names, _ = zip(*items)
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for name in list(params.keys()):
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if "__" not in name and name in item_names:
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self._replace_estimator(attr, name, params.pop(name))
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# 3. Step parameters and other initialisation arguments
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super().set_params(**params)
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return self
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def _replace_estimator(self, attr, name, new_val):
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# assumes `name` is a valid estimator name
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new_estimators = list(getattr(self, attr))
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for i, (estimator_name, _) in enumerate(new_estimators):
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if estimator_name == name:
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new_estimators[i] = (name, new_val)
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break
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setattr(self, attr, new_estimators)
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def _validate_names(self, names):
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if len(set(names)) != len(names):
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raise ValueError("Names provided are not unique: {0!r}".format(list(names)))
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invalid_names = set(names).intersection(self.get_params(deep=False))
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if invalid_names:
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raise ValueError(
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"Estimator names conflict with constructor arguments: {0!r}".format(
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sorted(invalid_names)
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)
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)
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invalid_names = [name for name in names if "__" in name]
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if invalid_names:
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raise ValueError(
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"Estimator names must not contain __: got {0!r}".format(invalid_names)
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)
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class _AvailableIfDescriptor:
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"""Implements a conditional property using the descriptor protocol.
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Using this class to create a decorator will raise an ``AttributeError``
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if check(self) returns a falsey value. Note that if check raises an error
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this will also result in hasattr returning false.
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See https://docs.python.org/3/howto/descriptor.html for an explanation of
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descriptors.
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"""
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def __init__(self, fn, check, attribute_name):
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self.fn = fn
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self.check = check
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self.attribute_name = attribute_name
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# update the docstring of the descriptor
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update_wrapper(self, fn)
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def __get__(self, obj, owner=None):
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attr_err = AttributeError(
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f"This {repr(owner.__name__)} has no attribute {repr(self.attribute_name)}"
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)
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if obj is not None:
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# delegate only on instances, not the classes.
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# this is to allow access to the docstrings.
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if not self.check(obj):
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raise attr_err
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# lambda, but not partial, allows help() to work with update_wrapper
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out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) # noqa
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else:
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def fn(*args, **kwargs):
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if not self.check(args[0]):
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raise attr_err
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return self.fn(*args, **kwargs)
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# This makes it possible to use the decorated method as an unbound method,
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# for instance when monkeypatching.
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out = lambda *args, **kwargs: fn(*args, **kwargs) # noqa
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# update the docstring of the returned function
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update_wrapper(out, self.fn)
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return out
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def available_if(check):
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"""An attribute that is available only if check returns a truthy value
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Parameters
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----------
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check : callable
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When passed the object with the decorated method, this should return
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a truthy value if the attribute is available, and either return False
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or raise an AttributeError if not available.
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Examples
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--------
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>>> from sklearn.utils.metaestimators import available_if
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>>> class HelloIfEven:
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... def __init__(self, x):
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... self.x = x
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...
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... def _x_is_even(self):
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... return self.x % 2 == 0
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...
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... @available_if(_x_is_even)
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... def say_hello(self):
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... print("Hello")
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...
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>>> obj = HelloIfEven(1)
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>>> hasattr(obj, "say_hello")
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False
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>>> obj.x = 2
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>>> hasattr(obj, "say_hello")
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True
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>>> obj.say_hello()
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Hello
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"""
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return lambda fn: _AvailableIfDescriptor(fn, check, attribute_name=fn.__name__)
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# TODO(1.3) remove
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class _IffHasAttrDescriptor(_AvailableIfDescriptor):
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"""Implements a conditional property using the descriptor protocol.
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Using this class to create a decorator will raise an ``AttributeError``
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if none of the delegates (specified in ``delegate_names``) is an attribute
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of the base object or the first found delegate does not have an attribute
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``attribute_name``.
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This allows ducktyping of the decorated method based on
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``delegate.attribute_name``. Here ``delegate`` is the first item in
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``delegate_names`` for which ``hasattr(object, delegate) is True``.
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See https://docs.python.org/3/howto/descriptor.html for an explanation of
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descriptors.
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"""
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def __init__(self, fn, delegate_names, attribute_name):
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super().__init__(fn, self._check, attribute_name)
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self.delegate_names = delegate_names
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def _check(self, obj):
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warnings.warn(
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"if_delegate_has_method was deprecated in version 1.1 and will be "
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"removed in version 1.3. Use if_available instead.",
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FutureWarning,
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)
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delegate = None
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for delegate_name in self.delegate_names:
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try:
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delegate = attrgetter(delegate_name)(obj)
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break
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except AttributeError:
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continue
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if delegate is None:
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return False
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# raise original AttributeError
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getattr(delegate, self.attribute_name)
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return True
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# TODO(1.3) remove
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def if_delegate_has_method(delegate):
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"""Create a decorator for methods that are delegated to a sub-estimator
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This enables ducktyping by hasattr returning True according to the
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sub-estimator.
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.. deprecated:: 1.3
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`if_delegate_has_method` is deprecated in version 1.1 and will be removed in
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version 1.3. Use `available_if` instead.
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Parameters
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----------
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delegate : str, list of str or tuple of str
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Name of the sub-estimator that can be accessed as an attribute of the
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base object. If a list or a tuple of names are provided, the first
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sub-estimator that is an attribute of the base object will be used.
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"""
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if isinstance(delegate, list):
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delegate = tuple(delegate)
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if not isinstance(delegate, tuple):
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delegate = (delegate,)
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return lambda fn: _IffHasAttrDescriptor(fn, delegate, attribute_name=fn.__name__)
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def _safe_split(estimator, X, y, indices, train_indices=None):
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"""Create subset of dataset and properly handle kernels.
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Slice X, y according to indices for cross-validation, but take care of
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precomputed kernel-matrices or pairwise affinities / distances.
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If ``estimator._pairwise is True``, X needs to be square and
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we slice rows and columns. If ``train_indices`` is not None,
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we slice rows using ``indices`` (assumed the test set) and columns
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using ``train_indices``, indicating the training set.
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Labels y will always be indexed only along the first axis.
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Parameters
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----------
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estimator : object
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Estimator to determine whether we should slice only rows or rows and
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columns.
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X : array-like, sparse matrix or iterable
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Data to be indexed. If ``estimator._pairwise is True``,
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this needs to be a square array-like or sparse matrix.
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y : array-like, sparse matrix or iterable
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Targets to be indexed.
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indices : array of int
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Rows to select from X and y.
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If ``estimator._pairwise is True`` and ``train_indices is None``
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then ``indices`` will also be used to slice columns.
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train_indices : array of int or None, default=None
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If ``estimator._pairwise is True`` and ``train_indices is not None``,
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then ``train_indices`` will be use to slice the columns of X.
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Returns
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-------
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X_subset : array-like, sparse matrix or list
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Indexed data.
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y_subset : array-like, sparse matrix or list
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Indexed targets.
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"""
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if _safe_tags(estimator, key="pairwise"):
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if not hasattr(X, "shape"):
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raise ValueError(
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"Precomputed kernels or affinity matrices have "
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"to be passed as arrays or sparse matrices."
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)
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# X is a precomputed square kernel matrix
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if X.shape[0] != X.shape[1]:
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raise ValueError("X should be a square kernel matrix")
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if train_indices is None:
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X_subset = X[np.ix_(indices, indices)]
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else:
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X_subset = X[np.ix_(indices, train_indices)]
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else:
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X_subset = _safe_indexing(X, indices)
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if y is not None:
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y_subset = _safe_indexing(y, indices)
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else:
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y_subset = None
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return X_subset, y_subset
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