219 lines
7.7 KiB
Python
219 lines
7.7 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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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 ..utils import _safe_indexing
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from ..base import BaseEstimator
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from ..base import _is_pairwise
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__all__ = ['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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"""
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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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out.update(estimators)
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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. Step replacement
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items = getattr(self, attr)
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names = []
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if items:
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names, _ = zip(*items)
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for name in list(params.keys()):
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if '__' not in name and name in 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: '
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'{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('Estimator names conflict with constructor '
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'arguments: {0!r}'.format(sorted(invalid_names)))
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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('Estimator names must not contain __: got '
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'{0!r}'.format(invalid_names))
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class _IffHasAttrDescriptor:
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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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self.fn = fn
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self.delegate_names = delegate_names
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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, type=None):
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# raise an AttributeError if the attribute is not present on the object
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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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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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except AttributeError:
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continue
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else:
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getattr(delegate, self.attribute_name)
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break
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else:
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attrgetter(self.delegate_names[-1])(obj)
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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)
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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 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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Parameters
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----------
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delegate : string, list of strings or tuple of strings
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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,
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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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.. deprecated:: 0.24
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The _pairwise attribute is deprecated in 0.24. From 1.1
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(renaming of 0.26) and onward, this function will check for the
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pairwise estimator tag.
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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 _is_pairwise(estimator):
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if not hasattr(X, "shape"):
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raise ValueError("Precomputed kernels or affinity matrices have "
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"to be passed as arrays or sparse matrices.")
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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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