521 lines
17 KiB
Python
521 lines
17 KiB
Python
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from functools import partial
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import numpy as np
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from numpy.testing import assert_array_equal
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from sklearn.base import (
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BaseEstimator,
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ClassifierMixin,
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MetaEstimatorMixin,
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RegressorMixin,
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TransformerMixin,
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clone,
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)
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from sklearn.metrics._scorer import _Scorer, mean_squared_error
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from sklearn.model_selection import BaseCrossValidator
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from sklearn.model_selection._split import GroupsConsumerMixin
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from sklearn.utils._metadata_requests import (
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SIMPLE_METHODS,
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)
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from sklearn.utils.metadata_routing import (
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MetadataRouter,
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MethodMapping,
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process_routing,
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)
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from sklearn.utils.multiclass import _check_partial_fit_first_call
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def record_metadata(obj, method, record_default=True, **kwargs):
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"""Utility function to store passed metadata to a method.
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If record_default is False, kwargs whose values are "default" are skipped.
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This is so that checks on keyword arguments whose default was not changed
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are skipped.
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"""
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if not hasattr(obj, "_records"):
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obj._records = {}
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if not record_default:
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kwargs = {
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key: val
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for key, val in kwargs.items()
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if not isinstance(val, str) or (val != "default")
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}
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obj._records[method] = kwargs
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def check_recorded_metadata(obj, method, split_params=tuple(), **kwargs):
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"""Check whether the expected metadata is passed to the object's method.
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Parameters
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----------
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obj : estimator object
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sub-estimator to check routed params for
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method : str
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sub-estimator's method where metadata is routed to
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split_params : tuple, default=empty
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specifies any parameters which are to be checked as being a subset
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of the original values
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**kwargs : dict
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passed metadata
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"""
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records = getattr(obj, "_records", dict()).get(method, dict())
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assert set(kwargs.keys()) == set(
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records.keys()
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), f"Expected {kwargs.keys()} vs {records.keys()}"
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for key, value in kwargs.items():
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recorded_value = records[key]
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# The following condition is used to check for any specified parameters
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# being a subset of the original values
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if key in split_params and recorded_value is not None:
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assert np.isin(recorded_value, value).all()
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else:
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if isinstance(recorded_value, np.ndarray):
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assert_array_equal(recorded_value, value)
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else:
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assert recorded_value is value, f"Expected {recorded_value} vs {value}"
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record_metadata_not_default = partial(record_metadata, record_default=False)
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def assert_request_is_empty(metadata_request, exclude=None):
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"""Check if a metadata request dict is empty.
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One can exclude a method or a list of methods from the check using the
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``exclude`` parameter. If metadata_request is a MetadataRouter, then
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``exclude`` can be of the form ``{"object" : [method, ...]}``.
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"""
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if isinstance(metadata_request, MetadataRouter):
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for name, route_mapping in metadata_request:
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if exclude is not None and name in exclude:
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_exclude = exclude[name]
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else:
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_exclude = None
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assert_request_is_empty(route_mapping.router, exclude=_exclude)
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return
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exclude = [] if exclude is None else exclude
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for method in SIMPLE_METHODS:
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if method in exclude:
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continue
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mmr = getattr(metadata_request, method)
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props = [
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prop
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for prop, alias in mmr.requests.items()
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if isinstance(alias, str) or alias is not None
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]
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assert not props
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def assert_request_equal(request, dictionary):
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for method, requests in dictionary.items():
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mmr = getattr(request, method)
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assert mmr.requests == requests
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empty_methods = [method for method in SIMPLE_METHODS if method not in dictionary]
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for method in empty_methods:
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assert not len(getattr(request, method).requests)
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class _Registry(list):
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# This list is used to get a reference to the sub-estimators, which are not
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# necessarily stored on the metaestimator. We need to override __deepcopy__
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# because the sub-estimators are probably cloned, which would result in a
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# new copy of the list, but we need copy and deep copy both to return the
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# same instance.
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def __deepcopy__(self, memo):
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return self
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def __copy__(self):
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return self
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class ConsumingRegressor(RegressorMixin, BaseEstimator):
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"""A regressor consuming metadata.
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Parameters
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----------
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registry : list, default=None
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If a list, the estimator will append itself to the list in order to have
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a reference to the estimator later on. Since that reference is not
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required in all tests, registration can be skipped by leaving this value
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as None.
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"""
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def __init__(self, registry=None):
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self.registry = registry
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def partial_fit(self, X, y, sample_weight="default", metadata="default"):
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if self.registry is not None:
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self.registry.append(self)
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record_metadata_not_default(
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self, "partial_fit", sample_weight=sample_weight, metadata=metadata
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)
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return self
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def fit(self, X, y, sample_weight="default", metadata="default"):
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if self.registry is not None:
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self.registry.append(self)
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record_metadata_not_default(
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self, "fit", sample_weight=sample_weight, metadata=metadata
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)
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return self
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def predict(self, X, y=None, sample_weight="default", metadata="default"):
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record_metadata_not_default(
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self, "predict", sample_weight=sample_weight, metadata=metadata
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)
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return np.zeros(shape=(len(X),))
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def score(self, X, y, sample_weight="default", metadata="default"):
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record_metadata_not_default(
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self, "score", sample_weight=sample_weight, metadata=metadata
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)
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return 1
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class NonConsumingClassifier(ClassifierMixin, BaseEstimator):
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"""A classifier which accepts no metadata on any method."""
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def __init__(self, alpha=0.0):
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self.alpha = alpha
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def fit(self, X, y):
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self.classes_ = np.unique(y)
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return self
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def partial_fit(self, X, y, classes=None):
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return self
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def decision_function(self, X):
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return self.predict(X)
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def predict(self, X):
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y_pred = np.empty(shape=(len(X),))
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y_pred[: len(X) // 2] = 0
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y_pred[len(X) // 2 :] = 1
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return y_pred
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class NonConsumingRegressor(RegressorMixin, BaseEstimator):
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"""A classifier which accepts no metadata on any method."""
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def fit(self, X, y):
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return self
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def partial_fit(self, X, y):
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return self
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def predict(self, X):
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return np.ones(len(X)) # pragma: no cover
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class ConsumingClassifier(ClassifierMixin, BaseEstimator):
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"""A classifier consuming metadata.
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Parameters
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----------
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registry : list, default=None
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If a list, the estimator will append itself to the list in order to have
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a reference to the estimator later on. Since that reference is not
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required in all tests, registration can be skipped by leaving this value
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as None.
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alpha : float, default=0
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This parameter is only used to test the ``*SearchCV`` objects, and
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doesn't do anything.
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"""
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def __init__(self, registry=None, alpha=0.0):
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self.alpha = alpha
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self.registry = registry
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def partial_fit(
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self, X, y, classes=None, sample_weight="default", metadata="default"
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):
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if self.registry is not None:
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self.registry.append(self)
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record_metadata_not_default(
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self, "partial_fit", sample_weight=sample_weight, metadata=metadata
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)
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_check_partial_fit_first_call(self, classes)
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return self
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def fit(self, X, y, sample_weight="default", metadata="default"):
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if self.registry is not None:
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self.registry.append(self)
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record_metadata_not_default(
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self, "fit", sample_weight=sample_weight, metadata=metadata
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)
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self.classes_ = np.unique(y)
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return self
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def predict(self, X, sample_weight="default", metadata="default"):
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record_metadata_not_default(
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self, "predict", sample_weight=sample_weight, metadata=metadata
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)
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y_score = np.empty(shape=(len(X),), dtype="int8")
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y_score[len(X) // 2 :] = 0
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y_score[: len(X) // 2] = 1
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return y_score
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def predict_proba(self, X, sample_weight="default", metadata="default"):
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record_metadata_not_default(
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self, "predict_proba", sample_weight=sample_weight, metadata=metadata
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)
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y_proba = np.empty(shape=(len(X), 2))
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y_proba[: len(X) // 2, :] = np.asarray([1.0, 0.0])
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y_proba[len(X) // 2 :, :] = np.asarray([0.0, 1.0])
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return y_proba
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def predict_log_proba(self, X, sample_weight="default", metadata="default"):
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pass # pragma: no cover
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# uncomment when needed
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# record_metadata_not_default(
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# self, "predict_log_proba", sample_weight=sample_weight, metadata=metadata
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# )
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# return np.zeros(shape=(len(X), 2))
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def decision_function(self, X, sample_weight="default", metadata="default"):
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record_metadata_not_default(
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self, "predict_proba", sample_weight=sample_weight, metadata=metadata
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)
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y_score = np.empty(shape=(len(X),))
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y_score[len(X) // 2 :] = 0
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y_score[: len(X) // 2] = 1
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return y_score
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# uncomment when needed
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# def score(self, X, y, sample_weight="default", metadata="default"):
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# record_metadata_not_default(
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# self, "score", sample_weight=sample_weight, metadata=metadata
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# )
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# return 1
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class ConsumingTransformer(TransformerMixin, BaseEstimator):
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"""A transformer which accepts metadata on fit and transform.
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Parameters
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----------
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registry : list, default=None
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If a list, the estimator will append itself to the list in order to have
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a reference to the estimator later on. Since that reference is not
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required in all tests, registration can be skipped by leaving this value
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as None.
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"""
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def __init__(self, registry=None):
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self.registry = registry
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def fit(self, X, y=None, sample_weight=None, metadata=None):
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if self.registry is not None:
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self.registry.append(self)
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record_metadata_not_default(
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self, "fit", sample_weight=sample_weight, metadata=metadata
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)
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return self
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def transform(self, X, sample_weight=None, metadata=None):
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record_metadata(
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self, "transform", sample_weight=sample_weight, metadata=metadata
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)
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return X
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def fit_transform(self, X, y, sample_weight=None, metadata=None):
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# implementing ``fit_transform`` is necessary since
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# ``TransformerMixin.fit_transform`` doesn't route any metadata to
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# ``transform``, while here we want ``transform`` to receive
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# ``sample_weight`` and ``metadata``.
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record_metadata(
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self, "fit_transform", sample_weight=sample_weight, metadata=metadata
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)
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return self.fit(X, y, sample_weight=sample_weight, metadata=metadata).transform(
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X, sample_weight=sample_weight, metadata=metadata
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)
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def inverse_transform(self, X, sample_weight=None, metadata=None):
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record_metadata(
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self, "inverse_transform", sample_weight=sample_weight, metadata=metadata
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)
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return X
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class ConsumingNoFitTransformTransformer(BaseEstimator):
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"""A metadata consuming transformer that doesn't inherit from
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TransformerMixin, and thus doesn't implement `fit_transform`. Note that
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TransformerMixin's `fit_transform` doesn't route metadata to `transform`."""
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def __init__(self, registry=None):
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self.registry = registry
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def fit(self, X, y=None, sample_weight=None, metadata=None):
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if self.registry is not None:
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self.registry.append(self)
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record_metadata(self, "fit", sample_weight=sample_weight, metadata=metadata)
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return self
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def transform(self, X, sample_weight=None, metadata=None):
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record_metadata(
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self, "transform", sample_weight=sample_weight, metadata=metadata
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)
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return X
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class ConsumingScorer(_Scorer):
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def __init__(self, registry=None):
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super().__init__(
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score_func=mean_squared_error, sign=1, kwargs={}, response_method="predict"
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)
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self.registry = registry
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def _score(self, method_caller, clf, X, y, **kwargs):
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if self.registry is not None:
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self.registry.append(self)
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record_metadata_not_default(self, "score", **kwargs)
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||
|
|
sample_weight = kwargs.get("sample_weight", None)
|
||
|
|
return super()._score(method_caller, clf, X, y, sample_weight=sample_weight)
|
||
|
|
|
||
|
|
|
||
|
|
class ConsumingSplitter(GroupsConsumerMixin, BaseCrossValidator):
|
||
|
|
def __init__(self, registry=None):
|
||
|
|
self.registry = registry
|
||
|
|
|
||
|
|
def split(self, X, y=None, groups="default", metadata="default"):
|
||
|
|
if self.registry is not None:
|
||
|
|
self.registry.append(self)
|
||
|
|
|
||
|
|
record_metadata_not_default(self, "split", groups=groups, metadata=metadata)
|
||
|
|
|
||
|
|
split_index = len(X) // 2
|
||
|
|
train_indices = list(range(0, split_index))
|
||
|
|
test_indices = list(range(split_index, len(X)))
|
||
|
|
yield test_indices, train_indices
|
||
|
|
yield train_indices, test_indices
|
||
|
|
|
||
|
|
def get_n_splits(self, X=None, y=None, groups=None, metadata=None):
|
||
|
|
return 2
|
||
|
|
|
||
|
|
def _iter_test_indices(self, X=None, y=None, groups=None):
|
||
|
|
split_index = len(X) // 2
|
||
|
|
train_indices = list(range(0, split_index))
|
||
|
|
test_indices = list(range(split_index, len(X)))
|
||
|
|
yield test_indices
|
||
|
|
yield train_indices
|
||
|
|
|
||
|
|
|
||
|
|
class MetaRegressor(MetaEstimatorMixin, RegressorMixin, BaseEstimator):
|
||
|
|
"""A meta-regressor which is only a router."""
|
||
|
|
|
||
|
|
def __init__(self, estimator):
|
||
|
|
self.estimator = estimator
|
||
|
|
|
||
|
|
def fit(self, X, y, **fit_params):
|
||
|
|
params = process_routing(self, "fit", **fit_params)
|
||
|
|
self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
|
||
|
|
|
||
|
|
def get_metadata_routing(self):
|
||
|
|
router = MetadataRouter(owner=self.__class__.__name__).add(
|
||
|
|
estimator=self.estimator,
|
||
|
|
method_mapping=MethodMapping().add(caller="fit", callee="fit"),
|
||
|
|
)
|
||
|
|
return router
|
||
|
|
|
||
|
|
|
||
|
|
class WeightedMetaRegressor(MetaEstimatorMixin, RegressorMixin, BaseEstimator):
|
||
|
|
"""A meta-regressor which is also a consumer."""
|
||
|
|
|
||
|
|
def __init__(self, estimator, registry=None):
|
||
|
|
self.estimator = estimator
|
||
|
|
self.registry = registry
|
||
|
|
|
||
|
|
def fit(self, X, y, sample_weight=None, **fit_params):
|
||
|
|
if self.registry is not None:
|
||
|
|
self.registry.append(self)
|
||
|
|
|
||
|
|
record_metadata(self, "fit", sample_weight=sample_weight)
|
||
|
|
params = process_routing(self, "fit", sample_weight=sample_weight, **fit_params)
|
||
|
|
self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def predict(self, X, **predict_params):
|
||
|
|
params = process_routing(self, "predict", **predict_params)
|
||
|
|
return self.estimator_.predict(X, **params.estimator.predict)
|
||
|
|
|
||
|
|
def get_metadata_routing(self):
|
||
|
|
router = (
|
||
|
|
MetadataRouter(owner=self.__class__.__name__)
|
||
|
|
.add_self_request(self)
|
||
|
|
.add(
|
||
|
|
estimator=self.estimator,
|
||
|
|
method_mapping=MethodMapping()
|
||
|
|
.add(caller="fit", callee="fit")
|
||
|
|
.add(caller="predict", callee="predict"),
|
||
|
|
)
|
||
|
|
)
|
||
|
|
return router
|
||
|
|
|
||
|
|
|
||
|
|
class WeightedMetaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
|
||
|
|
"""A meta-estimator which also consumes sample_weight itself in ``fit``."""
|
||
|
|
|
||
|
|
def __init__(self, estimator, registry=None):
|
||
|
|
self.estimator = estimator
|
||
|
|
self.registry = registry
|
||
|
|
|
||
|
|
def fit(self, X, y, sample_weight=None, **kwargs):
|
||
|
|
if self.registry is not None:
|
||
|
|
self.registry.append(self)
|
||
|
|
|
||
|
|
record_metadata(self, "fit", sample_weight=sample_weight)
|
||
|
|
params = process_routing(self, "fit", sample_weight=sample_weight, **kwargs)
|
||
|
|
self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def get_metadata_routing(self):
|
||
|
|
router = (
|
||
|
|
MetadataRouter(owner=self.__class__.__name__)
|
||
|
|
.add_self_request(self)
|
||
|
|
.add(
|
||
|
|
estimator=self.estimator,
|
||
|
|
method_mapping=MethodMapping().add(caller="fit", callee="fit"),
|
||
|
|
)
|
||
|
|
)
|
||
|
|
return router
|
||
|
|
|
||
|
|
|
||
|
|
class MetaTransformer(MetaEstimatorMixin, TransformerMixin, BaseEstimator):
|
||
|
|
"""A simple meta-transformer."""
|
||
|
|
|
||
|
|
def __init__(self, transformer):
|
||
|
|
self.transformer = transformer
|
||
|
|
|
||
|
|
def fit(self, X, y=None, **fit_params):
|
||
|
|
params = process_routing(self, "fit", **fit_params)
|
||
|
|
self.transformer_ = clone(self.transformer).fit(X, y, **params.transformer.fit)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def transform(self, X, y=None, **transform_params):
|
||
|
|
params = process_routing(self, "transform", **transform_params)
|
||
|
|
return self.transformer_.transform(X, **params.transformer.transform)
|
||
|
|
|
||
|
|
def get_metadata_routing(self):
|
||
|
|
return MetadataRouter(owner=self.__class__.__name__).add(
|
||
|
|
transformer=self.transformer,
|
||
|
|
method_mapping=MethodMapping()
|
||
|
|
.add(caller="fit", callee="fit")
|
||
|
|
.add(caller="transform", callee="transform"),
|
||
|
|
)
|