File size: 5,346 Bytes
fc0f7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
PREDICTOR_OR_ESTIMATOR_REQUIRED_ERROR_MESSAGE = "One of 'unconstrained_predictor' and " \
"'estimator' need to be passed."
EITHER_PREDICTOR_OR_ESTIMATOR_ERROR_MESSAGE = "Only one of 'unconstrained_predictor' and " \
"'estimator' can be passed."
MISSING_FIT_PREDICT_ERROR_MESSAGE = "The model does not have callable 'fit' or 'predict' methods."
MISSING_PREDICT_ERROR_MESSAGE = "The predictor does not have a callable 'predict' method."
class PostProcessing:
"""Abstract base class for postprocessing approaches for disparity mitigation.
:param unconstrained_predictor: A predictor with a :code:`predict(X)` method that has already
been trained on the training data; the predictor will subsequently be used in the mitigator
for unconstrained predictions; can only be specified if `estimator` is `None`
:type unconstrainted_predictor: predictor
:param estimator: An estimator implementing :code:`fit(X, y)` and :code:`predict(X)` methods
that will be trained on the training data and subsequently used in the mitigator for
unconstrained predictions; can only be specified if `unconstrainted_predictor` is `None`
:type estimator: estimator
:param constraints: the parity constraints to be enforced represented as a string
:type constraints: str
"""
def __init__(self, *, unconstrained_predictor=None, estimator=None,
constraints=None):
if unconstrained_predictor and estimator:
raise ValueError(EITHER_PREDICTOR_OR_ESTIMATOR_ERROR_MESSAGE)
elif unconstrained_predictor:
self._unconstrained_predictor = unconstrained_predictor
self._estimator = None
self._validate_predictor()
elif estimator:
self._unconstrained_predictor = None
self._estimator = estimator
self._validate_estimator()
else:
raise ValueError(PREDICTOR_OR_ESTIMATOR_REQUIRED_ERROR_MESSAGE)
def fit(self, X, y, *, sensitive_features, **kwargs):
"""Fits the model.
The fit is based on training features and labels, sensitive features,
as well as the fairness-unaware predictor or estimator. If an estimator was passed
in the constructor this fit method will call :code:`fit(X, y, **kwargs)` on said
estimator.
:param X: Feature matrix
:type X: numpy.ndarray or pandas.DataFrame
:param y: Label vector
:type y: numpy.ndarray, pandas.DataFrame, pandas.Series, or list
:param sensitive_features: Sensitive features to identify groups by, currently allows
only a single column
:type sensitive_features: currently 1D array as numpy.ndarray, list, pandas.DataFrame,
or pandas.Series
"""
raise NotImplementedError(self.fit.__name__ + " is not implemented")
def predict(self, X, *, sensitive_features):
"""Predict label for each sample in `X` while taking into account sensitive features.
:param X: Feature matrix
:type X: numpy.ndarray or pandas.DataFrame
:param sensitive_features: Sensitive features to identify groups by, currently allows
only a single column
:type sensitive_features: Currently 1D array as numpy.ndarray, list, pandas.DataFrame,
or pandas.Series
:return: predictions in numpy.ndarray
"""
raise NotImplementedError(self.predict.__name__ + " is not implemented")
def _pmf_predict(self, X, *, sensitive_features):
"""Probabilistic mass function.
:param X: Feature matrix
:type X: numpy.ndarray or pandas.DataFrame
:param sensitive_features: Sensitive features to identify groups by, currently allows
only a single column
:type sensitive_features: Currently 1D array as numpy.ndarray, list, pandas.DataFrame,
or pandas.Series
:return: array of tuples with probabilities for predicting 0 or 1, respectively. The sum
of the two numbers in each tuple needs to add up to 1.
:rtype: numpy.ndarray
"""
raise NotImplementedError(self._pmf_predict.__name__ + " is not implemented")
def _validate_predictor(self):
"""Validate that the _unconstrained_predictor member has a predict function."""
predict_function = getattr(self._unconstrained_predictor, "predict", None)
if not predict_function or not callable(predict_function):
raise ValueError(MISSING_PREDICT_ERROR_MESSAGE)
def _validate_estimator(self):
"""Validate that the `_estimator` member has both a fit and a predict function."""
fit_function = getattr(self._estimator, "fit", None)
predict_function = getattr(self._estimator, "predict", None)
if not predict_function or not fit_function or not callable(predict_function) or \
not callable(fit_function):
raise ValueError(MISSING_FIT_PREDICT_ERROR_MESSAGE)
# Ensure that PostProcessing shows up in correct place in documentation
# when it is used as a base class
PostProcessing.__module__ = "fairlearn.postprocessing"
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