forked from moreo/QuaPy
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
from quapy.data import LabelledCollection
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from .base import BaseQuantifier
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class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier):
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"""
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The `Maximum Likelihood Prevalence Estimation` (MLPE) method is a lazy method that assumes there is no prior
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probability shift between training and test instances (put it other way, that the i.i.d. assumpion holds).
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The estimation of class prevalence values for any test sample is always (i.e., irrespective of the test sample
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itself) the class prevalence seen during training. This method is considered to be a lower-bound quantifier that
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any quantification method should beat.
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"""
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def __init__(self):
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self._classes_ = None
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def fit(self, data: LabelledCollection):
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"""
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Computes the training prevalence and stores it.
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:param data: the training sample
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:return: self
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"""
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self._classes_ = data.classes_
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self.estimated_prevalence = data.prevalence()
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return self
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def quantify(self, instances):
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"""
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Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.
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:param instances: array-like (ignored)
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:return: the class prevalence seen during training
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"""
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return self.estimated_prevalence
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@property
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def classes_(self):
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"""
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Number of classes
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:return: integer
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"""
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return self._classes_
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def get_params(self, deep=True):
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"""
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Does nothing, since this learner has no parameters.
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:param deep: for compatibility with sklearn
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:return: `None`
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"""
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return None
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def set_params(self, **parameters):
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"""
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Does nothing, since this learner has no parameters.
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:param parameters: dictionary of param-value pairs (ignored)
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"""
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pass
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