forked from moreo/QuaPy
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
from abc import ABCMeta, abstractmethod
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from quapy.data import LabelledCollection
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# Base Quantifier abstract class
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# ------------------------------------
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class BaseQuantifier(metaclass=ABCMeta):
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@abstractmethod
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def fit(self, data: LabelledCollection): ...
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@abstractmethod
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def quantify(self, instances): ...
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@abstractmethod
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def set_params(self, **parameters): ...
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@abstractmethod
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def get_params(self, deep=True): ...
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# these methods allows meta-learners to reimplement the decision based on their constituents, and not
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# based on class structure
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@property
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def binary(self):
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return False
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@property
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def aggregative(self):
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return False
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@property
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def probabilistic(self):
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return False
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class BinaryQuantifier(BaseQuantifier):
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def _check_binary(self, data: LabelledCollection, quantifier_name):
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assert data.binary, f'{quantifier_name} works only on problems of binary classification. ' \
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f'Use the class OneVsAll to enable {quantifier_name} work on single-label data.'
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@property
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def binary(self):
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return True
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def isbinary(model:BaseQuantifier):
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return model.binary
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def isaggregative(model:BaseQuantifier):
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return model.aggregative
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def isprobabilistic(model:BaseQuantifier):
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return model.probabilistic
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# class OneVsAll:
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# """
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# Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary
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# quantifier for each class, and then l1-normalizes the outputs so that the class prevelences sum up to 1.
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# """
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#
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# def __init__(self, binary_method, n_jobs=-1):
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# self.binary_method = binary_method
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# self.n_jobs = n_jobs
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#
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# def fit(self, data: LabelledCollection, **kwargs):
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# assert not data.binary, f'{self.__class__.__name__} expect non-binary data'
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# assert isinstance(self.binary_method, BaseQuantifier), f'{self.binary_method} does not seem to be a Quantifier'
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# self.class_method = {c: deepcopy(self.binary_method) for c in data.classes_}
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# Parallel(n_jobs=self.n_jobs, backend='threading')(
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# delayed(self._delayed_binary_fit)(c, self.class_method, data, **kwargs) for c in data.classes_
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# )
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# return self
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#
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# def quantify(self, X, *args):
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# prevalences = np.asarray(
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# Parallel(n_jobs=self.n_jobs, backend='threading')(
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# delayed(self._delayed_binary_predict)(c, self.class_method, X) for c in self.classes
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# )
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# )
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# return F.normalize_prevalence(prevalences)
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#
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# @property
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# def classes(self):
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# return sorted(self.class_method.keys())
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#
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# def set_params(self, **parameters):
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# self.binary_method.set_params(**parameters)
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#
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# def get_params(self, deep=True):
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# return self.binary_method.get_params()
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#
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# def _delayed_binary_predict(self, c, learners, X):
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# return learners[c].quantify(X)[:,1] # the mean is the estimation for the positive class prevalence
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#
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# def _delayed_binary_fit(self, c, learners, data, **kwargs):
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# bindata = LabelledCollection(data.instances, data.labels == c, n_classes=2)
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# learners[c].fit(bindata, **kwargs)
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