forked from moreo/QuaPy
110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
from abc import ABCMeta, abstractmethod
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from copy import deepcopy
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from joblib import Parallel, delayed
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from sklearn.base import BaseEstimator
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import quapy as qp
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from quapy.data import LabelledCollection
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import numpy as np
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# Base Quantifier abstract class
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# ------------------------------------
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class BaseQuantifier(BaseEstimator):
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"""
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Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on
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:class:`quapy.data.base.LabelledCollection`, the method :meth:`quantify`, and the :meth:`set_params` and
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:meth:`get_params` for model selection (see :meth:`quapy.model_selection.GridSearchQ`)
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"""
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@abstractmethod
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def fit(self, data: LabelledCollection):
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"""
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Trains a quantifier.
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:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
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:return: self
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"""
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...
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@abstractmethod
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def quantify(self, instances):
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"""
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Generate class prevalence estimates for the sample's instances
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:param instances: array-like
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:return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.
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"""
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...
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class BinaryQuantifier(BaseQuantifier):
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"""
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Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes
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(typically, to be interpreted as one class and its complement).
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"""
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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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class OneVsAll:
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pass
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def newOneVsAll(binary_quantifier, n_jobs=None):
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assert isinstance(binary_quantifier, BaseQuantifier), \
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f'{binary_quantifier} does not seem to be a Quantifier'
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if isinstance(binary_quantifier, qp.method.aggregative.AggregativeQuantifier):
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return qp.method.aggregative.OneVsAllAggregative(binary_quantifier, n_jobs)
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else:
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return OneVsAllGeneric(binary_quantifier, n_jobs)
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class OneVsAllGeneric(OneVsAll, BaseQuantifier):
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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 prevelence values sum up to 1.
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"""
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def __init__(self, binary_quantifier, n_jobs=None):
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assert isinstance(binary_quantifier, BaseQuantifier), \
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f'{binary_quantifier} does not seem to be a Quantifier'
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if isinstance(binary_quantifier, qp.method.aggregative.AggregativeQuantifier):
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print('[warning] the quantifier seems to be an instance of qp.method.aggregative.AggregativeQuantifier; '
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f'you might prefer instantiating {qp.method.aggregative.OneVsAllAggregative.__name__}')
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self.binary_quantifier = binary_quantifier
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self.n_jobs = qp._get_njobs(n_jobs)
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def fit(self, data: LabelledCollection, fit_classifier=True):
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assert not data.binary, f'{self.__class__.__name__} expect non-binary data'
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assert fit_classifier == True, 'fit_classifier must be True'
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self.dict_binary_quantifiers = {c: deepcopy(self.binary_quantifier) for c in data.classes_}
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self._parallel(self._delayed_binary_fit, data)
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return self
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def _parallel(self, func, *args, **kwargs):
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return np.asarray(
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Parallel(n_jobs=self.n_jobs, backend='threading')(
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delayed(func)(c, *args, **kwargs) for c in self.classes_
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)
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)
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def quantify(self, instances):
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prevalences = self._parallel(self._delayed_binary_predict, instances)
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return qp.functional.normalize_prevalence(prevalences)
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@property
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def classes_(self):
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return sorted(self.dict_binary_quantifiers.keys())
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def _delayed_binary_predict(self, c, X):
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return self.dict_binary_quantifiers[c].quantify(X)[1]
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def _delayed_binary_fit(self, c, data):
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bindata = LabelledCollection(data.instances, data.labels == c, classes=[False, True])
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self.dict_binary_quantifiers[c].fit(bindata)
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