forked from moreo/QuaPy
110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
from abc import ABCMeta, abstractmethod
|
|
from copy import deepcopy
|
|
|
|
from joblib import Parallel, delayed
|
|
from sklearn.base import BaseEstimator
|
|
|
|
import quapy as qp
|
|
from quapy.data import LabelledCollection
|
|
import numpy as np
|
|
|
|
|
|
# Base Quantifier abstract class
|
|
# ------------------------------------
|
|
class BaseQuantifier(BaseEstimator):
|
|
"""
|
|
Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on
|
|
:class:`quapy.data.base.LabelledCollection`, the method :meth:`quantify`, and the :meth:`set_params` and
|
|
:meth:`get_params` for model selection (see :meth:`quapy.model_selection.GridSearchQ`)
|
|
"""
|
|
|
|
@abstractmethod
|
|
def fit(self, data: LabelledCollection):
|
|
"""
|
|
Trains a quantifier.
|
|
|
|
:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
|
|
:return: self
|
|
"""
|
|
...
|
|
|
|
@abstractmethod
|
|
def quantify(self, instances):
|
|
"""
|
|
Generate class prevalence estimates for the sample's instances
|
|
|
|
:param instances: array-like
|
|
:return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.
|
|
"""
|
|
...
|
|
|
|
|
|
class BinaryQuantifier(BaseQuantifier):
|
|
"""
|
|
Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes
|
|
(typically, to be interpreted as one class and its complement).
|
|
"""
|
|
|
|
def _check_binary(self, data: LabelledCollection, quantifier_name):
|
|
assert data.binary, f'{quantifier_name} works only on problems of binary classification. ' \
|
|
f'Use the class OneVsAll to enable {quantifier_name} work on single-label data.'
|
|
|
|
|
|
class OneVsAll:
|
|
pass
|
|
|
|
|
|
def getOneVsAll(binary_quantifier, n_jobs=None):
|
|
assert isinstance(binary_quantifier, BaseQuantifier), \
|
|
f'{binary_quantifier} does not seem to be a Quantifier'
|
|
if isinstance(binary_quantifier, qp.method.aggregative.AggregativeQuantifier):
|
|
return qp.method.aggregative.OneVsAllAggregative(binary_quantifier, n_jobs)
|
|
else:
|
|
return OneVsAllGeneric(binary_quantifier, n_jobs)
|
|
|
|
|
|
class OneVsAllGeneric(OneVsAll,BaseQuantifier):
|
|
"""
|
|
Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary
|
|
quantifier for each class, and then l1-normalizes the outputs so that the class prevelence values sum up to 1.
|
|
"""
|
|
|
|
def __init__(self, binary_quantifier, n_jobs=None):
|
|
assert isinstance(binary_quantifier, BaseQuantifier), \
|
|
f'{binary_quantifier} does not seem to be a Quantifier'
|
|
if isinstance(binary_quantifier, qp.method.aggregative.AggregativeQuantifier):
|
|
print('[warning] the quantifier seems to be an instance of qp.method.aggregative.AggregativeQuantifier; '
|
|
f'you might prefer instantiating {qp.method.aggregative.OneVsAllAggregative.__name__}')
|
|
self.binary_quantifier = binary_quantifier
|
|
self.n_jobs = qp._get_njobs(n_jobs)
|
|
|
|
def fit(self, data: LabelledCollection, fit_classifier=True):
|
|
assert not data.binary, f'{self.__class__.__name__} expect non-binary data'
|
|
assert fit_classifier == True, 'fit_classifier must be True'
|
|
|
|
self.dict_binary_quantifiers = {c: deepcopy(self.binary_quantifier) for c in data.classes_}
|
|
self._parallel(self._delayed_binary_fit, data)
|
|
return self
|
|
|
|
def _parallel(self, func, *args, **kwargs):
|
|
return np.asarray(
|
|
Parallel(n_jobs=self.n_jobs, backend='threading')(
|
|
delayed(func)(c, *args, **kwargs) for c in self.classes_
|
|
)
|
|
)
|
|
|
|
def quantify(self, instances):
|
|
prevalences = self._parallel(self._delayed_binary_predict, instances)
|
|
return qp.functional.normalize_prevalence(prevalences)
|
|
|
|
@property
|
|
def classes_(self):
|
|
return sorted(self.dict_binary_quantifiers.keys())
|
|
|
|
def _delayed_binary_predict(self, c, X):
|
|
return self.dict_binary_quantifiers[c].quantify(X)[1]
|
|
|
|
def _delayed_binary_fit(self, c, data):
|
|
bindata = LabelledCollection(data.instances, data.labels == c, classes=[False, True])
|
|
self.dict_binary_quantifiers[c].fit(bindata)
|