forked from moreo/QuaPy
1506 lines
71 KiB
Python
1506 lines
71 KiB
Python
from abc import ABC, abstractmethod
|
||
from copy import deepcopy
|
||
from typing import Callable, Union
|
||
import numpy as np
|
||
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
|
||
from scipy import optimize
|
||
from sklearn.base import BaseEstimator
|
||
from sklearn.calibration import CalibratedClassifierCV
|
||
from sklearn.metrics import confusion_matrix
|
||
from sklearn.model_selection import cross_val_predict
|
||
|
||
import quapy as qp
|
||
import quapy.functional as F
|
||
from quapy.functional import get_divergence
|
||
from quapy.classification.calibration import NBVSCalibration, BCTSCalibration, TSCalibration, VSCalibration
|
||
from quapy.classification.svmperf import SVMperf
|
||
from quapy.data import LabelledCollection
|
||
from quapy.method.base import BaseQuantifier, BinaryQuantifier, OneVsAllGeneric
|
||
|
||
|
||
# Abstract classes
|
||
# ------------------------------------
|
||
|
||
class AggregativeQuantifier(BaseQuantifier, ABC):
|
||
"""
|
||
Abstract class for quantification methods that base their estimations on the aggregation of classification
|
||
results. Aggregative quantifiers implement a pipeline that consists of generating classification predictions
|
||
and aggregating them. For this reason, the training phase is implemented by :meth:`classification_fit` followed
|
||
by :meth:`aggregation_fit`, while the testing phase is implemented by :meth:`classify` followed by
|
||
:meth:`aggregate`. Subclasses of this abstract class must provide implementations for these methods.
|
||
Aggregative quantifiers also maintain a :attr:`classifier` attribute.
|
||
|
||
The method :meth:`fit` comes with a default implementation based on :meth:`classification_fit`
|
||
and :meth:`aggregation_fit`.
|
||
|
||
The method :meth:`quantify` comes with a default implementation based on :meth:`classify`
|
||
and :meth:`aggregate`.
|
||
"""
|
||
|
||
val_split_ = None
|
||
|
||
@property
|
||
def val_split(self):
|
||
return self.val_split_
|
||
|
||
@val_split.setter
|
||
def val_split(self, val_split):
|
||
if isinstance(val_split, LabelledCollection):
|
||
print('warning: setting val_split with a LabelledCollection will be inefficient in'
|
||
'model selection. Rather pass the LabelledCollection at fit time')
|
||
self.val_split_ = val_split
|
||
|
||
def fit(self, data: LabelledCollection, fit_classifier=True, val_split=None):
|
||
"""
|
||
Trains the aggregative quantifier. This comes down to training a classifier and an aggregation function.
|
||
|
||
:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
|
||
:param fit_classifier: whether to train the learner (default is True). Set to False if the
|
||
learner has been trained outside the quantifier.
|
||
:return: self
|
||
"""
|
||
classif_predictions = self.classifier_fit_predict(data, fit_classifier, predict_on=val_split)
|
||
self.aggregation_fit(classif_predictions, data)
|
||
return self
|
||
|
||
def classifier_fit_predict(self, data: LabelledCollection, fit_classifier=True, predict_on=None):
|
||
"""
|
||
Trains the classifier if requested (`fit_classifier=True`) and generate the necessary predictions to
|
||
train the aggregation function.
|
||
|
||
:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
|
||
:param fit_classifier: whether to train the learner (default is True). Set to False if the
|
||
learner has been trained outside the quantifier.
|
||
:param predict_on: specifies the set on which predictions need to be issued. This parameter can
|
||
be specified as None (default) to indicate no prediction is needed; a float in (0, 1) to
|
||
indicate the proportion of instances to be used for predictions (the remainder is used for
|
||
training); an integer >1 to indicate that the predictions must be generated via k-fold
|
||
cross-validation, using this integer as k; or the data sample itself on which to generate
|
||
the predictions.
|
||
"""
|
||
assert isinstance(fit_classifier, bool), 'unexpected type for "fit_classifier", must be boolean'
|
||
|
||
self._check_classifier(adapt_if_necessary=(self._classifier_method() == 'predict_proba'))
|
||
|
||
if predict_on is None:
|
||
predict_on = self.val_split
|
||
|
||
if predict_on is None:
|
||
if fit_classifier:
|
||
self.classifier.fit(*data.Xy)
|
||
predictions = None
|
||
|
||
elif isinstance(predict_on, float):
|
||
if fit_classifier:
|
||
if not (0. < predict_on < 1.):
|
||
raise ValueError(f'proportion {predict_on=} out of range, must be in (0,1)')
|
||
train, val = data.split_stratified(train_prop=(1 - predict_on))
|
||
self.classifier.fit(*train.Xy)
|
||
predictions = LabelledCollection(self.classify(val.X), val.y, classes=data.classes_)
|
||
else:
|
||
raise ValueError(f'wrong type for predict_on: since fit_classifier=False, '
|
||
f'the set on which predictions have to be issued must be '
|
||
f'explicitly indicated')
|
||
|
||
elif isinstance(predict_on, LabelledCollection):
|
||
if fit_classifier:
|
||
self.classifier.fit(*data.Xy)
|
||
predictions = LabelledCollection(self.classify(predict_on.X), predict_on.y, classes=predict_on.classes_)
|
||
|
||
elif isinstance(predict_on, int):
|
||
if fit_classifier:
|
||
if predict_on <= 1:
|
||
raise ValueError(f'invalid value {predict_on} in fit. '
|
||
f'Specify a integer >1 for kFCV estimation.')
|
||
else:
|
||
predictions = cross_val_predict(
|
||
self.classifier, *data.Xy, cv=predict_on, n_jobs=self.n_jobs, method=self._classifier_method())
|
||
predictions = LabelledCollection(predictions, data.y, classes=data.classes_)
|
||
self.classifier.fit(*data.Xy)
|
||
else:
|
||
raise ValueError(f'wrong type for predict_on: since fit_classifier=False, '
|
||
f'the set on which predictions have to be issued must be '
|
||
f'explicitly indicated')
|
||
|
||
else:
|
||
raise ValueError(
|
||
f'error: param "predict_on" ({type(predict_on)}) not understood; '
|
||
f'use either a float indicating the split proportion, or a '
|
||
f'tuple (X,y) indicating the validation partition')
|
||
|
||
return predictions
|
||
|
||
@abstractmethod
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
"""
|
||
Trains the aggregation function.
|
||
|
||
:param classif_predictions: a LabelledCollection containing the label predictions issued
|
||
by the classifier
|
||
:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
|
||
"""
|
||
...
|
||
|
||
@property
|
||
def classifier(self):
|
||
"""
|
||
Gives access to the classifier
|
||
|
||
:return: the classifier (typically an sklearn's Estimator)
|
||
"""
|
||
return self.classifier_
|
||
|
||
@classifier.setter
|
||
def classifier(self, classifier):
|
||
"""
|
||
Setter for the classifier
|
||
|
||
:param classifier: the classifier
|
||
"""
|
||
self.classifier_ = classifier
|
||
|
||
@abstractmethod
|
||
def classify(self, instances):
|
||
"""
|
||
Provides the label predictions for the given instances. The predictions should respect the format expected by
|
||
:meth:`aggregate`, e.g., posterior probabilities for probabilistic quantifiers, or crisp predictions for
|
||
non-probabilistic quantifiers
|
||
|
||
:param instances: array-like of shape `(n_instances, n_features,)`
|
||
:return: np.ndarray of shape `(n_instances,)` with label predictions
|
||
"""
|
||
...
|
||
|
||
@abstractmethod
|
||
def _classifier_method(self):
|
||
"""
|
||
Name of the method that must be used for issuing label predictions.
|
||
|
||
:return: string
|
||
"""
|
||
...
|
||
|
||
@abstractmethod
|
||
def _check_classifier(self, adapt_if_necessary=False):
|
||
"""
|
||
Guarantees that the underlying classifier implements the method required for issuing predictions, i.e.,
|
||
the method indicated by the :meth:`_classifier_method`
|
||
|
||
:param adapt_if_necessary: if True, the method will try to comply with the required specifications
|
||
"""
|
||
...
|
||
|
||
def quantify(self, instances):
|
||
"""
|
||
Generate class prevalence estimates for the sample's instances by aggregating the label predictions generated
|
||
by the classifier.
|
||
|
||
:param instances: array-like
|
||
:return: `np.ndarray` of shape `(n_classes)` with class prevalence estimates.
|
||
"""
|
||
classif_predictions = self.classify(instances)
|
||
return self.aggregate(classif_predictions)
|
||
|
||
@abstractmethod
|
||
def aggregate(self, classif_predictions: np.ndarray):
|
||
"""
|
||
Implements the aggregation of label predictions.
|
||
|
||
:param classif_predictions: `np.ndarray` of label predictions
|
||
:return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.
|
||
"""
|
||
...
|
||
|
||
@property
|
||
def classes_(self):
|
||
"""
|
||
Class labels, in the same order in which class prevalence values are to be computed.
|
||
This default implementation actually returns the class labels of the learner.
|
||
|
||
:return: array-like
|
||
"""
|
||
return self.classifier.classes_
|
||
|
||
|
||
class AggregativeCrispQuantifier(AggregativeQuantifier, ABC):
|
||
"""
|
||
Abstract class for quantification methods that base their estimations on the aggregation of crips decisions
|
||
as returned by a hard classifier. Aggregative crisp quantifiers thus extend Aggregative
|
||
Quantifiers by implementing specifications about crisp predictions.
|
||
"""
|
||
|
||
def classify(self, instances):
|
||
"""
|
||
Provides the label (crisp) predictions for the given instances.
|
||
|
||
:param instances: array-like of shape `(n_instances, n_dimensions,)`
|
||
:return: np.ndarray of shape `(n_instances,)` with label predictions
|
||
"""
|
||
return self.classifier.predict(instances)
|
||
|
||
def _classifier_method(self):
|
||
"""
|
||
Name of the method that must be used for issuing label predictions.
|
||
|
||
:return: the string "predict", i.e., the standard method name for scikit-learn hard predictions
|
||
"""
|
||
return 'predict'
|
||
|
||
def _check_classifier(self, adapt_if_necessary=False):
|
||
"""
|
||
Guarantees that the underlying classifier implements the method indicated by the :meth:`_classifier_method`
|
||
|
||
:param adapt_if_necessary: unused, added for compatibility
|
||
"""
|
||
assert hasattr(self.classifier, self._classifier_method()), \
|
||
f"the method does not implement the required {self._classifier_method()} method"
|
||
|
||
|
||
class AggregativeSoftQuantifier(AggregativeQuantifier, ABC):
|
||
"""
|
||
Abstract class for quantification methods that base their estimations on the aggregation of posterior
|
||
probabilities as returned by a probabilistic classifier.
|
||
Aggregative soft quantifiers thus extend Aggregative Quantifiers by implementing specifications
|
||
about soft predictions.
|
||
"""
|
||
|
||
def classify(self, instances):
|
||
"""
|
||
Provides the posterior probabilities for the given instances.
|
||
|
||
:param instances: array-like of shape `(n_instances, n_dimensions,)`
|
||
:return: np.ndarray of shape `(n_instances, n_classes,)` with posterior probabilities
|
||
"""
|
||
return self.classifier.predict_proba(instances)
|
||
|
||
def _classifier_method(self):
|
||
"""
|
||
Name of the method that must be used for issuing label predictions.
|
||
|
||
:return: the string "predict_proba", i.e., the standard method name for scikit-learn soft predictions
|
||
"""
|
||
return 'predict_proba'
|
||
|
||
def _check_classifier(self, adapt_if_necessary=False):
|
||
"""
|
||
Guarantees that the underlying classifier implements the method indicated by the :meth:`_classifier_method`.
|
||
In case it does not, the classifier is calibrated (by means of the Platt's calibration method implemented by
|
||
scikit-learn in CalibratedClassifierCV, with cv=5). This calibration is only allowed if `adapt_if_necessary`
|
||
is set to True. If otherwise (i.e., the classifier is not probabilistic, and `adapt_if_necessary` is set
|
||
to False), an exception will be raised.
|
||
|
||
:param adapt_if_necessary: a hard classifier is turned into a soft classifier if `adapt_if_necessary==True`
|
||
"""
|
||
if not hasattr(self.classifier, self._classifier_method()):
|
||
if adapt_if_necessary:
|
||
print(f'warning: The learner {self.classifier.__class__.__name__} does not seem to be '
|
||
f'probabilistic. The learner will be calibrated (using CalibratedClassifierCV).')
|
||
self.classifier = CalibratedClassifierCV(self.classifier, cv=5)
|
||
else:
|
||
raise AssertionError(f'error: The learner {self.classifier.__class__.__name__} does not '
|
||
f'seem to be probabilistic. The learner cannot be calibrated since '
|
||
f'fit_classifier is set to False')
|
||
|
||
|
||
class BinaryAggregativeQuantifier(AggregativeQuantifier, BinaryQuantifier):
|
||
|
||
@property
|
||
def pos_label(self):
|
||
return self.classifier.classes_[1]
|
||
|
||
@property
|
||
def neg_label(self):
|
||
return self.classifier.classes_[0]
|
||
|
||
def fit(self, data: LabelledCollection, fit_classifier=True, val_split=None):
|
||
self._check_binary(data, self.__class__.__name__)
|
||
return super().fit(data, fit_classifier, val_split)
|
||
|
||
|
||
|
||
|
||
# Methods
|
||
# ------------------------------------
|
||
class CC(AggregativeCrispQuantifier):
|
||
"""
|
||
The most basic Quantification method. One that simply classifies all instances and counts how many have been
|
||
attributed to each of the classes in order to compute class prevalence estimates.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator):
|
||
self.classifier = classifier
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
"""
|
||
Nothing to do here!
|
||
|
||
:param classif_predictions: this is actually None
|
||
"""
|
||
pass
|
||
|
||
def aggregate(self, classif_predictions: np.ndarray):
|
||
"""
|
||
Computes class prevalence estimates by counting the prevalence of each of the predicted labels.
|
||
|
||
:param classif_predictions: array-like with label predictions
|
||
:return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.
|
||
"""
|
||
return F.prevalence_from_labels(classif_predictions, self.classes_)
|
||
|
||
|
||
class ACC(AggregativeCrispQuantifier):
|
||
"""
|
||
`Adjusted Classify & Count <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_,
|
||
the "adjusted" variant of :class:`CC`, that corrects the predictions of CC
|
||
according to the `misclassification rates`.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set (default 0.4); or as an integer, indicating that the predictions
|
||
are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
|
||
for `k`); or as a collection defining the specific set of data to use for validation.
|
||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated.
|
||
:param n_jobs: number of parallel workers
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None):
|
||
self.classifier = classifier
|
||
self.val_split = val_split
|
||
self.n_jobs = qp._get_njobs(n_jobs)
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
"""
|
||
Estimates the misclassification rates.
|
||
|
||
:param classif_predictions: classifier predictions with true labels
|
||
"""
|
||
pred_labels, true_labels = classif_predictions.Xy
|
||
self.cc = CC(self.classifier)
|
||
self.Pte_cond_estim_ = self.getPteCondEstim(self.classifier.classes_, true_labels, pred_labels)
|
||
|
||
@classmethod
|
||
def getPteCondEstim(cls, classes, y, y_):
|
||
# estimate the matrix with entry (i,j) being the estimate of P(yi|yj), that is, the probability that a
|
||
# document that belongs to yj ends up being classified as belonging to yi
|
||
conf = confusion_matrix(y, y_, labels=classes).T
|
||
conf = conf.astype(float)
|
||
class_counts = conf.sum(axis=0)
|
||
for i, _ in enumerate(classes):
|
||
if class_counts[i] == 0:
|
||
conf[i, i] = 1
|
||
else:
|
||
conf[:, i] /= class_counts[i]
|
||
return conf
|
||
|
||
def aggregate(self, classif_predictions):
|
||
prevs_estim = self.cc.aggregate(classif_predictions)
|
||
return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim)
|
||
|
||
@classmethod
|
||
def solve_adjustment(cls, PteCondEstim, prevs_estim):
|
||
"""
|
||
Solves the system linear system :math:`Ax = B` with :math:`A` = `PteCondEstim` and :math:`B` = `prevs_estim`
|
||
|
||
:param PteCondEstim: a `np.ndarray` of shape `(n_classes,n_classes,)` with entry `(i,j)` being the estimate
|
||
of :math:`P(y_i|y_j)`, that is, the probability that an instance that belongs to :math:`y_j` ends up being
|
||
classified as belonging to :math:`y_i`
|
||
:param prevs_estim: a `np.ndarray` of shape `(n_classes,)` with the class prevalence estimates
|
||
:return: an adjusted `np.ndarray` of shape `(n_classes,)` with the corrected class prevalence estimates
|
||
"""
|
||
A = PteCondEstim
|
||
B = prevs_estim
|
||
try:
|
||
adjusted_prevs = np.linalg.solve(A, B)
|
||
adjusted_prevs = np.clip(adjusted_prevs, 0, 1)
|
||
adjusted_prevs /= adjusted_prevs.sum()
|
||
except np.linalg.LinAlgError:
|
||
adjusted_prevs = prevs_estim # no way to adjust them!
|
||
return adjusted_prevs
|
||
|
||
|
||
class PCC(AggregativeSoftQuantifier):
|
||
"""
|
||
`Probabilistic Classify & Count <https://ieeexplore.ieee.org/abstract/document/5694031>`_,
|
||
the probabilistic variant of CC that relies on the posterior probabilities returned by a probabilistic classifier.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator):
|
||
self.classifier = classifier
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
"""
|
||
Nothing to do here!
|
||
|
||
:param classif_predictions: this is actually None
|
||
"""
|
||
pass
|
||
|
||
def aggregate(self, classif_posteriors):
|
||
return F.prevalence_from_probabilities(classif_posteriors, binarize=False)
|
||
|
||
|
||
class PACC(AggregativeSoftQuantifier):
|
||
"""
|
||
`Probabilistic Adjusted Classify & Count <https://ieeexplore.ieee.org/abstract/document/5694031>`_,
|
||
the probabilistic variant of ACC that relies on the posterior probabilities returned by a probabilistic classifier.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set (default 0.4); or as an integer, indicating that the predictions
|
||
are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
|
||
for `k`). Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated.
|
||
:param n_jobs: number of parallel workers
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None):
|
||
self.classifier = classifier
|
||
self.val_split = val_split
|
||
self.n_jobs = qp._get_njobs(n_jobs)
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
"""
|
||
Estimates the misclassification rates
|
||
|
||
:param classif_predictions: classifier soft predictions with true labels
|
||
"""
|
||
posteriors, true_labels = classif_predictions.Xy
|
||
self.pcc = PCC(self.classifier)
|
||
self.Pte_cond_estim_ = self.getPteCondEstim(self.classifier.classes_, true_labels, posteriors)
|
||
|
||
def aggregate(self, classif_posteriors):
|
||
prevs_estim = self.pcc.aggregate(classif_posteriors)
|
||
return ACC.solve_adjustment(self.Pte_cond_estim_, prevs_estim)
|
||
|
||
@classmethod
|
||
def getPteCondEstim(cls, classes, y, y_):
|
||
# estimate the matrix with entry (i,j) being the estimate of P(yi|yj), that is, the probability that a
|
||
# document that belongs to yj ends up being classified as belonging to yi
|
||
n_classes = len(classes)
|
||
confusion = np.eye(n_classes)
|
||
for i, class_ in enumerate(classes):
|
||
idx = y == class_
|
||
if idx.any():
|
||
confusion[i] = y_[idx].mean(axis=0)
|
||
|
||
return confusion.T
|
||
|
||
|
||
class EMQ(AggregativeSoftQuantifier):
|
||
"""
|
||
`Expectation Maximization for Quantification <https://ieeexplore.ieee.org/abstract/document/6789744>`_ (EMQ),
|
||
aka `Saerens-Latinne-Decaestecker` (SLD) algorithm.
|
||
EMQ consists of using the well-known `Expectation Maximization algorithm` to iteratively update the posterior
|
||
probabilities generated by a probabilistic classifier and the class prevalence estimates obtained via
|
||
maximum-likelihood estimation, in a mutually recursive way, until convergence.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
"""
|
||
|
||
MAX_ITER = 1000
|
||
EPSILON = 1e-4
|
||
|
||
def __init__(self, classifier: BaseEstimator):
|
||
self.classifier = classifier
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
self.train_prevalence = data.prevalence()
|
||
|
||
def aggregate(self, classif_posteriors, epsilon=EPSILON):
|
||
priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon)
|
||
return priors
|
||
|
||
def predict_proba(self, instances, epsilon=EPSILON):
|
||
"""
|
||
Returns the posterior probabilities updated by the EM algorithm.
|
||
|
||
:param instances: np.ndarray of shape `(n_instances, n_dimensions)`
|
||
:param epsilon: error tolerance
|
||
:return: np.ndarray of shape `(n_instances, n_classes)`
|
||
"""
|
||
classif_posteriors = self.classify(instances)
|
||
priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon)
|
||
return posteriors
|
||
|
||
@classmethod
|
||
def EM(cls, tr_prev, posterior_probabilities, epsilon=EPSILON):
|
||
"""
|
||
Computes the `Expectation Maximization` routine.
|
||
|
||
:param tr_prev: array-like, the training prevalence
|
||
:param posterior_probabilities: `np.ndarray` of shape `(n_instances, n_classes,)` with the
|
||
posterior probabilities
|
||
:param epsilon: float, the threshold different between two consecutive iterations
|
||
to reach before stopping the loop
|
||
:return: a tuple with the estimated prevalence values (shape `(n_classes,)`) and
|
||
the corrected posterior probabilities (shape `(n_instances, n_classes,)`)
|
||
"""
|
||
Px = posterior_probabilities
|
||
Ptr = np.copy(tr_prev)
|
||
qs = np.copy(Ptr) # qs (the running estimate) is initialized as the training prevalence
|
||
|
||
s, converged = 0, False
|
||
qs_prev_ = None
|
||
while not converged and s < EMQ.MAX_ITER:
|
||
# E-step: ps is Ps(y|xi)
|
||
ps_unnormalized = (qs / Ptr) * Px
|
||
ps = ps_unnormalized / ps_unnormalized.sum(axis=1, keepdims=True)
|
||
|
||
# M-step:
|
||
qs = ps.mean(axis=0)
|
||
|
||
if qs_prev_ is not None and qp.error.mae(qs, qs_prev_) < epsilon and s > 10:
|
||
converged = True
|
||
|
||
qs_prev_ = qs
|
||
s += 1
|
||
|
||
if not converged:
|
||
print('[warning] the method has reached the maximum number of iterations; it might have not converged')
|
||
|
||
return qs, ps
|
||
|
||
|
||
class EMQrecalib(AggregativeSoftQuantifier):
|
||
"""
|
||
`Expectation Maximization for Quantification <https://ieeexplore.ieee.org/abstract/document/6789744>`_ (EMQ),
|
||
aka `Saerens-Latinne-Decaestecker` (SLD) algorithm, with the heuristics proposed by
|
||
`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_.
|
||
|
||
These heuristics consist of using, as the training prevalence, an estimate of it obtained via k-fold cross
|
||
validation (instead of the true training prevalence), and to recalibrate the posterior probabilities of
|
||
the classifier.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: specifies the data used for generating classifier predictions. This specification
|
||
can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to
|
||
be extracted from the training set (default 0.4); or as an integer, indicating that the predictions
|
||
are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value
|
||
for `k`, default 5); or as a collection defining the specific set of data to use for validation.
|
||
Alternatively, this set can be specified at fit time by indicating the exact set of data
|
||
on which the predictions are to be generated.
|
||
:param exact_train_prev: set to True (default) for using, as the initial observation, the true training prevalence;
|
||
or set to False for computing the training prevalence as an estimate of it, i.e., as the expected
|
||
value of the posterior probabilities of the training instances
|
||
:param recalib: a string indicating the method of recalibration.
|
||
Available choices include "nbvs" (No-Bias Vector Scaling), "bcts" (Bias-Corrected Temperature Scaling,
|
||
default), "ts" (Temperature Scaling), and "vs" (Vector Scaling).
|
||
:param n_jobs: number of parallel workers
|
||
"""
|
||
|
||
MAX_ITER = 1000
|
||
EPSILON = 1e-4
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5, exact_train_prev=False, recalib='bcts', n_jobs=None):
|
||
self.classifier = classifier
|
||
self.val_split = val_split
|
||
self.exact_train_prev = exact_train_prev
|
||
self.recalib = recalib
|
||
self.n_jobs = n_jobs
|
||
|
||
def classify(self, instances):
|
||
"""
|
||
Provides the posterior probabilities for the given instances. If the classifier is
|
||
recalibrated, then these posteriors will be recalibrated accordingly.
|
||
|
||
:param instances: array-like of shape `(n_instances, n_dimensions,)`
|
||
:return: np.ndarray of shape `(n_instances, n_classes,)` with posterior probabilities
|
||
"""
|
||
posteriors = self.classifier.predict_proba(instances)
|
||
if hasattr(self, 'calibration_function') and self.calibration_function is not None:
|
||
posteriors = self.calibration_function(posteriors)
|
||
return posteriors
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
if self.recalib is not None:
|
||
P, y = classif_predictions.Xy
|
||
if self.recalib == 'nbvs':
|
||
calibrator = NoBiasVectorScaling()
|
||
elif self.recalib == 'bcts':
|
||
calibrator = TempScaling(bias_positions='all')
|
||
elif self.recalib == 'ts':
|
||
calibrator = TempScaling()
|
||
elif self.recalib == 'vs':
|
||
calibrator = VectorScaling()
|
||
else:
|
||
raise ValueError('invalid param argument for recalibration method; available ones are '
|
||
'"nbvs", "bcts", "ts", and "vs".')
|
||
|
||
self.calibration_function = calibrator(P, np.eye(data.n_classes)[y], posterior_supplied=True)
|
||
|
||
if self.exact_train_prev:
|
||
self.train_prevalence = F.prevalence_from_labels(data.labels, self.classes_)
|
||
else:
|
||
if self.recalib is not None:
|
||
train_posteriors = self.classify(data.X)
|
||
else:
|
||
train_posteriors = classif_predictions.X
|
||
|
||
self.train_prevalence = np.mean(train_posteriors, axis=0)
|
||
|
||
def aggregate(self, classif_posteriors, epsilon=EPSILON):
|
||
priors, posteriors = EMQ.EM(self.train_prevalence, classif_posteriors, epsilon)
|
||
return priors
|
||
|
||
def predict_proba(self, instances, epsilon=EPSILON):
|
||
classif_posteriors = self.classify(instances)
|
||
priors, posteriors = EMQ.EM(self.train_prevalence, classif_posteriors, epsilon)
|
||
return posteriors
|
||
|
||
|
||
class HDy(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
|
||
"""
|
||
`Hellinger Distance y <https://www.sciencedirect.com/science/article/pii/S0020025512004069>`_ (HDy).
|
||
HDy is a probabilistic method for training binary quantifiers, that models quantification as the problem of
|
||
minimizing the divergence (in terms of the Hellinger Distance) between two distributions of posterior
|
||
probabilities returned by the classifier. One of the distributions is generated from the unlabelled examples and
|
||
the other is generated from a validation set. This latter distribution is defined as a mixture of the
|
||
class-conditional distributions of the posterior probabilities returned for the positive and negative validation
|
||
examples, respectively. The parameters of the mixture thus represent the estimates of the class prevalence values.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a binary classifier
|
||
:param val_split: a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
|
||
validation distribution, or a :class:`quapy.data.base.LabelledCollection` (the split itself), or an integer indicating the number of folds (default 5)..
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5):
|
||
self.classifier = classifier
|
||
self.val_split = val_split
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
"""
|
||
Trains a HDy quantifier.
|
||
|
||
:param data: the training set
|
||
:param fit_classifier: set to False to bypass the training (the learner is assumed to be already fit)
|
||
:param val_split: either a float in (0,1) indicating the proportion of training instances to use for
|
||
validation (e.g., 0.3 for using 30% of the training set as validation data), or a
|
||
:class:`quapy.data.base.LabelledCollection` indicating the validation set itself
|
||
:return: self
|
||
"""
|
||
P, y = classif_predictions.Xy
|
||
Px = P[:, self.pos_label] # takes only the P(y=+1|x)
|
||
self.Pxy1 = Px[y == self.pos_label]
|
||
self.Pxy0 = Px[y == self.neg_label]
|
||
|
||
# pre-compute the histogram for positive and negative examples
|
||
self.bins = np.linspace(10, 110, 11, dtype=int) # [10, 20, 30, ..., 100, 110]
|
||
|
||
def hist(P, bins):
|
||
h = np.histogram(P, bins=bins, range=(0, 1), density=True)[0]
|
||
return h / h.sum()
|
||
|
||
self.Pxy1_density = {bins: hist(self.Pxy1, bins) for bins in self.bins}
|
||
self.Pxy0_density = {bins: hist(self.Pxy0, bins) for bins in self.bins}
|
||
|
||
return self
|
||
|
||
def aggregate(self, classif_posteriors):
|
||
# "In this work, the number of bins b used in HDx and HDy was chosen from 10 to 110 in steps of 10,
|
||
# and the final estimated a priori probability was taken as the median of these 11 estimates."
|
||
# (González-Castro, et al., 2013).
|
||
|
||
Px = classif_posteriors[:, self.pos_label] # takes only the P(y=+1|x)
|
||
|
||
prev_estimations = []
|
||
# for bins in np.linspace(10, 110, 11, dtype=int): #[10, 20, 30, ..., 100, 110]
|
||
# Pxy0_density, _ = np.histogram(self.Pxy0, bins=bins, range=(0, 1), density=True)
|
||
# Pxy1_density, _ = np.histogram(self.Pxy1, bins=bins, range=(0, 1), density=True)
|
||
for bins in self.bins:
|
||
Pxy0_density = self.Pxy0_density[bins]
|
||
Pxy1_density = self.Pxy1_density[bins]
|
||
|
||
Px_test, _ = np.histogram(Px, bins=bins, range=(0, 1), density=True)
|
||
|
||
# the authors proposed to search for the prevalence yielding the best matching as a linear search
|
||
# at small steps (modern implementations resort to an optimization procedure,
|
||
# see class DistributionMatching)
|
||
prev_selected, min_dist = None, None
|
||
for prev in F.prevalence_linspace(n_prevalences=101, repeats=1, smooth_limits_epsilon=0.0):
|
||
Px_train = prev * Pxy1_density + (1 - prev) * Pxy0_density
|
||
hdy = F.HellingerDistance(Px_train, Px_test)
|
||
if prev_selected is None or hdy < min_dist:
|
||
prev_selected, min_dist = prev, hdy
|
||
prev_estimations.append(prev_selected)
|
||
|
||
class1_prev = np.median(prev_estimations)
|
||
return np.asarray([1 - class1_prev, class1_prev])
|
||
|
||
|
||
class DyS(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
|
||
"""
|
||
`DyS framework <https://ojs.aaai.org/index.php/AAAI/article/view/4376>`_ (DyS).
|
||
DyS is a generalization of HDy method, using a Ternary Search in order to find the prevalence that
|
||
minimizes the distance between distributions.
|
||
Details for the ternary search have been got from <https://dl.acm.org/doi/pdf/10.1145/3219819.3220059>
|
||
|
||
:param classifier: a sklearn's Estimator that generates a binary classifier
|
||
:param val_split: a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
|
||
validation distribution, or a :class:`quapy.data.base.LabelledCollection` (the split itself), or an integer indicating the number of folds (default 5)..
|
||
:param n_bins: an int with the number of bins to use to compute the histograms.
|
||
:param divergence: a str indicating the name of divergence (currently supported ones are "HD" or "topsoe"), or a
|
||
callable function computes the divergence between two distributions (two equally sized arrays).
|
||
:param tol: a float with the tolerance for the ternary search algorithm.
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5, n_bins=8, divergence: Union[str, Callable]= 'HD', tol=1e-05):
|
||
self.classifier = classifier
|
||
self.val_split = val_split
|
||
self.tol = tol
|
||
self.divergence = divergence
|
||
self.n_bins = n_bins
|
||
|
||
def _ternary_search(self, f, left, right, tol):
|
||
"""
|
||
Find maximum of unimodal function f() within [left, right]
|
||
"""
|
||
while abs(right - left) >= tol:
|
||
left_third = left + (right - left) / 3
|
||
right_third = right - (right - left) / 3
|
||
|
||
if f(left_third) > f(right_third):
|
||
left = left_third
|
||
else:
|
||
right = right_third
|
||
|
||
# Left and right are the current bounds; the maximum is between them
|
||
return (left + right) / 2
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
Px, y = classif_predictions.Xy
|
||
Px = Px[:, self.pos_label] # takes only the P(y=+1|x)
|
||
self.Pxy1 = Px[y == self.pos_label]
|
||
self.Pxy0 = Px[y == self.neg_label]
|
||
self.Pxy1_density = np.histogram(self.Pxy1, bins=self.n_bins, range=(0, 1), density=True)[0]
|
||
self.Pxy0_density = np.histogram(self.Pxy0, bins=self.n_bins, range=(0, 1), density=True)[0]
|
||
return self
|
||
|
||
def aggregate(self, classif_posteriors):
|
||
Px = classif_posteriors[:, self.pos_label] # takes only the P(y=+1|x)
|
||
|
||
Px_test = np.histogram(Px, bins=self.n_bins, range=(0, 1), density=True)[0]
|
||
divergence = get_divergence(self.divergence)
|
||
|
||
def distribution_distance(prev):
|
||
Px_train = prev * self.Pxy1_density + (1 - prev) * self.Pxy0_density
|
||
return divergence(Px_train, Px_test)
|
||
|
||
class1_prev = self._ternary_search(f=distribution_distance, left=0, right=1, tol=self.tol)
|
||
return np.asarray([1 - class1_prev, class1_prev])
|
||
|
||
|
||
class SMM(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
|
||
"""
|
||
`SMM method <https://ieeexplore.ieee.org/document/9260028>`_ (SMM).
|
||
SMM is a simplification of matching distribution methods where the representation of the examples
|
||
is created using the mean instead of a histogram (conceptually equivalent to PACC).
|
||
|
||
:param classifier: a sklearn's Estimator that generates a binary classifier.
|
||
:param val_split: a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
|
||
validation distribution, or a :class:`quapy.data.base.LabelledCollection` (the split itself), or an integer indicating the number of folds (default 5)..
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5):
|
||
self.classifier = classifier
|
||
self.val_split = val_split
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
Px, y = classif_predictions.Xy
|
||
Px = Px[:, self.pos_label] # takes only the P(y=+1|x)
|
||
self.Pxy1 = Px[y == self.pos_label]
|
||
self.Pxy0 = Px[y == self.neg_label]
|
||
self.Pxy1_mean = np.mean(self.Pxy1) # equiv. TPR
|
||
self.Pxy0_mean = np.mean(self.Pxy0) # equiv. FPR
|
||
return self
|
||
|
||
def aggregate(self, classif_posteriors):
|
||
Px = classif_posteriors[:, self.pos_label] # takes only the P(y=+1|x)
|
||
Px_mean = np.mean(Px)
|
||
|
||
class1_prev = (Px_mean - self.Pxy0_mean)/(self.Pxy1_mean - self.Pxy0_mean)
|
||
class1_prev = np.clip(class1_prev, 0, 1)
|
||
|
||
return np.asarray([1 - class1_prev, class1_prev])
|
||
|
||
|
||
class DMy(AggregativeSoftQuantifier):
|
||
"""
|
||
Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of posterior
|
||
probabilities. This implementation takes the number of bins, the divergence, and the possibility to work on CDF
|
||
as hyperparameters.
|
||
|
||
:param classifier: a `sklearn`'s Estimator that generates a probabilistic classifier
|
||
:param val_split: indicates the proportion of data to be used as a stratified held-out validation set to model the
|
||
validation distribution.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the validation distribution should be estimated via
|
||
`k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a
|
||
:class:`quapy.data.base.LabelledCollection` (the split itself).
|
||
:param nbins: number of bins used to discretize the distributions (default 8)
|
||
:param divergence: a string representing a divergence measure (currently, "HD" and "topsoe" are implemented)
|
||
or a callable function taking two ndarrays of the same dimension as input (default "HD", meaning Hellinger
|
||
Distance)
|
||
:param cdf: whether to use CDF instead of PDF (default False)
|
||
:param n_jobs: number of parallel workers (default None)
|
||
"""
|
||
|
||
def __init__(self, classifier, val_split=5, nbins=8, divergence: Union[str, Callable]='HD',
|
||
cdf=False, search='optim_minimize', n_jobs=None):
|
||
self.classifier = classifier
|
||
self.val_split = val_split
|
||
self.nbins = nbins
|
||
self.divergence = divergence
|
||
self.cdf = cdf
|
||
self.search = search
|
||
self.n_jobs = n_jobs
|
||
|
||
# @classmethod
|
||
# def HDy(cls, classifier, val_split=5, n_jobs=None):
|
||
# from quapy.method.meta import MedianEstimator
|
||
#
|
||
# hdy = DMy(classifier=classifier, val_split=val_split, search='linear_search', divergence='HD')
|
||
# hdy = AggregativeMedianEstimator(hdy, param_grid={'nbins': np.linspace(10, 110, 11).astype(int)}, n_jobs=n_jobs)
|
||
# return hdy
|
||
|
||
def _get_distributions(self, posteriors):
|
||
histograms = []
|
||
post_dims = posteriors.shape[1]
|
||
if post_dims == 2:
|
||
# in binary quantification we can use only one class, since the other one is its complement
|
||
post_dims = 1
|
||
for dim in range(post_dims):
|
||
hist = np.histogram(posteriors[:, dim], bins=self.nbins, range=(0, 1))[0]
|
||
histograms.append(hist)
|
||
|
||
counts = np.vstack(histograms)
|
||
distributions = counts/counts.sum(axis=1)[:,np.newaxis]
|
||
if self.cdf:
|
||
distributions = np.cumsum(distributions, axis=1)
|
||
return distributions
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
"""
|
||
Trains the classifier (if requested) and generates the validation distributions out of the training data.
|
||
The validation distributions have shape `(n, ch, nbins)`, with `n` the number of classes, `ch` the number of
|
||
channels, and `nbins` the number of bins. In particular, let `V` be the validation distributions; then `di=V[i]`
|
||
are the distributions obtained from training data labelled with class `i`; while `dij = di[j]` is the discrete
|
||
distribution of posterior probabilities `P(Y=j|X=x)` for training data labelled with class `i`, and `dij[k]`
|
||
is the fraction of instances with a value in the `k`-th bin.
|
||
|
||
:param data: the training set
|
||
:param fit_classifier: set to False to bypass the training (the learner is assumed to be already fit)
|
||
:param val_split: either a float in (0,1) indicating the proportion of training instances to use for
|
||
validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection
|
||
indicating the validation set itself, or an int indicating the number k of folds to be used in kFCV
|
||
to estimate the parameters
|
||
"""
|
||
posteriors, true_labels = classif_predictions.Xy
|
||
n_classes = len(self.classifier.classes_)
|
||
|
||
self.validation_distribution = qp.util.parallel(
|
||
func=self._get_distributions,
|
||
args=[posteriors[true_labels==cat] for cat in range(n_classes)],
|
||
n_jobs=self.n_jobs,
|
||
backend='threading'
|
||
)
|
||
|
||
def aggregate(self, posteriors: np.ndarray):
|
||
"""
|
||
Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution
|
||
(the mixture) that best matches the test distribution, in terms of the divergence measure of choice.
|
||
In the multiclass case, with `n` the number of classes, the test and mixture distributions contain
|
||
`n` channels (proper distributions of binned posterior probabilities), on which the divergence is computed
|
||
independently. The matching is computed as an average of the divergence across all channels.
|
||
|
||
:param posteriors: posterior probabilities of the instances in the sample
|
||
:return: a vector of class prevalence estimates
|
||
"""
|
||
test_distribution = self._get_distributions(posteriors)
|
||
divergence = get_divergence(self.divergence)
|
||
n_classes, n_channels, nbins = self.validation_distribution.shape
|
||
def loss(prev):
|
||
prev = np.expand_dims(prev, axis=0)
|
||
mixture_distribution = (prev @ self.validation_distribution.reshape(n_classes,-1)).reshape(n_channels, -1)
|
||
divs = [divergence(test_distribution[ch], mixture_distribution[ch]) for ch in range(n_channels)]
|
||
return np.mean(divs)
|
||
|
||
return F.argmin_prevalence(loss, n_classes, method=self.search)
|
||
|
||
|
||
|
||
def newELM(svmperf_base=None, loss='01', C=1):
|
||
"""
|
||
Explicit Loss Minimization (ELM) quantifiers.
|
||
Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
`Joachims’ SVM perf <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_ structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
`script <https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh>`_).
|
||
This function equivalent to:
|
||
|
||
>>> CC(SVMperf(svmperf_base, loss, C))
|
||
|
||
:param svmperf_base: path to the folder containing the binary files of `SVM perf`; if set to None (default)
|
||
this path will be obtained from qp.environ['SVMPERF_HOME']
|
||
:param loss: the loss to optimize (see :attr:`quapy.classification.svmperf.SVMperf.valid_losses`)
|
||
:param C: trade-off between training error and margin (default 0.01)
|
||
:return: returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier
|
||
"""
|
||
if svmperf_base is None:
|
||
svmperf_base = qp.environ['SVMPERF_HOME']
|
||
assert svmperf_base is not None, \
|
||
'param svmperf_base was not specified, and the variable SVMPERF_HOME has not been set in the environment'
|
||
return CC(SVMperf(svmperf_base, loss=loss, C=C))
|
||
|
||
|
||
def newSVMQ(svmperf_base=None, C=1):
|
||
"""
|
||
SVM(Q) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the `Q` loss combining a
|
||
classification-oriented loss and a quantification-oriented loss, as proposed by
|
||
`Barranquero et al. 2015 <https://www.sciencedirect.com/science/article/pii/S003132031400291X>`_.
|
||
Equivalent to:
|
||
|
||
>>> CC(SVMperf(svmperf_base, loss='q', C=C))
|
||
|
||
Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
`Joachims’ SVM perf <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_ structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
`script <https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh>`_).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
|
||
|
||
:param svmperf_base: path to the folder containing the binary files of `SVM perf`; if set to None (default)
|
||
this path will be obtained from qp.environ['SVMPERF_HOME']
|
||
:param C: trade-off between training error and margin (default 0.01)
|
||
:return: returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier
|
||
"""
|
||
return newELM(svmperf_base, loss='q', C=C)
|
||
|
||
def newSVMKLD(svmperf_base=None, C=1):
|
||
"""
|
||
SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Kullback-Leibler Divergence
|
||
as proposed by `Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406>`_.
|
||
Equivalent to:
|
||
|
||
>>> CC(SVMperf(svmperf_base, loss='kld', C=C))
|
||
|
||
Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
`Joachims’ SVM perf <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_ structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
`script <https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh>`_).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
|
||
|
||
:param svmperf_base: path to the folder containing the binary files of `SVM perf`; if set to None (default)
|
||
this path will be obtained from qp.environ['SVMPERF_HOME']
|
||
:param C: trade-off between training error and margin (default 0.01)
|
||
:return: returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier
|
||
"""
|
||
return newELM(svmperf_base, loss='kld', C=C)
|
||
|
||
|
||
def newSVMKLD(svmperf_base=None, C=1):
|
||
"""
|
||
SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Kullback-Leibler Divergence
|
||
normalized via the logistic function, as proposed by
|
||
`Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406>`_.
|
||
Equivalent to:
|
||
|
||
>>> CC(SVMperf(svmperf_base, loss='nkld', C=C))
|
||
|
||
Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
`Joachims’ SVM perf <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_ structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
`script <https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh>`_).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
|
||
|
||
:param svmperf_base: path to the folder containing the binary files of `SVM perf`; if set to None (default)
|
||
this path will be obtained from qp.environ['SVMPERF_HOME']
|
||
:param C: trade-off between training error and margin (default 0.01)
|
||
:return: returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier
|
||
"""
|
||
return newELM(svmperf_base, loss='nkld', C=C)
|
||
|
||
def newSVMAE(svmperf_base=None, C=1):
|
||
"""
|
||
SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Absolute Error as first used by
|
||
`Moreo and Sebastiani, 2021 <https://arxiv.org/abs/2011.02552>`_.
|
||
Equivalent to:
|
||
|
||
>>> CC(SVMperf(svmperf_base, loss='mae', C=C))
|
||
|
||
Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
`Joachims’ SVM perf <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_ structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
`script <https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh>`_).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
|
||
|
||
:param svmperf_base: path to the folder containing the binary files of `SVM perf`; if set to None (default)
|
||
this path will be obtained from qp.environ['SVMPERF_HOME']
|
||
:param C: trade-off between training error and margin (default 0.01)
|
||
:return: returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier
|
||
"""
|
||
return newELM(svmperf_base, loss='mae', C=C)
|
||
|
||
def newSVMRAE(svmperf_base=None, C=1):
|
||
"""
|
||
SVM(KLD) is an Explicit Loss Minimization (ELM) quantifier set to optimize for the Relative Absolute Error as first
|
||
used by `Moreo and Sebastiani, 2021 <https://arxiv.org/abs/2011.02552>`_.
|
||
Equivalent to:
|
||
|
||
>>> CC(SVMperf(svmperf_base, loss='mrae', C=C))
|
||
|
||
Quantifiers based on ELM represent a family of methods based on structured output learning;
|
||
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
|
||
measure. This implementation relies on
|
||
`Joachims’ SVM perf <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_ structured output
|
||
learning algorithm, which has to be installed and patched for the purpose (see this
|
||
`script <https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh>`_).
|
||
This function is a wrapper around CC(SVMperf(svmperf_base, loss, C))
|
||
|
||
:param svmperf_base: path to the folder containing the binary files of `SVM perf`; if set to None (default)
|
||
this path will be obtained from qp.environ['SVMPERF_HOME']
|
||
:param C: trade-off between training error and margin (default 0.01)
|
||
:return: returns an instance of CC set to work with SVMperf (with loss and C set properly) as the
|
||
underlying classifier
|
||
"""
|
||
return newELM(svmperf_base, loss='mrae', C=C)
|
||
|
||
|
||
class ThresholdOptimization(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
|
||
"""
|
||
Abstract class of Threshold Optimization variants for :class:`ACC` as proposed by
|
||
`Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and
|
||
`Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_.
|
||
The goal is to bring improved stability to the denominator of the adjustment.
|
||
The different variants are based on different heuristics for choosing a decision threshold
|
||
that would allow for more true positives and many more false positives, on the grounds this
|
||
would deliver larger denominators.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
`k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a
|
||
:class:`quapy.data.base.LabelledCollection` (the split itself).
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None):
|
||
self.classifier = classifier
|
||
self.val_split = val_split
|
||
self.n_jobs = qp._get_njobs(n_jobs)
|
||
|
||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||
P, y = classif_predictions.Xy
|
||
self.tpr, self.fpr, self.threshold = self._optimize_threshold(y, P)
|
||
return self
|
||
|
||
@abstractmethod
|
||
def _condition(self, tpr, fpr) -> float:
|
||
"""
|
||
Implements the criterion according to which the threshold should be selected.
|
||
This function should return the (float) score to be minimized.
|
||
|
||
:param tpr: float, true positive rate
|
||
:param fpr: float, false positive rate
|
||
:return: float, a score for the given `tpr` and `fpr`
|
||
"""
|
||
...
|
||
|
||
def _optimize_threshold(self, y, probabilities):
|
||
"""
|
||
Seeks for the best `tpr` and `fpr` according to the score obtained at different
|
||
decision thresholds. The scoring function is implemented in function `_condition`.
|
||
|
||
:param y: predicted labels for the validation set (or for the training set via `k`-fold cross validation)
|
||
:param probabilities: array-like with the posterior probabilities
|
||
:return: best `tpr` and `fpr` and `threshold` according to `_condition`
|
||
"""
|
||
best_candidate_threshold_score = None
|
||
best_tpr = 0
|
||
best_fpr = 0
|
||
candidate_thresholds = np.unique(probabilities[:, self.pos_label])
|
||
for candidate_threshold in candidate_thresholds:
|
||
y_ = self.classes_[1*(probabilities[:,1]>candidate_threshold)]
|
||
#y_ = [self.pos_label if p > candidate_threshold else self.neg_label for p in probabilities[:, 1]]
|
||
TP, FP, FN, TN = self._compute_table(y, y_)
|
||
tpr = self._compute_tpr(TP, FP)
|
||
fpr = self._compute_fpr(FP, TN)
|
||
condition_score = self._condition(tpr, fpr)
|
||
if best_candidate_threshold_score is None or condition_score < best_candidate_threshold_score:
|
||
best_candidate_threshold_score = condition_score
|
||
best_tpr = tpr
|
||
best_fpr = fpr
|
||
|
||
return best_tpr, best_fpr, best_candidate_threshold_score
|
||
|
||
def aggregate(self, classif_predictions):
|
||
class_scores = classif_predictions[:, self.pos_label]
|
||
prev_estim = np.mean(class_scores > self.threshold)
|
||
if self.tpr - self.fpr != 0:
|
||
prevs_estim = np.clip((prev_estim - self.fpr) / (self.tpr - self.fpr), 0, 1)
|
||
prevs_estim = np.array((1 - prevs_estim, prevs_estim))
|
||
return prevs_estim
|
||
|
||
def _compute_table(self, y, y_):
|
||
TP = np.logical_and(y == y_, y == self.classes_[1]).sum()
|
||
FP = np.logical_and(y != y_, y == self.classes_[0]).sum()
|
||
FN = np.logical_and(y != y_, y == self.classes_[1]).sum()
|
||
TN = np.logical_and(y == y_, y == self.classes_[0]).sum()
|
||
return TP, FP, FN, TN
|
||
|
||
def _compute_tpr(self, TP, FP):
|
||
if TP + FP == 0:
|
||
return 1
|
||
return TP / (TP + FP)
|
||
|
||
def _compute_fpr(self, FP, TN):
|
||
if FP + TN == 0:
|
||
return 0
|
||
return FP / (FP + TN)
|
||
|
||
|
||
class T50(ThresholdOptimization):
|
||
"""
|
||
Threshold Optimization variant for :class:`ACC` as proposed by
|
||
`Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and
|
||
`Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that looks
|
||
for the threshold that makes `tpr` cosest to 0.5.
|
||
The goal is to bring improved stability to the denominator of the adjustment.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
`k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a
|
||
:class:`quapy.data.base.LabelledCollection` (the split itself).
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5):
|
||
super().__init__(classifier, val_split)
|
||
|
||
def _condition(self, tpr, fpr) -> float:
|
||
return abs(tpr - 0.5)
|
||
|
||
|
||
class MAX(ThresholdOptimization):
|
||
"""
|
||
Threshold Optimization variant for :class:`ACC` as proposed by
|
||
`Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and
|
||
`Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that looks
|
||
for the threshold that maximizes `tpr-fpr`.
|
||
The goal is to bring improved stability to the denominator of the adjustment.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
`k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a
|
||
:class:`quapy.data.base.LabelledCollection` (the split itself).
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5):
|
||
super().__init__(classifier, val_split)
|
||
|
||
def _condition(self, tpr, fpr) -> float:
|
||
# MAX strives to maximize (tpr - fpr), which is equivalent to minimize (fpr - tpr)
|
||
return (fpr - tpr)
|
||
|
||
|
||
class X(ThresholdOptimization):
|
||
"""
|
||
Threshold Optimization variant for :class:`ACC` as proposed by
|
||
`Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and
|
||
`Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that looks
|
||
for the threshold that yields `tpr=1-fpr`.
|
||
The goal is to bring improved stability to the denominator of the adjustment.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
`k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a
|
||
:class:`quapy.data.base.LabelledCollection` (the split itself).
|
||
"""
|
||
|
||
def __init__(self, classifier: BaseEstimator, val_split=5):
|
||
super().__init__(classifier, val_split)
|
||
|
||
def _condition(self, tpr, fpr) -> float:
|
||
return abs(1 - (tpr + fpr))
|
||
|
||
|
||
class MS(ThresholdOptimization):
|
||
"""
|
||
Median Sweep. Threshold Optimization variant for :class:`ACC` as proposed by
|
||
`Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and
|
||
`Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that generates
|
||
class prevalence estimates for all decision thresholds and returns the median of them all.
|
||
The goal is to bring improved stability to the denominator of the adjustment.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
`k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a
|
||
:class:`quapy.data.base.LabelledCollection` (the split itself).
|
||
"""
|
||
def __init__(self, classifier: BaseEstimator, val_split=5):
|
||
super().__init__(classifier, val_split)
|
||
|
||
def _condition(self, tpr, fpr) -> float:
|
||
pass
|
||
|
||
def _optimize_threshold(self, y, probabilities):
|
||
tprs = []
|
||
fprs = []
|
||
candidate_thresholds = np.unique(probabilities[:, 1])
|
||
for candidate_threshold in candidate_thresholds:
|
||
y_ = [self.classes_[1] if p > candidate_threshold else self.classes_[0] for p in probabilities[:, 1]]
|
||
TP, FP, FN, TN = self._compute_table(y, y_)
|
||
tpr = self._compute_tpr(TP, FP)
|
||
fpr = self._compute_fpr(FP, TN)
|
||
tprs.append(tpr)
|
||
fprs.append(fpr)
|
||
return np.median(tprs), np.median(fprs)
|
||
|
||
|
||
class MS2(MS):
|
||
"""
|
||
Median Sweep 2. Threshold Optimization variant for :class:`ACC` as proposed by
|
||
`Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and
|
||
`Forman 2008 <https://link.springer.com/article/10.1007/s10618-008-0097-y>`_ that generates
|
||
class prevalence estimates for all decision thresholds and returns the median of for cases in
|
||
which `tpr-fpr>0.25`
|
||
The goal is to bring improved stability to the denominator of the adjustment.
|
||
|
||
:param classifier: a sklearn's Estimator that generates a classifier
|
||
:param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the
|
||
misclassification rates are to be estimated.
|
||
This parameter can be indicated as a real value (between 0 and 1), representing a proportion of
|
||
validation data, or as an integer, indicating that the misclassification rates should be estimated via
|
||
`k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a
|
||
:class:`quapy.data.base.LabelledCollection` (the split itself).
|
||
"""
|
||
def __init__(self, classifier: BaseEstimator, val_split=5):
|
||
super().__init__(classifier, val_split)
|
||
|
||
def _optimize_threshold(self, y, probabilities):
|
||
tprs = [0, 1]
|
||
fprs = [0, 1]
|
||
candidate_thresholds = np.unique(probabilities[:, 1])
|
||
for candidate_threshold in candidate_thresholds:
|
||
y_ = [self.classes_[1] if p > candidate_threshold else self.classes_[0] for p in probabilities[:, 1]]
|
||
TP, FP, FN, TN = self._compute_table(y, y_)
|
||
tpr = self._compute_tpr(TP, FP)
|
||
fpr = self._compute_fpr(FP, TN)
|
||
if (tpr - fpr) > 0.25:
|
||
tprs.append(tpr)
|
||
fprs.append(fpr)
|
||
return np.median(tprs), np.median(fprs)
|
||
|
||
|
||
class OneVsAllAggregative(OneVsAllGeneric, AggregativeQuantifier):
|
||
"""
|
||
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 prevelences sum up to 1.
|
||
This variant was used, along with the :class:`EMQ` quantifier, in
|
||
`Gao and Sebastiani, 2016 <https://link.springer.com/content/pdf/10.1007/s13278-016-0327-z.pdf>`_.
|
||
|
||
:param binary_quantifier: a quantifier (binary) that will be employed to work on multiclass model in a
|
||
one-vs-all manner
|
||
:param n_jobs: number of parallel workers
|
||
:param parallel_backend: the parallel backend for joblib (default "loky"); this is helpful for some quantifiers
|
||
(e.g., ELM-based ones) that cannot be run with multiprocessing, since the temp dir they create during fit will
|
||
is removed and no longer available at predict time.
|
||
"""
|
||
|
||
def __init__(self, binary_quantifier, n_jobs=None, parallel_backend='multiprocessing'):
|
||
assert isinstance(binary_quantifier, BaseQuantifier), \
|
||
f'{self.binary_quantifier} does not seem to be a Quantifier'
|
||
assert isinstance(binary_quantifier, AggregativeQuantifier), \
|
||
f'{self.binary_quantifier} does not seem to be of type Aggregative'
|
||
self.binary_quantifier = binary_quantifier
|
||
self.n_jobs = qp._get_njobs(n_jobs)
|
||
self.parallel_backend = parallel_backend
|
||
|
||
def classify(self, instances):
|
||
"""
|
||
If the base quantifier is not probabilistic, returns a matrix of shape `(n,m,)` with `n` the number of
|
||
instances and `m` the number of classes. The entry `(i,j)` is a binary value indicating whether instance
|
||
`i `belongs to class `j`. The binary classifications are independent of each other, meaning that an instance
|
||
can end up be attributed to 0, 1, or more classes.
|
||
If the base quantifier is probabilistic, returns a matrix of shape `(n,m,2)` with `n` the number of instances
|
||
and `m` the number of classes. The entry `(i,j,1)` (resp. `(i,j,0)`) is a value in [0,1] indicating the
|
||
posterior probability that instance `i` belongs (resp. does not belong) to class `j`. The posterior
|
||
probabilities are independent of each other, meaning that, in general, they do not sum up to one.
|
||
|
||
:param instances: array-like
|
||
:return: `np.ndarray`
|
||
"""
|
||
|
||
classif_predictions = self._parallel(self._delayed_binary_classification, instances)
|
||
if isinstance(self.binary_quantifier, AggregativeSoftQuantifier):
|
||
return np.swapaxes(classif_predictions, 0, 1)
|
||
else:
|
||
return classif_predictions.T
|
||
|
||
def aggregate(self, classif_predictions):
|
||
prevalences = self._parallel(self._delayed_binary_aggregate, classif_predictions)
|
||
return F.normalize_prevalence(prevalences)
|
||
|
||
def _delayed_binary_classification(self, c, X):
|
||
return self.dict_binary_quantifiers[c].classify(X)
|
||
|
||
def _delayed_binary_aggregate(self, c, classif_predictions):
|
||
# the estimation for the positive class prevalence
|
||
return self.dict_binary_quantifiers[c].aggregate(classif_predictions[:, c])[1]
|
||
|
||
|
||
class AggregativeMedianEstimator(BinaryQuantifier):
|
||
"""
|
||
This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the
|
||
estimation returned by differently (hyper)parameterized base quantifiers.
|
||
The median of unit-vectors is only guaranteed to be a unit-vector for n=2 dimensions,
|
||
i.e., in cases of binary quantification.
|
||
|
||
:param base_quantifier: the base, binary quantifier
|
||
:param random_state: a seed to be set before fitting any base quantifier (default None)
|
||
:param param_grid: the grid or parameters towards which the median will be computed
|
||
:param n_jobs: number of parllel workes
|
||
"""
|
||
def __init__(self, base_quantifier: AggregativeQuantifier, param_grid: dict, random_state=None, n_jobs=None):
|
||
self.base_quantifier = base_quantifier
|
||
self.param_grid = param_grid
|
||
self.random_state = random_state
|
||
self.n_jobs = qp._get_njobs(n_jobs)
|
||
|
||
def get_params(self, deep=True):
|
||
return self.base_quantifier.get_params(deep)
|
||
|
||
def set_params(self, **params):
|
||
self.base_quantifier.set_params(**params)
|
||
|
||
def _delayed_fit(self, args):
|
||
with qp.util.temp_seed(self.random_state):
|
||
params, training = args
|
||
model = deepcopy(self.base_quantifier)
|
||
model.set_params(**params)
|
||
model.fit(training)
|
||
return model
|
||
|
||
def _delayed_fit_classifier(self, args):
|
||
with qp.util.temp_seed(self.random_state):
|
||
print('enter job')
|
||
cls_params, training, kwargs = args
|
||
model = deepcopy(self.base_quantifier)
|
||
model.set_params(**cls_params)
|
||
predictions = model.classifier_fit_predict(training, **kwargs)
|
||
print('exit job')
|
||
return (model, predictions)
|
||
|
||
def _delayed_fit_aggregation(self, args):
|
||
with qp.util.temp_seed(self.random_state):
|
||
((model, predictions), q_params), training = args
|
||
model = deepcopy(model)
|
||
model.set_params(**q_params)
|
||
model.aggregation_fit(predictions, training)
|
||
return model
|
||
|
||
|
||
def fit(self, training: LabelledCollection, **kwargs):
|
||
import itertools
|
||
|
||
self._check_binary(training, self.__class__.__name__)
|
||
|
||
if isinstance(self.base_quantifier, AggregativeQuantifier):
|
||
cls_configs, q_configs = qp.model_selection.group_params(self.param_grid)
|
||
|
||
if len(cls_configs) > 1:
|
||
models_preds = qp.util.parallel(
|
||
self._delayed_fit_classifier,
|
||
((params, training, kwargs) for params in cls_configs),
|
||
seed=qp.environ.get('_R_SEED', None),
|
||
n_jobs=self.n_jobs,
|
||
asarray=False,
|
||
backend='threading'
|
||
)
|
||
else:
|
||
print('only 1')
|
||
model = self.base_quantifier
|
||
model.set_params(**cls_configs[0])
|
||
predictions = model.classifier_fit_predict(training, **kwargs)
|
||
models_preds = [(model, predictions)]
|
||
|
||
self.models = qp.util.parallel(
|
||
self._delayed_fit_aggregation,
|
||
((setup, training) for setup in itertools.product(models_preds, q_configs)),
|
||
seed=qp.environ.get('_R_SEED', None),
|
||
n_jobs=self.n_jobs,
|
||
backend='threading'
|
||
)
|
||
else:
|
||
configs = qp.model_selection.expand_grid(self.param_grid)
|
||
self.models = qp.util.parallel(
|
||
self._delayed_fit,
|
||
((params, training) for params in configs),
|
||
seed=qp.environ.get('_R_SEED', None),
|
||
n_jobs=self.n_jobs,
|
||
backend='threading'
|
||
)
|
||
return self
|
||
|
||
def _delayed_predict(self, args):
|
||
model, instances = args
|
||
return model.quantify(instances)
|
||
|
||
def quantify(self, instances):
|
||
prev_preds = qp.util.parallel(
|
||
self._delayed_predict,
|
||
((model, instances) for model in self.models),
|
||
seed=qp.environ.get('_R_SEED', None),
|
||
n_jobs=self.n_jobs,
|
||
backend='threading'
|
||
)
|
||
return np.median(prev_preds, axis=0)
|
||
|
||
#---------------------------------------------------------------
|
||
# aliases
|
||
#---------------------------------------------------------------
|
||
|
||
ClassifyAndCount = CC
|
||
AdjustedClassifyAndCount = ACC
|
||
ProbabilisticClassifyAndCount = PCC
|
||
ProbabilisticAdjustedClassifyAndCount = PACC
|
||
ExpectationMaximizationQuantifier = EMQ
|
||
DistributionMatchingY = DMy
|
||
SLD = EMQ
|
||
HellingerDistanceY = HDy
|
||
MedianSweep = MS
|
||
MedianSweep2 = MS2
|