lorenzo.volpi
/
QuaPy
forked from moreo/QuaPy
1
0
Fork 0
Code Pull Requests Activity

merging from pool request and adding documentation

This commit is contained in:
Alejandro Moreo Fernandez 2022-03-15 14:16:37 +01:00
parent de9d5aaf5b
commit fa577abdd2
1 changed files with 439 additions and 108 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

547
quapy/method/aggregative.py
View File

@ -23,49 +23,109 @@ from quapy.method.base import BaseQuantifier, BinaryQuantifier
class AggregativeQuantifier(BaseQuantifier):
"""
Abstract class for quantification methods that base their estimations on the aggregation of classification
results. Aggregative Quantifiers thus implement a _classify_ method and maintain a _learner_ attribute.
results. Aggregative Quantifiers thus implement a :meth:`classify` method and maintain a :attr:`learner` attribute.
Subclasses of this abstract class must implement the method :meth:`aggregate` which computes the aggregation
of label predictions. The method :meth:`quantify` comes with a default implementation based on
:meth:`classify` and :meth:`aggregate`.
"""
@abstractmethod
def fit(self, data: LabelledCollection, fit_learner=True): ...
def fit(self, data: LabelledCollection, fit_learner=True):
"""
Trains the aggregative quantifier
:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
:param fit_learner: whether or not to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.
:return: self
"""
...
@property
def learner(self):
"""
Gives access to the classifier
:return: the classifier (typically an sklearn's Estimator)
"""
return self.learner_
@learner.setter
def learner(self, value):
self.learner_ = value
def learner(self, classifier):
"""
Setter for the classifier
def preclassify(self, instances):
return self.classify(instances)
:param classifier: the classifier
"""
self.learner_ = classifier
def classify(self, instances):
"""
Provides the label predictions for the given instances.
:param instances: array-like
:return: np.ndarray of shape `(n_instances,)` with label predictions
"""
return self.learner.predict(instances)
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 `(self.n_classes_,)` with class prevalence estimates.
"""
classif_predictions = self.classify(instances)
return self.aggregate(classif_predictions)
@abstractmethod
def aggregate(self, classif_predictions: np.ndarray): ...
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 `(self.n_classes_,)` with class prevalence estimates.
"""
...
def get_params(self, deep=True):
"""
Return the current parameters of the quantifier.
:param deep: for compatibility with sklearn
:return: a dictionary of param-value pairs
"""
return self.learner.get_params()
def set_params(self, **parameters):
"""
Set the parameters of the quantifier.
:param parameters: dictionary of param-value pairs
"""
self.learner.set_params(**parameters)
@property
def n_classes(self):
return len(self.classes_)
@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.learner.classes_
@property
def aggregative(self):
"""
Returns True, indicating the quantifier is of type aggregative.
:return: True
"""
return True
@ -77,9 +137,6 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier):
probabilities.
"""
def preclassify(self, instances):
return self.predict_proba(instances)
def posterior_probabilities(self, instances):
return self.learner.predict_proba(instances)
@ -102,23 +159,24 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier):
# Helper
# ------------------------------------
def training_helper(learner,
data: LabelledCollection,
fit_learner: bool = True,
ensure_probabilistic=False,
val_split: Union[LabelledCollection, float] = None):
def _training_helper(learner,
data: LabelledCollection,
fit_learner: bool = True,
ensure_probabilistic=False,
val_split: Union[LabelledCollection, float] = None):
"""
Training procedure common to all Aggregative Quantifiers.
:param learner: the learner to be fit
:param data: the data on which to fit the learner. If requested, the data will be split before fitting the learner.
:param fit_learner: whether or not to fit the learner (if False, then bypasses any action)
:param ensure_probabilistic: if True, guarantees that the resulting classifier implements predict_proba (if the
learner is not probabilistic, then a CalibratedCV instance of it is trained)
learner is not probabilistic, then a CalibratedCV instance of it is trained)
:param val_split: if specified as a float, indicates the proportion of training instances that will define the
validation split (e.g., 0.3 for using 30% of the training set as validation data); if specified as a
LabelledCollection, represents the validation split itself
validation split (e.g., 0.3 for using 30% of the training set as validation data); if specified as a
LabelledCollection, represents the validation split itself
:return: the learner trained on the training set, and the unused data (a _LabelledCollection_ if train_val_split>0
or None otherwise) to be used as a validation set for any subsequent parameter fitting
or None otherwise) to be used as a validation set for any subsequent parameter fitting
"""
if fit_learner:
if ensure_probabilistic:
@ -160,8 +218,10 @@ def training_helper(learner,
# ------------------------------------
class CC(AggregativeQuantifier):
"""
The most basic Quantification method. One that simply classifies all instances and countes how many have been
attributed each of the classes in order to compute class prevalence estimates.
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 learner: a sklearn's Estimator that generates a classifier
"""
def __init__(self, learner: BaseEstimator):
@ -169,19 +229,40 @@ class CC(AggregativeQuantifier):
def fit(self, data: LabelledCollection, fit_learner=True):
"""
Trains the Classify & Count method unless _fit_learner_ is False, in which case it is assumed to be already fit.
:param data: training data
Trains the Classify & Count method unless `fit_learner` is False, in which case, the classifier is assumed to
be already fit and there is nothing else to do.
:param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data
:param fit_learner: if False, the classifier is assumed to be fit
:return: self
"""
self.learner, _ = training_helper(self.learner, data, fit_learner)
self.learner, _ = _training_helper(self.learner, data, fit_learner)
return self
def aggregate(self, classif_predictions):
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 `(self.n_classes_,)` with class prevalence estimates.
"""
return F.prevalence_from_labels(classif_predictions, self.classes_)
class ACC(AggregativeQuantifier):
"""
`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 learner: 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, default 0.4), 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`), or as a
:class:`quapy.data.base.LabelledCollection` (the split itself).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
self.learner = learner
@ -189,13 +270,14 @@ class ACC(AggregativeQuantifier):
def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, int, LabelledCollection] = None):
"""
Trains a ACC quantifier
Trains a ACC quantifier.
:param data: the training set
:param fit_learner: 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
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 `k`-fold
cross validation to estimate the parameters
:return: self
"""
if val_split is None:
@ -211,7 +293,7 @@ class ACC(AggregativeQuantifier):
pbar.set_description(f'{self.__class__.__name__} fitting fold {k}')
training = data.sampling_from_index(training_idx)
validation = data.sampling_from_index(validation_idx)
learner, val_data = training_helper(self.learner, training, fit_learner, val_split=validation)
learner, val_data = _training_helper(self.learner, training, fit_learner, val_split=validation)
y_.append(learner.predict(val_data.instances))
y.append(val_data.labels)
@ -220,17 +302,15 @@ class ACC(AggregativeQuantifier):
class_count = data.counts()
# fit the learner on all data
self.learner, _ = training_helper(self.learner, data, fit_learner, val_split=None)
self.learner, _ = _training_helper(self.learner, data, fit_learner, val_split=None)
else:
self.learner, val_data = training_helper(self.learner, data, fit_learner, val_split=val_split)
self.learner, val_data = _training_helper(self.learner, data, fit_learner, val_split=val_split)
y_ = self.learner.predict(val_data.instances)
y = val_data.labels
self.cc = CC(self.learner)
# 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
self.Pte_cond_estim_ = self.getPteCondEstim(data.classes_, y, y_)
return self
@ -258,7 +338,15 @@ class ACC(AggregativeQuantifier):
@classmethod
def solve_adjustment(cls, PteCondEstim, prevs_estim):
# solve for the linear system Ax = B with A=PteCondEstim and B = 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:
@ -271,11 +359,18 @@ class ACC(AggregativeQuantifier):
class PCC(AggregativeProbabilisticQuantifier):
"""
`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 learner: a sklearn's Estimator that generates a classifier
"""
def __init__(self, learner: BaseEstimator):
self.learner = learner
def fit(self, data: LabelledCollection, fit_learner=True):
self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True)
self.learner, _ = _training_helper(self.learner, data, fit_learner, ensure_probabilistic=True)
return self
def aggregate(self, classif_posteriors):
@ -283,6 +378,18 @@ class PCC(AggregativeProbabilisticQuantifier):
class PACC(AggregativeProbabilisticQuantifier):
"""
`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 learner: 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, default 0.4), 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`), or as a
:class:`quapy.data.base.LabelledCollection` (the split itself).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
self.learner = learner
@ -290,7 +397,8 @@ class PACC(AggregativeProbabilisticQuantifier):
def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, int, LabelledCollection] = None):
"""
Trains a PACC quantifier
Trains a PACC quantifier.
:param data: the training set
:param fit_learner: 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
@ -313,7 +421,7 @@ class PACC(AggregativeProbabilisticQuantifier):
pbar.set_description(f'{self.__class__.__name__} fitting fold {k}')
training = data.sampling_from_index(training_idx)
validation = data.sampling_from_index(validation_idx)
learner, val_data = training_helper(
learner, val_data = _training_helper(
self.learner, training, fit_learner, ensure_probabilistic=True, val_split=validation)
y_.append(learner.predict_proba(val_data.instances))
y.append(val_data.labels)
@ -322,12 +430,12 @@ class PACC(AggregativeProbabilisticQuantifier):
y_ = np.vstack(y_)
# fit the learner on all data
self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True,
val_split=None)
self.learner, _ = _training_helper(self.learner, data, fit_learner, ensure_probabilistic=True,
val_split=None)
classes = data.classes_
else:
self.learner, val_data = training_helper(
self.learner, val_data = _training_helper(
self.learner, data, fit_learner, ensure_probabilistic=True, val_split=val_split)
y_ = self.learner.predict_proba(val_data.instances)
y = val_data.labels
@ -335,6 +443,13 @@ class PACC(AggregativeProbabilisticQuantifier):
self.pcc = PCC(self.learner)
# 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.empty(shape=(n_classes, n_classes))
for i, class_ in enumerate(classes):
confusion[i] = y_[y == class_].mean(axis=0)
self.Pte_cond_estim_ = self.getPteCondEstim(classes, y, y_)
return self
@ -344,7 +459,6 @@ class PACC(AggregativeProbabilisticQuantifier):
# 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.zeros(shape=(n_classes, n_classes))
confusion = np.eye(n_classes)
for i, class_ in enumerate(classes):
idx = y == class_
@ -363,34 +477,59 @@ class PACC(AggregativeProbabilisticQuantifier):
class EMQ(AggregativeProbabilisticQuantifier):
"""
The method is described in:
`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.
The `transform_prior` callback allows you to introduce ad-hoc regularizations which are not part of the
original EMQ algorithm. This callback can, for instance, enhance or diminish small class prevalences if
sparse or dense solutions should be promoted.
The original method is described in:
Saerens, M., Latinne, P., and Decaestecker, C. (2002).
Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure.
Neural Computation, 14(1): 21–41.
:param learner: a sklearn's Estimator that generates a classifier
:param transform_prior: an optional function :math:`R^c -> R^c` that transforms each intermediate estimate
"""
MAX_ITER = 1000
EPSILON = 1e-4
def __init__(self, learner: BaseEstimator):
def __init__(self, learner: BaseEstimator, transform_prior=None):
self.learner = learner
self.transform_prior = transform_prior
def fit(self, data: LabelledCollection, fit_learner=True):
self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True)
self.learner, _ = _training_helper(self.learner, data, fit_learner, ensure_probabilistic=True)
self.train_prevalence = F.prevalence_from_labels(data.labels, self.classes_)
return self
def aggregate(self, classif_posteriors, epsilon=EPSILON):
priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon)
priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon, self.transform_prior)
return priors
def predict_proba(self, instances, epsilon=EPSILON):
classif_posteriors = self.learner.predict_proba(instances)
priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon)
priors, posteriors = self.EM(self.train_prevalence, classif_posteriors, epsilon, self.transform_prior)
return posteriors
@classmethod
def EM(cls, tr_prev, posterior_probabilities, epsilon=EPSILON):
def EM(cls, tr_prev, posterior_probabilities, epsilon=EPSILON, transform_prior=None):
"""
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
:param transform_prior: an optional function :math:`R^c -> R^c` that transforms each intermediate estimate
: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
@ -411,17 +550,30 @@ class EMQ(AggregativeProbabilisticQuantifier):
qs_prev_ = qs
s += 1
# transformation of intermediate estimates
if transform_prior is not None and not converged:
qs = transform_prior(qs)
if not converged:
print('[warning] the method has reached the maximum number of iterations; it might have not converged')
return qs, ps
class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
"""
Implementation of the method based on the Hellinger Distance y (HDy) proposed by
González-Castro, V., Alaiz-Rodrı́guez, R., and Alegre, E. (2013). Class distribution
estimation based on the Hellinger distance. Information Sciences, 218:146–164.
`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 cumulative 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 learner: 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).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
@ -430,19 +582,20 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, LabelledCollection] = None):
"""
Trains a HDy quantifier
Trains a HDy quantifier.
:param data: the training set
:param fit_learner: 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
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
"""
if val_split is None:
val_split = self.val_split
self._check_binary(data, self.__class__.__name__)
self.learner, validation = training_helper(
self.learner, validation = _training_helper(
self.learner, data, fit_learner, ensure_probabilistic=True, val_split=val_split)
Px = self.posterior_probabilities(validation.instances)[:, 1] # takes only the P(y=+1|x)
self.Pxy1 = Px[validation.labels == self.learner.classes_[1]]
@ -473,7 +626,7 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
Px_test, _ = np.histogram(Px, bins=bins, range=(0, 1), density=True)
prev_selected, min_dist = None, None
for prev in F.prevalence_linspace(n_prevalences=100, repeat=1, smooth_limits_epsilon=0.0):
for prev in F.prevalence_linspace(n_prevalences=100, 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:
@ -485,6 +638,19 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
class ELM(AggregativeQuantifier, BinaryQuantifier):
"""
Class of 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>`_).
:param svmperf_base: path to the folder containing the binary files of `SVM perf`
:param loss: the loss to optimize (see :attr:`quapy.classification.svmperf.SVMperf.valid_losses`)
:param kwargs: rest of SVM perf's parameters
"""
def __init__(self, svmperf_base=None, loss='01', **kwargs):
self.svmperf_base = svmperf_base if svmperf_base is not None else qp.environ['SVMPERF_HOME']
@ -507,9 +673,15 @@ class ELM(AggregativeQuantifier, BinaryQuantifier):
class SVMQ(ELM):
"""
Barranquero, J., Díez, J., and del Coz, J. J. (2015).
Quantification-oriented learning based on reliable classifiers.
Pattern Recognition, 48(2):591–604.
SVM(Q), which attempts to minimize 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:
>>> ELM(svmperf_base, loss='q', **kwargs)
:param svmperf_base: path to the folder containing the binary files of `SVM perf`
:param kwargs: rest of SVM perf's parameters
"""
def __init__(self, svmperf_base=None, **kwargs):
@ -518,9 +690,14 @@ class SVMQ(ELM):
class SVMKLD(ELM):
"""
Esuli, A. and Sebastiani, F. (2015).
Optimizing text quantifiers for multivariate loss functions.
ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27.
SVM(KLD), which attempts to minimize the Kullback-Leibler Divergence as proposed by
`Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406>`_.
Equivalent to:
>>> ELM(svmperf_base, loss='kld', **kwargs)
:param svmperf_base: path to the folder containing the binary files of `SVM perf`
:param kwargs: rest of SVM perf's parameters
"""
def __init__(self, svmperf_base=None, **kwargs):
@ -529,9 +706,15 @@ class SVMKLD(ELM):
class SVMNKLD(ELM):
"""
Esuli, A. and Sebastiani, F. (2015).
Optimizing text quantifiers for multivariate loss functions.
ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27.
SVM(NKLD), which attempts to minimize a version of the 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:
>>> ELM(svmperf_base, loss='nkld', **kwargs)
:param svmperf_base: path to the folder containing the binary files of `SVM perf`
:param kwargs: rest of SVM perf's parameters
"""
def __init__(self, svmperf_base=None, **kwargs):
@ -539,25 +722,60 @@ class SVMNKLD(ELM):
class SVMAE(ELM):
"""
SVM(AE), which attempts to minimize Absolute Error as first used by
`Moreo and Sebastiani, 2021 <https://arxiv.org/abs/2011.02552>`_.
Equivalent to:
>>> ELM(svmperf_base, loss='mae', **kwargs)
:param svmperf_base: path to the folder containing the binary files of `SVM perf`
:param kwargs: rest of SVM perf's parameters
"""
def __init__(self, svmperf_base=None, **kwargs):
super(SVMAE, self).__init__(svmperf_base, loss='mae', **kwargs)
class SVMRAE(ELM):
"""
SVM(RAE), which attempts to minimize Relative Absolute Error as first used by
`Moreo and Sebastiani, 2021 <https://arxiv.org/abs/2011.02552>`_.
Equivalent to:
>>> ELM(svmperf_base, loss='mrae', **kwargs)
:param svmperf_base: path to the folder containing the binary files of `SVM perf`
:param kwargs: rest of SVM perf's parameters
"""
def __init__(self, svmperf_base=None, **kwargs):
super(SVMRAE, self).__init__(svmperf_base, loss='mrae', **kwargs)
class ThresholdOptimization(AggregativeQuantifier, BinaryQuantifier):
"""
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 learner: 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, default 0.4), 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`), or as a
:class:`quapy.data.base.LabelledCollection` (the split itself).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
self.learner = learner
self.val_split = val_split
@abstractmethod
def optimize_threshold(self, y, probabilities):
...
def fit(self, data: LabelledCollection, fit_learner=True, val_split: Union[float, int, LabelledCollection] = None):
self._check_binary(data, "Threshold Optimization")
@ -574,7 +792,7 @@ class ThresholdOptimization(AggregativeQuantifier, BinaryQuantifier):
pbar.set_description(f'{self.__class__.__name__} fitting fold {k}')
training = data.sampling_from_index(training_idx)
validation = data.sampling_from_index(validation_idx)
learner, val_data = training_helper(self.learner, training, fit_learner, val_split=validation)
learner, val_data = _training_helper(self.learner, training, fit_learner, val_split=validation)
probabilities.append(learner.predict_proba(val_data.instances))
y.append(val_data.labels)
@ -582,16 +800,16 @@ class ThresholdOptimization(AggregativeQuantifier, BinaryQuantifier):
probabilities = np.concatenate(probabilities)
# fit the learner on all data
self.learner, _ = training_helper(self.learner, data, fit_learner, val_split=None)
self.learner, _ = _training_helper(self.learner, data, fit_learner, val_split=None)
else:
self.learner, val_data = training_helper(self.learner, data, fit_learner, val_split=val_split)
self.learner, val_data = _training_helper(self.learner, data, fit_learner, val_split=val_split)
probabilities = self.learner.predict_proba(val_data.instances)
y = val_data.labels
self.cc = CC(self.learner)
self.tpr, self.fpr = self.optimize_threshold(y, probabilities)
self.tpr, self.fpr = self._optimize_threshold(y, probabilities)
return self
@ -599,20 +817,32 @@ class ThresholdOptimization(AggregativeQuantifier, BinaryQuantifier):
def _condition(self, tpr, fpr) -> float:
"""
Implements the criterion according to which the threshold should be selected.
This function should return a (float) score to be minimized.
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):
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` according to `_condition`
"""
best_candidate_threshold_score = None
best_tpr = 0
best_fpr = 0
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)
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
@ -629,25 +859,40 @@ class ThresholdOptimization(AggregativeQuantifier, BinaryQuantifier):
adjusted_prevs_estim = np.array((1 - adjusted_prevs_estim, adjusted_prevs_estim))
return adjusted_prevs_estim
def compute_table(self, y, y_):
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):
def _compute_tpr(self, TP, FP):
if TP + FP == 0:
return 0
return TP / (TP + FP)
def compute_fpr(self, FP, TN):
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 learner: 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, default 0.4), 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`), or as a
:class:`quapy.data.base.LabelledCollection` (the split itself).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
super().__init__(learner, val_split)
@ -657,6 +902,21 @@ class T50(ThresholdOptimization):
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 learner: 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, default 0.4), 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`), or as a
:class:`quapy.data.base.LabelledCollection` (the split itself).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
super().__init__(learner, val_split)
@ -667,6 +927,21 @@ class MAX(ThresholdOptimization):
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 learner: 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, default 0.4), 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`), or as a
:class:`quapy.data.base.LabelledCollection` (the split itself).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
super().__init__(learner, val_split)
@ -676,41 +951,70 @@ class X(ThresholdOptimization):
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 learner: 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, default 0.4), 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`), or as a
:class:`quapy.data.base.LabelledCollection` (the split itself).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
super().__init__(learner, val_split)
def _condition(self, tpr, fpr) -> float:
pass
def optimize_threshold(self, y, probabilities):
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)
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 learner: 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, default 0.4), 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`), or as a
:class:`quapy.data.base.LabelledCollection` (the split itself).
"""
def __init__(self, learner: BaseEstimator, val_split=0.4):
super().__init__(learner, val_split)
def optimize_threshold(self, y, probabilities):
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)
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)
@ -722,6 +1026,7 @@ AdjustedClassifyAndCount = ACC
ProbabilisticClassifyAndCount = PCC
ProbabilisticAdjustedClassifyAndCount = PACC
ExpectationMaximizationQuantifier = EMQ
SLD = EMQ
HellingerDistanceY = HDy
ExplicitLossMinimisation = ELM
MedianSweep = MS
@ -730,11 +1035,14 @@ MedianSweep2 = MS2
class OneVsAll(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 ExplicitLossMinimization quantifier in
Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
Social Network Analysis and Mining 6(19), 1–22 (2016)
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 learner: a sklearn's Estimator that generates a binary classifier
:param n_jobs: number of parallel workers
"""
def __init__(self, binary_quantifier, n_jobs=-1):
@ -753,18 +1061,30 @@ class OneVsAll(AggregativeQuantifier):
return self
def classify(self, instances):
# 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.
"""
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.
:param instances: array-like
:return: `np.ndarray`
"""
classif_predictions_bin = self.__parallel(self._delayed_binary_classification, instances)
return classif_predictions_bin.T
def posterior_probabilities(self, instances):
# 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.
"""
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`
"""
if not self.binary_quantifier.probabilistic:
raise NotImplementedError(f'{self.__class__.__name__} does not implement posterior_probabilities because '
f'the base quantifier {self.binary_quantifier.__class__.__name__} is not '
@ -811,7 +1131,7 @@ class OneVsAll(AggregativeQuantifier):
return self.binary_quantifier.get_params()
def _delayed_binary_classification(self, c, X):
return self.dict_binary_quantifiers[c].preclassify(X)
return self.dict_binary_quantifiers[c].classify(X)
def _delayed_binary_posteriors(self, c, X):
return self.dict_binary_quantifiers[c].posterior_probabilities(X)
@ -826,8 +1146,19 @@ class OneVsAll(AggregativeQuantifier):
@property
def binary(self):
"""
Informs that the classifier is not binary
:return: False
"""
return False
@property
def probabilistic(self):
return self.binary_quantifier.probabilistic
"""
Indicates if the classifier is probabilistic or not (depending on the nature of the base classifier).
:return: boolean
"""
return self.binary_quantifier.probabilistic