diff --git a/TODO.txt b/TODO.txt
index d632436..d25ed25 100644
--- a/TODO.txt
+++ b/TODO.txt
@@ -1,2 +1,3 @@
Documentation with sphinx
-The parallel training in svmperf seems not to work
\ No newline at end of file
+The parallel training in svmperf seems not to work
+Add "prepare svmperf for quantification" script
\ No newline at end of file
diff --git a/prepare_svmperf.sh b/prepare_svmperf.sh
new file mode 100755
index 0000000..aea1c08
--- /dev/null
+++ b/prepare_svmperf.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+set -x
+
+URL=http://download.joachims.org/svm_perf/current/svm_perf.tar.gz
+FILE=./svm_perf.tar.gz
+wget $URL $FILE
+mkdir ./svm_perf
+tar xvzf $FILE -C ./svm_perf
+rm $FILE
+
+#para crear el patch [para mi]
+#diff -Naur svm_perf svm_perf_quantification > svm-perf-quantification-ext.patch
+
+#para crear la modificacion
+#cp svm_perf svm_perf_quantification -r [ESTO NO HACE FALTA]
+patch -s -p0 < svm-perf-quantification-ext.patch
+mv svm_perf svm_perf_quantification
+cd svm_perf_quantification
+make
+
+
+
+
+
+
+
+
+
diff --git a/quapy/__init__.py b/quapy/__init__.py
new file mode 100644
index 0000000..59e21fe
--- /dev/null
+++ b/quapy/__init__.py
@@ -0,0 +1,6 @@
+from .dataset import *
+from . import functional
+from . import method
+from . import error
+
+
diff --git a/quapy/error.py b/quapy/error.py
new file mode 100644
index 0000000..ff9a6e0
--- /dev/null
+++ b/quapy/error.py
@@ -0,0 +1,47 @@
+from sklearn.metrics import f1_score
+from settings import SAMPLE_SIZE
+
+
+def f1e(y_true, y_pred):
+ return 1. - f1_score(y_true, y_pred, average='macro')
+
+
+def acce(y_true, y_pred):
+ acc = (y_true == y_pred).mean()
+ return 1. - acc
+
+
+def mae(prevs, prevs_hat):
+ return ae(prevs, prevs_hat).mean()
+
+
+def ae(p, p_hat):
+ assert p.shape == p_hat.shape, 'wrong shape'
+ return abs(p_hat-p).mean(axis=-1)
+
+
+def mrae(p, p_hat, eps=1./(2. * SAMPLE_SIZE)):
+ return rae(p, p_hat, eps).mean()
+
+
+def rae(p, p_hat, eps=1./(2. * SAMPLE_SIZE)):
+ p = smooth(p, eps)
+ p_hat = smooth(p_hat, eps)
+ return (abs(p-p_hat)/p).mean(axis=-1)
+
+
+def smooth(p, eps):
+ n_classes = p.shape[-1]
+ return (p+eps)/(eps*n_classes + 1)
+
+
+CLASSIFICATION_ERROR = {f1e, acce}
+QUANTIFICATION_ERROR = {mae, mrae}
+
+f1_error = f1e
+acc_error = acce
+mean_absolute_error = mae
+absolute_error = ae
+mean_relative_absolute_error = mrae
+relative_absolute_error = rae
+
diff --git a/quapy/functional.py b/quapy/functional.py
new file mode 100644
index 0000000..f44a85b
--- /dev/null
+++ b/quapy/functional.py
@@ -0,0 +1,49 @@
+from collections import defaultdict
+import numpy as np
+import itertools
+
+
+def artificial_prevalence_sampling(dimensions, n_prevalences=21, repeat=1, return_constrained_dim=False):
+ s = np.linspace(0., 1., n_prevalences, endpoint=True)
+ s = [s] * (dimensions - 1)
+ prevs = [p for p in itertools.product(*s, repeat=1) if sum(p)<=1]
+ if return_constrained_dim:
+ prevs = [p+(1-sum(p),) for p in prevs]
+ prevs = np.asarray(prevs).reshape(len(prevs), -1)
+ if repeat>1:
+ prevs = np.repeat(prevs, repeat, axis=0)
+ return prevs
+
+
+def prevalence_from_labels(labels, n_classes):
+ unique, counts = np.unique(labels, return_counts=True)
+ by_class = defaultdict(lambda:0, dict(zip(unique, counts)))
+ prevalences = np.asarray([by_class[ci] for ci in range(n_classes)], dtype=np.float)
+ prevalences /= prevalences.sum()
+ return prevalences
+
+
+def prevalence_from_probabilities(posteriors, binarize: bool = False):
+ if binarize:
+ predictions = np.argmax(posteriors, axis=-1)
+ return prevalence_from_labels(predictions, n_classes=posteriors.shape[1])
+ else:
+ prevalences = posteriors.mean(axis=0)
+ prevalences /= prevalences.sum()
+ return prevalences
+
+
+def strprev(prevalences, prec=3):
+ return '['+ ', '.join([f'{p:.{prec}f}' for p in prevalences]) + ']'
+
+
+def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
+ den = tpr - fpr
+ if den == 0:
+ den += 1e-8
+ adjusted = (prevalence_estim - fpr) / den
+ if clip:
+ adjusted = np.clip(adjusted, 0., 1.)
+ return adjusted
+
+
diff --git a/quapy/method/__init__.py b/quapy/method/__init__.py
new file mode 100644
index 0000000..a8e98d0
--- /dev/null
+++ b/quapy/method/__init__.py
@@ -0,0 +1,30 @@
+from . import aggregative as agg
+from . import non_aggregative as nagg
+
+
+AGGREGATIVE_METHODS = {
+ agg.ClassifyAndCount,
+ agg.AdjustedClassifyAndCount,
+ agg.ProbabilisticClassifyAndCount,
+ agg.ProbabilisticAdjustedClassifyAndCount,
+ agg.ExplicitLossMinimisation,
+ agg.ExpectationMaximizationQuantifier,
+}
+
+NON_AGGREGATIVE_METHODS = {
+ nagg.MaximumLikelihoodPrevalenceEstimation
+}
+
+QUANTIFICATION_METHODS = AGGREGATIVE_METHODS | NON_AGGREGATIVE_METHODS
+
+
+# common alisases
+CC = agg.ClassifyAndCount
+ACC = agg.AdjustedClassifyAndCount
+PCC = agg.ProbabilisticClassifyAndCount
+PACC = agg.ProbabilisticAdjustedClassifyAndCount
+ELM = agg.ExplicitLossMinimisation
+EMQ = agg.ExpectationMaximizationQuantifier
+MLPE = nagg.MaximumLikelihoodPrevalenceEstimation
+
+
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
new file mode 100644
index 0000000..ee16baf
--- /dev/null
+++ b/quapy/method/aggregative.py
@@ -0,0 +1,351 @@
+import numpy as np
+from .base import *
+from ..error import mae
+import functional as F
+from ..classification.svmperf import SVMperf
+from ..dataset import LabelledCollection
+from sklearn.metrics import confusion_matrix
+from sklearn.calibration import CalibratedClassifierCV
+from joblib import Parallel, delayed
+
+
+# Abstract classes
+# ------------------------------------
+
+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.
+ """
+
+ @abstractmethod
+ def fit(self, data: LabelledCollection, fit_learner=True, *args): ...
+
+ def classify(self, documents):
+ return self.learner.predict(documents)
+
+ def get_params(self, deep=True):
+ return self.learner.get_params()
+
+ def set_params(self, **parameters):
+ self.learner.set_params(**parameters)
+
+ @property
+ def n_classes(self):
+ return len(self.classes)
+
+ @property
+ def classes(self):
+ return self.learner.classes_
+
+
+class AggregativeProbabilisticQuantifier(AggregativeQuantifier):
+ """
+ Abstract class for quantification methods that base their estimations on the aggregation of posterior probabilities
+ as returned by a probabilistic classifier. Aggregative Probabilistic Quantifiers thus extend Aggregative
+ Quantifiersimplement by implementing a _soft_classify_ method returning values in [0,1] -- the posterior
+ probabilities.
+ """
+
+ def soft_classify(self, data):
+ return self.learner.predict_proba(data)
+
+ def set_params(self, **parameters):
+ if isinstance(self.learner, CalibratedClassifierCV):
+ parameters={'base_estimator__'+k:v for k,v in parameters.items()}
+ self.learner.set_params(**parameters)
+
+
+# Helper
+# ------------------------------------
+def training_helper(learner,
+ data: LabelledCollection,
+ fit_learner: bool = True,
+ ensure_probabilistic=False,
+ train_val_split=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
+ :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)
+ :param train_val_split: if specified, indicates the proportion of training documents on which to fit the learner
+ :return: the learner trained on the training set, and the unused data (a _LabelledCollection_ if train_val_split>0
+ or None otherwise)
+ """
+ if fit_learner:
+ if ensure_probabilistic:
+ if not hasattr(learner, 'predict_proba'):
+ print(f'The learner {learner.__class__.__name__} does not seem to be probabilistic. '
+ f'The learner will be calibrated.')
+ learner = CalibratedClassifierCV(learner, cv=5)
+ if train_val_split is not None:
+ if not (0 < train_val_split < 1):
+ raise ValueError(f'train/val split {train_val_split} out of range, must be in (0,1)')
+ train, unused = data.split_stratified(train_prop=train_val_split)
+ else:
+ train, unused = data, None
+ learner.fit(train.instances, train.labels)
+ else:
+ if ensure_probabilistic:
+ if not hasattr(learner, 'predict_proba'):
+ raise AssertionError('error: the learner cannot be calibrated since fit_learner is set to False')
+ unused = data
+
+ return learner, unused
+
+
+# Methods
+# ------------------------------------
+class ClassifyAndCount(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.
+ """
+
+ def __init__(self, learner):
+ self.learner = learner
+
+ def fit(self, data: LabelledCollection, fit_learner=True, *args):
+ """
+ Trains the Classify & Count method unless _fit_learner_ is False, in which case it is assumed to be already fit.
+ :param data: training data
+ :param fit_learner: if False, the classifier is assumed to be fit
+ :param args: unused
+ :return: self
+ """
+ self.learner, _ = training_helper(self.learner, data, fit_learner)
+ return self
+
+ def quantify(self, documents, *args):
+ classification = self.classify(documents) # classify
+ return F.prevalence_from_labels(classification, self.n_classes) # & count
+
+
+class AdjustedClassifyAndCount(AggregativeQuantifier):
+
+ def __init__(self, learner):
+ self.learner = learner
+
+ def fit(self, data: LabelledCollection, fit_learner=True, train_val_split=0.6):
+ self.learner, validation = training_helper(self.learner, data, fit_learner, train_val_split=train_val_split)
+ self.cc = ClassifyAndCount(self.learner)
+ y_ = self.cc.classify(validation.instances)
+ y = validation.labels
+ # 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_ = confusion_matrix(y,y_).T / validation.counts()
+ return self
+
+ def quantify(self, documents, *args):
+ prevs_estim = self.cc.quantify(documents)
+ # solve for the linear system Ax = B with A=Pte_cond_estim and B = prevs_estim
+ A = self.Pte_cond_estim_
+ 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
+
+ def classify(self, data):
+ return self.cc.classify(data)
+
+
+class ProbabilisticClassifyAndCount(AggregativeProbabilisticQuantifier):
+ def __init__(self, learner):
+ self.learner = learner
+
+ def fit(self, data : LabelledCollection, fit_learner=True, *args):
+ self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True)
+ return self
+
+ def quantify(self, documents, *args):
+ posteriors = self.soft_classify(documents) # classify
+ prevalences = F.prevalence_from_probabilities(posteriors, binarize=False) # & count
+ return prevalences
+
+
+class ProbabilisticAdjustedClassifyAndCount(AggregativeQuantifier):
+
+ def __init__(self, learner):
+ self.learner = learner
+
+ def fit(self, data: LabelledCollection, fit_learner=True, train_val_split=0.6):
+ self.learner, validation = training_helper(
+ self.learner, data, fit_learner, ensure_probabilistic=True, train_val_split=train_val_split
+ )
+ self.pcc = ProbabilisticClassifyAndCount(self.learner)
+ y_ = self.pcc.classify(validation.instances)
+ y = validation.labels
+ # 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_ = confusion_matrix(y, y_).T / validation.counts()
+ return self
+
+ def quantify(self, documents, *args):
+ prevs_estim = self.pcc.quantify(documents)
+ A = self.Pte_cond_estim_
+ 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
+
+ def classify(self, data):
+ return self.pcc.classify(data)
+
+
+class ExpectationMaximizationQuantifier(AggregativeProbabilisticQuantifier):
+
+ MAX_ITER = 1000
+ EPSILON = 1e-4
+
+ def __init__(self, learner, verbose=False):
+ self.learner = learner
+ self.verbose = verbose
+
+ def fit(self, data: LabelledCollection, fit_learner=True, *args):
+ self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True)
+ self.train_prevalence = F.prevalence_from_labels(data.labels, self.n_classes)
+ return self
+
+ def quantify(self, X, epsilon=EPSILON):
+ tr_prev=self.train_prevalence
+ posteriors = self.soft_classify(X)
+ return self.EM(tr_prev, posteriors, self.verbose, epsilon)
+
+ @classmethod
+ def EM(cls, tr_prev, posterior_probabilities, verbose=False, epsilon=EPSILON):
+ 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 < ExpectationMaximizationQuantifier.MAX_ITER:
+ # E-step: ps is Ps(y=+1|xi)
+ ps_unnormalized = (qs / Ptr) * Px
+ ps = ps_unnormalized / ps_unnormalized.sum(axis=1).reshape(-1,1)
+
+ # M-step: qs_pos is Ps+1(y=+1)
+ qs = ps.mean(axis=0)
+
+ if qs_prev_ is not None and mae(qs, qs_prev_) < epsilon and s>10:
+ converged = True
+
+ qs_prev_ = qs
+ s += 1
+
+ if verbose:
+ print('-'*80)
+
+ if not converged:
+ raise UserWarning('the method has reached the maximum number of iterations; it might have not converged')
+
+ return qs
+
+
+# todo: from here
+def train_task(c, learners, data):
+ learners[c].fit(data.documents, data.labels == c)
+
+
+def binary_quant_task(c, learners, X):
+ predictions_ci = learners[c].predict(X)
+ return predictions_ci.mean() # since the predictions array is binary
+
+
+class OneVsAllELM(AggregativeQuantifier):
+
+ def __init__(self, svmperf_base, loss, n_jobs=-1, **kwargs):
+ self.svmperf_base = svmperf_base
+ self.loss = loss
+ self.n_jobs = n_jobs
+ self.kwargs = kwargs
+
+ def fit(self, data: LabelledCollection, fit_learner=True, *args):
+ assert fit_learner, 'the method requires that fit_learner=True'
+
+ self.learners = {c: SVMperf(self.svmperf_base, loss=self.loss, **self.kwargs) for c in data.classes_}
+ Parallel(n_jobs=self.n_jobs, backend='threading')(
+ delayed(train_task)(c, self.learners, data) for c in self.learners.keys()
+ )
+ return self
+
+ def quantify(self, X, y=None):
+ prevalences = np.asarray(
+ Parallel(n_jobs=self.n_jobs, backend='threading')(
+ delayed(binary_quant_task)(c, self.learners, X) for c in self.learners.keys()
+ )
+ )
+ prevalences /= prevalences.sum()
+ return prevalences
+
+ @property
+ def classes(self):
+ return sorted(self.learners.keys())
+
+ def preclassify_collection(self, data: LabelledCollection):
+ classifications = []
+ for class_ in data.classes_:
+ classifications.append(self.learners[class_].predict(data.instances))
+ classifications = np.vstack(classifications).T
+ precomputed = LabelledCollection(classifications, data.labels)
+ return precomputed
+
+ def set_params(self, **parameters):
+ self.kwargs=parameters
+
+ def get_params(self, deep=True):
+ return self.kwargs
+
+
+class ExplicitLossMinimisation(AggregativeQuantifier):
+
+ def __init__(self, svmperf_base, loss, **kwargs):
+ self.learner = SVMperf(svmperf_base, loss=loss, **kwargs)
+
+ def fit(self, data: LabelledCollection, fit_learner=True, *args):
+ assert fit_learner, 'the method requires that fit_learner=True'
+ self.learner.fit(data.instances, data.labels)
+ return self
+
+ def quantify(self, X, y=None):
+ predictions = self.learner.predict(X)
+ return F.prevalence_from_labels(predictions, self.learner.n_classes_)
+
+ def classify(self, X, y=None):
+ return self.learner.predict(X)
+
+
+class SVMQ(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMQ, self).__init__(svmperf_base, loss='q', **kwargs)
+
+
+class SVMKLD(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMKLD, self).__init__(svmperf_base, loss='kld', **kwargs)
+
+
+class SVMNKLD(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMNKLD, self).__init__(svmperf_base, loss='nkld', **kwargs)
+
+
+class SVMAE(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMAE, self).__init__(svmperf_base, loss='mae', **kwargs)
+
+
+class SVMRAE(ExplicitLossMinimisation):
+ def __init__(self, svmperf_base, **kwargs):
+ super(SVMRAE, self).__init__(svmperf_base, loss='mrae', **kwargs)
+
diff --git a/quapy/method/base.py b/quapy/method/base.py
new file mode 100644
index 0000000..4679a8f
--- /dev/null
+++ b/quapy/method/base.py
@@ -0,0 +1,21 @@
+from abc import ABCMeta, abstractmethod
+import quapy as qp
+
+
+# Base Quantifier abstract class
+# ------------------------------------
+class BaseQuantifier(metaclass=ABCMeta):
+
+ @abstractmethod
+ def fit(self, data: qp.LabelledCollection, *args): ...
+
+ @abstractmethod
+ def quantify(self, documents, *args): ...
+
+ @abstractmethod
+ def set_params(self, **parameters): ...
+
+ @abstractmethod
+ def get_params(self, deep=True): ...
+
+
diff --git a/quapy/method/non_aggregative.py b/quapy/method/non_aggregative.py
new file mode 100644
index 0000000..1bd7c89
--- /dev/null
+++ b/quapy/method/non_aggregative.py
@@ -0,0 +1,21 @@
+from quapy import LabelledCollection
+from .base import BaseQuantifier
+
+
+
+class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier):
+
+ def __init__(self, **kwargs):
+ pass
+
+ def fit(self, data: LabelledCollection, *args):
+ self.estimated_prevalence = data.prevalence()
+
+ def quantify(self, documents, *args):
+ return self.estimated_prevalence
+
+ def get_params(self):
+ pass
+
+ def set_params(self, **parameters):
+ pass
diff --git a/svm-perf-quantification-ext.patch b/svm-perf-quantification-ext.patch
new file mode 100644
index 0000000..df59328
--- /dev/null
+++ b/svm-perf-quantification-ext.patch
@@ -0,0 +1,540 @@
+diff -ruN svm_perf/svm_struct/svm_struct_main.c svm_perf_quantification/svm_struct/svm_struct_main.c
+--- svm_perf/svm_struct/svm_struct_main.c 2020-10-28 12:23:19.000000000 +0100
++++ svm_perf_quantification/svm_struct/svm_struct_main.c 2020-10-28 12:23:53.000000000 +0100
+@@ -128,7 +128,8 @@
+ struct_parm->newconstretrain=100;
+ struct_parm->ccache_size=5;
+ struct_parm->batch_size=100;
+-
++ struct_parm->loss_parm=1.0;
++ struct_parm->beta=1.0; // AIC-QBETA
+ strcpy (modelfile, "svm_struct_model");
+ strcpy (learn_parm->predfile, "trans_predictions");
+ strcpy (learn_parm->alphafile, "");
+@@ -170,6 +171,7 @@
+ case 'p': i++; struct_parm->slack_norm=atol(argv[i]); break;
+ case 'e': i++; struct_parm->epsilon=atof(argv[i]); break;
+ case 'k': i++; struct_parm->newconstretrain=atol(argv[i]); break;
++ case 'j': i++; struct_parm->beta=atof(argv[i]); break; // AIC-QBETA
+ case 'h': i++; learn_parm->svm_iter_to_shrink=atol(argv[i]); break;
+ case '#': i++; learn_parm->maxiter=atol(argv[i]); break;
+ case 'm': i++; learn_parm->kernel_cache_size=atol(argv[i]); break;
+@@ -189,6 +191,7 @@
+ case '-': strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]);i++; strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]);break;
+ case 'v': i++; (*struct_verbosity)=atol(argv[i]); break;
+ case 'y': i++; (*verbosity)=atol(argv[i]); break;
++ case '!': i++; struct_parm->loss_parm=atof(argv[i]); break;
+ default: printf("\nUnrecognized option %s!\n\n",argv[i]);
+ print_help();
+ exit(0);
+@@ -396,6 +399,9 @@
+ printf(" (in the same order as in the training set)\n");
+ printf("Application-Specific Options:\n");
+ print_struct_help();
++ printf("*************************************************\n"); // AIC-QBETA
++ printf(" -j float -> parameter beta for qbeta-based loss functions (default: 1.0)\n");
++ printf("*************************************************\n");
+ wait_any_key();
+
+ printf("\nMore details in:\n");
+diff -ruN svm_perf/svm_struct_api.c svm_perf_quantification/svm_struct_api.c
+--- svm_perf/svm_struct_api.c 2020-10-28 12:23:19.000000000 +0100
++++ svm_perf_quantification/svm_struct_api.c 2020-10-28 12:23:53.000000000 +0100
+@@ -20,6 +20,7 @@
+ #include <stdio.h>
+ #include <stdlib.h>
+ #include <string.h>
++#include <math.h>
+ #include "svm_struct_api.h"
+ #include "svm_light/svm_common.h"
+ #include "svm_struct/svm_struct_common.h"
+@@ -27,7 +28,9 @@
+
+ #define MAX(x,y) ((x) < (y) ? (y) : (x))
+ #define MIN(x,y) ((x) > (y) ? (y) : (x))
++#define ABS(x) ((x) < (0) ? (-(x)) : (x))
+ #define SIGN(x) ((x) > (0) ? (1) : (((x) < (0) ? (-1) : (0))))
++#define PI (3.141592653589793)
+
+ int compareup(const void *a, const void *b)
+ {
+@@ -72,6 +75,16 @@
+ double rocarea(LABEL y, LABEL ybar);
+ double prbep(LABEL y, LABEL ybar);
+ double avgprec(LABEL y, LABEL ybar);
++/* AIC-QBETA */
++double gm(int a, int b, int c, int d);
++double nae(int a, int b, int c, int d);
++double ae(int a, int b, int c, int d);
++double rae(int a, int b, int c, int d);
++double Qbeta(int a, int b, int c, int d, double beta);
++double Qbeta_acc(int a, int b, int c, int d, double beta);
++double Qbeta_f1(int a, int b, int c, int d, double beta);
++double Qbeta_gm(int a, int b, int c, int d, double beta);
++/* AIC-QBETA */
+
+ double zeroone_loss(int a, int b, int c, int d);
+ double fone_loss(int a, int b, int c, int d);
+@@ -82,6 +95,23 @@
+ double swappedpairs_loss(LABEL y, LABEL ybar);
+ double avgprec_loss(LABEL y, LABEL ybar);
+
++double kldiv(int a, int b, int c, int d); // KLD
++double kldiv_loss(int a, int b, int c, int d); // KLD
++double nkldiv_loss(int a, int b, int c, int d); // KLD
++
++double milli_loss(int a, int b, int c, int d); //MILL
++
++/* AIC-QBETA */
++double gm_loss(int a, int b, int c, int d);
++double nae_loss(int a, int b, int c, int d);
++double ae_loss(int a, int b, int c, int d);
++double rae_loss(int a, int b, int c, int d);
++double Qbeta_loss(int a, int b, int c, int d, double beta);
++double Qbeta_acc_loss(int a, int b, int c, int d, double beta);
++double Qbeta_f1_loss(int a, int b, int c, int d, double beta);
++double Qbeta_gm_loss(int a, int b, int c, int d, double beta);
++/* AIC-QBETA */
++
+ void svm_struct_learn_api_init(int argc, char* argv[])
+ {
+ /* Called in learning part before anything else is done to allow
+@@ -181,10 +211,22 @@
+ /* change label value for better scaling using thresholdmetrics */
+ if((sparm->loss_function == ZEROONE)
+ || (sparm->loss_function == FONE)
++ || (sparm->loss_function == GM) // AIC-QBETA
++ || (sparm->loss_function == NAE) // AIC-QBETA
++ || (sparm->loss_function == AE) // AIC-QBETA
++ || (sparm->loss_function == RAE) // AIC-QBETA
++ || (sparm->loss_function == QBETA) // AIC-QBETA
++ || (sparm->loss_function == QBETA_ACC) // AIC-QBETA
++ || (sparm->loss_function == QBETA_F1) // AIC-QBETA
++ || (sparm->loss_function == QBETA_GM) // AIC-QBETA
+ || (sparm->loss_function == ERRORRATE)
+ || (sparm->loss_function == PRBEP)
+ || (sparm->loss_function == PREC_K)
+- || (sparm->loss_function == REC_K)) {
++ || (sparm->loss_function == REC_K)
++ || (sparm->loss_function == KLD)
++ || (sparm->loss_function == NKLD)
++ || (sparm->loss_function == MILLI)
++ ) {
+ for(i=0;i<sample.examples[0].x.totdoc;i++) {
+ if(sample.examples[0].y.class[i]>0)
+ sample.examples[0].y.class[i]=0.5*100.0/(numn+nump);
+@@ -520,10 +562,22 @@
+ LABEL ybar;
+ if((sparm->loss_function == ZEROONE)
+ || (sparm->loss_function == FONE)
++ || (sparm->loss_function == GM) // AIC-QBETA
++ || (sparm->loss_function == NAE) // AIC-QBETA
++ || (sparm->loss_function == AE) // AIC-QBETA
++ || (sparm->loss_function == RAE) // AIC-QBETA
++ || (sparm->loss_function == QBETA) // AIC-QBETA
++ || (sparm->loss_function == QBETA_ACC) // AIC-QBETA
++ || (sparm->loss_function == QBETA_F1) // AIC-QBETA
++ || (sparm->loss_function == QBETA_GM) // AIC-QBETA
+ || (sparm->loss_function == ERRORRATE)
+ || (sparm->loss_function == PRBEP)
+ || (sparm->loss_function == PREC_K)
+- || (sparm->loss_function == REC_K)) {
++ || (sparm->loss_function == REC_K)
++ || (sparm->loss_function == KLD)
++ || (sparm->loss_function == NKLD)
++ || (sparm->loss_function == MILLI)
++ ) {
+ ybar=find_most_violated_constraint_thresholdmetric(x,y,sm,sparm,
+ sparm->loss_type);
+ }
+@@ -562,9 +616,21 @@
+ sparm->loss_type); */
+ else if((sparm->loss_function == ZEROONE)
+ || (sparm->loss_function == FONE)
++ || (sparm->loss_function == GM) // AIC-QBETA
++ || (sparm->loss_function == NAE) // AIC-QBETA
++ || (sparm->loss_function == AE) // AIC-QBETA
++ || (sparm->loss_function == RAE) // AIC-QBETA
++ || (sparm->loss_function == QBETA) // AIC-QBETA
++ || (sparm->loss_function == QBETA_ACC) // AIC-QBETA
++ || (sparm->loss_function == QBETA_F1) // AIC-QBETA
++ || (sparm->loss_function == QBETA_GM) // AIC-QBETA
+ || (sparm->loss_function == PRBEP)
+ || (sparm->loss_function == PREC_K)
+- || (sparm->loss_function == REC_K))
++ || (sparm->loss_function == REC_K)
++ || (sparm->loss_function == KLD)
++ || (sparm->loss_function == NKLD)
++ || (sparm->loss_function == MILLI)
++ )
+ ybar=find_most_violated_constraint_thresholdmetric(x,y,sm,sparm,
+ sparm->loss_type);
+ else if((sparm->loss_function == SWAPPEDPAIRS))
+@@ -741,7 +807,23 @@
+ if(sparm->loss_function == ZEROONE)
+ loss=zeroone_loss(a,numn-d,nump-a,d);
+ else if(sparm->loss_function == FONE)
+- loss=fone_loss(a,numn-d,nump-a,d);
++ loss=fone_loss(a,numn-d,nump-a,d);
++ else if(sparm->loss_function == GM) // AIC-QBETA
++ loss=gm_loss(a,numn-d,nump-a,d);
++ else if(sparm->loss_function == NAE) // AIC-QBETA
++ loss=nae_loss(a,numn-d,nump-a,d);
++ else if(sparm->loss_function == AE) // AIC-QBETA
++ loss=ae_loss(a,numn-d,nump-a,d);
++ else if(sparm->loss_function == RAE) // AIC-QBETA
++ loss=rae_loss(a,numn-d,nump-a,d);
++ else if(sparm->loss_function == QBETA) // AIC-QBETA
++ loss=Qbeta_loss(a,numn-d,nump-a,d,sparm->beta);
++ else if(sparm->loss_function == QBETA_ACC) // AIC-QBETA
++ loss=Qbeta_acc_loss(a,numn-d,nump-a,d,sparm->beta);
++ else if(sparm->loss_function == QBETA_F1) // AIC-QBETA
++ loss=Qbeta_f1_loss(a,numn-d,nump-a,d,sparm->beta);
++ else if(sparm->loss_function == QBETA_GM) // AIC-QBETA
++ loss=Qbeta_gm_loss(a,numn-d,nump-a,d,sparm->beta);
+ else if(sparm->loss_function == ERRORRATE)
+ loss=errorrate_loss(a,numn-d,nump-a,d);
+ else if((sparm->loss_function == PRBEP) && (a+numn-d == nump))
+@@ -750,6 +832,12 @@
+ loss=prec_k_loss(a,numn-d,nump-a,d);
+ else if((sparm->loss_function == REC_K) && (a+numn-d <= prec_rec_k))
+ loss=rec_k_loss(a,numn-d,nump-a,d);
++ else if(sparm->loss_function == KLD) //KLD
++ loss=kldiv_loss(a,numn-d,nump-a,d); //KLD
++ else if(sparm->loss_function == NKLD) //KLD
++ loss=nkldiv_loss(a,numn-d,nump-a,d); //KLD
++ else if(sparm->loss_function == MILLI) //MILLI
++ loss=milli_loss(a,numn-d,nump-a,d); //MILLI
+ else {
+ loss=0;
+ }
+@@ -1213,6 +1301,7 @@
+ }
+ /* printf("%f %f\n",y.class[i],ybar.class[i]); */
+ }
++ //printf("********** loss %d (a,b,c,d) (%d,%d,%d,%d) beta=%f\n",sparm->loss_function,a,b,c,d,sparm->beta);
+ /* Return the loss according to the selected loss function. */
+ if(sparm->loss_function == ZEROONE) { /* type 0 loss: 0/1 loss */
+ /* return 0, if y==ybar. return 1 else */
+@@ -1221,6 +1310,30 @@
+ else if(sparm->loss_function == FONE) {
+ loss=fone_loss(a,b,c,d);
+ }
++ else if(sparm->loss_function == GM) { // AIC-QBETA
++ loss=gm_loss(a,b,c,d);
++ }
++ else if(sparm->loss_function == NAE) { // AIC-QBETA
++ loss=nae_loss(a,b,c,d);
++ }
++ else if(sparm->loss_function == AE) { // AIC-QBETA
++ loss=ae_loss(a,b,c,d);
++ }
++ else if(sparm->loss_function == RAE) { // AIC-QBETA
++ loss=rae_loss(a,b,c,d);
++ }
++ else if(sparm->loss_function == QBETA) { // AIC-QBETA
++ loss=Qbeta_loss(a,b,c,d,sparm->beta);
++ }
++ else if(sparm->loss_function == QBETA_ACC) { // AIC-QBETA
++ loss=Qbeta_acc_loss(a,b,c,d,sparm->beta);
++ }
++ else if(sparm->loss_function == QBETA_F1) { // AIC-QBETA
++ loss=Qbeta_f1_loss(a,b,c,d,sparm->beta);
++ }
++ else if(sparm->loss_function == QBETA_GM) { // AIC-QBETA
++ loss=Qbeta_gm_loss(a,b,c,d,sparm->beta);
++ }
+ else if(sparm->loss_function == ERRORRATE) {
+ loss=errorrate_loss(a,b,c,d);
+ }
+@@ -1242,6 +1355,15 @@
+ else if(sparm->loss_function == AVGPREC) {
+ loss=avgprec_loss(y,ybar);
+ }
++ else if(sparm->loss_function == KLD) { //KLD
++ loss=kldiv_loss(a,b,c,d); //KLD
++ } //KLD
++ else if(sparm->loss_function == NKLD) { //KLD
++ loss=nkldiv_loss(a,b,c,d); //KLD
++ } //KLD
++ else if(sparm->loss_function == MILLI) { //MILLI
++ loss=milli_loss(a,b,c,d); //MILLI
++ } //MILLI
+ else {
+ /* Put your code for different loss functions here. But then
+ find_most_violated_constraint_???(x, y, sm) has to return the
+@@ -1320,6 +1442,16 @@
+ printf("PRBEP : %5.2f\n",teststats->prbep);
+ printf("ROCArea : %5.2f\n",teststats->rocarea);
+ printf("AvgPrec : %5.2f\n",teststats->avgprec);
++ printf("Qb : %5.2f\n",teststats->Qbeta);
++ printf("Qb (Acc) : %5.2f\n",teststats->Qbeta_acc);
++ printf("Qb (F1) : %5.2f\n",teststats->Qbeta_f1);
++ printf("Qb (GM) : %5.2f\n",teststats->Qbeta_gm);
++ printf("NAE : %5.2f\n",teststats->nae);
++ printf("AE : %5.2f\n",teststats->ae);
++ printf("RAE : %5.2f\n",teststats->rae);
++ printf("GM : %5.2f\n",teststats->gm);
++ printf("KLD : %5.2f\n",teststats->kld);
++ printf("NKLD : %5.2f\n",teststats->nkld);
+ }
+ else {
+ printf("NOTE: %ld test examples are unlabeled, so performance cannot be computed. The\n",teststats->test_data_unlabeled);
+@@ -1352,6 +1484,29 @@
+ teststats->recall=100.0-loss(ex.y,ypred,sparm);
+ sparm->loss_function=FONE;
+ teststats->fone=100.0-loss(ex.y,ypred,sparm);
++
++ sparm->loss_function=GM; // AIC-QBETA
++ teststats->gm=100.0-loss(ex.y,ypred,sparm);
++ sparm->loss_function=NAE; // AIC-QBETA
++ teststats->nae=100.0-loss(ex.y,ypred,sparm);
++ sparm->loss_function=AE; // AIC-QBETA
++ teststats->ae=100.0-loss(ex.y,ypred,sparm);
++ sparm->loss_function=RAE; // AIC-QBETA
++ teststats->rae=100.0-loss(ex.y,ypred,sparm);
++ sparm->loss_function=QBETA; // AIC-QBETA
++ teststats->Qbeta=100.0-loss(ex.y,ypred,sparm);
++ sparm->loss_function=QBETA_ACC; // AIC-QBETA
++ teststats->Qbeta_acc=100.0-loss(ex.y,ypred,sparm);
++ sparm->loss_function=QBETA_F1; // AIC-QBETA
++ teststats->Qbeta_f1=100.0-loss(ex.y,ypred,sparm);
++ sparm->loss_function=QBETA_GM; // AIC-QBETA
++ teststats->Qbeta_gm=100.0-loss(ex.y,ypred,sparm);
++
++ sparm->loss_function=KLD; // KLD
++ teststats->kld=100-loss(ex.y,ypred,sparm);
++ sparm->loss_function=NKLD; // KLD
++ teststats->nkld=100.0-loss(ex.y,ypred,sparm);
++
+ teststats->prbep=prbep(ex.y,ypred);
+ teststats->rocarea=rocarea(ex.y,ypred);
+ teststats->avgprec=avgprec(ex.y,ypred);
+@@ -1403,6 +1558,7 @@
+ STRUCTMODEL sm;
+
+ sm.svm_model=read_model(file);
++ sparm->beta = 1; // AIC-QBETA *****************************
+ sparm->loss_function=ERRORRATE;
+ sparm->bias=0;
+ sparm->bias_featurenum=0;
+@@ -1514,6 +1670,18 @@
+ printf(" %2d Prec@k: 100 minus precision at k in percent.\n",PREC_K);
+ printf(" %2d Rec@k: 100 minus recall at k in percent.\n",REC_K);
+ printf(" %2d ROCArea: Percentage of swapped pos/neg pairs (i.e. 100 - ROCArea).\n\n",SWAPPEDPAIRS);
++ printf(" %2d Kullback-Leibler divergence.\n",KLD); //KLD
++ printf(" %2d Normalized Kullback-Leibler divergence.\n",NKLD); //KLD
++ printf(" %2d MILLI.\n",MILLI); //MILLI
++ printf(" %2d GM: geometric mean of tpr and tnr\n",GM); // AIC-QBETA
++ printf(" %2d NAE: normalized absolute error (Esuli & Sebastiani)\n",NAE); // AIC-QBETA
++ printf(" %2d AE: absolute error (Esuli & Sebastiani)\n",AE); // AIC-QBETA
++ printf(" %2d RAE: relative absolute error (Esuli & Sebastiani)\n",RAE); // AIC-QBETA
++ printf(" %2d Qbeta: 100 minus the Qbeta-score in percent (with recall)\n",QBETA); // AIC-QBETA
++ printf(" %2d Qbeta_acc: 100 minus the Qbeta-score in percent (with acc)\n",QBETA_ACC); // AIC-QBETA
++ printf(" %2d Qbeta_f1: 100 minus the Qbeta-score in percent (with F1)\n",QBETA_F1); // AIC-QBETA
++ printf(" %2d Qbeta_gm: 100 minus the Qbeta-score in percent (with GM)\n",QBETA_GM); // AIC-QBETA
++
+ printf("NOTE: The '-c' parameters in SVM-light and SVM-perf are related as\n");
+ printf(" c_light = c_perf*100/n for the 'Errorrate' loss function, where n is the\n");
+ printf(" number of training examples.\n\n");
+@@ -1785,7 +1953,65 @@
+ free(predset);
+ return(100.0*(apr/(double)(nump)));
+ }
+-
++/* AIC-QBETA */
++double tnr(int a, int b, int c, int d)
++{
++ /* Returns tnr as fractional value. */
++ if((b+d) == 0) return(0.0);
++ return((double)d/(double)(b+d));
++}
++double gm(int a, int b, int c, int d)
++{
++ double tprate = rec(a,b,c,d);
++ double tnrate = tnr(a,b,c,d);
++ return sqrt( tprate * tnrate );
++}
++double nae(int a, int b, int c, int d)
++{
++ double maximo = (a+c > b+d? a+c: b+d);
++ return 1.0 - ( (double)abs(c-b) / maximo);
++ //return 1.0 - ( (double)abs(c-b) / (double)(a+b+c+d)); // ABSERR
++}
++double ae(int a, int b, int c, int d)
++{
++ return (double)abs(c-b) / (double)(a+b+c+d) ; // ABSERR
++}
++double rae(int a, int b, int c, int d)
++{
++ double absdif = (double)abs(c-b) ;
++ double smooth_rae_pos = absdif / ((double)(a+c+0.5)) ;
++ double smooth_rae_neg = absdif / ((double)(b+d+0.5)) ;
++ return 0.5*(smooth_rae_pos + smooth_rae_neg) ;
++}
++double Qbeta(int a, int b, int c, int d, double beta)
++{
++ if(a+c == 0) return(0.0);
++ double qperf=nae(a,b,c,d);
++ double cperf=rec(a,b,c,d);
++ return((1+beta*beta)*qperf*cperf/((beta*beta*cperf)+qperf));
++}
++double Qbeta_acc(int a, int b, int c, int d, double beta)
++{
++ if(a+c == 0) return(0.0);
++ double qperf=nae(a,b,c,d);
++ double cperf=1.0-errorrate(a,b,c,d);
++ return((1+beta*beta)*qperf*cperf/((beta*beta*cperf)+qperf));
++}
++double Qbeta_f1(int a, int b, int c, int d, double beta)
++{
++ if(a+c == 0) return(0.0);
++ double qperf=nae(a,b,c,d);
++ double cperf=fone(a,b,c,d);
++ return((1+beta*beta)*qperf*cperf/((beta*beta*cperf)+qperf));
++}
++double Qbeta_gm(int a, int b, int c, int d, double beta)
++{
++ if(a+c == 0) return(0.0);
++ double qperf=nae(a,b,c,d);
++ double cperf=gm(a,b,c,d);
++ return((1+beta*beta)*qperf*cperf/((beta*beta*cperf)+qperf));
++}
++/* AIC-QBETA */
+ /*------- Loss functions based on performance measures --------*/
+
+ double zeroone_loss(int a, int b, int c, int d)
+@@ -1846,4 +2072,70 @@
+ }
+
+
++//KLD
++double kldiv(int a, int b, int c, int d)
++{
++ double sum = a+b+c+d+1.0;
++ double pab = (a+b+0.5)/sum;
++ double pac = (a+c+0.5)/sum;
++ double pbd = (b+d+0.5)/sum;
++ double pcd = (c+d+0.5)/sum;
++
++ double kl = pac*log(pac/pab)+pbd*log(pbd/pcd);
++
++ return kl;
++}
++
++//KLD
++double kldiv_loss(int a, int b, int c, int d)
++{
++ return kldiv(a,b,c,d);
++}
++
++//KLD
++double nkldiv_loss(int a, int b, int c, int d)
++{
++ return 100.0-(100.0*2.0/(1.0+exp(kldiv(a,b,c,d))));
++}
++
++//MILLI
++double milli_loss(int a, int b, int c, int d)
++{
++ int sum = a+b+c+d;
++ return 100.0*(b+c)*ABS(b-c);
++}
+
++/* AIC-QBETA */
++double gm_loss(int a, int b, int c, int d)
++{
++ return 100.0 * (1.0-gm(a,b,c,d));
++}
++double nae_loss(int a, int b, int c, int d)
++{
++ return 100.0 * (1.0-nae(a,b,c,d));
++}
++double ae_loss(int a, int b, int c, int d)
++{
++ return 100.0 * ae(a,b,c,d);
++}
++double rae_loss(int a, int b, int c, int d)
++{
++ return 100.0 * rae(a,b,c,d);
++}
++double Qbeta_loss(int a, int b, int c, int d,double beta)
++{
++ return(100.0*(1.0-Qbeta(a,b,c,d,beta)));
++}
++double Qbeta_acc_loss(int a, int b, int c, int d,double beta)
++{
++ return(100.0*(1.0-Qbeta_acc(a,b,c,d,beta)));
++}
++double Qbeta_f1_loss(int a, int b, int c, int d,double beta)
++{
++ return(100.0*(1.0-Qbeta_f1(a,b,c,d,beta)));
++}
++double Qbeta_gm_loss(int a, int b, int c, int d,double beta)
++{
++ return(100.0*(1.0-Qbeta_gm(a,b,c,d,beta)));
++}
++/* AIC-QBETA */
+diff -ruN svm_perf/svm_struct_api_types.h svm_perf_quantification/svm_struct_api_types.h
+--- svm_perf/svm_struct_api_types.h 2020-10-28 12:23:19.000000000 +0100
++++ svm_perf_quantification/svm_struct_api_types.h 2020-10-28 12:23:53.000000000 +0100
+@@ -28,14 +28,25 @@
+ # define INST_VERSION_DATE "15.07.2009"
+
+ /* Identifiers for loss functions */
+-#define ZEROONE 0
+-#define FONE 1
+-#define ERRORRATE 2
+-#define PRBEP 3
+-#define PREC_K 4
+-#define REC_K 5
+-#define SWAPPEDPAIRS 10
+-#define AVGPREC 11
++#define ZEROONE 0
++#define FONE 1
++#define ERRORRATE 2
++#define PRBEP 3
++#define PREC_K 4
++#define REC_K 5
++#define SWAPPEDPAIRS 10
++#define AVGPREC 11
++#define KLD 12 //KLD
++#define NKLD 13 //KLD
++#define MILLI 16 //MILLI
++#define GM 20 // AIC-QBETA
++#define NAE 21 // AIC-QBETA
++#define QBETA 22 // AIC-QBETA
++#define QBETA_ACC 23 // AIC-QBETA
++#define QBETA_F1 24 // AIC-QBETA
++#define QBETA_GM 25 // AIC-QBETA
++#define AE 26 // AIC-QBETA
++#define RAE 27 // AIC-QBETA
+
+ /* default precision for solving the optimization problem */
+ # define DEFAULT_EPS 0.1
+@@ -169,6 +180,8 @@
+ svm_perf_classify. This uses more
+ memory, but is faster if the support
+ vectors in the model are dense. */
++ double loss_parm;
++ double beta; /* AIC-QBETA */
+ } STRUCT_LEARN_PARM;
+
+ typedef struct struct_test_stats {
+@@ -183,6 +196,16 @@
+ double prbep;
+ double rocarea;
+ double avgprec;
++ double kld; //KLD
++ double nkld; //KLD
++ double gm; // AIC-QBETA
++ double nae; // AIC-QBETA
++ double ae; // AIC-QBETA
++ double rae; // AIC-QBETA
++ double Qbeta; // AIC-QBETA
++ double Qbeta_acc; // AIC-QBETA
++ double Qbeta_f1; // AIC-QBETA
++ double Qbeta_gm; // AIC-QBETA
+ } STRUCT_TEST_STATS;
+
+ typedef struct struct_id_score {