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merging with office branch

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Alejandro Moreo Fernandez 2025-06-15 11:59:32 +02:00
parent 48defb4261
commit 4cfb97c165
8 changed files with 26 additions and 23 deletions
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8
CHANGE_LOG.txt
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@ -4,7 +4,7 @@ Change Log 0.1.10
CLEAN TODO-FILE
- Base code Refactor:
- Removing coupling between LabelledCollection and quantification methods. E.g.:
- Removing coupling between LabelledCollection and quantification methods; the fit interface changes:
def fit(data:LabelledCollection): -> def fit(X, y):
- Adding function "predict" (function "quantify" is still present as an alias)
- Aggregative methods's behavior in terms of fit_classifier and how to treat the val_split is now
@ -14,13 +14,13 @@ CLEAN TODO-FILE
in which case the first argument is unused, and this was ambiguous with
my_acc.fit(the_data, fit_classifier=False)
in which case the_data is to be used for validation purposes. However, the val_split could be set as a fraction
indicating only part of the_data must be used for validation, and the rest wasted... it was confusing.
indicating only part of the_data must be used for validation, and the rest wasted... it was certainly confusing.
- EMQ has been modified, so that the representation function "classify" now only provides posterior
probabilities and, if required, these are recalibrated (e.g., by "bcts") during the aggregation function.
- A new parameter "on_calib_error" is passed to the constructor, which informs of the policy to follow
in case the calibration functions failed. Options include:
in case the abstention's calibration functions failed (which happens sometimes). Options include:
- 'raise': raises a RuntimeException (default)
- 'backup': avoids calibration
- 'backup': reruns avoiding calibration
- Parameter "recalib" has been renamed "calib"
- Added aggregative bootstrap for deriving confidence regions (confidence intervals, ellipses in the simplex, or
ellipses in the CLR space). This method is efficient as it leverages the two-phases of the aggregative quantifiers.

2
quapy/__init__.py
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@ -14,7 +14,7 @@ from . import model_selection
from . import classification
import os
__version__ = '0.1.10r'
__version__ = '0.2.0'
environ = {
'SAMPLE_SIZE': None,

5
quapy/data/datasets.py
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@ -548,25 +548,20 @@ def fetch_UCIBinaryLabelledCollection(dataset_name, data_home=None, standardize=
"""
if name == "acute.a":
X, y = data["X"], data["y"][:, 0]
# X, y = Xy[:, :-2], Xy[:, -2]
elif name == "acute.b":
X, y = data["X"], data["y"][:, 1]
# X, y = Xy[:, :-2], Xy[:, -1]
elif name == "wine-q-red":
X, y, color = data["X"], data["y"], data["color"]
# X, y, color = Xy[:, :-2], Xy[:, -2], Xy[:, -1]
red_idx = color == "red"
X, y = X[red_idx, :], y[red_idx]
y = (y > 5).astype(int)
elif name == "wine-q-white":
X, y, color = data["X"], data["y"], data["color"]
# X, y, color = Xy[:, :-2], Xy[:, -2], Xy[:, -1]
white_idx = color == "white"
X, y = X[white_idx, :], y[white_idx]
y = (y > 5).astype(int)
else:
X, y = data["X"], data["y"]
# X, y = Xy[:, :-1], Xy[:, -1]
y = binarize(y, pos_class=pos_class[name])

2
quapy/method/_threshold_optim.py
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@ -34,7 +34,7 @@ class ThresholdOptimization(BinaryAggregativeQuantifier):
"""
def __init__(self, classifier: BaseEstimator=None, fit_classifier=True, val_split=None, n_jobs=None):
super.__init__(classifier, fit_classifier, val_split)
super().__init__(classifier, fit_classifier, val_split)
self.n_jobs = qp._get_njobs(n_jobs)
@abstractmethod

12
quapy/method/aggregative.py
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@ -717,7 +717,7 @@ class EMQ(AggregativeSoftQuantifier):
super().__init__(classifier, fit_classifier, val_split)
self.exact_train_prev = exact_train_prev
self.calib = calib
self.on_calib_errors = on_calib_error
self.on_calib_error = on_calib_error
self.n_jobs = n_jobs
@classmethod
@ -790,9 +790,9 @@ class EMQ(AggregativeSoftQuantifier):
try:
self.calibration_function = calibrator(P, np.eye(n_classes)[y], posterior_supplied=True)
except Exception as e:
if self.on_calib_errors == 'raise':
if self.on_calib_error == 'raise':
raise RuntimeError(f'calibration {self.calib} failed at fit time: {e}')
elif self.on_calib_errors == 'backup':
elif self.on_calib_error == 'backup':
self.calibration_function = lambda P: P
def _calibrate_if_requested(self, uncalib_posteriors):
@ -800,12 +800,12 @@ class EMQ(AggregativeSoftQuantifier):
try:
calib_posteriors = self.calibration_function(uncalib_posteriors)
except Exception as e:
if self.on_calib_errors == 'raise':
if self.on_calib_error == 'raise':
raise RuntimeError(f'calibration {self.calib} failed at predict time: {e}')
elif self.on_calib_errors == 'backup':
elif self.on_calib_error == 'backup':
calib_posteriors = uncalib_posteriors
else:
raise ValueError(f'unexpected {self.on_calib_errors=}; '
raise ValueError(f'unexpected {self.on_calib_error=}; '
f'valid options are {EMQ.ON_CALIB_ERROR_VALUES}')
return calib_posteriors
return uncalib_posteriors

17
quapy/method/confidence.py
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@ -450,8 +450,17 @@ class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
:param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be
the one indicated in `qp.environ['DEFAULT_CLS']`
:param val_split: a float in (0, 1) indicating the proportion of the training data to be used,
as a stratified held-out validation set, for generating classifier predictions.
:param fit_classifier: whether to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.
: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; or as an integer (default 5), 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 tuple (X,y) defining the specific set of data to use for validation.
This hyperparameter is only meant to be used when the heuristics are to be applied, i.e., if a
calibration is required. The default value is None (meaning the calibration is not required). In
case this hyperparameter is set to a value other than None, but the calibration is not required
(calib=None), a warning message will be raised.
:param num_warmup: number of warmup iterations for the MCMC sampler (default 500)
:param num_samples: number of samples to draw from the posterior (default 1000)
:param mcmc_seed: random seed for the MCMC sampler (default 0)
@ -462,6 +471,7 @@ class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
"""
def __init__(self,
classifier: BaseEstimator=None,
fit_classifier=True,
val_split: int = 5,
num_warmup: int = 500,
num_samples: int = 1_000,
@ -480,8 +490,7 @@ class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
if _bayesian.DEPENDENCIES_INSTALLED is False:
raise ImportError("Auxiliary dependencies are required. Run `$ pip install quapy[bayes]` to install them.")
self.classifier = qp._get_classifier(classifier)
self.val_split = val_split
super().__init__(classifier, fit_classifier, val_split)
self.num_warmup = num_warmup
self.num_samples = num_samples
self.mcmc_seed = mcmc_seed

1
quapy/tests/test_datasets.py
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@ -106,7 +106,6 @@ class TestDatasets(unittest.TestCase):
self._check_samples(gen_val, q, max_samples_test=5, vectorizer=tfidf)
self._check_samples(gen_test, q, max_samples_test=5, vectorizer=tfidf)
def test_IFCB(self):
if os.environ.get('QUAPY_TESTS_OMIT_LARGE_DATASETS'):
print("omitting test_IFCB because QUAPY_TESTS_OMIT_LARGE_DATASETS is set")

2
quapy/tests/test_methods.py
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@ -64,7 +64,7 @@ class TestMethods(unittest.TestCase):
q = model()
print(f'testing {q} on dataset {dataset.name}')
q.fit(dataset.training)
q.fit(*dataset.training.Xy)
estim_prevalences = q.predict(dataset.test.X)
self.assertTrue(check_prevalence_vector(estim_prevalences))