1
0
Fork 0

svmperf in one-vs-all bugfix

This commit is contained in:
Alejandro Moreo Fernandez 2023-02-11 10:47:27 +01:00
parent 7b2d3cb7f1
commit 4c74ff02a3
3 changed files with 35 additions and 8 deletions

View File

@ -85,4 +85,5 @@ Things to fix:
--------------
- update unit tests
- improve plots
- svmperf clean temp dirs; check also ELM when instantiated using SVMperf directly

View File

@ -880,16 +880,27 @@ class ELM(AggregativeQuantifier, BinaryQuantifier):
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 classifier: an instance of `SVM perf` or None
: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):
def __init__(self, classifier=None, svmperf_base=None, loss='01', **kwargs):
self.svmperf_base = svmperf_base if svmperf_base is not None else qp.environ['SVMPERF_HOME']
self.loss = loss
self.kwargs = kwargs
assert classifier is None or isinstance(classifier, SVMperf), \
'param error "classifier": instances of ELM can only be instantiated with classifier SVMperf. ' \
'This parameter should either be an instance of SVMperf or None, in which case an SVMperf object ' \
'will be instantiaded using "svmperf_base" and "loss"'
if classifier is None:
self.classifier = SVMperf(self.svmperf_base, loss=self.loss, **self.kwargs)
else:
if classifier.loss != loss:
print(f'[warning]: the loss of the SVMperf object passed to arg "classifier" ({classifier.loss}) '
f'does not coincide with arg "loss" ({loss}); the latter will be ignored')
self.classifier = classifier
def fit(self, data: LabelledCollection, fit_classifier=True):
self._check_binary(data, self.__class__.__name__)
@ -913,11 +924,14 @@ class SVMQ(ELM):
>>> ELM(svmperf_base, loss='q', **kwargs)
:param classifier: not used, added for compatibility
: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):
def __init__(self, classifier=None, svmperf_base=None, **kwargs):
assert classifier == None, \
'param "classifier" should be None. SVMperf will be instantiated using "svmperf_base" path.'
super(SVMQ, self).__init__(svmperf_base, loss='q', **kwargs)
@ -929,11 +943,14 @@ class SVMKLD(ELM):
>>> ELM(svmperf_base, loss='kld', **kwargs)
:param classifier: not used, added for compatibility
: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):
def __init__(self, classifier=None, svmperf_base=None, **kwargs):
assert classifier == None, \
'param "classifier" should be None. SVMperf will be instantiated using "svmperf_base" path.'
super(SVMKLD, self).__init__(svmperf_base, loss='kld', **kwargs)
@ -946,11 +963,14 @@ class SVMNKLD(ELM):
>>> ELM(svmperf_base, loss='nkld', **kwargs)
:param classifier: not used, added for compatibility
: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):
def __init__(self, classifier=None, svmperf_base=None, **kwargs):
assert classifier == None, \
'param "classifier" should be None. SVMperf will be instantiated using "svmperf_base" path.'
super(SVMNKLD, self).__init__(svmperf_base, loss='nkld', **kwargs)
@ -962,11 +982,14 @@ class SVMAE(ELM):
>>> ELM(svmperf_base, loss='mae', **kwargs)
:param classifier: not used, added for compatibility
: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):
def __init__(self, classifier=None, svmperf_base=None, **kwargs):
assert classifier == None, \
'param "classifier" should be None. SVMperf will be instantiated using "svmperf_base" path.'
super(SVMAE, self).__init__(svmperf_base, loss='mae', **kwargs)
@ -978,11 +1001,14 @@ class SVMRAE(ELM):
>>> ELM(svmperf_base, loss='mrae', **kwargs)
:param classifier: not used, added for compatibility
: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):
def __init__(self, classifier=None, svmperf_base=None, **kwargs):
assert classifier == None, \
'param "classifier" should be None. SVMperf will be instantiated using "svmperf_base" path.'
super(SVMRAE, self).__init__(svmperf_base, loss='mrae', **kwargs)

View File

@ -38,7 +38,7 @@ def map_parallel(func, args, n_jobs):
return list(itertools.chain.from_iterable(results))
def parallel(func, args, n_jobs, seed = None):
def parallel(func, args, n_jobs, seed=None):
"""
A wrapper of multiprocessing: