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Alejandro Moreo Fernandez 2023-12-18 10:25:56 +01:00
commit c56fe9c09c
3 changed files with 139 additions and 168 deletions

View File

@ -53,6 +53,7 @@ with qp.util.temp_seed(0):
protocol=protocol,
error='mae', # the error to optimize is the MAE (a quantification-oriented loss)
refit=False, # retrain on the whole labelled set once done
raise_errors=False,
verbose=True # show information as the process goes on
).fit(training)
@ -66,5 +67,5 @@ model = model.best_model_
mae_score = qp.evaluation.evaluate(model, protocol=APP(test), error_metric='mae')
print(f'MAE={mae_score:.5f}')
print(f'model selection took {tend-tinit}s')
print(f'model selection took {tend-tinit:.1f}s')

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@ -5,7 +5,7 @@ from sklearn.neighbors import KernelDensity
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.aggregative import AggregativeProbabilisticQuantifier, cross_generate_predictions
from quapy.method.aggregative import AggregativeSoftQuantifier
import quapy.functional as F
from sklearn.metrics.pairwise import rbf_kernel
@ -33,7 +33,7 @@ class KDEBase:
class KDEyML(AggregativeProbabilisticQuantifier, KDEBase):
class KDEyML(AggregativeSoftQuantifier, KDEBase):
def __init__(self, classifier: BaseEstimator, val_split=10, bandwidth=0.1, n_jobs=None, random_state=0):
self._check_bandwidth(bandwidth)
@ -43,16 +43,8 @@ class KDEyML(AggregativeProbabilisticQuantifier, KDEBase):
self.n_jobs = n_jobs
self.random_state=random_state
def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, LabelledCollection] = None):
if val_split is None:
val_split = self.val_split
self.classifier, y, posteriors, _, _ = cross_generate_predictions(
data, self.classifier, val_split, probabilistic=True, fit_classifier=fit_classifier, n_jobs=self.n_jobs
)
self.mix_densities = self.get_mixture_components(posteriors, y, data.n_classes, self.bandwidth)
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
self.mix_densities = self.get_mixture_components(*classif_predictions.Xy, data.n_classes, self.bandwidth)
return self
def aggregate(self, posteriors: np.ndarray):
@ -76,7 +68,7 @@ class KDEyML(AggregativeProbabilisticQuantifier, KDEBase):
return F.optim_minimize(neg_loglikelihood, n_classes)
class KDEyHD(AggregativeProbabilisticQuantifier, KDEBase):
class KDEyHD(AggregativeSoftQuantifier, KDEBase):
def __init__(self, classifier: BaseEstimator, val_split=10, divergence: str='HD',
bandwidth=0.1, n_jobs=None, random_state=0, montecarlo_trials=10000):
@ -90,15 +82,8 @@ class KDEyHD(AggregativeProbabilisticQuantifier, KDEBase):
self.random_state=random_state
self.montecarlo_trials = montecarlo_trials
def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, LabelledCollection] = None):
if val_split is None:
val_split = self.val_split
self.classifier, y, posteriors, _, _ = cross_generate_predictions(
data, self.classifier, val_split, probabilistic=True, fit_classifier=fit_classifier, n_jobs=self.n_jobs
)
self.mix_densities = self.get_mixture_components(posteriors, y, data.n_classes, self.bandwidth)
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
self.mix_densities = self.get_mixture_components(*classif_predictions.Xy, data.n_classes, self.bandwidth)
N = self.montecarlo_trials
rs = self.random_state
@ -141,7 +126,7 @@ class KDEyHD(AggregativeProbabilisticQuantifier, KDEBase):
return F.optim_minimize(divergence, n_classes)
class KDEyCS(AggregativeProbabilisticQuantifier):
class KDEyCS(AggregativeSoftQuantifier):
def __init__(self, classifier: BaseEstimator, val_split=10, bandwidth=0.1, n_jobs=None, random_state=0):
KDEBase._check_bandwidth(bandwidth)
@ -163,19 +148,14 @@ class KDEyCS(AggregativeProbabilisticQuantifier):
gram = norm_factor * rbf_kernel(X, Y, gamma=gamma)
return gram.sum()
def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, LabelledCollection] = None):
if val_split is None:
val_split = self.val_split
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
self.classifier, y, posteriors, _, _ = cross_generate_predictions(
data, self.classifier, val_split, probabilistic=True, fit_classifier=fit_classifier, n_jobs=self.n_jobs
)
P, y = classif_predictions.Xy
n = data.n_classes
assert all(sorted(np.unique(y)) == np.arange(data.n_classes)), \
assert all(sorted(np.unique(y)) == np.arange(n)), \
'label name gaps not allowed in current implementation'
n = data.n_classes
P = posteriors
# counts_inv keeps track of the relative weight of each datapoint within its class
# (i.e., the weight in its KDE model)

View File

@ -23,54 +23,24 @@ class Status(Enum):
INVALID = 3
ERROR = 4
def check_status(func):
@wraps(func)
def wrapper(*args, **kwargs):
obj = args[0]
tinit = time()
job_descriptor = dict(args[1])
params = {**job_descriptor.get('cls-params', {}), **job_descriptor.get('q-params', {})}
class ConfigStatus:
def __init__(self, params, status, msg=''):
self.params = params
self.status = status
self.msg = msg
if obj.timeout > 0:
def handler(signum, frame):
raise TimeoutError()
def __str__(self):
return f':params:{self.params} :status:{self.status} ' + self.msg
signal.signal(signal.SIGALRM, handler)
signal.alarm(obj.timeout)
def __repr__(self):
return str(self)
try:
job_descriptor = func(*args, **kwargs)
def success(self):
return self.status == Status.SUCCESS
ttime = time() - tinit
score = job_descriptor.get('score', None)
if score is not None:
obj._sout(f'hyperparams=[{params}]\t got {obj.error.__name__} = {score:.5f} [took {ttime:.4f}s]')
if obj.timeout > 0:
signal.alarm(0)
exit_status = Status.SUCCESS
except TimeoutError:
obj._sout(f'timeout ({obj.timeout}s) reached for config {params}')
exit_status = Status.TIMEOUT
except ValueError as e:
obj._sout(f'the combination of hyperparameters {params} is invalid')
obj._sout(f'\tException: {e}')
exit_status = Status.INVALID
except Exception as e:
obj._sout(f'something went wrong for config {params}; skipping:')
obj._sout(f'\tException: {e}')
exit_status = Status.ERROR
job_descriptor['status'] = exit_status
job_descriptor['params'] = params
return job_descriptor
return wrapper
def failed(self):
return self.status != Status.SUCCESS
class GridSearchQ(BaseQuantifier):
@ -85,11 +55,14 @@ class GridSearchQ(BaseQuantifier):
:param protocol: a sample generation protocol, an instance of :class:`quapy.protocol.AbstractProtocol`
:param error: an error function (callable) or a string indicating the name of an error function (valid ones
are those in :class:`quapy.error.QUANTIFICATION_ERROR`
:param refit: whether or not to refit the model on the whole labelled collection (training+validation) with
:param refit: whether to refit the model on the whole labelled collection (training+validation) with
the best chosen hyperparameter combination. Ignored if protocol='gen'
:param timeout: establishes a timer (in seconds) for each of the hyperparameters configurations being tested.
Whenever a run takes longer than this timer, that configuration will be ignored. If all configurations end up
being ignored, a TimeoutError exception is raised. If -1 (default) then no time bound is set.
:param raise_errors: boolean, if True then raises an exception when a param combination yields any error, if
otherwise is False (default), then the combination is marked with an error status, but the process goes on.
However, if no configuration yields a valid model, then a ValueError exception will be raised.
:param verbose: set to True to get information through the stdout
"""
@ -101,6 +74,7 @@ class GridSearchQ(BaseQuantifier):
refit=True,
timeout=-1,
n_jobs=None,
raise_errors=False,
verbose=False):
self.model = model
@ -109,6 +83,7 @@ class GridSearchQ(BaseQuantifier):
self.refit = refit
self.timeout = timeout
self.n_jobs = qp._get_njobs(n_jobs)
self.raise_errors = raise_errors
self.verbose = verbose
self.__check_error(error)
assert isinstance(protocol, AbstractProtocol), 'unknown protocol'
@ -128,112 +103,97 @@ class GridSearchQ(BaseQuantifier):
raise ValueError(f'unexpected error type; must either be a callable function or a str representing\n'
f'the name of an error function in {qp.error.QUANTIFICATION_ERROR_NAMES}')
def _prepare_classifier(self, args):
cls_params = args['cls-params']
training = args['training']
def _prepare_classifier(self, cls_params):
model = deepcopy(self.model)
model.set_params(**cls_params)
predictions = model.classifier_fit_predict(training)
return {'model': model, 'predictions': predictions, 'cls-params': cls_params}
def job(cls_params):
model.set_params(**cls_params)
predictions = model.classifier_fit_predict(self._training)
return predictions
predictions, status, took = self._error_handler(job, cls_params)
self._sout(f'[classifier fit] hyperparams={cls_params} status={status} [took {took:.3f}s]')
return model, predictions, status, took
def _prepare_aggregation(self, args):
model = args['model']
predictions = args['predictions']
cls_params = args['cls-params']
q_params = args['q-params']
training = args['training']
model, predictions, cls_took, cls_params, q_params = args
model = deepcopy(model)
params = {**cls_params, **q_params}
def job(model):
tinit = time()
model = deepcopy(model)
# overrides default parameters with the parameters being explored at this iteration
def job(q_params):
model.set_params(**q_params)
model.aggregation_fit(predictions, training)
model.aggregation_fit(predictions, self._training)
score = evaluation.evaluate(model, protocol=self.protocol, error_metric=self.error)
ttime = time()-tinit
return score
return {
'model': model,
'cls-params':cls_params,
'q-params': q_params,
'params': params,
'score': score,
'ttime':ttime
}
score, status, aggr_took = self._error_handler(job, q_params)
self._print_status(params, score, status, aggr_took)
return model, params, score, status, (cls_took+aggr_took)
out, status = self._error_handler(job, args)
if status == Status.SUCCESS:
self._sout(f'hyperparams=[{params}]\t got {self.error.__name__} = {out["score"]:.5f} [took {out["time"]:.4f}s]')
elif status == Status.INVALID:
self._sout(f'the combination of hyperparameters {params} is invalid')
elif status == Status.
def _prepare_model(self, args):
params, training = args
def _prepare_nonaggr_model(self, params):
model = deepcopy(self.model)
# overrides default parameters with the parameters being explored at this iteration
model.set_params(**params)
model.fit(training)
score = evaluation.evaluate(model, protocol=self.protocol, error_metric=self.error)
return {'model': model, 'params': params, 'score': score}
def job(params):
model.set_params(**params)
model.fit(self._training)
score = evaluation.evaluate(model, protocol=self.protocol, error_metric=self.error)
return score
score, status, took = self._error_handler(job, params)
self._print_status(params, score, status, took)
return model, params, score, status, took
def _compute_scores_aggregative(self, training):
# break down the set of hyperparameters into two: classifier-specific, quantifier-specific
cls_configs, q_configs = group_params(self.param_grid)
# train all classifiers and get the predictions
partial_setups = qp.util.parallel(
self._training = training
cls_outs = qp.util.parallel(
self._prepare_classifier,
({'cls-params':params, 'training':training} for params in cls_configs),
seed=qp.environ.get('_R_SEED', None),
n_jobs=self.n_jobs,
asarray=False,
)
# filter out classifier configurations that yield any error
for setup in partial_setups:
if setup['status'] != Status.SUCCESS:
self._sout(f'-> classifier hyperparemters {setup["params"]} caused '
f'error {setup["status"]} and will be ignored')
partial_setups = [setup for setup in partial_setups if setup['status']==Status.SUCCESS]
if len(partial_setups) == 0:
raise ValueError('No valid configuration found for the classifier.')
# explore the quantifier-specific hyperparameters for each training configuration
scores = qp.util.parallel(
self._prepare_aggregation,
({'q-params': setup[1], 'training': training, **setup[0]} for setup in itertools.product(partial_setups, q_configs)),
cls_configs,
seed=qp.environ.get('_R_SEED', None),
n_jobs=self.n_jobs
)
return scores
# filter out classifier configurations that yielded any error
success_outs = []
for (model, predictions, status, took), cls_config in zip(cls_outs, cls_configs):
if status.success():
success_outs.append((model, predictions, took, cls_config))
else:
self.error_collector.append(status)
if len(success_outs) == 0:
raise ValueError('No valid configuration found for the classifier!')
# explore the quantifier-specific hyperparameters for each valid training configuration
aggr_configs = [(*out, q_config) for out, q_config in itertools.product(success_outs, q_configs)]
aggr_outs = qp.util.parallel(
self._prepare_aggregation,
aggr_configs,
seed=qp.environ.get('_R_SEED', None),
n_jobs=self.n_jobs
)
return aggr_outs
def _compute_scores_nonaggregative(self, training):
configs = expand_grid(self.param_grid)
# pass a seed to parallel, so it is set in child processes
self._training = training
scores = qp.util.parallel(
self._prepare_model,
((params, training) for params in configs),
self._prepare_nonaggr_model,
configs,
seed=qp.environ.get('_R_SEED', None),
n_jobs=self.n_jobs
)
return scores
def _compute_scores(self, training):
if isinstance(self.model, AggregativeQuantifier):
return self._compute_scores_aggregative(training)
def _print_status(self, params, score, status, took):
if status.success():
self._sout(f'hyperparams=[{params}]\t got {self.error.__name__} = {score:.5f} [took {took:.3f}s]')
else:
return self._compute_scores_nonaggregative(training)
self._sout(f'error={status}')
def fit(self, training: LabelledCollection):
""" Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing
@ -251,31 +211,41 @@ class GridSearchQ(BaseQuantifier):
tinit = time()
self.error_collector = []
self._sout(f'starting model selection with n_jobs={self.n_jobs}')
results = self._compute_scores(training)
if isinstance(self.model, AggregativeQuantifier):
results = self._compute_scores_aggregative(training)
else:
results = self._compute_scores_nonaggregative(training)
self.param_scores_ = {}
self.best_score_ = None
for job_result in results:
score = job_result.get('score', None)
params = job_result['params']
if score is not None:
for model, params, score, status, took in results:
if status.success():
if self.best_score_ is None or score < self.best_score_:
self.best_score_ = score
self.best_params_ = params
self.best_model_ = job_result['model']
self.best_model_ = model
self.param_scores_[str(params)] = score
else:
self.param_scores_[str(params)] = job_result['status']
self.param_scores_[str(params)] = status.status
self.error_collector.append(status)
tend = time()-tinit
if self.best_score_ is None:
raise TimeoutError('no combination of hyperparameters seem to work')
raise ValueError('no combination of hyperparameters seemed to work')
self._sout(f'optimization finished: best params {self.best_params_} (score={self.best_score_:.5f}) '
f'[took {tend:.4f}s]')
no_errors = len(self.error_collector)
if no_errors>0:
self._sout(f'warning: {no_errors} errors found')
for err in self.error_collector:
self._sout(f'\t{str(err)}')
if self.refit:
if isinstance(self.protocol, OnLabelledCollectionProtocol):
tinit = time()
@ -284,6 +254,7 @@ class GridSearchQ(BaseQuantifier):
tend = time() - tinit
self.refit_time_ = tend
else:
# already checked
raise RuntimeWarning(f'the model cannot be refit on the whole dataset')
return self
@ -324,23 +295,42 @@ class GridSearchQ(BaseQuantifier):
return self.best_model_
raise ValueError('best_model called before fit')
def _error_handler(self, func, params):
"""
Endorses one job with two returned values: the status, and the time of execution
def _error_handler(self, func, *args, **kwargs):
:param func: the function to be called
:param params: parameters of the function
:return: `tuple(out, status, time)` where `out` is the function output,
`status` is an enum value from `Status`, and `time` is the time it
took to complete the call
"""
output = None
def _handle(status, exception):
if self.raise_errors:
raise exception
else:
return ConfigStatus(params, status, str(e))
try:
with timeout(self.timeout):
output = func(*args, **kwargs)
return output, Status.SUCCESS
tinit = time()
output = func(params)
status = ConfigStatus(params, Status.SUCCESS)
except TimeoutError:
return None, Status.TIMEOUT
except TimeoutError as e:
status = _handle(Status.TIMEOUT, str(e))
except ValueError:
return None, Status.INVALID
except ValueError as e:
status = _handle(Status.INVALID, str(e))
except Exception:
return None, Status.ERROR
except Exception as e:
status = _handle(Status.ERROR, str(e))
took = time() - tinit
return output, status, took
def cross_val_predict(quantifier: BaseQuantifier, data: LabelledCollection, nfolds=3, random_state=0):