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QuaPy
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running ensembles

This commit is contained in:
Alejandro Moreo Fernandez 2021-01-25 08:54:10 +01:00
parent 03cf73aff6
commit 2001c6d852
3 changed files with 22 additions and 20 deletions
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35
TweetSentQuant/experiments.py
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@ -63,7 +63,8 @@ def quantification_ensembles():
'n_jobs': settings.ENSEMBLE_N_JOBS,
'param_grid': lr_params,
'param_mod_sel': param_mod_sel,
'val_split': 0.4
'val_split': 0.4,
'min_pos': 10
}
# hyperparameters will be evaluated within each quantifier of the ensemble, and so the typical model selection
@ -71,13 +72,13 @@ def quantification_ensembles():
hyper_none = None
yield 'epaccmaeptr', EPACC(newLR(), optim='mae', policy='ptr', **common), hyper_none
yield 'epaccmaemae', EPACC(newLR(), optim='mae', policy='mae', **common), hyper_none
yield 'esldmaeptr', EEMQ(newLR(), optim='mae', policy='ptr', **common), hyper_none
yield 'esldmaemae', EEMQ(newLR(), optim='mae', policy='mae', **common), hyper_none
#yield 'esldmaeptr', EEMQ(newLR(), optim='mae', policy='ptr', **common), hyper_none
#yield 'esldmaemae', EEMQ(newLR(), optim='mae', policy='mae', **common), hyper_none
yield 'epaccmraeptr', EPACC(newLR(), optim='mrae', policy='ptr', **common), hyper_none
yield 'epaccmraemrae', EPACC(newLR(), optim='mrae', policy='mrae', **common), hyper_none
yield 'esldmraeptr', EEMQ(newLR(), optim='mrae', policy='ptr', **common), hyper_none
yield 'esldmraemrae', EEMQ(newLR(), optim='mrae', policy='mrae', **common), hyper_none
#yield 'esldmraeptr', EEMQ(newLR(), optim='mrae', policy='ptr', **common), hyper_none
#yield 'esldmraemrae', EEMQ(newLR(), optim='mrae', policy='mrae', **common), hyper_none
def evaluate_experiment(true_prevalences, estim_prevalences):
@ -178,8 +179,8 @@ def run(experiment):
benchmark_eval.training.prevalence(), test_true_prevalence, test_estim_prevalence,
best_params)
if isinstance(model, QuaNet):
model.clean_checkpoint_dir()
#if isinstance(model, QuaNet):
#model.clean_checkpoint_dir()
if __name__ == '__main__':
@ -195,24 +196,24 @@ if __name__ == '__main__':
print(f'Result folder: {args.results}')
np.random.seed(0)
optim_losses = ['mae'] # ['mae', 'mrae']
optim_losses = ['mae', 'mrae']
datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
#models = quantification_models()
#Parallel(n_jobs=settings.N_JOBS)(
# delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
#)
models = quantification_models()
Parallel(n_jobs=settings.N_JOBS)(
delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
)
#models = quantification_cuda_models()
#Parallel(n_jobs=settings.CUDA_N_JOBS)(
# delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
#)
models = quantification_cuda_models()
Parallel(n_jobs=settings.CUDA_N_JOBS)(
delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
)
models = quantification_ensembles()
Parallel(n_jobs=1)(
delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
)
shutil.rmtree(args.checkpointdir, ignore_errors=True)
#shutil.rmtree(args.checkpointdir, ignore_errors=True)

2
TweetSentQuant/settings.py
View File

@ -1,7 +1,7 @@
import multiprocessing
N_JOBS = -2 #multiprocessing.cpu_count()
CUDA_N_JOBS = 1
CUDA_N_JOBS = 2
ENSEMBLE_N_JOBS = -2
SAMPLE_SIZE = 100

5
quapy/method/meta.py
View File

@ -72,7 +72,8 @@ class Ensemble(BaseQuantifier):
# randomly chooses the prevalences for each member of the ensemble (preventing classes with less than
# min_pos positive examples)
prevs = [_draw_simplex(ndim=data.n_classes, min_val=self.min_pos / len(data)) for _ in range(self.size)]
sample_size = len(data) if self.max_sample_size is None else min(self.max_sample_size, len(data))
prevs = [_draw_simplex(ndim=data.n_classes, min_val=self.min_pos / sample_size) for _ in range(self.size)]
posteriors = None
if self.policy == 'ds':
@ -80,7 +81,7 @@ class Ensemble(BaseQuantifier):
posteriors, self.post_proba_fn = self.ds_policy_get_posteriors(data)
is_static_policy = (self.policy in qp.error.QUANTIFICATION_ERROR_NAMES)
sample_size = len(data) if self.max_sample_size is None else min(self.max_sample_size, len(data))
self.ensemble = Parallel(n_jobs=self.n_jobs)(
delayed(_delayed_new_instance)(
self.base_quantifier, data, val_split, prev, posteriors, keep_samples=is_static_policy,