69 lines
4.1 KiB
Python
69 lines
4.1 KiB
Python
import quapy as qp
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from quapy.data.datasets import fetch_image_embeddings
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from quapy.method.meta import HistNetQ, GMNet
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from quapy.protocol import UPP
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# This example showcases HistNetQ and GMNet, two neural quantifiers that -- unlike the
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# classify-then-aggregate methods seen in most other examples (e.g., EMQ, PACC) -- are trained
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# end-to-end on whole samples ("bags") of known prevalence, instead of on individually labelled
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# instances. Both require `torch` to be installed (GMNet additionally requires `geotorch`); see
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# quapy.method._neural_bags.BagTrainedQuantifier, the base class they share, and the "Methods" manual
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# for further details.
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#
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# In particular, we exercise `fit_from_samples`, the entry point meant for the situation in which
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# individually labelled training instances are NOT available at all, and all one has access to is a
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# pool of pre-built bags of known prevalence -- e.g., as released for the LeQua challenges, or as
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# collected by a third party. We simulate such a pool here using CIFAR10 (see also example 20), by
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# drawing a fixed set of bags from its (otherwise fully instance-labelled) training collection with
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# the UPP protocol, and then training HistNetQ and GMNet as if that pool of bags -- and not the
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# underlying instance-level labels -- were all we had access to.
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#
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# The settings below (number of bags, training epochs, patience) are deliberately modest so that the
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# example runs in a reasonable time on a CPU; scale them up for serious experimentation.
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if __name__ == '__main__':
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qp.environ['SAMPLE_SIZE'] = 500
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# The datasets available in quapy do not consist of raw image files, but are instead
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# pre-generated embeddings (see the manuals for further information); 'features' corresponds to
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# the penultimate-layer representations of a resnet18, which is what HistNetQ and GMNet expect to
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# receive, since both operate on continuous instance representations.
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print('fetching cifar10 embeddings')
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train, test = fetch_image_embeddings(dataset_name='cifar10', embedding='features').train_test
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print('training:', train)
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print('test:', test)
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# Let us pretend that, instead of having access to the individually labelled instances of `train`,
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# we are only given a pool of 1000 pre-built bags of 500 instances each, with heterogeneous class
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# prevalence values (sampled uniformly at random from the simplex). This is exactly what
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# fit_from_samples expects: an AbstractProtocol that already yields (sample, prevalence) pairs.
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# UPP is deterministic by default (fixed random_state=0), so this pool is the same every time it
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# is iterated, as befits a fixed, pre-built collection of bags.
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print('simulating a pool of 1000 pre-built training bags (as if released by a third party)')
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bag_pool = UPP(train, repeats=1000, random_state=0)
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# Neither method requires a classifier, nor (in this case) a feature extraction module: since the
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# CIFAR10 embeddings are already vectorized, the default identity module is used, and both
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# networks operate directly on the 512-dimensional resnet18 features
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models = {
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'HistNetQ': HistNetQ(n_bins=8, bag_size=qp.environ['SAMPLE_SIZE'], train_epochs=50, patience=10, verbose=1),
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'GMNet': GMNet(n_gm_layers=8, num_gaussians=10, gaussian_dimensions=5, hidden_size_fe=(50,),
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bag_size=qp.environ['SAMPLE_SIZE'], train_epochs=50, patience=10, verbose=1),
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}
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for name, model in models.items():
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print(f'\nfitting {name} from the pre-built bag pool via fit_from_samples '
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f'(no instance-level labels are used)')
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model.fit_from_samples(bag_pool)
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# we now evaluate both quantifiers on cifar10's test set, using a fresh artificial-prevalence
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# protocol (note that, at prediction time, HistNetQ and GMNet behave just like any other
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# quantifier: the bag-based training is an internal detail of these two particular methods)
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test_prot = UPP(test, repeats=1000)
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for name, model in models.items():
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report = qp.evaluation.evaluation_report(model, protocol=test_prot, error_metrics=['mae'])
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print(f'\n{name}:')
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print(report.mean(numeric_only=True))
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