35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
import quapy as qp
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from quapy.classification.neural import CNNnet
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from quapy.classification.neural import NeuralClassifierTrainer
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from quapy.method.meta import QuaNet
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import quapy.functional as F
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"""
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This example shows how to train QuaNet. The internal classifier is a word-based CNN.
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"""
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# set the sample size in the environment
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qp.environ["SAMPLE_SIZE"] = 100
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# the dataset is textual (Kindle reviews from Amazon), so we need to index terms, i.e.,
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# we need to convert distinct terms into numerical ids
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dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
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qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
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train, test = dataset.train_test
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# train the text classifier:
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cnn_module = CNNnet(dataset.vocabulary_size, dataset.training.n_classes)
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cnn_classifier = NeuralClassifierTrainer(cnn_module, device='cuda')
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cnn_classifier.fit(*dataset.training.Xy)
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# train QuaNet (alternatively, we can set fit_classifier=True and let QuaNet train the classifier)
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quantifier = QuaNet(cnn_classifier, device='cuda')
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quantifier.fit(train, fit_classifier=False)
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# prediction and evaluation
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estim_prevalence = quantifier.quantify(test.instances)
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mae = qp.error.mae(test.prevalence(), estim_prevalence)
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print(f'true prevalence: {F.strprev(test.prevalence())}')
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print(f'estim prevalence: {F.strprev(estim_prevalence)}')
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print(f'MAE = {mae:.4f}') |