55 lines
2.4 KiB
Python
55 lines
2.4 KiB
Python
import quapy as qp
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from quapy.method.aggregative import MS2
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from quapy.method.base import newOneVsAll
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from quapy.model_selection import GridSearchQ
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from quapy.protocol import UPP
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from sklearn.linear_model import LogisticRegression
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import numpy as np
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"""
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In this example, we will create a quantifier for tweet sentiment analysis considering three classes: negative, neutral,
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and positive. We will use a one-vs-all approach using a binary quantifier for demonstration purposes.
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"""
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qp.environ['SAMPLE_SIZE'] = 100
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qp.environ['N_JOBS'] = -1
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"""
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Any binary quantifier can be turned into a single-label quantifier by means of getOneVsAll function.
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This function returns an instance of OneVsAll quantifier. Actually, it either returns the subclass OneVsAllGeneric
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when the quantifier is an instance of BaseQuantifier, and it returns OneVsAllAggregative when the quantifier is
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an instance of AggregativeQuantifier. Although OneVsAllGeneric works in all cases, using OneVsAllAggregative has
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some additional advantages (namely, all the advantages that AggregativeQuantifiers enjoy, i.e., faster predictions
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during evaluation).
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"""
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quantifier = newOneVsAll(MS2(LogisticRegression()))
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print(f'the quantifier is an instance of {quantifier.__class__.__name__}')
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# load a ternary dataset
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train_modsel, val = qp.datasets.fetch_twitter('hcr', for_model_selection=True, pickle=True).train_test
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"""
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model selection: for this example, we are relying on the UPP protocol, i.e., a variant of the
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artificial-prevalence protocol that generates random samples (100 in this case) for randomly picked priors
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from the unit simplex. The priors are sampled using the Kraemer algorithm. Note this is in contrast to the
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standard APP protocol, that instead explores a prefixed grid of prevalence values.
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"""
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param_grid = {
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'binary_quantifier__classifier__C': np.logspace(-2,2,5), # classifier-dependent hyperparameter
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'binary_quantifier__classifier__class_weight': ['balanced', None] # classifier-dependent hyperparameter
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}
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print('starting model selection')
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model_selection = GridSearchQ(quantifier, param_grid, protocol=UPP(val), verbose=True, refit=False)
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quantifier = model_selection.fit(train_modsel).best_model()
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print('training on the whole training set')
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train, test = qp.datasets.fetch_twitter('hcr', for_model_selection=False, pickle=True).train_test
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quantifier.fit(train)
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# evaluation
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mae = qp.evaluation.evaluate(quantifier, protocol=UPP(test), error_metric='mae')
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print(f'MAE = {mae:.4f}')
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