91 lines
3.7 KiB
Python
91 lines
3.7 KiB
Python
from typing import Union, Callable, Iterable
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import numpy as np
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from joblib import Parallel, delayed
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from tqdm import tqdm
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import quapy as qp
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from quapy.data import LabelledCollection
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from quapy.method.base import BaseQuantifier
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from quapy.util import temp_seed
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import quapy.functional as F
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def artificial_sampling_prediction(
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model: BaseQuantifier,
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test: LabelledCollection,
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sample_size,
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n_prevpoints=210,
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n_repetitions=1,
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n_jobs=1,
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random_seed=42,
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verbose=True
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):
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"""
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Performs the predictions for all samples generated according to the artificial sampling protocol.
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:param model: the model in charge of generating the class prevalence estimations
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:param test: the test set on which to perform arificial sampling
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:param sample_size: the size of the samples
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:param n_prevpoints: the number of different prevalences to sample
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:param n_repetitions: the number of repetitions for each prevalence
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:param n_jobs: number of jobs to be run in parallel
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:param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect
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any other random process.
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:param verbose: if True, shows a progress bar
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:return: two ndarrays of shape (m,n) with m the number of samples (n_prevpoints*n_repetitions) and n the
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number of classes. The first one contains the true prevalences for the samples generated while the second one
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contains the the prevalence estimations
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"""
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with temp_seed(random_seed):
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indexes = list(test.artificial_sampling_index_generator(sample_size, n_prevpoints, n_repetitions))
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if model.aggregative: #isinstance(model, qp.method.aggregative.AggregativeQuantifier):
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# print('\tinstance of aggregative-quantifier')
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quantification_func = model.aggregate
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if model.probabilistic: # isinstance(model, qp.method.aggregative.AggregativeProbabilisticQuantifier):
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# print('\t\tinstance of probabilitstic-aggregative-quantifier')
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preclassified_instances = model.posterior_probabilities(test.instances)
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else:
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# print('\t\tinstance of hard-aggregative-quantifier')
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preclassified_instances = model.classify(test.instances)
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test = LabelledCollection(preclassified_instances, test.labels)
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else:
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# print('\t\tinstance of base-quantifier')
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quantification_func = model.quantify
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def _predict_prevalences(index):
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sample = test.sampling_from_index(index)
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true_prevalence = sample.prevalence()
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estim_prevalence = quantification_func(sample.instances)
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return true_prevalence, estim_prevalence
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pbar = tqdm(indexes, desc='[artificial sampling protocol] predicting') if verbose else indexes
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results = Parallel(n_jobs=n_jobs)(
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delayed(_predict_prevalences)(index) for index in pbar
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)
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true_prevalences, estim_prevalences = zip(*results)
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true_prevalences = np.asarray(true_prevalences)
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estim_prevalences = np.asarray(estim_prevalences)
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return true_prevalences, estim_prevalences
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def evaluate(model: BaseQuantifier, test_samples:Iterable[LabelledCollection], err:Union[str, Callable], n_jobs:int=-1):
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if isinstance(err, str):
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err = getattr(qp.error, err)
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assert err.__name__ in qp.error.QUANTIFICATION_ERROR_NAMES, \
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f'error={err} does not seem to be a quantification error'
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scores = Parallel(n_jobs=n_jobs)(
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delayed(_delayed_eval)(model, Ti, err) for Ti in test_samples
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)
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return np.mean(scores)
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def _delayed_eval(model:BaseQuantifier, test:LabelledCollection, error:Callable):
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prev_estim = model.quantify(test.instances)
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prev_true = test.prevalence()
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return error(prev_true, prev_estim)
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