263 lines
10 KiB
Python
263 lines
10 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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import pandas as pd
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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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eval_budget: int = None,
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n_jobs=1,
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random_seed=42,
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verbose=False):
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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 (or set to None if eval_budget is specified)
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:param n_repetitions: the number of repetitions for each prevalence
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:param eval_budget: if specified, sets a ceil on the number of evaluations to perform. For example, if there are 3
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classes, n_repetitions=1 and eval_budget=20, then n_prevpoints will be set to 5, since this will generate 15
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different prevalences ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]) and since setting it n_prevpoints
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to 6 would produce more than 20 evaluations.
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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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n_prevpoints, _ = qp.evaluation._check_num_evals(test.n_classes, n_prevpoints, eval_budget, n_repetitions, verbose)
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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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return _predict_from_indexes(indexes, model, test, n_jobs, verbose)
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def natural_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_repetitions=1,
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n_jobs=1,
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random_seed=42,
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verbose=False):
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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_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_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.natural_sampling_index_generator(sample_size, n_repetitions))
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return _predict_from_indexes(indexes, model, test, n_jobs, verbose)
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def _predict_from_indexes(
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indexes,
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model: BaseQuantifier,
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test: LabelledCollection,
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n_jobs=1,
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verbose=False):
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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] generating predictions') if verbose else indexes
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results = qp.util.parallel(_predict_prevalences, pbar, n_jobs=n_jobs)
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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 artificial_sampling_report(
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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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eval_budget: int = None,
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n_jobs=1,
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random_seed=42,
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error_metrics:Iterable[Union[str,Callable]]='mae',
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verbose=False):
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true_prevs, estim_prevs = artificial_sampling_prediction(
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model, test, sample_size, n_prevpoints, n_repetitions, eval_budget, n_jobs, random_seed, verbose
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)
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return _sampling_report(true_prevs, estim_prevs, error_metrics)
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def natural_sampling_report(
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model: BaseQuantifier,
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test: LabelledCollection,
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sample_size,
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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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error_metrics:Iterable[Union[str,Callable]]='mae',
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verbose=False):
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true_prevs, estim_prevs = natural_sampling_prediction(
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model, test, sample_size, n_repetitions, n_jobs, random_seed, verbose
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)
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return _sampling_report(true_prevs, estim_prevs, error_metrics)
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def _sampling_report(
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true_prevs,
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estim_prevs,
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error_metrics: Iterable[Union[str, Callable]] = 'mae'):
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if isinstance(error_metrics, str):
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error_metrics = [error_metrics]
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error_names = [e if isinstance(e, str) else e.__name__ for e in error_metrics]
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error_funcs = [qp.error.from_name(e) if isinstance(e, str) else e for e in error_metrics]
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assert all(hasattr(e, '__call__') for e in error_funcs), 'invalid error functions'
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df = pd.DataFrame(columns=['true-prev', 'estim-prev'] + error_names)
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for true_prev, estim_prev in zip(true_prevs, estim_prevs):
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series = {'true-prev': true_prev, 'estim-prev': estim_prev}
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for error_name, error_metric in zip(error_names, error_funcs):
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score = error_metric(true_prev, estim_prev)
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series[error_name] = score
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df = df.append(series, ignore_index=True)
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return df
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def artificial_sampling_eval(
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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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eval_budget: int = None,
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n_jobs=1,
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random_seed=42,
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error_metric:Union[str,Callable]='mae',
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verbose=False):
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if isinstance(error_metric, str):
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error_metric = qp.error.from_name(error_metric)
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assert hasattr(error_metric, '__call__'), 'invalid error function'
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true_prevs, estim_prevs = artificial_sampling_prediction(
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model, test, sample_size, n_prevpoints, n_repetitions, eval_budget, n_jobs, random_seed, verbose
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)
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return error_metric(true_prevs, estim_prevs)
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def natural_sampling_eval(
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model: BaseQuantifier,
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test: LabelledCollection,
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sample_size,
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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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error_metric:Union[str,Callable]='mae',
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verbose=False):
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if isinstance(error_metric, str):
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error_metric = qp.error.from_name(error_metric)
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assert hasattr(error_metric, '__call__'), 'invalid error function'
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true_prevs, estim_prevs = natural_sampling_prediction(
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model, test, sample_size, n_repetitions, n_jobs, random_seed, verbose
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)
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return error_metric(true_prevs, estim_prevs)
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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 = qp.error.from_name(err)
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scores = qp.util.parallel(_delayed_eval, ((model, Ti, err) for Ti in test_samples), n_jobs=n_jobs)
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return np.mean(scores)
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def _delayed_eval(args):
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model, test, error = args
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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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def _check_num_evals(n_classes, n_prevpoints=None, eval_budget=None, n_repetitions=1, verbose=False):
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if n_prevpoints is None and eval_budget is None:
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raise ValueError('either n_prevpoints or eval_budget has to be specified')
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elif n_prevpoints is None:
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assert eval_budget > 0, 'eval_budget must be a positive integer'
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n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, n_repetitions)
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eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repetitions)
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if verbose:
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print(f'setting n_prevpoints={n_prevpoints} so that the number of '
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f'evaluations ({eval_computations}) does not exceed the evaluation '
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f'budget ({eval_budget})')
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elif eval_budget is None:
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eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repetitions)
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if verbose:
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print(f'{eval_computations} evaluations will be performed for each '
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f'combination of hyper-parameters')
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else:
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eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repetitions)
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if eval_computations > eval_budget:
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n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, n_repetitions)
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new_eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repetitions)
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if verbose:
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print(f'the budget of evaluations would be exceeded with '
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f'n_prevpoints={n_prevpoints}. Chaning to n_prevpoints={n_prevpoints}. This will produce '
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f'{new_eval_computations} evaluation computations for each hyper-parameter combination.')
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return n_prevpoints, eval_computations
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