from typing import Union, Callable, Iterable
import numpy as np
from tqdm import tqdm
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.base import BaseQuantifier
from quapy.util import temp_seed
import quapy.functional as F
import pandas as pd
def artificial_prevalence_prediction(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_prevpoints=210,
n_repetitions=1,
eval_budget: int = None,
n_jobs=1,
random_seed=42,
verbose=False):
"""
Performs the predictions for all samples generated according to the artificial sampling protocol.
:param model: the model in charge of generating the class prevalence estimations
:param test: the test set on which to perform arificial sampling
:param sample_size: the size of the samples
:param n_prevpoints: the number of different prevalences to sample (or set to None if eval_budget is specified)
:param n_repetitions: the number of repetitions for each prevalence
:param eval_budget: if specified, sets a ceil on the number of evaluations to perform. For example, if there are 3
classes, n_repetitions=1 and eval_budget=20, then n_prevpoints will be set to 5, since this will generate 15
different prevalences ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]) and since setting it n_prevpoints
to 6 would produce more than 20 evaluations.
:param n_jobs: number of jobs to be run in parallel
:param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect
any other random process.
:param verbose: if True, shows a progress bar
:return: two ndarrays of shape (m,n) with m the number of samples (n_prevpoints*n_repetitions) and n the
number of classes. The first one contains the true prevalences for the samples generated while the second one
contains the the prevalence estimations
"""
n_prevpoints, _ = qp.evaluation._check_num_evals(test.n_classes, n_prevpoints, eval_budget, n_repetitions, verbose)
with temp_seed(random_seed):
indexes = list(test.artificial_sampling_index_generator(sample_size, n_prevpoints, n_repetitions))
return _predict_from_indexes(indexes, model, test, n_jobs, verbose)
def natural_prevalence_prediction(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_repetitions=1,
n_jobs=1,
random_seed=42,
verbose=False):
"""
Performs the predictions for all samples generated according to the artificial sampling protocol.
:param model: the model in charge of generating the class prevalence estimations
:param test: the test set on which to perform arificial sampling
:param sample_size: the size of the samples
:param n_repetitions: the number of repetitions for each prevalence
:param n_jobs: number of jobs to be run in parallel
:param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect
any other random process.
:param verbose: if True, shows a progress bar
:return: two ndarrays of shape (m,n) with m the number of samples (n_repetitions) and n the
number of classes. The first one contains the true prevalences for the samples generated while the second one
contains the the prevalence estimations
"""
with temp_seed(random_seed):
indexes = list(test.natural_sampling_index_generator(sample_size, n_repetitions))
return _predict_from_indexes(indexes, model, test, n_jobs, verbose)
def _predict_from_indexes(
indexes,
model: BaseQuantifier,
test: LabelledCollection,
n_jobs=1,
verbose=False):
if model.aggregative: #isinstance(model, qp.method.aggregative.AggregativeQuantifier):
# print('\tinstance of aggregative-quantifier')
quantification_func = model.aggregate
if model.probabilistic: # isinstance(model, qp.method.aggregative.AggregativeProbabilisticQuantifier):
# print('\t\tinstance of probabilitstic-aggregative-quantifier')
preclassified_instances = model.posterior_probabilities(test.instances)
else:
# print('\t\tinstance of hard-aggregative-quantifier')
preclassified_instances = model.classify(test.instances)
test = LabelledCollection(preclassified_instances, test.labels)
else:
# print('\t\tinstance of base-quantifier')
quantification_func = model.quantify
def _predict_prevalences(index):
sample = test.sampling_from_index(index)
true_prevalence = sample.prevalence()
estim_prevalence = quantification_func(sample.instances)
return true_prevalence, estim_prevalence
pbar = tqdm(indexes, desc='[artificial sampling protocol] generating predictions') if verbose else indexes
results = qp.util.parallel(_predict_prevalences, pbar, n_jobs=n_jobs)
true_prevalences, estim_prevalences = zip(*results)
true_prevalences = np.asarray(true_prevalences)
estim_prevalences = np.asarray(estim_prevalences)
return true_prevalences, estim_prevalences
def artificial_prevalence_report(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_prevpoints=210,
n_repetitions=1,
eval_budget: int = None,
n_jobs=1,
random_seed=42,
error_metrics:Iterable[Union[str,Callable]]='mae',
verbose=False):
true_prevs, estim_prevs = artificial_prevalence_prediction(
model, test, sample_size, n_prevpoints, n_repetitions, eval_budget, n_jobs, random_seed, verbose
)
return _prevalence_report(true_prevs, estim_prevs, error_metrics)
def natural_prevalence_report(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_repetitions=1,
n_jobs=1,
random_seed=42,
error_metrics:Iterable[Union[str,Callable]]='mae',
verbose=False):
true_prevs, estim_prevs = natural_prevalence_prediction(
model, test, sample_size, n_repetitions, n_jobs, random_seed, verbose
)
return _prevalence_report(true_prevs, estim_prevs, error_metrics)
def _prevalence_report(
true_prevs,
estim_prevs,
error_metrics: Iterable[Union[str, Callable]] = 'mae'):
if isinstance(error_metrics, str):
error_metrics = [error_metrics]
error_names = [e if isinstance(e, str) else e.__name__ for e in error_metrics]
error_funcs = [qp.error.from_name(e) if isinstance(e, str) else e for e in error_metrics]
assert all(hasattr(e, '__call__') for e in error_funcs), 'invalid error functions'
df = pd.DataFrame(columns=['true-prev', 'estim-prev'] + error_names)
for true_prev, estim_prev in zip(true_prevs, estim_prevs):
series = {'true-prev': true_prev, 'estim-prev': estim_prev}
for error_name, error_metric in zip(error_names, error_funcs):
score = error_metric(true_prev, estim_prev)
series[error_name] = score
df = df.append(series, ignore_index=True)
return df
def artificial_prevalence_protocol(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_prevpoints=210,
n_repetitions=1,
eval_budget: int = None,
n_jobs=1,
random_seed=42,
error_metric:Union[str,Callable]='mae',
verbose=False):
if isinstance(error_metric, str):
error_metric = qp.error.from_name(error_metric)
assert hasattr(error_metric, '__call__'), 'invalid error function'
true_prevs, estim_prevs = artificial_prevalence_prediction(
model, test, sample_size, n_prevpoints, n_repetitions, eval_budget, n_jobs, random_seed, verbose
)
return error_metric(true_prevs, estim_prevs)
def natural_prevalence_protocol(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_repetitions=1,
n_jobs=1,
random_seed=42,
error_metric:Union[str,Callable]='mae',
verbose=False):
if isinstance(error_metric, str):
error_metric = qp.error.from_name(error_metric)
assert hasattr(error_metric, '__call__'), 'invalid error function'
true_prevs, estim_prevs = natural_prevalence_prediction(
model, test, sample_size, n_repetitions, n_jobs, random_seed, verbose
)
return error_metric(true_prevs, estim_prevs)
def evaluate(model: BaseQuantifier, test_samples:Iterable[LabelledCollection], err:Union[str, Callable], n_jobs:int=-1):
if isinstance(err, str):
err = qp.error.from_name(err)
scores = qp.util.parallel(_delayed_eval, ((model, Ti, err) for Ti in test_samples), n_jobs=n_jobs)
return np.mean(scores)
def _delayed_eval(args):
model, test, error = args
prev_estim = model.quantify(test.instances)
prev_true = test.prevalence()
return error(prev_true, prev_estim)
def _check_num_evals(n_classes, n_prevpoints=None, eval_budget=None, n_repetitions=1, verbose=False):
if n_prevpoints is None and eval_budget is None:
raise ValueError('either n_prevpoints or eval_budget has to be specified')
elif n_prevpoints is None:
assert eval_budget > 0, 'eval_budget must be a positive integer'
n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, n_repetitions)
eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repetitions)
if verbose:
print(f'setting n_prevpoints={n_prevpoints} so that the number of '
f'evaluations ({eval_computations}) does not exceed the evaluation '
f'budget ({eval_budget})')
elif eval_budget is None:
eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repetitions)
if verbose:
print(f'{eval_computations} evaluations will be performed for each '
f'combination of hyper-parameters')
else:
eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repetitions)
if eval_computations > eval_budget:
n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, n_repetitions)
new_eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repetitions)
if verbose:
print(f'the budget of evaluations would be exceeded with '
f'n_prevpoints={n_prevpoints}. Chaning to n_prevpoints={n_prevpoints}. This will produce '
f'{new_eval_computations} evaluation computations for each hyper-parameter combination.')
return n_prevpoints, eval_computations