import itertools
import signal
from copy import deepcopy
from typing import Union, Callable
import quapy as qp
import quapy.functional as F
from data.base import LabelledCollection
from quapy.evaluation import artificial_sampling_prediction
from quapy.method.aggregative import BaseQuantifier
class GridSearchQ(BaseQuantifier):
def __init__(self,
model: BaseQuantifier,
param_grid: dict,
sample_size: int,
n_prevpoints: int = None,
n_repetitions: int = 1,
eval_budget : int = None,
error: Union[Callable, str] = qp.error.mae,
refit=False,
n_jobs=-1,
random_seed=42,
timeout=-1,
verbose=False):
"""
Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation
protocol for quantification.
:param model: the quantifier to optimize
:param training: the training set on which to optimize the hyperparameters
:param validation: either a LabelledCollection on which to test the performance of the different settings, or
a float in [0,1] indicating the proportion of labelled data to extract from the training set
:param param_grid: a dictionary with keys the parameter names and values the list of values to explore for
that particular parameter
:param sample_size: the size of the samples to extract from the validation set
:param n_prevpoints: if specified, indicates the number of equally distant point to extract from the interval
[0,1] in order to define the prevalences of the samples; e.g., if n_prevpoints=5, then the prevalences for
each class will be explored in [0.00, 0.25, 0.50, 0.75, 1.00]. If not specified, then eval_budget is requested
:param n_repetitions: the number of repetitions for each combination of prevalences. This parameter is ignored
if eval_budget is set and is lower than the number of combinations that would be generated using the value
assigned to n_prevpoints (for the current number of classes and n_repetitions)
:param eval_budget: if specified, sets a ceil on the number of evaluations to perform for each hyper-parameter
combination. 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]
:param error: an error function (callable) or a string indicating the name of an error function (valid ones
are those in qp.error.QUANTIFICATION_ERROR
:param refit: whether or not to refit the model on the whole labelled collection (training+validation) with
the best chosen hyperparameter combination
:param n_jobs: number of parallel jobs
:param random_seed: set the seed of the random generator to replicate experiments
:param timeout: establishes a timer (in seconds) for each of the hyperparameters configurations being tested.
Whenever a run takes longer than this timer, that configuration will be ignored. If all configurations end up
being ignored, a TimeoutError exception is raised. If -1 (default) then no time bound is set.
:param verbose: set to True to get information through the stdout
"""
self.model = model
self.param_grid = param_grid
self.sample_size = sample_size
self.n_prevpoints = n_prevpoints
self.n_repetitions = n_repetitions
self.eval_budget = eval_budget
self.refit = refit
self.n_jobs = n_jobs
self.random_seed = random_seed
self.timeout = timeout
self.verbose = verbose
self.__check_error(error)
def sout(self, msg):
if self.verbose:
print(f'[{self.__class__.__name__}]: {msg}')
def __check_training_validation(self, training, validation):
if isinstance(validation, LabelledCollection):
return training, validation
elif isinstance(validation, float):
assert 0. < validation < 1., 'validation proportion should be in (0,1)'
training, validation = training.split_stratified(train_prop=1-validation)
return training, validation
else:
raise ValueError(f'"validation" must either be a LabelledCollection or a float in (0,1) indicating the'
f'proportion of training documents to extract (found) {type(validation)}')
def __check_num_evals(self, n_prevpoints, eval_budget, n_repetitions, n_classes):
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'
self.n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, n_repetitions)
eval_computations = F.num_prevalence_combinations(self.n_prevpoints, n_classes, n_repetitions)
self.sout(f'setting n_prevpoints={self.n_prevpoints} so that the number of \n'
f'evaluations ({eval_computations}) does not exceed the evaluation budget ({eval_budget})')
elif eval_budget is None:
self.n_prevpoints = n_prevpoints
eval_computations = F.num_prevalence_combinations(self.n_prevpoints, n_classes, n_repetitions)
self.sout(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:
self.n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, n_repetitions)
new_eval_computations = F.num_prevalence_combinations(self.n_prevpoints, n_classes, n_repetitions)
self.sout(f'the budget of evaluations would be exceeded with\n'
f'n_prevpoints={n_prevpoints}. Chaning to n_prevpoints={self.n_prevpoints}. This will produce\n'
f'{new_eval_computations} evaluation computations for each hyper-parameter combination.')
def __check_error(self, error):
if error in qp.error.QUANTIFICATION_ERROR:
self.error = error
elif isinstance(error, str):
assert error in qp.error.QUANTIFICATION_ERROR_NAMES, \
f'unknown error name; valid ones are {qp.error.QUANTIFICATION_ERROR_NAMES}'
self.error = getattr(qp.error, error)
else:
raise ValueError(f'unexpected error type; must either be a callable function or a str representing\n'
f'the name of an error function in {qp.error.QUANTIFICATION_ERROR_NAMES}')
def fit(self, training: LabelledCollection, val_split: Union[LabelledCollection, float]=0.4):
"""
:param training: the training set on which to optimize the hyperparameters
:param val_split: either a LabelledCollection on which to test the performance of the different settings, or
a float in [0,1] indicating the proportion of labelled data to extract from the training set
"""
training, val_split = self.__check_training_validation(training, val_split)
assert isinstance(self.sample_size, int) and self.sample_size > 0, 'sample_size must be a positive integer'
self.__check_num_evals(self.n_prevpoints, self.eval_budget, self.n_repetitions, training.n_classes)
# print(f'training size={len(training)}')
# print(f'validation size={len(val_split)}')
params_keys = list(self.param_grid.keys())
params_values = list(self.param_grid.values())
model = self.model
n_jobs = self.n_jobs
if self.timeout > 0:
def handler(signum, frame):
self.sout('timeout reached')
raise TimeoutError()
signal.signal(signal.SIGALRM, handler)
self.sout(f'starting optimization with n_jobs={n_jobs}')
self.param_scores_ = {}
self.best_score_ = None
some_timeouts = False
for values in itertools.product(*params_values):
params = dict({k: values[i] for i, k in enumerate(params_keys)})
if self.timeout > 0:
signal.alarm(self.timeout)
try:
# overrides default parameters with the parameters being explored at this iteration
model.set_params(**params)
model.fit(training)
true_prevalences, estim_prevalences = artificial_sampling_prediction(
model, val_split, self.sample_size, self.n_prevpoints, self.n_repetitions, n_jobs, self.random_seed,
verbose=False
)
score = self.error(true_prevalences, estim_prevalences)
self.sout(f'checking hyperparams={params} got {self.error.__name__} score {score:.5f}')
if self.best_score_ is None or score < self.best_score_:
self.best_score_ = score
self.best_params_ = params
if not self.refit:
self.best_model_ = deepcopy(model)
self.param_scores_[str(params)] = score
if self.timeout > 0:
signal.alarm(0)
except TimeoutError:
print(f'timeout reached for config {params}')
some_timeouts = True
if self.best_score_ is None and some_timeouts:
raise TimeoutError('all jobs took more than the timeout time to end')
self.sout(f'optimization finished: best params {self.best_params_} (score={self.best_score_:.5f})')
model.set_params(**self.best_params_)
self.best_model_ = deepcopy(model)
if self.refit:
self.sout(f'refitting on the whole development set')
self.best_model_.fit(training + val_split)
return self
def quantify(self, instances):
return self.best_model_.quantify(instances)
def set_params(self, **parameters):
self.param_grid = parameters
def get_params(self, deep=True):
return self.param_grid
def best_model(self):
if hasattr(self, 'best_model_'):
return self.best_model_
raise ValueError('best_model called before fit')