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, val_split=0.4, 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.val_split = val_split 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]=None): """ :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 """ if val_split is None: val_split = self.val_split 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')