172 lines
8.9 KiB
Python
172 lines
8.9 KiB
Python
import itertools
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import quapy as qp
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from evaluation import artificial_sampling_prediction
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from data.base import LabelledCollection
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from method.aggregative import BaseQuantifier
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from typing import Union, Callable
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import functional as F
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from copy import deepcopy
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class GridSearchQ(BaseQuantifier):
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def __init__(self,
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model: BaseQuantifier,
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param_grid: dict,
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sample_size: int,
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n_prevpoints: int = None,
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n_repetitions: int = 1,
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eval_budget : int = None,
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error: Union[Callable, str] = qp.error.mae,
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refit=False,
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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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Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation
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protocol for quantification.
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:param model: the quantifier to optimize
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:param training: the training set on which to optimize the hyperparameters
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:param validation: either a LabelledCollection on which to test the performance of the different settings, or
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a float in [0,1] indicating the proportion of labelled data to extract from the training set
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:param param_grid: a dictionary with keys the parameter names and values the list of values to explore for
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that particular parameter
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:param sample_size: the size of the samples to extract from the validation set
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:param n_prevpoints: if specified, indicates the number of equally distant point to extract from the interval
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[0,1] in order to define the prevalences of the samples; e.g., if n_prevpoints=5, then the prevalences for
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each class will be explored in [0.00, 0.25, 0.50, 0.75, 1.00]. If not specified, then eval_budget is requested
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:param n_repetitions: the number of repetitions for each combination of prevalences. This parameter is ignored
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if eval_budget is set and is lower than the number of combinations that would be generated using the value
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assigned to n_prevpoints (for the current number of classes and n_repetitions)
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:param eval_budget: if specified, sets a ceil on the number of evaluations to perform for each hyper-parameter
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combination. For example, if there are 3 classes, n_repetitions=1 and eval_budget=20, then n_prevpoints will be
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set to 5, since this will generate 15 different prevalences:
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[0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]
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:param error: an error function (callable) or a string indicating the name of an error function (valid ones
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are those in qp.error.QUANTIFICATION_ERROR
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:param refit: whether or not to refit the model on the whole labelled collection (training+validation) with
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the best chosen hyperparameter combination
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:param n_jobs: number of parallel jobs
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:param random_seed: set the seed of the random generator to replicate experiments
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:param verbose: set to True to get information through the stdout
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"""
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self.model = model
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self.param_grid = param_grid
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self.sample_size = sample_size
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self.n_prevpoints = n_prevpoints
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self.n_repetitions = n_repetitions
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self.eval_budget = eval_budget
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self.refit = refit
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self.n_jobs = n_jobs
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self.random_seed = random_seed
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self.verbose = verbose
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self.__check_error(error)
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def sout(self, msg):
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if self.verbose:
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print(f'[{self.__class__.__name__}]: {msg}')
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def __check_training_validation(self, training, validation):
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if isinstance(validation, LabelledCollection):
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return training, validation
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elif isinstance(validation, float):
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assert 0. < validation < 1., 'validation proportion should be in (0,1)'
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training, validation = training.split_stratified(train_prop=1-validation)
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return training, validation
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else:
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raise ValueError('"validation" must either be a LabelledCollection or a float in (0,1) indicating the'
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'proportion of training documents to extract')
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def __check_num_evals(self, n_prevpoints, eval_budget, n_repetitions, n_classes):
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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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self.n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, n_repetitions)
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eval_computations = F.num_prevalence_combinations(self.n_prevpoints, n_classes, n_repetitions)
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self.sout(f'setting n_prevpoints={self.n_prevpoints} so that the number of \n'
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f'evaluations ({eval_computations}) does not exceed the evaluation budget ({eval_budget})')
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elif eval_budget is None:
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self.n_prevpoints = n_prevpoints
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eval_computations = F.num_prevalence_combinations(self.n_prevpoints, n_classes, n_repetitions)
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self.sout(f'{eval_computations} evaluations will be performed for each\n'
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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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self.n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, n_repetitions)
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new_eval_computations = F.num_prevalence_combinations(self.n_prevpoints, n_classes, n_repetitions)
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self.sout(f'the budget of evaluations would be exceeded with\n'
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f'n_prevpoints={n_prevpoints}. Chaning to n_prevpoints={self.n_prevpoints}. This will produce\n'
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f'{new_eval_computations} evaluation computations for each hyper-parameter combination.')
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def __check_error(self, error):
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if error in qp.error.QUANTIFICATION_ERROR:
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self.error = error
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elif isinstance(error, str):
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assert error in qp.error.QUANTIFICATION_ERROR_NAMES, \
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f'unknown error name; valid ones are {qp.error.QUANTIFICATION_ERROR_NAMES}'
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self.error = getattr(qp.error, error)
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else:
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raise ValueError(f'unexpected error type; must either be a callable function or a str representing\n'
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f'the name of an error function in {qp.error.QUANTIFICATION_ERROR_NAMES}')
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def fit(self, training: LabelledCollection, validation: Union[LabelledCollection, float]=0.3):
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"""
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:param training: the training set on which to optimize the hyperparameters
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:param validation: either a LabelledCollection on which to test the performance of the different settings, or
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a float in [0,1] indicating the proportion of labelled data to extract from the training set
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"""
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training, validation = self.__check_training_validation(training, validation)
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self.__check_num_evals(self.n_prevpoints, self.eval_budget, self.n_repetitions, training.n_classes)
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params_keys = list(self.param_grid.keys())
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params_values = list(self.param_grid.values())
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model = self.model
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n_jobs = self.n_jobs
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self.sout(f'starting optimization with n_jobs={n_jobs}')
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self.param_scores_ = {}
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self.best_score_ = None
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for values in itertools.product(*params_values):
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params = {k: values[i] for i, k in enumerate(params_keys)}
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# overrides default parameters with the parameters being explored at this iteration
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model.set_params(**params)
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model.fit(training)
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true_prevalences, estim_prevalences = artificial_sampling_prediction(
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model, validation, self.sample_size, self.n_prevpoints, self.n_repetitions, n_jobs, self.random_seed,
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verbose=False
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)
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score = self.error(true_prevalences, estim_prevalences)
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self.sout(f'checking hyperparams={params} got {self.error.__name__} score {score:.5f}')
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if self.best_score_ is None or score < self.best_score_:
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self.best_score_ = score
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self.best_params_ = params
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if not self.refit:
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self.best_model_ = deepcopy(model)
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self.param_scores_[str(params)] = score
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self.sout(f'optimization finished: best params {self.best_params_} (score={self.best_score_:.5f})')
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model.set_params(**self.best_params_)
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self.best_model_ = deepcopy(model)
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if self.refit:
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self.sout(f'refitting on the whole development set')
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self.best_model_.fit(training + validation)
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return self
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def quantify(self, instances):
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return self.best_model_.quantify(instances)
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def set_params(self, **parameters):
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self.param_grid = parameters
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def get_params(self, deep=True):
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return self.param_grid
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