232 lines
8.7 KiB
Python
232 lines
8.7 KiB
Python
import itertools
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import signal
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from copy import deepcopy
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from typing import Union, Callable
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import numpy as np
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from sklearn import clone
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import quapy as qp
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from quapy import evaluation
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from quapy.protocol import AbstractProtocol, OnLabelledCollectionProtocol
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from quapy.data.base import LabelledCollection
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from quapy.method.aggregative import BaseQuantifier
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from time import time
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class GridSearchQ(BaseQuantifier):
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"""Grid Search optimization targeting a quantification-oriented metric.
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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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:type model: BaseQuantifier
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:param param_grid: a dictionary with keys the parameter names and values the list of values to explore
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:param protocol: a sample generation protocol, an instance of :class:`quapy.protocol.AbstractProtocol`
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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 :class:`quapy.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. Ignored if protocol='gen'
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:param timeout: establishes a timer (in seconds) for each of the hyperparameters configurations being tested.
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Whenever a run takes longer than this timer, that configuration will be ignored. If all configurations end up
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being ignored, a TimeoutError exception is raised. If -1 (default) then no time bound is set.
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:param verbose: set to True to get information through the stdout
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"""
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def __init__(self,
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model: BaseQuantifier,
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param_grid: dict,
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protocol: AbstractProtocol,
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error: Union[Callable, str] = qp.error.mae,
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refit=True,
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timeout=-1,
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n_jobs=None,
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verbose=False):
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self.model = model
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self.param_grid = param_grid
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self.protocol = protocol
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self.refit = refit
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self.timeout = timeout
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self.n_jobs = qp._get_njobs(n_jobs)
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self.verbose = verbose
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self.__check_error(error)
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assert isinstance(protocol, AbstractProtocol), 'unknown protocol'
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def _sout(self, msg):
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if self.verbose:
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print(f'[{self.__class__.__name__}:{self.model.__class__.__name__}]: {msg}')
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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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self.error = qp.error.from_name(error)
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elif hasattr(error, '__call__'):
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self.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):
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""" Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing
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the error metric.
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:param training: the training set on which to optimize the hyperparameters
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:return: self
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"""
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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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protocol = self.protocol
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self.param_scores_ = {}
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self.best_score_ = None
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tinit = time()
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hyper = [dict({k: val[i] for i, k in enumerate(params_keys)}) for val in itertools.product(*params_values)]
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self._sout(f'starting model selection with {self.n_jobs =}')
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#pass a seed to parallel so it is set in clild processes
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scores = qp.util.parallel(
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self._delayed_eval,
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((params, training) for params in hyper),
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seed=qp.environ.get('_R_SEED', None),
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n_jobs=self.n_jobs
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)
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for params, score, model in scores:
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if score is not None:
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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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self.best_model_ = model
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self.param_scores_[str(params)] = score
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else:
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self.param_scores_[str(params)] = 'timeout'
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tend = time()-tinit
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if self.best_score_ is None:
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raise TimeoutError('no combination of hyperparameters seem to work')
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self._sout(f'optimization finished: best params {self.best_params_} (score={self.best_score_:.5f}) '
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f'[took {tend:.4f}s]')
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if self.refit:
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if isinstance(protocol, OnLabelledCollectionProtocol):
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self._sout(f'refitting on the whole development set')
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self.best_model_.fit(training + protocol.get_labelled_collection())
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else:
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raise RuntimeWarning(f'"refit" was requested, but the protocol does not '
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f'implement the {OnLabelledCollectionProtocol.__name__} interface')
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return self
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def _delayed_eval(self, args):
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params, training = args
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protocol = self.protocol
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error = self.error
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if self.timeout > 0:
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def handler(signum, frame):
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raise TimeoutError()
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signal.signal(signal.SIGALRM, handler)
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tinit = time()
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if self.timeout > 0:
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signal.alarm(self.timeout)
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try:
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model = deepcopy(self.model)
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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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score = evaluation.evaluate(model, protocol=protocol, error_metric=error)
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ttime = time()-tinit
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self._sout(f'hyperparams={params}\t got {error.__name__} score {score:.5f} [took {ttime:.4f}s]')
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if self.timeout > 0:
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signal.alarm(0)
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except TimeoutError:
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self._sout(f'timeout ({self.timeout}s) reached for config {params}')
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score = None
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except ValueError as e:
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self._sout(f'the combination of hyperparameters {params} is invalid')
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raise e
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except Exception as e:
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self._sout(f'something went wrong for config {params}; skipping:')
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self._sout(f'\tException: {e}')
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score = None
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return params, score, model
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def quantify(self, instances):
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"""Estimate class prevalence values using the best model found after calling the :meth:`fit` method.
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:param instances: sample contanining the instances
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:return: a ndarray of shape `(n_classes)` with class prevalence estimates as according to the best model found
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by the model selection process.
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"""
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assert hasattr(self, 'best_model_'), 'quantify called before fit'
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return self.best_model().quantify(instances)
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def set_params(self, **parameters):
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"""Sets the hyper-parameters to explore.
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:param parameters: a dictionary with keys the parameter names and values the list of values to explore
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"""
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self.param_grid = parameters
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def get_params(self, deep=True):
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"""Returns the dictionary of hyper-parameters to explore (`param_grid`)
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:param deep: Unused
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:return: the dictionary `param_grid`
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"""
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return self.param_grid
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def best_model(self):
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"""
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Returns the best model found after calling the :meth:`fit` method, i.e., the one trained on the combination
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of hyper-parameters that minimized the error function.
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:return: a trained quantifier
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"""
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if hasattr(self, 'best_model_'):
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return self.best_model_
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raise ValueError('best_model called before fit')
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def cross_val_predict(quantifier: BaseQuantifier, data: LabelledCollection, nfolds=3, random_state=0):
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"""
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Akin to `scikit-learn's cross_val_predict <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html>`_
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but for quantification.
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:param quantifier: a quantifier issuing class prevalence values
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:param data: a labelled collection
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:param nfolds: number of folds for k-fold cross validation generation
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:param random_state: random seed for reproducibility
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:return: a vector of class prevalence values
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"""
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total_prev = np.zeros(shape=data.n_classes)
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for train, test in data.kFCV(nfolds=nfolds, random_state=random_state):
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quantifier.fit(train)
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fold_prev = quantifier.quantify(test.X)
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rel_size = len(test.X)/len(data)
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total_prev += fold_prev*rel_size
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return total_prev
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