205 lines
7.0 KiB
Python
205 lines
7.0 KiB
Python
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import itertools
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from copy import deepcopy
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from time import time
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from typing import Callable, Union
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from quapy.data import LabelledCollection
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from quapy.protocol import AbstractProtocol, OnLabelledCollectionProtocol
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import quacc as qc
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import quacc.evaluation.method as evaluation
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from quacc.data import ExtendedCollection
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from quacc.method.base import BaseAccuracyEstimator
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class GridSearchAE(BaseAccuracyEstimator):
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def __init__(
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self,
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model: BaseAccuracyEstimator,
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param_grid: dict,
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protocol: AbstractProtocol,
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error: Union[Callable, str] = qc.error.maccd,
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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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):
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self.model = model
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self.param_grid = self.__normalize_params(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__}]: {msg}")
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def __normalize_params(self, params):
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__remap = {}
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for key in params.keys():
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k, delim, sub_key = key.partition("__")
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if delim and k == "q":
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__remap[key] = f"quantifier__{sub_key}"
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return {(__remap[k] if k in __remap else k): v for k, v in params.items()}
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def __check_error(self, error):
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if error in qc.error.ACCURACY_ERROR:
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self.error = error
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elif isinstance(error, str):
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self.error = qc.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(
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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 {qc.error.ACCURACY_ERROR_NAMES}"
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)
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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 = [
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dict(zip(params_keys, val)) for val in itertools.product(*params_values)
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]
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# self._sout(f"starting model selection with {self.n_jobs =}")
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self._sout("starting model selection")
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scores = [self.__params_eval(params, training) for params in hyper]
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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(
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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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)
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if self.refit:
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if isinstance(protocol, OnLabelledCollectionProtocol):
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self._sout("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(
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f'"refit" was requested, but the protocol does not '
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f"implement the {OnLabelledCollectionProtocol.__name__} interface"
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)
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return self
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def __params_eval(self, params, training):
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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(
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f"hyperparams={params}\t got score {score:.5f} [took {ttime:.4f}s]"
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)
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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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# traceback(e)
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score = None
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return params, score, model
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def extend(self, coll: LabelledCollection, pred_proba=None) -> ExtendedCollection:
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assert hasattr(self, "best_model_"), "quantify called before fit"
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return self.best_model().extend(coll, pred_proba=pred_proba)
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def estimate(self, instances, ext=False):
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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().estimate(instances, ext=ext)
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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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