def __init__(self,
estimator,
param_grid,
scoring=None,
cv=4,
refit=True,
verbose=False,
population_size=50,
mutation_prob=0.10,
tournament_size=3,
generations_number=10,
n_jobs=1,
iid=True,
pre_dispatch='2*n_jobs',
error_score='raise',
fit_params=None):
super(EvolutionaryAlgorithmSearchCV,
self).__init__(estimator, scoring, fit_params, n_jobs, iid,
refit, cv, pre_dispatch, error_score)
_check_param_grid(param_grid)
self.param_grid = param_grid
self.possible_params = list(ParameterGrid(self.param_grid))
self.individual_size = int(ceil(log(len(self.possible_params), 2)))
self.population_size = population_size
self.generations_number = generations_number
self.best_estimator_ = None
self.best_score_ = None
self.best_params_ = None
self._individual_evals = {}
self.mutation_prob = mutation_prob
self.tournament_size = tournament_size
def __init__(self, estimator, param_grid, scoring=None, loss_func=None,
score_func=None, fit_params=None, n_jobs=1, iid=True,
refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs'):
super(ExtendedGridSearchCV, self).__init__(
estimator, scoring, loss_func, score_func, fit_params, n_jobs, iid,
refit, cv, verbose, pre_dispatch)
self.param_grid = param_grid
_check_param_grid(param_grid)
def __init__(self, estimator, param_grid, scoring=None, fit_params=None,
iid=True, refit=True, cv=None, verbose=0):
super(GridSearchCV, self).__init__(
estimator, scoring, fit_params, iid,
refit, cv, verbose)
self.param_grid = param_grid
_check_param_grid(param_grid)
def __init__(self, estimator, param_grid=None, refit=True, cv=2, verbose=0):
self.estimator = estimator
_check_param_grid(param_grid)
self.param_grid = param_grid
self.refit = refit
self.cv = cv
self.verbose = verbose
self.dataset = None
def __init__(self, sc, estimator, param_grid, scoring=None, fit_params=None,
n_jobs=1, iid=True, refit=True, cv=None, verbose=0,
pre_dispatch='2*n_jobs', error_score='raise'):
super(GridSearchCV, self).__init__(
estimator, scoring, fit_params, n_jobs, iid,
refit, cv, verbose, pre_dispatch, error_score)
self.sc = sc
self.param_grid = param_grid
_check_param_grid(param_grid)
def __init__(self, estimator, param_grid, dataset_filenames=None,
sync=True, scoring=None,
loss_func=None, score_func=None, fit_params=None, n_jobs=1, iid=True,
refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs'):
super(IPyGridSearchCV, self).__init__(
estimator, scoring, loss_func, score_func, fit_params, n_jobs, iid,
refit, cv, verbose, pre_dispatch)
self.param_grid = param_grid
self.dataset_filenames = dataset_filenames
self.sync = sync
_check_param_grid(param_grid)
def __init__(self, estimator, param_grid, scoring=None, loss_func=None,
score_func=None, fit_params=None, iid=True,
refit=True, cv=None, verbose=0, client=None,
return_train_scores=False, tmp_dir='.'):
super(DistributedGridSearchCV, self).__init__(
estimator, scoring=scoring, loss_func=loss_func,
score_func=score_func, fit_params=fit_params, iid=iid,
refit=refit, cv=cv, verbose=verbose, client=client,
return_train_scores=return_train_scores, tmp_dir=tmp_dir)
self.param_grid = param_grid
_check_param_grid(param_grid)
def __init__(self, estimator, param_grid, profile='net', grid_parallel=True, scoring=None, loss_func=None,
score_func=None, fit_params=None, n_jobs=-1, iid=True,
refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs',
error_score='raise'):
super(GridSearchCV, self).__init__(
estimator=estimator, scoring=scoring, fit_params=fit_params, n_jobs=n_jobs, iid=iid,
refit=refit, cv=cv, verbose=verbose, pre_dispatch=pre_dispatch)
self.param_grid = param_grid
self.profile = profile
self.grid_parallel = grid_parallel
grid_search._check_param_grid(param_grid)
def __init__(self,
estimator,
param_grid,
scoring=None,
fit_params=None,
iid=True,
refit=True,
cv=None,
verbose=0):
super(GridSearchCV, self).__init__(estimator, scoring, fit_params, iid,
refit, cv, verbose)
self.param_grid = param_grid
_check_param_grid(param_grid)
def __init__(self, estimator, param_grid, scoring=None, cv=None, inner_cv=None,
profile=None):
self.scoring = scoring
self.estimator = estimator
if isinstance(param_grid, Mapping):
self.param_grid = [param_grid]
else:
self.param_grid = param_grid
self.scoring = scoring
self.cv = cv
self.inner_cv = inner_cv
self.profile = profile
_check_param_grid(param_grid)
def fit(self, X, y):
X, y = check_X_y(X,
y,
force_all_finite=False,
multi_output=self.multi_output)
_check_param_grid(self.param_grid)
cv = _check_cv(self.cv, X, y, classifier=is_classifier(self.estimator))
self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)
if comm_rank == 0:
self._fit_master(X, y, cv)
else:
self._fit_slave()
return self
def fit(self, X, y):
if master:
LOG.info("comm_size:" + str(comm_size))
X, y = check_X_y(X, y, force_all_finite=False, multi_output=self.multi_output, accept_sparse='csr')
_check_param_grid(self.param_grid)
cv = check_cv(self.cv, X, y, classifier=is_classifier(self.estimator))
if master:
LOG.info("cv length:" + str(len(cv)))
self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)
if master:
self._fit_master(X, y, cv)
else:
self._fit_slave()
return self
def __init__(self, estimator, data_home, dataset, param_grid=None, refit=True, verbose=0, holdout=None,
queue=None, scorer=None, fit_params=None, error_score='raise'):
self.error_score = error_score
if not scorer:
scorer = estimator.score
self.scorer = scorer
self.dataset = dataset
self.data_home = data_home
self.estimator = estimator
_check_param_grid(param_grid)
self.param_grid = param_grid
self.refit = refit
self.verbose = verbose
self.holdout = holdout
if queue is None:
queue = HTCondorQueue()
self.queue = queue
self.fit_params = fit_params
def __init__(self,
estimator,
param_grid,
scoring=None,
fit_params=None,
iid=True,
refit=True,
cv=None,
get=None):
super(GridSearchCV, self).__init__(estimator=estimator,
scoring=scoring,
fit_params=fit_params,
iid=iid,
refit=refit,
cv=cv,
get=get)
_check_param_grid(param_grid)
self.param_grid = param_grid
def __init__(self, estimator, param_grid, scoring=None, cv=4,
refit=True, verbose=False, population_size=50, mutation_prob=0.10,
tournament_size=3, generations_number=10,
n_jobs=1, iid=True, pre_dispatch='2*n_jobs', error_score='raise',
fit_params=None):
super(EvolutionaryAlgorithmSearchCV, self).__init__(
estimator, scoring, fit_params, n_jobs, iid,
refit, cv, pre_dispatch, error_score)
_check_param_grid(param_grid)
self.param_grid = param_grid
self.possible_params = list(ParameterGrid(self.param_grid))
self.individual_size = int(ceil(log(len(self.possible_params), 2)))
self.population_size = population_size
self.generations_number = generations_number
self.best_estimator_ = None
self.best_score_ = None
self.best_params_ = None
self._individual_evals = {}
self.mutation_prob = mutation_prob
self.tournament_size = tournament_size
def __init__(self,
estimator,
param_grid,
scoring=None,
fit_params=None,
n_duplicates=1,
n_splits=100,
iid=True,
refit=True,
cv=None,
verbose=0):
self.param_grid = param_grid
_check_param_grid(param_grid)
self.n_duplicates = n_duplicates
self.n_splits = n_splits
super(FlyGridCV, self).__init__(estimator=estimator,
scoring=scoring,
fit_params=fit_params,
iid=iid,
refit=refit,
cv=cv,
verbose=verbose)
def __init__(self, param_grid, n_evaluations, random_state=None):
"""
The aim of this class is to generate new points, where the function (estimator) will be computed.
:type param_grid: OrderedDict, the grid with parameters to optimize on
:type n_evaluations: int, the number of evaluations to do
:type random_state: int | RandomState | None
"""
assert isinstance(param_grid, dict), 'the passed param_grid should be of OrderedDict class'
self.param_grid = OrderedDict(param_grid)
_check_param_grid(self.param_grid)
self.dimensions = list([len(param_values) for param, param_values in self.param_grid.items()])
size = numpy.prod(self.dimensions)
assert size > 1, 'The space of parameters contains only %i points' % size
if n_evaluations > size / 2:
warn('The number of evaluations was decreased to %i' % (size // 2), UserWarning)
n_evaluations = size // 2
self.n_evaluations = n_evaluations
# results on different parameters
self.grid_scores_ = OrderedDict()
# all the tasks that are being computed or already computed
self.queued_tasks_ = set()
self.random_state = check_random_state(random_state)
self.evaluations_done = 0
