def launch_for_arrays(self,
model,
X,
y,
train_sizes,
n_cv_iter=5,
params=None,
test_ratio=0.25,
pre_warm=True,
folder=u".",
name=None,
random_state=None):
cv_split_dict = {}
for train_size in train_sizes:
test_size = int(test_ratio * train_size)
name = "{0}_{1}".format(name, train_size)
cv_split_dict[train_size] = persist_cv_splits(
X,
y,
n_cv_iter=n_cv_iter,
train_size=int(train_size),
test_size=test_size,
name=name,
folder=folder,
random_state=random_state)
return self.launch_for_splits(model,
cv_split_dict,
pre_warm=pre_warm,
params=params,
collect_files_on_reset=True)
def launch_for_arrays(self,
model,
parameter_grid,
X,
y,
n_cv_iter=5,
train_size=None,
test_size=0.25,
pre_warm=True,
folder=".",
name=None,
random_state=None):
cv_split_filenames = persist_cv_splits(X,
y,
n_cv_iter=n_cv_iter,
train_size=train_size,
test_size=test_size,
name=name,
folder=folder,
random_state=random_state)
return self.launch_for_splits(model,
parameter_grid,
cv_split_filenames,
pre_warm=pre_warm,
collect_files_on_reset=True)
def launch_for_arrays(self, model, parameter_grid, X, y, n_cv_iter=3, train_size=None,
test_size=0.25, pre_warm=True, folder=".", name=None,
random_state=None):
cv_split_filenames = persist_cv_splits(
X, y, n_cv_iter=n_cv_iter, train_size=train_size, test_size=test_size,
name=name, folder=folder, random_state=random_state)
return self.launch_for_splits(model, parameter_grid,
cv_split_filenames, pre_warm=pre_warm, collect_files_on_reset=True)
def launch_for_arrays(self, model, X, y, train_sizes, n_cv_iter=5, params=None,
test_ratio=0.25, pre_warm=True, folder=u".", name=None,
random_state=None):
cv_split_dict = {}
for train_size in train_sizes:
test_size = int(test_ratio*train_size)
name = "{0}_{1}".format(name,train_size)
cv_split_dict[train_size] = persist_cv_splits(
X, y, n_cv_iter=n_cv_iter, train_size=int(train_size), test_size=test_size,
name=name, folder=folder, random_state=random_state)
return self.launch_for_splits(model, cv_split_dict,
pre_warm=pre_warm, params=params, collect_files_on_reset=True)