def __init__(self, node, **kwargs):
super(CVBestSearchRefitParallel, self).__init__(wrapped_node=None)
#### 'y/test/score_recall_mean'
default_score = "y" + conf.SEP + \
conf.TEST + conf.SEP + \
conf.SCORE_RECALL_MEAN
score = kwargs.pop("score") if "score" in kwargs else default_score
arg_max = kwargs.pop("arg_max") if "arg_max" in kwargs else True
from epac.workflow.splitters import CV
# methods = Methods(*tasks)
cv_node = CV(node=node,
reducer=ClassificationReport(keep=False),
**kwargs)
self.add_child(cv_node)
self.score = score
self.arg_max = arg_max
self.refited = None
self.best_params = None
self.reducer = CVBestSearchRefitPReducer(self)
def __init__(self, node, **kwargs):
super(CVBestSearchRefitParallel, self).__init__(wrapped_node=None)
#### 'y/test/score_recall_mean'
default_score = "y" + conf.SEP + \
conf.TEST + conf.SEP + \
conf.SCORE_RECALL_MEAN
score = kwargs.pop("score") if "score" in kwargs else default_score
arg_max = kwargs.pop("arg_max") if "arg_max" in kwargs else True
from epac.workflow.splitters import CV
# methods = Methods(*tasks)
cv_node = CV(node=node,
reducer=ClassificationReport(keep=False),
**kwargs)
self.add_child(cv_node)
self.score = score
self.arg_max = arg_max
self.refited = None
self.best_params = None
self.reducer = CVBestSearchRefitPReducer(self)
class CVBestSearchRefitParallel(Wrapper):
"""Cross-validation + grid-search then refit with optimals parameters.
Average results over first axis, then find the arguments that maximize or
minimise a "score" over other axis.
Parameters
----------
See CV parameters, plus other parameters:
score: string
the score name to be optimized (default "mean_score_te").
arg_max: boolean
True/False take parameters that maximize/minimize the score. Default
is True.
Example
-------
>>> from sklearn import datasets
>>> from sklearn.svm import SVC
>>> from epac import Methods
>>> from epac.workflow.splitters import CVBestSearchRefitParallel
>>> X, y = datasets.make_classification(n_samples=12,
... n_features=10,
... n_informative=2,
... random_state=1)
>>> n_folds_nested = 2
>>> C_values = [.1, 0.5, 1, 2, 5]
>>> kernels = ["linear", "rbf"]
>>> methods = Methods(*[SVC(C=C, kernel=kernel)
... for C in C_values for kernel in kernels])
>>> wf = CVBestSearchRefitParallel(methods, n_folds=n_folds_nested)
>>> wf.run(X=X, y=y)
[[{'y/test/pred': array([0, 0, 1, 0, 0, 0]), 'y/train/pred': array([0, 0, 0, 0, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}], [{'y/test/pred': array([0, 1, 1, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 1, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 0, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 1, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 0, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 1, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 0, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 0, 1, 0]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 0, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 0, 1, 0]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}]]
>>> wf.reduce()
ResultSet(
[{'key': CVBestSearchRefitParallel, 'best_params': [{'kernel': 'rbf', 'C': 0.1, 'name': 'SVC'}], 'y/true': [1 0 0 1 0 0 1 0 1 1 0 1], 'y/pred': [1 0 0 1 0 0 1 0 1 1 0 1]}])
"""
def __init__(self, node, **kwargs):
super(CVBestSearchRefitParallel, self).__init__(wrapped_node=None)
#### 'y/test/score_recall_mean'
default_score = "y" + conf.SEP + \
conf.TEST + conf.SEP + \
conf.SCORE_RECALL_MEAN
score = kwargs.pop("score") if "score" in kwargs else default_score
arg_max = kwargs.pop("arg_max") if "arg_max" in kwargs else True
from epac.workflow.splitters import CV
# methods = Methods(*tasks)
cv_node = CV(node=node,
reducer=ClassificationReport(keep=False),
**kwargs)
self.add_child(cv_node)
self.score = score
self.arg_max = arg_max
self.refited = None
self.best_params = None
self.reducer = CVBestSearchRefitPReducer(self)
def get_signature(self):
return self.__class__.__name__
def transform(self, **Xy):
Xy_train, Xy_test = train_test_split(Xy)
result = Result(key=self.get_signature(), **Xy)
if not self.store:
self.store = StoreMem()
self.save_results(ResultSet(result))
if Xy_train is Xy_test:
return Xy
else:
return Xy_train
def _results2dict(self, **cpXy):
res_dict = {}
for key in cpXy[self.get_signature()]:
if not key == "key":
res_dict[key] = cpXy[self.get_signature()][key]
return res_dict
def reduce(self, store_results=True):
children_results = [
child.reduce(store_results=False) for child in self.children
]
results = ResultSet(*children_results)
if self.reducer:
to_refit, best_params = self.reducer.reduce(results)
Xy = self.load_results()
Xy = self._results2dict(**Xy)
self.refited = to_refit
self.best_params = best_params
out = self.refited.top_down(**Xy)
out[conf.BEST_PARAMS] = best_params
result = Result(key=self.get_signature(), **out)
return ResultSet(result)
return results
class CVBestSearchRefitParallel(Wrapper):
"""Cross-validation + grid-search then refit with optimals parameters.
Average results over first axis, then find the arguments that maximize or
minimise a "score" over other axis.
Parameters
----------
See CV parameters, plus other parameters:
score: string
the score name to be optimized (default "mean_score_te").
arg_max: boolean
True/False take parameters that maximize/minimize the score. Default
is True.
Example
-------
>>> from sklearn import datasets
>>> from sklearn.svm import SVC
>>> from epac import Methods
>>> from epac.workflow.splitters import CVBestSearchRefitParallel
>>> X, y = datasets.make_classification(n_samples=12,
... n_features=10,
... n_informative=2,
... random_state=1)
>>> n_folds_nested = 2
>>> C_values = [.1, 0.5, 1, 2, 5]
>>> kernels = ["linear", "rbf"]
>>> methods = Methods(*[SVC(C=C, kernel=kernel)
... for C in C_values for kernel in kernels])
>>> wf = CVBestSearchRefitParallel(methods, n_folds=n_folds_nested)
>>> wf.run(X=X, y=y)
[[{'y/test/pred': array([0, 0, 1, 0, 0, 0]), 'y/train/pred': array([0, 0, 0, 0, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}, {'y/test/pred': array([1, 1, 1, 0, 1, 1]), 'y/train/pred': array([0, 0, 1, 1, 0, 1]), 'y/test/true': array([1, 0, 0, 1, 0, 1])}], [{'y/test/pred': array([0, 1, 1, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 1, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 0, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 1, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 0, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 1, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 0, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 0, 1, 0]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 0, 0, 1, 1]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}, {'y/test/pred': array([0, 1, 1, 0, 1, 0]), 'y/train/pred': array([1, 0, 0, 1, 0, 1]), 'y/test/true': array([0, 0, 1, 1, 0, 1])}]]
>>> wf.reduce()
ResultSet(
[{'key': CVBestSearchRefitParallel, 'best_params': [{'kernel': 'rbf', 'C': 0.1, 'name': 'SVC'}], 'y/true': [1 0 0 1 0 0 1 0 1 1 0 1], 'y/pred': [1 0 0 1 0 0 1 0 1 1 0 1]}])
"""
def __init__(self, node, **kwargs):
super(CVBestSearchRefitParallel, self).__init__(wrapped_node=None)
#### 'y/test/score_recall_mean'
default_score = "y" + conf.SEP + \
conf.TEST + conf.SEP + \
conf.SCORE_RECALL_MEAN
score = kwargs.pop("score") if "score" in kwargs else default_score
arg_max = kwargs.pop("arg_max") if "arg_max" in kwargs else True
from epac.workflow.splitters import CV
# methods = Methods(*tasks)
cv_node = CV(node=node,
reducer=ClassificationReport(keep=False),
**kwargs)
self.add_child(cv_node)
self.score = score
self.arg_max = arg_max
self.refited = None
self.best_params = None
self.reducer = CVBestSearchRefitPReducer(self)
def get_signature(self):
return self.__class__.__name__
def transform(self, **Xy):
Xy_train, Xy_test = train_test_split(Xy)
result = Result(key=self.get_signature(), **Xy)
if not self.store:
self.store = StoreMem()
self.save_results(ResultSet(result))
if Xy_train is Xy_test:
return Xy
else:
return Xy_train
def _results2dict(self, **cpXy):
res_dict = {}
for key in cpXy[self.get_signature()]:
if not key == "key":
res_dict[key] = cpXy[self.get_signature()][key]
return res_dict
def reduce(self, store_results=True):
children_results = [child.reduce(store_results=False) for
child in self.children]
results = ResultSet(*children_results)
if self.reducer:
to_refit, best_params = self.reducer.reduce(results)
Xy = self.load_results()
Xy = self._results2dict(**Xy)
self.refited = to_refit
self.best_params = best_params
out = self.refited.top_down(**Xy)
out[conf.BEST_PARAMS] = best_params
result = Result(key=self.get_signature(), **out)
return ResultSet(result)
return results
