def _select_best(features, results, method):
features = identity(features)
return mapreduce(_select_min,
_evaluate_solution,
[(features, r, method) for r in results],
reduce_step=32,
map_step=8)
def nfoldcrossvalidation(features, labels, **kwargs):
"""
jug_task = nfoldcrossvalidation(features, labels, **kwargs)
A jug Task that perform n-foldcrossvalidation
N-fold cross validation is inherently parallel. This function returns a
``jug.Task`` which performs n-fold crossvalidation which jug can
parallelise.
Parameters
----------
features : sequence of features
labels : sequence
kwargs : any
This will be passed down to ``milk.nfoldcrossvalidation``
Returns
-------
jug_task : a jug.Task
A Task object
See Also
--------
milk.nfoldcrossvalidation : The same functionality as a "normal" function
jug.CompoundTask : This function can be used as argument to CompoundTask
"""
mapper = _nfold_one(features, labels, kwargs)
nfolds = kwargs.get("nfolds", 10)
return mapreduce(_nfold_reduce, mapper, range(nfolds), map_step=1, reduce_step=(nfolds + 1))
def nfoldcrossvalidation(features, labels, **kwargs):
'''
jug_task = nfoldcrossvalidation(features, labels, **kwargs)
A jug Task that perform n-foldcrossvalidation
N-fold cross validation is inherently parallel. This function returns a
``jug.Task`` which performs n-fold crossvalidation which jug can
parallelise.
Parameters
----------
features : sequence of features
labels : sequence
kwargs : any
This will be passed down to ``milk.nfoldcrossvalidation``
Returns
-------
jug_task : a jug.Task
A Task object
See Also
--------
milk.nfoldcrossvalidation : The same functionality as a "normal" function
jug.CompoundTask : This function can be used as argument to CompoundTask
'''
mapper = identity(_nfold_one(features, labels, kwargs))
nfolds = kwargs.get('nfolds', 10)
return mapreduce(_nfold_reduce, mapper, range(nfolds), map_step=1, reduce_step=(nfolds+1))
def _select_best(features, results, method):
features = identity(features)
return mapreduce(_select_min, _evaluate_solution, [(features,r,method) for r in results], reduce_step=32, map_step=8)
