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)