Source code for crossvalidation

# Copyright 2011-2014 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import fastr
from fastr.core.samples import SampleIndex, SampleItem
from fastr.plugins import FlowInterface

def kfold(n_items, n_folds):
    items_per_fold = n_items // n_folds
    remaining_items = n_items % n_folds
    fold_sizes = [items_per_fold + 1 if x < remaining_items else items_per_fold for x in range(n_folds)]
    return [(list(range(0, sum(fold_sizes[:x]))) + list(range(sum(fold_sizes[:x+1]), n_items)), list(range(sum(fold_sizes[:x]), sum(fold_sizes[:x+1])))) for x in range(n_folds)]

class CrossValidation(FlowInterface.flow_plugin_type):
    Advanced flow plugin that generated a cross-validation data flow. The node
    need an input with data and an input number of folds. Based on that the
    outputs test and train will be supplied with a number of data sets.
[docs] @staticmethod def execute(payload): log_data = None items = payload['items'] method = sum( for x in payload['method']).sequence_part() number_of_folds = sum( for x in payload['number_of_folds']).sequence_part() labels = None if len(method) != 1: raise ValueError('Can only handle 1 method for cross validation!') method = method[0].value if len(number_of_folds) != 1: raise ValueError('Can only handle 1 number_of_folds for cross validation!') number_of_folds = number_of_folds[0].value fastr.log.debug('CV Plugin items: {!r}'.format(items))'CV Plugin method: {!r}'.format(method))'CV Plugin number_of_folds: {!r}'.format(number_of_folds)) if labels is not None and len(labels) != len(items): raise ValueError('If given, the number of labels should match the number of items!') if method == 'KFold': cv_iterator = kfold(len(items), n_folds=number_of_folds) else: raise ValueError('Invalid method selected!') train_data = {} test_data = {} for fold, (train, test) in enumerate(cv_iterator): fold_id = 'fold_{}'.format(fold) for k, index in enumerate(train): item = items[index] new_index = SampleIndex([k, fold]) new_id = + fold_id train_data[new_index, new_id] = SampleItem(new_index, new_id,, for k, index in enumerate(test): item = items[index] new_index = SampleIndex([k, fold]) new_id = + fold_id test_data[new_index, new_id] = SampleItem(new_index, new_id,, result_data = {'train': train_data, 'test': test_data} for key, value in result_data.items(): values = [(, x.index, for x in value.values()] fastr.log.debug('Result data {}: {}'.format(key, values)) return result_data, log_data