def apply_classifier(self):
parallel_classifier = ParallelClassifier()
number_examples, number_attributes = self.dataset.shape
examples = self.dataset[:, range(number_attributes - 2)]
examples_classes = self.dataset[:, [-2]]
train_test_folds = []
fold = np.zeros([number_examples])
map_folds = {}
for example in range(number_examples):
fold[example] = self.dataset[example][-1]
dict.setdefault(map_folds, fold[example], set())
map_folds[fold[example]].add(example)
for fold_examples in map_folds.values():
train_test_folds.append(
(list(set(range(number_examples)) - fold_examples),
list(fold_examples)))
score = parallel_classifier.get_final_score(
self.classifiers[self.classifier](**self.classifier_params),
examples, examples_classes, train_test_folds,
self.metrics[self.metric])
return score
def apply_classifier(self):
parallel_classifier = ParallelClassifier()
number_examples, number_attributes = self.dataset.shape
examples = self.dataset[:,range(number_attributes-2)]
examples_classes = self.dataset[:,[-2]]
train_test_folds = []
fold = np.zeros([number_examples])
map_folds = {}
for example in range(number_examples):
fold[example] = self.dataset[example][-1]
dict.setdefault(map_folds, fold[example], set())
map_folds[fold[example]].add(example)
for fold_examples in map_folds.values():
train_test_folds.append((list(set(range(number_examples)) - fold_examples), list(fold_examples)))
score = parallel_classifier.get_final_score(self.classifiers[self.classifier](**self.classifier_params), examples, examples_classes, train_test_folds, self.metrics[self.metric])
return score
class SupervisedLinkPrediction:
"""
A supervised learning link predictor.
This class builds a classifier model based on the classification dataset provider, and uses this model to predict the existence
or absence of a link in a set of pairs of nodes.
"""
def __init__(self, dataset, folds_number, classifier = "NB", classifier_params = {}, metric = "precision"):
self.dataset = dataset
self.classifier_params = classifier_params
self.metric = metric
self.folds_number = folds_number
self.classifier = ParallelClassifier(classifier, classifier_params, metric)
def set_classifier(self, classifier, classifier_params, metric):
self.classifier.set_classifier(classifier, classifier_params, metric)
def set_folds_number(self, folds_number):
self.folds_number = folds_number
def apply_classifier(self):
number_examples, number_attributes = self.dataset.shape
examples = self.dataset[:,range(number_attributes-2)]
examples_classes = self.dataset[:,[-2]]
train_test_folds = []
fold = np.zeros([number_examples])
map_folds = {}
for example in range(number_examples):
fold[example] = self.dataset[example][-1]
dict.setdefault(map_folds, fold[example], set())
map_folds[fold[example]].add(example)
for fold_examples in map_folds.values():
train_test_folds.append((list(set(range(number_examples)) - fold_examples), list(fold_examples)))
score = self.classifier.get_final_score(examples, examples_classes, train_test_folds)
return score
def __init__(self, dataset, folds_number, classifier = "NB", classifier_params = {}, metric = "precision"):
self.dataset = dataset
self.classifier_params = classifier_params
self.metric = metric
self.folds_number = folds_number
self.classifier = ParallelClassifier(classifier, classifier_params, metric)
