def _addTrainingData(self, labeled_pairs):
"""
Appends training data to the training data collection.
"""
for label, examples in labeled_pairs.items():
n_examples = len(examples)
labels = [label] * n_examples
new_data = numpy.empty(n_examples, dtype=self.training_data.dtype)
new_data["label"] = labels
new_data["distances"] = core.fieldDistances(examples, self.data_model)
self.training_data = numpy.append(self.training_data, new_data)
def _addTrainingData(self, labeled_pairs):
"""
Appends training data to the training data collection.
"""
for label, examples in labeled_pairs.items():
n_examples = len(examples)
labels = [label] * n_examples
new_data = numpy.empty(n_examples, dtype=self.training_data.dtype)
new_data['label'] = labels
new_data['distances'] = core.fieldDistances(
examples, self.data_model)
self.training_data = numpy.append(self.training_data, new_data)
def goodThreshold(self,
blocks,
constrained_matching=False,
recall_weight=1.5):
"""
Returns the threshold that maximizes the expected F score,
a weighted average of precision and recall for a sample of
blocked data.
Keyword arguments:
blocks -- Sequence of tuples of records, where each
tuple is a set of records covered by a blocking
predicate
recall_weight -- Sets the tradeoff between precision and
recall. I.e. if you care twice as much about
recall as you do precision, set recall_weight
to 2.
"""
blocked_records = (block.values() for block in blocks)
candidates = core.blockedPairs(blocked_records, constrained_matching)
field_distances = core.fieldDistances(candidates, self.data_model)
probability = core.scorePairs(field_distances, self.data_model)
probability.sort()
probability = probability[::-1]
expected_dupes = numpy.cumsum(probability)
recall = expected_dupes / expected_dupes[-1]
precision = expected_dupes / numpy.arange(1, len(expected_dupes) + 1)
score = recall * precision / (recall + recall_weight**2 * precision)
i = numpy.argmax(score)
logging.info('Maximum expected recall and precision')
logging.info('recall: %2.3f', recall[i])
logging.info('precision: %2.3f', precision[i])
logging.info('With threshold: %2.3f', probability[i])
return probability[i]
def goodThreshold(self, blocks, recall_weight=1.5):
"""
Returns the threshold that maximizes the expected F score,
a weighted average of precision and recall for a sample of
blocked data.
Keyword arguments:
blocks -- Sequence of tuples of records, where each
tuple is a set of records covered by a blocking
predicate
recall_weight -- Sets the tradeoff between precision and
recall. I.e. if you care twice as much about
recall as you do precision, set recall_weight
to 2.
"""
blocked_records = (block.values() for block in blocks)
candidates = core.blockedPairs(blocked_records)
field_distances = core.fieldDistances(candidates, self.data_model)
probability = core.scorePairs(field_distances, self.data_model)
probability.sort()
probability = probability[::-1]
expected_dupes = numpy.cumsum(probability)
recall = expected_dupes / expected_dupes[-1]
precision = expected_dupes / numpy.arange(1, len(expected_dupes) + 1)
score = recall * precision / (recall + recall_weight ** 2 * precision)
i = numpy.argmax(score)
logging.info("Maximum expected recall and precision")
logging.info("recall: %2.3f", recall[i])
logging.info("precision: %2.3f", precision[i])
logging.info("With threshold: %2.3f", probability[i])
return probability[i]
