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'headers': dict(get_headers(environ)),
'env': dict(get_environ(environ)),
""remote_ip"": request.META[""REMOTE_ADDR""],
""parameters"": parameters,
""action"": view.__name__,
""application"": view.__module__,
""method"": request.method,
""url"": request.build_absolute_uri()
}"
1058,"def run(self, training_set, trainer, filename = ""bootstrapped_model.hdf5"", force = False):
""""""run(training_set, trainer, [filename], [force]) -> model
Runs the bootstrapped training of a strong classifier using the given training data and a strong classifier trainer.
The training set need to contain extracted features already, as this function will need the features several times.
**Parameters:**
``training_set`` : :py:class:`TrainingSet`
The training set containing pre-extracted feature files
``trainer`` : :py:class:`bob.learn.boosting.Boosting`
A strong boosting trainer to use for selecting the weak classifiers and their weights for each round.
``filename`` : str
A filename, where to write the resulting strong classifier to.
This filename is also used as a base to compute filenames of intermediate files, which store results of each of the bootstrapping steps.
``force`` : bool
If set to ``False`` (the default), the bootstrapping will continue the round, where it has been stopped during the last run (reading the current stage from respective files).
If set to ``True``, the training will start from the beginning.
**Returns:**
``model`` : :py:class:`bob.learn.boosting.BoostedMachine`
The resulting strong classifier, a weighted combination of weak classifiers.
""""""
feature_extractor = training_set.feature_extractor()
training_data = None
training_labels = None
model = None
positive_indices, negative_indices = set(), set()
for b in range(self.m_number_of_rounds):
# check if old results are present
temp_file = ""%s_round_%d.hdf5"" % (os.path.splitext(filename)[0], b+1)
if os.path.exists(temp_file) and not force:
logger.info(""Loading already computed stage %d from %s."", b+1, temp_file)
model, positives, negatives = self._load(bob.io.base.HDF5File(temp_file))
positive_indices |= positives
negative_indices |= negatives
else:
if positive_indices or negative_indices:
# load data from previous rounds
logger.info(""Getting training data of previous rounds"")
training_data, training_labels = training_set.sample(positive_indices = positive_indices, negative_indices = negative_indices)
positive_indices, negative_indices = set(), set()
# get data for current round
logger.info(""Getting new data for bootstrapping round %d"", b+1)
new_data, new_labels = training_set.sample(model, self.m_number_of_positive_examples_per_round, self.m_number_of_negative_examples_per_round)
if training_data is None:
training_data = new_data
else:
training_data = numpy.append(training_data, new_data, axis=0)
if training_labels is None:
training_labels = new_labels
else:
training_labels = numpy.append(training_labels, new_labels, axis=0)
logger.info(""Starting training with %d examples"", training_data.shape[0])
model = trainer.train(training_data, training_labels, self.m_number_of_weak_learners_per_round[b], model)
# write model and extractor to temporary file to be able to catch up later
logger.info(""Saving results for stage %d to file %s"", b+1, temp_file)
self._save(bob.io.base.HDF5File(temp_file, 'w'), model, training_set.positive_indices, training_set.negative_indices)
feature_extractor.model_indices = model.indices
# finally, return the trained model
return model"
1059,"def _save(self, hdf5, model, positives, negatives):
""""""Saves the given intermediate state of the bootstrapping to file.""""""
# write the model and the training set indices to the given HDF5 file
hdf5.set(""PositiveIndices"", sorted(list(positives)))
hdf5.set(""NegativeIndices"", sorted(list(negatives)))
hdf5.create_group(""Model"")
hdf5.cd(""Model"")
model.save(hdf5)
del hdf5"
1060,"def _load(self, hdf5):
""""""Loads the intermediate state of the bootstrapping from file.""""""
positives = set(hdf5.get(""PositiveIndices""))
negatives = set(hdf5.get(""NegativeIndices""))
hdf5.cd(""Model"")
model = bob.learn.boosting.BoostedMachine(hdf5)
return model, positives, negatives"