class PredictionVectorSinkNode(BaseNode):
""" Collect all :class:`~pySPACE.resources.data_types.prediction_vector.PredictionVectorDataset` elements
that are passed through it in a collection of type
:mod:`~pySPACE.resources.dataset_defs.prediction_vector`.
.. note::
The code is heavily based on its counterpart for
:class:`~pySPACE.resources.data_types.feature_vector.FeatureVector`
elements that can be found in :mod:`~pySPACE.missions.nodes.sink.feature_vector_sink.FeatureVectorSinkNode`
**Exemplary Call**
.. code-block:: yaml
-
node: PredictionVectorSink
:input: PredictionVector
:output: PredictionVectorDataset
:Author: Andrei Ignat ([email protected])
:Created: 2014/10/15
"""
input_types = ["PredictionVector"]
def __init__(self, **kwargs):
super(PredictionVectorSinkNode, self).__init__(**kwargs)
self.set_permanent_attributes(prediction_vector_collection=None)
def reset(self):
""" Reset the state of the object to the clean state it had after its
initialization
"""
tmp = self.permanent_state
# TODO: just a hack to get it working quickly...
tmp["prediction_vector_collection"] = self.prediction_vector_collection
self.__dict__ = copy.copy(tmp)
self.permanent_state = tmp
def is_trainable(self):
""" Returns whether this node is trainable.
Since we want to sink the training examples as well, this
function wil return True
"""
return True
def is_supervised(self):
""" Returns whether this node requires supervised training """
return True
def _train(self, data, label):
# We do nothing
pass
def _create_result_sets(self):
""" Instantiate the :class:`~pySPACE.resources.data_types.prediction_vector.PredictionVectorDataset`
"""
self.prediction_vector_collection = PredictionVectorDataset()
def process_current_split(self):
""" Compute the results of this sink node for the current split
of the data into train and test data
"""
# Compute the prediction vectors for the data used for training
for prediction_vector, label in self.input_node.request_data_for_training(False):
if prediction_vector.tag != "Discard":
if self.prediction_vector_collection is None:
# create the dataset if it does not already exist
self._create_result_sets()
self.prediction_vector_collection.add_sample(prediction_vector,
label,
train=True,
split=self.current_split,
run=self.run_number)
# Compute the prediction vectors for the data used for testing
for prediction_vector, label in self.input_node.request_data_for_testing():
# If Prediction Vectors need to be discarded, that is done here
if prediction_vector.tag != "Discard":
# Do lazy initialization of the class
# (maybe there were no training examples)
if self.prediction_vector_collection is None:
self._create_result_sets()
# Add sample
self.prediction_vector_collection.add_sample(prediction_vector,
label,
train=False,
split=self.current_split,
run=self.run_number)
def get_result_dataset(self):
""" Return the result dataset """
return self.prediction_vector_collection
class PredictionVectorSinkNode(BaseNode):
""" Collect all :class:`~pySPACE.resources.data_types.prediction_vector.PredictionVectorDataset` elements
that are passed through it in a collection of type
:mod:`~pySPACE.resources.dataset_defs.prediction_vector`.
.. note::
The code is heavily based on its counterpart for
:class:`~pySPACE.resources.data_types.feature_vector.FeatureVector`
elements that can be found in :mod:`~pySPACE.missions.nodes.sink.feature_vector_sink.FeatureVectorSinkNode`
**Exemplary Call**
.. code-block:: yaml
-
node: PredictionVectorSink
:input: PredictionVector
:output: PredictionVectorDataset
:Author: Andrei Ignat ([email protected])
:Created: 2014/10/15
"""
input_types = ["PredictionVector"]
def __init__(self, **kwargs):
super(PredictionVectorSinkNode, self).__init__(**kwargs)
self.set_permanent_attributes(prediction_vector_collection=None)
def reset(self):
""" Reset the state of the object to the clean state it had after its
initialization
"""
tmp = self.permanent_state
# TODO: just a hack to get it working quickly...
tmp["prediction_vector_collection"] = self.prediction_vector_collection
self.__dict__ = copy.copy(tmp)
self.permanent_state = tmp
def is_trainable(self):
""" Returns whether this node is trainable.
Since we want to sink the training examples as well, this
function wil return True
"""
return True
def is_supervised(self):
""" Returns whether this node requires supervised training """
return True
def _train(self, data, label):
# We do nothing
pass
def _create_result_sets(self):
""" Instantiate the :class:`~pySPACE.resources.data_types.prediction_vector.PredictionVectorDataset`
"""
self.prediction_vector_collection = PredictionVectorDataset()
def process_current_split(self):
""" Compute the results of this sink node for the current split
of the data into train and test data
"""
# Compute the prediction vectors for the data used for training
for prediction_vector, label in self.input_node.request_data_for_training(
False):
if prediction_vector.tag != "Discard":
if self.prediction_vector_collection is None:
# create the dataset if it does not already exist
self._create_result_sets()
self.prediction_vector_collection.add_sample(
prediction_vector,
label,
train=True,
split=self.current_split,
run=self.run_number)
# Compute the prediction vectors for the data used for testing
for prediction_vector, label in self.input_node.request_data_for_testing(
):
# If Prediction Vectors need to be discarded, that is done here
if prediction_vector.tag != "Discard":
# Do lazy initialization of the class
# (maybe there were no training examples)
if self.prediction_vector_collection is None:
self._create_result_sets()
# Add sample
self.prediction_vector_collection.add_sample(
prediction_vector,
label,
train=False,
split=self.current_split,
run=self.run_number)
def get_result_dataset(self):
""" Return the result dataset """
return self.prediction_vector_collection
def _create_result_sets(self):
""" Instantiate the :class:`~pySPACE.resources.data_types.prediction_vector.PredictionVectorDataset`
"""
self.prediction_vector_collection = PredictionVectorDataset()
def _create_result_sets(self):
""" Instantiate the :class:`~pySPACE.resources.data_types.prediction_vector.PredictionVectorDataset`
"""
self.prediction_vector_collection = PredictionVectorDataset()
