def test_construction():
minimal_event_list = [{
'scene_label': 'A'
}, {
'scene_label': 'A'
}, {
'scene_label': 'B'
}, {
'scene_label': 'B'
}, {
'scene_label': 'C'
}]
meta = MetaDataContainer(minimal_event_list)
target_binary_matrix = numpy.array([
[1., 0., 0.], # 0
[1., 0., 0.], # 1
[1., 0., 0.], # 2
]).T
# Test #1
binary_matrix = OneHotEncoder(label_list=['A', 'B', 'C'],
time_resolution=1.0).encode(
label='A',
length_seconds=3,
)
numpy.testing.assert_array_equal(target_binary_matrix, binary_matrix.data)
nose.tools.assert_equal(binary_matrix.shape[0],
target_binary_matrix.shape[0])
nose.tools.assert_equal(binary_matrix.shape[1],
target_binary_matrix.shape[1])
target_binary_matrix = numpy.array([
[0., 1., 0.], # 0
[0., 1., 0.], # 1
[0., 1., 0.], # 2
]).T
# Test #1
binary_matrix = OneHotEncoder(label_list=['A', 'B', 'C'],
time_resolution=1.0).encode(
label='B',
length_seconds=3,
)
numpy.testing.assert_array_equal(target_binary_matrix, binary_matrix.data)
nose.tools.assert_equal(binary_matrix.shape[0],
target_binary_matrix.shape[0])
nose.tools.assert_equal(binary_matrix.shape[1],
target_binary_matrix.shape[1])
def test_unknown_label():
with dcase_util.utils.DisableLogger():
OneHotEncoder(label_list=['A', 'B', 'C'],
time_resolution=1.0,
filename='test.cpickle').encode(
label='BB',
length_seconds=3,
)
def __init__(self, label_list=None, focus_field='scene_label', time_resolution=1.0,
length_frames=1, length_seconds=None, allow_unknown_labels=False,
**kwargs):
"""Constructor
Parameters
----------
label_list : list
List of labels in correct order
focus_field : str
Field from the meta data item to be used in encoding
time_resolution : float > 0.0
Time resolution used when converting event into event roll.
length_frames : int
Length of encoded segment in frames
length_seconds : float > 0.0
Length of encoded segment in seconds
allow_unknown_labels : bool
Allow unknown labels in the decoding. If False, labels not in the given label_list will produce an error.
Default value False
"""
# Inject initialization parameters back to kwargs
kwargs.update(
{
'label_list': label_list,
'time_resolution': time_resolution,
'length_frames': length_frames,
'length_seconds': length_seconds,
'allow_unknown_labels': allow_unknown_labels
}
)
# Run super init to call init of mixins too
super(OneHotEncodingProcessor, self).__init__(**kwargs)
self.encoder = OneHotEncoder(**self.init_parameters)
self.focus_field = focus_field
def test_log():
with dcase_util.utils.DisableLogger():
OneHotEncoder(label_list=['A', 'B', 'C'],
time_resolution=1.0,
filename='test.cpickle').log()
class OneHotEncodingProcessor(Processor):
"""One hot encoding processor"""
input_type = ProcessingChainItemType.METADATA #: Input data type
output_type = ProcessingChainItemType.DATA_CONTAINER #: Output data type
def __init__(self,
label_list=None,
focus_field='scene_label',
time_resolution=1.0,
length_frames=1,
length_seconds=None,
**kwargs):
"""Constructor
Parameters
----------
label_list : list
List of labels in correct order
focus_field : str
Field from the meta data item to be used in encoding
time_resolution : float > 0.0
Time resolution used when converting event into event roll.
length_frames : int
Length of encoded segment in frames
length_seconds : float > 0.0
Length of encoded segment in seconds
"""
# Inject initialization parameters back to kwargs
kwargs.update({
'label_list': label_list,
'time_resolution': time_resolution,
'length_frames': length_frames,
'length_seconds': length_seconds
})
# Run super init to call init of mixins too
super(OneHotEncodingProcessor, self).__init__(**kwargs)
self.encoder = OneHotEncoder(**self.init_parameters)
self.focus_field = focus_field
def process(self,
data=None,
label=None,
focus_field=None,
length_frames=None,
length_seconds=None,
store_processing_chain=False,
**kwargs):
"""Encode metadata
Parameters
----------
data : MetaDataContainer
Meta data to encode. Give data in either through meta data container or directly with label parameter.
label : str
Class label to be hot
focus_field : str
Field from the meta data item to be used in encoding. If None, one given as parameter for class
constructor is used.
length_frames : int
Length of encoded segment in frames. If None, one given as parameter for class constructor is used.
length_seconds : float > 0.0
Length of encoded segment in seconds. If None, one given as parameter for class constructor is used.
store_processing_chain : bool
Store processing chain to data container returned
Default value False
Returns
-------
BinaryMatrixContainer
"""
if data is None and label is None:
message = '{name}: Give data or label parameter.'.format(
name=self.__class__.__name__)
self.logger.exception(message)
raise ValueError(message)
from dcase_util.containers import MetaDataContainer
if data is not None and not isinstance(data, MetaDataContainer):
message = '{name}: Wrong input data type, type required [{input_type}].'.format(
name=self.__class__.__name__, input_type=self.input_type)
self.logger.exception(message)
raise ValueError(message)
if focus_field is None:
focus_field = self.focus_field
if data is not None and len(data) > 0 and label is None:
label = data[0].get(focus_field)
# Do processing
self.encoder.encode(label=label,
length_frames=length_frames,
length_seconds=length_seconds)
if store_processing_chain:
# Get processing chain item
processing_chain_item = self.get_processing_chain_item()
if 'process_parameters' not in processing_chain_item:
processing_chain_item['process_parameters'] = {}
processing_chain_item['process_parameters'][
'focus_field'] = focus_field
processing_chain_item['process_parameters'][
'length_frames'] = length_frames
# Create processing chain to be stored in the container, and push chain item into it
if hasattr(data, 'processing_chain'):
data.processing_chain.push_processor(**processing_chain_item)
processing_chain = data.processing_chain
else:
processing_chain = ProcessingChain().push_processor(
**processing_chain_item)
else:
processing_chain = None
from dcase_util.containers import BinaryMatrix2DContainer
container = BinaryMatrix2DContainer(
data=self.encoder.data,
label_list=self.encoder.label_list,
time_resolution=self.encoder.time_resolution,
processing_chain=processing_chain)
return container