dcase_framework.learners.EventDetector

class dcase_framework.learners.EventDetector(*args, **kwargs)

Event detector (Frame classifier / Multi-class - Multi-label)

Constructor

Parameters:

method : str

Method label Default value “None”

class_labels : list of strings

List of class labels Default value “[]”

params : dict or DottedDict

Parameters

feature_masker : FeatureMasker or class inherited from FeatureMasker

Feature masker instance Default value “None”

feature_normalizer : FeatureNormalizer or class inherited from FeatureNormalizer

Feature normalizer instance Default value “None”

feature_stacker : FeatureStacker or class inherited from FeatureStacker

Feature stacker instance Default value “None”

feature_aggregator : FeatureAggregator or class inherited from FeatureAggregator

Feature aggregator instance Default value “None”

logger : logging

Instance of logging Default value “None”

disable_progress_bar : bool

Disable progress bar in console Default value “False”

log_progress : bool

Show progress in log. Default value “False”

show_extra_debug : bool

Show extra debug information Default value “True”

__init__(*args, **kwargs)

Constructor

Parameters:

method : str

Method label Default value “None”

class_labels : list of strings

List of class labels Default value “[]”

params : dict or DottedDict

Parameters

feature_masker : FeatureMasker or class inherited from FeatureMasker

Feature masker instance Default value “None”

feature_normalizer : FeatureNormalizer or class inherited from FeatureNormalizer

Feature normalizer instance Default value “None”

feature_stacker : FeatureStacker or class inherited from FeatureStacker

Feature stacker instance Default value “None”

feature_aggregator : FeatureAggregator or class inherited from FeatureAggregator

Feature aggregator instance Default value “None”

logger : logging

Instance of logging Default value “None”

disable_progress_bar : bool

Disable progress bar in console Default value “False”

log_progress : bool

Show progress in log. Default value “False”

show_extra_debug : bool

Show extra debug information Default value “True”

Methods

__init__(\*args, \*\*kwargs)	Constructor
clear(() -> None.  Remove all items from D.)
copy(() -> a shallow copy of D)
detect_file_format(filename)	Detect file format from extension
empty()	Check if file is empty
exists()	Checks that file exists
fromkeys(...)	v defaults to None.
get((k[,d]) -> D[k] if k in D, ...)
get_dump_content(data)	Clean internal content for saving
get_file_information()	Get file information, filename
get_hash([data])	Get unique hash string (md5) for given parameter dict
get_hash_for_path([dotted_path])
get_path(dotted_path[, default, data])	Get value from nested dict with dotted path
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
itervalues(...)
keys(() -> list of D’s keys)
learn(data, annotations[, data_filenames])
load(\*args, \*\*kwargs)	Load file
log([level])	Log container content
merge(override[, target])	Recursive dict merge
pop((k[,d]) -> v, ...)	If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...)	2-tuple; but raise KeyError if D is empty.
save(\*args, \*\*kwargs)	Save file
set_path(dotted_path, new_value[, data])	Set value in nested dict with dotted path
set_seed([seed])	Set randomization seeds
setdefault((k[,d]) -> D.get(k,d), ...)
show()	Print container content
update(([E, ...)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)
viewitems(...)
viewkeys(...)
viewvalues(...)

Attributes

class_labels	Class labels
feature_aggregator	Feature aggregator instance
feature_masker	Feature masker instance
feature_normalizer	Feature normalizer instance
feature_stacker	Feature stacker instance
learner_params	Get learner parameters from parameter container
method	Learner method label
model	Acoustic model
params	Parameters
valid_formats