def to_picklable_dict(self):
""" Produces a picklable representation of model as a dict. """
from jazzparser.utils.nltk.storage import object_to_dict
if self.backoff_model is not None:
backoff_model = self.backoff_model.to_picklable_dict()
else:
backoff_model = None
return {
'order':
self.order,
'root_transition_counts':
object_to_dict(self.root_transition_counts),
'schema_transition_counts':
object_to_dict(self.schema_transition_counts),
'emission_counts':
object_to_dict(self.emission_counts),
'estimator':
self._estimator,
'backoff_model':
backoff_model,
'chord_vocab':
self.chord_vocab,
'schemata':
self.schemata,
'history':
self.history,
}
def to_picklable_dict(self):
"""
Produces a picklable representation of model as a dict.
"""
from jazzparser.utils.nltk.storage import object_to_dict
return {
'label_dom' : self.label_dom,
'emission_dom' : self.emission_dom,
'label_dist' : object_to_dict(self.label_dist),
'emission_dist' : object_to_dict(self.emission_dist),
}
def to_picklable_dict(self):
"""
Produces a picklable representation of model as a dict.
"""
from jazzparser.utils.nltk.storage import object_to_dict
return {
'label_dom': self.label_dom,
'emission_dom': self.emission_dom,
'label_dist': object_to_dict(self.label_dist),
'emission_dist': object_to_dict(self.emission_dist),
}
def _get_model_data(self):
data = {
'parents' : object_to_dict(self._parent_counts),
'expansions' : object_to_dict(self._expansion_type_counts),
'heads' : object_to_dict(self._head_expansion_counts),
'non_heads' : object_to_dict(self._non_head_expansion_counts),
'words' : object_to_dict(self._lexical_counts),
'cutoff' : self.cutoff,
'cat_bins' : self.cat_bins,
'estimator': self._estimator,
'grammar' : self.grammar,
'lexical' : self.lexical,
'chordmap' : self.chordmap.name,
}
return data
def _get_model_data(self):
data = {
'parents': object_to_dict(self._parent_counts),
'expansions': object_to_dict(self._expansion_type_counts),
'heads': object_to_dict(self._head_expansion_counts),
'non_heads': object_to_dict(self._non_head_expansion_counts),
'words': object_to_dict(self._lexical_counts),
'cutoff': self.cutoff,
'cat_bins': self.cat_bins,
'estimator': self._estimator,
'grammar': self.grammar,
'lexical': self.lexical,
'chordmap': self.chordmap.name,
}
return data
def to_picklable_dict(self):
"""
Produces a picklable representation of model as a dict.
You can't just pickle the object directly because some of the
NLTK classes can't be pickled. You can pickle this dict and
reconstruct the model using NgramModel.from_picklable_dict(dict).
"""
from jazzparser.utils.nltk.storage import object_to_dict
return {
'key_transition_dist' : object_to_dict(self.key_transition_dist),
'chord_transition_dist' : object_to_dict(self.chord_transition_dist),
'emission_dist' : object_to_dict(self.emission_dist),
'chord_dist' : object_to_dict(self.chord_dist),
'history' : self.history,
'description' : self.description,
'chord_set' : self.chord_set,
}
def to_picklable_dict(self):
"""
Produces a picklable representation of model as a dict.
You can't just pickle the object directly because some of the
NLTK classes can't be pickled. You can pickle this dict and
reconstruct the model using NgramModel.from_picklable_dict(dict).
"""
from jazzparser.utils.nltk.storage import object_to_dict
return {
'key_transition_dist': object_to_dict(self.key_transition_dist),
'chord_transition_dist':
object_to_dict(self.chord_transition_dist),
'emission_dist': object_to_dict(self.emission_dist),
'chord_dist': object_to_dict(self.chord_dist),
'history': self.history,
'description': self.description,
'chord_set': self.chord_set,
}
def to_picklable_dict(self):
""" Produces a picklable representation of model as a dict. """
from jazzparser.utils.nltk.storage import object_to_dict
if self.backoff_model is not None:
backoff_model = self.backoff_model.to_picklable_dict()
else:
backoff_model = None
return {
'order' : self.order,
'root_transition_counts' : object_to_dict(self.root_transition_counts),
'schema_transition_counts' : object_to_dict(self.schema_transition_counts),
'emission_counts' : object_to_dict(self.emission_counts),
'estimator' : self._estimator,
'backoff_model' : backoff_model,
'chord_vocab' : self.chord_vocab,
'schemata' : self.schemata,
'history' : self.history,
}
def to_picklable_dict(self):
from jazzparser.utils.nltk.storage import object_to_dict
if self.backoff_model is not None:
backoff_model = self.backoff_model.to_picklable_dict()
else:
backoff_model = None
return {
'order' : self.order,
'point_transition_counts' : object_to_dict(self.point_transition_counts),
'fn_transition_counts' : object_to_dict(self.fn_transition_counts),
'type_emission_counts' : object_to_dict(self.type_emission_counts),
'subst_emission_counts' : object_to_dict(self.subst_emission_counts),
'estimator' : self._estimator,
'backoff_model' : backoff_model,
'chord_map' : self.chord_map.name,
'vector_dom' : self.vector_dom,
'point_dom' : self.point_dom,
'history' : self.history,
}
def to_picklable_dict(self):
from jazzparser.utils.nltk.storage import object_to_dict
if self.backoff_model is not None:
backoff_model = self.backoff_model.to_picklable_dict()
else:
backoff_model = None
return {
'order': self.order,
'point_transition_counts':
object_to_dict(self.point_transition_counts),
'fn_transition_counts': object_to_dict(self.fn_transition_counts),
'type_emission_counts': object_to_dict(self.type_emission_counts),
'subst_emission_counts':
object_to_dict(self.subst_emission_counts),
'estimator': self._estimator,
'backoff_model': backoff_model,
'chord_map': self.chord_map.name,
'vector_dom': self.vector_dom,
'point_dom': self.point_dom,
'history': self.history,
}
def to_picklable_dict(self):
"""
Produces a picklable representation of model as a dict.
You can't just pickle the object directly because some of the
NLTK classes can't be pickled. You can pickle this dict and
reconstruct the model using NgramModel.from_picklable_dict(dict).
"""
from jazzparser.utils.nltk.storage import object_to_dict
return {
'schema_transition_dist' : object_to_dict(self.schema_transition_dist),
'root_transition_dist' : object_to_dict(self.root_transition_dist),
'emission_dist' : object_to_dict(self.emission_dist),
'emission_number_dist' : object_to_dict(self.emission_number_dist),
'initial_state_dist' : object_to_dict(self.initial_state_dist),
'schemata' : self.schemata,
'history' : self.history,
'description' : self.description,
'chord_class_mapping' : self.chord_class_mapping,
'chord_classes' : self.chord_classes,
'illegal_transitions' : self.illegal_transitions,
'fixed_root_transitions' : self.fixed_root_transitions,
}
def _object_to_dict(obj):
if type(obj) == MutableProbDist:
# Convert to a normal dict prob dist
obj = prob_dist_to_dictionary_prob_dist(obj)
return object_to_dict(obj)