Package sentence :: Module coupleddataset :: Class CoupledDataSet

Class CoupledDataSet

     object --+    
              |    
dataset.DataSet --+
                  |
                 CoupledDataSet

Known Subclasses:

CoupledDataSetDisk

A coupled data set contains all possible couples of parallel sentences of a simple dataset

Instance Methods

[hide private]

__init__(self, **kwargs)
x.__init__(...) initializes x; see help(type(x)) for signature

source code

DataSet

get_single_set(self, critical_attribute=None)
Reconstructs the original data set, with only one sentence per entry.

source code

DataSet

get_single_set_with_soft_ranks(self, attribute1='', attribute2='', critical_attribute='rank_soft_predicted')
Reconstructs the original data set, with only one sentence per entry. source code

DataSet

get_single_set_with_hard_ranks(self, critical_attribute=None)
Reconstructs the original data set, with only one sentence per entry.

source code

get_nested_attribute_names(self)

source code

Inherited from dataset.DataSet: __eq__, __iter__, add_attribute_vector, append_dataset, clone, compare, confirm_attributes, ensure_judgment_ids, get_all_attribute_names, get_annotations, get_attribute_names, get_discrete_attribute_values, get_head_sentences, get_multisource_strings, get_parallelsentences, get_parallelsentences_per_sentence_id, get_parallelsentences_with_judgment_ids, get_singlesource_strings, get_size, get_tail_sentences, get_target_strings, get_translations_count_vector, import_target_attributes_onsystem, merge_dataset, merge_dataset_symmetrical, merge_references_symmetrical, modify_singlesource_strings, modify_target_strings, remove_ties, select_attribute_names, split, write_singlesource_strings_file

Inherited from dataset.DataSet (private): _retrieve_attribute_names

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Instance Variables

[hide private]

[ParallelSentence, ...] parallelsentences
a list of the contained parallel sentence instances

Inherited from dataset.DataSet: attribute_names, attribute_names_found

Properties

[hide private]

Inherited from object: __class__

Method Details

[hide private]

init(self, **kwargs)
(Constructor)

source code

x.__init__(...) initializes x; see help(type(x)) for signature

Parameters:

parallelsentence_list - the parallelsentences to be wrapped in the dataset
attributes_list - if the names of the attributes for the parallelsentences are known, they can be given here, in order to avoid extra processing. Otherwise they will be computed when needed. @type [str, ...]
annotations - Not implemented @type list
existing_item (DataSet or [CoupledParallelSentence, ...])

Overrides: object.__init__

get_single_set(self, critical_attribute=None)

source code

Reconstructs the original data set, with only one sentence per entry.

Returns: DataSet: Simple dataset that contains the simplified parallel sentences

get_single_set_with_soft_ranks(self, attribute1=`''`, attribute2=`''`, critical_attribute=`'rank_soft_predicted'`)

source code

Reconstructs the original data set, with only one sentence per entry.

Returns: DataSet: Simple dataset that contains the simplified parallel sentences

get_single_set_with_hard_ranks(self, critical_attribute=None)

source code

Reconstructs the original data set, with only one sentence per entry.

Returns: DataSet: Simple dataset that contains the simplified parallel sentences

get_nested_attribute_names(self)

source code

Overrides: dataset.DataSet.get_nested_attribute_names

Class CoupledDataSet

__init__(self, **kwargs) (Constructor)

get_single_set(self, critical_attribute=None)

get_single_set_with_soft_ranks(self, attribute1='', attribute2='', critical_attribute='rank_soft_predicted')

get_single_set_with_hard_ranks(self, critical_attribute=None)

get_nested_attribute_names(self)

init(self, **kwargs)
(Constructor)

get_single_set_with_soft_ranks(self, attribute1=`''`, attribute2=`''`, critical_attribute=`'rank_soft_predicted'`)