def fit(self, data_pack: DataPack, verbose=1):
"""
Fit pre-processing context for transformation.
:param data_pack: data_pack to be preprocessed.
:param verbose: Verbosity.
:return: class:`BasicPreprocessor` instance.
"""
units = self._default_processor_units()
data_pack = data_pack.apply_on_text(chain_transform(units),
verbose=verbose)
fitted_filter_unit = build_unit_from_data_pack(self._filter_unit,
data_pack,
flatten=False,
mode='right',
verbose=verbose)
data_pack = data_pack.apply_on_text(fitted_filter_unit.transform,
mode='right',
verbose=verbose)
self._context['filter_unit'] = fitted_filter_unit
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['vocab_unit'] = vocab_unit
self._context['vocab_size'] = len(vocab_unit.state['term_index']) + 1
self._context['input_shapes'] = [(self._fixed_length_left, ),
(self._fixed_length_right, )]
return self
def build_unit_from_data_pack(unit: StatefulUnit,
data_pack: mz.DataPack,
mode: str = 'both',
flatten: bool = True,
verbose: int = 1) -> StatefulUnit:
"""
Build a :class:`StatefulUnit` from a :class:`DataPack` object.
:param unit: :class:`StatefulUnit` object to be built.
:param data_pack: The input :class:`DataPack` object.
:param mode: One of 'left', 'right', and 'both', to determine the source
data for building the :class:`VocabularyUnit`.
:param flatten: Flatten the datapack or not. `True` to organize the
:class:`DataPack` text as a list, and `False` to organize
:class:`DataPack` text as a list of list.
:param verbose: Verbosity.
:return: A built :class:`StatefulUnit` object.
"""
corpus = []
if flatten:
data_pack.apply_on_text(corpus.extend, mode=mode, verbose=verbose)
else:
data_pack.apply_on_text(corpus.append, mode=mode, verbose=verbose)
if verbose:
description = 'Building ' + unit.__class__.__name__ + \
' from a datapack.'
corpus = tqdm(corpus, desc=description)
unit.fit(corpus)
return unit
def fit(self, data_pack: DataPack, verbose: int = 1):
## 经过分词、去标点以及去停用词
data_pack = data_pack.apply_on_text(chain_transform(self._units),
verbose=verbose)
## 过滤高频词和低频词
## 先通过build进行统计
fitted_filter_unit = build_unit_from_data_pack(self._filter_unit,
data_pack,
flatten=False,
mode='right',
verbose=verbose)
### 基于上面统计的结果进行转换并保存模型
data_pack = data_pack.apply_on_text(fitted_filter_unit.transform,
mode='right',
verbose=verbose)
self._context['filter_unit'] = fitted_filter_unit
## 构建词表
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['vocab_unit'] = vocab_unit
vocab_size = len(vocab_unit.state['term_index'])
self._context['vocab_size'] = vocab_size
self._context['embedding_input_dim'] = vocab_size
return self
def transform(self, pack: matchzoo.DataPack):
""" Converting the raw path to mapped indices """
def left_to_indices(images: List[str]):
images_indices = [
self.left_img_path2index[p]
for p in images[:self.max_num_left_images]
]
images_indices += [0] * (
self.max_num_left_images - len(images_indices)) # padding
return images_indices
def right_to_indices(images: List[str]):
images_indices = [
self.right_img_path2index[p]
for p in images[:self.max_num_right_images]
]
images_indices += [0] * (
self.max_num_right_images - len(images_indices)) # padding
return images_indices
pack.left["images_left"] = pack.left["images_left"].apply(
left_to_indices)
pack.right["images_right"] = pack.right["images_right"].apply(
right_to_indices)
return pack
def fit(self, data_pack: DataPack, verbose: int = 1):
"""
Fit pre-processing context for transformation.
:param data_pack: data_pack to be preprocessed.
:param verbose: Verbosity.
:return: class:'DIINPreprocessor' instance.
"""
func = chain_transform(self._units)
data_pack = data_pack.apply_on_text(func, mode='both', verbose=verbose)
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
vocab_size = len(vocab_unit.state['term_index'])
self._context['vocab_unit'] = vocab_unit
self._context['vocab_size'] = vocab_size
self._context['embedding_input_dim'] = vocab_size
data_pack = data_pack.apply_on_text(
units.NgramLetter(ngram=1, reduce_dim=True).transform,
mode='both', verbose=verbose)
char_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['char_unit'] = char_unit
self._context['input_shapes'] = [
(self._fixed_length_left,),
(self._fixed_length_right,),
(self._fixed_length_left, self._fixed_length_word,),
(self._fixed_length_right, self._fixed_length_word,),
(self._fixed_length_left,),
(self._fixed_length_right,)
]
return self
def fit(self, data_pack: DataPack, verbose: int = 1):
"""
Fit pre-processing context for transformation.
:param data_pack: data_pack to be preprocessed.
:param verbose: Verbosity.
:return: class:`BasicPreprocessor` instance.
"""
data_pack = data_pack.apply_on_text(chain_transform(self._units),
verbose=verbose)
fitted_filter_unit = build_unit_from_data_pack(self._filter_unit,
data_pack,
flatten=False,
mode='right',
verbose=verbose)
data_pack = data_pack.apply_on_text(fitted_filter_unit.transform,
mode='right',
verbose=verbose)
self._context['filter_unit'] = fitted_filter_unit
vocab_unit = build_vocab_unit(
data_pack, verbose=verbose,
mode="right") # only rely on the right side
self._context['vocab_unit'] = vocab_unit
vocab_size = len(
vocab_unit.state['term_index']) # + 1 # +1 for padding
self._context['vocab_size'] = vocab_size
self._context['embedding_input_dim'] = vocab_size
self._context['input_shapes'] = [(self._fixed_length_left, ),
(self._fixed_length_right, )]
return self
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
"""
Apply transformation on data, create `letter-ngram` representation.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
data_pack = data_pack.copy()
func = chain_transform(self._default_units())
data_pack.apply_on_text(func, inplace=True, verbose=verbose)
data_pack.apply_on_text(self._left_fixedlength_unit.transform,
mode='left',
inplace=True,
verbose=verbose)
data_pack.apply_on_text(self._right_fixedlength_unit.transform,
mode='right',
inplace=True,
verbose=verbose)
post_units = [units.NgramLetter(reduce_dim=False)]
if self._with_word_hashing:
term_index = self._context['vocab_unit'].state['term_index']
post_units.append(units.WordHashing(term_index))
data_pack.apply_on_text(chain_transform(post_units),
inplace=True,
verbose=verbose)
return data_pack
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
"""
Apply transformation on data, create fixed length representation.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
data_pack = data_pack.copy()
data_pack.apply_on_text(chain_transform(self._units),
inplace=True,
verbose=verbose)
data_pack.apply_on_text(self._context['filter_unit'].transform,
mode='right',
inplace=True,
verbose=verbose)
def convert_to_bow(input_: List[str]):
"""the list of tokens will be converted to """
vocab_unit = self._context['vocab_unit']
ans = [0.0] * self._context['vocab_size']
for token in input_:
index = vocab_unit._state['term_index'][token]
ans[index] = 1.0
return ans
data_pack.apply_on_text(convert_to_bow,
mode='both',
inplace=True,
verbose=verbose)
data_pack.right['images_right'] = data_pack.right[
"images_right"].progress_apply(self._images_unit.transform)
return data_pack
def __init__(self,
data_pack: mz.DataPack,
mode='point',
num_dup: int = 1,
num_neg: int = 1,
resample: bool = True,
batch_size: int = 128,
shuffle: bool = True,
callbacks: typing.List[Callback] = None):
"""Init."""
if callbacks is None:
callbacks = []
if mode not in ('point', 'pair', 'list'):
raise ValueError(f"{mode} is not a valid mode type."
f"Must be one of `point`, `pair` or `list`.")
self._mode = mode
self._num_dup = num_dup
self._num_neg = num_neg
self._batch_size = batch_size
self._shuffle = shuffle
self._resample = resample
self._orig_relation = data_pack.relation
self._callbacks = callbacks
if mode == 'pair':
data_pack.relation = self._reorganize_pair_wise(data_pack.relation,
num_dup=num_dup,
num_neg=num_neg)
self._data_pack = data_pack
self._batch_indices = None
self.reset_index()
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
"""
Apply transformation on data.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
data_pack.apply_on_text(self._tokenizer.encode,
mode='both',
inplace=True,
verbose=verbose)
data_pack.append_text_length(inplace=True, verbose=verbose)
return data_pack
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
"""
Apply transformation on data, create truncated length representation.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
units_ = self._default_units()
units_.append(self._context['vocab_unit'])
units_.append(
units.TruncatedLength(text_length=30, truncate_mode='post'))
func = chain_transform(units_)
data_pack.apply_on_text(func, inplace=True, verbose=verbose)
data_pack.append_text_length(inplace=True, verbose=verbose)
return data_pack
def __init__(
self,
data_pack: mz.DataPack,
mode='point',
num_dup: int = 1,
num_neg: int = 1,
batch_size: int = 32,
resample: bool = False,
shuffle: bool = True,
sort: bool = False,
callbacks: typing.List[BaseCallback] = None
):
"""Init."""
if callbacks is None:
callbacks = []
if mode not in ('point', 'pair', 'list'):
raise ValueError(f"{mode} is not a valid mode type."
f"Must be one of `point`, `pair` or `list`.")
if shuffle and sort:
raise ValueError(f"parameters `shuffle` and `sort` conflict, "
f"should not both be `True`.")
data_pack = data_pack.copy()
self._mode = mode
self._num_dup = num_dup
self._num_neg = num_neg
self._batch_size = batch_size
self._resample = (resample if mode != 'point' else False)
self._shuffle = shuffle
self._sort = sort
self._orig_relation = data_pack.relation
self._callbacks = callbacks
if mode == 'pair':
data_pack.relation = self._reorganize_pair_wise(
relation=self._orig_relation,
num_dup=num_dup,
num_neg=num_neg
)
self._data_pack = data_pack
self._batch_indices = None
self.reset_index()
def transform(self, data_pack: DataPack, verbose=1) -> DataPack:
"""
Apply transformation on data, create `tri-letter` representation.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
data_pack = data_pack.copy()
units = self._default_processor_units()
if self._with_word_hashing:
term_index = self._context['vocab_unit'].state['term_index']
units.append(processor_units.WordHashingUnit(term_index))
data_pack.apply_on_text(chain_transform(units),
inplace=True,
verbose=verbose)
return data_pack
def data_pack():
relation = [['qid0', 'did0', 1], ['qid1', 'did1', 0]]
left = [['qid0', [1, 2]], ['qid1', [2, 3]]]
right = [['did0', [2, 3, 4]], ['did1', [3, 4, 5]]]
relation = pd.DataFrame(relation, columns=['id_left', 'id_right', 'label'])
left = pd.DataFrame(left, columns=['id_left', 'text_left'])
left.set_index('id_left', inplace=True)
right = pd.DataFrame(right, columns=['id_right', 'text_right'])
right.set_index('id_right', inplace=True)
return DataPack(relation=relation, left=left, right=right)
def fit(self, data_pack: DataPack, verbose: int = 1):
"""
Fit pre-processing context for transformation.
:param data_pack: data_pack to be preprocessed.
:param verbose: Verbosity.
:return: class:`BasicPreprocessor` instance.
"""
data_pack = data_pack.apply_on_text(
ChainTransform(self._units),
multiprocessing=self.multiprocessing,
verbose=verbose)
fitted_filter_unit = build_unit_from_data_pack(self._filter_unit,
data_pack,
flatten=False,
mode='right',
verbose=verbose)
data_pack = data_pack.apply_on_text(ChainTransform(fitted_filter_unit),
mode='right',
multiprocessing=False,
verbose=verbose)
self._context['filter_unit'] = fitted_filter_unit
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
if self.extra_terms:
vocab_unit.fit_incrementally(self.extra_terms)
self._context['vocab_unit'] = vocab_unit
vocab_size = len(vocab_unit.state['term_index'])
self._context['vocab_size'] = vocab_size
self._context['embedding_input_dim'] = vocab_size
if self._ngram_size:
data_pack = data_pack.apply_on_text(
ChainTransform(self._context['ngram_process_unit']),
mode='both',
multiprocessing=self.multiprocessing,
verbose=verbose)
ngram_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['ngram_vocab_unit'] = ngram_unit
self._context['ngram_vocab_size'] = len(
ngram_unit.state['term_index'])
return self
def fit(self, data_pack: DataPack, verbose: int = 1):
"""
Fit pre-processing context for transformation.
:param data_pack: data_pack to be preprocessed.
:param verbose: Verbosity.
:return: class:`NaivePreprocessor` instance.
"""
func = chain_transform(self._default_units())
data_pack = data_pack.apply_on_text(func, verbose=verbose)
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['vocab_unit'] = vocab_unit
return self
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
"""
Apply transformation on data, create `tri-letter` representation.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
units = self._default_processor_units()
units.append(self._context['vocab_unit'])
units.append(processor_units.FixedLengthUnit(text_length=30,
pad_mode='post'))
return data_pack.apply_on_text(chain_transform(units), verbose=verbose)
def fit(self, data_pack: DataPack, verbose: int = 1):
"""
Fit pre-processing context for transformation.
:param data_pack: data_pack to be preprocessed.
:param verbose: Verbosity.
:return: class:`BasicPreprocessor` instance.
"""
data_pack = data_pack.apply_on_text(chain_transform(self._units),
verbose=verbose)
# fitted_filter_unit = build_unit_from_data_pack(self._filter_unit,
# data_pack,
# flatten=False,
# mode='right',
# verbose=verbose)
# data_pack = data_pack.apply_on_text(fitted_filter_unit.transform,
# mode='right', verbose=verbose)
# self._context['filter_unit'] = fitted_filter_unit
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['vocab_unit'] = vocab_unit
vocab_size = len(vocab_unit.state['term_index'])
self._context['vocab_size'] = vocab_size
self._context['embedding_input_dim'] = vocab_size
if self._ngram_size:
data_pack = data_pack.apply_on_text(
self._context['ngram_process_unit'].transform,
mode='both',
verbose=verbose)
ngram_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['ngram_vocab_unit'] = ngram_unit
self._context['ngram_vocab_size'] = len(
ngram_unit.state['term_index'])
return self
def fit(self, data_pack: DataPack, verbose=1):
"""
Fit pre-processing context for transformation.
:param verbose: Verbosity.
:param data_pack: data_pack to be preprocessed.
:return: class:`DSSMPreprocessor` instance.
"""
units = self._default_processor_units()
data_pack = data_pack.apply_on_text(chain_transform(units),
verbose=verbose)
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['vocab_unit'] = vocab_unit
triletter_dim = len(vocab_unit.state['term_index']) + 1
self._context['input_shapes'] = [(triletter_dim, ), (triletter_dim, )]
return self
def fit(self, data_pack: DataPack, verbose: int = 1):
"""
Fit pre-processing context for transformation.
:param verbose: Verbosity.
:param data_pack: Data_pack to be preprocessed.
:return: class:`CDSSMPreprocessor` instance.
"""
fit_units = self._default_units() + [units.NgramLetter()]
func = chain_transform(fit_units)
data_pack = data_pack.apply_on_text(func, verbose=verbose)
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['vocab_unit'] = vocab_unit
vocab_size = len(vocab_unit.state['term_index'])
self._context['vocab_size'] = vocab_size
self._context['embedding_input_dim'] = vocab_size
return self
def fit(self, data_pack: DataPack, verbose=1):
"""
Fit pre-processing context for transformation.
:param verbose: Verbosity.
:param data_pack: Data_pack to be preprocessed.
:return: class:`CDSSMPreprocessor` instance.
"""
units = self._default_processor_units()
units.append(processor_units.NgramLetterUnit())
data_pack = data_pack.apply_on_text(chain_transform(units),
verbose=verbose)
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['vocab_unit'] = vocab_unit
vocab_size = len(vocab_unit.state['term_index']) + 1
self._context['input_shapes'] = [(self._fixed_length_left, vocab_size),
(self._fixed_length_right, vocab_size)
]
return self
def transform(self, data_pack: DataPack, verbose=1) -> DataPack:
"""
Apply transformation on data, create fixed length representation.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
data_pack = data_pack.copy()
units = self._default_processor_units()
data_pack.apply_on_text(chain_transform(units),
inplace=True,
verbose=verbose)
data_pack.apply_on_text(self._context['filter_unit'].transform,
mode='right',
inplace=True,
verbose=verbose)
data_pack.apply_on_text(self._context['vocab_unit'].transform,
mode='both',
inplace=True,
verbose=verbose)
data_pack.append_text_length(inplace=True, verbose=verbose)
data_pack.apply_on_text(self._left_fixedlength_unit.transform,
mode='left',
inplace=True,
verbose=verbose)
data_pack.apply_on_text(self._right_fixedlength_unit.transform,
mode='right',
inplace=True,
verbose=verbose)
max_len_left = self._fixed_length_left
max_len_right = self._fixed_length_right
data_pack.left['length_left'] = data_pack.left['length_left'].apply(
lambda val: val if val <= max_len_left else max_len_left)
data_pack.right['length_right'] = data_pack.right[
'length_right'].apply(lambda val: val
if val <= max_len_right else max_len_right)
return data_pack
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
"""
Apply transformation on data, create truncated length representation.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
data_pack = data_pack.copy()
data_pack.apply_on_text(ChainTransform(self._units),
inplace=True,
multiprocessing=self.multiprocessing,
verbose=verbose)
data_pack.apply_on_text(ChainTransform(self._context['filter_unit']),
mode='right',
inplace=True,
verbose=verbose)
data_pack.apply_on_text(ChainTransform(self._context['vocab_unit']),
mode='both',
inplace=True,
verbose=verbose)
if self._truncated_length_left:
data_pack.apply_on_text(ChainTransform(
self._left_truncatedlength_unit),
mode='left',
inplace=True,
verbose=verbose)
if self._truncated_length_right:
data_pack.apply_on_text(ChainTransform(
self._right_truncatedlength_unit),
mode='right',
inplace=True,
verbose=verbose)
data_pack.append_text_length(inplace=True, verbose=verbose)
data_pack.drop_empty(inplace=True)
return data_pack
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
data_pack = data_pack.copy()
data_pack.apply_on_text(chain_transform(self._units), verbose=verbose)
data_pack.apply_on_text(self._context['filter_unit'].transform,
mode='right',
inplace=True,
verbose=verbose)
data_pack.apply_on_text(self._context['vocab_unit'].transform,
mode='both',
inplace=True,
verbose=verbose)
if self._truncated_length_left:
data_pack.apply_on_text(self._left_truncatedlength_unit.transform,
mode='left',
inplace=True,
verbose=verbose)
if self._truncated_length_right:
data_pack.apply_on_text(self._right_truncatedlength_unit.transform,
mode='right',
inplace=True,
verbose=verbose)
data_pack.append_text_length(inplace=True, verbose=verbose)
data_pack.drop_empty(inplace=True)
return data_pack
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
"""
Apply transformation on data, create fixed length representation.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:`DataPack` object.
"""
data_pack = data_pack.copy()
data_pack.apply_on_text(chain_transform(self._units),
inplace=True,
verbose=verbose)
# data_pack.apply_on_text(self._context['filter_unit'].transform,
# mode='right', inplace=True, verbose=verbose)
# data_pack.apply_on_text(self._char_left.transform, mode='left', inplace=True, verbose=verbose, rename="char_left")
# data_pack.apply_on_text(self._char_right.transform, mode='right', inplace=True, verbose=verbose, rename="char_right")
data_pack.apply_on_text(self._context['vocab_unit'].transform,
mode='both',
inplace=True,
verbose=verbose)
data_pack.append_text_length(inplace=True, verbose=verbose)
data_pack.apply_on_text(self._left_fixedlength_unit.transform,
mode='left',
inplace=True,
verbose=verbose)
data_pack.apply_on_text(self._right_fixedlength_unit.transform,
mode='right',
inplace=True,
verbose=verbose)
def process_decoder_input_output(text: str):
tokens = chain_transform(self._units)(text)
tokens = self._context['vocab_unit'].transform(tokens)
return self._right_fixedlength_unit.transform(tokens)
data_pack.right[KeyWordSettings.TextRightInput] = data_pack.right[
KeyWordSettings.TextRightInput].apply(process_decoder_input_output)
data_pack.right[KeyWordSettings.TextRightOutput] = data_pack.right[
KeyWordSettings.TextRightOutput].apply(
process_decoder_input_output)
max_len_left = self._fixed_length_left
max_len_right = self._fixed_length_right
data_pack.left['length_left'] = \
data_pack.left['length_left'].apply(
lambda val: min(val, max_len_left))
data_pack.right['length_right'] = \
data_pack.right['length_right'].apply(
lambda val: min(val, max_len_right))
return data_pack
def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
"""
Apply transformation on data.
:param data_pack: Inputs to be preprocessed.
:param verbose: Verbosity.
:return: Transformed data as :class:'DataPack' object.
"""
data_pack = data_pack.copy()
data_pack.apply_on_text(
chain_transform(self._units),
mode='both', inplace=True, verbose=verbose)
# Process character representation
data_pack.apply_on_text(
units.NgramLetter(ngram=1, reduce_dim=False).transform,
rename=('char_left', 'char_right'),
mode='both', inplace=True, verbose=verbose)
char_index_dict = self._context['char_unit'].state['term_index']
left_charindex_unit = units.CharacterIndex(
char_index_dict, self._fixed_length_left, self._fixed_length_word)
right_charindex_unit = units.CharacterIndex(
char_index_dict, self._fixed_length_right, self._fixed_length_word)
data_pack.left['char_left'] = data_pack.left['char_left'].apply(
left_charindex_unit.transform)
data_pack.right['char_right'] = data_pack.right['char_right'].apply(
right_charindex_unit.transform)
# Process word representation
data_pack.apply_on_text(
self._context['vocab_unit'].transform,
mode='both', inplace=True, verbose=verbose)
# Process exact match representation
frame = data_pack.relation.join(
data_pack.left, on='id_left', how='left'
).join(data_pack.right, on='id_right', how='left')
left_exactmatch_unit = units.WordExactMatch(
self._fixed_length_left, match='text_left', to_match='text_right')
right_exactmatch_unit = units.WordExactMatch(
self._fixed_length_right, match='text_right', to_match='text_left')
data_pack.relation['match_left'] = frame.apply(
left_exactmatch_unit.transform, axis=1)
data_pack.relation['match_right'] = frame.apply(
right_exactmatch_unit.transform, axis=1)
data_pack.apply_on_text(
self._left_fixedlength_unit.transform,
mode='left', inplace=True, verbose=verbose)
data_pack.apply_on_text(
self._right_fixedlength_unit.transform,
mode='right', inplace=True, verbose=verbose)
return data_pack
