def test_tagged_frame_to_tokens_with_tf_threshold_and_not_threshold_tf_mask(tagged_frame: pd.DataFrame):
extract_opts = ExtractTaggedTokensOpts(
lemmatize=True,
pos_includes=None,
pos_excludes=None,
global_tf_threshold=2,
global_tf_threshold_mask=False,
**SPARV_TAGGED_COLUMNS,
)
""" Alternative #1: tagged_frame_to_tokens does the filtering """
token2id: Token2Id = Token2Id().ingest(["*", GLOBAL_TF_THRESHOLD_MASK_TOKEN]).ingest(tagged_frame.baseform)
expected_count = len(
tagged_frame[
tagged_frame.baseform.apply(lambda x: token2id.tf[token2id[x]] >= extract_opts.global_tf_threshold)
]
)
df: pd.DataFrame = tagged_frame.copy()
tokens = tagged_frame_to_tokens(df, token2id=token2id, extract_opts=extract_opts)
assert len(tokens) == expected_count
assert set(tokens) == set(['i', 'i', '.', 'valv', 'valv', '.', '.'])
""" Alternative #2: Use token2id to mask low TF tokens"""
df: pd.DataFrame = tagged_frame.copy()
token2id: Token2Id = Token2Id().ingest(["*", GLOBAL_TF_THRESHOLD_MASK_TOKEN]).ingest(df.baseform)
"""Note that translation must be used to map token-ids if used elsewhere"""
_, translation = token2id.compress(tf_threshold=2, inplace=True) # pylint: disable=unused-variable
token2id.close()
tokens = tagged_frame_to_tokens(df, token2id=token2id, extract_opts=extract_opts)
assert len(tokens) == expected_count
assert set(tokens) == set(['i', 'i', '.', 'valv', 'valv', '.', '.'])
def test_tagged_frame_to_tokens_with_tf_threshold_and_threshold_tf_mask(tagged_frame: pd.DataFrame):
extract_opts = ExtractTaggedTokensOpts(lemmatize=True, pos_includes=None, pos_excludes=None, **SPARV_TAGGED_COLUMNS)
""" Alternative #1: tagged_frame_to_tokens does the filtering """
df: pd.DataFrame = tagged_frame.copy()
extract_opts.global_tf_threshold = 2
extract_opts.global_tf_threshold_mask = True
token2id: Token2Id = Token2Id().ingest(["*", GLOBAL_TF_THRESHOLD_MASK_TOKEN]).ingest(df.baseform)
tokens = tagged_frame_to_tokens(df, token2id=token2id, extract_opts=extract_opts)
assert len(tokens) == len(df)
assert set(tokens) == set([GLOBAL_TF_THRESHOLD_MASK_TOKEN, 'i', 'i', '.', 'valv', 'valv', '.', '.'])
""" Alternative #2: Use token2id to mask low TF tokens"""
df: pd.DataFrame = tagged_frame.copy()
token2id: Token2Id = Token2Id().ingest(["*", GLOBAL_TF_THRESHOLD_MASK_TOKEN]).ingest(df.baseform)
"""Note that translation must be used to map token-ids if used elsewhere"""
token2id.compress(tf_threshold=2, inplace=True)
token2id.close()
tokens = tagged_frame_to_tokens(df, token2id=token2id, extract_opts=extract_opts)
assert len(tokens) == len(df)
assert set(tokens) == set([GLOBAL_TF_THRESHOLD_MASK_TOKEN, 'i', 'i', '.', 'valv', 'valv', '.', '.'])
def test_token2id_store_and_load():
os.makedirs('./tests/output', exist_ok=True)
token2id: Token2Id = Token2Id().ingest(TEST_TOKENS_STREAM1)
filename = './tests/output/test_vocabulary.zip'
tf_filename = path_add_suffix(filename, "_tf", new_extension=".pbz2")
token2id.store(filename=filename)
assert os.path.isfile(filename) and os.path.isfile(tf_filename)
token2id_loaded: Token2Id = Token2Id.load(filename=filename)
assert token2id_loaded is not None
assert token2id_loaded.tf is not None
assert token2id_loaded.data == {
'adam': 0,
'anton': 1,
'beatrice': 2,
'felicia': 3,
'niklas': 4
}
assert dict(token2id_loaded.tf) == {0: 3, 1: 2, 2: 1, 3: 1, 4: 1}
def translate_id_pair_to_token(self, pair2id: Token2Id,
token2id: Token2Id) -> None:
"""Translates `id pairs` (w1_id, w2_id) to pair-token `w1/w2`"""
_single_without_sep = {
w_id: w.replace(WORD_PAIR_DELIMITER, '')
for w_id, w in token2id.id2token.items()
}
sg = _single_without_sep.get
pair2id.replace(
data={
sj([sg(w1_id), sg(w2_id)]): pair_id
for (w1_id, w2_id), pair_id in pair2id.data.items()
})
def test_interfaces_token2id_store():
os.makedirs('./tests/output', exist_ok=True)
filename: str = './tests/output/test_interfaces_token2id_store.zip'
token2id = Token2Id()
token2id.ingest(['apa', 'banan', 'soffa'])
token2id.store(filename)
assert pathlib.Path(filename).exists()
token2id_loaded: Token2Id = Token2Id.load(filename)
assert token2id.data == token2id_loaded.data
def test_term_term_matrix_to_co_occurrences_with_ignore_ids():
text_corpus = very_simple_corpus(data=[
('tran_2019_01_test.txt', ['*', 'b', 'c', 'c']),
('tran_2019_02_test.txt', ['a', '*', '*', 'd']),
('tran_2019_03_test.txt', ['a', 'e', 'e', 'b']),
('tran_2020_01_test.txt', ['*', 'c', 'd', 'a']),
('tran_2020_02_test.txt', ['a', 'b', '*', '*']),
])
token2id: Token2Id = Token2Id(text_corpus.token2id)
term_term_matrix = (dtm.CorpusVectorizer().fit_transform(
text_corpus, already_tokenized=True,
vocabulary=text_corpus.token2id).co_occurrence_matrix())
pad_id = token2id['*']
co_occurrences = term_term_matrix_to_co_occurrences(
term_term_matrix=term_term_matrix,
threshold_count=1,
ignore_ids=set([pad_id]),
)
assert not (co_occurrences.w1_id == pad_id).any()
assert not (co_occurrences.w2_id == pad_id).any()
def get_topic_token_weights(
self,
vocabulary: Any,
n_tokens: int = 200,
minimum_probability: float = 0.000001) -> pd.DataFrame:
"""Compile document topic weights. Return DataFrame."""
id2token: dict = Token2Id.any_to_id2token(vocabulary)
topic_data: list[tuple[
int, TokenWeights]] = self.get_topic_token_weights_data(
n_tokens=n_tokens, id2term=id2token)
topic_token_weights: pd.DataFrame = pd.DataFrame(
[(topic_id, token, weight) for topic_id, tokens in topic_data
for token, weight in tokens if weight > minimum_probability],
columns=['topic_id', 'token', 'weight'],
)
topic_token_weights['topic_id'] = topic_token_weights.topic_id.astype(
np.uint16)
fg = {v: k for k, v in id2token.items()}.get
topic_token_weights['token_id'] = topic_token_weights.token.apply(fg)
return topic_token_weights[['topic_id', 'token_id', 'token', 'weight']]
def test_interfaces_token2id_get():
token2id = Token2Id()
token_id = token2id['apa']
assert token_id == 0
assert 'apa' in token2id
def test_interfaces_token2id_reverse():
token2id: Token2Id = Token2Id()
id2token = token2id.ingest(['apa', 'banan', 'soffa']).id2token
assert id2token[0] == 'apa'
assert id2token[1] == 'banan'
assert id2token[2] == 'soffa'
def test_interfaces_token2id_ingest():
token2id = Token2Id()
token2id.ingest(['apa', 'banan', 'soffa'])
assert 'apa' in token2id
assert 'banan' in token2id
assert 'soffa' in token2id
def _token2id(self) -> Optional[Token2Id]:
"""Returns dictionary stored in archive, or None if not found in archive"""
if DICTIONARY_FILENAME not in self.namelist():
return None
return Token2Id(
zip_utils.read_json(zip_or_filename=self,
filename=DICTIONARY_FILENAME))
def process_stream(self) -> Iterable[DocumentPayload]:
if self.document_index is None:
raise CoOccurrenceError(
"expected document index found no such thing")
token2id: Token2Id = self.pipeline.payload.token2id
pair2id: Token2Id = Token2Id()
normal_builder: CoOccurrenceCorpusBuilder = CoOccurrenceCorpusBuilder(
VectorizeType.Normal, self.document_index, pair2id, token2id)
concept_builder: CoOccurrenceCorpusBuilder = (
CoOccurrenceCorpusBuilder(VectorizeType.Concept,
self.document_index, pair2id, token2id)
if self.context_opts.concept else None)
coo_payloads: Iterable[CoOccurrencePayload] = (
payload.content
for payload in self.prior.outstream(desc="Ingest",
total=len(self.document_index))
if payload.content is not None)
for coo_payload in coo_payloads:
normal_builder.ingest_pairs(coo_payload).add(payload=coo_payload)
if concept_builder:
concept_builder.add(payload=coo_payload)
pair2id.close()
"""Translation between id-pair (single vocab IDs) and pair-pid (pair vocab IDs)"""
token_ids_2_pair_id: Mapping[Tuple[int, int], int] = dict(pair2id.data)
self.translate_id_pair_to_token(pair2id, token2id)
concept_corpus: VectorizedCorpus = (concept_builder.corpus.remember(
window_counts=self.get_window_counts(concept_builder))
if concept_builder else None)
corpus: VectorizedCorpus = normal_builder.corpus.remember(
window_counts=self.get_window_counts(normal_builder))
bundle: Bundle = Bundle(
corpus=corpus,
token2id=token2id,
document_index=self.document_index,
concept_corpus=concept_corpus,
compute_options=self.pipeline.payload.stored_opts(),
vocabs_mapping=token_ids_2_pair_id,
)
if self.compress:
bundle.compress()
payload: DocumentPayload = DocumentPayload(content=bundle)
yield payload
def setup(self) -> ITask:
self.target = self.get_column_name(self.token_type)
# if self.pipeline.get_next_to(self).in_content_type == ContentType.TAGGED_FRAME:
# if self.token_type is None:
# raise ValueError("token_type text or lemma not specfied")
self.token2id: Token2Id = self.token2id or Token2Id()
self.pipeline.payload.token2id = self.token2id
return self
def test_replace():
tokens = ['a', 'a', 'b', 'c']
ingested: Token2Id = Token2Id().ingest(tokens)
token2id: Token2Id = Token2Id()
token2id.replace(data={
'a': 0,
'b': 1,
'c': 2
},
tf=Counter({
0: 2,
1: 1,
2: 1
}))
assert dict(token2id.data) == dict(ingested.data)
assert dict(token2id.tf) == dict(ingested.tf)
def test_tagged_frame_to_tokens_with_global_tf_threshold(tagged_frame: pd.DataFrame):
tagged_frame: pd.DataFrame = tagged_frame.copy()
expected_counts: dict = {
'.': 3,
'bakom': 1,
'den': 1,
'fladdra_omkring': 1,
'gapa': 1,
'halvmörker': 1,
'i': 2,
'ingen': 1,
'inne': 1,
'kyrka': 1,
'ljuslåga': 1,
'någon': 1,
'och': 1,
'romansk': 1,
'tränga': 1,
'turist': 1,
'valv': 2,
'väldig': 1,
'överblick': 1,
}
extract_opts = ExtractTaggedTokensOpts(lemmatize=True, pos_includes=None, pos_excludes=None, **SPARV_TAGGED_COLUMNS)
tokens = tagged_frame_to_tokens(tagged_frame, extract_opts=extract_opts)
assert set(expected_counts.keys()) == set(tokens)
"""TF threshold resets to 1 if token2id not supplied (i.e. token2id.TF is needed)"""
extract_opts.global_tf_threshold = 2
tokens = tagged_frame_to_tokens(tagged_frame, token2id=None, extract_opts=extract_opts)
assert extract_opts.global_tf_threshold == 1
token2id: Token2Id = Token2Id().ingest(["*", GLOBAL_TF_THRESHOLD_MASK_TOKEN]).ingest(tagged_frame.baseform)
extract_opts.global_tf_threshold = 2
extract_opts.global_tf_threshold_mask = False
tokens = tagged_frame_to_tokens(tagged_frame, token2id=token2id, extract_opts=extract_opts)
assert tokens == ['i', 'i', '.', 'valv', 'valv', '.', '.']
extract_opts.global_tf_threshold = 2
extract_opts.global_tf_threshold_mask = True
tokens = tagged_frame_to_tokens(tagged_frame, token2id=token2id, extract_opts=extract_opts)
assert len(tokens) == len(tagged_frame)
assert set(tokens) == set([GLOBAL_TF_THRESHOLD_MASK_TOKEN, 'i', 'i', '.', 'valv', 'valv', '.', '.'])
extract_opts.global_tf_threshold = 2
extract_opts.global_tf_threshold_mask = True
extract_opts.passthrough_tokens = {'överblick'}
tokens = tagged_frame_to_tokens(tagged_frame, token2id=token2id, extract_opts=extract_opts)
assert len(tokens) == len(tagged_frame)
assert set(tokens) == set([GLOBAL_TF_THRESHOLD_MASK_TOKEN, 'i', 'i', '.', 'valv', 'valv', '.', '.', 'överblick'])
def test_token2id_compress_with_ingested_mask_token_and_threshold_has_correct_magic_token_sum(
):
token2id: Token2Id = Token2Id().ingest(TEST_TOKENS_STREAM2).ingest(
[GLOBAL_TF_THRESHOLD_MASK_TOKEN]).close()
_, translation = token2id.compress(tf_threshold=2, inplace=True)
assert dict(token2id.data) == {
'adam': 0,
'anton': 1,
'beata': 2,
GLOBAL_TF_THRESHOLD_MASK_TOKEN: 3
}
assert dict(token2id.tf) == {0: 3, 1: 2, 2: 2, 3: 4}
assert translation == {0: 0, 1: 1, 5: 2, 6: 3}
def test_token2id_find():
token2id: Token2Id = Token2Id({
'adam': 0,
'anton': 1,
'beatrice': 2,
'felicia': 3,
'niklas': 4
})
assert set(token2id.find(what='adam')) == set([0])
assert set(token2id.find(what='a*')) == set([0, 1])
assert set(token2id.find(what=['a*', 'f*'])) == set([0, 1, 3])
assert set(token2id.find(what=['a*', 'beatrice'])) == set([0, 1, 2])
def test_interfaces_token2id_close():
token2id = Token2Id()
token2id.ingest(['apa', 'banan', 'soffa'])
token2id.close()
with pytest.raises(KeyError):
_ = token2id['hängmatta']
token2id.open()
_ = token2id['hängmatta']
assert 'hängmatta' in token2id
def test_TTM_to_co_occurrence_DTM_using_LIL_matrix():
source_corpus = very_simple_corpus(SIMPLE_CORPUS_ABCDE_5DOCS)
token2id = Token2Id(source_corpus.token2id)
document_index: DocumentIndex = source_corpus.document_index
stream: Iterable[CoOccurrencePayload] = (CoOccurrencePayload(
document_id,
document_name="-",
ttm_data_map={
VectorizeType.Normal:
VectorizedTTM(
vectorize_type=VectorizeType.Normal,
term_term_matrix=CorpusVectorizer().fit_transform(
[doc], already_tokenized=True,
vocabulary=token2id.data).co_occurrence_matrix(),
term_window_counts={},
document_id=document_id,
)
},
) for document_id, doc in enumerate(source_corpus))
pair2id: Token2Id = Token2Id()
builder: CoOccurrenceCorpusBuilder = CoOccurrenceCorpusBuilder(
vectorize_type=VectorizeType.Normal,
document_index=document_index,
pair2id=pair2id,
token2id=token2id,
)
for payload in stream:
builder.ingest_pairs(payload).add(payload)
corpus: VectorizedCorpus = builder.corpus
assert corpus is not None
def test_translation():
data = {'*': 0, '__low-tf__': 1, 'a': 2, 'b': 3, 'c': 4, 'd': 5, 'e': 6}
tf = {0: 1, 1: 1, 2: 5, 3: 2, 4: 5, 5: 2, 6: 4}
token2id = Token2Id(data=data, tf=tf)
_, translation = token2id.compress(tf_threshold=4, inplace=True)
tf = {0: 1, 1: 1, 2: 5, 3: 2, 4: 5, 5: 2, 6: 4}
assert dict(token2id.data) == {
'*': 0,
'__low-tf__': 1,
'a': 2,
'c': 3,
'e': 4
}
assert dict(token2id.tf) == {0: 1, 1: 5, 2: 5, 3: 5, 4: 4}
assert dict(translation) == {0: 0, 1: 1, 2: 2, 4: 3, 6: 4}
def test_translate():
data = {'*': 0, '__low-tf__': 1, 'a': 2, 'b': 3, 'c': 4, 'd': 5, 'e': 6}
tf = {0: 1, 1: 1, 2: 5, 3: 2, 4: 5, 5: 2, 6: 4}
ids_translation = {0: 0, 1: 1, 2: 2, 4: 3, 6: 4}
token2id = Token2Id(data=data, tf=tf)
token2id.translate(ids_translation=ids_translation, inplace=True)
assert dict(token2id.data) == {
'*': 0,
'__low-tf__': 1,
'a': 2,
'c': 3,
'e': 4
}
"""Note that translate doesn't add LF-counts to LF-marker"""
assert dict(token2id.tf) == {0: 1, 1: 1, 2: 5, 3: 5, 4: 4}
def test_token2id_compress_with_threshold_and_keeps_adds_masked_magic_token_with_correct_sum(
):
mask_token: str = GLOBAL_TF_THRESHOLD_MASK_TOKEN
token2id: Token2Id = Token2Id().ingest(TEST_TOKENS_STREAM2).close()
token2id_compressed, _ = token2id.compress(tf_threshold=2,
keeps={4},
inplace=False)
assert mask_token not in token2id
assert mask_token in token2id_compressed
sum_of_masked_tokens = sum([
v for k, v in EXPECTED_COUNTS2.items() if k not in token2id_compressed
])
assert token2id_compressed.tf[
token2id_compressed[mask_token]] == sum_of_masked_tokens
def test_token2id_inplace_compress_with_threshold_and_no_keeps():
token2id: Token2Id = Token2Id().ingest(TEST_TOKENS_STREAM2).close()
token2id_compressed, translation = token2id.compress(tf_threshold=2,
inplace=False)
assert dict(token2id_compressed.data) == {
'adam': 0,
'anton': 1,
'beata': 2,
GLOBAL_TF_THRESHOLD_MASK_TOKEN: 3
}
assert dict(token2id_compressed.tf) == {0: 3, 1: 2, 2: 2, 3: 3}
assert translation == {0: 0, 1: 1, 5: 2}
assert token2id.fallback_token_id is None
assert token2id_compressed.fallback_token_id is not None
assert token2id_compressed["roger"] == token2id_compressed[
GLOBAL_TF_THRESHOLD_MASK_TOKEN]
assert "roger" not in token2id_compressed
def test_token2id_compress_with_no_threshold_and_no_keeps_returns_self():
token2id: Token2Id = Token2Id().ingest(TEST_TOKENS_STREAM2).close()
assert token2id.data == {
'adam': 0,
'anton': 1,
'beatrice': 2,
'felicia': 3,
'niklas': 4,
'beata': 5
}
assert dict(token2id.tf) == {0: 3, 1: 2, 2: 1, 3: 1, 4: 1, 5: 2}
token2id_compressed, translation = token2id.compress(tf_threshold=1,
inplace=False,
keeps=None)
assert token2id_compressed is token2id
assert translation is None
def test_co_occurrences_to_co_occurrence_corpus():
folder, tag = './tests/test_data/ABCDEFG_7DOCS_CONCEPT', "ABCDEFG_7DOCS_CONCEPT"
co_occurrences: CoOccurrenceDataFrame = co_occurrence.load_co_occurrences(
co_occurrence_filename(folder, tag))
document_index: DocumentIndex = DocumentIndexHelper.load(
document_index_filename(folder, tag)).document_index
token2id: Token2Id = Token2Id.load(vocabulary_filename(folder, tag))
corpus = LegacyCoOccurrenceMixIn.from_co_occurrences(
co_occurrences=co_occurrences,
document_index=document_index,
token2id=token2id,
)
assert corpus.data.sum() == co_occurrences.value.sum()
assert corpus.data.shape[0] == len(document_index)
assert corpus.data.shape[1] == len(co_occurrences[["w1_id", "w2_id"
]].drop_duplicates())
def test_token2id_compress_with_threshold_and_keeps_scuccee3():
token2id: Token2Id = Token2Id().ingest(TEST_TOKENS_STREAM2).close()
_, translation = token2id.compress(tf_threshold=2,
inplace=True,
keeps={token2id["felicia"]})
assert dict(token2id.data) == {
'adam': 0,
'anton': 1,
'felicia': 2,
'beata': 3,
GLOBAL_TF_THRESHOLD_MASK_TOKEN: 4
}
assert tf_to_string(token2id) == {
'adam': 3,
'anton': 2,
'felicia': 1,
'beata': 2,
'__low-tf__': 2
}
assert translation == {0: 0, 1: 1, 3: 2, 5: 3}
def test_token2id_ingest():
token2id: Token2Id = Token2Id().ingest(TEST_TOKENS_STREAM1)
assert token2id.data == {
'adam': 0,
'anton': 1,
'beatrice': 2,
'felicia': 3,
'niklas': 4
}
assert token2id.tf is not None
assert dict(token2id.tf) == {0: 3, 1: 2, 2: 1, 3: 1, 4: 1}
assert tf_to_string(token2id) == {
'adam': 3,
'anton': 2,
'beatrice': 1,
'felicia': 1,
'niklas': 1
}
def filter_tagged_frame(
tagged_frame: pd.DataFrame,
*,
extract_opts: ExtractTaggedTokensOpts,
token2id: Token2Id = None,
pos_schema: PoS_Tag_Scheme = None,
normalize_column_names: bool = True,
transform_opts: TokensTransformOpts = None,
) -> pd.DataFrame:
"""Filters tagged frame (text or numeric). Returns tagged frame
Args:
tagged_frame ([pd.DataFrame]): Document frame to be filtered, can be text or numeric
extract_opts (ExtractTaggedTokensOpts): PoS and lemma extract/filter opts
token2id (Token2Id, optional): Vocabulary. Defaults to None.
pos_schema (PoS_Tag_Scheme, optional): PoS schema. Defaults to None.
transform_opts (TokensTransformOpts, optional): Filters and transforms. Defaults to None.
normalize_column_names (bool, optional): If text, rename columns to `token` and `pos`. Defaults to True.
Raises:
Token2IdMissingError: Token2Id is mandatory if frame is numeric.
PoSTagSchemaMissingError: PoS-schema is mandatory if frame is numeric.
TaggedFrameColumnNameError: Missing target column (corrupt data)
Returns:
pd.DataFrame: Filtered and transformed document frame.
"""
if len(tagged_frame) == 0:
return []
is_numeric_frame: bool = is_encoded_tagged_frame(tagged_frame)
to_lower: bool = transform_opts and transform_opts.to_lower
if is_numeric_frame:
if token2id is None:
raise Token2IdMissingError(
"filter_tagged_frame: cannot filter tagged id frame without vocabulary"
)
if pos_schema is None:
raise PoSTagSchemaMissingError(
"filter_tagged_frame: cannot filter tagged id frame without pos_schema"
)
if to_lower:
logger.warning(
"lowercasing not implemented for numeric tagged frames")
to_lower = False
if not is_numeric_frame and extract_opts.lemmatize is None and extract_opts.target_override is None:
raise ValueError("a valid target not supplied (no lemmatize or target")
target_column: str = extract_opts.target_column
pos_column: str = extract_opts.pos_column
if target_column not in tagged_frame.columns:
raise TaggedFrameColumnNameError(
f"{target_column} is not valid target for given document (missing column)"
)
if pos_column not in tagged_frame.columns:
raise ValueError(f"configuration error: {pos_column} not in document")
passthroughs: Set[str] = extract_opts.get_passthrough_tokens()
blocks: Set[str] = extract_opts.get_block_tokens().union('')
if is_numeric_frame:
passthroughs = token2id.to_id_set(passthroughs)
blocks = token2id.to_id_set(blocks)
if not is_numeric_frame and (extract_opts.lemmatize or to_lower):
tagged_frame[target_column] = tagged_frame[target_column].str.lower()
# pd.Series([x.lower() for x in tagged_frame[target_column]])
passthroughs = {x.lower() for x in passthroughs}
# if extract_opts.block_chars:
# for char in extract_opts.block_chars:
# doc[target] = doc[target].str.replace(char, '', regex=False)
""" Phrase detection """
if extract_opts.phrases:
if is_numeric_frame:
logger.warning(
"phrase detection not implemented for numeric tagged frames")
extract_opts.phrases = None
else:
found_phrases = detect_phrases(tagged_frame[target_column],
extract_opts.phrases,
ignore_case=to_lower)
if found_phrases:
tagged_frame = merge_phrases(tagged_frame,
found_phrases,
target_column=target_column,
pad=PHRASE_PAD)
passthroughs = passthroughs.union(
{'_'.join(x[1])
for x in found_phrases})
mask = np.repeat(True, len(tagged_frame.index))
if extract_opts.filter_opts and extract_opts.filter_opts.data:
mask &= extract_opts.filter_opts.mask(tagged_frame)
pos_includes: Set[str] = extract_opts.get_pos_includes()
pos_excludes: Set[str] = extract_opts.get_pos_excludes()
pos_paddings: Set[str] = extract_opts.get_pos_paddings()
if is_numeric_frame:
pg = pos_schema.pos_to_id.get
pos_includes = {pg(x) for x in pos_includes}
pos_excludes = {pg(x) for x in pos_excludes}
pos_paddings = {pg(x) for x in pos_paddings}
if pos_includes:
"""Don't filter if PoS-include is empty - and don't filter out PoS tokens that should be padded"""
mask &= tagged_frame[pos_column].isin(pos_includes.union(pos_paddings))
if pos_excludes:
mask &= ~(tagged_frame[pos_column].isin(pos_excludes))
if transform_opts and transform_opts.has_effect:
mask &= transform_opts.mask(tagged_frame[target_column],
token2id=token2id)
if len(passthroughs) > 0:
mask |= tagged_frame[target_column].isin(passthroughs)
if len(blocks) > 0:
mask &= ~tagged_frame[target_column].isin(blocks)
filtered_data: pd.DataFrame = tagged_frame.loc[mask][[
target_column, pos_column
]]
if extract_opts.global_tf_threshold > 1:
if token2id is None or token2id.tf is None:
logger.error(
"Cannot apply TF filter since token2id has no term frequencies"
)
extract_opts.global_tf_threshold = 1
else:
filtered_data = filter_tagged_frame_by_term_frequency(
tagged_frame=filtered_data,
target_column=target_column,
token2id=token2id,
extract_opts=extract_opts,
passthroughs=passthroughs,
)
if not is_numeric_frame and normalize_column_names:
filtered_data.rename(columns={
target_column: 'token',
pos_column: 'pos'
},
inplace=True)
return filtered_data
def tagged_frame_to_tokens( # pylint: disable=too-many-arguments, too-many-statements
doc: pd.DataFrame,
extract_opts: ExtractTaggedTokensOpts,
token2id: Token2Id = None,
transform_opts: TokensTransformOpts = None,
pos_schema: PoS_Tag_Scheme = None,
) -> Iterable[str | int]:
"""Extracts tokens from a tagged document represented as a Pandas data frame.
Args:
extract_opts (ExtractTaggedTokensOpts): Part-of-speech/lemma extract options (e.g. PoS-filter)
Returns:
Iterable[str]: Sequence of extracted tokens
"""
if len(doc) == 0:
return []
is_numeric_frame: bool = is_encoded_tagged_frame(doc)
if isinstance(extract_opts, str):
passthrough_column: str = extract_opts
"""If extracts_opts is a string: return column with what name"""
if transform_opts is not None:
raise ValueError(
"transform_opts must be None when passthrough is specified")
if transform_opts is not None:
raise ValueError(
"transform_opts must be None when passthrough is specified")
return doc[passthrough_column].tolist()
# if is_numeric_frame:
# raise NotImplementedError("tagged_frame_to_tokens cannot handle encoded frames yet")
pad: str = "*"
pos_paddings: Set[str] = extract_opts.get_pos_paddings()
phrase_pad: str = PHRASE_PAD
filtered_data = filter_tagged_frame(
tagged_frame=doc,
extract_opts=extract_opts,
token2id=token2id,
transform_opts=transform_opts,
pos_schema=pos_schema,
)
target_columns = ['token_id', 'pos_id'
] if is_numeric_frame else ['token', 'pos']
token_pos_tuples = filtered_data[target_columns].itertuples(index=False,
name=None)
if len(pos_paddings) > 0:
passthroughs: Set[str] = extract_opts.get_passthrough_tokens()
if is_numeric_frame:
pos_paddings = {pos_schema.pos_to_id.get(x) for x in pos_paddings}
passthroughs = token2id.to_id_set(passthroughs)
token_pos_tuples = ((pad, x[1]) if x[1] in pos_paddings
and x[0] not in passthroughs else x
for x in token_pos_tuples)
if extract_opts.append_pos:
if is_numeric_frame:
raise NotImplementedError(
"tagged_frame_to_tokens: PoS tag on encoded frame")
return [
pad if x[0] == pad else f"{x[0].replace(' ', '_')}@{x[1]}"
for x in token_pos_tuples if x[0] != phrase_pad
]
if not is_numeric_frame and extract_opts.phrases and len(
extract_opts.phrases) > 0:
return [
x[0].replace(' ', '_') for x in token_pos_tuples
if x[0] != phrase_pad
]
return [x[0] for x in token_pos_tuples]
def filter_tagged_frame_by_term_frequency( # pylint: disable=too-many-arguments, too-many-statements
tagged_frame: pd.DataFrame,
target_column: str,
token2id: Token2Id,
extract_opts: ExtractTaggedTokensOpts,
passthroughs: Set[str] = None,
) -> pd.DataFrame:
"""Filter tagged frame `tagged_frame` based on `extract_opts`.
Return tagged frame with columns `token` and `pos`.
Columns `token` is lemmatized word or source word depending on `extract_opts.lemmatize`.
Args:
tagged_frame (pd.DataFrame): tagged frame to be filtered
extract_opts (ExtractTaggedTokensOpts): Part-of-speech/lemma extract options (e.g. PoS-filter)
token2id (Token2Id, optional): Vocabulary.
Returns:
pd.DataFrame: Filtered tagged frame
"""
is_numeric_frame: bool = is_encoded_tagged_frame(tagged_frame)
if extract_opts.global_tf_threshold <= 1:
return tagged_frame
if token2id is None or token2id.tf is None:
raise ValueError("token2id or token2id.tf is not defined")
if target_column not in tagged_frame.columns:
raise ValueError(
f"{target_column} is not valid target for given document (missing column)"
)
"""
If global_tf_threshold_mask then filter out tokens below threshold
Otherwise replace token with `GLOBAL_TF_THRESHOLD_MASK_TOKEN`
Alternativ implementation:
1. Compress Token2Id (remove low frequency words)
2. Remove or mask tokens not in compressed token2id
"""
tg = token2id.get
mask_token_id: int = tg(GLOBAL_TF_THRESHOLD_MASK_TOKEN)
mask_token: str | int = mask_token_id if is_numeric_frame else GLOBAL_TF_THRESHOLD_MASK_TOKEN
"""Set low TF to 0"""
mask_token_id_tf: int = token2id.tf.get(mask_token_id, 0)
token2id.tf[mask_token_id] = 0
cg = token2id.tf.get
low_frequency_mask: pd.Series = (
tagged_frame[target_column].apply(tg).apply(cg)
if not is_numeric_frame else tagged_frame[target_column].apply(cg)
).fillna(0) < extract_opts.global_tf_threshold
"""Reset low TF count"""
token2id.tf[mask_token_id] = mask_token_id_tf
if passthroughs:
low_frequency_mask &= ~tagged_frame[target_column].isin(passthroughs)
if extract_opts.global_tf_threshold_mask:
"""Mask low frequency terms"""
tagged_frame[target_column] = tagged_frame[target_column].where(
~low_frequency_mask, mask_token)
else:
"""Filter out low frequency terms"""
tagged_frame = tagged_frame[~low_frequency_mask]
return tagged_frame
