def about_version(version=RuSentiFramesVersions.V20):
stemmer = MystemWrapper()
pos_tagger = POSMystemWrapper(stemmer.MystemInstance)
frames_collection = RuSentiFramesCollection.read_collection(
version=version)
print("Lexicon version:", version)
return __about(frames_collection=frames_collection, pos_tagger=pos_tagger)
def __read_synonyms_collection(self):
# Initializing stemmer
stemmer = MystemWrapper()
# Reading synonyms collection.
return RuSentRelSynonymsCollectionProvider.load_collection(
stemmer=stemmer, version=self.__rusentrel_version)
def __init__(self, init_word_embedding=True):
self.__stemmer = MystemWrapper()
self.__synonym_collection = RuSentRelSynonymsCollection.load_collection(
stemmer=self.__stemmer, is_read_only=True)
self.__opinion_formatter = RuSentRelOpinionCollectionFormatter
# You may manually select three-scale mode by commenting one of these two lines below.
self.__neutral_annotator = self.__init_two_scale_neutral_annotator()
# self.__neutral_annotator = self.__init_three_scale_neutral_annotator()
self.__word_embedding = self.__create_word_embedding(
) if init_word_embedding else None
self.__cv_folding_algorithm = self.__init_sentence_based_cv_folding_algorithm(
)
def setUpClass(cls):
cls.stemmer = MystemWrapper()
cls.entities_formatter = RussianEntitiesCasedFormatter(
pos_tagger=POSMystemWrapper(Mystem(entire_input=False)))
cls.synonyms = RuSentRelSynonymsCollectionProvider.load_collection(
stemmer=cls.stemmer)
cls.frames_collection = RuSentiFramesCollection.read_collection(
version=RuSentiFramesVersions.V10)
cls.unique_frame_variants = FrameVariantsCollection()
cls.unique_frame_variants.fill_from_iterable(
variants_with_id=cls.frames_collection.iter_frame_id_and_variants(
),
overwrite_existed_variant=True,
raise_error_on_existed_variant=False)
def test(self):
frame_variants_collection = self.__create_frames_variants_collection()
stemmer = MystemWrapper()
p = LemmasBasedFrameVariantsParser(
save_lemmas=False,
stemmer=stemmer,
frame_variants=frame_variants_collection)
ctx = PipelineContext(
d={"src": "мы пытались его осудить но не получилось".split()})
p.apply(ctx)
for t in ctx.provide("src"):
s = "[{}]".format(t.Variant.get_value()) if isinstance(
t, TextFrameVariant) else t
print(s)
def test_output_formatter(self):
stemmer = MystemWrapper()
synonyms = RuSentRelSynonymsCollectionProvider.load_collection(
stemmer=stemmer)
label_scaler = ThreeLabelScaler()
# sample_storage = BaseRowsStorage.from_tsv(filepath=self.__input_samples_filepath)
output_storage = BaseRowsStorage.from_tsv(
filepath=self.__output_filepath)
linkages_view = MultilableOpinionLinkagesView(
labels_scaler=label_scaler, storage=output_storage)
opinion_storage = BaseRowsStorage.from_tsv(
filepath=self.__input_opinions_filepath)
opinion_view = BaseOpinionStorageView(opinion_storage)
ppl = output_to_opinion_collections_pipeline(
create_opinion_collection_func=lambda: OpinionCollection(
opinions=[],
synonyms=synonyms,
error_on_duplicates=True,
error_on_synonym_end_missed=True),
doc_ids_set={1},
labels_scaler=label_scaler,
iter_opinion_linkages_func=lambda doc_id: linkages_view.
iter_opinion_linkages(doc_id=doc_id, opinions_view=opinion_view),
label_calc_mode=LabelCalculationMode.AVERAGE)
doc_ids = set(
opinion_storage.iter_column_values(column_name=const.DOC_ID,
dtype=int))
print(doc_ids)
pipeline_ctx = PipelineContext({"src": doc_ids})
# Running pipeline.
ppl.run(pipeline_ctx)
# Iterate over the result.
for doc_id, collection in pipeline_ctx.provide("src"):
print("d:{}, ct:{}, count:{}".format(doc_id, type(collection),
len(collection)))
def __init__(self):
self.__stemmer = MystemWrapper()
self.__synonyms = RuSentRelSynonymsCollection.read_collection(stemmer=self.__stemmer,
is_read_only=True)
self.__pnc = ParsedNewsCollection()
for doc_id in RuSentRelIOUtils.iter_collection_indices():
entities = RuSentRelDocumentEntityCollection.read_collection(doc_id=doc_id,
stemmer=self.__stemmer,
synonyms=self.__synonyms)
news = RuSentRelNews.read_document(doc_id=doc_id, entities=entities)
parsed_news = RuSentRelParsedNewsHelper.create_parsed_news(rusentrel_news_id=doc_id,
rusentrel_news=news,
keep_tokens=False,
stemmer=self.__stemmer)
self.__pnc.add(parsed_news)
def test(self):
stemmer = MystemWrapper()
actual_synonyms = RuSentRelSynonymsCollectionProvider.load_collection(
stemmer=stemmer, version=RuSentRelVersions.V11)
result = calculate_results(
doc_ids=RuSentRelIOUtils.iter_test_indices(RuSentRelVersions.V11),
evaluator=ThreeClassEvaluator(DataType.Test),
iter_etalon_opins_by_doc_id_func=lambda doc_id:
create_etalon_with_neutral(
collection=OpinionCollection(None,
synonyms=actual_synonyms,
error_on_duplicates=True,
error_on_synonym_end_missed=True),
etalon_opins=RuSentRelOpinionCollection.iter_opinions_from_doc(
doc_id=doc_id),
neut_opins=CustomZippedResultsIOUtils.iter_doc_opinions(
doc_id=doc_id,
result_version=Results.Etalon,
labels_fmt=CustomRuSentRelLabelsFormatter(),
opin_path_fmt=u"art{doc_id}.neut.Test.txt")),
iter_result_opins_by_doc_id_func=lambda doc_id: OpinionCollection(
opinions=CustomZippedResultsIOUtils.iter_doc_opinions(
doc_id=doc_id,
labels_fmt=RuSentRelLabelsFormatter(),
opin_path_fmt=u"{doc_id}.opin.txt",
result_version=Results.Test),
synonyms=actual_synonyms,
error_on_duplicates=False,
error_on_synonym_end_missed=False))
# logging all the result information.
for doc_id, doc_info in result.iter_document_results():
print u"{}:\t{}".format(doc_id, doc_info)
print "------------------------"
print str(result.TotalResult)
print "------------------------"
def test_parsing(self):
# Initializing logger.
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logging.basicConfig(level=logging.DEBUG)
# Initializing stemmer.
stemmer = MystemWrapper()
# frame and variants.
frames = RuSentiFramesCollection.read_collection(version=RuSentiFramesVersions.V20)
frame_variants = FrameVariantsCollection()
frame_variants.fill_from_iterable(variants_with_id=frames.iter_frame_id_and_variants(),
overwrite_existed_variant=True,
raise_error_on_existed_variant=False)
text_parser = BaseTextParser(pipeline=[RuSentRelTextEntitiesParser(),
DefaultTextTokenizer(keep_tokens=True),
LemmasBasedFrameVariantsParser(frame_variants=frame_variants,
stemmer=stemmer,
save_lemmas=False),
FrameVariantsSentimentNegation()])
# Reading synonyms collection.
synonyms = RuSentRelSynonymsCollectionProvider.load_collection(stemmer=stemmer)
version = RuSentRelVersions.V11
for doc_id in RuSentRelIOUtils.iter_collection_indices(version):
# Parsing
news = RuSentRelNews.read_document(doc_id=doc_id,
synonyms=synonyms,
version=version)
# Perform text parsing.
parsed_news = NewsParser.parse(news=news, text_parser=text_parser)
debug_show_news_terms(parsed_news=parsed_news)
def test_rusentrel_news_text_parsing(self):
version = RuSentRelVersions.V11
text_parser = BaseTextParser(pipeline=[
RuSentRelTextEntitiesParser(),
DefaultTextTokenizer(keep_tokens=True)
])
stemmer = MystemWrapper()
synonyms = RuSentRelSynonymsCollectionProvider.load_collection(
stemmer=stemmer, version=version)
news = RuSentRelNews.read_document(doc_id=1,
synonyms=synonyms,
version=version)
# Parse news via external parser.
parsed_news = NewsParser.parse(news=news, text_parser=text_parser)
# Display result
for parsed_text in parsed_news:
self.__print_parsed_text(parsed_text)
assert (isinstance(parsed_news, ParsedNews))
class StemmerArg(BaseArg):
default = u"mystem"
supported = {
u"mystem": MystemWrapper()
}
def __init__(self):
pass
@staticmethod
def read_argument(args):
return StemmerArg.supported[args.stemmer]
@staticmethod
def add_argument(parser):
parser.add_argument('--stemmer',
dest='stemmer',
type=unicode,
choices=list(StemmerArg.supported.keys()),
default=StemmerArg.default,
nargs='?',
help='Stemmer (Default: {})'.format(StemmerArg.default))
DistanceInTermsBetweenAttitudeEndsArg.add_argument(parser)
# Parsing arguments.
args = parser.parse_args()
# Reading arguments.
embedding_filepath = RusVectoresEmbeddingFilepathArg.read_argument(args)
exp_type = ExperimentTypeArg.read_argument(args)
labels_count = LabelsCountArg.read_argument(args)
terms_per_context = TermsPerContextArg.read_argument(args)
entity_fmt = EntityFormatterTypesArg.read_argument(args)
stemmer = StemmerArg.read_argument(args)
use_balancing = UseBalancingArg.read_argument(args)
dist_in_terms_between_attitude_ends = DistanceInTermsBetweenAttitudeEndsArg.read_argument(
args)
pos_tagger = POSMystemWrapper(MystemWrapper().MystemInstance)
synonyms_collection = RuSentRelSynonymsCollectionProvider.load_collection(
stemmer=stemmer)
annot_algo = PairBasedAnnotationAlgorithm(
dist_in_terms_bound=None,
label_provider=PairSingleLabelProvider(
label_instance=ExperimentNeutralLabel()))
exp_name = Common.create_exp_name(rusentrel_version=rusentrel_version,
ra_version=ra_version,
folding_type=folding_type)
extra_name_suffix = Common.create_exp_name_suffix(
use_balancing=use_balancing,
def run_data_serialization_pipeline(sentences, terms_per_context,
entities_parser, embedding_path,
entity_fmt_type, stemmer):
assert (isinstance(sentences, list))
assert (isinstance(entities_parser, BasePipelineItem)
or entities_parser is None)
assert (isinstance(terms_per_context, int))
assert (isinstance(embedding_path, str))
assert (isinstance(entity_fmt_type, EntityFormatterTypes))
labels_scaler = BaseLabelScaler(uint_dict=OrderedDict([(NoLabel(), 0)]),
int_dict=OrderedDict([(NoLabel(), 0)]))
label_provider = MultipleLabelProvider(label_scaler=labels_scaler)
sentences = list(map(lambda text: BaseNewsSentence(text), sentences))
annot_algo = PairBasedAnnotationAlgorithm(
dist_in_terms_bound=None,
label_provider=PairSingleLabelProvider(label_instance=NoLabel()))
frames_collection = create_frames_collection()
frame_variants_collection = create_and_fill_variant_collection(
frames_collection)
# Step 1. Annotate text.
synonyms = RuSentRelSynonymsCollectionProvider.load_collection(
stemmer=stemmer, version=RuSentRelVersions.V11)
# Step 2. Parse text.
news = News(doc_id=0, sentences=sentences)
text_parser = BaseTextParser(pipeline=[
TermsSplitterParser(),
TextEntitiesParser() if entities_parser is None else entities_parser,
EntitiesGroupingPipelineItem(synonyms.get_synonym_group_index),
DefaultTextTokenizer(keep_tokens=True),
FrameVariantsParser(frame_variants=frame_variants_collection),
LemmasBasedFrameVariantsParser(
save_lemmas=False,
stemmer=stemmer,
frame_variants=frame_variants_collection),
FrameVariantsSentimentNegation()
])
embedding = RusvectoresEmbedding.from_word2vec_format(
filepath=embedding_path, binary=True)
embedding.set_stemmer(stemmer)
exp_ctx = RuSentRelExperimentSerializationContext(
labels_scaler=label_provider.LabelScaler,
stemmer=stemmer,
embedding=embedding,
annotator=DefaultAnnotator(annot_algo=annot_algo),
terms_per_context=terms_per_context,
str_entity_formatter=create_entity_formatter(entity_fmt_type),
pos_tagger=POSMystemWrapper(MystemWrapper().MystemInstance),
name_provider=create_infer_experiment_name_provider(),
data_folding=NoFolding(doc_ids_to_fold=[0],
supported_data_types=[DataType.Test]))
labels_fmt = StringLabelsFormatter(stol={"neu": NoLabel})
exp_io = InferIOUtils(exp_ctx)
# Step 3. Serialize data
experiment = CustomExperiment(exp_io=exp_io,
exp_ctx=exp_ctx,
doc_ops=SingleDocOperations(
exp_ctx=exp_ctx,
news=news,
text_parser=text_parser),
labels_formatter=labels_fmt,
synonyms=synonyms,
neutral_labels_fmt=labels_fmt)
NetworkInputHelper.prepare(
exp_ctx=experiment.ExperimentContext,
exp_io=experiment.ExperimentIO,
doc_ops=experiment.DocumentOperations,
opin_ops=experiment.OpinionOperations,
terms_per_context=terms_per_context,
balance=False,
value_to_group_id_func=synonyms.get_synonym_group_index)
return experiment.ExperimentIO
def create_stemmer():
# This is the only stemmer supported by the experiment.
return MystemWrapper()
def __create_stemmer():
return MystemWrapper()
