def main():
log.info("*****Patent2Vec Application*****")
# Preprocess patent documents
log.info("Preprocessing patent documents")
patents = PatentDocument(SOURCE_DATASET,
extension="",
use_conceptualizer=True,
transform_conceptualizer=True,
enable_sampling=True,
train_ratio=1.0,
test_ratio=0.0)
# Create Patent2Vec model
models = OrderedDict()
# PV-DM with average
models["PV_DM_Mean"] = \
Patent2Vec(dm=1, dm_mean=1, dm_concat=0, min_word_count=5, size=500,
context_window_size=8, negative=2, iter=50, workers=CPU_CORE,
use_less_memory=False, docvecs_mapfile=DOCVECS_MAP)
models["PV_DM_Mean"].build(patents)
models["PV_DM_Mean"].intersect_with_pretrained_embedding(
PRETRAINED_EMBEDDING, binary=False)
# models["PV_DM_Mean"].load(PATENT2VEC_MODEL)
# # PV-DM with concatenation
# models["PV_DM_Concatenation"] = \
# Patent2Vec(dm=1, dm_mean=0, dm_concat=1, min_word_count=5, size=500,
# context_window_size=8, negative=2, iter=50, workers=CPU_CORE,
# use_less_memory=False, docvecs_mapfile=DOCVECS_MAP)
# models["PV_DM_Concatenation"].reuse_from(models["PV_DM_Mean"])
# # models["PV_DM_Concatenation"].build(patents)
# # models["PV_DM_Concatenation"].intersect_with_pretrained_embedding(PRETRAINED_EMBEDDING,
# # binary=False)
# # # models["PV_DM_Concatenation"].load(PATENT2VEC_MODEL)
# # PV-DBOW
# models["PV_DBOW"] = \
# Patent2Vec(dm=0, dm_mean=0, dm_concat=0, min_word_count=5, size=500,
# context_window_size=8, negative=2, iter=50, workers=CPU_CORE,
# use_less_memory=False, docvecs_mapfile=DOCVECS_MAP)
# models["PV_DBOW"].reuse_from(models["PV_DM_Mean"])
# # models["PV_DBOW"].build(patents)
# # models["PV_DBOW"].intersect_with_pretrained_embedding(PRETRAINED_EMBEDDING,
# # binary=False)
# # # models["PV_DBOW"].load(PATENT2VEC_MODEL)
# # Mixed models
# models["DBOW + DM with average"] = ConcatenatedPatent2Vec([models["PV_DBOW"],
# models["PV_DM_Mean"]])
# models["DBOW + DM with concatenation"] = ConcatenatedPatent2Vec([models["PV_DBOW"],
# models["PV_DM_Concatenation"]])
for name, model in models.items():
# Train Patent2Vec model
start_time = time.time()
model.train(patents,
alpha=0.1,
min_alpha=0.0001,
passes=10,
fixed_alpha=False)
end_time = time.time()
log.info("Total time elapsed: %r", (end_time - start_time))
# Evaluate Patent2Vec model
model.evaluate()
# Save Patent2Vec model
model.save(model=PATENT2VEC_MODEL)
# Create a database object
db = Database(verbose=True)
# Connect to database
db.connect(in_memory=True)
# Create a new table for storing document embeddings
db.create_table(table=PATENT_EMBEDDING_TABLE,
primary_column=PRIMARY_KEY,
other_columns=FIELDS)
# Save document embeddings
model.save_document_embeddings(document_embeddings=PATENT_EMBEDDING,
rows=len(patents),
columns=500,
database=db,
table_name=PATENT_EMBEDDING_TABLE,
save_patent_category=True,
prepend_document_category=True)
# Test documents
if not os.path.exists(TESTING_DATA):
raise PathNotFoundError("Path does not exist: %s" % TESTING_DATA)
with open(TESTING_DATA, "r") as t:
test_documents = t.readlines()
test_documents = map(lambda x: x.strip(), test_documents)
test_documents = filter(None, test_documents)
# Preprocessed test documents
preprocessed_test_documents = patents.get_preprocessed_corpus(
test_documents)
# Predict document embeddings
model.predict(preprocessed_test_documents,
alpha=0.1,
min_alpha=0.0001,
steps=50,
save=True,
database=db,
table_name=PATENT_EMBEDDING_TABLE,
save_patent_category=True,
prepend_document_category=True)
# Create an index on document embedding table
db.create_index(index=PATENT_EMBEDDING_INDEX,
table=PATENT_EMBEDDING_TABLE,
index_by_column=PRIMARY_KEY[0])
# Close database connection
db.close(save_to=PATENT_EMBEDDING_DATABASE)
# Delete temporary training data
model.clean()
# Test document for checking the quality of Patent2Vec model
patents.set_token_only(True)
preprocessed_test_document = patents.get_preprocessed_document(
TEST_DOCUMENT)
patents.set_token_only(False)
# Check quality of Patent2Vec model
if preprocessed_test_document is not None:
log.info("Check quality of Patent2Vec model")
log.info("Top matches for test document: %s", TEST_DOCUMENT)
for name, model in models.items():
embedding = model.infer(preprocessed_test_document)
top_matches = model.model.docvecs.most_similar(
positive=[embedding], negative=[], topn=10)
top_matches = map(lambda x: x[0] + "\t\t" + str(x[1]), top_matches)
for top_match in top_matches:
log.info(top_match)
# Clean all un-necessary files
clean(cleanSample=True,
cleanModel=False,
cleanDocvecs=True,
cleanDatabase=False,
cleanClusters=False,
filter=[])
