def index():
with Corpus_DB() as corpus_db:
with BOW_DB() as bow_db:
with LDA_DB() as lda_db:
handler = GroupInBoxHandler(request, response, corpus_db, bow_db, lda_db)
response.delimiters = ('[[', ']]')
return handler.GenerateResponse()
def TermFrequencyModel(): with BOW_DB() as bow_db: with LDA_DB() as lda_db: handler = TermTopicMatrix1(request, response, bow_db, lda_db) data = handler.GetTermFrequencyModel() dataStr = json.dumps(data, encoding='utf-8', indent=2, sort_keys=True) response.headers['Content-Type'] = 'application/json' return dataStr
def GroupInBox():
with Corpus_DB() as corpus_db:
with BOW_DB() as bow_db:
with LDA_DB() as lda_db:
handler = GroupInBoxHandler(request, response, corpus_db,
bow_db, lda_db)
handler.LoadGIB()
return handler.GenerateResponse()
def Inspect():
with Corpus_DB() as corpus_db:
with BOW_DB() as bow_db:
with LDA_DB() as lda_db:
handler = GroupInBoxHandler(request, response, corpus_db,
bow_db, lda_db)
handler.InspectModel()
return handler.GenerateResponse()
def ImportSTM( app_name, model_path, corpus_path, database_path, is_quiet, force_overwrite ):
logger = logging.getLogger( 'termite' )
logger.addHandler( logging.StreamHandler() )
logger.setLevel( logging.INFO if is_quiet else logging.DEBUG )
app_path = 'apps/{}'.format( app_name )
corpus_filename = '{}/corpus.txt'.format( corpus_path )
database_filename = '{}/corpus.db'.format( database_path )
logger.info( '--------------------------------------------------------------------------------' )
logger.info( 'Import an STM topic model as a web2py application...' )
logger.info( ' app_name = %s', app_name )
logger.info( ' app_path = %s', app_path )
logger.info( ' model_path = %s', model_path )
logger.info( ' corpus_filename = %s', corpus_filename )
logger.info( ' database_filename = %s', database_filename )
logger.info( '--------------------------------------------------------------------------------' )
if force_overwrite or not os.path.exists( app_path ):
with CreateApp(app_name) as app:
# Import corpus (models/corpus.db, data/corpus.txt, data/sentences.txt)
app_database_filename = '{}/corpus.db'.format( app.GetDatabasePath() )
app_corpus_filename = '{}/corpus.txt'.format( app.GetDataPath() )
app_sentences_filename = '{}/sentences.txt'.format( app.GetDataPath() )
logger.info( 'Copying [%s] --> [%s]', database_filename, app_database_filename )
shutil.copy( database_filename, app_database_filename )
logger.info( 'Copying [%s] --> [%s]', corpus_filename, app_corpus_filename )
shutil.copy( corpus_filename, app_corpus_filename )
logger.info( 'Extracting [%s] --> [%s]', corpus_filename, app_sentences_filename )
SplitSentences( corpus_filename, app_sentences_filename )
# Import model (data/*)
app_model_path = '{}/stm'.format( app.GetDataPath() )
logger.info( 'Copying [%s] --> [%s]', model_path, app_model_path )
shutil.copytree( model_path, app_model_path )
for stm_filename in [ 'doc-index.json', 'term-index.json', 'topic-index.json', 'doc-topic-matrix.txt', 'term-topic-matrix.txt' ]:
source_filename = '{}/{}'.format(corpus_path, stm_filename)
target_filename = '{}/{}'.format(app_model_path, stm_filename)
logger.info( 'Copying [%s] --> [%s]', source_filename, target_filename )
shutil.copy( source_filename, target_filename )
db_path = app.GetDatabasePath()
with Corpus_DB(db_path) as corpus_db:
# Create a bow-of-words language model
with BOW_DB(db_path, isInit=True) as bow_db:
bow_computer = BOW_ComputeStats(bow_db, corpus_db, app_corpus_filename, app_sentences_filename)
bow_computer.Execute()
# Compute derived-statistics about an LDA-like topic model
with LDA_DB(db_path, isInit=True) as lda_db:
stm_reader = STMReader(lda_db, app_model_path, corpus_db)
stm_reader.Execute()
lda_computer = LDA_ComputeStats(lda_db, corpus_db)
lda_computer.Execute()
else:
logger.info( ' Already available: %s', app_path )
def gib(): with Corpus_DB() as corpus_db: with BOW_DB() as bow_db: with LDA_DB() as lda_db: handler = GroupInBoxHandler(request, response, corpus_db, bow_db, lda_db) handler.UpdateModel() handler.InspectModel() handler.LoadGIB() dataStr = json.dumps(handler.content, encoding='utf-8', indent=2, sort_keys=True) response.headers['Content-Type'] = 'application/json' return dataStr
def index(): with BOW_DB() as bow_db: with LDA_DB() as lda_db: handler = TermTopicMatrix1(request, response, bow_db, lda_db) return handler.GenerateResponse()
def index():
with BOW_DB() as bow_db:
handler = BOW_Core(request, response, bow_db)
return handler.GenerateResponse()
def SentenceG2():
with BOW_DB() as bow_db:
handler = BOW_Core(request, response, bow_db)
handler.LoadSentenceG2()
return handler.GenerateResponse()
def TermCoProbs():
with BOW_DB() as bow_db:
handler = BOW_Core(request, response, bow_db)
handler.LoadTermCoProbs()
return handler.GenerateResponse()
