def setUp(self):
self.docs = mock_documents('en', '')
try:
import Pyro4
self.server = Pyro4.Proxy('PYRONAME:gensim.testserver')
logger.info(self.server.status())
except Exception, e:
logger.info(
"could not locate running SessionServer; starting a local server"
)
self.server = SessionServer(gensim.utils.randfname())
def commit_indexing_set_old(self):
''' after filling an indexing set the actual indexing needs to be done
'''
import sqlite3
training_id = str(self.training_id)
conn = sqlite3.connect(self.sqlserver)
c = conn.cursor()
# fetch the content
sql = "SELECT * FROM gensimIndexingSet"+str(training_id)
print ( sql )
c.execute( sql )
# just fetch all items
indexing_data = c.fetchmany(500);
#service = similarities.SessionServer(self.rootlocation, autosession=True)
service = SessionServer(self.rootlocation + 'gensimTraining'+str(self.training_id), autosession=True) # create a local server
import Pyro4
service = Pyro4.Proxy(Pyro4.locateNS().lookup('gensim.testserver'))
print self.rootlocation + 'gensimTraining'+str(self.training_id)
while len(indexing_data) > 0 :
corpus = [{'id': str(id), 'tokens': utils.simple_preprocess(text)}
for (text, id) in indexing_data]
service.index(corpus) ## TODO we don't have a corpus yet, but we definatly need one big
indexing_data = c.fetchmany(500)
service.autosession = True
time.sleep(0.5)
# sql = "DROP TABLE IF EXISTS gensimIndexingSet"+str(training_id)
# c.execute( sql )
self.delete_set()
self.init_indexing_set()
return 'indexing done'
def commit_WIKIPEDIA_training_set(self, ):
''' after filling a training set the actual training needs to be done
'''
from packages.controller.gensim_sim import gensim_sim
w = gensim_sim()
service = SessionServer(self.rootlocation + 'gensimTraining'+str(self.training_id), autosession=True) # create a local server
factor=20000
for d in range (0, 100):
print "currently working on text row " + str( d*factor ) + "up to" + str( (d+1)*factor )
training_data = w.init_sql_connection(d*factor, factor )
corpus = [{'id': id, 'tokens': utils.simple_preprocess(text)}
for (id, text) in training_data]
service.train(corpus, method='lsi') ## TODO we don't have a corpus yet, but we definatly need one big
#self.init_training_set()
return 'training done'