def _filter_terms(self):
'''
Removes tokens that appear either too often or too rarely. It returns the corpus of
filtered tokens and also changes the token and word_frequencies lists of each document
according to the filtered tokens.
'''
index = Index("kmeans_index")
index.add_documents(self.db_documents)
index.finalize()
filtered_terms = index.get_filtered_terms(lowestf=0.1, highestf=0.2)
corpus = []
for id, document in self.document_dict.iteritems():
filtered_tokens = []
for token in document.tokens:
if token in filtered_terms:
filtered_tokens.append(token)
if len(filtered_tokens) == 0: #if all the tokens are removed then this document is worthless
self.document_dict.pop(id)
else: #if there are still tokens ammend the token list and the word frequencies list to include only the relevant tokens
self.document_dict[id].tokens = filtered_tokens
self.document_dict[id].word_frequencies = [item for item in self.document_dict[id].word_frequencies if item.word in filtered_tokens]
corpus.append(filtered_tokens)
return corpus
@author: george
My playground!
'''
import unittest, os
from analysis.index import Index
from database.warehouse import WarehouseServer
from database.model.tweets import TwoGroupsTweet
BASE_PATH = os.path.expanduser("~/virtualenvfyp/pythia/data/")
index_path = BASE_PATH + "test_index"
ws = WarehouseServer()
sample_docs = ws.get_n_documents(100, type=TwoGroupsTweet)
index = Index(index_path)
for doc in sample_docs:
index.add_document(doc)
index.finalize()
class TestPlayground(unittest.TestCase):
def test_searching(self):
results = index.search_by_term("sales")
calculated = []
for doc in results:
calculated.append(doc.get('id'))
expected = ['4f2d602780286c38a7000013', '4f2d603280286c38a700001e']
self.assertEqual(expected, calculated)
try:
os.makedirs(index_path)
except os.error:
raise Exception(index_path + " could not be created.")
#Save the tweets in the db
f = CrawlerFactory()
t = f.get_crawler("topsy")
search_hashtags = "#25jan OR #jan25 OR #egypt OR #tahrir OR #fuckmubarak OR #mubarak \
OR #suez OR #DownWithMubarak OR #NOSCAF OR #SCAF OR #cairo"
t.search_for(search_hashtags)
from_date=datetime.datetime(2011, 01, 27, 23, 55, 0)
to_date=datetime.datetime(2011, 01, 29, 0, 0, 0)
t.search_between(from_date=from_date,
to_date=to_date,
granularity_days=0,
granularity_hours=0,
granularity_mins=5)
t.retrieve_items_of_type(EgyptTweet)
t.crawl(only_english=True)
#Index all the documents
docs = ws.get_documents_by_date(from_date, to_date, type=EgyptTweet)
index = Index(index_path)
print 'Started indexing'
index.add_documents(docs)
index.finalize()
print 'Started indexing'
for term in index.get_top_terms(limit=100):
print term
@author: george
My playground!
'''
import unittest, os
from analysis.index import Index
from database.warehouse import WarehouseServer
from database.model.tweets import TwoGroupsTweet
BASE_PATH = os.path.expanduser("~/virtualenvfyp/pythia/data/")
index_path = BASE_PATH + "test_index"
ws = WarehouseServer()
sample_docs = ws.get_n_documents(100, type=TwoGroupsTweet)
index = Index(index_path)
for doc in sample_docs:
index.add_document(doc)
index.finalize()
class TestPlayground(unittest.TestCase):
def test_searching(self):
results = index.search_by_term("sales")
calculated = []
for doc in results:
calculated.append(doc.get('id'))
expected = ['4f2d602780286c38a7000013', '4f2d603280286c38a700001e']
self.assertEqual(expected, calculated)
except os.error:
raise Exception(index_path + " could not be created.")
#Save the tweets in the db
f = CrawlerFactory()
t = f.get_crawler("topsy")
search_hashtags = "#25jan OR #jan25 OR #egypt OR #tahrir OR #fuckmubarak OR #mubarak \
OR #suez OR #DownWithMubarak OR #NOSCAF OR #SCAF OR #cairo"
t.search_for(search_hashtags)
##Last update ended at 2011-01-27 09:00:00
from_date = datetime.datetime(2011, 01, 24, 0, 0, 0)
to_date = datetime.datetime(2011, 01, 25, 0, 0, 0)
t.search_between(from_date=from_date,
to_date=to_date,
granularity_days=0,
granularity_hours=0,
granularity_mins=5)
t.retrieve_items_of_type(EgyptTweet)
t.crawl(only_english=True)
#Index all the documents
docs = ws.get_documents_by_date(from_date, to_date, type=EgyptTweet)
index = Index(index_path)
print 'Started indexing'
index.add_documents(docs)
index.finalize()
print 'Started indexing'
for term in index.get_top_terms(limit=100):
print term
