class WhooshSearchInterface(BaseSearchInterface):
"""
A search interface making use of the Whoosh indexing library - and the ifind search components.
Set model = 0 for TFIDIF
Set model = 1 for BM25 (defaults to b=0.75), set pval to change b.
Set model = 2 for PL2 (defaults to c=10.), set pval to change c.
"""
def __init__(self, whoosh_index_dir, model=2, implicit_or=True, pval=None):
super(WhooshSearchInterface, self).__init__()
log.debug("Whoosh Index to open: {0}".format(whoosh_index_dir))
self.__index = open_dir(whoosh_index_dir)
self.__reader = self.__index.reader()
self.__engine = Whooshtrec(whoosh_index_dir=whoosh_index_dir,model=model,implicit_or=implicit_or)
if pval:
self.__engine.set_model(model, pval)
def issue_query(self, query, top=100):
"""
Allows one to issue a query to the underlying search engine. Takes an ifind Query object.
"""
query.top = top
response = self.__engine.search(query)
self._last_query = query
self._last_response = response
return response
def get_document(self, document_id):
"""
Retrieves a Document object for the given document specified by parameter document_id.
"""
fields = self.__reader.stored_fields(int(document_id))
title = fields['title']
content = fields['content']
document_num = fields['docid']
document_date = fields['timedate']
document_source = fields['source']
document = Document(id=document_id, title=title, content=content)
document.date = document_date
document.doc_id = document_num
document.source = document_source
return document
def __init__(self, whoosh_index_dir, model=2, implicit_or=True, pval=None):
super(WhooshSearchInterface, self).__init__()
log.debug("Whoosh Index to open: {0}".format(whoosh_index_dir))
self.__index = open_dir(whoosh_index_dir)
self.__reader = self.__index.reader()
self.__engine = Whooshtrec(whoosh_index_dir=whoosh_index_dir,model=model,implicit_or=implicit_or)
if pval:
self.__engine.set_model(model, pval)
def __init__(self,
whoosh_index_dir,
model=2,
implicit_or=True,
pval=None,
frag_type=2,
frag_size=2,
frag_surround=40,
host=None,
port=0):
super(WhooshSearchInterface, self).__init__()
log.debug("Whoosh Index to open: {0}".format(whoosh_index_dir))
self.__index = open_dir(whoosh_index_dir)
self.__reader = self.__index.reader()
self.__redis_conn = None
if host is None:
self.__engine = Whooshtrec(whoosh_index_dir=whoosh_index_dir,
model=model,
implicit_or=implicit_or)
else:
self.__engine = Whooshtrec(whoosh_index_dir=whoosh_index_dir,
model=model,
implicit_or=implicit_or,
cache='engine',
host=host,
port=port)
# Update (2017-05-02) for snippet fragment tweaking.
# SIGIR Study (2017) uses frag_type==1 (2 doesn't give sensible results), surround==40, snippet_sizes==2,0,1,4
self.__engine.snippet_size = frag_size
self.__engine.set_fragmenter(frag_type=frag_type,
surround=frag_surround)
if pval:
self.__engine.set_model(model, pval)
from ifind.common.language_model import LanguageModel
from compute_snippet_len_gain import make_query, get_words_from_snippet, compute_length, compute_info_gain
import sys
from ifind.search.engines.whooshtrec import Whooshtrec
from ifind.search import Query
bm25_search_engine = Whooshtrec(
whoosh_index_dir='/Users/david/Workspace/indexes/aquaint_test500_whoosh/',
stopwords_file='',
model=1,
newschema=True)
bm25_search_engine.snippet_size = 40
def main():
log_file = sys.argv[1]
lm = LanguageModel(file='vocab.in')
# Interface... 1 2 3 4
snippet_sizes = [2, 0, 1, 4]
snippet_surround = [40, 40, 40, 40]
with open(log_file) as f:
for s in f:
fields = s.strip().split()
amtid = fields[3]
interface = fields[5]
order = fields[6]
topic = fields[7]
class WhooshSearchInterface(BaseSearchInterface):
"""
A search interface making use of the Whoosh indexing library - and the ifind search components.
Set model = 0 for TFIDIF
Set model = 1 for BM25 (defaults to b=0.75), set pval to change b.
Set model = 2 for PL2 (defaults to c=10.), set pval to change c.
"""
def __init__(self,
whoosh_index_dir,
model=2,
implicit_or=True,
pval=None,
frag_type=2,
frag_size=2,
frag_surround=40,
host=None,
port=0):
super(WhooshSearchInterface, self).__init__()
log.debug("Whoosh Index to open: {0}".format(whoosh_index_dir))
self.__index = open_dir(whoosh_index_dir)
self.__reader = self.__index.reader()
self.__redis_conn = None
if host is None:
self.__engine = Whooshtrec(whoosh_index_dir=whoosh_index_dir,
model=model,
implicit_or=implicit_or)
else:
self.__engine = Whooshtrec(whoosh_index_dir=whoosh_index_dir,
model=model,
implicit_or=implicit_or,
cache='engine',
host=host,
port=port)
# Update (2017-05-02) for snippet fragment tweaking.
# SIGIR Study (2017) uses frag_type==1 (2 doesn't give sensible results), surround==40, snippet_sizes==2,0,1,4
self.__engine.snippet_size = frag_size
self.__engine.set_fragmenter(frag_type=frag_type,
surround=frag_surround)
if pval:
self.__engine.set_model(model, pval)
def issue_query(self, query, top=100):
"""
Allows one to issue a query to the underlying search engine. Takes an ifind Query object.
"""
query.top = top
response = self.__engine.search(query)
self._last_query = query
self._last_response = response
return response
def get_document(self, document_id):
"""
Retrieves a Document object for the given document specified by parameter document_id.
"""
fields = self.__reader.stored_fields(int(document_id))
title = fields['title']
content = fields['content']
document_num = fields['docid']
document_date = fields['timedate']
document_source = fields['source']
document = Document(id=document_id, title=title, content=content)
document.date = document_date
document.doc_id = document_num
document.source = document_source
return document
def main():
bm25 = Whooshtrec(
whoosh_index_dir='fullindex/',
stopwords_file='',
model=1,
newschema=True)
query = Query('Sea Pirates')
query.skip = 1
query.top = 5
bm25.snippet_size = 3
response = bm25.search(query)
i = 1
for result in response.results:
print i,len(result.summary)
#print result.summary
#print "--------------"
soup = BeautifulSoup(result.summary,'html.parser')
text = soup.getText()
#print text
print "--------------"
n = extract_nouns(text)
print set(n)
print "--------------"
sentences = nltk.sent_tokenize(text)
tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
#print tokenized_sentences
cat_sentences = []
for ts in tokenized_sentences:
for w in ts:
cat_sentences.append(w)
#print cat_sentences
tagged = nltk.pos_tag(cat_sentences)
nouns = [word for word,pos in tagged if (pos == 'NN' or pos == 'NNP' or pos == 'NNS' or pos == 'NNPS')]
downcased = [x.lower() for x in nouns]
joined = " ".join(downcased).encode('utf-8')
into_string = str(nouns)
print (into_string)
#print tokenized_sentences
print "--------------"
tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
chunked_sentences = nltk.chunk.ne_chunk_sents(tagged_sentences, binary=True)
entity_names = []
for tree in chunked_sentences:
# Print results per sentence
# print extract_entity_names(tree)
entity_names.extend(extract_entity_names(tree))
print set(entity_names)
i+=1
'pretask/3/', 'search/3/',
'pretask/4/', 'search/4/','endexperiment/',
'logout/'
]
suggestion_trie = AutocompleteTrie(
min_occurrences=3,
suggestion_count=8,
include_stopwords=False,
stopwords_path=os.path.join(work_dir, "data/stopwords.txt"),
vocab_path=os.path.join(work_dir, "data/vocab.txt"),
vocab_trie_path=os.path.join(work_dir, "data/vocab_trie.dat"))
search_engine = Whooshtrec(
whoosh_index_dir=my_whoosh_doc_index_dir,
stopwords_file=stopword_file,
model=1,
newschema=True)
search_engine.key_name = 'bm25'
search_engine.set_fragmenter(frag_type=2, surround=30)
exp_test = ExperimentSetup(
workflow=snippet_flow,
engine=search_engine,
practice_topic='367',
topics=['347', '341', '435','408'],
rpp=10,
practice_interface=1,
interface=[1, 2, 3, 4],
rotation_type=1,
import sys
from ifind.search import Query
from ifind.search.engines.whooshtrec import Whooshtrec
from whoosh.index import open_dir
from whoosh.qparser import QueryParser
whoosh_path = sys.argv[1]
stopwords_path = sys.argv[2]
page = 3
page_len = 10
search_engine = Whooshtrec(whoosh_index_dir=whoosh_path,
stopwords_file=stopwords_path,
model=1,
newschema=True)
query = Query('wildlife extinction')
query.skip = page
query.top = page_len
response = search_engine.search(query)
for result in response:
print '{0} {1}'.format(result.whooshid, result.rank)
print response.result_total
print response.results_on_page
print response.actual_page
