def index_lastloc(self):
lastloc = None
for (_,idx) in self.iteridxs():
(ndocs,_) = idx_info(idx)
(lastloc,_) = idx_docid2info(idx, ndocs-1)
# the first index must be newest, so we stop here.
break
return lastloc
def index_lastloc(self):
lastloc = None
for (_, idx) in self.iteridxs():
(ndocs, _) = idx_info(idx)
(lastloc, _) = idx_docid2info(idx, ndocs - 1)
# the first index must be newest, so we stop here.
break
return lastloc
def load_status(self, x):
from base64 import b64decode
try:
(idxid0, docid0, found_docs) = unpack('>HiH', b64decode(x))
except:
return
# put dummy locs (max. 65535)
self.start_loc = (idxid0, docid0)
self.found_docs = [None] * found_docs
searched_docs0 = 0
for (idxid,idx) in self._indexdb.iteridxs(end=idxid0-1):
(ndocs, _) = idx_info(idx)
searched_docs0 += ndocs
self.searched_docs = (searched_docs0, ndocs-docid0)
return
def load_status(self, x):
from base64 import b64decode
try:
(idxid0, docid0, found_docs) = unpack('>HiH', b64decode(x))
except:
return
# put dummy locs (max. 65535)
self.start_loc = (idxid0, docid0)
self.found_docs = [None] * found_docs
searched_docs0 = 0
for (idxid, idx) in self._indexdb.iteridxs(end=idxid0 - 1):
(ndocs, _) = idx_info(idx)
searched_docs0 += ndocs
self.searched_docs = (searched_docs0, ndocs - docid0)
return
def open(self):
if not self.cdb:
self.cdb = cdb.init(self.fname)
(self.ndocs, self.nterms) = idx_info(self.cdb)
return
def open(self):
if not self.cdb:
self.cdb = cdb.init(self.fname)
(self.ndocs, self.nterms) = idx_info(self.cdb)
return
def get_docids(self):
"Returns a list of DocIDs that have a given feature."
(start_idx, start_docid0) = self.start_loc
(end_idx, end_docid0) = self.end_loc
# We maintain the number of docs that have been searched so far.
# But this is separeted into two parts:
# "all the docs included up to the previous index" +
# "the number of docs that have been searched within the current index"
# This way we can compute the number of searched docs deterministicly
# without any cumulative counting within iterators
# (no worry for double counting!).
(searched_docs0, _) = self.searched_docs
# start_idx <= idxid <= end_idx.
# start_docid-1 >= docid >= end_docid.
for (idxid,idx) in self._indexdb.iteridxs(start_idx, end_idx):
assert isinstance(idxid, int)
try:
(ndocs, _) = idx_info(idx)
except KeyError:
continue
if idxid == start_idx:
start_docid = min(start_docid0, ndocs)
else:
start_docid = ndocs
if idxid == end_idx:
end_docid = end_docid0
else:
end_docid = 0
if self.pos_preds:
conj = False
docs = {}
else:
# no positive predicate.
conj = True
docs = dict( (docid,[]) for docid in xrange(start_docid-1,-1,-1) )
# Get a set of narrowed documents for each predicate.
for pred in (self.pos_preds + self.neg_preds):
# locs: docids must be in decending order. (ie. start_docid > end_docid)
locs = [ (docid,sentid) for (docid,sentid) in pred.narrow_docids(idx)
if start_docid >= docid and docid > end_docid ]
locs = [ (docid,sentid) for (docid,sentid) in locs
if pred.check_docid(docid) and pred.check_sentid(sentid) ]
if not locs:
if pred.neg:
continue
elif not self.disjunctive:
docs.clear()
break
if self.disjunctive:
# disjunctive (OR) search.
docs1 = {}
for (docid,sentid) in locs:
if docid not in docs1:
sentids = array('i')
docs1[docid] = sentids
else:
sentids = docs1[docid]
assert isinstance(sentids, array)
sentids.append(sentid)
# combine with the previous docs.
for (docid,sentids) in docs1.iteritems():
if docid not in docs:
r = []
docs[docid] = r
else:
r = docs[docid]
x = (sentids, pred.checkpat)
r.append(x)
elif pred.neg:
# negative conjunctive (-AND) search.
for (docid,sentid) in locs:
if docid in docs:
del docs[docid]
else:
# positive conjunctive (+AND) search.
docs1 = {}
for (docid,sentid) in locs:
if conj and (docid not in docs): continue
if docid not in docs1:
sentids = array('i')
docs1[docid] = sentids
else:
sentids = docs1[docid]
assert isinstance(sentids, array)
sentids.append(sentid)
if conj:
# intersect with the previous docs.
tmp = {}
for (docid,sentids) in docs1.iteritems():
r = docs[docid]
x = (sentids, pred.checkpat)
r.append(x)
tmp[docid] = r
docs = tmp
else:
# first positive predicate.
conj = True
for (docid,sentids) in docs1.iteritems():
docs[docid] = [(sentids, pred.checkpat)]
# docs: the candidate documents in the current index file.
docs2 = docs.items()
docs2.sort(reverse=True)
found = set()
for (docid,contexts) in docs2:
self.start_loc = (idxid, docid)
self.searched_docs = (searched_docs0, ndocs-docid)
# Skip if the document is already in the list.
if docid in found: continue
found.add(docid)
yield (idx,docid,contexts)
# Finished this index.
searched_docs0 += ndocs
self.searched_docs = (searched_docs0, 0)
return
def total_docs(self):
total = 0
for (_,idx) in self.iteridxs():
(ndocs,_) = idx_info(idx)
total += ndocs
return total
def get_docids(self):
"Returns a list of DocIDs that have a given feature."
(start_idx, start_docid0) = self.start_loc
(end_idx, end_docid0) = self.end_loc
# We maintain the number of docs that have been searched so far.
# But this is separeted into two parts:
# "all the docs included up to the previous index" +
# "the number of docs that have been searched within the current index"
# This way we can compute the number of searched docs deterministicly
# without any cumulative counting within iterators
# (no worry for double counting!).
(searched_docs0, _) = self.searched_docs
# start_idx <= idxid <= end_idx.
# start_docid-1 >= docid >= end_docid.
for (idxid, idx) in self._indexdb.iteridxs(start_idx, end_idx):
assert isinstance(idxid, int)
try:
(ndocs, _) = idx_info(idx)
except KeyError:
continue
if idxid == start_idx:
start_docid = min(start_docid0, ndocs)
else:
start_docid = ndocs
if idxid == end_idx:
end_docid = end_docid0
else:
end_docid = 0
if self.pos_preds:
conj = False
docs = {}
else:
# no positive predicate.
conj = True
docs = dict(
(docid, []) for docid in xrange(start_docid - 1, -1, -1))
# Get a set of narrowed documents for each predicate.
for pred in (self.pos_preds + self.neg_preds):
# locs: docids must be in decending order. (ie. start_docid > end_docid)
locs = [(docid, sentid)
for (docid, sentid) in pred.narrow_docids(idx)
if start_docid >= docid and docid > end_docid]
locs = [
(docid, sentid) for (docid, sentid) in locs
if pred.check_docid(docid) and pred.check_sentid(sentid)
]
if not locs:
if pred.neg:
continue
elif not self.disjunctive:
docs.clear()
break
if self.disjunctive:
# disjunctive (OR) search.
docs1 = {}
for (docid, sentid) in locs:
if docid not in docs1:
sentids = array('i')
docs1[docid] = sentids
else:
sentids = docs1[docid]
assert isinstance(sentids, array)
sentids.append(sentid)
# combine with the previous docs.
for (docid, sentids) in docs1.iteritems():
if docid not in docs:
r = []
docs[docid] = r
else:
r = docs[docid]
x = (sentids, pred.checkpat)
r.append(x)
elif pred.neg:
# negative conjunctive (-AND) search.
for (docid, sentid) in locs:
if docid in docs:
del docs[docid]
else:
# positive conjunctive (+AND) search.
docs1 = {}
for (docid, sentid) in locs:
if conj and (docid not in docs): continue
if docid not in docs1:
sentids = array('i')
docs1[docid] = sentids
else:
sentids = docs1[docid]
assert isinstance(sentids, array)
sentids.append(sentid)
if conj:
# intersect with the previous docs.
tmp = {}
for (docid, sentids) in docs1.iteritems():
r = docs[docid]
x = (sentids, pred.checkpat)
r.append(x)
tmp[docid] = r
docs = tmp
else:
# first positive predicate.
conj = True
for (docid, sentids) in docs1.iteritems():
docs[docid] = [(sentids, pred.checkpat)]
# docs: the candidate documents in the current index file.
docs2 = docs.items()
docs2.sort(reverse=True)
found = set()
for (docid, contexts) in docs2:
self.start_loc = (idxid, docid)
self.searched_docs = (searched_docs0, ndocs - docid)
# Skip if the document is already in the list.
if docid in found: continue
found.add(docid)
yield (idx, docid, contexts)
# Finished this index.
searched_docs0 += ndocs
self.searched_docs = (searched_docs0, 0)
return
def total_docs(self):
total = 0
for (_, idx) in self.iteridxs():
(ndocs, _) = idx_info(idx)
total += ndocs
return total
