def __init__(self, ms, slop=1, ordered=True, mindist=1):
self.ms = ms
self.slop = slop
self.ordered = ordered
self.mindist = mindist
isect = make_binary_tree(binary.IntersectionMatcher, ms)
super(SpanNear2.SpanNear2Matcher, self).__init__(isect)
def __init__(self, ms, slop=1, ordered=True, mindist=1):
self.ms = ms
self.slop = slop
self.ordered = ordered
self.mindist = mindist
isect = make_binary_tree(binary.IntersectionMatcher, ms)
super(SpanNear2.SpanNear2Matcher, self).__init__(isect)
def __init__(self, ms, slop=1, ordered=True, mindist=1):
self.ms = ms
self.slop = slop
self.ordered = ordered
self.mindist = mindist
isect = make_binary_tree(
cylleneus.engine.matching.binary.IntersectionMatcher, ms)
super(SpanWith2.SpanWith2Matcher, self).__init__(isect)
def word_graph(self, fieldname):
from whoosh.support.dawg import UnionNode
from whoosh.util import make_binary_tree
if not self.has_word_graph(fieldname):
raise Exception("No word graph for field %r" % fieldname)
graphs = [r.word_graph(fieldname) for r in self.readers
if r.has_word_graph(fieldname)]
if len(graphs) == 1:
return graphs[0]
return make_binary_tree(UnionNode, graphs)
def word_graph(self, fieldname):
from whoosh.support.dawg import UnionNode
from whoosh.util import make_binary_tree
if not self.has_word_graph(fieldname):
raise Exception("No word graph for field %r" % fieldname)
graphs = [r.word_graph(fieldname) for r in self.readers
if r.has_word_graph(fieldname)]
if len(graphs) == 1:
return graphs[0]
return make_binary_tree(UnionNode, graphs)
def word_graph(self, fieldname):
from whoosh.automata.fst import UnionNode
from whoosh.util import make_binary_tree
if not self.has_word_graph(fieldname):
raise Exception("No word graph for field %r" % fieldname)
graphs = [r.word_graph(fieldname) for r in self.readers
if r.has_word_graph(fieldname)]
if len(graphs) == 0:
raise KeyError("No readers have graph for %r" % fieldname)
if len(graphs) == 1:
return graphs[0]
return make_binary_tree(UnionNode, graphs)
def word_graph(self, fieldname):
from whoosh.automata.fst import UnionNode
from whoosh.util import make_binary_tree
if not self.has_word_graph(fieldname):
raise Exception("No word graph for field %r" % fieldname)
graphs = [
r.word_graph(fieldname) for r in self.readers
if r.has_word_graph(fieldname)
]
if len(graphs) == 0:
raise KeyError("No readers have graph for %r" % fieldname)
if len(graphs) == 1:
return graphs[0]
return make_binary_tree(UnionNode, graphs)
def test_random_union():
testcount = 100
rangelimits = (2, 10)
clauselimits = (2, 10)
vals = list(range(100))
for _ in xrange(testcount):
target = set()
matchers = []
for _ in xrange(randint(*clauselimits)):
nums = sample(vals, randint(*rangelimits))
target = target.union(nums)
matchers.append(matching.ListMatcher(sorted(nums)))
target = sorted(target)
um = make_binary_tree(matching.UnionMatcher, matchers)
assert_equal(list(um.all_ids()), target)
def test_random_union():
testcount = 100
rangelimits = (2, 10)
clauselimits = (2, 10)
vals = list(range(100))
for _ in xrange(testcount):
target = set()
matchers = []
for _ in xrange(randint(*clauselimits)):
nums = sample(vals, randint(*rangelimits))
target = target.union(nums)
matchers.append(matching.ListMatcher(sorted(nums)))
target = sorted(target)
um = make_binary_tree(matching.UnionMatcher, matchers)
assert list(um.all_ids()) == target
def phrase(cls, fieldname, words, slop=1, ordered=True):
"""Returns a tree of SpanNear queries to match a list of terms.
This class method is a convenience for constructing a phrase query
using a binary tree of SpanNear queries::
SpanNear.phrase("content", ["alfa", "bravo", "charlie", "delta"])
:param fieldname: the name of the field to search in.
:param words: a sequence of token texts to search for.
:param slop: the number of positions within which the terms must
occur. Default is 1, meaning the terms must occur right next
to each other.
:param ordered: whether the terms must occur in order. Default is True.
"""
terms = [Term(fieldname, word) for word in words]
return make_binary_tree(cls, terms, slop=slop, ordered=ordered)
def phrase(cls, fieldname, words, slop=1, ordered=True):
"""Returns a tree of SpanNear queries to match a list of terms.
This class method is a convenience for constructing a phrase query
using a binary tree of SpanNear queries::
SpanNear.phrase("content", ["alfa", "bravo", "charlie", "delta"])
:param fieldname: the name of the field to search in.
:param words: a sequence of texts to search for.
:param slop: the number of positions within which the terms must
occur. Default is 1, meaning the terms must occur right next
to each other.
:param ordered: whether the terms must occur in order. Default is True.
"""
terms = [Term(fieldname, word) for word in words]
return make_binary_tree(cls, terms, slop=slop, ordered=ordered)
def phrase(cls, fieldname, words, slop=1, ordered=True):
"""Returns a tree of SpanNear queries to match a list of terms.
This class method is a convenience for constructing a phrase query
using a binary tree of SpanNear queries.
>>> SpanNear.phrase("f", [u"a", u"b", u"c", u"d"])
SpanNear(SpanNear(Term("f", u"a"), Term("f", u"b")), SpanNear(Term("f", u"c"), Term("f", u"d")))
:param fieldname: the name of the field to search in.
:param words: a sequence of token texts to search for.
:param slop: the number of positions within which the terms must
occur. Default is 1, meaning the terms must occur right next
to each other.
:param ordered: whether the terms must occur in order. Default is True.
"""
terms = [Term(fieldname, word) for word in words]
return make_binary_tree(cls, terms, slop=slop, ordered=ordered)
def _matcher(self, matchercls, q_weight_fn, searcher, weighting=None,
**kwargs):
# q_weight_fn is a function which is called on each query and returns a
# "weight" value which is used to build a huffman-like matcher tree. If
# q_weight_fn is None, an order-preserving binary tree is used instead.
# Pull any queries inside a Not() out into their own list
subs, nots = self._split_queries()
if not subs:
return matching.NullMatcher()
# Create a matcher from the list of subqueries
subms = [q.matcher(searcher, weighting=weighting) for q in subs]
if len(subms) == 1:
m = subms[0]
elif q_weight_fn is None:
m = make_binary_tree(matchercls, subms)
else:
w_subms = [(q_weight_fn(q), m) for q, m in zip(subs, subms)]
m = make_weighted_tree(matchercls, w_subms)
# If there were queries inside Not(), make a matcher for them and
# wrap the matchers in an AndNotMatcher
if nots:
if len(nots) == 1:
notm = nots[0].matcher(searcher)
else:
r = searcher.reader()
notms = [(q.estimate_size(r), q.matcher(searcher))
for q in nots]
notm = make_weighted_tree(matching.UnionMatcher, notms)
if notm.is_active():
m = matching.AndNotMatcher(m, notm)
# If this query had a boost, add a wrapping matcher to apply the boost
if self.boost != 1.0:
m = matching.WrappingMatcher(m, self.boost)
return m
def _matcher(self, matchercls, q_weight_fn, searcher, weighting=None,
**kwargs):
# q_weight_fn is a function which is called on each query and returns a
# "weight" value which is used to build a huffman-like matcher tree. If
# q_weight_fn is None, an order-preserving binary tree is used instead.
# Pull any queries inside a Not() out into their own list
subs, nots = self._split_queries()
if not subs:
return matching.NullMatcher()
# Create a matcher from the list of subqueries
subms = [q.matcher(searcher, weighting=weighting) for q in subs]
if len(subms) == 1:
m = subms[0]
elif q_weight_fn is None:
m = make_binary_tree(matchercls, subms)
else:
w_subms = [(q_weight_fn(q), m) for q, m in zip(subs, subms)]
m = make_weighted_tree(matchercls, w_subms)
# If there were queries inside Not(), make a matcher for them and
# wrap the matchers in an AndNotMatcher
if nots:
if len(nots) == 1:
notm = nots[0].matcher(searcher)
else:
r = searcher.reader()
notms = [(q.estimate_size(r), q.matcher(searcher))
for q in nots]
notm = make_weighted_tree(matching.UnionMatcher, notms)
if notm.is_active():
m = matching.AndNotMatcher(m, notm)
# If this query had a boost, add a wrapping matcher to apply the boost
if self.boost != 1.0:
m = matching.WrappingMatcher(m, self.boost)
return m
def _tree_matcher(self, subs, mcls, searcher, context, q_weight_fn,
**kwargs):
# q_weight_fn is a function which is called on each query and returns a
# "weight" value which is used to build a huffman-like matcher tree. If
# q_weight_fn is None, an order-preserving binary tree is used instead.
# Create a matcher from the list of subqueries
subms = [q.matcher(searcher, context) for q in subs]
if len(subms) == 1:
m = subms[0]
elif q_weight_fn is None:
m = make_binary_tree(mcls, subms, **kwargs)
else:
w_subms = [(q_weight_fn(q), m) for q, m in zip(subs, subms)]
m = make_weighted_tree(mcls, w_subms, **kwargs)
# If this query had a boost, add a wrapping matcher to apply the boost
if self.boost != 1.0:
m = matching.WrappingMatcher(m, self.boost)
return m
def _tree_matcher(self, subs, mcls, searcher, context, q_weight_fn,
**kwargs):
# q_weight_fn is a function which is called on each query and returns a
# "weight" value which is used to build a huffman-like matcher tree. If
# q_weight_fn is None, an order-preserving binary tree is used instead.
# Create a matcher from the list of subqueries
subms = [q.matcher(searcher, context) for q in subs]
if len(subms) == 1:
m = subms[0]
elif q_weight_fn is None:
m = make_binary_tree(mcls, subms, **kwargs)
else:
w_subms = [(q_weight_fn(q), m) for q, m in zip(subs, subms)]
m = make_weighted_tree(mcls, w_subms, **kwargs)
# If this query had a boost, add a wrapping matcher to apply the boost
if self.boost != 1.0:
m = matching.WrappingMatcher(m, self.boost)
return m
def matcher(self, searcher, context=None):
matchers = [q.matcher(searcher, context) for q in self.subqs]
return make_binary_tree(SpanOr.SpanOrMatcher, matchers)
def matcher(self, searcher, weighting=None):
matchers = [q.matcher(searcher, weighting=weighting)
for q in self.subqs]
return make_binary_tree(SpanOr.SpanOrMatcher, matchers)
def matcher(self, searcher, weighting=None):
matchers = [q.matcher(searcher, weighting=weighting) for q in self.subqs]
return make_binary_tree(SpanOr.SpanOrMatcher, matchers)
def matcher(self, searcher, exclude_docs=None):
matchers = [q.matcher(searcher, exclude_docs=exclude_docs)
for q in self.subqs]
return make_binary_tree(SpanOr.SpanOrMatcher, matchers)
def matcher(self, searcher, exclude_docs=None):
matchers = [
q.matcher(searcher, exclude_docs=exclude_docs) for q in self.subqs
]
return make_binary_tree(SpanOr.SpanOrMatcher, matchers)
def matcher(self, searcher, context=None):
matchers = [q.matcher(searcher, context) for q in self.subqs]
return make_binary_tree(SpanOr.SpanOrMatcher, matchers)