2026-1-6

venv/Lib/site-packages/whoosh/matching/combo.py

@@ -0,0 +1,312 @@
+# Copyright 2010 Matt Chaput. All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+#    1. Redistributions of source code must retain the above copyright notice,
+#       this list of conditions and the following disclaimer.
+#
+#    2. Redistributions in binary form must reproduce the above copyright
+#       notice, this list of conditions and the following disclaimer in the
+#       documentation and/or other materials provided with the distribution.
+#
+# THIS SOFTWARE IS PROVIDED BY MATT CHAPUT ``AS IS'' AND ANY EXPRESS OR
+# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
+# EVENT SHALL MATT CHAPUT OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
+# OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
+# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+# The views and conclusions contained in the software and documentation are
+# those of the authors and should not be interpreted as representing official
+# policies, either expressed or implied, of Matt Chaput.
+
+from __future__ import division
+from array import array
+
+from whoosh.compat import xrange
+from whoosh.matching import mcore
+
+
+class CombinationMatcher(mcore.Matcher):
+    def __init__(self, submatchers, boost=1.0):
+        self._submatchers = submatchers
+        self._boost = boost
+
+    def supports_block_quality(self):
+        return all(m.supports_block_quality() for m in self._submatchers)
+
+    def max_quality(self):
+        return max(m.max_quality() for m in self._submatchers
+                   if m.is_active()) * self._boost
+
+    def supports(self, astype):
+        return all(m.supports(astype) for m in self._submatchers)
+
+    def children(self):
+        return iter(self._submatchers)
+
+    def score(self):
+        return sum(m.score() for m in self._submatchers) * self._boost
+
+
+class PreloadedUnionMatcher(CombinationMatcher):
+    """Instead of marching the sub-matchers along in parallel, this
+    matcher pre-reads the scores for EVERY MATCHING DOCUMENT, trading memory
+    for speed.
+
+    This is faster than the implementation using a binary tree of
+    :class:`~whoosh.matching.binary.UnionMatcher` objects (possibly just
+    because of less overhead), but it doesn't allow getting information about
+    the "current" document other than the score, because there isn't really a
+    current document, just an array of scores.
+    """
+
+    def __init__(self, submatchers, doccount, boost=1.0, scored=True):
+        CombinationMatcher.__init__(self, submatchers, boost=boost)
+
+        self._doccount = doccount
+
+        a = array("d")
+        active = [subm for subm in self._submatchers if subm.is_active()]
+        if active:
+            offset = self._docnum = min(m.id() for m in active)
+            for m in active:
+                while m.is_active():
+                    if scored:
+                        score = m.score() * boost
+                    else:
+                        score = boost
+
+                    docnum = m.id()
+                    place = docnum - offset
+                    if len(a) <= place:
+                        a.extend(0 for _ in xrange(place - len(a) + 1))
+                    a[place] += score
+                    m.next()
+            self._a = a
+            self._offset = offset
+        else:
+            self._docnum = 0
+            self._offset = 0
+        self._a = a
+
+    def is_active(self):
+        return self._docnum - self._offset < len(self._a)
+
+    def id(self):
+        return self._docnum
+
+    def score(self):
+        return self._a[self._docnum - self._offset]
+
+    def next(self):
+        a = self._a
+        offset = self._offset
+        place = self._docnum - offset
+
+        place += 1
+        while place < len(a) and a[place] == 0:
+            place += 1
+        self._docnum = place + offset
+
+    def max_quality(self):
+        return max(self._a[self._docnum - self._offset:])
+
+    def block_quality(self):
+        return self.max_quality()
+
+    def skip_to(self, docnum):
+        if docnum < self._docnum:
+            return
+
+        self._docnum = docnum
+        i = docnum - self._offset
+        if i < len(self._a) and self._a[i] == 0:
+            self.next()
+
+    def skip_to_quality(self, minquality):
+        a = self._a
+        offset = self._offset
+        place = self._docnum - offset
+
+        skipped = 0
+        while place < len(a) and a[place] <= minquality:
+            place += 1
+            skipped = 1
+
+        self._docnum = place + offset
+        return skipped
+
+    def supports(self, astype):
+        # This matcher doesn't support any posting values
+        return False
+
+    def all_ids(self):
+        a = self._a
+        offset = self._offset
+        place = self._docnum - offset
+
+        while place < len(a):
+            if a[place] > 0:
+                yield place + offset
+            place += 1
+
+
+class ArrayUnionMatcher(CombinationMatcher):
+    """Instead of marching the sub-matchers along in parallel, this matcher
+    pre-reads the scores for a large block of documents at a time from each
+    matcher, accumulating the scores in an array.
+
+    This is faster than the implementation using a binary tree of
+    :class:`~whoosh.matching.binary.UnionMatcher` objects (possibly just
+    because of less overhead), but it doesn't allow getting information about
+    the "current" document other than the score, because there isn't really a
+    current document, just an array of scores.
+    """
+
+    def __init__(self, submatchers, doccount, boost=1.0, scored=True,
+                 partsize=2048):
+        CombinationMatcher.__init__(self, submatchers, boost=boost)
+        self._scored = scored
+        self._doccount = doccount
+
+        if not partsize:
+            partsize = doccount
+        self._partsize = partsize
+
+        self._a = array("d", (0 for _ in xrange(self._partsize)))
+        self._docnum = self._min_id()
+        self._read_part()
+
+    def __repr__(self):
+        return ("%s(%r, boost=%f, scored=%r, partsize=%d)"
+                % (self.__class__.__name__, self._submatchers, self._boost,
+                   self._scored, self._partsize))
+
+    def _min_id(self):
+        active = [subm for subm in self._submatchers if subm.is_active()]
+        if active:
+            return min(subm.id() for subm in active)
+        else:
+            return self._doccount
+
+    def _read_part(self):
+        scored = self._scored
+        boost = self._boost
+        limit = min(self._docnum + self._partsize, self._doccount)
+        offset = self._docnum
+        a = self._a
+
+        # Clear the array
+        for i in xrange(self._partsize):
+            a[i] = 0
+
+        # Add the scores from the submatchers into the array
+        for m in self._submatchers:
+            while m.is_active() and m.id() < limit:
+                i = m.id() - offset
+                if scored:
+                    a[i] += m.score() * boost
+                else:
+                    a[i] = 1
+                m.next()
+
+        self._offset = offset
+        self._limit = limit
+
+    def _find_next(self):
+        a = self._a
+        docnum = self._docnum
+        offset = self._offset
+        limit = self._limit
+
+        while docnum < limit:
+            if a[docnum - offset] > 0:
+                break
+            docnum += 1
+
+        if docnum == limit:
+            self._docnum = self._min_id()
+            self._read_part()
+        else:
+            self._docnum = docnum
+
+    def supports(self, astype):
+        # This matcher doesn't support any posting values
+        return False
+
+    def is_active(self):
+        return self._docnum < self._doccount
+
+    def max_quality(self):
+        return max(m.max_quality() for m in self._submatchers)
+
+    def block_quality(self):
+        return max(self._a)
+
+    def skip_to(self, docnum):
+        if docnum < self._offset:
+            # We've already passed it
+            return
+        elif docnum < self._limit:
+            # It's in the current part
+            self._docnum = docnum
+            self._find_next()
+            return
+
+        # Advance all active submatchers
+        submatchers = self._submatchers
+        active = False
+        for subm in submatchers:
+            if subm.is_active():
+                subm.skip_to(docnum)
+
+        if any(subm.is_active() for subm in submatchers):
+            # Rebuffer
+            self._docnum = self._min_id()
+            self._read_part()
+        else:
+            self._docnum = self._doccount
+
+    def skip_to_quality(self, minquality):
+        skipped = 0
+        while self.is_active() and self.block_quality() <= minquality:
+            skipped += 1
+            self._docnum = self._limit
+            self._read_part()
+        if self.is_active():
+            self._find_next()
+        return skipped
+
+    def id(self):
+        return self._docnum
+
+    def all_ids(self):
+        doccount = self._doccount
+        docnum = self._docnum
+        offset = self._offset
+        limit = self._limit
+
+        a = self._a
+        while docnum < doccount:
+            if a[docnum - offset] > 0:
+                yield docnum
+
+            docnum += 1
+            if docnum == limit:
+                self._docnum = docnum
+                self._read_part()
+                offset = self._offset
+                limit = self._limit
+
+    def next(self):
+        self._docnum += 1
+        return self._find_next()
+
+    def score(self):
+        return self._a[self._docnum - self._offset]