# Copyright 2011 Matt Chaput. All rights reserved.
#
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# 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.
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# 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 array import array
from collections import defaultdict
# Faceting objects
[docs]class FacetType:
"""Base class for "facets", aspects that can be sorted/faceted."""
maptype = None
[docs] def categorizer(self, global_searcher):
"""Returns a :class:`Categorizer` corresponding to this facet.
:param global_searcher: A parent searcher. You can use this searcher if
you need global document ID references.
"""
raise NotImplementedError
def map(self, default=None):
t = self.maptype
if t is None:
t = default
if t is None:
return OrderedList()
elif type(t) is type:
return t()
else:
return t
def default_name(self):
return "facet"
[docs]class Categorizer:
"""Base class for categorizer objects which compute a key value for a
document based on certain criteria, for use in sorting/faceting.
Categorizers are created by FacetType objects through the
:meth:`FacetType.categorizer` method. The
:class:`whoosh.searching.Searcher` object passed to the ``categorizer``
method may be a composite searcher (that is, wrapping a multi-reader), but
categorizers are always run **per-segment**, with segment-relative document
numbers.
The collector will call a categorizer's ``set_searcher`` method as it
searches each segment to let the cateogorizer set up whatever segment-
specific data it needs.
``Collector.allow_overlap`` should be ``True`` if the caller can use the
``keys_for`` method instead of ``key_for`` to group documents into
potentially overlapping groups. The default is ``False``.
If a categorizer subclass can categorize the document using only the
document number, it should set ``Collector.needs_current`` to ``False``
(this is the default) and NOT USE the given matcher in the ``key_for`` or
``keys_for`` methods, since in that case ``segment_docnum`` is not
guaranteed to be consistent with the given matcher. If a categorizer
subclass needs to access information on the matcher, it should set
``needs_current`` to ``True``. This will prevent the caller from using
optimizations that might leave the matcher in an inconsistent state.
"""
allow_overlap = False
needs_current = False
[docs] def set_searcher(self, segment_searcher, docoffset):
"""Called by the collector when the collector moves to a new segment.
The ``segment_searcher`` will be atomic. The ``docoffset`` is the
offset of the segment's document numbers relative to the entire index.
You can use the offset to get absolute index docnums by adding the
offset to segment-relative docnums.
"""
pass
[docs] def key_for(self, matcher, segment_docnum):
"""Returns a key for the current match.
:param matcher: a :class:`whoosh.matching.Matcher` object. If
``self.needs_current`` is ``False``, DO NOT use this object,
since it may be inconsistent. Use the given ``segment_docnum``
instead.
:param segment_docnum: the segment-relative document number of the
current match.
"""
# Backwards compatibility
if hasattr(self, "key_for_id"):
return self.key_for_id(segment_docnum)
elif hasattr(self, "key_for_matcher"):
return self.key_for_matcher(matcher)
raise NotImplementedError(self.__class__)
[docs] def keys_for(self, matcher, segment_docnum):
"""Yields a series of keys for the current match.
This method will be called instead of ``key_for`` if
``self.allow_overlap`` is ``True``.
:param matcher: a :class:`whoosh.matching.Matcher` object. If
``self.needs_current`` is ``False``, DO NOT use this object,
since it may be inconsistent. Use the given ``segment_docnum``
instead.
:param segment_docnum: the segment-relative document number of the
current match.
"""
# Backwards compatibility
if hasattr(self, "keys_for_id"):
return self.keys_for_id(segment_docnum)
raise NotImplementedError(self.__class__)
[docs] def key_to_name(self, key):
"""Returns a representation of the key to be used as a dictionary key
in faceting. For example, the sorting key for date fields is a large
integer; this method translates it into a ``datetime`` object to make
the groupings clearer.
"""
return key
# General field facet
[docs]class FieldFacet(FacetType):
"""Sorts/facets by the contents of a field.
For example, to sort by the contents of the "path" field in reverse order,
and facet by the contents of the "tag" field::
paths = FieldFacet("path", reverse=True)
tags = FieldFacet("tag")
results = searcher.search(myquery, sortedby=paths, groupedby=tags)
This facet returns different categorizers based on the field type.
"""
def __init__(self, fieldname, reverse=False, allow_overlap=False, maptype=None):
"""
:param fieldname: the name of the field to sort/facet on.
:param reverse: if True, when sorting, reverse the sort order of this
facet.
:param allow_overlap: if True, when grouping, allow documents to appear
in multiple groups when they have multiple terms in the field.
"""
self.fieldname = fieldname
self.reverse = reverse
self.allow_overlap = allow_overlap
self.maptype = maptype
def default_name(self):
return self.fieldname
def categorizer(self, global_searcher):
# The searcher we're passed here may wrap a multireader, but the
# actual key functions will always be called per-segment following a
# Categorizer.set_searcher method call
fieldname = self.fieldname
fieldobj = global_searcher.schema[fieldname]
# If we're grouping with allow_overlap=True, all we can use is
# OverlappingCategorizer
if self.allow_overlap:
return OverlappingCategorizer(global_searcher, fieldname)
if global_searcher.reader().has_column(fieldname):
coltype = fieldobj.column_type
if coltype.reversible or not self.reverse:
c = ColumnCategorizer(global_searcher, fieldname, self.reverse)
else:
c = ReversedColumnCategorizer(global_searcher, fieldname)
else:
c = PostingCategorizer(global_searcher, fieldname, self.reverse)
return c
class ColumnCategorizer(Categorizer):
def __init__(self, global_searcher, fieldname, reverse=False):
self._fieldname = fieldname
self._fieldobj = global_searcher.schema[self._fieldname]
self._column_type = self._fieldobj.column_type
self._reverse = reverse
# The column reader is set in set_searcher() as we iterate over the
# sub-searchers
self._creader = None
def __repr__(self):
return "{}({!r}, {!r}, reverse={!r})".format(
self.__class__.__name__,
self._fieldobj,
self._fieldname,
self._reverse,
)
def set_searcher(self, segment_searcher, docoffset):
r = segment_searcher.reader()
self._creader = r.column_reader(
self._fieldname, reverse=self._reverse, translate=False
)
def key_for(self, matcher, segment_docnum):
return self._creader.sort_key(segment_docnum)
def key_to_name(self, key):
return self._fieldobj.from_column_value(key)
class ReversedColumnCategorizer(ColumnCategorizer):
"""Categorizer that reverses column values for columns that aren't
naturally reversible.
"""
def __init__(self, global_searcher, fieldname):
ColumnCategorizer.__init__(self, global_searcher, fieldname)
reader = global_searcher.reader()
self._doccount = reader.doc_count_all()
global_creader = reader.column_reader(fieldname, translate=False)
self._values = sorted(set(global_creader))
def key_for(self, matcher, segment_docnum):
value = self._creader[segment_docnum]
order = self._values.index(value)
# Subtract from 0 to reverse the order
return 0 - order
def key_to_name(self, key):
# Re-reverse the key to get the index into _values
key = self._values[0 - key]
return ColumnCategorizer.key_to_name(self, key)
class OverlappingCategorizer(Categorizer):
allow_overlap = True
def __init__(self, global_searcher, fieldname):
self._fieldname = fieldname
self._fieldobj = global_searcher.schema[fieldname]
field = global_searcher.schema[fieldname]
reader = global_searcher.reader()
self._use_vectors = bool(field.vector)
self._use_column = (
reader.has_column(fieldname) and field.column_type.stores_lists()
)
# These are set in set_searcher() as we iterate over the sub-searchers
self._segment_searcher = None
self._creader = None
self._lists = None
def set_searcher(self, segment_searcher, docoffset):
fieldname = self._fieldname
self._segment_searcher = segment_searcher
reader = segment_searcher.reader()
if self._use_vectors:
pass
elif self._use_column:
self._creader = reader.column_reader(fieldname, translate=False)
else:
# Otherwise, cache the values in each document in a huge list
# of lists
dc = segment_searcher.doc_count_all()
field = segment_searcher.schema[fieldname]
from_bytes = field.from_bytes
self._lists = [[] for _ in range(dc)]
for btext in field.sortable_terms(reader, fieldname):
text = from_bytes(btext)
postings = reader.postings(fieldname, btext)
for docid in postings.all_ids():
self._lists[docid].append(text)
def keys_for(self, matcher, docid):
if self._use_vectors:
try:
v = self._segment_searcher.vector(docid, self._fieldname)
return list(v.all_ids())
except KeyError:
return []
elif self._use_column:
return self._creader[docid]
else:
return self._lists[docid] or [None]
def key_for(self, matcher, docid):
if self._use_vectors:
try:
v = self._segment_searcher.vector(docid, self._fieldname)
return v.id()
except KeyError:
return None
elif self._use_column:
return self._creader.sort_key(docid)
else:
ls = self._lists[docid]
if ls:
return ls[0]
else:
return None
class PostingCategorizer(Categorizer):
"""
Categorizer for fields that don't store column values. This is very
inefficient. Instead of relying on this categorizer you should plan for
which fields you'll want to sort on and set ``sortable=True`` in their
field type.
This object builds an array caching the order of all documents according to
the field, then uses the cached order as a numeric key. This is useful when
a field cache is not available, and also for reversed fields (since field
cache keys for non- numeric fields are arbitrary data, it's not possible to
"negate" them to reverse the sort order).
"""
def __init__(self, global_searcher, fieldname, reverse):
self.reverse = reverse
if fieldname in global_searcher._field_caches:
self.values, self.array = global_searcher._field_caches[fieldname]
else:
# Cache the relative positions of all docs with the given field
# across the entire index
reader = global_searcher.reader()
dc = reader.doc_count_all()
self._fieldobj = global_searcher.schema[fieldname]
from_bytes = self._fieldobj.from_bytes
self.values = []
self.array = array("i", [dc + 1] * dc)
btexts = self._fieldobj.sortable_terms(reader, fieldname)
for i, btext in enumerate(btexts):
self.values.append(from_bytes(btext))
# Get global docids from global reader
postings = reader.postings(fieldname, btext)
for docid in postings.all_ids():
self.array[docid] = i
global_searcher._field_caches[fieldname] = (self.values, self.array)
def set_searcher(self, segment_searcher, docoffset):
self._searcher = segment_searcher
self.docoffset = docoffset
def key_for(self, matcher, segment_docnum):
global_docnum = self.docoffset + segment_docnum
i = self.array[global_docnum]
if self.reverse:
i = len(self.values) - i
return i
def key_to_name(self, i):
if i >= len(self.values):
return None
if self.reverse:
i = len(self.values) - i
return self.values[i]
# Special facet types
[docs]class QueryFacet(FacetType):
"""Sorts/facets based on the results of a series of queries."""
def __init__(self, querydict, other=None, allow_overlap=False, maptype=None):
"""
:param querydict: a dictionary mapping keys to
:class:`whoosh.query.Query` objects.
:param other: the key to use for documents that don't match any of the
queries.
"""
self.querydict = querydict
self.other = other
self.maptype = maptype
self.allow_overlap = allow_overlap
def categorizer(self, global_searcher):
return self.QueryCategorizer(self.querydict, self.other, self.allow_overlap)
class QueryCategorizer(Categorizer):
def __init__(self, querydict, other, allow_overlap=False):
self.querydict = querydict
self.other = other
self.allow_overlap = allow_overlap
def set_searcher(self, segment_searcher, offset):
self.docsets = {}
for qname, q in self.querydict.items():
docset = set(q.docs(segment_searcher))
if docset:
self.docsets[qname] = docset
self.offset = offset
def key_for(self, matcher, docid):
for qname in self.docsets:
if docid in self.docsets[qname]:
return qname
return self.other
def keys_for(self, matcher, docid):
found = False
for qname in self.docsets:
if docid in self.docsets[qname]:
yield qname
found = True
if not found:
yield None
[docs]class RangeFacet(QueryFacet):
"""Sorts/facets based on numeric ranges. For textual ranges, use
:class:`QueryFacet`.
For example, to facet the "price" field into $100 buckets, up to $1000::
prices = RangeFacet("price", 0, 1000, 100)
results = searcher.search(myquery, groupedby=prices)
The ranges/buckets are always **inclusive** at the start and **exclusive**
at the end.
"""
def __init__(self, fieldname, start, end, gap, hardend=False, maptype=None):
"""
:param fieldname: the numeric field to sort/facet on.
:param start: the start of the entire range.
:param end: the end of the entire range.
:param gap: the size of each "bucket" in the range. This can be a
sequence of sizes. For example, ``gap=[1,5,10]`` will use 1 as the
size of the first bucket, 5 as the size of the second bucket, and
10 as the size of all subsequent buckets.
:param hardend: if True, the end of the last bucket is clamped to the
value of ``end``. If False (the default), the last bucket is always
``gap`` sized, even if that means the end of the last bucket is
after ``end``.
"""
self.fieldname = fieldname
self.start = start
self.end = end
self.gap = gap
self.hardend = hardend
self.maptype = maptype
self._queries()
def default_name(self):
return self.fieldname
def _rangetype(self):
from whoosh import query
return query.NumericRange
def _range_name(self, startval, endval):
return (startval, endval)
def _queries(self):
if not self.gap:
raise Exception(f"No gap secified ({self.gap!r})")
if isinstance(self.gap, (list, tuple)):
gaps = self.gap
gapindex = 0
else:
gaps = [self.gap]
gapindex = -1
rangetype = self._rangetype()
self.querydict = {}
cstart = self.start
while cstart < self.end:
thisgap = gaps[gapindex]
if gapindex >= 0:
gapindex += 1
if gapindex == len(gaps):
gapindex = -1
cend = cstart + thisgap
if self.hardend:
cend = min(self.end, cend)
rangename = self._range_name(cstart, cend)
q = rangetype(self.fieldname, cstart, cend, endexcl=True)
self.querydict[rangename] = q
cstart = cend
def categorizer(self, global_searcher):
return QueryFacet(self.querydict).categorizer(global_searcher)
[docs]class DateRangeFacet(RangeFacet):
"""Sorts/facets based on date ranges. This is the same as RangeFacet
except you are expected to use ``daterange`` objects as the start and end
of the range, and ``timedelta`` or ``relativedelta`` objects as the gap(s),
and it generates :class:`~whoosh.query.DateRange` queries instead of
:class:`~whoosh.query.TermRange` queries.
For example, to facet a "birthday" range into 5 year buckets::
from datetime import datetime
from whoosh.support.relativedelta import relativedelta
startdate = datetime(1920, 0, 0)
enddate = datetime.now()
gap = relativedelta(years=5)
bdays = DateRangeFacet("birthday", startdate, enddate, gap)
results = searcher.search(myquery, groupedby=bdays)
The ranges/buckets are always **inclusive** at the start and **exclusive**
at the end.
"""
def _rangetype(self):
from whoosh import query
return query.DateRange
[docs]class ScoreFacet(FacetType):
"""Uses a document's score as a sorting criterion.
For example, to sort by the ``tag`` field, and then within that by relative
score::
tag_score = MultiFacet(["tag", ScoreFacet()])
results = searcher.search(myquery, sortedby=tag_score)
"""
def categorizer(self, global_searcher):
return self.ScoreCategorizer(global_searcher)
class ScoreCategorizer(Categorizer):
needs_current = True
def __init__(self, global_searcher):
w = global_searcher.weighting
self.use_final = w.use_final
if w.use_final:
self.final = w.final
def set_searcher(self, segment_searcher, offset):
self.segment_searcher = segment_searcher
def key_for(self, matcher, docid):
score = matcher.score()
if self.use_final:
score = self.final(self.segment_searcher, docid, score)
# Negate the score so higher values sort first
return 0 - score
[docs]class FunctionFacet(FacetType):
"""This facet type is low-level. In most cases you should use
:class:`TranslateFacet` instead.
This facet type ets you pass an arbitrary function that will compute the
key. This may be easier than subclassing FacetType and Categorizer to set up
the desired behavior.
The function is called with the arguments ``(searcher, docid)``, where the
``searcher`` may be a composite searcher, and the ``docid`` is an absolute
index document number (not segment-relative).
For example, to use the number of words in the document's "content" field
as the sorting/faceting key::
fn = lambda s, docid: s.doc_field_length(docid, "content")
lengths = FunctionFacet(fn)
"""
def __init__(self, fn, maptype=None):
self.fn = fn
self.maptype = maptype
def categorizer(self, global_searcher):
return self.FunctionCategorizer(global_searcher, self.fn)
class FunctionCategorizer(Categorizer):
def __init__(self, global_searcher, fn):
self.global_searcher = global_searcher
self.fn = fn
def set_searcher(self, segment_searcher, docoffset):
self.offset = docoffset
def key_for(self, matcher, docid):
return self.fn(self.global_searcher, docid + self.offset)
class TranslateFacet(FacetType):
"""Lets you specify a function to compute the key based on a key generated
by a wrapped facet.
This is useful if you want to use a custom ordering of a sortable field. For
example, if you want to use an implementation of the Unicode Collation
Algorithm (UCA) to sort a field using the rules from a particular language::
from pyuca import Collator
# The Collator object has a sort_key() method which takes a unicode
# string and returns a sort key
c = Collator("allkeys.txt")
# Make a facet object for the field you want to sort on
facet = sorting.FieldFacet("name")
# Wrap the facet in a TranslateFacet with the translation function
# (the Collator object's sort_key method)
facet = sorting.TranslateFacet(c.sort_key, facet)
# Use the facet to sort the search results
results = searcher.search(myquery, sortedby=facet)
You can pass multiple facets to the
"""
def __init__(self, fn, *facets):
"""
:param fn: The function to apply. For each matching document, this
function will be called with the values of the given facets as
arguments.
:param facets: One or more :class:`FacetType` objects. These facets are
used to compute facet value(s) for a matching document, and then the
value(s) is/are passed to the function.
"""
self.fn = fn
self.facets = facets
self.maptype = None
def categorizer(self, global_searcher):
catters = [facet.categorizer(global_searcher) for facet in self.facets]
return self.TranslateCategorizer(self.fn, catters)
class TranslateCategorizer(Categorizer):
def __init__(self, fn, catters):
self.fn = fn
self.catters = catters
def set_searcher(self, segment_searcher, docoffset):
for catter in self.catters:
catter.set_searcher(segment_searcher, docoffset)
def key_for(self, matcher, segment_docnum):
keys = [catter.key_for(matcher, segment_docnum) for catter in self.catters]
return self.fn(*keys)
[docs]class StoredFieldFacet(FacetType):
"""Lets you sort/group using the value in an unindexed, stored field (e.g.
:class:`whoosh.fields.STORED`). This is usually slower than using an indexed
field.
For fields where the stored value is a space-separated list of keywords,
(e.g. ``"tag1 tag2 tag3"``), you can use the ``allow_overlap`` keyword
argument to allow overlapped faceting on the result of calling the
``split()`` method on the field value (or calling a custom split function
if one is supplied).
"""
def __init__(self, fieldname, allow_overlap=False, split_fn=None, maptype=None):
"""
:param fieldname: the name of the stored field.
:param allow_overlap: if True, when grouping, allow documents to appear
in multiple groups when they have multiple terms in the field. The
categorizer uses ``string.split()`` or the custom ``split_fn`` to
convert the stored value into a list of facet values.
:param split_fn: a custom function to split a stored field value into
multiple facet values when ``allow_overlap`` is True. If not
supplied, the categorizer simply calls the value's ``split()``
method.
"""
self.fieldname = fieldname
self.allow_overlap = allow_overlap
self.split_fn = split_fn
self.maptype = maptype
def default_name(self):
return self.fieldname
def categorizer(self, global_searcher):
return self.StoredFieldCategorizer(
self.fieldname, self.allow_overlap, self.split_fn
)
class StoredFieldCategorizer(Categorizer):
def __init__(self, fieldname, allow_overlap, split_fn):
self.fieldname = fieldname
self.allow_overlap = allow_overlap
self.split_fn = split_fn
def set_searcher(self, segment_searcher, docoffset):
self.segment_searcher = segment_searcher
def keys_for(self, matcher, docid):
d = self.segment_searcher.stored_fields(docid)
value = d.get(self.fieldname)
if self.split_fn:
return self.split_fn(value)
else:
return value.split()
def key_for(self, matcher, docid):
d = self.segment_searcher.stored_fields(docid)
return d.get(self.fieldname)
[docs]class MultiFacet(FacetType):
"""Sorts/facets by the combination of multiple "sub-facets".
For example, to sort by the value of the "tag" field, and then (for
documents where the tag is the same) by the value of the "path" field::
facet = MultiFacet([FieldFacet("tag"), FieldFacet("path")])
results = searcher.search(myquery, sortedby=facet)
As a shortcut, you can use strings to refer to field names, and they will
be assumed to be field names and turned into FieldFacet objects::
facet = MultiFacet(["tag", "path"])
You can also use the ``add_*`` methods to add criteria to the multifacet::
facet = MultiFacet()
facet.add_field("tag")
facet.add_field("path", reverse=True)
facet.add_query({"a-m": TermRange("name", "a", "m"),
"n-z": TermRange("name", "n", "z")})
"""
def __init__(self, items=None, maptype=None):
self.facets = []
if items:
for item in items:
self._add(item)
self.maptype = maptype
def __repr__(self):
return f"{self.__class__.__name__}({self.facets!r}, {self.maptype!r})"
@classmethod
def from_sortedby(cls, sortedby):
multi = cls()
if isinstance(sortedby, str):
multi._add(sortedby)
elif isinstance(sortedby, (list, tuple)) or hasattr(sortedby, "__iter__"):
for item in sortedby:
multi._add(item)
else:
multi._add(sortedby)
return multi
def _add(self, item):
if isinstance(item, FacetType):
self.add_facet(item)
elif isinstance(item, str):
self.add_field(item)
else:
raise Exception(f"Don't know what to do with facet {item!r}")
def add_field(self, fieldname, reverse=False):
self.facets.append(FieldFacet(fieldname, reverse=reverse))
return self
def add_query(self, querydict, other=None, allow_overlap=False):
self.facets.append(
QueryFacet(querydict, other=other, allow_overlap=allow_overlap)
)
return self
def add_score(self):
self.facets.append(ScoreFacet())
return self
def add_facet(self, facet):
if not isinstance(facet, FacetType):
raise TypeError(
f"{facet!r} is not a facet object, perhaps you meant add_field()"
)
self.facets.append(facet)
return self
def categorizer(self, global_searcher):
if not self.facets:
raise Exception("No facets")
elif len(self.facets) == 1:
catter = self.facets[0].categorizer(global_searcher)
else:
catter = self.MultiCategorizer(
[facet.categorizer(global_searcher) for facet in self.facets]
)
return catter
class MultiCategorizer(Categorizer):
def __init__(self, catters):
self.catters = catters
@property
def needs_current(self):
return any(c.needs_current for c in self.catters)
def set_searcher(self, segment_searcher, docoffset):
for catter in self.catters:
catter.set_searcher(segment_searcher, docoffset)
def key_for(self, matcher, docid):
return tuple(catter.key_for(matcher, docid) for catter in self.catters)
def key_to_name(self, key):
return tuple(
catter.key_to_name(keypart)
for catter, keypart in zip(self.catters, key)
)
[docs]class Facets:
"""Maps facet names to :class:`FacetType` objects, for creating multiple
groupings of documents.
For example, to group by tag, and **also** group by price range::
facets = Facets()
facets.add_field("tag")
facets.add_facet("price", RangeFacet("price", 0, 1000, 100))
results = searcher.search(myquery, groupedby=facets)
tag_groups = results.groups("tag")
price_groups = results.groups("price")
(To group by the combination of multiple facets, use :class:`MultiFacet`.)
"""
def __init__(self, x=None):
self.facets = {}
if x:
self.add_facets(x)
@classmethod
def from_groupedby(cls, groupedby):
facets = cls()
if isinstance(groupedby, (cls, dict)):
facets.add_facets(groupedby)
elif isinstance(groupedby, str):
facets.add_field(groupedby)
elif isinstance(groupedby, FacetType):
facets.add_facet(groupedby.default_name(), groupedby)
elif isinstance(groupedby, (list, tuple)):
for item in groupedby:
facets.add_facets(cls.from_groupedby(item))
else:
raise Exception(f"Don't know what to do with groupedby={groupedby!r}")
return facets
[docs] def names(self):
"""Returns an iterator of the facet names in this object."""
return iter(self.facets)
[docs] def items(self):
"""Returns a list of (facetname, facetobject) tuples for the facets in
this object.
"""
return self.facets.items()
[docs] def add_field(self, fieldname, **kwargs):
"""Adds a :class:`FieldFacet` for the given field name (the field name
is automatically used as the facet name).
"""
self.facets[fieldname] = FieldFacet(fieldname, **kwargs)
return self
[docs] def add_query(self, name, querydict, **kwargs):
"""Adds a :class:`QueryFacet` under the given ``name``.
:param name: a name for the facet.
:param querydict: a dictionary mapping keys to
:class:`whoosh.query.Query` objects.
"""
self.facets[name] = QueryFacet(querydict, **kwargs)
return self
[docs] def add_facet(self, name, facet):
"""Adds a :class:`FacetType` object under the given ``name``."""
if not isinstance(facet, FacetType):
raise Exception(f"{name!r}:{facet!r} is not a facet")
self.facets[name] = facet
return self
[docs] def add_facets(self, facets, replace=True):
"""Adds the contents of the given ``Facets`` or ``dict`` object to this
object.
"""
if not isinstance(facets, (dict, Facets)):
raise Exception(f"{facets!r} is not a Facets object or dict")
for name, facet in facets.items():
if replace or name not in self.facets:
self.facets[name] = facet
return self
# Objects for holding facet groups
[docs]class FacetMap:
"""Base class for objects holding the results of grouping search results by
a Facet. Use an object's ``as_dict()`` method to access the results.
You can pass a subclass of this to the ``maptype`` keyword argument when
creating a ``FacetType`` object to specify what information the facet
should record about the group. For example::
# Record each document in each group in its sorted order
myfacet = FieldFacet("size", maptype=OrderedList)
# Record only the count of documents in each group
myfacet = FieldFacet("size", maptype=Count)
"""
[docs] def add(self, groupname, docid, sortkey):
"""Adds a document to the facet results.
:param groupname: the name of the group to add this document to.
:param docid: the document number of the document to add.
:param sortkey: a value representing the sort position of the document
in the full results.
"""
raise NotImplementedError
[docs] def as_dict(self):
"""Returns a dictionary object mapping group names to
implementation-specific values. For example, the value might be a list
of document numbers, or a integer representing the number of documents
in the group.
"""
raise NotImplementedError
[docs]class OrderedList(FacetMap):
"""Stores a list of document numbers for each group, in the same order as
they appear in the search results.
The ``as_dict`` method returns a dictionary mapping group names to lists
of document numbers.
"""
def __init__(self):
self.dict = defaultdict(list)
def __repr__(self):
return f"<{self.__class__.__name__} {self.dict!r}>"
def add(self, groupname, docid, sortkey):
self.dict[groupname].append((sortkey, docid))
def as_dict(self):
d = {}
for key, items in self.dict.items():
d[key] = [docnum for _, docnum in sorted(items)]
return d
[docs]class UnorderedList(FacetMap):
"""Stores a list of document numbers for each group, in arbitrary order.
This is slightly faster and uses less memory than
:class:`OrderedListResult` if you don't care about the ordering of the
documents within groups.
The ``as_dict`` method returns a dictionary mapping group names to lists
of document numbers.
"""
def __init__(self):
self.dict = defaultdict(list)
def __repr__(self):
return f"<{self.__class__.__name__} {self.dict!r}>"
def add(self, groupname, docid, sortkey):
self.dict[groupname].append(docid)
def as_dict(self):
return dict(self.dict)
[docs]class Count(FacetMap):
"""Stores the number of documents in each group.
The ``as_dict`` method returns a dictionary mapping group names to
integers.
"""
def __init__(self):
self.dict = defaultdict(int)
def __repr__(self):
return f"<{self.__class__.__name__} {self.dict!r}>"
def add(self, groupname, docid, sortkey):
self.dict[groupname] += 1
def as_dict(self):
return dict(self.dict)
[docs]class Best(FacetMap):
"""Stores the "best" document in each group (that is, the one with the
highest sort key).
The ``as_dict`` method returns a dictionary mapping group names to
docnument numbers.
"""
def __init__(self):
self.bestids = {}
self.bestkeys = {}
def __repr__(self):
return f"<{self.__class__.__name__} {self.bestids!r}>"
def add(self, groupname, docid, sortkey):
if groupname not in self.bestids or sortkey < self.bestkeys[groupname]:
self.bestids[groupname] = docid
self.bestkeys[groupname] = sortkey
def as_dict(self):
return self.bestids
# Helper functions
def add_sortable(writer, fieldname, facet, column=None):
"""Adds a per-document value column to an existing field which was created
without the ``sortable`` keyword argument.
>>> from whoosh import index, sorting
>>> ix = index.open_dir("indexdir")
>>> with ix.writer() as w:
... facet = sorting.FieldFacet("price")
... sorting.add_sortable(w, "price", facet)
...
:param writer: a :class:`whoosh.writing.IndexWriter` object.
:param fieldname: the name of the field to add the per-document sortable
values to. If this field doesn't exist in the writer's schema, the
function will add a :class:`whoosh.fields.COLUMN` field to the schema,
and you must specify the column object to using the ``column`` keyword
argument.
:param facet: a :class:`FacetType` object to use to generate the
per-document values.
:param column: a :class:`whosh.columns.ColumnType` object to use to store
the per-document values. If you don't specify a column object, the
function will use the default column type for the given field.
"""
storage = writer.storage
schema = writer.schema
field = None
if fieldname in schema:
field = schema[fieldname]
if field.column_type:
raise Exception(f"{fieldname!r} field is already sortable")
if column:
if fieldname not in schema:
from whoosh.fields import COLUMN
field = COLUMN(column)
schema.add(fieldname, field)
else:
if fieldname in schema:
column = field.default_column()
else:
raise Exception(f"Field {fieldname!r} does not exist")
searcher = writer.searcher()
catter = facet.categorizer(searcher)
for subsearcher, docoffset in searcher.leaf_searchers():
catter.set_searcher(subsearcher, docoffset)
reader = subsearcher.reader()
if reader.has_column(fieldname):
raise Exception(f"{fieldname!r} field already has a column")
codec = reader.codec()
segment = reader.segment()
colname = codec.column_filename(segment, fieldname)
colfile = storage.create_file(colname)
try:
colwriter = column.writer(colfile)
for docnum in reader.all_doc_ids():
v = catter.key_to_name(catter.key_for(None, docnum))
cv = field.to_column_value(v)
colwriter.add(docnum, cv)
colwriter.finish(reader.doc_count_all())
finally:
colfile.close()
field.column_type = column