# Copyright 2011 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 array import array from collections import defaultdict from whoosh.compat import string_type from whoosh.compat import iteritems, izip, xrange # Faceting objects class FacetType(object): """Base class for "facets", aspects that can be sorted/faceted. """ maptype = None 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" class Categorizer(object): """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 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 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__) 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__) 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 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 "%s(%r, %r, reverse=%r)" % (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 xrange(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 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 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("No gap secified (%r)" % self.gap) 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) 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 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 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) 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) 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 "%s(%r, %r)" % (self.__class__.__name__, self.facets, self.maptype) @classmethod def from_sortedby(cls, sortedby): multi = cls() if isinstance(sortedby, string_type): 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, string_type): self.add_field(item) else: raise Exception("Don't know what to do with facet %r" % (item,)) 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("%r is not a facet object, perhaps you meant " "add_field()" % (facet,)) 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 izip(self.catters, key)) class Facets(object): """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, string_type): 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("Don't know what to do with groupedby=%r" % groupedby) return facets def names(self): """Returns an iterator of the facet names in this object. """ return iter(self.facets) def items(self): """Returns a list of (facetname, facetobject) tuples for the facets in this object. """ return self.facets.items() 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 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 def add_facet(self, name, facet): """Adds a :class:`FacetType` object under the given ``name``. """ if not isinstance(facet, FacetType): raise Exception("%r:%r is not a facet" % (name, facet)) self.facets[name] = facet return self 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("%r is not a Facets object or dict" % facets) 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 class FacetMap(object): """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) """ 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 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 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 "<%s %r>" % (self.__class__.__name__, self.dict) def add(self, groupname, docid, sortkey): self.dict[groupname].append((sortkey, docid)) def as_dict(self): d = {} for key, items in iteritems(self.dict): d[key] = [docnum for _, docnum in sorted(items)] return d 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 "<%s %r>" % (self.__class__.__name__, self.dict) def add(self, groupname, docid, sortkey): self.dict[groupname].append(docid) def as_dict(self): return dict(self.dict) 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 "<%s %r>" % (self.__class__.__name__, self.dict) def add(self, groupname, docid, sortkey): self.dict[groupname] += 1 def as_dict(self): return dict(self.dict) 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 "<%s %r>" % (self.__class__.__name__, self.bestids) 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("%r field is already sortable" % fieldname) 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("Field %r does not exist" % fieldname) 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("%r field already has a column" % fieldname) 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