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R3600/modules/bibauthorid/lib/bibauthorid_cluster_set.py
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bibauthorid_cluster_set.py
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# -*- coding: utf-8 -*-
##
## This file is part of Invenio.
## Copyright (C) 2011 CERN.
##
## Invenio is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License as
## published by the Free Software Foundation; either version 2 of the
## License, or (at your option) any later version.
##
## Invenio is distributed in the hope that it will be useful, but
## WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with Invenio; if not, write to the Free Software Foundation, Inc.,
## 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA.
from itertools import chain, groupby
from operator import itemgetter
from bibauthorid_matrix_optimization import maximized_mapping
from bibauthorid_backinterface import save_cluster
from bibauthorid_backinterface import get_all_papers_of_pids
from bibauthorid_backinterface import get_bib10x, get_bib70x
from bibauthorid_backinterface import get_all_valid_bibrecs
from bibauthorid_backinterface import get_bibrefrec_subset
from bibauthorid_backinterface import remove_result_cluster
from bibauthorid_name_utils import generate_last_name_cluster_str
class Blob:
def __init__(self, personid_records):
'''
@param personid_records:
A list of tuples: (personid, bibrefrec, flag).
Notice that all bibrefrecs should be the same
since the Blob represents only one bibrefrec.
'''
self.bib = personid_records[0][1]
assert all(p[1] == self.bib for p in personid_records)
self.claimed = set()
self.assigned = set()
self.rejected = set()
for pid, unused, flag in personid_records:
if flag > 1:
self.claimed.add(pid)
elif flag >= -1:
self.assigned.add(pid)
else:
self.rejected.add(pid)
def create_blobs_by_pids(pids):
'''
Returs a list of blobs by a given set of personids.
Blob is an object which describes all information
for a bibrefrec in the personid table.
@type pids: iterable of integers
'''
all_bibs = get_all_papers_of_pids(pids)
all_bibs = ((x[0], (int(x[1]), x[2], x[3]), x[4]) for x in all_bibs)
bibs_dict = groupby(sorted(all_bibs, key=itemgetter(1)), key=itemgetter(1))
blobs = [Blob(list(bibs)) for unused, bibs in bibs_dict]
return blobs
def group_blobs(blobs):
'''
Separates the blobs into two groups
of objects - those with claims and
those without.
'''
# created from blobs, which are claimed
# [(bibrefrec, personid)]
union = []
# created from blobs, which are not claimed
# [(bibrefrec, personid/None, [personid])]
independent = []
for blob in blobs:
assert len(blob.claimed) + len(blob.assigned) == 1
if len(blob.claimed) > 0:
union.append((blob.bib, list(blob.claimed)[0]))
else:
independent.append((blob.bib, list(blob.assigned)[0], list(blob.rejected)))
return (union, independent)
class Cluster_set:
class Cluster:
def __init__(self, bibs, hate = []):
# hate is a symetrical relation
self.bibs = set(bibs)
self.hate = set(hate)
def hates(self, other):
return other in self.hate
def quarrel(self, cl2):
self.hate.add(cl2)
cl2.hate.add(self)
def _debug_test_hate_relation(self):
for cl2 in self.hate:
if not self.hates(cl2) or not cl2.hates(self):
return False
return True
def __init__(self):
self.clusters = []
def create_skeleton(self, personids, last_name):
blobs = create_blobs_by_pids(personids)
self.last_name = last_name
union, independent = group_blobs(blobs)
union_clusters = {}
for uni in union:
union_clusters[uni[1]] = union_clusters.get(uni[1], []) + [uni[0]]
cluster_dict = dict((personid, self.Cluster(bibs)) for personid, bibs in union_clusters.items())
self.clusters = cluster_dict.values()
for i, cl in enumerate(self.clusters):
cl.hate = set(chain(self.clusters[:i], self.clusters[i+1:]))
for ind in independent:
bad_clusters = [cluster_dict[i] for i in ind[2] if i in cluster_dict]
cl = self.Cluster([ind[0]], bad_clusters)
for bcl in bad_clusters:
bcl.hate.add(cl)
self.clusters.append(cl)
# Creates a cluster set, ignoring the claims and the
# rejected papers.
def create_pure(self, personids, last_name):
blobs = create_blobs_by_pids(personids)
self.last_name = last_name
self.clusters = [self.Cluster((blob.bib,)) for blob in blobs]
# no longer used
def create_body(self, blobs):
union, independent = group_blobs(blobs)
arranged_clusters = {}
for cls in chain(union, independent):
arranged_clusters[cls[1]] = arranged_clusters.get(cls[1], []) + [cls[0]]
for pid, bibs in arranged_clusters.items():
cl = self.Cluster(bibs)
cl.personid = pid
self.clusters.append(cl)
# a *very* slow fucntion checking when the hate relation is no longer symetrical
def _debug_test_hate_relation(self):
for cl1 in self.clusters:
if not cl1._debug_test_hate_relation():
return False
return True
# similar to the function above
def _debug_duplicated_recs(self, mapping=None):
for cl in self.clusters:
if mapping:
setty = set(mapping[x][2] for x in cl.bibs)
else:
setty = set(x[2] for x in cl.bibs)
if len(cl.bibs) != len(setty):
return False
return True
# No longer used but it might be handy.
@staticmethod
def match_cluster_sets(cs1, cs2):
"""
This functions tries to generate the best matching
between cs1 and cs2 acoarding to the shared bibrefrecs.
It returns a dictionary with keys, clsuters in cs1,
and values, clusters in cs2.
@param and type of cs1 and cs2: cluster_set
@return: dictionary with the matching clusters.
@return type: { cluster : cluster }
"""
matr = [[len(cl1.bibs & cl2.bibs) for cl2 in cs2.clusters] for cl1 in cs1.clusters]
mapping = maximized_mapping(matr)
return dict((cs1.clusters[mappy[0]], cs2.clusters[mappy[1]]) for mappy in mapping)
def store(self):
'''
Stores the cluster set in a special table.
This is used to store the results of
tortoise/wedge in a table and later merge them
with personid.
'''
remove_result_cluster("%s." % self.last_name)
named_clusters = (("%s.%d" % (self.last_name, idx), cl) for idx, cl in enumerate(self.clusters))
map(save_cluster, named_clusters)
def cluster_sets_from_marktables():
# { (100, 123) -> name }
ref100 = get_bib10x()
ref700 = get_bib70x()
bibref_2_name = dict([((100, ref), generate_last_name_cluster_str(name)) for ref, name in ref100] +
[((700, ref), generate_last_name_cluster_str(name)) for ref, name in ref700])
all_recs = get_all_valid_bibrecs()
all_bibrefrecs = chain(set((100, ref, rec) for rec, ref in get_bibrefrec_subset(100, all_recs, map(itemgetter(0), ref100))),
set((700, ref, rec) for rec, ref in get_bibrefrec_subset(700, all_recs, map(itemgetter(0), ref700))))
last_name_2_bibs = {}
for bibrefrec in all_bibrefrecs:
table, ref, unused = bibrefrec
name = bibref_2_name[(table, ref)]
last_name_2_bibs[name] = last_name_2_bibs.get(name, []) + [bibrefrec]
cluster_sets = []
for name, bibrecrefs in last_name_2_bibs.items():
new_cluster_set = Cluster_set()
new_cluster_set.clusters = [Cluster_set.Cluster([bib]) for bib in bibrecrefs]
new_cluster_set.last_name = name
cluster_sets.append(new_cluster_set)
return cluster_sets
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