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R3600/modules/bibauthorid/lib/bibauthorid_prob_matrix.py
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bibauthorid_prob_matrix.py
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# -*- coding: utf-8 -*-
##
## This file is part of Invenio.
## Copyright (C) 2011, 2012 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.
import gc
import invenio.bibauthorid_config as bconfig
from invenio.bibauthorid_comparison import compare_bibrefrecs
from invenio.bibauthorid_comparison import clear_all_caches as clear_comparison_caches
from invenio.bibauthorid_backinterface import get_modified_papers_before
from invenio.bibauthorid_general_utils import bibauthor_print \
, update_status \
, update_status_final \
, is_eq
#import pyximport; pyximport.install()
from invenio.bibauthorid_bib_matrix import Bib_matrix
if bconfig.DEBUG_CHECKS:
def _debug_is_eq_v(vl1, vl2):
if isinstance(vl1, str) and isinstance(vl2, str):
return vl1 == vl2
if isinstance(vl1, tuple) and isinstance(vl2, tuple):
return is_eq(vl1[0], vl2[0]) and is_eq(vl1[1], vl2[1])
return False
class ProbabilityMatrix(object):
'''
This class contains and maintains the comparison
between all virtual authors. It is able to write
and read from the database and update the results.
'''
def __init__(self, name):
self._bib_matrix = Bib_matrix(name)
def load(self, load_map=True, load_matrix=True):
update_status(0., "Loading probability matrix...")
self._bib_matrix.load()
update_status_final("Probability matrix loaded.")
def store(self):
update_status(0., "Saving probability matrix...")
self._bib_matrix.store()
update_status_final("Probability matrix saved.")
def __getitem__(self, bibs):
return self._bib_matrix[bibs[0], bibs[1]]
def getitem_numeric(self, bibs):
return self._bib_matrix.getitem_numeric(bibs)
def __get_up_to_date_bibs(self, bib_matrix):
return frozenset(get_modified_papers_before(
bib_matrix.get_keys(),
bib_matrix.creation_time))
def is_up_to_date(self, cluster_set):
return self.__get_up_to_date_bibs(self._bib_matrix) >= frozenset(cluster_set.all_bibs())
def recalculate(self, cluster_set):
'''
Constructs probability matrix. If use_cache is true, it will
try to load old computations from the database. If save cache
is true it will save the current results into the database.
@param cluster_set: A cluster set object, used to initialize
the matrix.
'''
last_cleaned = 0
self._bib_matrix.store()
try:
old_matrix = Bib_matrix(self._bib_matrix.name+'copy')
old_matrix.duplicate_existing(self._bib_matrix.name, self._bib_matrix.name+'copy')
old_matrix.load()
cached_bibs = self.__get_up_to_date_bibs(old_matrix)
have_cached_bibs = bool(cached_bibs)
except IOError:
old_matrix.destroy()
cached_bibs = None
have_cached_bibs = False
self._bib_matrix.destroy()
self._bib_matrix = Bib_matrix(cluster_set.last_name, cluster_set=cluster_set)
ncl = cluster_set.num_all_bibs
expected = ((ncl * (ncl - 1)) / 2)
if expected == 0:
expected = 1
try:
cur_calc, opti, prints_counter = 0, 0, 0
for cl1 in cluster_set.clusters:
if cur_calc+opti - prints_counter > 100000 or cur_calc == 0:
update_status((float(opti) + cur_calc) / expected, "Prob matrix: calc %d, opti %d." % (cur_calc, opti))
prints_counter = cur_calc+opti
# #clean caches
if cur_calc - last_cleaned > 20000000:
gc.collect()
# clear_comparison_caches()
last_cleaned = cur_calc
for cl2 in cluster_set.clusters:
if id(cl1) < id(cl2) and not cl1.hates(cl2):
for bib1 in cl1.bibs:
for bib2 in cl2.bibs:
if have_cached_bibs:
try:
val = old_matrix[bib1, bib2]
opti += 1
if bconfig.DEBUG_CHECKS:
assert _debug_is_eq_v(val, compare_bibrefrecs(bib1, bib2))
except KeyError:
cur_calc += 1
val = compare_bibrefrecs(bib1, bib2)
if not val:
cur_calc += 1
val = compare_bibrefrecs(bib1, bib2)
else:
cur_calc += 1
val = compare_bibrefrecs(bib1, bib2)
self._bib_matrix[bib1, bib2] = val
except Exception, e:
raise Exception("""Error happened in prob_matrix.recalculate with
val:%s
original_exception: %s
"""%(str(val),str(e)))
clear_comparison_caches()
update_status_final("Matrix done. %d calc, %d opt." % (cur_calc, opti))
def prepare_matirx(cluster_set, force):
if bconfig.DEBUG_CHECKS:
assert cluster_set._debug_test_hate_relation()
assert cluster_set._debug_duplicated_recs()
matr = ProbabilityMatrix(cluster_set.last_name)
matr.load(load_map=True, load_matrix=False)
if not force and matr.is_up_to_date(cluster_set):
bibauthor_print("Cluster %s is up-to-date and therefore will not be computed."
% cluster_set.last_name)
return False
matr.load(load_map=False, load_matrix=True)
matr.recalculate(cluster_set)
matr.store()
return True
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