def one_click_search(ini, query_str, outputs):
if bool(ini.get('condition_no_cclparser', '')) or \
bool(ini.get('condition_baseline', '')):
parser.USE_CCLPARSER = False
if bool(ini.get('condition_no_boilerplate', '')) or \
bool(ini.get('condition_baseline', '')):
html_to_trec.USE_BOILERPLATE = False
if bool(ini.get('condition_patterns', '')):
nugget_finder.USE_PATTERNS = True
if bool(ini.get('condition_candidate_scorer', '')):
nugget_finder.USE_CANDIDATE_SCORER = True
if bool(ini.get('condition_no_ilp', '')):
USE_ILP = False
else:
USE_ILP = True
####
# fetch results from Web search engine (or cache)
#
(htmls, html_urls) = query_web_search(query_str, ini)
print "found", len(htmls), "pages"
####
# extract relevant nuggets
#
(scored_candidates, parsed_query, path_to_index) = find_nuggets(ini, htmls, query_str)
####
# final output
#
final_passages = do_search(parsed_query, ini.get('search_command', './cpp/Search'),
path_to_index, int(ini.get('main_search_passage_count', 3)))
results = {}
if USE_ILP:
####
# assemble final output
#
for (final_length, output_type) in outputs:
results[output_type] = assemble_output_ilp(final_passages, scored_candidates, final_length)
else:
####
# score final passages
#
final_passages_scored = score_passages(final_passages, scored_candidates)
####
# assemble final output
#
for (final_length, output_type) in outputs:
results[output_type] = assemble_output(final_passages_scored, final_length)
return (results, html_urls)
def __call__(self, query):
from nugget_finder import load_ini, do_search, identify_candidates
return do_search(query, self.search_command, self.index_path,
self.ret_size)
def __call__(self, query):
from nugget_finder import load_ini, do_search, identify_candidates
return do_search(query, self.search_command, self.index_path, self.ret_size)
def gen_nugget_train(ini, htmls, query_str, good_text):
from nugget_finder import load_ini, do_search, identify_candidates
tmp_folder = ini.get('tmp_folder', './tmp')
good_text = good_text.lower()
####
# extract text from the HTML documents
#
sys.stderr.write("Extracting text...\n")
path_to_corpus = "%s/to_index" % (tmp_folder,)
if not os.path.exists(path_to_corpus):
os.makedirs(path_to_corpus)
html_count = 0
for html in htmls:
outfile = "%s/%s.txt" % (path_to_corpus, html_count)
cached_detag = "%s.txt" % (html,)
if os.path.exists(cached_detag):
copyfile(cached_detag, outfile)
else:
detag_html_file(infile=html,outfile=outfile,id=html_count)
copyfile(outfile, cached_detag)
html_count += 1
####
# build index
#
sys.stderr.write("Indexing...\n")
path_to_index = "%s/index" % (tmp_folder,)
if not os.path.exists(path_to_index):
os.makedirs(path_to_index)
config_template = file(ini.get('index_config_template', "./indexing.template")).read()
config_filename = "%s/indexing.param" % (tmp_folder,)
config_file = open(config_filename, "w")
config_file.write(config_template.format(path_to_corpus=path_to_corpus,
path_to_index=path_to_index))
config_file.close()
index_command = ini.get('index_command', 'IndriBuildIndex')
retcode=subprocess.call([ index_command, config_filename ], stdout=sys.stderr, stderr=sys.stderr)
assert retcode==0
####
# generate query
#
parsed_query = parse_into_chunks(query_str)
if bool(ini.get('condition_baseline', '')):
print "baseline run."
return ([], parsed_query, path_to_index)
####
# main search
#
sys.stderr.write("Main search...\n")
search_command = ini.get('search_command', './cpp/Search')
main_passages = do_search(parsed_query, search_command, path_to_index, 2000)
#print 'passage num:', len(main_passages)
####
# identify candidates
#
sys.stderr.write("Identifying candidates...\n")
top_documents = int(ini.get('top_documents_for_candidate', '20'))
candidates, main_evidence = identify_candidates(main_passages,
int(ini.get('main_search_passage_count', 3)),
top_documents)
print 'candidate num:', len(candidates)
###
# evidence search
#
sys.stderr.write("Evidence searching...\n")
evidence = dict()
t0 = time.time()
searcher = Searcher(search_command, path_to_index,
int(ini.get('evidence_search_passage_count', 10)))
p = Pool(8)
queries = map(lambda candidate: list(parsed_query) + [('NE', candidate[1] )], candidates)
evidence_passages_list = p.map(searcher, queries, 50)
p.close()
print 'pool map evidence search:', time.time() - t0;
for i in xrange(len(candidates)):
candidate = candidates[i]
evidence[candidate[0]] = filter(lambda passage:
all(map(lambda token: token.lower() in passage[1].lower(), candidate[1])), evidence_passages_list[i])
####
# evaluate evidence
#
sys.stderr.write("Generating Training...\n")
instances = []
total = len(evidence)
t0 = time.time()
gen = TrainGenerator(ini.get('dumpindex_command', 'dumpindex'), ini.get('stat_index'))
inputs = map(lambda candidate: (candidate, evidence[candidate], main_evidence[candidate], good_text), evidence.keys())
p = Pool(8)
instances = p.map(gen, inputs, 50)
p.close()
print 'pool map evaluating:', time.time() - t0
####
# clean up
#
for i in xrange(0, html_count):
try:
os.unlink("%s/to_index/%s.txt" % (tmp_folder, i))
except:
pass
return instances
def one_click_search(ini, query_str, outputs):
if bool(ini.get('condition_no_cclparser', '')) or \
bool(ini.get('condition_baseline', '')):
parser.USE_CCLPARSER = False
if bool(ini.get('condition_no_boilerplate', '')) or \
bool(ini.get('condition_baseline', '')):
html_to_trec.USE_BOILERPLATE = False
if bool(ini.get('condition_patterns', '')):
nugget_finder.USE_PATTERNS = True
if bool(ini.get('condition_candidate_scorer', '')):
nugget_finder.USE_CANDIDATE_SCORER = True
if bool(ini.get('condition_no_ilp', '')):
USE_ILP = False
else:
USE_ILP = True
####
# fetch results from Web search engine (or cache)
#
(htmls, html_urls) = query_web_search(query_str, ini)
print "found", len(htmls), "pages"
####
# extract relevant nuggets
#
(scored_candidates, parsed_query,
path_to_index) = find_nuggets(ini, htmls, query_str)
####
# final output
#
final_passages = do_search(parsed_query,
ini.get('search_command', './cpp/Search'),
path_to_index,
int(ini.get('main_search_passage_count', 3)))
results = {}
if USE_ILP:
####
# assemble final output
#
for (final_length, output_type) in outputs:
results[output_type] = assemble_output_ilp(final_passages,
scored_candidates,
final_length)
else:
####
# score final passages
#
final_passages_scored = score_passages(final_passages,
scored_candidates)
####
# assemble final output
#
for (final_length, output_type) in outputs:
results[output_type] = assemble_output(final_passages_scored,
final_length)
return (results, html_urls)
def gen_nugget_train(ini, htmls, query_str, good_text):
from nugget_finder import load_ini, do_search, identify_candidates
tmp_folder = ini.get('tmp_folder', './tmp')
good_text = good_text.lower()
####
# extract text from the HTML documents
#
sys.stderr.write("Extracting text...\n")
path_to_corpus = "%s/to_index" % (tmp_folder, )
if not os.path.exists(path_to_corpus):
os.makedirs(path_to_corpus)
html_count = 0
for html in htmls:
outfile = "%s/%s.txt" % (path_to_corpus, html_count)
cached_detag = "%s.txt" % (html, )
if os.path.exists(cached_detag):
copyfile(cached_detag, outfile)
else:
detag_html_file(infile=html, outfile=outfile, id=html_count)
copyfile(outfile, cached_detag)
html_count += 1
####
# build index
#
sys.stderr.write("Indexing...\n")
path_to_index = "%s/index" % (tmp_folder, )
if not os.path.exists(path_to_index):
os.makedirs(path_to_index)
config_template = file(
ini.get('index_config_template', "./indexing.template")).read()
config_filename = "%s/indexing.param" % (tmp_folder, )
config_file = open(config_filename, "w")
config_file.write(
config_template.format(path_to_corpus=path_to_corpus,
path_to_index=path_to_index))
config_file.close()
index_command = ini.get('index_command', 'IndriBuildIndex')
retcode = subprocess.call([index_command, config_filename],
stdout=sys.stderr,
stderr=sys.stderr)
assert retcode == 0
####
# generate query
#
parsed_query = parse_into_chunks(query_str)
if bool(ini.get('condition_baseline', '')):
print "baseline run."
return ([], parsed_query, path_to_index)
####
# main search
#
sys.stderr.write("Main search...\n")
search_command = ini.get('search_command', './cpp/Search')
main_passages = do_search(parsed_query, search_command, path_to_index,
2000)
#print 'passage num:', len(main_passages)
####
# identify candidates
#
sys.stderr.write("Identifying candidates...\n")
top_documents = int(ini.get('top_documents_for_candidate', '20'))
candidates, main_evidence = identify_candidates(
main_passages, int(ini.get('main_search_passage_count', 3)),
top_documents)
print 'candidate num:', len(candidates)
###
# evidence search
#
sys.stderr.write("Evidence searching...\n")
evidence = dict()
t0 = time.time()
searcher = Searcher(search_command, path_to_index,
int(ini.get('evidence_search_passage_count', 10)))
p = Pool(8)
queries = map(
lambda candidate: list(parsed_query) + [('NE', candidate[1])],
candidates)
evidence_passages_list = p.map(searcher, queries, 50)
p.close()
print 'pool map evidence search:', time.time() - t0
for i in xrange(len(candidates)):
candidate = candidates[i]
evidence[candidate[0]] = filter(
lambda passage: all(
map(lambda token: token.lower() in passage[1].lower(),
candidate[1])), evidence_passages_list[i])
####
# evaluate evidence
#
sys.stderr.write("Generating Training...\n")
instances = []
total = len(evidence)
t0 = time.time()
gen = TrainGenerator(ini.get('dumpindex_command', 'dumpindex'),
ini.get('stat_index'))
inputs = map(
lambda candidate:
(candidate, evidence[candidate], main_evidence[candidate], good_text),
evidence.keys())
p = Pool(8)
instances = p.map(gen, inputs, 50)
p.close()
print 'pool map evaluating:', time.time() - t0
####
# clean up
#
for i in xrange(0, html_count):
try:
os.unlink("%s/to_index/%s.txt" % (tmp_folder, i))
except:
pass
return instances
