def main():
engine = EngineFactory(
engine='Sitebing',
api_key="msRh5UoZzyV3qvroEpzXMzbZEVjW3ENfTGMAQO1yuRc",
throttle=0.1,
cache='engine')
query_generator = TriTermQueryGeneration(minlen=TERM_LEN,
stopwordfile=STOPWORD_FILE,
maxsize=100)
tuple_list = get_trending_queries(URL_FILE)
page_calculator = PageRetrievabilityCalculator(engine=engine,
cutoff=CUTOFF,
generator=query_generator)
with open(RESULT_FILE, 'a') as f:
for tuple in tuple_list:
url = tuple[1]
findability = tuple[0]
category_name = tuple[2]
retrievability = page_calculator.score_page(url)
s = page_calculator.stats()
f.write('{0},{1},{2},{3},{4},{5}\n'.format(category_name, url,
findability,
retrievability,
s['retrieved'],
s['query_count']))
def main():
engine = EngineFactory(engine='Sitebing',
api_key="msRh5UoZzyV3qvroEpzXMzbZEVjW3ENfTGMAQO1yuRc",
throttle=0.1,
cache='engine')
query_generator = TriTermQueryGeneration(minlen=TERM_LEN, stopwordfile=STOPWORD_FILE, maxsize=100)
tuple_list = get_trending_queries(URL_FILE)
page_calculator = PageRetrievabilityCalculator(engine=engine, cutoff=CUTOFF, generator=query_generator)
with open(RESULT_FILE, 'a') as f:
for tuple in tuple_list:
url = tuple[1]
findability = tuple[0]
category_name = tuple[2]
retrievability = page_calculator.score_page(url)
s = page_calculator.stats()
f.write('{0},{1},{2},{3},{4},{5}\n'.format(category_name,
url,
findability,
retrievability,
s['retrieved'],
s['query_count']))
def main():
"""
:return:
"""
parser = argparse.ArgumentParser(
description="Page Calculator for pages")
parser.add_argument("-u", "--url", type=str,
help="url address")
parser.add_argument("-e","--engine",type=str,
help="Name of search engine: " + ENGINE_LIST.__str__())
parser.add_argument("-k","--key",type=str,
help="API Key for search engine (if applicable)")
parser.add_argument("-c","--cutoff", type=int,
help ="The cutoff value for queries")
parser.add_argument("-m","--maxqueries", type=int,
help ="The maximum number of queries per page")
parser.add_argument("-s","--stopwordfile", type=str,
help ="The filename name containing stopwords")
parser.add_argument("-b","--backgroundfile", type=str,
help ="The filename name containing background term counts")
parser.add_argument("-ca", "--cache",
action="store_true", default=False,
help="use cache")
args = parser.parse_args()
if not args.url:
print "Check your URL argument"
parser.print_help()
return 2
cache = None
if args.cache:
cache = 'engine'
if args.key:
engine = EngineFactory(engine=args.engine, api_key=args.key, throttle=0.1, cache=cache)
else:
print "cache is ", cache
engine = EngineFactory(engine=args.engine, cache=cache, throttle=0.1)
stopwordfile = None
if args.stopwordfile:
stopwordfile = args.stopwordfile
mq = 50
if args.maxqueries:
mq = args.maxqueries
backgroundfile = 'background.txt'
if args.backgroundfile:
backgroundfile = args.backgroundfile
doc_extractor = SingleQueryGeneration(minlen=3,stopwordfile=stopwordfile)
query_generator = BiTermQueryGeneration(minlen=3, stopwordfile=stopwordfile)
print "Loading background distribution"
colLM = LanguageModel(file=backgroundfile)
print "Background loaded, number of terms: ", colLM.get_num_terms()
print "Fetching page: %s" % (args.url)
pc = PageCapture(args.url)
page_html = pc.get_page_sourcecode()
print "Page loaded"
doc_extractor.extract_queries_from_html(page_html)
doc_term_counts = doc_extractor.query_count
print "Number of terms in document: %d" % (len(doc_term_counts))
docLM = LanguageModel(term_dict=doc_term_counts)
slm = BayesLanguageModel(docLM=docLM, colLM=colLM, beta=500)
query_list = query_generator.extract_queries_from_html(page_html)
print "Queries generated: ", len(query_list)
qr = OddsRatioQueryRanker(smoothed_language_model=slm)
scored_queries = qr.calculate_query_list_probabilities(query_list)
queries = qr.get_top_queries(mq)
query_list = []
for query in queries:
query_list.append(query[0])
prc = PageRetrievabilityCalculator(engine=engine)
prc.score_page(args.url, query_list)
print "\nRetrievability Scores for cumulative c=20"
prc.calculate_page_retrievability(c=20)
prc.report()
print "\nRetrievability Scores for gravity beta=1.0"
prc.calculate_page_retrievability(c=20, beta=1.0)
prc.report()
print "Done!"
return 0
def setup(self):
"""
:return:
"""
parser = argparse.ArgumentParser(
description="Page Calculator for pages")
parser.add_argument("-u", "--url", type=str,
help="url address")
parser.add_argument("-e","--engine",type=str,
help="Name of search engine: " + ENGINE_LIST.__str__())
parser.add_argument("-k","--key",type=str,
help="API Key for search engine (if applicable)")
parser.add_argument("-d","--domain",type=str,
help="domain for search engine (if applicable, i.e. engine is sitebing, default is gla.ac.uk)")
parser.add_argument("-c","--cutoff", type=int,
help ="The cutoff value for queries")
parser.add_argument("-m","--maxqueries", type=int,
help ="The maximum number of queries per page")
parser.add_argument("-s","--stopwordfile", type=str,
help ="The filename name containing stopwords")
parser.add_argument("-ca", "--cache",
action="store_true", default=False,
help="use cache")
#parser.add_argument("-ex","--experiment", type=int, help=" experiment number 1 - x")
args = parser.parse_args()
if not args.url:
print "Check your URL argument"
parser.print_help()
return 2
else:
self.url = args.url
# cache = None
# if args.cache:
# cache = 'engine'
# if args.key:
# engine = EngineFactory(engine=args.engine, api_key=args.key, throttle=0.1, cache=cache)
# else:
# print "cache is ", cache
# engine = EngineFactory(engine=args.engine, cache=cache, throttle=0.1)
cache = None
if args.cache:
self.cache = 'engine'
else:
self.cache = cache
if args.key:
self.engine = EngineFactory(engine=args.engine, api_key=args.key, throttle=0.1, cache=self.cache)
else:
self.engine = EngineFactory(engine=args.engine, cache=self.cache, throttle=0.1)
if args.domain:
self.engine.site = args.domain
stopwordfile = None
if args.stopwordfile:
self.stopwordfile = args.stopwordfile
else:
self.stopwordfile = None
self.mq = 250
if args.maxqueries:
self.mq = args.maxqueries
print "Fetching page: %s" % (args.url)
pc = PageCapture(args.url)
self.page_html = pc.get_page_sourcecode()
print "Page loaded"
self.page_text = ''
# answer = raw_input("Do you want to use a percentage of this page? Enter y or n \n")
# if answer == 'y':
# percent = raw_input("What percentage do you want to use? \n")
# if self.is_integer(percent):
# self.page_text = self.reduce_page(percentage=percent)
# else:
# print "input error, will exit"
# sys.exit(2)
# #todo update so asks again, not exit
# else:
self.page_text = self.page_html
query_list = []
answer = raw_input("Do you want to use only a position based extractor? Enter y or n \n")
if answer == 'y' or answer != 'n': #if enter is hit then assume y
text = self.get_position_text()
#todo at this stage this could be single, bi or tri terms
query_gen = None
if self.stopwordfile:
query_gen = BiTermQueryGeneration(minlen=3, stopwordfile=self.stopwordfile)
else:
query_gen = BiTermQueryGeneration(minlen=3)
query_list = query_gen.extract_queries_from_text(text)
elif answer == 'n':
answer = raw_input("Do you want to use only a rank based extractor? Enter y or n \n")
if answer == 'y' or answer != 'n': #if enter is hit then assume y:
query_list = self.get_ranked_queries()
elif answer == 'n':
answer = raw_input("Do you want to use a rank based extractor combined with a position extractor? Enter y or n \n")
if answer == 'y' or answer != 'n': #if enter is hit then assume y:
text = self.get_position_text()
query_list = self.get_ranked_queries(text)
elif answer == 'n':
print "sorry, that's all the options, system will exit"
sys.exit(0)
print "Queries generated: ", len(query_list)
prc = None
if args.cutoff:
prc = PageRetrievabilityCalculator(engine=self.engine, max_queries=self.mq)
else:
prc = PageRetrievabilityCalculator(engine=self.engine, max_queries=self.mq)
prc.score_page(self.url, query_list)
print "\nRetrievability Scores for cumulative pce=20"
prc.calculate_page_retrievability(c=20)
prc.report()
print "\nRetrievability Scores for gravity beta=1.0"
prc.calculate_page_retrievability(c=20, beta=1.0)
prc.report()
print "Done!"
return 0
def process_queries(self):
prc = None
if self.maxqueries:
prc = PageRetrievabilityCalculator(engine=self.engine, max_queries=self.maxqueries)
else:
prc = PageRetrievabilityCalculator(engine=self.engine)
prc.score_page(self.url, self.query_list)
#rose todo refactor following code, not optimal and a bit messy
prc.calculate_page_retrievability(c=20)
c_20_summary_data = prc.output_summary_report()
c_20_score = str(c_20_summary_data['score'])
c_20_breakdown_data = prc.output_query_report()
prc.calculate_page_retrievability(c=10)
c_10_summary_data = prc.output_summary_report()
c_10_score = str(c_10_summary_data['score'])
c_10_breakdown_data = prc.output_query_report()
prc.calculate_page_retrievability(c=5)
c_5_summary_data = prc.output_summary_report()
c_5_score = str(c_5_summary_data['score'])
c_5_breakdown_data = prc.output_query_report()
prc.calculate_page_retrievability(c=20, beta=1.0)
g_1_summary_data = prc.output_summary_report()
g_1_score = str(g_1_summary_data['score'])
g_1_breakdown_data = prc.output_query_report()
prc.calculate_page_retrievability(c=20, beta=0.5)
g_pt5_summary_data = prc.output_summary_report()
g_pt5_score = str(g_pt5_summary_data['score'])
g_pt5_breakdown_data = prc.output_query_report()
#print "c breakdown is NOW ", str(c_20_breakdown_data["internationally excellent site:gla.ac.uk"].ret_score)
#print "c breakdown is " , c_20_breakdown_data
#print "g breakdown is " , g_1_breakdown_data
summary_report = ""
#add all the details for cumulative to the summary report
for value in c_20_summary_data.values():
summary_report += str(value) + " "
#at the end add in the remaining c and gravity scores for the same page and a new line
summary_report += c_10_score + " " + c_5_score + " " + g_1_score + " " + g_pt5_score + "\n"
breakdown_report = ""
for q_terms, curr_query in c_20_breakdown_data.items():
q_c20_score = str(curr_query.ret_score)
q_c10_score = str(c_10_breakdown_data[q_terms].ret_score)
q_c5_score = str(c_5_breakdown_data[q_terms].ret_score)
q_g1_score = str(g_1_breakdown_data[q_terms].ret_score)
q_gpt5_score = str(g_pt5_breakdown_data[q_terms].ret_score)
breakdown_report += self.url + " " + q_terms + " " + str(curr_query.rank) + " " + q_c20_score \
+ " " + q_c10_score + " " + q_c5_score + " " + q_g1_score + " " + q_gpt5_score + "\n"
self.write_output_files(summary_report,breakdown_report)
def process_queries(self):
prc = None
if self.maxqueries:
prc = PageRetrievabilityCalculator(engine=self.engine, max_queries=self.maxqueries)
else:
prc = PageRetrievabilityCalculator(engine=self.engine)
prc.score_page(self.url, self.query_list)
# rose todo refactor following code, not optimal and a bit messy
prc.calculate_page_retrievability(c=20)
c_20_summary_data = prc.output_summary_report()
c_20_score = str(c_20_summary_data["score"])
c_20_breakdown_data = prc.output_query_report()
prc.calculate_page_retrievability(c=10)
c_10_summary_data = prc.output_summary_report()
c_10_score = str(c_10_summary_data["score"])
c_10_breakdown_data = prc.output_query_report()
prc.calculate_page_retrievability(c=5)
c_5_summary_data = prc.output_summary_report()
c_5_score = str(c_5_summary_data["score"])
c_5_breakdown_data = prc.output_query_report()
prc.calculate_page_retrievability(c=20, beta=1.0)
g_1_summary_data = prc.output_summary_report()
g_1_score = str(g_1_summary_data["score"])
g_1_breakdown_data = prc.output_query_report()
prc.calculate_page_retrievability(c=20, beta=0.5)
g_pt5_summary_data = prc.output_summary_report()
g_pt5_score = str(g_pt5_summary_data["score"])
g_pt5_breakdown_data = prc.output_query_report()
# print "c breakdown is NOW ", str(c_20_breakdown_data["internationally excellent site:gla.ac.uk"].ret_score)
# print "c breakdown is " , c_20_breakdown_data
# print "g breakdown is " , g_1_breakdown_data
summary_report = ""
# add all the details for cumulative to the summary report
for value in c_20_summary_data.values():
summary_report += str(value) + " "
# at the end add in the remaining c and gravity scores for the same page and a new line
summary_report += c_10_score + " " + c_5_score + " " + g_1_score + " " + g_pt5_score + "\n"
breakdown_report = ""
for q_terms, curr_query in c_20_breakdown_data.items():
q_c20_score = str(curr_query.ret_score)
q_c10_score = str(c_10_breakdown_data[q_terms].ret_score)
q_c5_score = str(c_5_breakdown_data[q_terms].ret_score)
q_g1_score = str(g_1_breakdown_data[q_terms].ret_score)
q_gpt5_score = str(g_pt5_breakdown_data[q_terms].ret_score)
breakdown_report += (
self.url
+ " "
+ q_terms
+ " "
+ str(curr_query.rank)
+ " "
+ q_c20_score
+ " "
+ q_c10_score
+ " "
+ q_c5_score
+ " "
+ q_g1_score
+ " "
+ q_gpt5_score
+ "\n"
)
self.write_output_files(summary_report, breakdown_report)