def task3a(model, raw_corpus_directory):
project_directory = os.getcwd()
p = Indexer.Parser()
corpus_directory = p.build_corpus(raw_corpus_directory, stopped=True)
output_directory = os.path.join(project_directory, "output")
I = Indexer.InvertedIndexer(corpus_directory)
I.ngram_indexer(1) # builds a unigram indexes for each word
r = Retriever.Retriever(
corpus_directory, I, project_directory
) # create a Retriever class, which contains different retrieval model
os.chdir(raw_corpus_directory)
os.chdir(os.pardir)
f = open('cacm.query.txt', 'r')
soup = BeautifulSoup(f.read(), 'html.parser')
f.close()
f_stop_words = open('common_words.txt', 'r')
stop_words_list = f_stop_words.readlines()
stop_words = [i.strip() for i in stop_words_list]
f_stop_words.close()
file_name = os.path.join(output_directory, 'task3a_' + model + '.txt')
f = open(file_name, 'w') # open file for writing results
for i in range(64):
query_no = (soup.find('docno')).text.encode(
'utf-8') # extract query number and query
(soup.find('docno')).decompose()
query = (soup.find('doc')).text.encode('utf-8')
(soup.find('doc')).decompose()
r.process_query(query, stopped=True,
stopwords=stop_words) # parse the query
# r.clean_content(query)
docs_and_scores = r.get_scores_for_docs(
model, int(query_no)) # retrieve relevant documents
# save results into appropriate file
docs = docs_and_scores[0]
scores = docs_and_scores[1]
for i in range(100):
f.write(str(query_no) \
+ " Q0 " \
+ str(docs[i]) + ' ' \
+ str((i+1)) + " " \
+ str(scores[i]) + " " \
+ model + "\n")
f.close()
reverse=True)
expanded_query_list = [i for i, j in top_terms][:len(query_terms) + n]
expanded_query = " ".join(expanded_query_list)
return expanded_query
print("Select a task number\n\t1. Task - 1 \n\t2. Task - 2")
task = int(raw_input())
model = raw_input("Enter the model: ")
raw_corpus_directory = raw_input(
"Enter the raw corpus directory (html files): ")
project_directory = os.getcwd()
output_directory = os.path.join(project_directory, "output")
# Parser (to process the raw corpus (no stopping))
p = Indexer.Parser()
corpus_directory = p.build_corpus(raw_corpus_directory)
# Indexer - Builds the inverted indexes for the processed corpus
I = Indexer.InvertedIndexer(corpus_directory)
I.ngram_indexer(1) # builds a unigram indexes for each word
# Retriever - based on the model specified, this object can be
# used to get the results.
r = Retriever.Retriever(corpus_directory, I, project_directory)
# Get the queries from the given file
query_dic = {} # stores the queries; key - query ID, token - query
os.chdir(project_directory)
f = open('cacm.query.txt', 'r')
soup = BeautifulSoup(f.read(), 'html.parser')
def run_task(task, model, raw_corpus_directory):
project_directory = os.getcwd()
output_directory = os.path.join(project_directory, "output")
# Parser (to process the raw corpus (no stopping))
p = Indexer.Parser()
corpus_directory = p.build_corpus(raw_corpus_directory)
# Indexer - Builds the inverted indexes for the processed corpus
I = Indexer.InvertedIndexer(corpus_directory)
I.ngram_indexer(1) # builds a unigram indexes for each word
# Retriever - based on the model specified, this object can be
# used to get the results.
r = Retriever.Retriever(corpus_directory, I, project_directory)
# Get the queries from the given file
query_dic = {} # stores the queries; key - query ID, token - query
os.chdir(project_directory)
f = open('cacm.query.txt', 'r')
soup = BeautifulSoup(f.read(), 'html.parser')
f.close()
for i in range(64):
query_no = (soup.find('docno')).text.encode(
'utf-8') # extract query number and query
(soup.find('docno')).decompose()
query = (soup.find('doc')).text.encode('utf-8')
(soup.find('doc')).decompose()
query_dic[int(query_no)] = query
# task 1
if task == 1:
os.chdir(project_directory)
if not os.path.exists(output_directory):
os.mkdir(output_directory, 0755)
os.chdir(output_directory)
f = open('task1_' + model + '.txt', 'w')
for query_no in range(len(query_dic)):
r.process_query(query_dic[query_no + 1]) # parse the query
docs_and_scores = r.get_scores_for_docs(
model, (query_no + 1)) # retrieve relevant documents
# save results into appropriate file
docs = docs_and_scores[0]
scores = docs_and_scores[1]
for i in range(100):
f.write(str(query_no + 1) \
+ " Q0 " \
+ str(docs[i]) + ' ' \
+ str((i+1)) + " " \
+ str(scores[i]) + " " \
+ model + "\n")
f.close()
# task 2
if task == 2:
# read output files from task 1
file_name = 'task1_' + model + '.txt'
try:
f = open(os.path.join(output_directory, file_name), 'r')
except:
print "Run Task - 1 before Task - 2"
exit()
data = f.readlines()
f.close()
list_of_lines = []
for each_line in data:
list_of_lines.append(each_line.split(
)) # contains parsed lines from the task1 output file
task1_output = {
} # results for each; key = query ID(number), value = list of relevant files
for each_line in list_of_lines:
task1_output.setdefault(int(each_line[0]), []).append(each_line[2])
# get stopwords
f_stop_words = open('common_words.txt', 'r')
stop_words_list = f_stop_words.readlines()
stop_words = [i.strip() for i in stop_words_list]
f_stop_words.close()
# get corpus
os.chdir(corpus_directory)
files_list = glob.glob('*.html')
corpus = {}
for each_file in files_list:
doc_name = each_file[:len(each_file) - 5]
text = open(each_file).read()
corpus[doc_name] = text.split()
file_name = 'expanded_queries_' + model + '.txt'
f = open(os.path.join(output_directory, file_name), 'w')
expanded_query_dic = {}
for query_no, query in query_dic.viewitems():
processed_query = r.process_query(query, True)
expanded_query_dic[query_no] = Query_Expander(
processed_query,
task1_output[query_no],
corpus,
stopwords=stop_words)
f.write(str(query_no) + " " + expanded_query_dic[query_no] + "\n")
f.close()
file_name = 'task2_' + model + '.txt'
f = open(os.path.join(output_directory, file_name), 'w')
for query_no, query in expanded_query_dic.viewitems():
r.process_query(query) # parse the query
# r.clean_content(query)
docs_and_scores = r.get_scores_for_docs(
model, query_no) # retrieve relevant documents
# save results into appropriate file
docs = docs_and_scores[0]
scores = docs_and_scores[1]
for i in range(100):
f.write(str(query_no) \
+ " Q0 " \
+ str(docs[i]) + ' ' \
+ str((i + 1)) + " " \
+ str(scores[i]) + " " \
+ model + "\n")
f.close()
print "Results stored in " + output_directory + " directory"
