def get_top_members(results, classes, x):
k = int(0.1 * len(results))
top_few = results[:k]
members = []
for result in top_few:
try:
members.extend(classes[result])
except KeyError:
pass
return helper.get_top_k(members, x)
def get_top_classes(results, classes, x):
k = int(0.1 * len(results))
top_few = results[:k]
PC = []
for result in top_few:
try:
PC.append(classes[result])
except KeyError:
pass
return helper.get_top_k(PC, x)
def get_top_members(results, classes, x):
k = int(0.1 * len(results))
top_few = results[:k]
members = []
for result in top_few:
try:
members.extend(classes[result])
except KeyError:
pass
return helper.get_top_k(members, x)
def get_top_classes(results, classes, x):
k = int(0.1 * len(results))
top_few = results[:k]
PC = []
for result in top_few:
try:
PC.append(classes[result])
except KeyError:
pass
return helper.get_top_k(PC, x)
def build(index_directory, dictionary_file, postings_file):
files_to_index = [f for f in listdir(index_directory) if isfile(join(index_directory, f))]
index = []
doc_lengths = {}
IPC_class = {}
UPC_class = {}
family_members = {}
cited_by = {}
counter = 0
doc_top_terms = {}
for file_name in files_to_index:
file_path = format_directory_path(index_directory) + file_name
# Read XML
tree = ET.parse(file_path)
root = tree.getroot()
tokens = []
for child in root:
attr_name = child.attrib['name']
if attr_name == 'Title' or attr_name == 'Abstract':
t = build_tokens(child.text)
tokens.extend(t)
elif attr_name == 'IPC Class':
IPC_class[remove_file_ext(file_name)] = child.text.strip()
elif attr_name == 'UPC Class':
UPC_class[remove_file_ext(file_name)] = child.text.strip()
elif attr_name == 'Family Members':
members = child.text.strip().split('|')
m = []
for member in members:
m.append(member.strip())
family_members[remove_file_ext(file_name)] = m
elif attr_name == 'Cited By':
members = child.text.strip().split('|')
m = []
for member in members:
m.append(member.strip())
cited_by[remove_file_ext(file_name)] = m
tokens = helper.remove_stop_words(helper.filter_invalid_characters(tokens))
doc_top_terms[remove_file_ext(file_name)] = helper.get_top_k(tokens, 10)
# build tokens
doc_lengths[remove_file_ext(file_name)] = get_doc_length(tokens)
index_entries = add_doc_id_to_tokens(tokens, remove_file_ext(file_name))
index.extend(index_entries)
counter += 1
if counter % 300 == 0:
print 'indexing ............... {}% completed'.format(round(float(counter)/len(files_to_index)*100, 2))
print 'Writing index to disk...'
index = sort_inverted_index(index)
index = group_index(index)
write_index_to_disk(index, dictionary_file, postings_file)
write_meta_data_to_disk(doc_lengths, len(files_to_index), doc_top_terms, UPC_class, IPC_class, family_members, cited_by)
def expand_query(results, doc_top_terms, inverted_index, meta_data):
"""
To deal with the anomalous state of knowledge problem
We take top 10% of documents. For each document, pick the 10 most frequent words (already indexed)
From this pool of words, pick the final top 10 by frequency.
Run query again and return results
"""
k = int(0.1 * len(results))
top_few = results[:k]
pool_of_words = []
for result in top_few:
pool_of_words.extend(doc_top_terms[result])
new_query = helper.get_top_k(pool_of_words, 10)
return execute_query([], new_query, [], inverted_index, meta_data)
def expand_query(results, doc_top_terms, inverted_index, meta_data):
"""
To deal with the anomalous state of knowledge problem
We take top 10% of documents. For each document, pick the 10 most frequent words (already indexed)
From this pool of words, pick the final top 10 by frequency.
Run query again and return results
"""
k = int(0.1 * len(results))
top_few = results[:k]
pool_of_words = []
for result in top_few:
pool_of_words.extend(doc_top_terms[result])
new_query = helper.get_top_k(pool_of_words, 10)
return execute_query([], new_query, [], inverted_index, meta_data)
