def BM25_parameter_tests(self):
# Load the inverted indexes and the document lengths
inverted_indexes = load_pickle(Constants.path_inverted_indexes)
document_lengths = load_pickle(Constants.path_document_lengths)
ks = [5.0] # 5.0 appears to be more or less the best value for k
bs = [0.8] # 0.8 appears to be more or less the best value for b
for k in ks:
for b in bs:
parameters = ParametersBM25(k=k, b=b)
path_results_dir = Constants.path_results_dir + r"BM25_parameter_tests/"
results_file_name = (f"results_BM25-k={k}-b={b}")
parameters.print_parameters()
# Rank the documents
document_ranker = DocumentRanker()
document_ranker.rank_documents(inverted_indexes,
document_lengths,
parameters,
Constants.path_topics,
path_results_dir,
results_file_name)
del inverted_indexes, document_lengths
print("Done ranking documents.")
def rank_documents_rocchio(self):
"""Score and rank each document for each query."""
# Load the inverted indexes and the document lengths
inverted_indexes = load_pickle(Constants.path_inverted_indexes)
document_lengths = load_pickle(Constants.path_document_lengths)
documents = load_pickle(Constants.path_final_documents)
document_lengths_bm25f = load_pickle(
Constants.path_document_length_info_bm25f)
inverted_indexes_bm25f = load_pickle(
Constants.path_inverted_indexes_bm25f)
# Rank the documents
# self.document_ranker.rank_documents_rocchio(inverted_indexes,
# document_lengths,
# documents,
# Constants.path_topics,
# Constants.path_results_dir,
# Constants.results_file_name)
self.document_ranker.rank_documents_rocchio_with_bm25f(
inverted_indexes, document_lengths, document_lengths_bm25f,
inverted_indexes_bm25f, documents, Constants.path_topics,
Constants.path_results_dir, Constants.results_file_name)
print("Done ranking documents.")
del inverted_indexes, document_lengths, documents
def rank_documents_with_reranker(self):
"""Score and rank each document for each query, then rerank them."""
# Load the required data
inverted_indexes = load_pickle(Constants.path_inverted_indexes)
doc_lengths = load_pickle(Constants.path_document_lengths)
documents_dictionary = load_pickle(Constants.path_documents_dictionary)
parameters = ParametersBM25()
self.document_ranker.rank_with_reranker(
inverted_indexes, doc_lengths, documents_dictionary, parameters,
Constants.path_topics, Constants.path_results_dir,
Constants.results_rerank_file_name)
del inverted_indexes, doc_lengths, documents_dictionary
def rank_documents(self):
"""Score and rank each document for each query."""
# Load the inverted indexes and the document lengths
inverted_indexes = load_pickle(Constants.path_inverted_indexes)
document_lengths = load_pickle(Constants.path_document_lengths)
parameters = ParametersBM25()
# Rank the documents
self.document_ranker.rank_documents(inverted_indexes, document_lengths,
parameters, Constants.path_topics,
Constants.path_results_dir,
Constants.results_file_name)
del inverted_indexes, document_lengths
print("Done ranking documents.")
def rank_documents_BM25F(self):
"""Score and rank each document for each query."""
# Load the inverted indexes and the document lengths
inverted_indexes_bm25f = load_pickle(
Constants.path_inverted_indexes_bm25f)
doc_length_info_bm25f = load_pickle(
Constants.path_doc_length_info_bm25f)
parameters = ParametersBM25F()
# Rank the documents
self.document_ranker.rank_documents_bm25f(
inverted_indexes_bm25f, doc_length_info_bm25f, parameters,
Constants.path_topics, Constants.path_results_dir, "results_BM25F")
del inverted_indexes_bm25f, doc_length_info_bm25f
print("Done ranking documents.")
def rank_documents_BM25F_with_reranker(self):
"""Score and rank each document for each query with BM25F, then rerank them."""
# Load the required data
inverted_indexes_bm25f = load_pickle(
Constants.path_inverted_indexes_bm25f)
doc_length_info_bm25f = load_pickle(
Constants.path_doc_length_info_bm25f)
documents_dictionary = load_pickle(Constants.path_documents_dictionary)
parameters = ParametersBM25F()
self.document_ranker.rank_BM25F_with_reranker(
inverted_indexes_bm25f,
doc_length_info_bm25f,
documents_dictionary,
parameters,
Constants.path_topics,
Constants.path_results_dir,
results_file_name="results_BM25F_rerank")
del inverted_indexes_bm25f, doc_length_info_bm25f, documents_dictionary
def create_inverted_indexes_bm25f(self):
# Load the documents
documents = load_pickle(Constants.path_documents)
# Create the inverted indexes, also retrieve information on number of terms per field
inverted_indexes, doc_length_info = self.index_creator.create_BM25_inverted_indexes(
documents)
# Save the retrieved information
save_pickle(inverted_indexes, 'inverted_indexes_bm25f')
save_pickle(doc_length_info, 'doc_length_info_bm25f')
del inverted_indexes, doc_length_info
print("Done creating inverted indexes for the bm25f algorithm.")
def process_documents(self, path_linked_documents, path_unlinked_documents,
path_parsed_documents, path_merged_documents,
path_final_documents):
""" (Deprecated!) Process the documents into their final form and store them."""
# Load all document info
linked_documents = load_pickle(path_linked_documents)
unlinked_documents = load_pickle(path_unlinked_documents)
parsed_documents = load_pickle(path_parsed_documents)
# Merge the appropriate documents
near_matches = self.find_near_matches(unlinked_documents,
parsed_documents)
self.merge_documents(unlinked_documents, parsed_documents,
near_matches, path_merged_documents)
del near_matches, unlinked_documents, parsed_documents
# Load the newly created merged documents
merged_documents = load_pickle(path_merged_documents)
# Create a final list of documents and store it
final_documents = linked_documents + merged_documents
save_pickle(final_documents, path_final_documents)
del linked_documents, merged_documents, final_documents
def filter_judged_documents(self, path_final_documents,
path_relevance_judgements, path_judged_documents):
judged_cord_uids = set()
with open(path_relevance_judgements, 'r') as f:
for line in f:
judged_cord_uid = line.split(" ")[2]
judged_cord_uids.add(judged_cord_uid)
print(f"Retrieved {len(judged_cord_uids)} cord_uids of judged documents.")
final_documents = load_pickle(path_final_documents)
judged_documents = []
for document in final_documents:
if document.cord_uid in judged_cord_uids:
judged_documents.append(document)
print(f"Filtered {len(judged_documents)} judged documents.")
save_pickle(judged_documents, path_judged_documents)
def create_inverted_indexes(self):
"""
For the complete documents: create inverted indexes and determine the
length of each document.
"""
# Load the documents
documents = load_pickle(Constants.path_documents)
# Create the inverted indexes
inverted_indexes, document_lengths = self.index_creator.create_inverted_indexes(
documents)
# Store the inverted indexes and the document lengths
save_pickle(inverted_indexes, Constants.path_inverted_indexes)
save_pickle(document_lengths, Constants.path_document_lengths)
del inverted_indexes, document_lengths
print("Done creating inverted indexes for the complete documents.")
# Constants.path_topics,
# results_file_name="results_judged")
# del judged_inverted_indexes
# del judged_doc_lengths
# =============================================================================
# =============================================================================
# judged_documents = load_pickle(Constants.path_judged_documents)
# judged_document_lengths = load_pickle(Constants.path_judged_document_lengths)
# Util.compute_document_statistics(judged_documents, judged_document_lengths,
# Constants.path_relevance_judgements)
# del judged_documents
# del judged_document_lengths
# =============================================================================
from Util import load_pickle
inverted_indexes = load_pickle(Constants.path_inverted_indexes)
doc_lengths = load_pickle(Constants.path_document_lengths)
documents_dict = load_pickle(Constants.path_doc_dict)
search_system.document_ranker.rank_with_rerank_light(
inverted_indexes,
doc_lengths,
Constants.path_topics,
documents_dict,
path_results_dir=r"../trec_eval-master/our_data/",
results_file_name="results_rerank")
def filter_judged_documents(self, path_final_documents,
path_relevance_judgements, path_judged_documents):
def BM25F_parameter_tests(self):
# The (non-field-specific) 'b' parameter is included by mistake
def run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k, b,
weight_title, weight_author,
weight_abstract, weight_sections,
b_title, b_author,
b_abstract, b_sections):
parameters = ParametersBM25F(k=k,
weight_title=weight_title, weight_author=weight_author,
weight_abstract=weight_abstract, weight_sections=weight_sections,
b_title=b_title, b_author=b_author,
b_abstract=b_abstract, b_sections=b_sections)
parameters.print_parameters()
path_results_dir = Constants.path_results_dir + r"BM25F_parameter_tests/"
results_file_name = (f"results_BM25F_test_{test_id}")
document_ranker = DocumentRanker()
document_ranker.rank_documents_bm25f(inverted_indexes_bm25f,
doc_length_info_bm25f,
parameters,
Constants.path_topics,
path_results_dir,
results_file_name)
inverted_indexes_bm25f = load_pickle(Constants.path_inverted_indexes_bm25f)
doc_length_info_bm25f = load_pickle(Constants.path_doc_length_info_bm25f)
round_nr = 11
if round_nr == 0: # round 0 - getting a feel of impact field weights
test_id = "00"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=1.0,
weight_abstract=1.0, weight_sections=1.0,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.1872
test_id = "01"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.2,
weight_abstract=1.0, weight_sections=1.0,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.1872
test_id = "02"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=1.0,
weight_abstract=1.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2331
test_id = "03"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=1.0,
weight_abstract=1.0, weight_sections=0.5,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2093
test_id = "04"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=1.0,
weight_abstract=1.0, weight_sections=1.5,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.1754
# Conclusions:
# -author field can probably be disregarded (as expected)
# -section field should have relatively low weight
elif round_nr == 1: # round 1 - getting a better feel of field weights
test_id = "10"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=1.0, weight_sections=1.0,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.1872
test_id = "11"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=5.0,
weight_abstract=1.0, weight_sections=1.0,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.1871
test_id = "12"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=0.5, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2132
test_id = "13"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2331
test_id = "14"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=1.5, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2390
test_id = "15"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=0.5, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2218
test_id = "16"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2331
test_id = "17"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.5, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2398
# Conclusions:
# -author field be disregarded set to 0
# -abstract field should have a relatively high weight (as expected)
# -title field should have a relatively hight weight (as expected)
elif round_nr == 2: # round 2 - focussing on abstract weights
test_id = "20"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.01,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2016
test_id = "21"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.05,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2210
test_id = "22"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2331
test_id = "23"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.3,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2248
# Conclusions:
# -With the other weights as they are, sections weight of 0.2 is good
elif round_nr == 3: # round 3
test_id = "30"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=0.8, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2409
test_id = "31"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=1.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2465
test_id = "32"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=1.5, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=2536
test_id = "33"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2540
test_id = "34"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=3.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2493
test_id = "35"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=5.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2348
# Conclusions:
# -With the other weights as they are, abstract weight of 2.0 is good
elif round_nr == 4: # round 4 - focussing on title weights
test_id = "40"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=0.8, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2357
test_id = "41"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2390
test_id = "42"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=1.5, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2459
test_id = "43"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=2.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2499
test_id = "44"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2536
test_id = "45"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=5.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2514
# Conclusions:
# -With the other weights as they are, title weight of 3.0 is good
elif round_nr == 5: # round 5 - focussing on parameter 'k'
test_id = "50"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=1.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2371
test_id = "51"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=2.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2480
test_id = "52"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2510
test_id = "53"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=4.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2509
test_id = "54"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=4.5, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2502
test_id = "55"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2493
test_id = "56"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=5.5, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2481
test_id = "57"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=6.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2470
test_id = "58"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=7.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2449
# Conclusions:
# -With the other weights as they are, a 'k' of 3.0 is good
elif round_nr == 6: # round 6 - focussing on parameter 'b' (which does not exist)
test_id = "60"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.2,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2596
test_id = "61"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.5,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2596
test_id = "62"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.6,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2596
test_id = "63"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.7,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2596
test_id = "64"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2596
test_id = "65"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.9,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2596
# Conclusions:
# -There is no general b parameter...
elif round_nr == 7: # round 7 - focussing on field 'b' parameters
test_id = "70"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.7, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2596
test_id = "71"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.9, b_author=0.8,
b_abstract=0.8, b_sections=0.8)
# map=0.2595
test_id = "72"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.7, b_sections=0.8)
# map=0.2615
test_id = "73"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.9, b_sections=0.8)
# map=0.2563
test_id = "74"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.7)
# map=0.2595
test_id = "75"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.8, b_sections=0.9)
# map=0.2589
test_id = "76"
b_fields = 0.7
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=b_fields)
# map=0.2613
test_id = "77"
b_fields = 0.9
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=b_fields)
# map=0.2556
# Conclusions:
# -Except for sections 0.7 appears to be a better 'b' than 0.8
elif round_nr == 8: # round 8 - Focussing again on field 'b' parameters
test_id = "80"
b_fields = 0.4
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=0.8)
# map=0.2637
test_id = "81"
b_fields = 0.5
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=0.8)
# map=0.2634
test_id = "82"
b_fields = 0.6
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=0.8)
# map=0.2628
test_id = "83"
b_fields = 0.7
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=0.8)
# map=0.2615
test_id = "84"
b_fields = 0.8
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=0.8)
# map=0.2596
test_id = "85"
b_fields = 0.9
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=0.8)
# map=0.2562
test_id = "86"
b_fields = 0.7
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=0.7)
# map=0.2613
# Conclusions:
# -For sections the 'b' parameter of 0.8 is good
# -For title and abstract the 'b' parameter of either or both
# may be best below 0.4
elif round_nr == 9: # round 9 - Focussing again on field 'b' parameters
test_id = "90"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.3, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2633
test_id = "91"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.5, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2638
test_id = "92"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.4, b_author=0.8,
b_abstract=0.3, b_sections=0.8)
# map=0.2634
test_id = "93"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.4, b_author=0.8,
b_abstract=0.5, b_sections=0.8)
# map=0.2632
test_id = "94"
b_fields = 0.4
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=b_fields,
b_abstract=b_fields, b_sections=0.8)
# map=0.2637
test_id = "95"
b_fields = 0.4
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=0.8,
b_abstract=b_fields, b_sections=0.8)
# map=0.2637
test_id = "96"
b_fields = 0.3
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=0.8,
b_abstract=b_fields, b_sections=0.8)
# map=0.2632
test_id = "97"
b_fields = 0.2
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=0.8,
b_abstract=b_fields, b_sections=0.8)
# map=0.2617
test_id = "98"
b_fields = 0.1
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=b_fields, b_author=0.8,
b_abstract=b_fields, b_sections=0.8)
# map=0.2587
# Conclusions:
# -For the title a 'b' parameter of 0.5 or higher appears best
# -For the abstract a 'b' parameter of 0.4 appears best
elif round_nr == 10: # round 10 - Final b test for title
test_id = "100"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.3, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2633
test_id = "101"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.4, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2637
test_id = "102"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.5, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2638
test_id = "103"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.6, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2640
test_id = "104"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2642
test_id = "105"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.8, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2641
test_id = "106"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.2,
b_title=0.9, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2641
# Conclusions:
# -For the title a 'b' parameter of 0.7 appears best
elif round_nr == 11: # round 11 - Some final tweaking
test_id = "110"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.1,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2537
test_id = "111"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.3,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2648
test_id = "112"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.4,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2601
test_id = "113"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.5,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2528
test_id = "114"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.7,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2373
test_id = "115"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=0.8,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2300
test_id = "116"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=1.0,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.2173
test_id = "117"
run_test(test_id, inverted_indexes_bm25f, doc_length_info_bm25f,
k=3.0, b=0.8,
weight_title=3.0, weight_author=0.0,
weight_abstract=2.0, weight_sections=1.5,
b_title=0.7, b_author=0.8,
b_abstract=0.4, b_sections=0.8)
# map=0.1937
# Conclusions:
# - The following appear to be good parameters:
# - k=3.0, b=0.8
# - weight_title=3.0, weight_author=0.0, weight_abstract=2.0, weight_sections=0.3
# - b_title=0.7, b_author=0.8, b_abstract=0.4, b_sections=0.8
print("Done testing BM25F parameters.")
def create_complete_documents(self, path_merged_documents,
path_linked_cord_uids, path_all_documents,
path_documents):
"""
Complete document information where necessary and possible.
A number of documents have no direct reference to their full-text
parse. Previous functions have linked these documents to full-text
parses by matching their titles. This function integrates this
information into a single 'complete' document set.
"""
# Load the required data
merged_documents = load_pickle(path_merged_documents)
linked_cord_uids = load_pickle(path_linked_cord_uids)
all_documents = load_pickle(path_all_documents)
# This can be used to look up documents by cord_uid
all_documents_dictionary = create_document_dictionary(all_documents)
i = ti = au = ab = 0
for merged_doc in merged_documents:
cord_uid = merged_doc.cord_uid
completed_documents = []
completed_cord_uids = set()
# If the document does not already have body text...
if cord_uid not in linked_cord_uids:
unlinked_doc = all_documents_dictionary[cord_uid]
# If the title is missing, retrieve title information
if is_empty(unlinked_doc.title):
unlinked_doc.title = merged_doc.title
ti += 1
# If the abstract is missing, retrieve abstract information
if is_empty(unlinked_doc.abstract):
unlinked_doc.abstract = merged_doc.abstract
ab += 1
# If the authors are missing, retrieve author information
unlinked_author_string = (
"" if unlinked_doc.authors == None else " ".join(
filter(None, unlinked_doc.authors)))
if is_empty(unlinked_author_string):
unlinked_doc.authors = merged_doc.authors
au += 1
# Retrieve the body text
unlinked_doc.sections = merged_doc.sections
# Track the cord_uids of documents to which info will be added
completed_cord_uids.add(cord_uid)
# Store the unlinked and now completed document
completed_documents.append(unlinked_doc)
i += 1
if i % 1000 == 0:
print(
f"iteration={i}, potentially retrieved information on:"
+ f" {ti} titles, {ab} abstracts, and {au} authors.")
print(f"Potentially retrieved information on: {ti} titles," +
f" {ab} abstracts, and {au} authors.")
# Add documents that were already complete to the now completed documents
for document in all_documents:
# Only the documents that were already completed will have to be added
if document.cord_uid not in completed_cord_uids:
completed_documents.append(document)
# Save the completed documents
save_pickle(completed_documents, path_documents)
# Free memory
del merged_documents, linked_cord_uids, all_documents
del all_documents_dictionary, completed_documents, completed_cord_uids
