def test_run_rbo():
with open('./example/rpd_b.txt') as _base_file, open(
'./example/rpd_a.txt') as _adv_file:
_base_run = pytrec_eval.parse_run(_base_file)
_adv_run = pytrec_eval.parse_run(_adv_file)
_rbo = rpd_eval.rbo(run_b_rep=_base_run, run_a_rep=_adv_run)
assert 'baseline' in _rbo.keys()
assert rbo_base == _rbo.get('baseline')
assert 'advanced' in _rbo.keys()
assert rbo_adv == _rbo.get('advanced')
def test_run_ktu():
with open('./example/rpd_b.txt') as _base_file, open(
'./example/rpd_a.txt') as _adv_file:
_base_run = pytrec_eval.parse_run(_base_file)
_adv_run = pytrec_eval.parse_run(_adv_file)
_ktu = rpd_eval.ktau_union(run_b_rep=_base_run, run_a_rep=_adv_run)
assert 'baseline' in _ktu.keys()
assert ktu_base == _ktu.get('baseline')
assert 'advanced' in _ktu.keys()
assert ktu_adv == _ktu.get('advanced')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('qrel')
parser.add_argument('run')
parser.add_argument('measure')
args = parser.parse_args()
assert os.path.exists(args.qrel)
assert os.path.exists(args.run)
with open(args.qrel, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(args.run, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
evaluator = pytrec_eval.RelevanceEvaluator(
qrel, {args.measure})
results = evaluator.evaluate(run)
def print_line(measure, scope, value):
#scope = query_id = topic_id
print('{:25s}{:8s}{:.22f}'.format(measure, scope, value))
avg_DCG = []
for query_id, query_measures in results.items():
for measure, value in sorted(query_measures.items()):
avg_DCG.append(value)
print_line(measure, query_id, value)
print(avg_DCG)
print(mean(avg_DCG))
print(' avg of nDCG {:f}'.format(mean(avg_DCG)))
def get_metric(self,
qrels: str,
trec: str,
metric: str = 'ndcg_cut_10',
split: dict = None,
split_idx: int = -1) -> Dict[str, float]:
with open(qrels, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(trec, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
# partial evaluation
if split is not None and split_idx >= 0:
for qid in copy.deepcopy(run):
if qid not in split[split_idx]:
_ = run.pop(qid)
evaluator = pytrec_eval.RelevanceEvaluator(
qrel, pytrec_eval.supported_measures)
results = evaluator.evaluate(run)
for query_id, query_measures in sorted(results.items()):
pass
mes = {}
for measure in sorted(query_measures.keys()):
mes[measure] = pytrec_eval.compute_aggregated_measure(
measure, [
query_measures[measure]
for query_measures in results.values()
])
return mes[metric]
def read_ranking(bow_model_path):
"""return BoW ranking as dict of dicts {qid: {doc_id: score, ...}, ...}"""
print('read BoW ranking')
with open(bow_model_path, 'r') as f:
# pytrec_eval loads ranking as dict of dicts
run = pytrec_eval.parse_run(f)
return run
def qrel_metrics(qrel_file, run_file, metrics=('ndcg', 'map')):
"""Get metrics (ndcg and map by default) for a run compared to a qrel file.
Arguments:
qrel_file -- qrel file with ground truth data
run_file -- predictions from the run
metrics -- which metrics to evaluate on,
can use any valid metrics that the trec_eval tool accepts
Returns:
metric_values -- dictionary of metric values (out of 100), rounded to two decimal places
"""
with open(qrel_file, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(run_file, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
evaluator = pytrec_eval.RelevanceEvaluator(qrel, set(metrics))
results = evaluator.evaluate(run)
metric_values = {}
for measure in sorted(metrics):
res = pytrec_eval.compute_aggregated_measure(
measure,
[query_measures[measure] for query_measures in results.values()]
)
metric_values[measure] = np.round(100 * res, 2)
return metric_values
def cal_ndcg(qrels, trec, k):
with open(qrels, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(trec, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
evaluator = pytrec_eval.RelevanceEvaluator(qrel,
pytrec_eval.supported_measures)
results = evaluator.evaluate(run)
for query_id, query_measures in sorted(results.items()):
pass
mes = {}
for measure in sorted(query_measures.keys()):
mes[measure] = pytrec_eval.compute_aggregated_measure(
measure,
[query_measures[measure] for query_measures in results.values()])
metric = 'ndcg_cut_%d' % k
if metric not in mes:
print('Depth of NDCG not available.')
exit()
ndcg = mes[metric]
return ndcg
def evaluate(eval_path, qrel_path, res_path):
measures = {"map", "ndcg_cut", "recall", "P"}
with open(qrel_path, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
evaluator = pytrec_eval.RelevanceEvaluator(qrel, measures)
with open(res_path, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
all_metrics = evaluator.evaluate(run)
metrics = {
'P_5': 0,
'P_10': 0,
'P_20': 0,
'ndcg_cut_5': 0,
'ndcg_cut_10': 0,
'ndcg_cut_20': 0,
'ndcg_cut_100': 0,
'map': 0,
'recall_100': 0
}
nb_queries = len(all_metrics)
for key, values in all_metrics.items():
for metric in metrics:
metrics[metric] += values[metric] / nb_queries
with open(eval_path, 'w') as f:
json.dump(metrics, f)
def main():
rpd_eval = RpdEvaluator(qrel_orig_path=QREL,
run_b_orig_path=ORIG_B,
run_a_orig_path=ORIG_A,
run_b_rep_path=None,
run_a_rep_path=None)
rpd_eval.trim()
rpd_eval.evaluate()
for run_name, info in runs_rpd.items():
with open(info.get('path')) as run_file:
info['run'] = pytrec_eval.parse_run(run_file)
trim(info['run'])
info['scores'] = rpd_eval.evaluate(info['run'])
info['rmse'] = rpd_eval.rmse(run_b_score=info['scores'])
baseline_runs = [
'rpd_wcr04_tf_1', 'rpd_wcr04_tf_2', 'rpd_wcr04_tf_3', 'rpd_wcr04_tf_4',
'rpd_wcr04_tf_5'
]
advanced_runs = [
'rpd_wcr0405_tf_1', 'rpd_wcr0405_tf_2', 'rpd_wcr0405_tf_3',
'rpd_wcr0405_tf_4', 'rpd_wcr0405_tf_5'
]
cutoffs = ['5', '10', '15', '20', '30', '100', '200', '500', '1000']
df_content = {}
for run_name in baseline_runs:
df_content[run_name] = [
runs_rpd[run_name]['rmse']['baseline']['ndcg_cut_' + co]
for co in cutoffs
]
df = pd.DataFrame(df_content, index=cutoffs)
ax = df.plot.line(style='o-')
ax.set_xlabel('Cut-off values')
ax.set_ylabel('RMSE')
ax.get_figure().savefig('data/plots/rpd_b_rmse.pdf',
format='pdf',
bbox_inches='tight')
plt.show()
df_content = {}
for run_name in advanced_runs:
df_content[run_name] = [
runs_rpd[run_name]['rmse']['baseline']['ndcg_cut_' + co]
for co in cutoffs
]
df = pd.DataFrame(df_content, index=cutoffs)
ax = df.plot.line(style='o-')
ax.set_xlabel('Cut-off values')
ax.set_ylabel('RMSE')
ax.get_figure().savefig('data/plots/rpd_a_rmse.pdf',
format='pdf',
bbox_inches='tight')
plt.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('qrel')
parser.add_argument('run', nargs=2)
# A bit too strict, as it does not allow for parametrized measures,
# but sufficient for the example.
parser.add_argument(
'--measure',
#choices=pytrec_eval.supported_measures,
required=True)
args = parser.parse_args()
assert os.path.exists(args.qrel)
assert all(map(os.path.exists, args.run))
with open(args.qrel, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(args.run[0], 'r') as f_run:
first_run = pytrec_eval.parse_run(f_run)
with open(args.run[1], 'r') as f_run:
second_run = pytrec_eval.parse_run(f_run)
evaluator = pytrec_eval.RelevanceEvaluator(qrel, {args.measure})
first_results = evaluator.evaluate(first_run)
print(first_results.keys())
second_results = evaluator.evaluate(second_run)
query_ids = list(set(first_results.keys()) & set(second_results.keys()))
first_scores = [
first_results[query_id][args.measure] for query_id in query_ids
]
second_scores = [
second_results[query_id][args.measure] for query_id in query_ids
]
print(scipy.stats.ttest_rel(first_scores, second_scores))
def pytrec_evaluation(runfile, qrelfile, measures = pytrec_eval.supported_measures):
""" run trec_eval with "measures" from the Python interface """
with open(runfile, "r") as ranking:
run = pytrec_eval.parse_run(ranking)
with open(qrelfile, "r") as qrel:
qrel = pytrec_eval.parse_qrel(qrel)
evaluator = pytrec_eval.RelevanceEvaluator(
qrel, measures)
return evaluator.evaluate(run)
def main():
rpl_eval = RplEvaluator(qrel_orig_path=QREL,
run_b_orig_path=ORIG_B,
run_a_orig_path=ORIG_A,
run_b_rep_path=None,
run_a_rep_path=None,
qrel_rpd_path=QREL_RPL)
rpl_eval.trim()
rpl_eval.evaluate()
for run_name, info in runs_rpl.items():
with open(info.get('path')) as run_file:
info['run'] = pytrec_eval.parse_run(run_file)
trim(info['run'])
info['scores'] = rpl_eval.evaluate(info['run'])
pairs = [('rpl_wcr04_tf_1', 'rpl_wcr0405_tf_1'),
('rpl_wcr04_tf_2', 'rpl_wcr0405_tf_2'),
('rpl_wcr04_tf_3', 'rpl_wcr0405_tf_3'),
('rpl_wcr04_tf_4', 'rpl_wcr0405_tf_4'),
('rpl_wcr04_tf_5', 'rpl_wcr0405_tf_5')]
df_content = {
'P_10': [
rpl_eval.er(run_b_score=runs_rpl[pair[0]]['scores'],
run_a_score=runs_rpl[pair[1]]['scores'])['P_10']
for pair in pairs
],
'ndcg': [
rpl_eval.er(run_b_score=runs_rpl[pair[0]]['scores'],
run_a_score=runs_rpl[pair[1]]['scores'])['ndcg']
for pair in pairs
],
'map': [
rpl_eval.er(run_b_score=runs_rpl[pair[0]]['scores'],
run_a_score=runs_rpl[pair[1]]['scores'])['map']
for pair in pairs
],
}
df = pd.DataFrame(df_content,
index=['tf_1', 'tf_2', 'tf_3', 'tf_4', 'tf_5'])
orig_val = 1
ax = df.plot.bar(rot=0)
ax.hlines(orig_val, -.5, 5.5, linestyles='dashed', color='black')
ax.annotate(' ', (3, orig_val), color='black')
ax.set_xlabel("Replicated Run")
ax.set_ylabel("Effect Ratio (ER)")
ax.get_figure().savefig('data/plots/rpl_er.pdf',
format='pdf',
bbox_inches='tight')
plt.show()
def compute_metrics(coll_path,
Collection,
queries_index,
qrel,
results,
model_name,
save_res=False):
"""Function that saves the results of retrieval: the top_k documents according to their score for
a certain model identified by model_name. Then, it computes different metrics for IR using the pytrec_eval
package""" #HR
Collection.save_results(queries_index, results, model_name, top_k=1000)
with open(model_name, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
if not save_res:
os.remove(model_name)
#measures = {"map", "ndcg_cut", "recall", "P"}
measures = {"ndcg_cut"}
evaluator = pytrec_eval.RelevanceEvaluator(qrel, measures)
all_metrics = evaluator.evaluate(run)
metrics = {
'P_5': 0,
'P_10': 0,
'P_20': 0,
'ndcg_cut_5': 0,
'ndcg_cut_10': 0,
'ndcg_cut_20': 0,
'ndcg_cut_1000': 0,
'map': 0,
'recall_1000': 0
}
nb_queries = len(all_metrics)
for key, values in all_metrics.items():
for metric in metrics:
metrics[metric] += values[metric] / nb_queries
return metrics
def main():
parser = argparse.ArgumentParser()
parser.add_argument('qrel')
parser.add_argument('run')
args = parser.parse_args()
assert os.path.exists(args.qrel)
assert os.path.exists(args.run)
with open(args.qrel, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(args.run, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
evaluator = pytrec_eval.RelevanceEvaluator(
qrel, pytrec_eval.supported_measures)
results = evaluator.evaluate(run)
def print_line(measure, scope, value):
print('{:25s}{:8s}{:.4f}'.format(measure, scope, value))
for query_id, query_measures in sorted(results.items()):
for measure, value in sorted(query_measures.items()):
print_line(measure, query_id, value)
# Scope hack: use query_measures of last item in previous loop to
# figure out all unique measure names.
#
# TODO(cvangysel): add member to RelevanceEvaluator
# with a list of measure names.
for measure in sorted(query_measures.keys()):
print_line(
measure,
'all',
pytrec_eval.compute_aggregated_measure(
measure,
[query_measures[measure]
for query_measures in results.values()]))
def main():
rpd_eval = RpdEvaluator(qrel_orig_path=QREL,
run_b_orig_path=ORIG_B,
run_a_orig_path=ORIG_A,
run_b_rep_path=None,
run_a_rep_path=None)
rpd_eval.trim()
rpd_eval.evaluate()
for run_name, info in runs_rpd.items():
with open(info.get('path')) as run_file:
info['run'] = pytrec_eval.parse_run(run_file)
trim(info['run'])
info['scores'] = rpd_eval.evaluate(info['run'])
average_retrieval_performance(
rpd_eval.run_b_orig_score, {
'tf_1': runs_rpd.get('rpd_wcr04_tf_1').get('scores'),
'tf_2': runs_rpd.get('rpd_wcr04_tf_2').get('scores'),
'tf_3': runs_rpd.get('rpd_wcr04_tf_3').get('scores'),
'tf_4': runs_rpd.get('rpd_wcr04_tf_4').get('scores'),
'tf_5': runs_rpd.get('rpd_wcr04_tf_5').get('scores'),
},
measures=['P_10', 'ndcg', 'bpref', 'map'],
xlabel='Reproduced run (wcr04)',
ylabel='Score',
outfile='data/plots/rpd_b_arp.pdf')
average_retrieval_performance(
rpd_eval.run_a_orig_score, {
'tf_1': runs_rpd.get('rpd_wcr0405_tf_1').get('scores'),
'tf_2': runs_rpd.get('rpd_wcr0405_tf_2').get('scores'),
'tf_3': runs_rpd.get('rpd_wcr0405_tf_3').get('scores'),
'tf_4': runs_rpd.get('rpd_wcr0405_tf_4').get('scores'),
'tf_5': runs_rpd.get('rpd_wcr0405_tf_5').get('scores'),
},
measures=['P_10', 'ndcg', 'bpref', 'map'],
xlabel='Reproduced run (wcr0405)',
ylabel='Score',
outfile='data/plots/rpd_a_arp.pdf')
def get_metric(self,
qrels: str,
trec: str,
metric: str = 'ndcg_cut_10') -> Dict[str, float]:
with open(qrels, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(trec, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
evaluator = pytrec_eval.RelevanceEvaluator(
qrel, pytrec_eval.supported_measures)
results = evaluator.evaluate(run)
for query_id, query_measures in sorted(results.items()):
pass
mes = {}
for measure in sorted(query_measures.keys()):
mes[measure] = pytrec_eval.compute_aggregated_measure(
measure, [
query_measures[measure]
for query_measures in results.values()
])
return mes[metric]
def __test(self):
with open(os.path.join(TREC_EVAL_TEST_DIR, ground_truth_filename)) as \
f_trec_eval:
trec_eval_output = parse_trec_eval(f_trec_eval)
measures = set(
measure if measure in pytrec_eval.supported_measures else
prefix_match(measure, pytrec_eval.supported_measures)
for measure in trec_eval_output['all'].keys())
with open(os.path.join(TREC_EVAL_TEST_DIR, qrel_filename)) as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(os.path.join(TREC_EVAL_TEST_DIR, run_filename)) as f_run:
run = pytrec_eval.parse_run(f_run)
evaluator = pytrec_eval.RelevanceEvaluator(qrel, measures, **kwargs)
results = evaluator.evaluate(run)
expected_measures = trec_eval_output['all']
for measure in expected_measures:
agg_measure_value = pytrec_eval.compute_aggregated_measure(
measure, [
query_measure_values[measure]
for query_measure_values in results.values()
])
ground_truth_agg_measure_value = \
trec_eval_output['all'][measure]
self.assertAlmostEqual(agg_measure_value,
ground_truth_agg_measure_value,
places=3,
msg=measure)
def main():
os.chdir(os.path.dirname(os.path.realpath('__file__')))
# set folders
corpus_folder = 'corpus/' + FLAGS.corpus_name + '/' + FLAGS.corpus_name
index_folder = 'corpus/' + FLAGS.corpus_name + '/index'
# model_folder = 'corpus/' + FLAGS.corpus_name + '/models/' + FLAGS.model_name
data_folder = 'corpus/' + FLAGS.corpus_name + '/data'
query_folder = 'corpus/' + FLAGS.corpus_name + '/queries'
qrels_folder = 'corpus/' + FLAGS.corpus_name + '/qrels'
rankings_folder = 'corpus/' + FLAGS.corpus_name + '/rankings/' + FLAGS.model_name
# create folders
if not os.path.exists(rankings_folder):
os.makedirs(rankings_folder)
# if not os.path.exists(model_folder):
# os.makedirs(model_folder)
if not os.path.exists(query_folder) or not os.path.exists(qrels_folder):
print(
'folders containing queries and qrels are required - please add them'
)
return False
# set random seed - enable reproducibility
np.random.seed(FLAGS.seed)
# establish connection with UMLS db
umls_lookup = umls.UMLSLookup()
# load required data
print(
'load processed data required to retrofit word vectors and perform retrieval tasks'
)
with open(data_folder + '/docs.json', 'r') as df:
corpus = json.load(df)
with open(data_folder + '/idfs.json', 'r') as wf:
idfs = json.load(wf)
with open(data_folder + '/cfs.json', 'r') as cff:
cfs = json.load(cff)
with open(data_folder + '/word_dict.json', 'r') as wdf:
word_dict = json.load(wdf)
# compute reverse word dict
reverse_word_dict = dict(zip(word_dict.values(), word_dict.keys()))
# store docnos and docs as separate lists
docnos = list(corpus.keys())
docs = list(corpus.values())
del corpus # free memory space
# pre process relational data
if not os.path.exists(data_folder + '/term2cui.json'):
# map terms to cuis using QuickUMLS
term2cui = tf_utils.get_term2cui(word_dict,
data_folder,
threshold=FLAGS.threshold,
stypes_fname=FLAGS.stypes_fname)
else:
# laod (term, cui) pairs
print('load (term, cui) pairs')
with open(data_folder + '/term2cui.json', 'r') as tcf:
term2cui = json.load(tcf)
"""
SEMANTIC PROCESSING
"""
# load semantic model
print('load semantic model')
with tf.Session() as sess:
# restore model and get required tensors
saver = tf.train.import_meta_graph(FLAGS.semantic_model + '.ckpt.meta')
saver.restore(sess, FLAGS.semantic_model + '.ckpt')
word_embs = sess.run(tf.get_default_graph().get_tensor_by_name(
'embeddings/word_embs:0'))
"""
RETROFITTING
"""
if FLAGS.retrofit:
# get synonyms for each word within vocabulary
print('get synonyms')
syns = tf_utils.get_syns(term2cui, word_dict, umls_lookup)
if FLAGS.syn_weights:
# convert collection frequencies from list to dict
cfs = dict(cfs)
else:
cfs = None
# retrofit word vectors
print('retrofit word vectors for {} iterations'.format(
FLAGS.iterations))
word_embs = retrofit(word_embs,
syns,
reverse_word_dict,
FLAGS.iterations,
alpha=1.0,
beta=FLAGS.beta,
cfs=cfs)
# compute doc embeddings
print('compute document vectors w/ retrofitted word vectors')
doc_embs, filt_ids = tf_utils.compute_doc_embs(docs, word_dict, word_embs,
idfs)
if not FLAGS.reranking:
"""
RETRIEVAL
"""
print('perform retrieval over the entire collection')
# load queries
q = tf_utils.read_ohsu_queries(query_folder + '/' + FLAGS.query_fname)
# set query embs and ids
q_embs = []
q_ids = []
# loop over queries and generate rankings
for qid, qtext in q.items():
# prepare queries for semantic matching
q_proj = tf_utils.prepare_query(qtext[FLAGS.qfield], word_dict,
word_embs)
if q_proj is None:
print('query {} does not contain known terms'.format(qid))
else:
q_embs.append(q_proj)
q_ids.append(qid)
q_embs = np.array(q_embs)
# perform search and evaluate model effectiveness
tf_utils.semantic_search(docnos, doc_embs, q_ids, q_embs,
rankings_folder, FLAGS.model_name)
scores = tf_utils.evaluate(
['Rprec', 'P_5', 'P_10', 'P_20', 'ndcg', 'map'], rankings_folder,
FLAGS.model_name, qrels_folder, FLAGS.qrels_fname)
else:
"""
RE-RANKING
"""
print('perform re-ranking over top 1000 documents from a baseline run')
# parse and store qrels
with open(qrels_folder + '/' + FLAGS.qrels_fname + '.txt',
'r') as qrelf:
qrels = pytrec_eval.parse_qrel(qrelf)
# initialize evaluator over qrels
evaluator = pytrec_eval.RelevanceEvaluator(
qrels, {'P'}) # evaluate on Precision
# parse input run
print('parse input run')
with open(FLAGS.run_path, 'r') as runf:
run = pytrec_eval.parse_run(runf)
# load queries
q = tf_utils.read_ohsu_queries(query_folder + '/' + FLAGS.query_fname)
# get query ids
qids = list(q.keys())
# shuffle query ids
np.random.shuffle(qids)
if FLAGS.fixed_gamma:
# perform re-ranking based on a fixed value of gamma
print('perform re-ranking w/ gamma=%.2f' % (FLAGS.fixed_gamma))
# initialize combined (output) run
crun = trec_utils.OnlineTRECRun(FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma))
# combine rankings using fixed gamma
comb_run = tf_utils.compute_combined_run(
run, FLAGS.qfield, q, docnos, doc_embs, word_dict, word_embs,
SCORE_NORMALIZERS[FLAGS.normalizer], FLAGS.fixed_gamma)
# store test ranking in combined run
for qid, doc_ids_and_scores in comb_run.items():
crun.add_ranking(
qid, [(score, docno)
for docno, score in doc_ids_and_scores.items()])
# close and store run
crun.close_and_write(out_path=rankings_folder + '/' +
FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma) + '.txt',
overwrite=True)
print('combined run stored in {}'.format(rankings_folder))
# evalaute combined run
print('evaluate run combined w/ gamma=%.2f' % (FLAGS.fixed_gamma))
tf_utils.evaluate(['map', 'P_10', 'ndcg'], rankings_folder,
FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma), qrels_folder,
FLAGS.qrels_fname)
else:
# learn optimal weight to combine runs
print("learn optimal weight to combine runs with sweep: {}".format(
FLAGS.sweep))
# set variable to store scores and weights
scores_and_weights = []
# initialize kfold with FLAGS.num_folds
kfold = sklearn.model_selection.KFold(n_splits=FLAGS.num_folds)
for fold, (train_qids, test_qids) in enumerate(kfold.split(qids)):
print('fold n. {}'.format(fold))
# restrict queries to train_qids and test_qids
qtrain = {qids[ix]: q[qids[ix]] for ix in train_qids}
qtest = {qids[ix]: q[qids[ix]] for ix in test_qids}
# obtain best combination on training queries
train_score, best_train_weight = max(
tf_utils.perform_reranking(
run, FLAGS.qfield, qtrain, docnos, doc_embs, word_dict,
word_embs, FLAGS.sweep,
SCORE_NORMALIZERS[FLAGS.normalizer], FLAGS.ref_measure,
evaluator))
print(
'fold %d: best_train_weight=%.2f, %s =%.4f' %
(fold, best_train_weight, FLAGS.ref_measure, train_score))
# compute combined run with best combination on test queries
test_crun = tf_utils.compute_combined_run(
run, FLAGS.qfield, qtest, docnos, doc_embs, word_dict,
word_embs, SCORE_NORMALIZERS[FLAGS.normalizer],
best_train_weight)
# evaluate test run
test_res = evaluator.evaluate(test_crun)
# compute aggregated measure score for test queries
test_score = pytrec_eval.compute_aggregated_measure(
FLAGS.ref_measure, [
qscore[FLAGS.ref_measure]
for qscore in test_res.values()
])
# store averaged scores w/ best weights
scores_and_weights.append(
(np.mean([train_score, test_score]), best_train_weight))
# get (best) weight that produces the highest averaged score
best_score, best_weight = max(scores_and_weights)
print('found best weight=%.2f' % (best_weight))
# initialize combined (output) run
crun = trec_utils.OnlineTRECRun(FLAGS.model_name +
'_best_weight_' +
str(FLAGS.best_weight))
# compute combined run based on test weight
comb_run = tf_utils.compute_combined_run(
run, FLAGS.qfield, q, docnos, doc_embs, word_dict, word_embs,
SCORE_NORMALIZERS[FLAGS.normalizer], best_weight)
# store ranking in crun
for qid, doc_ids_and_scores in comb_run.items():
crun.add_ranking(
qid, [(score, doc_id)
for doc_id, score in doc_ids_and_scores.items()])
# close and store run
crun.close_and_write(out_path=rankings_folder + '/' +
FLAGS.model_name + '_best_weight_' +
str(FLAGS.best_weight) + '.txt',
overwrite=True)
print('combined run stored in {}'.format(rankings_folder))
# evalaute combined run
print(
'evaluate run combined w/ {}-fold cross validation and best weight={}'
.format(FLAGS.num_folds, FLAGS.best_weight))
tf_utils.evaluate(['map', 'P_10', 'ndcg'], rankings_folder,
FLAGS.model_name + '_best_weight_' +
str(FLAGS.best_weight), qrels_folder,
FLAGS.qrels_fname)
with open(qrel_path, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
evaluator = pytrec_eval.RelevanceEvaluator(qrel, {'ndcg_cut'})
redirects = {}
with open(redirects_path) as f:
for line in f:
if not line.startswith('#'):
subj, pred, obj = line.split(maxsplit=2)
obj = obj[:obj.rfind('.')].strip()
redirects[subj] = obj
with open(ir_run_path, "r") as ir_run_file:
ir_run = pytrec_eval.parse_run(ir_run_file)
model = Word2Vec.load(args.model)
entityv = KeyedVectors(model.vector_size * 2)
entityv_entities = []
entityv_weights = []
wordv = KeyedVectors(model.vector_size * 2)
wordv_entities = []
wordv_weights = []
for entity, vocab in model.wv.vocab.items():
if entity.startswith('<'):
entityv_entities.append(entity)
entityv_weights.append(
np.concatenate(
(model.syn1neg[vocab.index], model.wv.syn0[vocab.index])))
def main():
cutoffs = [1000, 100, 50, 20, 10, 5]
# BASELINE
for run_name, info in zip(
list(runs_rpd.keys())[::2],
list(runs_rpd.values())[::2]):
rpd_eval = RpdEvaluator(qrel_orig_path=QREL,
run_b_orig_path=ORIG_B,
run_a_orig_path=ORIG_A,
run_b_rep_path=None,
run_a_rep_path=None)
rpd_eval.trim()
rpd_eval.evaluate()
with open(info.get('path')) as run_file:
info['run'] = pytrec_eval.parse_run(run_file)
for cutoff in cutoffs:
rpd_eval.trim(cutoff)
rpd_eval.trim(cutoff, info['run'])
info['ktu_' + str(cutoff)] = arp(
rpd_eval.ktau_union(info['run'])['baseline'])
df_content = {}
for run_name, info in zip(
list(runs_rpd.keys())[::2],
list(runs_rpd.values())[::2]):
df_content[run_name] = [
info.get('ktu_' + str(cutoff)) for cutoff in cutoffs[::-1]
]
ax = pd.DataFrame(data=df_content,
index=[str(cutoff)
for cutoff in cutoffs[::-1]]).plot(style='-*')
ax.set_xlabel('Cut-off values')
ax.set_ylabel(r"Kendall's $\tau$")
ax.get_figure().savefig('data/plots/rpd_b_ktu.pdf',
format='pdf',
bbox_inches='tight')
plt.show()
# ADVANCED
for run_name, info in zip(
list(runs_rpd.keys())[1::2],
list(runs_rpd.values())[1::2]):
rpd_eval = RpdEvaluator(qrel_orig_path=QREL,
run_b_orig_path=ORIG_B,
run_a_orig_path=ORIG_A,
run_b_rep_path=None,
run_a_rep_path=None)
rpd_eval.trim()
rpd_eval.evaluate()
with open(info.get('path')) as run_file:
info['run'] = pytrec_eval.parse_run(run_file)
for cutoff in cutoffs:
rpd_eval.trim(cutoff)
rpd_eval.trim(cutoff, info['run'])
# scores = rpl_eval.evaluate(info['run'])
info['ktu_' + str(cutoff)] = arp(
rpd_eval.ktau_union(info['run'])['baseline'])
df_content = {}
for run_name, info in zip(
list(runs_rpd.keys())[1::2],
list(runs_rpd.values())[1::2]):
df_content[run_name] = [
info.get('ktu_' + str(cutoff)) for cutoff in cutoffs[::-1]
]
ax = pd.DataFrame(data=df_content,
index=[str(cutoff)
for cutoff in cutoffs[::-1]]).plot(style='-*')
ax.set_xlabel('Cut-off values')
ax.set_ylabel(r"Kendall's $\tau$")
ax.get_figure().savefig('data/plots/rpd_a_ktu.pdf',
format='pdf',
bbox_inches='tight')
plt.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', nargs="?", type=str)
args = parser.parse_args()
config = json.load(open(args.config, 'r'))
IR_models = [
mz.models.list_available()[i] for i in config["index_mz_models"]
]
with open(config["collection_path"] + '/test/qrels', 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
evaluator = pytrec_eval.RelevanceEvaluator(qrel, set(config["measures"]))
bm25_res = json.load(
open(config["collection_path"] + '/test/' + 'BM25.metrics.json', 'r'))
with open(config["collection_path"] + '/test/' + 'BM25.res', 'r') as f_run:
bm25_run = pytrec_eval.parse_run(f_run)
bm25_results = evaluator.evaluate(bm25_run)
_ = ""
for key, value in bm25_res.items():
if key in config["print_measures"]:
_ += str(value)[:6] + " & "
print('BM25 & ' + _[:-2] + '\\\\')
all_res = dict()
for model_class in IR_models:
validation_path = config[
"collection_path"] + '/validation/' + model_class.__name__
test_path = config["collection_path"] + '/test/' + model_class.__name__
if os.path.exists(validation_path) and os.path.exists(test_path):
best_model = ""
best_metric = 0
for file in os.listdir(validation_path):
if '.json' in file:
val_res = json.load(open(validation_path + '/' + file,
'r'))
if val_res[config["optim_measure"]] > best_metric:
best_model = file
best_metric = val_res[config["optim_measure"]]
if best_model != "" and os.path.exists(test_path + '/' +
best_model):
test_res = json.load(open(test_path + '/' + best_model, 'r'))
all_res[model_class.__name__] = [best_model, test_res]
with open(
config["collection_path"] + '/test/' +
model_class.__name__ + '/' + best_model[:-12] + 'res',
'r') as f_run:
run = pytrec_eval.parse_run(f_run)
results = evaluator.evaluate(run)
query_ids = list(
set(bm25_results.keys()) & set(results.keys()))
_ = ""
for key, value in test_res.items():
if key in config["print_measures"]:
bm25_scores = [
bm25_results[query_id][key]
for query_id in query_ids
]
scores = [
results[query_id][key] for query_id in query_ids
]
test = scipy.stats.ttest_rel(bm25_scores, scores)
_ += str(value)[:6]
if test[0] < 0:
if test[1] < 0.01 / len(config["print_measures"]):
_ += "\\textsuperscript{\\textbf{++}}"
elif test[1] < 0.05 / len(
config["print_measures"]):
_ += "\\textsuperscript{\\textbf{+}}"
else:
if test[1] < 0.01 / len(config["print_measures"]):
_ += "\\textsuperscript{\\textbf{-\,-}}"
elif test[1] < 0.05 / len(
config["print_measures"]):
_ += "\\textsuperscript{\\textbf{-}}"
_ += " & "
print(model_class.__name__ + ' & ' + _[:-2] + '\\\\')
def main():
rpd_eval = RpdEvaluator(qrel_orig_path=QREL,
run_b_orig_path=ORIG_B,
run_a_orig_path=ORIG_A,
run_b_rep_path=None,
run_a_rep_path=None)
rpd_eval.trim()
rpd_eval.evaluate()
for run_name, info in runs_rpd.items():
with open(info.get('path')) as run_file:
info['run'] = pytrec_eval.parse_run(run_file)
trim(info['run'])
info['scores'] = rpd_eval.evaluate(info['run'])
dri_er = {
'wcr_tf_1': {
'er':
rpd_eval.er(runs_rpd['rpd_wcr04_tf_1']['scores'],
runs_rpd['rpd_wcr0405_tf_1']['scores']),
'dri':
rpd_eval.dri(runs_rpd['rpd_wcr04_tf_1']['scores'],
runs_rpd['rpd_wcr0405_tf_1']['scores'])
},
'wcr_tf_2': {
'er':
rpd_eval.er(runs_rpd['rpd_wcr04_tf_2']['scores'],
runs_rpd['rpd_wcr0405_tf_2']['scores']),
'dri':
rpd_eval.dri(runs_rpd['rpd_wcr04_tf_2']['scores'],
runs_rpd['rpd_wcr0405_tf_2']['scores'])
},
'wcr_tf_3': {
'er':
rpd_eval.er(runs_rpd['rpd_wcr04_tf_3']['scores'],
runs_rpd['rpd_wcr0405_tf_3']['scores']),
'dri':
rpd_eval.dri(runs_rpd['rpd_wcr04_tf_3']['scores'],
runs_rpd['rpd_wcr0405_tf_3']['scores'])
},
'wcr_tf_4': {
'er':
rpd_eval.er(runs_rpd['rpd_wcr04_tf_4']['scores'],
runs_rpd['rpd_wcr0405_tf_4']['scores']),
'dri':
rpd_eval.dri(runs_rpd['rpd_wcr04_tf_4']['scores'],
runs_rpd['rpd_wcr0405_tf_4']['scores'])
},
'wcr_tf_5': {
'er':
rpd_eval.er(runs_rpd['rpd_wcr04_tf_5']['scores'],
runs_rpd['rpd_wcr0405_tf_5']['scores']),
'dri':
rpd_eval.dri(runs_rpd['rpd_wcr04_tf_5']['scores'],
runs_rpd['rpd_wcr0405_tf_5']['scores'])
},
}
measures = ['P_10', 'map', 'ndcg']
marker_color = [('o', 'b'), ('^', 'g'), ('v', 'r')]
fig, ax1 = plt.subplots()
ax1.set_xlabel('Effect Ratio (ER)')
ax1.set_ylabel(u'Delta Relative Improvement (ΔRI)')
for measure, mk in zip(measures, marker_color):
ax1.plot([dri_er[r]['er'][measure] for r in dri_er.keys()],
[dri_er[r]['dri'][measure] for r in dri_er.keys()],
marker=mk[0],
color=mk[1],
linestyle='None',
label=measure)
ax1.tick_params(axis='y', labelcolor='k')
fig.tight_layout()
plt.axhline(0, color='grey')
plt.axvline(1, color='grey')
plt.legend()
plt.title('Reproducibility')
plt.savefig('data/plots/rpd_dri_vs_er.pdf',
format='pdf',
bbox_inches='tight')
plt.show()
def main():
os.chdir(os.path.dirname(os.path.realpath('__file__')))
# set folders
corpus_folder = 'corpus/' + FLAGS.corpus_name + '/' + FLAGS.corpus_name
data_folder = 'corpus/' + FLAGS.corpus_name + '/data'
query_folder = 'corpus/' + FLAGS.corpus_name + '/queries'
qrels_folder = 'corpus/' + FLAGS.corpus_name + '/qrels'
rankings_folder = 'corpus/' + FLAGS.corpus_name + '/rankings/' + FLAGS.model_name
# create folders
if not os.path.exists(rankings_folder):
os.makedirs(rankings_folder)
if not os.path.exists(query_folder) or not os.path.exists(qrels_folder):
print(
'folders containing queries and qrels are required - please add them'
)
return False
# parse and store qrels
if FLAGS.qrels_fname:
with open(qrels_folder + '/' + FLAGS.qrels_fname + '.txt',
'r') as qrelf:
qrels = pytrec_eval.parse_qrel(qrelf)
# initialize evaluator over qrels
evaluator = pytrec_eval.RelevanceEvaluator(
qrels, {'P'}) # evaluate on Precision
else:
print("please provide qrels filename")
return False
"""
LEXICAL PREPROCESSING
"""
# parse input run
print('parse input run')
with open(FLAGS.run_path, 'r') as runf:
run = pytrec_eval.parse_run(runf)
"""
SEMANTIC PREPROCESSING
"""
# load required data
print(
'load processed data required to perform re-ranking over lexical model w/ semantic model'
)
with open(data_folder + '/docs.json', 'r') as cf:
corpus = json.load(cf)
with open(data_folder + '/idfs.json', 'r') as wf:
idfs = json.load(wf)
with open(data_folder + '/cfs.json', 'r') as cff:
cfs = json.load(cff)
with open(data_folder + '/word_dict.json', 'r') as wdf:
word_dict = json.load(wdf)
# compute reverse word dictionary
reverse_word_dict = dict(zip(word_dict.values(), word_dict.keys()))
# store docnos and docs as separate lists
docnos = list(corpus.keys())
docs = list(corpus.values())
del corpus # free memory space
# load semantic model
print('load semantic model')
with tf.Session() as sess:
# restore model and get required tensors
saver = tf.train.import_meta_graph(FLAGS.semantic_model + '.ckpt.meta')
saver.restore(sess, FLAGS.semantic_model + '.ckpt')
word_embs = sess.run(tf.get_default_graph().get_tensor_by_name(
'embeddings/word_embs:0'))
# compute doc embeddings
doc_embs, filt_ids = tf_utils.compute_doc_embs(docs, word_dict, word_embs,
idfs)
"""
COMPUTE RE-RANKING
"""
# set random seed
np.random.seed(FLAGS.seed)
# load queries
q = tf_utils.read_ohsu_queries(query_folder + '/' + FLAGS.query_fname)
# get query ids
qids = list(q.keys())
# shuffle query ids
np.random.shuffle(qids)
if FLAGS.fixed_gamma:
# perform re-ranking based on a fixed value of gamma
print('perform re-ranking w/ gamma=%.2f' % (FLAGS.fixed_gamma))
# initialize combined (output) run
crun = trec_utils.OnlineTRECRun(FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma))
# combine rankings using fixed gamma
comb_run = tf_utils.compute_combined_run(
run, FLAGS.qfield, q, docnos, doc_embs, word_dict, word_embs,
SCORE_NORMALIZERS[FLAGS.normalizer], FLAGS.fixed_gamma)
# store test ranking in combined run
for qid, doc_ids_and_scores in comb_run.items():
crun.add_ranking(qid,
[(score, docno)
for docno, score in doc_ids_and_scores.items()])
# close and store run
crun.close_and_write(out_path=rankings_folder + '/' +
FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma) + '.txt',
overwrite=True)
print('combined run stored in {}'.format(rankings_folder))
# evalaute combined run
print('evaluate run combined w/ gamma=%.2f' % (FLAGS.fixed_gamma))
tf_utils.evaluate(['map', 'P_10', 'ndcg'], rankings_folder,
FLAGS.model_name + '_gamma_' +
str(FLAGS.fixed_gamma), qrels_folder,
FLAGS.qrels_fname)
else:
# learn optimal weight to combine runs
print("learn optimal weight to combine runs with sweep: {}".format(
FLAGS.sweep))
# set variable to store scores and weights
scores_and_weights = []
# initialize kfold with FLAGS.num_folds
kfold = sklearn.model_selection.KFold(n_splits=FLAGS.num_folds)
for fold, (train_qids, test_qids) in enumerate(kfold.split(qids)):
print('fold n. {}'.format(fold))
# restrict queries to train_qids and test_qids
qtrain = {qids[ix]: q[qids[ix]] for ix in train_qids}
qtest = {qids[ix]: q[qids[ix]] for ix in test_qids}
# obtain best combination on training queries
train_score, best_train_weight = max(
tf_utils.perform_reranking(run, FLAGS.qfield, qtrain, docnos,
doc_embs, word_dict, word_embs,
FLAGS.sweep,
SCORE_NORMALIZERS[FLAGS.normalizer],
FLAGS.ref_measure, evaluator))
print('fold %d: best_train_weight=%.2f, %s =%.4f' %
(fold, best_train_weight, FLAGS.ref_measure, train_score))
# compute combined run with best combination on test queries
test_crun = tf_utils.compute_combined_run(
run, FLAGS.qfield, qtest, docnos, doc_embs, word_dict,
word_embs, SCORE_NORMALIZERS[FLAGS.normalizer],
best_train_weight)
# evaluate test run
test_res = evaluator.evaluate(test_crun)
# compute aggregated measure score for test queries
test_score = pytrec_eval.compute_aggregated_measure(
FLAGS.ref_measure,
[qscore[FLAGS.ref_measure] for qscore in test_res.values()])
# store averaged scores w/ best weights
scores_and_weights.append(
(np.mean([train_score, test_score]), best_train_weight))
# get (best) weight that produces the highest averaged score
best_score, best_weight = max(scores_and_weights)
print('found best weight=%.2f' % (best_weight))
# initialize combined (output) run
crun = trec_utils.OnlineTRECRun(FLAGS.model_name + '_best_weight_' +
str(FLAGS.best_weight))
# compute combined run based on test weight
comb_run = tf_utils.compute_combined_run(
run, FLAGS.qfield, q, docnos, doc_embs, word_dict, word_embs,
SCORE_NORMALIZERS[FLAGS.normalizer], best_weight)
# store ranking in crun
for qid, doc_ids_and_scores in comb_run.items():
crun.add_ranking(qid,
[(score, doc_id)
for doc_id, score in doc_ids_and_scores.items()])
# close and store run
crun.close_and_write(out_path=rankings_folder + '/' +
FLAGS.model_name + '_best_weight_' +
str(FLAGS.best_weight) + '.txt',
overwrite=True)
print('combined run stored in {}'.format(rankings_folder))
# evalaute combined run
print(
'evaluate run combined w/ {}-fold cross validation and best weight={}'
.format(FLAGS.num_folds, FLAGS.best_weight))
tf_utils.evaluate(['map', 'P_10', 'ndcg'], rankings_folder,
FLAGS.model_name + '_best_weight_' +
str(FLAGS.best_weight), qrels_folder,
FLAGS.qrels_fname)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--qrel',
default=
'/Users/woffee/www/emse-apiqa/QA2021/data/QA2021_stackoverflow4_qrel.txt'
)
parser.add_argument(
'--run',
default='/Users/woffee/www/emse-apiqa/QA2021/data/pyltr_pred.txt')
args = parser.parse_args()
print("args.qrel:", args.qrel)
print("args.run", args.run)
assert os.path.exists(args.qrel)
assert os.path.exists(args.run)
final_auc, final_accuracy = calc_auc(args.qrel, args.run)
with open(args.qrel, 'r') as f_qrel:
qrel = pytrec_eval.parse_qrel(f_qrel)
with open(args.run, 'r') as f_run:
run = pytrec_eval.parse_run(f_run)
evaluator = pytrec_eval.RelevanceEvaluator(qrel,
pytrec_eval.supported_measures)
results = evaluator.evaluate(run)
def print_line(measure, scope, value):
print('{:25s}{:8s}{:.4f}'.format(measure, scope, value))
total = len(results.items())
sum_map = 0.0
for query_id, query_measures in sorted(results.items()):
for measure, value in sorted(query_measures.items()):
# print_line(measure, query_id, value)
pass
# Scope hack: use query_measures of last item in previous loop to
# figure out all unique measure names.
#
# TODO(cvangysel): add member to RelevanceEvaluator
# with a list of measure names.
print("==========")
selected_measures = [
'map', 'recip_rank', 'P_5', 'P_10', 'P_15', 'P_20', 'recall_5',
'recall_10', 'recall_15', 'recall_20', 'ndcg'
]
eva_values = {}
for measure in selected_measures:
eva_values[measure] = pytrec_eval.compute_aggregated_measure(
measure,
[query_measures[measure] for query_measures in results.values()])
# print_line( measure, 'all', eva_values[measure])
for measure in selected_measures:
print_line(measure, 'all', eva_values[measure])
print(
"%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f"
% (final_auc, final_accuracy, eva_values['map'],
eva_values['recip_rank'], eva_values['P_5'], eva_values['P_10'],
eva_values['P_15'], eva_values['P_20'], eva_values['recall_5'],
eva_values['recall_10'], eva_values['recall_15'],
eva_values['recall_20'], eva_values['ndcg']))
