def evaluate(ranking, gold):
for qid in gold:
gold_sorted = sorted(gold[qid], key=itemgetter(2), reverse=True)
pred_sorted = ranking[qid]
pred_sorted = sorted(pred_sorted, key=itemgetter(2), reverse=True)
gold[qid], ranking[qid] = [], []
for i, row in enumerate(gold_sorted):
relevant, gold_score, aid = row
gold[qid].append((relevant, gold_score, aid))
pred_score = pred_sorted[i][1]
ranking[qid].append((relevant, pred_score, aid))
for qid in gold:
# Sort by IR score.
gold_sorted = sorted(gold[qid], key=itemgetter(1), reverse=True)
# Sort by SVM prediction score.
pred_sorted = ranking[qid]
pred_sorted = sorted(pred_sorted, key=itemgetter(1), reverse=True)
gold[qid] = [rel for rel, score, aid in gold_sorted]
ranking[qid] = [rel for rel, score, aid in pred_sorted]
map_gold = metrics.map(gold, 10)
map_pred = metrics.map(ranking, 10)
return map_gold, map_pred
def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=50,
verbose=False,
reranking_th=-100.0,
ignore_noanswer=False, ignore_allanswer=False):
ir, svm = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer,
ignore_allanswer=ignore_allanswer)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir)
map_svm = metrics.map(svm)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
'''
print "%13s %5s" %("IR", "SVM")
print "MRR: %5.2f %5.2f" %(mrr_se, mrr_svm)
print "MAP: %5.4f %5.4f" %(map_se, map_svm)
print "AvgRec: %5.2f %5.2f" %(avg_acc1_ir, avg_acc1_svm)
print "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SVM", "IR", "SVM", "IR", "SVM", "IR", "SVM")
'''
rec1_se =-10
rec1_svm = -10
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
#print "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2)
if (rec1_se<-5):
rec1_se = p_se
rec1_svm = p_svm
'''
print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)"
print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
print "AC2 - the absolute number of correct answers at @X"
'''
print "Table view"
print " MRR MAP P@1"
print "REF_FILE %5.2f %5.2f %5.2f" % (mrr_se, map_se*100, rec1_se)
print "SVM %5.2f %5.2f %5.2f" % (mrr_svm, map_svm*100, rec1_svm)
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print "acc\tf1\tMAP\tMRR\tAvgRec"
print "%.4f %4.4f %4.4f %4.4f %4.4f" % (acc, f1, map_svm, mrr_svm,
avg_acc1_svm)
def eval_search_engine(res_fname, format, th=50):
ir = read_res_file(res_fname, format)
# evaluate IR
rec = metrics.recall_of_1(ir, th)
acc = metrics.accuracy(ir, th)
acc1 = metrics.accuracy1(ir, th)
acc2 = metrics.accuracy2(ir, th)
mrr = metrics.mrr(ir, th)
# MAP
map_ir = metrics.map(ir)
print "%10s" % "IR"
print "MRR: %5.2f" % mrr
print "MAP: %5.2f" % map_ir
for i, (r, a, a1, a2) in enumerate(zip(rec, acc, acc1, acc2), 1):
print "REC-1@%02d: %6.2f ACC@%02d: %6.2f AC1@%02d: %6.2f AC2@%02d: %4.0f" % (
i, r, i, a, i, a1, i, a2)
print
print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)"
print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
print "AC2 - the absolute number of correct answers at @X"
def eval_search_engine(res_fname, format, th=50): ir = read_res_file(res_fname, format) # evaluate IR rec = metrics.recall_of_1(ir, th) acc = metrics.accuracy(ir, th) acc1 = metrics.accuracy1(ir, th) acc2 = metrics.accuracy2(ir, th) mrr = metrics.mrr(ir, th) # MAP map_ir = metrics.map(ir) print "%10s" %"IR" print "MRR: %5.2f" % mrr print "MAP: %5.2f" % map_ir for i, (r, a, a1, a2) in enumerate(zip(rec, acc, acc1, acc2), 1): print "REC-1@%02d: %6.2f ACC@%02d: %6.2f AC1@%02d: %6.2f AC2@%02d: %4.0f" %(i, r, i, a, i, a1, i, a2) print print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)" print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions" print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)" print "AC2 - the absolute number of correct answers at @X"
def eval_reranker(resPredIterable,
th=10,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(resPredIterable, reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (conf_matrix['true']['true'] + conf_matrix['false']['false']) / (conf_matrix['true']['true'] + conf_matrix['false']['false'] + conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
mrr_svm = metrics.mrr(svm, th)
map_svm = metrics.map(svm, th)
scores = {}
scores['map'] = map_svm
scores['accuracy'] = acc
scores['precision'] = p
scores['recall'] = r
scores['f1'] = f1
scores['mrr'] = mrr_svm
return scores
def check_engine_quality(self, query_num, list_of_docs):
"""
:param query_num:
:param list_of_docs:
:return: no return. prints metrics of the query. precision, recall, map.
"""
benchmark_path = "data\\benchmark_lbls_train.csv"
df = pd.read_csv(benchmark_path)
df_prec = df[df['query'] == query_num]
df_prec = df_prec[df_prec['tweet'].isin(list_of_docs)]
dict_for_data = df_prec.set_index('tweet')['y_true'].to_dict()
rmv_lst = []
ranking = []
# Add to list for rank
for doc in list_of_docs:
try:
ranking.append(dict_for_data[int(doc)])
except:
rmv_lst.append(doc)
for d in rmv_lst:
list_of_docs.remove(d)
data_df = pd.DataFrame({
'query': query_num,
'tweet': list_of_docs,
'y_true': ranking
})
df_rec = df[df['query'] == query_num]
recall_total = len(df_rec[df_rec['y_true'] == 1.0])
# print("total Relevant doc found with tag 1 :" , len (data_df[data_df['y_true'] == 1.0]))
# print("total NON relevant doc found with tag 0 :" , len (data_df[data_df['y_true'] == 0]))
# print("found total of", len(df_prec), "tagged docs")
# Calculate and print
prec5 = metrics.precision_at_n(data_df, query_num, 5)
prec10 = metrics.precision_at_n(data_df, query_num, 10)
prec50 = metrics.precision_at_n(data_df, query_num, 50)
prec_total = metrics.precision(data_df, True, query_number=query_num)
map_of_query = metrics.map(data_df)
recall_val = metrics.recall_single(data_df, recall_total, query_num)
self.map_list.append(map_of_query)
self.prec5_list.append(prec5)
self.prec10_list.append(prec10)
self.prec50_list.append(prec50)
self.prec_total_list.append(prec_total)
self.recall_list.append(recall_val)
print()
print("precision at 5 of query", query_num, "is :", prec5)
print("precision at 10 of query", query_num, "is :", prec10)
print("precision at 50 of query", query_num, "is :", prec50)
print("precision of query", query_num, "is :", prec_total)
print("recall of query", query_num, "is :", recall_val)
print("map of query", query_num, "is :", map_of_query)
def get_cv_evaluation_results(qid_aid_label_list, y_pred):
predictions_dict = get_cv_ranked_predictions_dict(qid_aid_label_list,
y_pred)
logging.debug("Num of questions: %d" % (len(predictions_dict)))
mrr_score = m.mrr(predictions_dict, 1000)
map_score = m.map(predictions_dict) * 100
p1_score = m.recall_of_1(predictions_dict, 1000)[0]
return mrr_score, map_score, p1_score
def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=50,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir)
map_svm = metrics.map(svm)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print "%13s %5s" %("IR", "SVM")
print "MRR: %5.2f %5.2f" %(mrr_se, mrr_svm)
print "MAP: %5.4f %5.4f" %(map_se, map_svm)
print "AvgRec: %5.2f %5.2f" %(avg_acc1_ir, avg_acc1_svm)
print "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SVM", "IR", "SVM", "IR", "SVM", "IR", "SVM")
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
print "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2)
print
print "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks were questions have at most one correct answer)"
print "ACC - accuracy, i.e. number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
print "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
print "AC2 - the absolute number of correct answers at @X"
def get_evaluation_results(df,
y_pred,
skip_all_positives_and_all_negatives=True):
predictions_dict = get_ranked_predictions_dict(
df,
y_pred,
skip_all_positives_and_all_negatives=
skip_all_positives_and_all_negatives)
logging.debug("Num of questions: %d" % (len(predictions_dict)))
mrr_score = m.mrr(predictions_dict, 1000)
map_score = m.map(predictions_dict) * 100
p1_score = m.recall_of_1(predictions_dict, 1000)[0]
return mrr_score, map_score, p1_score
def cal_score(ref_lines, probs):
reranking_th = 0.0
line_count = 0
pred_lines = defaultdict(list)
for ref_line in ref_lines:
qid, aid, lbl = ref_line[0], ref_line[1], ref_line[2]
pred_lines[qid].append((lbl, probs[line_count][0], aid))
line_count += 1
# for qid in pred_lines.keys():
# candidates = pred_lines[qid]
# if all(relevant == "false" for relevant, _, _ in candidates):
# del pred_lines[qid]
for qid in pred_lines.keys():
pred_sorted = pred_lines[qid]
max_score = max([score for rel, score, aid in pred_sorted])
if max_score >= reranking_th:
pred_sorted = sorted(pred_sorted, key=itemgetter(1), reverse=True)
pred_lines[qid] = [rel for rel, score, aid in pred_sorted]
MAP = metrics.map(pred_lines, 10)
MRR = metrics.mrr(pred_lines, 10)
return MAP, MRR
def test(engine, options):
queries = pd.read_csv(os.path.join('data', 'queries_train.tsv'), sep='\t')
bench_lbls = pd.read_csv(os.path.join('data', 'benchmark_lbls_train.csv'),
dtype={
'query': int,
'tweet': str,
'y_true': int
})
q2n_relevant = bench_lbls.groupby('query')['y_true'].sum().to_dict()
queries_results = []
q_times = []
for i, row in queries.iterrows():
q_id = row['query_id']
q_keywords = row['keywords']
start_time = time.time()
q_n_res, q_res = engine.search(q_keywords, options['methods'])
end_time = time.time()
q_time = end_time - start_time
q_times.append(q_time)
queries_results.extend([(q_id, str(doc_id)) for doc_id in q_res])
if q_time > 10:
print(f'Query time exceeded: {options}')
queries_results = pd.DataFrame(queries_results, columns=['query', 'tweet'])
q_results_labeled = pd.merge(queries_results,
bench_lbls,
on=['query', 'tweet'],
how='inner',
suffixes=('_result', '_bench'))
options['max_q_time'] = max(q_times)
options['avg_q_time'] = sum(q_times) / len(q_times)
options['MAP'] = metrics.map(q_results_labeled)
options['precision'] = metrics.precision(q_results_labeled)
options['precision@5'] = metrics.precision(
q_results_labeled.groupby('query').head(5))
options['precision@10'] = metrics.precision(
q_results_labeled.groupby('query').head(10))
options['precision@50'] = metrics.precision(
q_results_labeled.groupby('query').head(50))
options['recall'] = metrics.recall(q_results_labeled, q2n_relevant)
save_to_csv(options)
def _run_evaluation_on_selected_users(self, recommender_object,
usersToEvaluate):
start_time = time.time()
start_time_print = time.time()
results_dict = {}
for cutoff in self.cutoff_list:
results_dict[cutoff] = create_empty_metrics_dict(
self.n_items, self.n_users, recommender_object.URM_train,
self.ignore_items_ID, self.ignore_users_ID, cutoff,
self.diversity_object)
n_users_evaluated = 0
for test_user in usersToEvaluate:
# Being the URM CSR, the indices are the non-zero column indexes
relevant_items = self.get_user_relevant_items(test_user)
n_users_evaluated += 1
recommended_items = recommender_object.recommend(
test_user,
remove_seen_flag=self.exclude_seen,
cutoff=self.max_cutoff,
remove_top_pop_flag=False,
remove_CustomItems_flag=self.ignore_items_flag)
is_relevant = np.in1d(recommended_items,
relevant_items,
assume_unique=True)
for cutoff in self.cutoff_list:
results_current_cutoff = results_dict[cutoff]
is_relevant_current_cutoff = is_relevant[0:cutoff]
recommended_items_current_cutoff = recommended_items[0:cutoff]
results_current_cutoff[
EvaluatorMetrics.ROC_AUC.value] += roc_auc(
is_relevant_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.PRECISION.value] += precision(
is_relevant_current_cutoff, len(relevant_items))
results_current_cutoff[
EvaluatorMetrics.RECALL.value] += recall(
is_relevant_current_cutoff, relevant_items)
results_current_cutoff[EvaluatorMetrics.RECALL_TEST_LEN.
value] += recall_min_test_len(
is_relevant_current_cutoff,
relevant_items)
results_current_cutoff[EvaluatorMetrics.MAP.value] += map(
is_relevant_current_cutoff, relevant_items)
results_current_cutoff[EvaluatorMetrics.MRR.value] += rr(
is_relevant_current_cutoff)
results_current_cutoff[EvaluatorMetrics.NDCG.value] += ndcg(
recommended_items_current_cutoff,
relevant_items,
relevance=self.get_user_test_ratings(test_user),
at=cutoff)
results_current_cutoff[
EvaluatorMetrics.HIT_RATE.
value] += is_relevant_current_cutoff.sum()
results_current_cutoff[EvaluatorMetrics.ARHR.value] += arhr(
is_relevant_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.NOVELTY.value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_GINI.value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[EvaluatorMetrics.SHANNON_ENTROPY.
value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.COVERAGE_ITEM.value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.COVERAGE_USER.value].add_recommendations(
recommended_items_current_cutoff, test_user)
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_MEAN_INTER_LIST.
value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[EvaluatorMetrics.DIVERSITY_HERFINDAHL.
value].add_recommendations(
recommended_items_current_cutoff)
if EvaluatorMetrics.DIVERSITY_SIMILARITY.value in results_current_cutoff:
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_SIMILARITY.
value].add_recommendations(
recommended_items_current_cutoff)
if time.time() - start_time_print > 30 or n_users_evaluated == len(
self.usersToEvaluate):
print(
"SequentialEvaluator: Processed {} ( {:.2f}% ) in {:.2f} seconds. Users per second: {:.0f}"
.format(
n_users_evaluated, 100.0 * float(n_users_evaluated) /
len(self.usersToEvaluate),
time.time() - start_time,
float(n_users_evaluated) / (time.time() - start_time)))
sys.stdout.flush()
sys.stderr.flush()
start_time_print = time.time()
return results_dict, n_users_evaluated
bench_lbls,
on=['query', 'tweet'],
how='inner',
suffixes=('_result',
'_bench'))
# q_results_labeled.rename(columns={'y_true': 'label'})
zero_recall_qs = [q_id for q_id, rel in q2n_relevant.items() \
if metrics.recall_single(q_results_labeled, rel, q_id) == 0]
if len(zero_recall_qs) > 0:
logging.warning(
f"{engine_module}'s recall for the following queries was zero {zero_recall_qs}."
)
if q_results_labeled is not None:
# test that MAP > 0
results_map = metrics.map(q_results_labeled)
logging.debug(
f"{engine_module} results have MAP value of {results_map}."
)
if results_map <= 0 or results_map > 1:
logging.error(
f'{engine_module} results MAP value is out of range (0,1).'
)
# test that the average across queries of precision,
# precision@5, precision@10, precision@50, and recall
# is in [0,1].
prec, p5, p10, p50, recall = \
metrics.precision(q_results_labeled), \
metrics.precision(q_results_labeled.groupby('query').head(5)), \
metrics.precision(q_results_labeled.groupby('query').head(10)), \
relevant_items = test[test_user].indices
if len(relevant_items) > 0:
neval += 1
#
# TODO: Here you can write to file the recommendations for each user in the test split.
# WARNING: there is a catch with the item idx!
#
# this will rank *all* items
recommended_items = recommender.recommend(user_profile,
exclude_seen=True)
# use this to have the *top-k* recommended items (warning: this can underestimate ROC-AUC for small k)
# recommended_items = recommender.recommend(user_profile, k=at, exclude_seen=True)
roc_auc_ += roc_auc(recommended_items, relevant_items)
precision_ += precision(recommended_items, relevant_items, at=at)
recall_ += recall(recommended_items, relevant_items, at=at)
map_ += map(recommended_items, relevant_items, at=at)
mrr_ += rr(recommended_items, relevant_items, at=at)
ndcg_ += ndcg(recommended_items,
relevant_items,
relevance=test[test_user].data,
at=at)
roc_auc_ /= neval
precision_ /= neval
recall_ /= neval
map_ /= neval
mrr_ /= neval
ndcg_ /= neval
logger.info('Ranking quality')
logger.info('ROC-AUC: {:.4f}'.format(roc_auc_))
logger.info('Precision@{}: {:.4f}'.format(at, precision_))
def stats_cv(path=".", format="trec", prefix="svm", th=50, verbose=False):
mrrs_se = []
mrrs_svm = []
abs_mrrs = []
rel_mrrs = []
maps_se = []
maps_svm = []
abs_maps = []
rel_maps = []
recalls1_se = []
recalls1_svm = []
abs_recalls = []
rel_recalls = []
oracle_mrrs = []
oracle_maps = []
oracle_recs1 = []
num_folds = 0
print "%13s %5s %7s %7s" %("IR", "SVM", "(abs)", "(rel)")
for fold in sorted(os.listdir(path)):
currentFold = os.path.join(path, fold)
if not os.path.isdir(currentFold):
continue
if not fold.startswith("fold"):
logging.warn("Directories containing CV folds should start with 'fold'")
continue
print fold
# Relevancy file
res_fname = os.path.join(currentFold, "%s.test.res" % prefix)
if not os.path.exists(res_fname):
logging.error("Relevancy file not found: %s", res_fname)
sys.exit(1)
# Predictions file
pred_fname = os.path.join(currentFold, "%s.pred" % prefix)
if not os.path.exists(pred_fname):
logging.error("SVM prediction file not found: %s", pred_fname)
sys.exit(1)
try:
ir, svm = read_res_pred_files(res_fname, pred_fname, format, verbose)
except:
logging.error("Failed to process input files: %s %s", res_fname, pred_fname)
logging.error("Check that the input file format is correct")
sys.exit(1)
# MRR
mrr_se = metrics.mrr(ir, th) or 1
mrr_svm = metrics.mrr(svm, th)
mrrs_se.append(mrr_se)
mrrs_svm.append(mrr_svm)
# improvement
abs_mrr_diff = mrr_svm - mrr_se
rel_mrr_diff = (mrr_svm - mrr_se)*100/mrr_se
abs_mrrs.append(abs_mrr_diff)
rel_mrrs.append(rel_mrr_diff)
print "MRR: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (mrr_se, mrr_svm, abs_mrr_diff, rel_mrr_diff)
# MAP
map_se = metrics.map(ir) or 1
map_svm = metrics.map(svm)
maps_se.append(map_se)
maps_svm.append(map_svm)
# improvement
abs_map_diff = map_svm - map_se
rel_map_diff = (map_svm - map_se)*100/map_se
abs_maps.append(abs_map_diff)
rel_maps.append(rel_map_diff)
print "MAP: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (map_se, map_svm, abs_map_diff, rel_map_diff)
# Recall-of-1@1
rec_se = metrics.recall_of_1(ir, th)[0] or 1
rec_svm = metrics.recall_of_1(svm, th)[0]
recalls1_se.append(rec_se)
recalls1_svm.append(rec_svm)
# improvement
abs_rec_diff = rec_svm - rec_se
rel_rec_diff = (rec_svm - rec_se)*100/rec_se
abs_recalls.append(abs_rec_diff)
rel_recalls.append(rel_rec_diff)
print "P@1: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (rec_se, rec_svm, abs_rec_diff, rel_rec_diff)
num_folds += 1
'''
mrr_oracle = metrics.oracle_mrr(ir, th)
map_oracle = metrics.oracle_map(ir)
prec_oracle = metrics.oracle_precision(ir, th)[0]
rec1_oracle = metrics.oracle_recall_of_1(ir, th)[0]
oracle_mrrs.append(mrr_oracle)
oracle_maps.append(map_oracle)
oracle_recs1.append(rec1_oracle)
print "Oracle MRR: %5.2f, Oracle MAP: %5.2f, Oracle prec: %5.2f, Oracle rec@1: %5.2f" % (mrr_oracle, map_oracle, prec_oracle, rec1_oracle)
'''
# mrrs
avg_mrr_se, std_mrr_se = mean_and_std(mrrs_se)
avg_mrr_svm, std_mrr_svm = mean_and_std(mrrs_svm)
avg_abs_impr_mrr, std_abs_impr_mrr = mean_and_std(abs_mrrs)
avg_rel_impr_mrr, std_rel_impr_mrr = mean_and_std(rel_mrrs)
#oracle_avg_mrr, std_oracle_avg_mrr = mean_and_std(oracle_mrrs)
# maps
avg_map_se, std_map_se = mean_and_std(maps_se)
avg_map_svm, std_map_svm = mean_and_std(maps_svm)
avg_abs_impr_map, std_abs_impr_map = mean_and_std(abs_maps)
avg_rel_impr_map, std_rel_impr_map = mean_and_std(rel_maps)
#oracle_avg_map, std_oracle_avg_map = mean_and_std(oracle_maps)
# recall
avg_rec1_se, std_rec1_se = mean_and_std(recalls1_se) # se
avg_rec1_svm, std_rec1_svm = mean_and_std(recalls1_svm) # svm
avg_abs_impr_rec1, std_abs_impr_rec1 = mean_and_std(abs_recalls) # absolute
avg_rel_impr_rec1, std_rel_impr_rec1 = mean_and_std(rel_recalls) # relative
#oracle_avg_rec1, std_oracle_avg_rec1 = mean_and_std(oracle_recs1)
FMT = u"%3s: %5.2f \u00B1 %4.2f %5.2f \u00B1 %4.2f %+6.2f%% \u00B1 %4.2f %+6.2f%% \u00B1 %4.2f"
#ORACLE_FMT = u"Oracle MRR: %5.2f \u00B1 %4.2f, Oracle MAP: %5.2f \u00B1 %4.2f, Oracle P@1: %5.2f \u00B1 %4.2f"
print
print "Averaged over %s folds" % num_folds
print "%17s %12s %14s %14s" %("IR", "SVM", "(abs)", "(rel)")
print FMT % ("MRR", avg_mrr_se, std_mrr_se, avg_mrr_svm, std_mrr_svm, avg_abs_impr_mrr, std_abs_impr_mrr, avg_rel_impr_mrr, std_rel_impr_mrr)
print FMT % ("MAP", avg_map_se, std_map_se, avg_map_svm, std_map_svm, avg_abs_impr_map, std_abs_impr_map, avg_rel_impr_map, std_rel_impr_map)
print FMT % ("P@1", avg_rec1_se, std_rec1_se, avg_rec1_svm, std_rec1_svm, avg_abs_impr_rec1, std_abs_impr_rec1, avg_rel_impr_rec1, std_rel_impr_rec1)
def stats_cv(path=".",
format="trec",
prefix="svm",
th=50,
suf="",
verbose=False,
truth_file=None,
ignore_noanswer=False,
cut_truth_map_at_N=None):
mrrs_se = []
mrrs_svm = []
abs_mrrs = []
rel_mrrs = []
maps_se = []
maps_svm = []
abs_maps = []
rel_maps = []
recalls1_se = []
recalls1_svm = []
abs_recalls = []
rel_recalls = []
num_folds = 0
truth = read_truth_file(truth_file, format, cut_truth_map_at_N)
print "%13s %5s %7s %7s" % ("IR", "SVM", "(abs)", "(rel)")
for fold in sorted(os.listdir(path)):
currentFold = os.path.join(path, fold)
if not os.path.isdir(currentFold):
continue
if not fold.startswith("fold"):
logging.warn(
"Directories containing CV folds should start with 'fold'")
continue
print fold
# Relevancy file
res_fname = os.path.join(currentFold, "%s.relevancy" % prefix)
if not os.path.exists(res_fname):
logging.error("Relevancy file not found: %s", res_fname)
sys.exit(1)
# Predictions file
pred_fname = os.path.join(currentFold, "%s.pred" % (prefix + suf))
if not os.path.exists(pred_fname):
logging.error("SVM prediction file not found: %s", pred_fname)
sys.exit(1)
try:
ir, svm = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
ignore_noanswer=ignore_noanswer,
truth_map=truth)
except:
logging.error("Failed to process input files: %s %s", res_fname,
pred_fname)
logging.error("Check that the input file format is correct")
sys.exit(1)
# MRR
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
mrrs_se.append(mrr_se)
mrrs_svm.append(mrr_svm)
# improvement
abs_mrr_diff = mrr_svm - mrr_se
rel_mrr_diff = (mrr_svm - mrr_se) * 100 / mrr_se
abs_mrrs.append(abs_mrr_diff)
rel_mrrs.append(rel_mrr_diff)
print "MRR: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (
mrr_se, mrr_svm, abs_mrr_diff, rel_mrr_diff)
# MAP
map_se = metrics.map(ir)
map_svm = metrics.map(svm)
maps_se.append(map_se)
maps_svm.append(map_svm)
# improvement
abs_map_diff = map_svm - map_se
rel_map_diff = (map_svm - map_se) * 100 / map_se
abs_maps.append(abs_map_diff)
rel_maps.append(rel_map_diff)
print "MAP: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (
map_se * 100, map_svm * 100, abs_map_diff, rel_map_diff)
# Recall-of-1@1
rec_se = metrics.recall_of_1(ir, th)[0]
rec_svm = metrics.recall_of_1(svm, th)[0]
recalls1_se.append(rec_se)
recalls1_svm.append(rec_svm)
# improvement
abs_rec_diff = rec_svm - rec_se
rel_rec_diff = (rec_svm - rec_se) * 100 / rec_se
abs_recalls.append(abs_rec_diff)
rel_recalls.append(rel_rec_diff)
print "P@1: %5.2f %5.2f %+6.2f%% %+6.2f%%" % (
rec_se, rec_svm, abs_rec_diff, rel_rec_diff)
num_folds += 1
# mrrs
avg_mrr_se, std_mrr_se = mean_and_std(mrrs_se)
avg_mrr_svm, std_mrr_svm = mean_and_std(mrrs_svm)
avg_abs_impr_mrr, std_abs_impr_mrr = mean_and_std(abs_mrrs)
avg_rel_impr_mrr, std_rel_impr_mrr = mean_and_std(rel_mrrs)
# maps
avg_map_se, std_map_se = mean_and_std(maps_se)
avg_map_svm, std_map_svm = mean_and_std(maps_svm)
avg_abs_impr_map, std_abs_impr_map = mean_and_std(abs_maps)
avg_rel_impr_map, std_rel_impr_map = mean_and_std(rel_maps)
# recall
avg_rec1_se, std_rec1_se = mean_and_std(recalls1_se) # se
avg_rec1_svm, std_rec1_svm = mean_and_std(recalls1_svm) # svm
avg_abs_impr_rec1, std_abs_impr_rec1 = mean_and_std(
abs_recalls) # absolute
avg_rel_impr_rec1, std_rel_impr_rec1 = mean_and_std(
rel_recalls) # relative
FMT = u"%3s: %5.2f \u00B1 %4.2f %5.2f \u00B1 %4.2f %+6.2f%% \u00B1 %4.2f %+6.2f%% \u00B1 %4.2f"
print
print "Averaged over %s folds" % num_folds
print "%17s %12s %14s %14s" % ("IR", "SVM", "(abs)", "(rel)")
print FMT % ("MRR", avg_mrr_se, std_mrr_se, avg_mrr_svm, std_mrr_svm,
avg_abs_impr_mrr, std_abs_impr_mrr, avg_rel_impr_mrr,
std_rel_impr_mrr)
print FMT % ("MAP", avg_map_se * 100, std_map_se, avg_map_svm * 100,
std_map_svm, avg_abs_impr_map, std_abs_impr_map,
avg_rel_impr_map, std_rel_impr_map)
print FMT % ("P@1", avg_rec1_se, std_rec1_se, avg_rec1_svm, std_rec1_svm,
avg_abs_impr_rec1, std_abs_impr_rec1, avg_rel_impr_rec1,
std_rel_impr_rec1)
print "Table view"
print " MRR MAP P@1"
print u"IR %5.2f\u00B1%4.2f %5.2f\u00B1%4.2f %5.2f\u00B1%4.2f" % (
avg_mrr_se, std_mrr_se, avg_map_se * 100, std_map_se * 100,
avg_rec1_se, std_rec1_se)
print u"SVM %5.2f\u00B1%4.2f %5.2f\u00B1%4.2f %5.2f\u00B1%4.2f" % (
avg_mrr_svm, std_mrr_svm, avg_map_svm * 100, std_map_svm * 100,
avg_rec1_svm, std_rec1_svm)
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print("")
print("*** Official score (MAP for SYS): %5.4f" % (map_svm))
print("")
print("")
print("******************************")
print("*** Classification results ***")
print("******************************")
print("")
print("Acc = %5.4f" % (acc))
print("P = %5.4f" % (p))
print("R = %5.4f" % (r))
print("F1 = %5.4f" % (f1))
print("")
print("")
print("********************************")
print("*** Detailed ranking results ***")
print("********************************")
print("")
print("IR -- Score for the output of the IR system (baseline).")
print("SYS -- Score for the output of the tested system.")
print("")
print("%13s %5s" % ("IR", "SYS"))
print("MAP : %5.4f %5.4f" % (map_se, map_svm))
print("AvgRec: %5.4f %5.4f" % (avg_acc1_ir, avg_acc1_svm))
print("MRR : %6.2f %6.2f" % (mrr_se, mrr_svm))
print("%16s %6s %14s %6s %14s %6s %12s %4s" %
("IR", "SYS", "IR", "SYS", "IR", "SYS", "IR", "SYS"))
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2,
a_svm2) in enumerate(
zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1,
acc_se2, acc_svm2), 1):
print(
"REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f"
% (i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2,
a_svm2))
print()
print(
"REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks where questions have at most one correct answer)"
)
print(
"ACC - accuracy, i.e., number of correct answers retrieved at rank @X normalized by the rank and the total number of questions"
)
print(
"AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)"
)
print("AC2 - the absolute number of correct answers at @X")
def _run_evaluation_on_selected_users(self,
recommender_object,
usersToEvaluate,
block_size=1000):
start_time = time.time()
start_time_print = time.time()
results_dict = {}
for cutoff in self.cutoff_list:
results_dict[cutoff] = create_empty_metrics_dict(
self.n_items, self.n_users, recommender_object.get_URM_train(),
self.ignore_items_ID, self.ignore_users_ID, cutoff,
self.diversity_object)
n_users_evaluated = 0
# Start from -block_size to ensure it to be 0 at the first block
user_batch_start = 0
user_batch_end = 0
while user_batch_start < len(self.usersToEvaluate):
user_batch_end = user_batch_start + block_size
user_batch_end = min(user_batch_end, len(usersToEvaluate))
test_user_batch_array = np.array(
usersToEvaluate[user_batch_start:user_batch_end])
user_batch_start = user_batch_end
# Compute predictions for a batch of users using vectorization, much more efficient than computing it one at a time
recommended_items_batch_list = recommender_object.recommend(
test_user_batch_array,
remove_seen_flag=self.exclude_seen,
cutoff=self.max_cutoff,
remove_top_pop_flag=False,
remove_CustomItems_flag=self.ignore_items_flag)
# Compute recommendation quality for each user in batch
for batch_user_index in range(len(recommended_items_batch_list)):
user_id = test_user_batch_array[batch_user_index]
recommended_items = recommended_items_batch_list[
batch_user_index]
# Being the URM CSR, the indices are the non-zero column indexes
relevant_items = self.get_user_relevant_items(user_id)
is_relevant = np.in1d(recommended_items,
relevant_items,
assume_unique=True)
n_users_evaluated += 1
for cutoff in self.cutoff_list:
results_current_cutoff = results_dict[cutoff]
is_relevant_current_cutoff = is_relevant[0:cutoff]
recommended_items_current_cutoff = recommended_items[
0:cutoff]
results_current_cutoff[
EvaluatorMetrics.ROC_AUC.value] += roc_auc(
is_relevant_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.PRECISION.value] += precision(
is_relevant_current_cutoff, len(relevant_items))
results_current_cutoff[
EvaluatorMetrics.RECALL.value] += recall(
is_relevant_current_cutoff, relevant_items)
results_current_cutoff[EvaluatorMetrics.RECALL_TEST_LEN.
value] += recall_min_test_len(
is_relevant_current_cutoff,
relevant_items)
results_current_cutoff[EvaluatorMetrics.MAP.value] += map(
is_relevant_current_cutoff, relevant_items)
results_current_cutoff[EvaluatorMetrics.MRR.value] += rr(
is_relevant_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.NDCG.value] += ndcg(
recommended_items_current_cutoff,
relevant_items,
relevance=self.get_user_test_ratings(user_id),
at=cutoff)
results_current_cutoff[
EvaluatorMetrics.HIT_RATE.
value] += is_relevant_current_cutoff.sum()
results_current_cutoff[
EvaluatorMetrics.ARHR.value] += arhr(
is_relevant_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.NOVELTY.value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_GINI.
value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.SHANNON_ENTROPY.
value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.COVERAGE_ITEM.
value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.COVERAGE_USER.
value].add_recommendations(
recommended_items_current_cutoff, user_id)
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_MEAN_INTER_LIST.
value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_HERFINDAHL.
value].add_recommendations(
recommended_items_current_cutoff)
if EvaluatorMetrics.DIVERSITY_SIMILARITY.value in results_current_cutoff:
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_SIMILARITY.
value].add_recommendations(
recommended_items_current_cutoff)
if time.time(
) - start_time_print > 30 or n_users_evaluated == len(
self.usersToEvaluate):
print(
"SequentialEvaluator: Processed {} ( {:.2f}% ) in {:.2f} seconds. Users per second: {:.0f}"
.format(
n_users_evaluated,
100.0 * float(n_users_evaluated) /
len(self.usersToEvaluate),
time.time() - start_time,
float(n_users_evaluated) /
(time.time() - start_time)))
sys.stdout.flush()
sys.stderr.flush()
start_time_print = time.time()
return results_dict, n_users_evaluated
def eval_reranker(res_fname="svm.test.res",
pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname,
pred_fname,
format,
verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (
conf_matrix['true']['true'] + conf_matrix['false']['false']) / (
conf_matrix['true']['true'] + conf_matrix['false']['false'] +
conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (
conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
#print ""
#print "*** Official score (MAP for SYS): %5.4f" %(map_svm)
#print ""
#print ""
#print "******************************"
#print "*** Classification results ***"
#print "******************************"
#print ""
#print "Acc = %5.4f" %(acc)
#print "P = %5.4f" %(p)
#print "R = %5.4f" %(r)
#print "F1 = %5.4f" %(f1)
#print ""
#print ""
#print "********************************"
#print "*** Detailed ranking results ***"
#print "********************************"
#print ""
#print "IR -- Score for the output of the IR system (baseline)."
#print "SYS -- Score for the output of the tested system."
#print ""
#print "%13s %5s" %("IR", "SYS")
#print "MAP : %5.4f %5.4f" %(map_se, map_svm)
#print "AvgRec: %5.4f %5.4f" %(avg_acc1_ir, avg_acc1_svm)
#print "MRR : %6.2f %6.2f" %(mrr_se, mrr_svm)
print "MAP : %5.4f\tMRR : %5.4f\tAvgRec: %5.4f" % (map_svm, mrr_svm,
avg_acc1_svm)
#print "Acc : %5.4f" %(acc)
#print "P : %5.4f" %(p)
#print "R : %5.4f" %(r)
#print "F1 : %5.4f" %(f1)
"""
def evaluateRecommender(self, recommender_object):
"""
:param recommender_object: the trained recommender object, a Recommender subclass
:param URM_test_list: list of URMs to test the recommender against, or a single URM object
:param cutoff_list: list of cutoffs to be use to report the scores, or a single cutoff
"""
results_dict = {}
for cutoff in self.cutoff_list:
results_dict[cutoff] = create_empty_metrics_dict(
self.n_items, self.n_users, recommender_object.URM_train,
self.ignore_items_ID, self.ignore_users_ID, cutoff,
self.diversity_object)
start_time = time.time()
start_time_print = time.time()
n_eval = 0
self.__all_items = np.arange(0, self.n_items, dtype=np.int)
self.__all_items = set(self.__all_items)
if self.ignore_items_flag:
recommender_object.set_items_to_ignore(self.ignore_items_ID)
for test_user in self.usersToEvaluate:
# Being the URM CSR, the indices are the non-zero column indexes
relevant_items = self.get_user_relevant_items(test_user)
n_eval += 1
self.user_specific_remove_items(recommender_object, test_user)
# recommended_items = recommender_object.recommend(np.array(test_user), remove_seen_flag=self.exclude_seen,
# cutoff = self.max_cutoff, remove_top_pop_flag=False, remove_CustomItems_flag=self.ignore_items_flag)
recommended_items = recommender_object.recommend(
np.atleast_1d(test_user),
remove_seen_flag=self.exclude_seen,
cutoff=self.max_cutoff,
remove_top_pop_flag=False,
remove_CustomItems_flag=self.ignore_items_flag)
recommended_items = np.array(recommended_items[0])
recommender_object.reset_items_to_ignore()
is_relevant = np.in1d(recommended_items,
relevant_items,
assume_unique=True)
for cutoff in self.cutoff_list:
results_current_cutoff = results_dict[cutoff]
is_relevant_current_cutoff = is_relevant[0:cutoff]
recommended_items_current_cutoff = recommended_items[0:cutoff]
results_current_cutoff[
EvaluatorMetrics.ROC_AUC.value] += roc_auc(
is_relevant_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.PRECISION.value] += precision(
is_relevant_current_cutoff, len(relevant_items))
results_current_cutoff[
EvaluatorMetrics.RECALL.value] += recall(
is_relevant_current_cutoff, relevant_items)
results_current_cutoff[EvaluatorMetrics.RECALL_TEST_LEN.
value] += recall_min_test_len(
is_relevant_current_cutoff,
relevant_items)
results_current_cutoff[EvaluatorMetrics.MAP.value] += map(
is_relevant_current_cutoff, relevant_items)
results_current_cutoff[EvaluatorMetrics.MRR.value] += rr(
is_relevant_current_cutoff)
results_current_cutoff[EvaluatorMetrics.NDCG.value] += ndcg(
recommended_items_current_cutoff,
relevant_items,
relevance=self.get_user_test_ratings(test_user),
at=cutoff)
results_current_cutoff[
EvaluatorMetrics.HIT_RATE.
value] += is_relevant_current_cutoff.sum()
results_current_cutoff[EvaluatorMetrics.ARHR.value] += arhr(
is_relevant_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.NOVELTY.value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_GINI.value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[EvaluatorMetrics.SHANNON_ENTROPY.
value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.COVERAGE_ITEM.value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[
EvaluatorMetrics.COVERAGE_USER.value].add_recommendations(
recommended_items_current_cutoff, test_user)
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_MEAN_INTER_LIST.
value].add_recommendations(
recommended_items_current_cutoff)
results_current_cutoff[EvaluatorMetrics.DIVERSITY_HERFINDAHL.
value].add_recommendations(
recommended_items_current_cutoff)
if EvaluatorMetrics.DIVERSITY_SIMILARITY.value in results_current_cutoff:
results_current_cutoff[
EvaluatorMetrics.DIVERSITY_SIMILARITY.
value].add_recommendations(
recommended_items_current_cutoff)
if time.time() - start_time_print > 30 or n_eval == len(
self.usersToEvaluate):
print(
"SequentialEvaluator: Processed {} ( {:.2f}% ) in {:.2f} seconds. Users per second: {:.0f}"
.format(n_eval,
100.0 * float(n_eval) / len(self.usersToEvaluate),
time.time() - start_time,
float(n_eval) / (time.time() - start_time)))
sys.stdout.flush()
sys.stderr.flush()
start_time_print = time.time()
if (n_eval > 0):
for cutoff in self.cutoff_list:
results_current_cutoff = results_dict[cutoff]
for key in results_current_cutoff.keys():
value = results_current_cutoff[key]
if isinstance(value, Metrics_Object):
results_current_cutoff[key] = value.get_metric_value()
else:
results_current_cutoff[key] = value / n_eval
precision_ = results_current_cutoff[
EvaluatorMetrics.PRECISION.value]
recall_ = results_current_cutoff[EvaluatorMetrics.RECALL.value]
if precision_ + recall_ != 0:
results_current_cutoff[EvaluatorMetrics.F1.value] = 2 * (
precision_ * recall_) / (precision_ + recall_)
else:
print(
"WARNING: No users had a sufficient number of relevant items")
if self.ignore_items_flag:
recommender_object.reset_items_to_ignore()
results_run_string = self.get_result_string(results_dict)
return (results_dict, results_run_string)
def eval_reranker(res_fname="svm.test.res", pred_fname="svm.train.pred",
format="trec",
th=10,
verbose=False,
reranking_th=0.0,
ignore_noanswer=False):
ir, svm, conf_matrix = read_res_pred_files(res_fname, pred_fname, format, verbose,
reranking_th=reranking_th,
ignore_noanswer=ignore_noanswer)
# Calculate standard P, R, F1, Acc
acc = 1.0 * (conf_matrix['true']['true'] + conf_matrix['false']['false']) / (conf_matrix['true']['true'] + conf_matrix['false']['false'] + conf_matrix['true']['false'] + conf_matrix['false']['true'])
p = 0
if (conf_matrix['true']['true'] + conf_matrix['false']['true']) > 0:
p = 1.0 * (conf_matrix['true']['true']) / (conf_matrix['true']['true'] + conf_matrix['false']['true'])
r = 0
if (conf_matrix['true']['true'] + conf_matrix['true']['false']) > 0:
r = 1.0 * (conf_matrix['true']['true']) / (conf_matrix['true']['true'] + conf_matrix['true']['false'])
f1 = 0
if (p + r) > 0:
f1 = 2.0 * p * r / (p + r)
# evaluate IR
prec_se = metrics.recall_of_1(ir, th)
acc_se = metrics.accuracy(ir, th)
acc_se1 = metrics.accuracy1(ir, th)
acc_se2 = metrics.accuracy2(ir, th)
# evaluate SVM
prec_svm = metrics.recall_of_1(svm, th)
acc_svm = metrics.accuracy(svm, th)
acc_svm1 = metrics.accuracy1(svm, th)
acc_svm2 = metrics.accuracy2(svm, th)
mrr_se = metrics.mrr(ir, th)
mrr_svm = metrics.mrr(svm, th)
map_se = metrics.map(ir, th)
map_svm = metrics.map(svm, th)
avg_acc1_svm = metrics.avg_acc1(svm, th)
avg_acc1_ir = metrics.avg_acc1(ir, th)
print ("")
print ("*** Official score (MAP for SYS): %5.4f" %(map_svm))
print ("")
print ("")
print( "******************************")
print( "*** Classification results ***")
print( "******************************")
print( "")
print( "Acc = %5.4f" %(acc))
print( "P = %5.4f" %(p))
print( "R = %5.4f" %(r))
print( "F1 = %5.4f" %(f1))
print( "")
print( "")
print( "********************************")
print( "*** Detailed ranking results ***")
print( "********************************")
print( "")
print( "IR -- Score for the output of the IR system (baseline).")
print( "SYS -- Score for the output of the tested system.")
print( "")
print( "%13s %5s" %("IR", "SYS"))
print( "MAP : %5.4f %5.4f" %(map_se, map_svm))
print( "AvgRec: %5.4f %5.4f" %(avg_acc1_ir, avg_acc1_svm))
print( "MRR : %6.2f %6.2f" %(mrr_se, mrr_svm))
print( "%16s %6s %14s %6s %14s %6s %12s %4s" % ("IR", "SYS", "IR", "SYS", "IR", "SYS", "IR", "SYS"))
for i, (p_se, p_svm, a_se, a_svm, a_se1, a_svm1, a_se2, a_svm2) in enumerate(zip(prec_se, prec_svm, acc_se, acc_svm, acc_se1, acc_svm1, acc_se2, acc_svm2), 1):
print( "REC-1@%02d: %6.2f %6.2f ACC@%02d: %6.2f %6.2f AC1@%02d: %6.2f %6.2f AC2@%02d: %4.0f %4.0f" %(i, p_se, p_svm, i, a_se, a_svm, i, a_se1, a_svm1, i, a_se2, a_svm2))
print( "REC-1 - percentage of questions with at least 1 correct answer in the top @X positions (useful for tasks where questions have at most one correct answer)")
print( "ACC - accuracy, i.e., number of correct answers retrieved at rank @X normalized by the rank and the total number of questions")
print( "AC1 - the number of correct answers at @X normalized by the number of maximum possible answers (perfect re-ranker)")
print( "AC2 - the absolute number of correct answers at @X")
return map_svm
def calcQueryEvaluation(q_res, num_q):
queries_results = pd.DataFrame(q_res, columns=['query', 'tweet'])
if bench_lbls is not None and len(queries_results) > 0:
q_results_labeled = pd.merge(queries_results,
bench_lbls,
on=['query', 'tweet'],
how='inner',
suffixes=('_result', '_bench'))
# q_results_labeled.rename(columns={'y_true': 'label'})
zero_recall_qs = [q_id for q_id, rel in q2n_relevant.items() \
if metrics.recall_single(q_results_labeled, rel, q_id) == 0]
if len(zero_recall_qs) > 0:
logging.warning(
f"{engine_module}'s recall for the following queries was zero {zero_recall_qs}."
)
if q_results_labeled is not None:
# test that MAP > 0
results_map = metrics.map(q_results_labeled)
logging.debug(
f"{engine_module} results have MAP value of {results_map}."
)
if results_map <= 0 or results_map > 1:
logging.error(
f'{engine_module} results MAP value is out of range (0,1).'
)
prec, p5, p10, p50, recall = \
metrics.precision(q_results_labeled), \
metrics.precision(q_results_labeled.groupby('query').head(5)), \
metrics.precision(q_results_labeled.groupby('query').head(10)), \
metrics.precision(q_results_labeled.groupby('query').head(50)), \
metrics.recall_single(q_results_labeled, q2n_relevant[num_q], num_q)
# logging.debug(f"{engine_module} on query {num_q} results produced average precision of {prec}.")
# logging.debug(f"{engine_module} on query {num_q} results produced average precision@5 of {p5}.")
# logging.debug(f"{engine_module} on query {num_q} results produced average precision@10 of {p10}.")
# logging.debug(f"{engine_module} on query {num_q} results produced average precision@50 of {p50}.")
# logging.debug(f"{engine_module} on query {num_q} results produced average recall of {recall}.")
if prec < 0 or prec > 1:
logging.error(
f"The average precision for {engine_module} is out of range [0,1]."
)
if p5 < 0 or p5 > 1:
logging.error(
f"The average precision@5 for {engine_module} is out of range [0,1]."
)
if p5 < 0 or p5 > 1:
logging.error(
f"The average precision@5 for {engine_module} is out of range [0,1]."
)
if p50 < 0 or p50 > 1:
logging.error(
f"The average precision@50 for {engine_module} is out of range [0,1]."
)
if recall < 0 or recall > 1:
logging.error(
f"The average recall for {engine_module} is out of range [0,1]."
)
precision_list.append(prec)
precisiion_5.append(p5)
precisiom_10.append(p10)
presicion_50.append(p50)
recall_list.append(recall)
