models['avg_retro_weighted'] = ''.join(
[utils.ACOUSTICBRAINZ_EMBS_DIR, "/avg_retro_weighted_embs.csv"])
models['sif_init'] = ''.join(
[utils.ACOUSTICBRAINZ_EMBS_DIR, "/sif_initial_embs.csv"])
models['sif_retro_unweighted'] = ''.join(
[utils.ACOUSTICBRAINZ_EMBS_DIR, "/sif_retro_unweighted_embs.csv"])
models['sif_retro_weighted'] = ''.join(
[utils.ACOUSTICBRAINZ_EMBS_DIR, "/sif_retro_weighted_embs.csv"])
for k in models:
print("Initializing model", k)
translators[k] = EnglishLangEmbsTranslator(tm, models[k], ['en'])
print("Evaluating the translators")
for k in translators:
print(k)
tr = translators[k]
results = []
for fold in range(4):
eval_data, eval_target = dhelper.get_test_data(tm, fold=fold)
eval_target = eval_target.astype("float32")
print("Computing KB results for fold {}".format(fold))
res = judge.compute_macro_metrics(eval_target,
tr.predict_scores(eval_data))
print('auc_macro', utils.truncate(res * 100, 1))
results.append(res)
print('auc_macro mean', utils.truncate(np.mean(results) * 100, 1))
print('auc_macro std', utils.truncate(np.std(results) * 100, 1))
print(datetime.now() - startTime)
