def lower_than(val_x, threshold):
return (val_x < threshold)
threshold_val = 0.5
filtered_results = filter_results(full_results_LOADED,
threshold_val,
greater_than,
sim_measure='ed')
# print("++++++++++")
# print("****FILTEREDRESULTS******")
# print(filtered_results)
sim_phrase_true_path = "/media/sirivasv/DATAL/MCC/DATASUBSET/MTC-ANN-2.0/metadata/MTC-ANN-phrase-similarity.csv"
sim_phrase_true_keys = ["filename", "phrase_id", "ann1", "ann2", "ann3"]
sim_phrase_true_dict = csv_to_dict(sim_phrase_true_path, sim_phrase_true_keys)
# print("++++++++++")
# print("*****TRUELABELS*****")
# print(sim_phrase_true_dict)
pattern_prec_recall = pattern_precision_recall(full_results_LOADED,
melody_dict_LOADED,
sim_phrase_true_dict, 'ed',
"ann1")
# print("++++++++++")
# print("*****PRECRECALL*****")
# print(pattern_prec_recall)
sum_prec = 0.0
sum_recall = 0.0
for pattrn in pattern_prec_recall:
sum_prec += pattrn["precision"]
tune_family_data_path = "/media/sirivasv/DATAL/MCC/DATASUBSET/MTC-ANN-2.0/metadata/MTC-ANN-tune-family-labels.csv"
tune_family_data_keys = ["filename", "tunefamily"]
tune_family_id_path = "/media/sirivasv/DATAL/MCC/DATASUBSET/mtc-fs-1.0/MTC-FS-1.0/metadata/MTC-FS.csv"
tune_family_id_keys = [
"filename", "songid", "source_id", "serial_number", "page", "singer_id_s",
"date_of_recording", "place_of_recording", "latitude", "longitude",
"title", "firstline", "textfamily_id", "tunefamily_id", "tunefamily",
"type", "voice_strophe_number", "voice_strophe", "image_filename_s",
"audio_filename", "variation", "confidence"
]
music_files_path = "/media/sirivasv/DATAL/MCC/DATASUBSET/MTC-ANN-2.0/krn/"
meta_dict = csv_to_dict(tune_family_data_path, tune_family_data_keys)
for m_i, m in enumerate(meta_dict):
meta_dict[m_i]["tunefamily_separated"] = meta_dict[m_i][
"tunefamily"].replace("_", " ")
meta_dict = add_tunefamily_ids(meta_dict, tune_family_id_path,
tune_family_id_keys)
ts = time.time()
with open('./MetaDict_{0}.json'.format(str(ts)), 'w') as outfile:
json.dump(meta_dict, outfile)
melody_dict = extract_melodies_from_corpus(music_files_path, meta_dict)
ts = time.time()
pickle.dump(melody_dict, open('./MelodyDict_{0}.pkl'.format(str(ts)), "wb"))
phrase_dict = filter_phrases(melody_dict)
ts = time.time()