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01_test.py
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01_test.py
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########################################################################
# import default libraries
########################################################################
import os
import csv
import sys
import gc
########################################################################
########################################################################
# import additional libraries
########################################################################
import numpy as np
import scipy.stats
# from import
from tqdm import tqdm
from sklearn import metrics
try:
from sklearn.externals import joblib
except:
import joblib
# original lib
import common as com
import keras_model
########################################################################
########################################################################
# load parameter.yaml
########################################################################
param = com.yaml_load()
#######################################################################
########################################################################
# output csv file
########################################################################
def save_csv(save_file_path,
save_data):
with open(save_file_path, "w", newline="") as f:
writer = csv.writer(f, lineterminator='\n')
writer.writerows(save_data)
########################################################################
########################################################################
# main 01_test.py
########################################################################
if __name__ == "__main__":
# check mode
# "development": mode == True
# "evaluation": mode == False
mode = com.command_line_chk()
if mode is None:
sys.exit(-1)
# make output result directory
os.makedirs(param["result_directory"], exist_ok=True)
# load base directory
dirs = com.select_dirs(param=param, mode=mode)
# initialize lines in csv for AUC and pAUC
csv_lines = []
if mode:
performance_over_all = []
# loop of the base directory
for idx, target_dir in enumerate(dirs):
print("\n===========================")
print("[{idx}/{total}] {target_dir}".format(target_dir=target_dir, idx=idx+1, total=len(dirs)))
machine_type = os.path.split(target_dir)[1]
print("============== MODEL LOAD ==============")
# load model file
model_file = "{model}/model_{machine_type}.hdf5".format(model=param["model_directory"],
machine_type=machine_type)
if not os.path.exists(model_file):
com.logger.error("{} model not found ".format(machine_type))
sys.exit(-1)
model = keras_model.load_model(model_file)
model.summary()
# load anomaly score distribution for determining threshold
score_distr_file_path = "{model}/score_distr_{machine_type}.pkl".format(model=param["model_directory"],
machine_type=machine_type)
shape_hat, loc_hat, scale_hat = joblib.load(score_distr_file_path)
# determine threshold for decision
decision_threshold = scipy.stats.gamma.ppf(q=param["decision_threshold"], a=shape_hat, loc=loc_hat, scale=scale_hat)
if mode:
# results for each machine type
csv_lines.append([machine_type])
csv_lines.append(["section", "domain", "AUC", "pAUC", "precision", "recall", "F1 score"])
performance = []
dir_names = ["source_test", "target_test"]
for dir_name in dir_names:
#list machine id
section_names = com.get_section_names(target_dir, dir_name=dir_name)
for section_name in section_names:
# load test file
files, y_true = com.file_list_generator(target_dir=target_dir,
section_name=section_name,
dir_name=dir_name,
mode=mode)
# setup anomaly score file path
anomaly_score_csv = "{result}/anomaly_score_{machine_type}_{section_name}_{dir_name}.csv".format(result=param["result_directory"],
machine_type=machine_type,
section_name=section_name,
dir_name=dir_name)
anomaly_score_list = []
# setup decision result file path
decision_result_csv = "{result}/decision_result_{machine_type}_{section_name}_{dir_name}.csv".format(result=param["result_directory"],
machine_type=machine_type,
section_name=section_name,
dir_name=dir_name)
decision_result_list = []
print("\n============== BEGIN TEST FOR A SECTION ==============")
y_pred = [0. for k in files]
for file_idx, file_path in tqdm(enumerate(files), total=len(files)):
try:
data = com.file_to_vectors(file_path,
n_mels=param["feature"]["n_mels"],
n_frames=param["feature"]["n_frames"],
n_fft=param["feature"]["n_fft"],
hop_length=param["feature"]["hop_length"],
power=param["feature"]["power"])
except:
com.logger.error("File broken!!: {}".format(file_path))
y_pred[file_idx] = np.mean(np.square(data - model.predict(data)))
# store anomaly scores
anomaly_score_list.append([os.path.basename(file_path), y_pred[file_idx]])
# store decision results
if y_pred[file_idx] > decision_threshold:
decision_result_list.append([os.path.basename(file_path), 1])
else:
decision_result_list.append([os.path.basename(file_path), 0])
# output anomaly scores
save_csv(save_file_path=anomaly_score_csv, save_data=anomaly_score_list)
com.logger.info("anomaly score result -> {}".format(anomaly_score_csv))
# output decision results
save_csv(save_file_path=decision_result_csv, save_data=decision_result_list)
com.logger.info("decision result -> {}".format(decision_result_csv))
if mode:
# append AUC and pAUC to lists
auc = metrics.roc_auc_score(y_true, y_pred)
p_auc = metrics.roc_auc_score(y_true, y_pred, max_fpr=param["max_fpr"])
tn, fp, fn, tp = metrics.confusion_matrix(y_true, [1 if x > decision_threshold else 0 for x in y_pred]).ravel()
prec = tp / np.maximum(tp + fp, sys.float_info.epsilon)
recall = tp / np.maximum(tp + fn, sys.float_info.epsilon)
f1 = 2.0 * prec * recall / np.maximum(prec + recall, sys.float_info.epsilon)
csv_lines.append([section_name.split("_", 1)[1], dir_name.split("_", 1)[0], auc, p_auc, prec, recall, f1])
performance.append([auc, p_auc, prec, recall, f1])
performance_over_all.append([auc, p_auc, prec, recall, f1])
com.logger.info("AUC : {}".format(auc))
com.logger.info("pAUC : {}".format(p_auc))
com.logger.info("precision : {}".format(prec))
com.logger.info("recall : {}".format(recall))
com.logger.info("F1 score : {}".format(f1))
print("\n============ END OF TEST FOR A SECTION ============")
if mode:
# calculate averages for AUCs and pAUCs
amean_performance = np.mean(np.array(performance, dtype=float), axis=0)
csv_lines.append(["arithmetic mean", ""] + list(amean_performance))
hmean_performance = scipy.stats.hmean(np.maximum(np.array(performance, dtype=float), sys.float_info.epsilon), axis=0)
csv_lines.append(["harmonic mean", ""] + list(hmean_performance))
csv_lines.append([])
del data
del model
keras_model.clear_session()
gc.collect()
if mode:
csv_lines.append(["", "", "AUC", "pAUC", "precision", "recall", "F1 score"])
# calculate averages for AUCs and pAUCs
amean_performance = np.mean(np.array(performance_over_all, dtype=float), axis=0)
csv_lines.append(["arithmetic mean over all machine types, sections, and domains", ""] + list(amean_performance))
hmean_performance = scipy.stats.hmean(np.maximum(np.array(performance_over_all, dtype=float), sys.float_info.epsilon), axis=0)
csv_lines.append(["harmonic mean over all machine types, sections, and domains", ""] + list(hmean_performance))
csv_lines.append([])
# output results
result_path = "{result}/{file_name}".format(result=param["result_directory"], file_name=param["result_file"])
com.logger.info("results -> {}".format(result_path))
save_csv(save_file_path=result_path, save_data=csv_lines)