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lof.py
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lof.py
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import pathlib
import sys
from sklearn.neighbors import lof
from utils import *
def LOFModel(_abnormal_data, _neighbor=5, _job=2, _contamination=0.05, _metric='euclidean'):
clf = lof.LocalOutlierFactor(n_neighbors=_neighbor, n_jobs=_job, metric=_metric)
y_pred = clf.fit_predict(_abnormal_data)
score = clf.negative_outlier_factor_
return score, y_pred
def RunModel(_file_name, _choice, _neighbor=5, _job=2, _contamination=0.1, _metric='euclidean'):
if _choice == 1:
abnormal_data, abnormal_label = ReadGDDataset(_file_name)
if _choice == 2:
abnormal_data, abnormal_label = ReadHSSDataset(_file_name)
if _choice == 3:
abnormal_data, abnormal_label = ReadS5Dataset(_file_name)
if _choice == 4:
abnormal_data, abnormal_label = ReadNABDataset(_file_name)
if _choice == 5:
abnormal_data, abnormal_label = Read2DDataset(_file_name)
if _choice == 6:
abnormal_data, abnormal_label = ReadUAHDataset(_file_name)
if _choice == 7:
abnormal_data, abnormal_label = ReadECGDataset(_file_name)
score, y_pred = LOFModel(abnormal_data, _neighbor, _job, _metric)
score_pred_label = np.c_[score, y_pred, abnormal_label]
np.savetxt('./saved_result/' + pathlib.Path(_file_name).parts[0] + '/lof_' + os.path.basename(_file_name) + '_score.txt', score_pred_label, delimiter=',')
x = abnormal_label[np.where(abnormal_label == -1)]
y = y_pred[np.where(y_pred == -1)]
precision, recall, f1 = CalculatePrecisionRecallF1Metrics(abnormal_label, y_pred)
# PrintPrecisionRecallF1Metrics(precision, recall, f1)
# k_number = [20, 40, 60, 80, 100]
# for k in k_number:
# precision_at_k = CalculatePrecisionAtK(abnormal_label, -score, k, _type=1)
# print('precision at ' + str(k) + '=' + str(precision_at_k))
fpr, tpr, roc_auc = CalculateROCAUCMetrics(abnormal_label, score)
# PlotROCAUC(fpr, tpr, roc_auc)
# print('roc_auc=' + str(roc_auc))
precision_curve, recall_curve, average_precision = CalculatePrecisionRecallCurve(abnormal_label, score)
# PlotPrecisionRecallCurve(precision_curve, recall_curve, average_precision)
# print('pr_auc=' + str(average_precision))
cks = CalculateCohenKappaMetrics(abnormal_label, y_pred)
# print('cohen_kappa=' + str(cks))
return precision, recall, f1, roc_auc, average_precision, cks
if __name__ == '__main__':
try:
sys.argv[1]
except IndexError:
for n in range(1, 7):
dataset = n
if dataset == 1:
file_name = './GD/data/Genesis_AnomalyLabels.csv'
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
print('avg_precision=' + str(precision))
print('avg_recall=' + str(recall))
print('avg_f1=' + str(f1))
print('avg_roc_auc=' + str(roc_auc))
print('avg_pr_auc=' + str(pr_auc))
print('avg_cks=' + str(cks))
if dataset == 2:
file_name = './HSS/data/HRSS_anomalous_standard.csv'
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
print('avg_precision=' + str(precision))
print('avg_recall=' + str(recall))
print('avg_f1=' + str(f1))
print('avg_roc_auc=' + str(roc_auc))
print('avg_pr_auc=' + str(pr_auc))
print('avg_cks=' + str(cks))
if dataset == 3:
for root, dirs, _ in os.walk('./YAHOO/data'):
for dir in dirs:
k_partition = 10
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 4:
for root, dirs, _ in os.walk('./NAB/data'):
for dir in dirs:
k_partition = 10
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 5:
for root, dirs, _ in os.walk('./2D/test'):
for dir in dirs:
k_partition = 10
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 6:
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for root, dirs, files in os.walk('./UAH/'):
for file in files:
file_name = os.path.join(root, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 7:
for root, dirs, _ in os.walk('./ECG/'):
for dir in dirs:
k_partition = 3
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
else:
dataset = int(sys.argv[1])
if dataset == 1:
file_name = './GD/data/Genesis_AnomalyLabels.csv'
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
print('avg_precision=' + str(precision))
print('avg_recall=' + str(recall))
print('avg_f1=' + str(f1))
print('avg_roc_auc=' + str(roc_auc))
print('avg_pr_auc=' + str(pr_auc))
print('avg_cks=' + str(cks))
if dataset == 2:
file_name = './HSS/data/HRSS_anomalous_standard.csv'
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
print('avg_precision=' + str(precision))
print('avg_recall=' + str(recall))
print('avg_f1=' + str(f1))
print('avg_roc_auc=' + str(roc_auc))
print('avg_pr_auc=' + str(pr_auc))
print('avg_cks=' + str(cks))
if dataset == 3:
for root, dirs, _ in os.walk('./YAHOO/data'):
for dir in dirs:
k_partition = 10
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 4:
for root, dirs, _ in os.walk('./NAB/data'):
for dir in dirs:
k_partition = 10
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 5:
for root, dirs, _ in os.walk('./2D/test'):
for dir in dirs:
k_partition = 10
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 6:
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for root, dirs, files in os.walk('./UAH/'):
for dir in dirs:
folder_name = os.path.join(root, dir)
print(folder_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(folder_name, dataset)
print('########################################')
print('precision=' + str(precision))
print('recall=' + str(recall))
print('f1=' + str(f1))
print('roc_auc=' + str(roc_auc))
print('pr_auc=' + str(pr_auc))
print('cks=' + str(cks))
print('########################################')
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 7:
for root, dirs, _ in os.walk('./ECG/'):
for dir in dirs:
k_partition = 3
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
precision, recall, f1, roc_auc, pr_auc, cks = RunModel(file_name, dataset)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')