def findAnomalies(self, saveChart=False, saveEvaluation=False):
predicted_outlier = []
list_of_df = self.dataCollector.getWithAnomaly()
for df in list_of_df:
if df.shape[0] > 0:
# clf = SSkNNO()
data = df.drop(['anomaly', 'changepoint'], axis=1)
self.st_tr_time.append(datetime.datetime.now().timestamp())
prediction = pd.Series(self.fit_predict(data.to_numpy()), index=df.index) \
.rolling(5) \
.median() \
.fillna(0)
self.en_tr_time.append(datetime.datetime.now().timestamp())
# predicted outliers saving
predicted_outlier.append(prediction)
df['knno_anomaly'] = prediction
true_outlier = [df.anomaly for df in list_of_df]
if saveChart:
for i in range(len(predicted_outlier)):
plt.figure()
plt.rcParams["font.family"] = "Times New Roman"
csfont = {'fontname': 'Times New Roman'}
plt.xlabel('Time', **csfont)
plt.ylabel('Value', **csfont)
plt.title('KNNO On File [{}]'.format(i + 1), **csfont)
predicted_outlier[i].plot(figsize=(12, 6), label='predictions', marker='o', markersize=5)
true_outlier[i].plot(marker='o', markersize=2)
# data = list_of_df[i]
# plt.scatter(x=data[data['forest_anomaly'] == data['anomaly']].index,
# y=data[data['forest_anomaly'] == data['anomaly']]['anomaly'], label='True Prediction'
# , c='g', zorder=4)
# plt.scatter(x=data[data['forest_anomaly'] != data['anomaly']].index,
# y=data[data['forest_anomaly'] != data['anomaly']]['anomaly'], label='False Prediction'
# , c='r', zorder=5)
plt.legend(loc='upper right')
plt.savefig(self.path_to_plt + 'anom/knno-pre-{}.png'.format(i + 1), format='png')
print('Chart {} is Generated'.format(i + 1))
plt.clf()
plt.close('all')
if saveChart:
ts = 1
for df in list_of_df:
data = df.drop(['anomaly', 'changepoint'], axis=1)
pc = PCA(n_components=2).fit_transform(data)
df[['X', 'Y']] = pc
plt.figure()
sb.set(font='Times New Roman')
sns = sb.scatterplot(data=df, x='X', y='Y', hue='knno_anomaly', palette='bright')
sns.set_title('The Anomaly Detected By KNNO, File {}'.format(ts))
sns.set_title('The Anomaly Detected By KNNO, File {}'.format(ts))
sns.figure.savefig(self.path_to_plt + 'chart/chart-{}.png'.format(ts))
plt.close('all')
print('The Chart of File {} is Generated.'.format(ts))
ts += 1
if saveEvaluation:
evaluator = Evaluator(true_outlier, predicted_outlier, metric='binary', numenta_time='30 sec')
metrics = evaluator.getConfusionMetrics()
TP = metrics['TP']
TN = metrics['TN']
FP = metrics['FP']
FN = metrics['FN']
print('\n-----------------------------------------------------')
print('KNNO Outputs: ')
print(f'\t False Alarm Rate: {round(FP / (FP + TN) * 100, 2)} %')
print(f'\t Missing Alarm Rate: {round(FN / (FN + TP) * 100, 2)} %')
print(f'\t Accuracy Rate: {round((TP + TN) / (TP + TN + FN + FP) * 100, 2)} %')
trainTime = np.array(self.en_tr_time).sum() - np.array(self.st_tr_time).sum()
print(f'\t Train & Test Time {round(trainTime, 2)}s')
data = {'far': round(FP / (FP + TN) * 100, 2),
'mar': round(FN / (FN + TP) * 100, 2),
'acc': round((TP + TN) / (TP + TN + FN + FP) * 100, 2),
'tr': trainTime,
'te': 0,
'tp': TP,
'tn': TN,
'fp': FP,
'fn': FN}
output = OutputWriter(self.path_to_plt, 'KNNO', data)
output.write()
