Detección de anomalías②
Vamos a realizar detección de anomalías utilizando Anomaly Detection Toolkit (ADTK). Aplicaremos la detección de anomalías a datos artificiales multidimensionales. Esta vez, trabajaremos con datos de múltiples dimensiones.
import numpy as np
import pandas as pd
from adtk.data import validate_series
s_train = pd.read_csv("./training.csv", index_col="timestamp", parse_dates=True)
s_train = validate_series(s_train)
s_train["value2"] = s_train["value"].apply(lambda v: np.sin(v) + np.cos(v))
s_train
| value | value2 | |
|---|---|---|
| timestamp | ||
| 2014-04-01 00:00:00 | 18.090486 | 0.037230 |
| 2014-04-01 00:05:00 | 20.359843 | 1.058643 |
| 2014-04-01 00:10:00 | 21.105470 | 0.141581 |
| 2014-04-01 00:15:00 | 21.151585 | 0.076564 |
| 2014-04-01 00:20:00 | 18.137141 | 0.103122 |
| ... | ... | ... |
| 2014-04-14 23:35:00 | 18.269290 | 0.288071 |
| 2014-04-14 23:40:00 | 19.087351 | 1.207420 |
| 2014-04-14 23:45:00 | 19.594689 | 1.413067 |
| 2014-04-14 23:50:00 | 19.767817 | 1.401750 |
| 2014-04-14 23:55:00 | 20.479156 | 0.939501 |
4032 rows × 2 columns
from adtk.visualization import plot
plot(s_train)
Comparación de métodos de detección de anomalías
Realizaremos la detección de anomalías utilizando SeasonalAD. Para otros métodos, consulte Detector.
import matplotlib.pyplot as plt
from adtk.detector import OutlierDetector, PcaAD, RegressionAD
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import LocalOutlierFactor
model_dict = {
"OutlierDetector": OutlierDetector(LocalOutlierFactor(contamination=0.05)),
"RegressionAD": RegressionAD(regressor=LinearRegression(), target="value2", c=3.0),
"PcaAD": PcaAD(k=2),
}
for model_name, model in model_dict.items():
anomalies = model.fit_detect(s_train)
plot(
s_train,
anomaly=anomalies,
ts_linewidth=1,
ts_markersize=3,
anomaly_color="red",
anomaly_alpha=0.3,
curve_group="all",
)
plt.title(model_name)
plt.show()
