from typing import Optional
import numpy as np
from dtaianomaly.anomaly_detection.BaseDetector import BaseDetector, Supervision
[docs]
class AlwaysNormal(BaseDetector):
"""
Baseline anomaly detector, which predicts that all observations are normal.
This detector should only be used for sanity-check, and not to effectively
detect anomalies in time series data.
"""
def __init__(self):
super().__init__(Supervision.UNSUPERVISED)
def _fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
"""Should not do anything."""
def _decision_function(self, X: np.ndarray) -> np.array:
return np.zeros(shape=X.shape[0])
[docs]
class AlwaysAnomalous(BaseDetector):
"""
Baseline anomaly detector, which predicts that all observations are anomalous.
This detector should only be used for sanity-check, and not to effectively
detect anomalies in time series data.
"""
def __init__(self):
super().__init__(Supervision.UNSUPERVISED)
def _fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
"""Should not do anything."""
def _decision_function(self, X: np.ndarray) -> np.array:
return np.ones(shape=X.shape[0])
[docs]
class RandomDetector(BaseDetector):
"""
Baseline anomaly detector, which assigns random anomaly scores. This detector
should only be used for sanity-check, and not to effectively detect anomalies
in time series data.
Parameters
----------
seed: int, default=None
The seed to use for generating anomaly scores. If None, no seed will be used.
"""
seed: Optional[int]
def __init__(self, seed: Optional[int] = None):
super().__init__(Supervision.UNSUPERVISED)
self.seed = seed
def _fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> None:
"""Should not do anything."""
def _decision_function(self, X: np.ndarray) -> np.array:
return np.random.default_rng(seed=self.seed).random(size=X.shape[0])