Custom models

Even though dtaianomaly already offers a lot of functionality, there is always room for more enhancements. dtaianomaly is designed with flexibility in mind: it is extremely easy to integrate a new component in dtaianomaly. These new components can be either existing methods that haven’t been implemented yet, or new state-of-the-art time series anomaly detection methods. By implementing your new component in dtaianomaly, y ou can seamlessly use the existing tools - such as the Pipeline and Workflow - as if it were a native part of dtaianomaly.

Below, we illustrate how you can implement your own (1) anomaly detector, (2) dataloader, (3) preprocessor, (4) thresholding, and (5) evaluation.

Custom anomaly detector

The core functionality of dtaianomaly - time series anomaly detection - is extended by implementing the BaseDetector. To achieve this, you need to implement the fit(), and decision_function() methods. Below, we implement an anomaly detector that detects anomalies when the distance between an observation and the mean value exceeds a specified number of standard deviations (also known as the 3-sigma rule. The methods have the following functionality:

fit(): learn the mean and standard deviation of the training data. These values are stored in the attributes mean_ and std_.
decision_function(): compute the values that have distance larger than nb_sigmas times the learned standard deviation from the learned mean. These values are considered anomalies.

from dtaianomaly.anomaly_detection import BaseDetector

class NbSigmaAnomalyDetector(BaseDetector):
    nb_sigmas: float
    mean_: float
    std_: float

    def __init__(self, nb_sigmas: float = 3.0):
        self.nb_sigmas = nb_sigmas

    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'NbSigmaAnomalyDetector':
        """ Compute the mean and standard deviation of the given time series. """
        self.mean_ = X.mean()
        self.std_ = X.std()
        return self

    def decision_function(self, X: np.ndarray) -> np.ndarray:
        """ Compute which values are too far from the mean. """
        return np.abs(X - self.mean_) > self.nb_sigmas * self.std_

Custom data loader

Some dataloaders are provided within dtaianomaly, but often we want to detect anomalies in our own data. Typically, for such custom data, there is no dataloader available within dtaianomaly. To address this, you can implement a new dataloader by extending the LazyDataLoader, along with the load() method. Upon initialization of the custom data loader, a path parameter is required, which points to the location of the data. The load() function will then effectively load this dataset and return a DataSet object, which combines the data X and ground truth labels y.

Implementing a custom dataloader is especially useful for quantitatively evaluating the anomaly detectors on your own data, as you can pass the loader to a Workflow and easily analyze multiple detectors simultaneously.

from dtaianomaly.data import LazyDataLoader, DataSet

class SimpleDataLoader(LazyDataLoader):

    def load(self)-> DataSet:
        """ Read a data frame with the data in column 'X' and the labels in column 'y'. """
        df = pd.read_clipboard(self.path)
        return DataSet(x=df['X'].values, y=df['y'].values)

Custom preprocessor

The preprocessors will perform some processing on the time series, after which the transformed time series can be used for anomaly detection. Below, we implement a custom preprocessor by extending the Preprocessor class. Our preprocessor replaces all missing values (i.e., the NaN values) with the mean of the training data. Specifically, we need to implement following methods:

_fit(): learns the mean value of the given time series and stores it as the fill_value_ attribute.
_transform(): fills in all missing values with the given time series by the learned mean value. This method returns both a transformed X and y, because some preprocessors also change the labels y (for example, the SamplingRateUnderSampler).

Notice that we implement the _fit() and _transform() methods (with a starting underscore), while we can call the fit() and transform() methods (without the underscore) on an instance of our Imputer. This is because the public methods will first check if the input is valid using the check_preprocessing_inputs() method, and only then call the protected methods with starting underscores, ensuring that valid data is passed to these methods.

from dtaianomaly.preprocessing import Preprocessor

class Imputer(Preprocessor):
    fill_value_: float

    def _fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'Preprocessor':
        self.fill_value_ = np.nanmean(X, axis=0)
        return self

    def _transform(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> Tuple[np.ndarray, Optional[np.ndarray]]:
        X[np.isnan(X)] = self.fill_value_
        return X, y

Custom thresholding

Many anomaly detectors compute continuous anomaly scores (“how anomalous is the sample?), while many practical applications prefer binary labels (“is the sample an anomaly?”). Converting the continuous scores to binary labels can be done via thresholding. The most common thresholding strategies have already been implemented in dtaianomaly, but is possible to add a new thresholding technique, as we do below. For this, we extend the Thresholding object and implement the threshold method. Our custom thresholding technique sets a dynamic threshold, such that observations with an anomaly score larger than a specified number of standard deviations above the mean anomaly score are considered anomalous.

from dtaianomaly.thresholding import Thresholding

class DynamicThreshold(Thresholding):
    factor: float

    def __init__(self, factor: float):
        self.factor = factor

    def threshold(self, scores: np.ndarray) -> np.ndarray:
        threshold = scores.mean() + self.factor * scores.std()
        return scores > threshold

Custom evaluation

Various performance metrics exist to evaluate an anomaly detector. There are two types of metrics in dtaianomaly:

BinaryMetric: the provided anomaly scores must be binary anomaly labels. An example of such metric is the precision.
ProbaMetric:: the provided anomaly scores are expected to be continuous scores. An example of such metric is the area under the ROC curve (AUC-ROC).

Custom evaluation metrics can be implemented in dtaianomaly. Below, we implement accuracy by extending the BinaryMetric class (since accuracy requires binary labels) and implementing the _compute() method. Similar to the custom preprocessor above,we implement the _compute() method with starting underscore, while we call the compute() method to measure the metric. This is because the public compute() method performs checks on the input, ensuring that valid data is passed to the _compute() method.

Warning

Anomaly detection is typically a highly unbalanced problem: anomalies are, by definition, rare. Therefore, it is not recommended to use accuracy for evaluation (time series) anomaly detection!

from dtaianomaly.evaluation import BinaryMetric

class Accuracy(BinaryMetric):

    def _compute(self, y_true: np.ndarray, y_pred: np.ndarray):
        """ Compute the accuracy. """
        return np.nanmean(y_true == y_pred)