RADAR.static_data.algorithms
Submodules
RADAR.static_data.algorithms.pyod module
- class RADAR.static_data.algorithms.pyod.PyodAnomalyDetection(**kwargs)[source]
Bases:
BaseAnomalyDetection- decision_function(X)[source]
Predict raw anomaly score of X using the fitted detector.
- Parameters:
X (numpy array of shape (n_samples, n_features)) – The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
- Returns:
anomaly_scores – The anomaly score of the input samples.
- Return type:
numpy array of shape (n_samples,)
- evaluate(X, y=None)[source]
Evaluates the model on data X (and optionally y). Uses decision_function to get anomaly scores and predict to get labels. If y is provided, prints metrics using print_metrics.
- Parameters:
X (numpy array of shape (n_samples, n_features)) – The input samples.
y (numpy array of shape (n_samples,), optional) – Ground truth labels (0 for normal, 1 for anomaly).
- Returns:
results – Dictionary containing: - ‘scores’: anomaly scores from decision_function - ‘labels_preds’: predicted labels (0 for normal, 1 for anomaly) - ‘labels_true’: ground truth labels (if y is provided)
- Return type:
dict
- fit(X, y=None)[source]
Fit detector. y is ignored in unsupervised methods.
- Parameters:
X (numpy array of shape (n_samples, n_features)) – The input samples.
y (Ignored) – Not used, present for API consistency by convention.
- Returns:
self – Fitted estimator.
- Return type:
object
- get_default_params(**params)[source]
Get DEFAULT parameters for this estimator, params is used to configure positional parameters in order to obtain default parameters of the object.
- Returns:
params – Parameter names mapped to their values.
- Return type:
mapping of string to any
- get_params()[source]
Get parameters for this estimator.
- Returns:
params – Parameter names mapped to their values.
- Return type:
mapping of string to any
- predict(X)[source]
Predict raw anomaly scores of X using the fitted detector.
The anomaly score of an input sample is computed based on the fitted detector. For consistency, outliers are assigned with higher anomaly scores.
If label_parser is an attribute, then we execute the particular predict function
- Parameters:
X (numpy array of shape (n_samples, n_features)) – The input samples. Sparse matrices are accepted only if they are supported by the base estimator.
- Returns:
anomaly_scores – The anomaly score of the input samples.
- Return type:
numpy array of shape (n_samples,)
- classmethod register_algorithm(name, model_class)[source]
Register a new algorithm in the class. :param - name: The name of the new algorithm. :type - name: str :param - model_class: The class implementing the anomaly detection model. :type - model_class: class
- The class should have:
An __init__ method that accepts model-specific parameters.
A fit(X, y) method to train the model.
A predict(X) method to make predictions.
Optionally, a decision_function(X) for scoring anomalies.
RADAR.static_data.algorithms.sklearn module
- class RADAR.static_data.algorithms.sklearn.SkLearnAnomalyDetection(**kwargs)[source]
Bases:
BaseAnomalyDetection- decision_function(X)[source]
Predict raw anomaly score of X using the fitted detector.
- Parameters:
X (numpy array of shape (n_samples, n_features)) – The training input samples.
- Returns:
anomaly_scores – The anomaly score of the input samples.
- Return type:
numpy array of shape (n_samples,)
- evaluate(X, y=None)[source]
Evaluates the model on data X (and optionally y). Uses decision_function to get anomaly scores and predict to get labels. Note: sklearn uses -1 for outliers and +1 for inliers, which are converted to 0/1 for metrics. If y is provided, prints metrics using print_metrics.
- Parameters:
X (numpy array of shape (n_samples, n_features)) – The input samples.
y (numpy array of shape (n_samples,), optional) – Ground truth labels (0 for normal, 1 for anomaly). Note: If y uses -1/+1 format, it will be converted to 0/1 automatically.
- Returns:
results – Dictionary containing: - ‘scores’: anomaly scores from decision_function - ‘labels_preds’: predicted labels (0 for normal, 1 for anomaly) - ‘labels_true’: ground truth labels
- Return type:
dict
- fit(X, y=None)[source]
Fit detector. y is ignored in unsupervised methods.
- Parameters:
X (numpy array of shape (n_samples, n_features)) – The input samples.
y (Ignored) – Not used, present for API consistency by convention.
- Returns:
self – Fitted estimator.
- Return type:
object
- get_default_params(**params)[source]
Get DEFAULT parameters for this estimator, params is used to configure positional parameters in order to obtain default parameters of the object.
- Returns:
params – Parameter names mapped to their values.
- Return type:
mapping of string to any
- get_params()[source]
Get parameters for this estimator.
- Returns:
params – Parameter names mapped to their values.
- Return type:
mapping of string to any
- predict(X)[source]
Predict labels (1 inlier, -1 outlier) of X according to fitted model.
- Parameters:
X (array-like of shape (n_samples, n_features)) – The data matrix.
- Returns:
is_inlier – Returns -1 for anomalies/outliers and +1 for inliers.
- Return type:
ndarray of shape (n_samples,)
- classmethod register_algorithm(name, model_class)[source]
Register a new algorithm in the class. :param - name: The name of the new algorithm. :type - name: str :param - model_class: The class implementing the anomaly detection model. :type - model_class: class
- The class should have:
An __init__ method that accepts model-specific parameters.
A fit(X, y) method to train the model.
A predict(X) method to make predictions.
Optionally, a decision_function(X) for scoring anomalies.