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� ��'j� � �� � d Z ddlmZmZ ddlZddlZddlm Z ddl m Z ddl m Z ddlmZ ddlmZ d Zd eeej( f d eeej( f fd �Z G d � de e � Zy)z�Outlier/anomaly detection utilities for MySQL Connector/Python. Provides a scikit-learn compatible wrapper using HeatWave to score anomalies. � )�Optional�UnionN)� OutlierMixin)� MyBaseMLModel)�ML_TASK)� copy_dict)�MySQLConnectionAbstractg�h㈵��>�prob�returnc �| � t j | t dt z � }t j |d|z z � S )z� Compute logit (logodds) for a probability, clipping to avoid numerical overflow. Args: prob: Scalar or array of probability values in (0,1). Returns: logit-transformed probabilities. � )�np�clip�EPS�log)r �results �D/opt/nydus/tmp/pip-target-k0fmikie/lib/python/mysql/ai/ml/outlier.py� _get_logitsr 0 s2 � � �W�W�T�3��C�� (�F� �6�6�&�A��J�'� (�(� c �D � e Zd ZdZ d dedee dee dee fd�Zde e j e j f d e j fd �Zde e j e j f d e j fd �Zde e j e j f d e j fd �Zy)�MyAnomalyDetectora� MySQL HeatWave scikit-learn compatible anomaly/outlier detector. Flags samples as outliers when the probability of being an anomaly exceeds a user-tunable threshold. Includes helpers to obtain decision scores and anomaly probabilities for ranking. Args: db_connection (MySQLConnectionAbstract): Active MySQL DB connection. model_name (str, optional): Custom model name in the database. fit_extra_options (dict, optional): Extra options for fitting. score_extra_options (dict, optional): Extra options for scoring/prediction. Attributes: boundary: Decision threshold boundary in logit space. Derived from trained model's catalog info Methods: predict(X): Predict outlier/inlier labels. score_samples(X): Compute anomaly (normal class) logit scores. decision_function(X): Compute signed score above/below threshold for ranking. N� db_connection� model_name�fit_extra_options�score_extra_optionsc � � t j | |t j ||�� t |� | _ d| _ y)a2 Initialize an anomaly detector instance with threshold and extra options. Args: db_connection: Active MySQL DB connection. model_name: Optional model name in DB. fit_extra_options: Optional extra fit options. score_extra_options: Optional extra scoring options. Raises: ValueError: If outlier_threshold is not in (0,1). DatabaseError: If a database connection issue occurs. If an operational error occurs during execution. )r r N)r �__init__r �ANOMALY_DETECTIONr r �boundary)�selfr r r r s r r zMyAnomalyDetector.__init__W s> � �, � � � � � � %� %�!� /� � $-�-@�#A�� �)-�� r �Xr c �T � t j | j |� dk dd� S )a� Predict outlier/inlier binary labels for input samples. Args: X: Samples to predict on. Returns: ndarray: Values are -1 for outliers, +1 for inliers, as per scikit-learn convention. Raises: DatabaseError: If provided options are invalid or unsupported, or if the model is not initialized, i.e., fit or import has not been called If a database connection issue occurs. If an operational error occurs during execution. DatabaseError: If provided options are invalid or unsupported, or if the model is not initialized, i.e., fit or import has not been called If a database connection issue occurs. If an operational error occurs during execution. g ���r )r �where�decision_function)r r! s r �predictzMyAnomalyDetector.predictw s' � �6 �x�x��.�.�q�1�C�7��Q�?�?r c � � | j |� }| j �U| j � }|� t d� �|d d j dd� }|� t d� �t d|z � | _ || j z S )a� Compute signed distance to the outlier threshold. Args: X: Samples to predict on. Returns: ndarray: Score > 0 means inlier, < 0 means outlier; |value| gives margin. Raises: DatabaseError: If provided options are invalid or unsupported, or if the model is not initialized, i.e., fit or import has not been called If a database connection issue occurs. If an operational error occurs during execution. ValueError: If the provided model info does not provide threshold Nz Model does not exist in catalog.�model_metadata�training_params�anomaly_detection_thresholdzzTrained model is outdated and does not support threshold. Try retraining or using an existing, trained model with MyModel.g �?)� score_samplesr �get_model_info� ValueError�getr )r r! � sample_scores� model_info� thresholds r r% z#MyAnomalyDetector.decision_function� s� � �. �*�*�1�-� � �=�=� � �,�,�.�J��!� �!C�D�D�"�#3�4�5F�G�K�K�-�t��I� � � �W�� � (��i�� 8�D�M� �t�}�}�,�,r c � � | j j || j �� }t |d j d� � j � � S )aJ Compute normal probability logit score for each sample. Used for ranking, thresholding. Args: X: Samples to score. Returns: ndarray: Logit scores based on "normal" class probability. Raises: DatabaseError: If provided options are invalid or unsupported, or if the model is not initialized, i.e., fit or import has not been called If a database connection issue occurs. If an operational error occurs during execution. )�options� ml_resultsc � � | d d S )N� probabilities�normal� )�xs r �<lambda>z1MyAnomalyDetector.score_samples.<locals>.<lambda>� s � �Q�� /�� 9r )�_modelr& r r �apply�to_numpy)r r! r s r r+ z MyAnomalyDetector.score_samples� sJ � �, ���$�$�Q��0H�0H�$�I��� �<� � �U�9� :� �X�Z� � r )NNN)�__name__� __module__� __qualname__�__doc__r r �str�dictr r �pd� DataFramer �ndarrayr& r% r+ r8 r r r r > s� � ��6 %)�,0�.2� .�.� .� �S�M� .� $�D�>� .� &�d�^� .�@@� �� � �r�z�z�)� *�@� ���@�:+-� �� � �r�z�z�)� *�+-� ���+-�Z � �� � �r�z�z�)� *� � ��� r r )rA �typingr r �numpyr �pandasrD � sklearn.baser �mysql.ai.ml.baser �mysql.ai.ml.modelr �mysql.ai.utilsr �mysql.connector.abstractsr r �floatrF r r r8 r r �<module>rP sh ��:� #� � � %� *� %� $� =� �� )�e�E�2�:�:� -�.� )�5��� � �9J�3K� )� _ � �|� _ r
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