# alibi\_detect.od.llr ## Constants ### `logger` ```python logger: logging.Logger = ``` Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting. ## `LLR` *Inherits from:* `BaseDetector`, `FitMixin`, `ThresholdMixin`, `ABC` ### Constructor ```python LLR(self, threshold: float = None, model: Union[keras.src.models.model.Model, tensorflow_probability.python.distributions.distribution.Distribution, alibi_detect.models.tensorflow.pixelcnn.PixelCNN] = None, model_background: Union[keras.src.models.model.Model, tensorflow_probability.python.distributions.distribution.Distribution, alibi_detect.models.tensorflow.pixelcnn.PixelCNN] = None, log_prob: Callable = None, sequential: bool = False, data_type: str = None) -> None ``` | Name | Type | Default | Description | | ------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------- | | `threshold` | `Optional[float]` | `None` | Threshold used for the likelihood ratio (LLR) to determine outliers. | | `model` | `Union[keras.src.models.model.Model, tensorflow_probability.python.distributions.distribution.Distribution, alibi_detect.models.tensorflow.pixelcnn.PixelCNN, None]` | `None` | Generative model, defaults to PixelCNN. | | `model_background` | `Union[keras.src.models.model.Model, tensorflow_probability.python.distributions.distribution.Distribution, alibi_detect.models.tensorflow.pixelcnn.PixelCNN, None]` | `None` | Optional model for the background. Only needed if it is different from `model`. | | `log_prob` | `Optional[Callable]` | `None` | Function used to evaluate log probabilities under the model if the model does not have a `log_prob` function. | | `sequential` | `bool` | `False` | Whether the data is sequential. Used to create targets during training. | | `data_type` | `Optional[str]` | `None` | Optionally specify the data type (tabular, image or time-series). Added to metadata. | ### Methods #### `feature_score` ```python feature_score(X: numpy.ndarray, batch_size: int = 10000000000) -> numpy.ndarray ``` Feature-level negative likelihood ratios. | Name | Type | Default | Description | | ------------ | --------------- | ------------- | ----------- | | `X` | `numpy.ndarray` | | | | `batch_size` | `int` | `10000000000` | | **Returns** * Type: `numpy.ndarray` #### `fit` ```python fit(X: numpy.ndarray, mutate_fn: Callable = , mutate_fn_kwargs: dict = {'rate': 0.2, 'seed': 0, 'feature_range': (0, 255)}, mutate_batch_size: int = 10000000000, loss_fn: .tensorflow.keras.losses = None, loss_fn_kwargs: dict = None, optimizer: Union[ForwardRef('tf.keras.optimizers.Optimizer'), ForwardRef('tf.keras.optimizers.legacy.Optimizer'), type[ForwardRef('tf.keras.optimizers.Optimizer')], type[ForwardRef('tf.keras.optimizers.legacy.Optimizer')]] = , epochs: int = 20, batch_size: int = 64, verbose: bool = True, log_metric: Tuple[str, ForwardRef('tf.keras.metrics')] = None, callbacks: .tensorflow.keras.callbacks = None) -> None ``` Train semantic and background generative models. | Name | Type | Default | Description | | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | | `X` | `numpy.ndarray` | | Training batch. | | `mutate_fn` | `Callable` | `` | Mutation function used to generate the background dataset. | | `mutate_fn_kwargs` | `dict` | `{'rate': 0.2, 'seed': 0, 'feature_range': (0, 255)}` | Kwargs for the mutation function used to generate the background dataset. Default values set for an image dataset. | | `mutate_batch_size` | `int` | `10000000000` | Batch size used to generate the mutations for the background dataset. | | `loss_fn` | `.tensorflow.keras.losses` | `None` | Loss function used for training. | | `loss_fn_kwargs` | `dict` | `None` | Kwargs for loss function. | | `optimizer` | `Union[ForwardRef('tf.keras.optimizers.Optimizer'), ForwardRef('tf.keras.optimizers.legacy.Optimizer'), type[ForwardRef('tf.keras.optimizers.Optimizer')], type[ForwardRef('tf.keras.optimizers.legacy.Optimizer')]]` | `` | Optimizer used for training. | | `epochs` | `int` | `20` | Number of training epochs. | | `batch_size` | `int` | `64` | Batch size used for training. | | `verbose` | `bool` | `True` | Whether to print training progress. | | `log_metric` | `Tuple[str, ForwardRef('tf.keras.metrics')]` | `None` | Additional metrics whose progress will be displayed if verbose equals True. | | `callbacks` | `.tensorflow.keras.callbacks` | `None` | Callbacks used during training. | **Returns** * Type: `None` #### `infer_threshold` ```python infer_threshold(X: numpy.ndarray, outlier_type: str = 'instance', threshold_perc: float = 95.0, batch_size: int = 10000000000) -> None ``` Update LLR threshold by a value inferred from the percentage of instances considered to be outliers in a sample of the dataset. | Name | Type | Default | Description | | ---------------- | --------------- | ------------- | ----------------------------------------------------------------------------------------- | | `X` | `numpy.ndarray` | | Batch of instances. | | `outlier_type` | `str` | `'instance'` | Predict outliers at the 'feature' or 'instance' level. | | `threshold_perc` | `float` | `95.0` | Percentage of sorted feature level outlier scores used to predict instance level outlier. | | `batch_size` | `int` | `10000000000` | Batch size for the generative model evaluations. | **Returns** * Type: `None` #### `instance_score` ```python instance_score(X: numpy.ndarray, batch_size: int = 10000000000) -> numpy.ndarray ``` Instance-level negative likelihood ratios. | Name | Type | Default | Description | | ------------ | --------------- | ------------- | ----------- | | `X` | `numpy.ndarray` | | | | `batch_size` | `int` | `10000000000` | | **Returns** * Type: `numpy.ndarray` #### `llr` ```python llr(X: numpy.ndarray, return_per_feature: bool, batch_size: int = 10000000000) -> numpy.ndarray ``` Compute likelihood ratios. | Name | Type | Default | Description | | -------------------- | --------------- | ------------- | ------------------------------------------------ | | `X` | `numpy.ndarray` | | Batch of instances. | | `return_per_feature` | `bool` | | Return likelihood ratio per feature. | | `batch_size` | `int` | `10000000000` | Batch size for the generative model evaluations. | **Returns** * Type: `numpy.ndarray` #### `logp` ```python logp(dist, X: numpy.ndarray, return_per_feature: bool = False, batch_size: int = 10000000000) -> numpy.ndarray ``` Compute log probability of a batch of instances under the generative model. | Name | Type | Default | Description | | -------------------- | --------------- | ------------- | ------------------------------------------------ | | `dist` | | | Distribution of the model. | | `X` | `numpy.ndarray` | | Batch of instances. | | `return_per_feature` | `bool` | `False` | Return log probability per feature. | | `batch_size` | `int` | `10000000000` | Batch size for the generative model evaluations. | **Returns** * Type: `numpy.ndarray` #### `logp_alt` ```python logp_alt(model: keras.src.models.model.Model, X: numpy.ndarray, return_per_feature: bool = False, batch_size: int = 10000000000) -> numpy.ndarray ``` Compute log probability of a batch of instances using the log\_prob function defined by the user. | Name | Type | Default | Description | | -------------------- | ------------------------------ | ------------- | ------------------------------------------------ | | `model` | `keras.src.models.model.Model` | | Trained model. | | `X` | `numpy.ndarray` | | Batch of instances. | | `return_per_feature` | `bool` | `False` | Return log probability per feature. | | `batch_size` | `int` | `10000000000` | Batch size for the generative model evaluations. | **Returns** * Type: `numpy.ndarray` #### `predict` ```python predict(X: numpy.ndarray, outlier_type: str = 'instance', batch_size: int = 10000000000, return_feature_score: bool = True, return_instance_score: bool = True) -> Dict[Dict[str, str], Dict[numpy.ndarray, numpy.ndarray]] ``` Predict whether instances are outliers or not. | Name | Type | Default | Description | | ----------------------- | --------------- | ------------- | ------------------------------------------------------------------ | | `X` | `numpy.ndarray` | | Batch of instances. | | `outlier_type` | `str` | `'instance'` | Predict outliers at the 'feature' or 'instance' level. | | `batch_size` | `int` | `10000000000` | Batch size used when making predictions with the generative model. | | `return_feature_score` | `bool` | `True` | Whether to return feature level outlier scores. | | `return_instance_score` | `bool` | `True` | Whether to return instance level outlier scores. | **Returns** * Type: `Dict[Dict[str, str], Dict[numpy.ndarray, numpy.ndarray]]` #### `score` ```python score(X: numpy.ndarray, batch_size: int = 10000000000) -> Tuple[numpy.ndarray, numpy.ndarray] ``` Feature-level and instance-level outlier scores. The scores are equal to the negative likelihood ratios. | Name | Type | Default | Description | | ------------ | --------------- | ------------- | ----------- | | `X` | `numpy.ndarray` | | | | `batch_size` | `int` | `10000000000` | | **Returns** * Type: `Tuple[numpy.ndarray, numpy.ndarray]` ## Functions ### `build_model` ```python build_model(dist: Union[tensorflow_probability.python.distributions.distribution.Distribution, alibi_detect.models.tensorflow.pixelcnn.PixelCNN], input_shape: Optional[tuple] = None, filepath: Optional[str] = None) -> Tuple[keras.src.models.model.Model, Union[tensorflow_probability.python.distributions.distribution.Distribution, alibi_detect.models.tensorflow.pixelcnn.PixelCNN]] ``` Create tf.keras.Model from TF distribution. | Name | Type | Default | Description | | ------------- | -------------------------------------------------------------------------------------------------------------------------------- | ------- | -------------------------------- | | `dist` | `Union[tensorflow_probability.python.distributions.distribution.Distribution, alibi_detect.models.tensorflow.pixelcnn.PixelCNN]` | | TensorFlow distribution. | | `input_shape` | `Optional[tuple]` | `None` | Input shape of the model. | | `filepath` | `Optional[str]` | `None` | File to load model weights from. | **Returns** * Type: `Tuple[keras.src.models.model.Model, Union[tensorflow_probability.python.distributions.distribution.Distribution, alibi_detect.models.tensorflow.pixelcnn.PixelCNN]]` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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