# alibi\_detect.od.vae ## 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. ## `OutlierVAE` *Inherits from:* `BaseDetector`, `FitMixin`, `ThresholdMixin`, `ABC` ### Constructor ```python OutlierVAE(self, threshold: float = None, score_type: str = 'mse', vae: keras.src.models.model.Model = None, encoder_net: keras.src.models.model.Model = None, decoder_net: keras.src.models.model.Model = None, latent_dim: int = None, samples: int = 10, beta: float = 1.0, data_type: str = None) -> None ``` | Name | Type | Default | Description | | ------------- | ---------------------------------------- | ------- | ---------------------------------------------------------------------------------------------------------------------- | | `threshold` | `Optional[float]` | `None` | Threshold used for outlier score to determine outliers. | | `score_type` | `str` | `'mse'` | Metric used for outlier scores. Either 'mse' (mean squared error) or 'proba' (reconstruction probabilities) supported. | | `vae` | `Optional[keras.src.models.model.Model]` | `None` | A trained tf.keras model if available. | | `encoder_net` | `Optional[keras.src.models.model.Model]` | `None` | Layers for the encoder wrapped in a tf.keras.Sequential class if no 'vae' is specified. | | `decoder_net` | `Optional[keras.src.models.model.Model]` | `None` | Layers for the decoder wrapped in a tf.keras.Sequential class if no 'vae' is specified. | | `latent_dim` | `Optional[int]` | `None` | Dimensionality of the latent space. | | `samples` | `int` | `10` | Number of samples sampled to evaluate each instance. | | `beta` | `float` | `1.0` | Beta parameter for KL-divergence loss term. | | `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_orig: numpy.ndarray, X_recon: numpy.ndarray) -> numpy.ndarray ``` Compute feature level outlier scores. | Name | Type | Default | Description | | --------- | --------------- | ------- | --------------------------------- | | `X_orig` | `numpy.ndarray` | | Batch of original instances. | | `X_recon` | `numpy.ndarray` | | Batch of reconstructed instances. | **Returns** * Type: `numpy.ndarray` #### `fit` ```python fit(X: numpy.ndarray, loss_fn: .tensorflow.keras.losses = , 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')]] = , cov_elbo: dict = {'sim': 0.05}, 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 VAE model. | Name | Type | Default | Description | | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `X` | `numpy.ndarray` | | Training batch. | | `loss_fn` | `.tensorflow.keras.losses` | `` | Loss function used for training. | | `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. | | `cov_elbo` | `dict` | `{'sim': 0.05}` | Dictionary with covariance matrix options in case the elbo loss function is used. Either use the full covariance matrix inferred from X (dict(cov\_full=None)), only the variance (dict(cov\_diag=None)) or a float representing the same standard deviation for each feature (e.g. dict(sim=.05)). | | `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', outlier_perc: float = 100.0, threshold_perc: float = 95.0, batch_size: int = 10000000000) -> None ``` Update 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. | | `outlier_perc` | `float` | `100.0` | Percentage of sorted feature level outlier scores used to predict instance level outlier. | | `threshold_perc` | `float` | `95.0` | Percentage of X considered to be normal based on the outlier score. | | `batch_size` | `int` | `10000000000` | Batch size used when making predictions with the VAE. | **Returns** * Type: `None` #### `instance_score` ```python instance_score(fscore: numpy.ndarray, outlier_perc: float = 100.0) -> numpy.ndarray ``` Compute instance level outlier scores. | Name | Type | Default | Description | | -------------- | --------------- | ------- | ----------------------------------------------------------------------------------------- | | `fscore` | `numpy.ndarray` | | Feature level outlier scores. | | `outlier_perc` | `float` | `100.0` | Percentage of sorted feature level outlier scores used to predict instance level outlier. | **Returns** * Type: `numpy.ndarray` #### `predict` ```python predict(X: numpy.ndarray, outlier_type: str = 'instance', outlier_perc: float = 100.0, 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. | | `outlier_perc` | `float` | `100.0` | Percentage of sorted feature level outlier scores used to predict instance level outlier. | | `batch_size` | `int` | `10000000000` | Batch size used when making predictions with the VAE. | | `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, outlier_perc: float = 100.0, batch_size: int = 10000000000) -> Tuple[numpy.ndarray, numpy.ndarray] ``` Compute feature and instance level outlier scores. | Name | Type | Default | Description | | -------------- | --------------- | ------------- | ----------------------------------------------------------------------------------------- | | `X` | `numpy.ndarray` | | Batch of instances. | | `outlier_perc` | `float` | `100.0` | Percentage of sorted feature level outlier scores used to predict instance level outlier. | | `batch_size` | `int` | `10000000000` | Batch size used when making predictions with the VAE. | **Returns** * Type: `Tuple[numpy.ndarray, numpy.ndarray]` --- # 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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