alibi_detect.od.llr module
- class alibi_detect.od.llr.LLR(threshold=None, model=None, model_background=None, log_prob=None, sequential=False, data_type=None)[source]
Bases:
BaseDetector,FitMixin,ThresholdMixin- __init__(threshold=None, model=None, model_background=None, log_prob=None, sequential=False, data_type=None)[source]
Likelihood Ratios for Out-of-Distribution Detection. Ren, J. et al. NeurIPS 2019. https://arxiv.org/abs/1906.02845
- Parameters:
threshold (
Optional[float]) – Threshold used for the likelihood ratio (LLR) to determine outliers.model (
Union[Model,Distribution,PixelCNN,None]) – Generative model, defaults to PixelCNN.model_background (
Union[Model,Distribution,PixelCNN,None]) – Optional model for the background. Only needed if it is different from model.log_prob (
Optional[Callable]) – Function used to evaluate log probabilities under the model if the model does not have a log_prob function.sequential (
bool) – Whether the data is sequential. Used to create targets during training.data_type (
Optional[str]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.
- feature_score(X, batch_size=10000000000)[source]
Feature-level negative likelihood ratios.
- Return type:
ndarray
- fit(X, mutate_fn=<function mutate_categorical>, mutate_fn_kwargs={'feature_range': (0, 255), 'rate': 0.2, 'seed': 0}, mutate_batch_size=10000000000, loss_fn=None, loss_fn_kwargs=None, optimizer=tensorflow.keras.optimizers.Adam, epochs=20, batch_size=64, verbose=True, log_metric=None, callbacks=None)[source]
Train semantic and background generative models.
- Parameters:
X – Training batch.
mutate_fn – Mutation function used to generate the background dataset.
mutate_fn_kwargs – Kwargs for the mutation function used to generate the background dataset. Default values set for an image dataset.
mutate_batch_size – Batch size used to generate the mutations for the background dataset.
loss_fn – Loss function used for training.
loss_fn_kwargs – Kwargs for loss function.
optimizer – Optimizer used for training.
epochs – Number of training epochs.
batch_size – Batch size used for training.
verbose – Whether to print training progress.
log_metric – Additional metrics whose progress will be displayed if verbose equals True.
callbacks – Callbacks used during training.
- infer_threshold(X, outlier_type='instance', threshold_perc=95.0, batch_size=10000000000)[source]
Update LLR threshold by a value inferred from the percentage of instances considered to be outliers in a sample of the dataset.
- Parameters:
- Return type:
- instance_score(X, batch_size=10000000000)[source]
Instance-level negative likelihood ratios.
- Return type:
ndarray
- logp(dist, X, return_per_feature=False, batch_size=10000000000)[source]
Compute log probability of a batch of instances under the generative model.
- logp_alt(model, X, return_per_feature=False, batch_size=10000000000)[source]
Compute log probability of a batch of instances using the log_prob function defined by the user.
- predict(X, outlier_type='instance', batch_size=10000000000, return_feature_score=True, return_instance_score=True)[source]
Predict whether instances are outliers or not.
- Parameters:
X (
ndarray) – Batch of instances.outlier_type (
str) – Predict outliers at the ‘feature’ or ‘instance’ level.batch_size (
int) – Batch size used when making predictions with the generative model.return_feature_score (
bool) – Whether to return feature level outlier scores.return_instance_score (
bool) – Whether to return instance level outlier scores.
- Return type:
- Returns:
Dictionary containing
'meta'and'data'dictionaries. –'meta'has the model’s metadata.'data'contains the outlier predictions and both feature and instance level outlier scores.