alibi_detect.cd.learned_kernel module
- class alibi_detect.cd.learned_kernel.LearnedKernelDrift(x_ref, kernel, backend='tensorflow', p_val=0.05, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, n_permutations=100, batch_size_permutations=1000000, var_reg=1e-05, reg_loss_fn=<function LearnedKernelDrift.<lambda>>, train_size=0.75, retrain_from_scratch=True, optimizer=None, learning_rate=0.001, batch_size=32, batch_size_predict=32, preprocess_batch_fn=None, epochs=3, num_workers=0, verbose=0, train_kwargs=None, device=None, dataset=None, dataloader=None, input_shape=None, data_type=None)[source]
Bases:
DriftConfigMixin
- __init__(x_ref, kernel, backend='tensorflow', p_val=0.05, x_ref_preprocessed=False, preprocess_at_init=True, update_x_ref=None, preprocess_fn=None, n_permutations=100, batch_size_permutations=1000000, var_reg=1e-05, reg_loss_fn=<function LearnedKernelDrift.<lambda>>, train_size=0.75, retrain_from_scratch=True, optimizer=None, learning_rate=0.001, batch_size=32, batch_size_predict=32, preprocess_batch_fn=None, epochs=3, num_workers=0, verbose=0, train_kwargs=None, device=None, dataset=None, dataloader=None, input_shape=None, data_type=None)[source]
Maximum Mean Discrepancy (MMD) data drift detector where the kernel is trained to maximise an estimate of the test power. The kernel is trained on a split of the reference and test instances and then the MMD is evaluated on held out instances and a permutation test is performed.
For details see Liu et al (2020): Learning Deep Kernels for Non-Parametric Two-Sample Tests (https://arxiv.org/abs/2002.09116)
- Parameters:
x_ref (
Union
[ndarray
,list
]) – Data used as reference distribution.kernel (
Callable
) – Trainable PyTorch or TensorFlow module that returns a similarity between two instances.backend (
str
) – Backend used by the kernel and training loop.p_val (
float
) – p-value used for the significance of the test.x_ref_preprocessed (
bool
) – Whether the given reference data x_ref has been preprocessed yet. If x_ref_preprocessed=True, only the test data x will be preprocessed at prediction time. If x_ref_preprocessed=False, the reference data will also be preprocessed.preprocess_at_init (
bool
) – Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference data will be preprocessed at prediction time. Only applies if x_ref_preprocessed=False.update_x_ref (
Optional
[Dict
[str
,int
]]) – Reference data can optionally be updated to the last n instances seen by the detector or via reservoir sampling with size n. For the former, the parameter equals {‘last’: n} while for reservoir sampling {‘reservoir_sampling’: n} is passed.preprocess_fn (
Optional
[Callable
]) – Function to preprocess the data before applying the kernel.n_permutations (
int
) – The number of permutations to use in the permutation test once the MMD has been computed.batch_size_permutations (
int
) – KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations. Only relevant for ‘keops’ backend.var_reg (
float
) – Constant added to the estimated variance of the MMD for stability.reg_loss_fn (
Callable
) – The regularisation term reg_loss_fn(kernel) is added to the loss function being optimized.train_size (
Optional
[float
]) – Optional fraction (float between 0 and 1) of the dataset used to train the kernel. The drift is detected on 1 - train_size.retrain_from_scratch (
bool
) – Whether the kernel should be retrained from scratch for each set of test data or whether it should instead continue training from where it left off on the previous set.optimizer (
Optional
[Callable
]) – Optimizer used during training of the kernel.learning_rate (
float
) – Learning rate used by optimizer.batch_size (
int
) – Batch size used during training of the kernel.batch_size_predict (
int
) – Batch size used for the trained drift detector predictions.preprocess_batch_fn (
Optional
[Callable
]) – Optional batch preprocessing function. For example to convert a list of objects to a batch which can be processed by the kernel.epochs (
int
) – Number of training epochs for the kernel. Corresponds to the smaller of the reference and test sets.num_workers (
int
) – Number of workers for the dataloader. The default (num_workers=0) means multi-process data loading is disabled. Setting num_workers>0 may be unreliable on Windows.verbose (
int
) – Verbosity level during the training of the kernel. 0 is silent, 1 a progress bar.train_kwargs (
Optional
[dict
]) – Optional additional kwargs when training the kernel.device (
Union
[Literal
[‘cuda’, ‘gpu’, ‘cpu’],device
,None
]) – Device type used. The default tries to use the GPU and falls back on CPU if needed. Can be specified by passing either'cuda'
,'gpu'
,'cpu'
or an instance oftorch.device
. Relevant for ‘pytorch’ and ‘keops’ backends.dataset (
Optional
[Callable
]) – Dataset object used during training.dataloader (
Optional
[Callable
]) – Dataloader object used during training. Relevant for ‘pytorch’ and ‘keops’ backends.data_type (
Optional
[str
]) – Optionally specify the data type (tabular, image or time-series). Added to metadata.
- predict(x, return_p_val=True, return_distance=True, return_kernel=True)[source]
Predict whether a batch of data has drifted from the reference data.
- Parameters:
- Return type:
Dict
[Dict
[str
,str
],Dict
[str
,Union
[int
,float
,Callable
]]]- Returns:
Dictionary containing
'meta'
and'data'
dictionaries. –'meta'
has the detector’s metadata.'data'
contains the drift prediction and optionally the p-value, threshold, MMD metric and trained kernel.