import numpy as np
from typing import Callable, Dict, Optional, Union
from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, has_keops, BackendValidator, Framework
from alibi_detect.utils.warnings import deprecated_alias
from alibi_detect.base import DriftConfigMixin
from alibi_detect.utils._types import TorchDeviceType
if has_pytorch:
from torch.utils.data import DataLoader
from alibi_detect.cd.pytorch.learned_kernel import LearnedKernelDriftTorch
from alibi_detect.utils.pytorch.data import TorchDataset
if has_tensorflow:
from alibi_detect.cd.tensorflow.learned_kernel import LearnedKernelDriftTF
from alibi_detect.utils.tensorflow.data import TFDataset
if has_keops:
from alibi_detect.cd.keops.learned_kernel import LearnedKernelDriftKeops
[docs]
class LearnedKernelDrift(DriftConfigMixin):
[docs]
@deprecated_alias(preprocess_x_ref='preprocess_at_init')
def __init__(
self,
x_ref: Union[np.ndarray, list],
kernel: Callable,
backend: str = 'tensorflow',
p_val: float = .05,
x_ref_preprocessed: bool = False,
preprocess_at_init: bool = True,
update_x_ref: Optional[Dict[str, int]] = None,
preprocess_fn: Optional[Callable] = None,
n_permutations: int = 100,
batch_size_permutations: int = 1000000,
var_reg: float = 1e-5,
reg_loss_fn: Callable = (lambda kernel: 0),
train_size: Optional[float] = .75,
retrain_from_scratch: bool = True,
optimizer: Optional[Callable] = None,
learning_rate: float = 1e-3,
batch_size: int = 32,
batch_size_predict: int = 32,
preprocess_batch_fn: Optional[Callable] = None,
epochs: int = 3,
num_workers: int = 0,
verbose: int = 0,
train_kwargs: Optional[dict] = None,
device: TorchDeviceType = None,
dataset: Optional[Callable] = None,
dataloader: Optional[Callable] = None,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None
) -> None:
"""
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
Data used as reference distribution.
kernel
Trainable PyTorch or TensorFlow module that returns a similarity between two instances.
backend
Backend used by the kernel and training loop.
p_val
p-value used for the significance of the test.
x_ref_preprocessed
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
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
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
Function to preprocess the data before applying the kernel.
n_permutations
The number of permutations to use in the permutation test once the MMD has been computed.
batch_size_permutations
KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations.
Only relevant for 'keops' backend.
var_reg
Constant added to the estimated variance of the MMD for stability.
reg_loss_fn
The regularisation term reg_loss_fn(kernel) is added to the loss function being optimized.
train_size
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
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
Optimizer used during training of the kernel.
learning_rate
Learning rate used by optimizer.
batch_size
Batch size used during training of the kernel.
batch_size_predict
Batch size used for the trained drift detector predictions.
preprocess_batch_fn
Optional batch preprocessing function. For example to convert a list of objects to a batch which can be
processed by the kernel.
epochs
Number of training epochs for the kernel. Corresponds to the smaller of the reference and test sets.
num_workers
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
Verbosity level during the training of the kernel. 0 is silent, 1 a progress bar.
train_kwargs
Optional additional kwargs when training the kernel.
device
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 of
``torch.device``. Relevant for 'pytorch' and 'keops' backends.
dataset
Dataset object used during training.
dataloader
Dataloader object used during training. Relevant for 'pytorch' and 'keops' backends.
input_shape
Shape of input data.
data_type
Optionally specify the data type (tabular, image or time-series). Added to metadata.
"""
super().__init__()
# Set config
self._set_config(locals())
backend = backend.lower()
BackendValidator(
backend_options={Framework.TENSORFLOW: [Framework.TENSORFLOW],
Framework.PYTORCH: [Framework.PYTORCH],
Framework.KEOPS: [Framework.KEOPS]},
construct_name=self.__class__.__name__
).verify_backend(backend)
kwargs = locals()
args = [kwargs['x_ref'], kwargs['kernel']]
pop_kwargs = ['self', 'x_ref', 'kernel', 'backend', '__class__']
if kwargs['optimizer'] is None:
pop_kwargs += ['optimizer']
[kwargs.pop(k, None) for k in pop_kwargs]
if backend == Framework.TENSORFLOW:
pop_kwargs = ['device', 'dataloader', 'batch_size_permutations', 'num_workers']
[kwargs.pop(k, None) for k in pop_kwargs]
if dataset is None:
kwargs.update({'dataset': TFDataset})
detector = LearnedKernelDriftTF
else:
if dataset is None:
kwargs.update({'dataset': TorchDataset})
if dataloader is None:
kwargs.update({'dataloader': DataLoader})
if backend == Framework.PYTORCH:
pop_kwargs = ['batch_size_permutations']
[kwargs.pop(k, None) for k in pop_kwargs]
detector = LearnedKernelDriftTorch
else:
detector = LearnedKernelDriftKeops
self._detector = detector(*args, **kwargs)
self.meta = self._detector.meta
[docs]
def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True,
return_distance: bool = True, return_kernel: bool = True) \
-> Dict[Dict[str, str], Dict[str, Union[int, float, Callable]]]:
"""
Predict whether a batch of data has drifted from the reference data.
Parameters
----------
x
Batch of instances.
return_p_val
Whether to return the p-value of the permutation test.
return_distance
Whether to return the MMD metric between the new batch and reference data.
return_kernel
Whether to return the updated kernel trained to discriminate reference and test instances.
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.
"""
return self._detector.predict(x, return_p_val, return_distance, return_kernel)