import logging
import numpy as np
from typing import Callable, Dict, Optional, Union, Tuple
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
if has_pytorch:
from alibi_detect.cd.pytorch.mmd import MMDDriftTorch
if has_tensorflow:
from alibi_detect.cd.tensorflow.mmd import MMDDriftTF
if has_keops and has_pytorch:
from alibi_detect.cd.keops.mmd import MMDDriftKeops
logger = logging.getLogger(__name__)
[docs]class MMDDrift(DriftConfigMixin):
[docs] @deprecated_alias(preprocess_x_ref='preprocess_at_init')
def __init__(
self,
x_ref: Union[np.ndarray, list],
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,
kernel: Callable = None,
sigma: Optional[np.ndarray] = None,
configure_kernel_from_x_ref: bool = True,
n_permutations: int = 100,
batch_size_permutations: int = 1000000,
device: Optional[str] = None,
input_shape: Optional[tuple] = None,
data_type: Optional[str] = None
) -> None:
"""
Maximum Mean Discrepancy (MMD) data drift detector using a permutation test.
Parameters
----------
x_ref
Data used as reference distribution.
backend
Backend used for the MMD implementation.
p_val
p-value used for the significance of the permutation 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 computing the data drift metrics.
kernel
Kernel used for the MMD computation, defaults to Gaussian RBF kernel.
sigma
Optionally set the GaussianRBF kernel bandwidth. Can also pass multiple bandwidth values as an array.
The kernel evaluation is then averaged over those bandwidths.
configure_kernel_from_x_ref
Whether to already configure the kernel bandwidth from the reference data.
n_permutations
Number of permutations used in the permutation test.
batch_size_permutations
KeOps computes the n_permutations of the MMD^2 statistics in chunks of batch_size_permutations.
Only relevant for 'keops' backend.
device
Device type used. The default None tries to use the GPU and falls back on CPU if needed.
Can be specified by passing either 'cuda', 'gpu' or 'cpu'. Only relevant for 'pytorch' backend.
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']]
pop_kwargs = ['self', 'x_ref', 'backend', '__class__']
if backend == Framework.TENSORFLOW:
pop_kwargs += ['device', 'batch_size_permutations']
detector = MMDDriftTF
elif backend == Framework.PYTORCH:
pop_kwargs += ['batch_size_permutations']
detector = MMDDriftTorch
else:
detector = MMDDriftKeops
[kwargs.pop(k, None) for k in pop_kwargs]
if kernel is None:
if backend == Framework.TENSORFLOW:
from alibi_detect.utils.tensorflow.kernels import GaussianRBF
elif backend == Framework.PYTORCH:
from alibi_detect.utils.pytorch.kernels import GaussianRBF # type: ignore
else:
from alibi_detect.utils.keops.kernels import GaussianRBF # type: ignore
kwargs.update({'kernel': GaussianRBF})
self._detector = detector(*args, **kwargs) # type: ignore
self.meta = self._detector.meta
[docs] def predict(self, x: Union[np.ndarray, list], return_p_val: bool = True, return_distance: bool = True) \
-> Dict[Dict[str, str], Dict[str, Union[int, float]]]:
"""
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.
Returns
-------
Dictionary containing 'meta' and 'data' dictionaries.
'meta' has the model's metadata.
'data' contains the drift prediction and optionally the p-value, threshold and MMD metric.
"""
return self._detector.predict(x, return_p_val, return_distance)
[docs] def score(self, x: Union[np.ndarray, list]) -> Tuple[float, float, float]:
"""
Compute the p-value resulting from a permutation test using the maximum mean discrepancy
as a distance measure between the reference data and the data to be tested.
Parameters
----------
x
Batch of instances.
Returns
-------
p-value obtained from the permutation test, the MMD^2 between the reference and test set,
and the MMD^2 threshold above which drift is flagged.
"""
return self._detector.score(x)