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Classifier
Overview
The classifier-based drift detector Lopez-Paz and Oquab, 2017 simply tries to correctly distinguish instances from the reference set vs. the test set. The classifier is trained to output the probability that a given instance belongs to the test set. If the probabilities it assigns to unseen test instances are significantly higher (as determined by a Kolmogorov-Smirnov test) to those it assigns to unseen reference instances then the test set must differ from the reference set and drift is flagged. Alternatively, the detector also allows to binarize the classifier predictions (0 or 1) and apply a binomial test on the binarized predictions of the reference vs. the test data. To leverage all the available reference and test data, stratified cross-validation can be applied and the out-of-fold predictions are used for the significance test. Note that a new classifier is trained for each test set or even each fold within the test set.
Usage
Initialize
Arguments:
x_ref
: Data used as reference distribution.model
: Binary classification model used for drift detection. TensorFlow, PyTorch and Sklearn models are supported.
Keyword arguments:
backend
: Specify the backend (tensorflow, pytorch or sklearn). This depends on the framework of themodel
. Defaults to tensorflow.p_val
: p-value threshold used for the significance of the test.preprocess_at_init
: Whether to already apply the (optional) preprocessing step to the reference data at initialization and store the preprocessed data. Dependent on the preprocessing step, this can reduce the computation time for the predict step significantly, especially when the reference dataset is large. Defaults to True. It is possible that it needs to be set to False if the preprocessing step requires statistics from both the reference and test data, such as the mean or standard deviation.x_ref_preprocessed
: Whether or not the reference datax_ref
has already been preprocessed. If True, the reference data will be skipped and preprocessing will only be applied to the test data passed topredict
.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. If the input data type is of typeList[Any]
thenupdate_x_ref
needs to be set to None and the reference set remains fixed.preprocess_fn
: Function to preprocess the data before computing the data drift metrics.preds_type
: Whether the model outputs ‘probs’ (probabilities - for ‘tensorflow’, ‘pytorch’, ‘sklearn’ models), ‘logits’ (for ‘pytorch’, ‘tensorflow’ models), ‘scores’ (for ‘sklearn’ models ifdecision_function
is supported).binarize_preds
: Whether to test for discrepancy on soft (e.g. probs/logits/scores) model predictions directly with a K-S test or binarise to 0-1 prediction errors and apply a binomial test. Defaults to False and therefore applies the K-S test.train_size
: Optional fraction (float between 0 and 1) of the dataset used to train the classifier. The drift is detected on 1 - train_size. Cannot be used in combination withn_folds
.n_folds
: Optional number of stratified folds used for training. The model preds are then calculated on all the out-of-fold predictions. This allows to leverage all the reference and test data for drift detection at the expense of longer computation. If bothtrain_size
andn_folds
are specified,n_folds
is prioritized.seed
: Optional random seed for fold selection.optimizer
: Optimizer used during training of the classifier. Fromtorch.optim
for PyTorch andtf.keras.optimizers
for TensorFlow.learning_rate
: Learning rate for the optimizer. Only relevant for tensorflow and pytorch backends.batch_size
: Batch size used during training of the classifier.Only relevant for tensorflow and pytorch backends.epochs
: Number of training epochs for the classifier. Applies to each fold ifn_folds
is specified. Only relevant for tensorflow and pytorch backends.verbose
: Verbosity level during the training of the classifier. 0 is silent and 1 prints a progress bar. Only relevant for tensorflow and pytorch backends.train_kwargs
: Optional additional kwargs for the built-in TensorFlow (from alibi_detect.models.tensorflow import trainer
) or PyTorch (from alibi_detect.models.pytorch import trainer
) trainer functions.dataset
: Dataset object used during training of the classifier. Defaults toalibi_detect.utils.pytorch.TorchDataset
(an instance oftorch.utils.data.Dataset
) for the PyTorch backend andalibi_detect.utils.tensorflow.TFDataset
(an instance oftf.keras.utils.Sequence
) for the TensorFlow backend. For PyTorch, the dataset should only take the data x and the array of labels y as input, so when e.g. TorchDataset is passed to the detector at initialisation, during training TorchDataset(x, y) is used. For TensorFlow, the dataset is an instance oftf.keras.utils.Sequence
, so when e.g. TFDataset is passed to the detector at initialisation, during training TFDataset(x, y, batch_size=batch_size, shuffle=True) is used. x can be of type np.ndarray or List[Any] while y is of type np.ndarray.input_shape
: Shape of input data.data_type
: Optionally specify the data type (e.g. tabular, image or time-series). Added to metadata.
Additional PyTorch keyword arguments:
device
: cuda or gpu to use the GPU and cpu for the CPU. If the device is not specified, the detector will try to leverage the GPU if possible and otherwise fall back on CPU.dataloader
: Dataloader object used during training of the model. Defaults totorch.utils.data.DataLoader
. The dataloader is not initialized yet, this is done during init off the detector using thebatch_size
. Custom dataloaders can be passed as well, e.g. for graph data we can usetorch_geometric.data.DataLoader
.
Additional Sklearn keyword arguments:
use_calibration
: Whether to use calibration. Calibration can be used on top of any model. Only relevant for ‘sklearn’ backend.calibration_kwargs
: Optional additional kwargs for calibration. Only relevant for ‘sklearn’ backend. See https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html for more details.use_oob
: Whether to use out-of-bag(OOB) predictions. Supported only forRandomForestClassifier
.
Initialized TensorFlow drift detector example:
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, Flatten, Input
from alibi_detect.cd import ClassifierDrift
model = tf.keras.Sequential(
[
Input(shape=(32, 32, 3)),
Conv2D(8, 4, strides=2, padding='same', activation=tf.nn.relu),
Conv2D(16, 4, strides=2, padding='same', activation=tf.nn.relu),
Conv2D(32, 4, strides=2, padding='same', activation=tf.nn.relu),
Flatten(),
Dense(2, activation='softmax')
]
)
cd = ClassifierDrift(x_ref, model, p_val=.05, preds_type='probs', n_folds=5, epochs=2)
A similar detector using PyTorch:
import torch.nn as nn
model = nn.Sequential(
nn.Conv2d(3, 8, 4, stride=2, padding=0),
nn.ReLU(),
nn.Conv2d(8, 16, 4, stride=2, padding=0),
nn.ReLU(),
nn.Conv2d(16, 32, 4, stride=2, padding=0),
nn.ReLU(),
nn.Flatten(),
nn.Linear(128, 2)
)
cd = ClassifierDrift(x_ref, model, backend='pytorch', p_val=.05, preds_type='logits')
Detect Drift
We detect data drift by simply calling predict
on a batch of instances x
. return_p_val
equal to True will also return the p-value of the test, return_distance
equal to True will return a notion of strength of the drift and return_probs
equals True also returns the out-of-fold classifier model prediction probabilities on the reference and test data (0 = reference data, 1 = test data) as well as the associated out-of-fold reference and test instances.
The prediction takes the form of a dictionary with meta
and data
keys. meta
contains the detector’s metadata while data
is also a dictionary which contains the actual predictions stored in the following keys:
is_drift
: 1 if the sample tested has drifted from the reference data and 0 otherwise.threshold
: the user-defined threshold defining the significance of the testp_val
: the p-value of the test ifreturn_p_val
equals True.distance
: a notion of strength of the drift ifreturn_distance
equals True. Equal to the K-S test statistic assumingbinarize_preds
equals False or the relative error reduction over the baseline error expected under the null ifbinarize_preds
equals True.probs_ref
: the instance level prediction probability for the reference datax_ref
(0 = reference data, 1 = test data) ifreturn_probs
is True.probs_test
: the instance level prediction probability for the test datax
ifreturn_probs
is true.x_ref_oof
: the instances associated withprobs_ref
ifreturn_probs
equals True.x_test_oof
: the instances associated withprobs_test
ifreturn_probs
equals True.
preds = cd.predict(x)