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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 tests instances are significantly higher (as determined by a Kolmogorov-Smirnoff test) to those it assigns to unseen reference instances then the test set must differ from the reference set and drift is flagged. 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.




  • x_ref: Data used as reference distribution.

  • model: Classification model used for drift detection. Both TensorFlow and PyTorch models are supported.

Keyword arguments:

  • backend: Specify the backend (tensorflow or pytorch). This depends on the framework of the model. Defaults to tensorflow.

  • p_val: p-value threshold used for the significance of the test.

  • preprocess_x_ref: 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.

  • 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.

  • preds_type: Whether the model outputs ‘probs’ or ‘logits’.

  • binarize_preds: Whether to test for discrepency on soft (e.g. probs/logits) model predictions directly with a K-S test or binarise to 0-1 prediction errors and apply a binomial 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 with n_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 both train_size and n_folds are specified, n_folds is prioritized.

  • seed: Optional random seed for fold selection.

  • optimizer: Optimizer used during training of the classifier. From torch.optim for PyTorch and tf.keras.optimizers for TensorFlow.

  • learning_rate: Learning rate for the optimizer.

  • batch_size: Batch size used during training of the classifier.

  • epochs: Number of training epochs for the classifier. Applies to each fold if n_folds is specified.

  • verbose: Verbosity level during the training of the classifier. 0 is silent, 1 a progress bar and 2 prints the statistics after each epoch.

  • train_kwargs: Optional additional kwargs for when fitting the classifier for TensorFlow or for the built-in PyTorch trainer function (from alibi_detect.models.pytorch import trainer).

  • data_type: Optionally specify the data type (e.g. tabular, image or time-series). Added to metadata.

Additional TensorFlow keyword arguments:

  • compile_kwargs: Optional additional kwargs for model.compile() when compiling the classifier.

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.

Initialized TensorFlow drift detector example:

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, Flatten, Input
from 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),
      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.Conv2d(8, 16, 4, stride=2, padding=0),
    nn.Conv2d(16, 32, 4, stride=2, padding=0),
    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 and return_distance equal to True will return a notion of strength of the drift.

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 test

  • p_val: the p-value of the test if return_p_val equals True.

  • distance: a notion of strength of the drift if return_distance equals True. Equal to the K-S test statistic assuming binarize_preds equals False or the relative error reduction over the baseline error expected under the null if binarize_preds equals True.

preds = cd.predict(x)

Saving and loading

The drift detectors can be saved and loaded in the same way as other detectors:

from alibi_detect.utils.saving import save_detector, load_detector

filepath = 'my_path'
save_detector(cd, filepath)
cd = load_detector(filepath)

Currently on the TensorFlow backend is supported for save_detector and load_detector. Adding PyTorch support is a near term priority.