Saving and Loading

Alibi Detect includes support for saving and loading detectors to disk. To save a detector, simply call the save_detector method and provide a path to a directory (a new one will be created if it doesn’t exist):

from alibi_detect.od import OutlierVAE
from alibi_detect.saving import save_detector

od = OutlierVAE(...) 

filepath = './my_detector/'
save_detector(od, filepath)

To load a previously saved detector, use the load_detector method and provide it with the path to the detector’s directory:

from alibi_detect.saving import load_detector

filepath = './my_detector/'
od = load_detector(filepath)

Warning

When loading a saved detector, a warning will be issued if the runtime alibi-detect version is different from the version used to save the detector. It is highly recommended to use the same alibi-detect, Python and dependency versions as were used to save the detector to avoid potential bugs and incompatibilities.

Formats

Detectors can be saved using two formats:

• Config format: For drift detectors, by default save_detector serializes the detector via a config file named config.toml, stored in filepath. The TOML format is human-readable, which makes the config files useful for record keeping, and allows a detector to be edited before it is reloaded. For more details, see Detector Configuration Files.

• Legacy format: Outlier and adversarial detectors are saved to dill files stored within filepath. Drift detectors can also be saved in this legacy format by running save_detector with legacy=True. Loading is performed in the same way, by simply running load_detector(filepath).

Supported detectors

The following tables list the current state of save/load support for each detector. Adding full support for the remaining detectors is in the Roadmap.

Drift detectors

Detector	Legacy save/load	Config save/load
Kolmogorov-Smirnov	✅	✅
Cramér-von Mises	✅	✅
Fisher’s Exact Test	✅	✅
Least-Squares Density Difference	❌	✅
Maximum Mean Discrepancy	✅	✅
Learned Kernel MMD	❌	✅
Chi-Squared	✅	✅
Mixed-type tabular	✅	✅
Classifier	✅	✅
Spot-the-diff	❌	✅
Classifier Uncertainty	❌	✅
Regressor Uncertainty	❌	✅
Online Cramér-von Mises	❌	✅
Online Fisher’s Exact Test	❌	✅
Online Least-Squares Density Difference	❌	✅
Online Maximum Mean Discrepancy	❌	✅

Outlier detectors

Detector	Legacy save/load	Config save/load
Isolation Forest	✅	❌
Mahalanobis Distance	✅	❌
AE	✅	❌
VAE	✅	❌
AEGMM	✅	❌
VAEGMM	✅	❌
Likelihood Ratios	✅	❌
Prophet	✅	❌
Spectral Residual	✅	❌
Seq2Seq	✅	❌

Adversarial detectors

Detector	Legacy save/load	Config save/load
Adversarial AE	✅	❌
Model distillation	✅	❌

Note

Saving/loading of detectors using PyTorch models and/or a PyTorch backend is currently not supported.