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)

Note: 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.

```{tab-item} Drift detectors
| Detector                                                                       | Legacy save/load | Config save/load |
|:-------------------------------------------------------------------------------|:----------------:|:----------------:|
| [Kolmogorov-Smirnov](../cd/methods/ksdrift.ipynb)                              |        ✅         |        ✅         |
| [Cramér-von Mises](../cd/methods/cvmdrift.ipynb)                               |        ❌         |        ✅         |
| [Fisher's Exact Test](../cd/methods/fetdrift.ipynb)                            |        ❌         |        ✅         |
| [Least-Squares Density Difference](../cd/methods/lsdddrift.ipynb)              |        ❌         |        ✅         |
| [Maximum Mean Discrepancy](../cd/methods/mmddrift.ipynb)                       |        ✅         |        ✅         |
| [Learned Kernel MMD](../cd/methods/learnedkerneldrift.ipynb)                   |        ❌         |        ✅         |
| [Chi-Squared](../cd/methods/chisquaredrift.ipynb)                              |        ✅         |        ✅         |
| [Mixed-type tabular](../cd/methods/tabulardrift.ipynb)                         |        ✅         |        ✅         |
| [Classifier](../cd/methods/classifierdrift.ipynb)                              |        ✅         |        ✅         |
| [Spot-the-diff](../cd/methods/spotthediffdrift.ipynb)                          |        ❌         |        ✅         |
| [Classifier Uncertainty](../cd/methods/modeluncdrift.ipynb)                    |        ❌         |        ✅         |
| [Regressor Uncertainty](../cd/methods/modeluncdrift.ipynb)                     |        ❌         |        ✅         |
| [Online Cramér-von Mises](../cd/methods/onlinecvmdrift.ipynb)                  |        ❌         |        ✅         |
| [Online Fisher's Exact Test](../cd/methods/onlinefetdrift.ipynb)               |        ❌         |        ✅         |
| [Online Least-Squares Density Difference](../cd/methods/onlinelsdddrift.ipynb) |        ❌         |        ✅         |
| [Online Maximum Mean Discrepancy](../cd/methods/onlinemmddrift.ipynb)          |        ❌         |        ✅         |
```

```{tab-item} Outlier detectors
| Detector                                                | Legacy save/load | Config save/load |
|:--------------------------------------------------------|:----------------:|:----------------:|
| [Isolation Forest](../od/methods/iforest.ipynb)         |         ✅       |       ❌          |         
| [Mahalanobis Distance](../od/methods/mahalanobis.ipynb) |         ✅       |       ❌          |
| [AE](../od/methods/ae.ipynb)                            |         ✅       |       ❌          |
| [VAE](../od/methods/vae.ipynb)                          |         ✅       |       ❌          |
| [AEGMM](../od/methods/aegmm.ipynb)                      |         ✅       |       ❌          |
| [VAEGMM](../od/methods/vaegmm.ipynb)                    |         ✅       |       ❌          |
| [Likelihood Ratios](../od/methods/llr.ipynb)            |         ✅       |       ❌          |
| [Prophet](../od/methods/prophet.ipynb)                  |         ✅       |       ❌          |
| [Spectral Residual](../od/methods/sr.ipynb)             |         ✅       |       ❌          |
| [Seq2Seq](../od/methods/seq2seq.ipynb)                  |         ✅       |       ❌          |

```

```{tab-item} Adversarial detectors
| Detector                                                    | Legacy save/load | Config save/load |
|:------------------------------------------------------------|:----------------:|:----------------:|
| [Adversarial AE](../ad/methods/adversarialae.ipynb)         |        ✅        |        ❌         |
| [Model distillation](../ad/methods/modeldistillation.ipynb) |        ✅        |        ❌         |
```

Drift detectors

Outlier detectors

Adversarial detectors

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

Supported ML models

Alibi Detect drift detectors offer the option to perform preprocessing with user-defined machine learning models:

model = ... # A TensorFlow model
preprocess_fn = partial(preprocess_drift, model=model, batch_size=128)
cd = MMDDrift(x_ref, backend='tensorflow', p_val=.05, preprocess_fn=preprocess_fn)

Additionally, some detectors are built upon models directly, for example the Classifier drift detector requires a model to be passed as an argument:

cd = ClassifierDrift(x_ref, model, backend='sklearn', p_val=.05, preds_type='probs')

In order for a detector to be saveable and loadable, any models contained within it (or referenced within a detector configuration file) must fall within the family of supported models:

TensorFlow

Alibi Detect supports serialization of any TensorFlow model that can be serialized to the HDF5 format. Custom objects should be pre-registered with register_keras_serializable.

PyTorch

PyTorch models are serialized by saving the entire model using the dill module. Therefore, Alibi Detect should support any PyTorch model that can be saved and loaded with torch.save(..., pickle_module=dill) and torch.load(..., pickle_module=dill).

Scikit-learn

Scikit-learn models are serialized using joblib. Any scikit-learn model that is a subclass of sklearn.base.BaseEstimator is supported, including xgboost models following the scikit-learn API.

Online detectors

Online drift detectors are stateful, with their state updated each timestep t (each time .predict() is called). {func}~alibi_detect.saving.save_detector will save the state of online detectors to disk if t > 0. At load time, {func}~alibi_detect.saving.load_detector will load this state. For example:

from alibi_detect.cd import LSDDDriftOnline
from alibi_detect.saving import save_detector, load_detector

# Init detector (t=0)
dd = LSDDDriftOnline(x_ref, window_size=10, ert=50)

# Run 2 predictions
pred_1 = dd.predict(x_1)  # t=1 
pred_2 = dd.predict(x_2)  # t=2

# Save detector (state will be saved since t>0)
save_detector(dd, filepath)

# Load detector
dd_new = load_detector(filepath)  # detector will start at t=2

To save a clean (stateless) detector, it should be reset before saving:

dd.reset_state()  # reset to t=0
save_detector(dd, filepath)  # save the detector without state