GITBOOK-4: No subject

Author: Rajakavitha1

docs-gb/overview/saving.md

@@ -1,8 +1,6 @@
 # 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):
+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):
 
 ```python
 from alibi_detect.od import OutlierVAE
@@ -14,8 +12,7 @@ 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:
+To load a previously saved detector, use the `load_detector` method and provide it with the path to the detector's directory:
 
 ```python
 from alibi_detect.saving import load_detector
@@ -24,31 +21,20 @@ 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**.
-```
+{% hint style="info" %}
+**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**.
+{% endhint %}
 
 ## 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](https://toml.io/en/) 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](config_files.md).
 
-- **Legacy format**: Outlier and adversarial detectors are saved to [dill](https://dill.readthedocs.io/en/latest/dill.html) 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)`.
+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](https://toml.io/en/) 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](config\_files.md).
+* **Legacy format**: Outlier and adversarial detectors are saved to [dill](https://dill.readthedocs.io/en/latest/dill.html) 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](roadmap.md).
-
+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](roadmap.md).
 
 ````{tab-set}
 
@@ -97,58 +83,58 @@ for the remaining detectors is in the [Roadmap](roadmap.md).
 ```
 ````
 
-(supported_models)=
+{% tabs %}
+{% tab title="Drift detectors" %}
+
+{% endtab %}
+
+{% tab title="Outlier detectors" %}
+
+{% endtab %}
+
+{% tab title="Adversarial detectors       " %}
+| Detector           | Legacy save/load  | Config save/load |
+| ------------------ | ----------------- | ---------------- |
+| Adversarial AE     | ✅                 | ❌                |
+| Model distillation | ✅                 | ❌                |
+{% endtab %}
+{% endtabs %}
+
 ## Supported ML models
 
-Alibi Detect drift detectors offer the option to perform [preprocessing](../cd/background.md#input-preprocessing)
-with user-defined machine learning models:
+Alibi Detect drift detectors offer the option to perform [preprocessing](../cd/background.md#input-preprocessing) with user-defined machine learning models:
 
 ```python
 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](../cd/methods/classifierdrift.ipynb) drift detector requires a `model` to be passed
-as an argument:
+Additionally, some detectors are built upon models directly, for example the [Classifier](../cd/methods/classifierdrift.ipynb) drift detector requires a `model` to be passed as an argument:
 
 ```python
 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](config_files.md#specifying-artefacts)) must fall within the family of supported models:
-
-````{tab-set}
+In order for a detector to be saveable and loadable, any models contained within it (or referenced within a [detector configuration file](config\_files.md#specifying-artefacts)) must fall within the family of supported models:
 
-```{tab-item} TensorFlow
-Alibi Detect supports serialization of any TensorFlow model that can be serialized to the 
-[HDF5](https://www.tensorflow.org/guide/keras/save_and_serialize#keras_h5_format) format. 
-Custom objects should be pre-registered with 
-[register_keras_serializable](https://www.tensorflow.org/api_docs/python/tf/keras/utils/register_keras_serializable).
-```
+{% tabs %}
+{% tab title="TensorFlow" %}
+Alibi Detect supports serialization of any TensorFlow model that can be serialized to the [HDF5](https://www.tensorflow.org/guide/keras/save\_and\_serialize#keras\_h5\_format) format. Custom objects should be pre-registered with [register\_keras\_serializable](https://www.tensorflow.org/api\_docs/python/tf/keras/utils/register\_keras\_serializable).
+{% endtab %}
 
-```{tab-item} PyTorch
-PyTorch models are serialized by saving the [entire model](https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-load-entire-model)
-using the [dill](https://dill.readthedocs.io/en/latest/index.html) 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)`.
-```
+{% tab title="PyTorch" %}
+PyTorch models are serialized by saving the [entire model](https://pytorch.org/tutorials/beginner/saving\_loading\_models.html#save-load-entire-model) using the [dill](https://dill.readthedocs.io/en/latest/index.html) 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)`.
+{% endtab %}
 
-```{tab-item} Scikit-learn
-Scikit-learn models are serialized using [joblib](https://joblib.readthedocs.io/en/latest/persistence.html).
-Any scikit-learn model that is a subclass of {py:class}`sklearn.base.BaseEstimator` is supported, including 
-[xgboost](https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn) models following 
-the scikit-learn API.
-```
-````
+{% tab title="Scikit-learn" %}
+Scikit-learn models are serialized using [joblib](https://joblib.readthedocs.io/en/latest/persistence.html). Any scikit-learn model that is a subclass of [`sklearn.base.BaseEstimator`](https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator) is supported, including [xgboost](https://xgboost.readthedocs.io/en/latest/python/python\_api.html#module-xgboost.sklearn) models following the scikit-learn API.
+{% endtab %}
+{% endtabs %}
 
 ## Online detectors
 
-[Online drift detectors](../cd/methods.md#online) 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:
+[Online drift detectors](../cd/methods.md#online) 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:
 
 ```python
 from alibi_detect.cd import LSDDDriftOnline
@@ -173,4 +159,4 @@ To save a clean (stateless) detector, it should be reset before saving:
 ```python
 dd.reset_state()  # reset to t=0
 save_detector(dd, filepath)  # save the detector without state
-```
+```