Skip to content

Latest commit

 

History

History
294 lines (263 loc) · 7.83 KB

getting_started.md

File metadata and controls

294 lines (263 loc) · 7.83 KB
Raw

Getting Started

Installation

Alibi works with Python 3.7+ and can be installed from PyPI or conda-forge by following the instructions below.

```{div} sd-mb-3
- Alibi can be installed from [PyPI](https://pypi.org/project/alibi/) with `pip`:
```

`````{tab-set}

````{tab-item} Standard
:sync: label-standard
:class-label: sd-pt-0
```{div} sd-mb-1
Default installation.
```
```bash
pip install alibi
```
````

````{tab-item} SHAP
:sync: label-shap
:class-label: sd-pt-0
```{div} sd-mb-1
Installation with support for computing [SHAP](https://shap.readthedocs.io/en/stable/index.html) values.
```
```bash
pip install alibi[shap]
```
````

````{tab-item} Distributed
:class-label: sd-pt-0
:sync: label-dist
```{div} sd-mb-1
Installation with support for 
[distributed Kernel SHAP](../examples/distributed_kernel_shap_adult_lr.ipynb).
```
```bash
pip install alibi[ray]
```
````

````{tab-item} TensorFlow
:class-label: sd-pt-0
:sync: label-tensorflow
```{div} sd-mb-1
Installation with support for tensorflow backends. Required for 
- [Contrastive Explanation Method (CEM)](../methods/CEM.ipynb) 
- [Counterfactuals Guided by Prototypes](../methods/CFProto.ipynb) 
- [Counterfactual Instances](../methods/CF.ipynb)
- [Integrated gradients](../methods/IntegratedGradients.ipynb) 
- [Anchors on Textual data](../examples/anchor_text_movie.ipynb) with `sampling_strategy='language_model'` 
- One of Torch or TensorFlow is required for the [Counterfactuals with RL](../methods/CFRL.ipynb) methods
```
```bash
pip install alibi[tensorflow]
```
````

````{tab-item} Torch
:class-label: sd-pt-0
:sync: label-torch
```{div} sd-mb-1
Installation with support for torch backends. One of Torch or TensorFlow is required for: 
- [Counterfactuals with RL](../methods/CFRL.ipynb)
- [Similarity explanations](../methods/Similarity.ipynb)
```
```bash
pip install alibi[torch]
```
````

````{tab-item} All
:class-label: sd-pt-0
:sync: label-all
```{div} sd-mb-1
Installs all optional dependencies.
```
```bash
pip install alibi[all]
```
````
`````

```{div} sd-mb-3
- To install the conda-forge version it is recommended to use [mamba](https://mamba.readthedocs.io/en/stable/), 
which can be installed to the *base* conda enviroment with:
```
```bash
conda install mamba -n base -c conda-forge
```
```{div} sd-mb-3
- `mamba` can then be used to install alibi in a conda enviroment:
```

`````{tab-set}

````{tab-item} Standard
:sync: label-standard
:class-label: sd-pt-0
```{div} sd-mb-1
Default installation.
```
```bash
mamba install -c conda-forge alibi
```
````

````{tab-item} SHAP
:sync: label-shap
:class-label: sd-pt-0
```{div} sd-mb-1
Installation with support for computing [SHAP](https://shap.readthedocs.io/en/stable/index.html) values.
```
```bash
mamba install -c conda-forge alibi shap
```
````

````{tab-item} Distributed
:sync: label-dist
:class-label: sd-pt-0
```{div} sd-mb-1
Installation with support for distributed computation of explanations.
```
```bash
mamba install -c conda-forge alibi ray 
```
````

`````

Features

Alibi is a Python package designed to help explain the predictions of machine learning models and gauge the confidence of predictions. The focus of the library is to support the widest range of models using black-box methods where possible.

To get a list of the latest available model explanation algorithms, you can type:

import alibi
alibi.explainers.__all__
['ALE', 
'AnchorTabular',
'DistributedAnchorTabular', 
'AnchorText', 
'AnchorImage', 
'CEM', 
'Counterfactual', 
'CounterfactualProto', 
'CounterfactualRL', 
'CounterfactualRLTabular',
'PartialDependence',
'TreePartialDependence',
'PartialDependenceVariance',
'PermutationImportance',
'plot_ale',
'plot_pd',
'plot_pd_variance',
'plot_permutation_importance',
'IntegratedGradients', 
'KernelShap', 
'TreeShap',
'GradientSimilarity']

For gauging model confidence:

alibi.confidence.__all__
['linearity_measure',
 'LinearityMeasure',
 'TrustScore']

For dataset summarization

alibi.prototypes.__all__
['ProtoSelect',
 'visualize_image_prototypes']

For detailed information on the methods:

Basic Usage

The alibi explanation API takes inspiration from scikit-learn, consisting of distinct initialize, fit and explain steps. We will use the Anchor method on tabular data to illustrate the API.

First, we import the explainer:

from alibi.explainers import AnchorTabular

Next, we initialize it by passing it a prediction function and any other necessary arguments:

explainer = AnchorTabular(predict_fn, feature_names)

Some methods require an additional .fit step which requires access to the training set the model was trained on:

explainer.fit(X_train)
AnchorTabular(meta={
    'name': 'AnchorTabular',
    'type': ['blackbox'],
    'explanations': ['local'],
    'params': {'seed': None, 'disc_perc': (25, 50, 75)}
})

Finally, we can call the explainer on a test instance which will return an Explanation object containing the explanation and any additional metadata returned by the computation:

 explanation = explainer.explain(x)

The returned Explanation object has meta and data attributes which are dictionaries containing any explanation metadata (e.g. parameters, type of explanation) and the explanation itself respectively:

explanation.meta
{'name': 'AnchorTabular',
 'type': ['blackbox'],
 'explanations': ['local'],
 'params': {'seed': None,
  'disc_perc': (25, 50, 75),
  'threshold': 0.95,
  'delta': ...truncated output...

explanation.data
{'anchor': ['petal width (cm) > 1.80', 'sepal width (cm) <= 2.80'],
 'precision': 0.9839228295819936,
 'coverage': 0.31724137931034485,
 'raw': {'feature': [3, 1],
  'mean': [0.6453362255965293, 0.9839228295819936],
  'precision': [0.6453362255965293, 0.9839228295819936],
  'coverage': [0.20689655172413793, 0.31724137931034485],
  'examples': ...truncated output...

The top level keys of both meta and data dictionaries are also exposed as attributes for ease of use of the explanation:

explanation.anchor
['petal width (cm) > 1.80', 'sepal width (cm) <= 2.80']

Some algorithms, such as Kernel SHAP, can run batches of explanations in parallel, if the number of cores is specified in the algorithm constructor:

distributed_ks = KernelShap(predict_fn, distributed_opts={'n_cpus': 10})

Note that this requires the user to run pip install alibi[ray] to install dependencies of the distributed backend.

The exact details will vary slightly from method to method, so we encourage the reader to become familiar with the types of algorithms supported in Alibi.