Algorithms for explaining machine learning models
This is a patch release to correct a regression in AnchorImage introduced in v0.6.3.
v0.6.3 where AnchorImage would ignore user segmentation_kwargs (#581).Pillow and scikit-image have been bumped to 9.x and 0.19.x respectively.IntegratedGradients via the target_fn argument, in order to calculate the scalar target dimension from the model output. This is to bypass the requirement of passing target directly to explain when the target of interest may depend on the prediction output. See the example in the docs. (#523).AnchorImage leading to an error (#542).CounterfactualRLTabular (#550).numpy typing has been updated to be compatible with numpy 1.22 (#543). This is a prerequisite for upgrading to tensorflow 2.7.Optional type-checking with mypy has been reinstated (#541).tensorflow version has been bumped from 2.6 to 2.7 (#377).AnchorTabular, AnchorImage and AnchorText now expose an additional dtype keyword argument with a default value of np.float32. This is to ensure that whenever a user predictor is called internally with dummy data a correct data type can be ensured (#506).alibi.exceptions defining the alibi exception hierarchy. This introduces two exceptions, AlibiPredictorCallException and AlibiPredictorReturnTypeError. See #520 for more details.AnchorImage, coerce image_shape argument into a tuple to implicitly allow passing a list input which eases use of configuration files. In the future the typing will be improved to be more explicit about allowed types with runtime type checking.shap version to the latest 0.40.0 as this fixes an installation issue if alibi and shap are installed with the same command.transformers library (#528).IntegratedGradients with forward_kwargs not always being correctly passed (#525).TreeShap predictor (#534).readthedocs Docker image in our CI to replicate the doc building environment exactly. Also enabled readthedocs build on PR feature which allows browsing the built docs on every PR.myst (a markdown superset) for more flexible documentation (#482).alibi.explainers.CounterfactualRL and alibi.explainers.CounterfactualRLTabular classes. The method is model-agnostic and the implementation is written in both PyTorch and TensorFlow. See docs for more information.CounterFactual and CounterFactualProto classes have been changed to Counterfactual and CounterfactualProto respectively for consistency and correctness. The old class names continue working for now but emit a deprecation warning message and will be removed in an upcoming version.dill behaviour was changed to not extend the pickle protocol so that standard usage of pickle in a session with alibi does not change expected pickle behaviour. See discussion.AnchorImage internals refactored to avoid persistent state between explain calls.pandoc version for docs building updated to 1.19.2 which is what is used on readthedocs.AnchorText now supports sampling according to masked language models via the transformers library. See docs and the example for using the new functionality.AnchorText the public API for the constructor has changed. See docs for a full description of the new API.AnchorTabular now supports one-hot encoded categorical variables in addition to the default ordinal/label encoded representation of categorical variables.IntegratedGradients changes to allow explaining a wider variety of models. In particular, a new forward_kwargs argument to explain allows passing additional arguments to the model and attribute_to_layer_inputs flag to allow calculating attributions with respect to layer input instead of output if set to True. The API and capabilities now track more closely to the captum.ai PyTorch implementation.IntegratedGradients to explain transformer models.IntegratedGradients - fix the path definition for attributions calculated with respect to an internal layer. Previously the paths were defined in terms of the inputs and baselines, now they are correctly defined in terms of the corresponding layer input/output.dill. See docs for more details.model.layers for IntegratedGradients.numpy 1.20.KernelShap and TreeShap now requires installing the shap dependency explicitly after installing alibi. This can be achieved by running pip install alibi && pip install alibi[shap]. The reason for this is that the build process for the upstream shap package is not well configured resulting in broken installations as detailed in https://github.com/SeldonIO/alibi/pull/376 and https://github.com/slundberg/shap/pull/1802. We expect this to be a temporary change until changes are made upstream.reset_predictor method for black-box explainers. The intended use case for this is for deploying an already configured explainer to work with a remote predictor endpoint instead of the local predictor used in development.alibi.datasets.load_cats function which loads a small sample of cat images shipped with the library to be used in examples.alibi.datasets.fetch_imagenet function as the Imagenet API is no longer available.IntegratedGradients now works with subclassed TensorFlow models.IntegratedGradients as this was not working properly and is difficult to do in the general case.AnchorTabular tests not being picked up due to a name change of test data fixtures.IntegratedGradients now supports models with multiple inputs. For each input of the model, attributions are calculated and returned in a list. Also extends the method allowing to calculate attributions for multiple internal layers. If a list of layers is passed, a list of attributions is returned. See https://github.com/SeldonIO/alibi/pull/321.ALE now supports selecting a subset of features to explain. This can be useful to reduce runtime if only some features are of interest and also indirectly helps dealing with categorical variables by being able to exclude them (as ALE does not support categorical variables).AnchorTabular coverage calculation was incorrect which was caused by incorrectly indexing a list, this is now resolved.ALE was causing an error when a constant feature was present. This is now handled explicitly and the user has control over how to handle these features. See https://docs.seldon.io/projects/alibi/en/latest/api/alibi.explainers.ale.html#alibi.explainers.ale.ALE for more details.AnchorText functionality as the way lexeme_prob tables are loaded was changed. This is now fixed by explicitly handling the loading depending on the spacy version.Explanation object instead of the old dict object.CounterFactual, CounterFactualProto and CEM docs to explain the necessity of clearing the TensorFlow graph if switching to a new model in the same session.requirements/dev.txt and requirements/docs.txt..readthedocs.yml to control how user-facing docs are built directly from the repo.setup.py as the workflow is both unused and outdated.shap==0.38.1 as a dependency as it assumes IPython is installed and breaks the installation.ray. To use, install ray using pip install alibi[ray].KernelShap distributed version using the new distributed backend.data['raw']['instances'] which is a batch-wise version of the existing data['raw']['instance']. This is in preparation for the eventual batch support for anchor methods.pyupgrade via nbqa for formatting example notebooks using Python 3.6+ syntax.data['raw']['prediction'] is now batch-wise, i.e. for AnchorTabular and AnchorImage it is a 1-dimensional numpy array whilst for AnchorText it is a list of strings. This is in preparation for the eventual batch support for anchor methods.prettyprinter and substituted with a slightly modified standard library version of PrettyPrinter. This is to prepare for a conda release which requires all dependencies to also be published on conda.update_metadata method for any Explainer object to enable easy book-keeping for algorithm parametersKernelShap wrapper to work with the newest shap>=0.36 libraryKernelShap and TreeShap