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Change Log

v0.9.0 (2023-xx-xx)

Full Changelog

Added

  • New feature PermutationImportance explainer implementing the permutation feature importance global explanations. Also included is a plot_permutation_importance utility function for flexible plotting of the resulting feature importance scores (docs, #798).
  • New feature PartialDependenceVariance explainer implementing partial dependence variance global explanations. Also included is a plot_pd_variance utility function for flexible plotting of the resulting PD variance plots (docs, #758).

Fixed

  • GradientSimilarity explainer now automatically handles sparse tensors in the model by converting the gradient tensors to dense ones before calculating similarity. This used to be a source of bugs when calculating similarity for models with embedding layers for which gradients tensors are sparse by default. Additionally, it now filters any non-trainable parameters and doesn't consider those in the calculation as no gradients exist. A warning is raised if any non-trainable layers or parameters are detected (#829).
  • Updated the discussion of the interpretation of ALE. The previous examples and documentation had some misleading claims; these have been removed and reworked with an emphasis on the mostly qualitative interpretation of ALE plots (#838, #846).

Changed

  • Deprecated the use of the legacy Boston housing dataset in examples and testing. The new examples now use the California housing dataset (#838, #834).
  • Modularized the computation of prototype importances and plotting for ProtoSelect, allowing greater flexibility to the end user (#826).
  • Roadmap documentation page removed due to going out of date (#842).

Development

  • Tests added for tensorflow models used in CounterfactualRL (#793).
  • Tests added for pytorch models used in CounterfactualRL (#799).
  • Tests added for ALE plotting functionality (#816).
  • Tests added for PartialDependence plotting functionality (#819).
  • Tests added for PartialDependenceVariance plotting functionality (#820).
  • Tests added for PermutationImportance plotting functionality (#824).
  • Tests addef for ProtoSelect plotting functionality (#841).
  • Tests added for the datasets subpackage (#814).
  • Fixed optional dependency installation during CI to make sure dependencies are consistent (#817).
  • Synchronize notebook CI workflow with the main CI workflow (#818).
  • Version of pytest-cov bumped to 4.x (#794).
  • Version of pytest-xdist bumped to 3.x (#808).
  • Version of tox bumped to 4.x (#832).

v0.8.0 (2022-09-26)

Full Changelog

Added

  • New feature PartialDependence and TreePartialDependence explainers implementing partial dependence (PD) global explanations. Also included is a plot_pd utility function for flexible plotting of the resulting PD plots (docs, #721).
  • New exceptions.NotFittedError exception which is raised whenever a compulsory call to a fit method has not been carried out. Specifically, this is now raised in AnchorTabular.explain when AnchorTabular.fit has been skipped (#732).
  • Various improvements to docs and examples (#695, #701, #698, #703, #717, #711, #750, #784).

Fixed

  • Edge case in AnchorTabular where an error is raised during an explain call if the instance contains a categorical feature value not seen in the training data (#742).

Changed

  • Improved handling of custom grid_points for the ALE explainer (#731).
  • Renamed our custom exception classes to remove the verbose Alibi* prefix and standardised the *Error suffix. Concretely:
    • exceptions.AlibiPredictorCallException is now exceptions.PredictorCallError
    • exceptions.AlibiPredictorReturnTypeError is now exceptions.PredictorReturnTypeError. Backwards compatibility has been maintained by subclassing the new exception classes by the old ones, but these will likely be removed in a future version (#733).
  • Warn users when TreeShap is used with more than 100 samples in the background dataset which is due to a limitation in the upstream shap package (#710).
  • Minimum version of scikit-learn bumped to 1.0.0 mainly due to upcoming deprecations (#776).
  • Minimum version of scikit-image bumped to 0.17.2 to fix a possible bug when using the slic segmentation function with AnchorImage (#753).
  • Maximum supported version of attrs bumped to 22.x (#727).
  • Maximum supported version of tensorflow bumped to 2.10.x (#745).
  • Maximum supported version of ray bumped to 2.x (#740).
  • Maximum supported version of numba bumped to 0.56.x (#724).
  • Maximum supported version of shap bumped to 0.41.x (#702).
  • Updated shap example notebooks to recommend installing matplotlib==3.5.3 due to failure of shap plotting functions with matplotlib==3.6.0 (#776).

Development

  • Extend optional dependency checks to ensure the correct submodules are present (#714).
  • Introduce pytest-custom_exit_code to let notebook CI pass when no notebooks are selected for tests (#728).
  • Use UTF-8 encoding when loading README.md in setup.py to avoid a possible failure of installation for some users (#744).
  • Updated guidance for class docstrings (#743).
  • Reinstate ray tests (#756).
  • We now exclude test files from test coverage for a more accurate representation of coverage (#751). Note that this has led to a drop in code covered which will be addressed in due course (#760).
  • The Python 3.10.x version on CI has been pinned to 3.10.6 due to typechecking failures, pending a new release of mypy (#761).
  • The test_changed_notebooks workflow can now be triggered manually and is run on push/PR for any branch (#762).
  • Use codecov flags for more granular reporting of code coverage (#759).
  • Option to ssh into Github Actions runs for remote debugging of CI pipelines (#770).
  • Version of sphinx bumped to 5.x but capped at <5.1.0 to avoid CI failures (#722).
  • Version of myst-parser bumped to 0.18.x (#693).
  • Version of flake8 bumped to 5.x (#729).
  • Version of ipykernel bumped to 6.x (#431).
  • Version of ipython bumped to 8.x (#572).
  • Version of pytest bumped to 7.x (#591).
  • Version of sphinx-design bumped to 0.3.0 (#739).
  • Version of nbconvert bumped to 7.x (#738).

v0.7.0 (2022-05-18)

Full Changelog

This release introduces two new methods, a GradientSimilarity explainer and a ProtoSelect data summarisation algorithm.

Added

  • New feature GradientSimilarity explainer for explaining predictions of gradient-based (PyTorch and TensorFlow) models by returning the most similar training data points from the point of view of the model (docs).
  • New feature We have introduced a new subpackage alibi.prototypes which contains the ProtoSelect algorithm for summarising datasets with a representative set of "prototypes" (docs).
  • ALE explainer now can take a custom grid-point per feature to evaluate the ALE on. This can help in certain situations when grid-points defined by quantiles might not be the best choice (docs).
  • Extended the IntegratedGradients method target selection to handle explaining any scalar dimension of tensors of any rank (previously only rank-1 and rank-2 were supported). See #635.
  • Python 3.10 support. Note that PyTorch at the time of writing doesn't support Python 3.10 on Windows.

Fixed

  • Fixed a bug which incorrectly handled multi-dimensional scaling in CounterfactualProto (#646).
  • Fixed a bug in the example using CounterfactualRLTabular (#651).

Changed

  • tensorflow is now an optional dependency. To use methods that require tensorflow you can install alibi using pip install alibi[tensorflow] which will pull in a supported version. For full instructions for the recommended way of installing optional dependencies please refer to Installation docs.
  • Updated sklearn version bounds to scikit-learn>=0.22.0, <2.0.0.
  • Updated tensorflow maximum allowed version to 2.9.x.

Development

  • This release introduces a way to manage the absence of optional dependencies. In short, the design is such that if an optional dependency is required for an algorithm but missing, at import time the corresponding public (or private in the case of the optional dependency being required for a subset of the functionality of a private class) algorithm class will be replaced by a MissingDependency object. For full details on developing alibi with optional dependencies see Contributing: Optional Dependencies.
  • The CONTRIBUTING.md has been updated with further instructions for managing optional dependencies (see point above) and more conventions around docstrings.
  • We have split the Explainer base class into Base and Explainer to facilitate reusability and better class hierarchy semantics with introducing methods that are not explainers (#649).
  • mypy has been updated to ~=0.900 which requires additional development dependencies for type stubs, currently only types-requests has been necessary to add to requirements/dev.txt.
  • Fron this release onwards we exclude the directories doc/ and examples/ from the source distribution (by adding prune directives in MANIFEST.in). This results in considerably smaller file sizes for the source distribution.

v0.6.5 (2022-03-18)

Full Changelog

This is a patch release to correct a regression in CounterfactualProto introduced in v0.6.3.

Added

Fixed

  • Fix a bug introduced in v0.6.3 which prevented CounterfactualProto working with categorical features (#612).
  • Fix an issue with the LanguageModelSampler where it would sometimes sample punctuation (#585).

Development

  • The maximum tensorflow version has been bumped from 2.7 to 2.8 (#588).

v0.6.4 (2022-02-28)

Full Changelog

This is a patch release to correct a regression in AnchorImage introduced in v0.6.3.

Fixed

  • Fix a bug introduced in v0.6.3 where AnchorImage would ignore user segmentation_kwargs (#581).

Development

  • The maximum versions of Pillow and scikit-image have been bumped to 9.x and 0.19.x respectively.

v0.6.3 (2022-01-18)

Full Changelog

Added

  • New feature A callback can now be passed to 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).
  • A new comprehensive Introduction to explainability added to the documentation (#510).

Changed

  • Python 3.6 has been deprecated from the supported versions as it has reached end-of-life.

Fixed

  • Fix a bug with passing background images to AnchorImage leading to an error (#542).
  • Fix a bug with rounding errors being introduced in CounterfactualRLTabular (#550).

Development

  • Docstrings have been updated and consolidated (#548). For developers, docstring conventions have been documented in CONTRIBUTING.md.
  • numpy typing has been updated to be compatible with numpy 1.22 (#543). This is a prerequisite for upgrading to tensorflow 2.7.
  • To further improve reliability, strict Optional type-checking with mypy has been reinstated (#541).
  • The Alibi CI tests now include Windows and MacOS platforms (#575).
  • The maximum tensorflow version has been bumped from 2.6 to 2.7 (#377).

v0.6.2 (2021-11-18)

Full Changelog

Added

  • Documentation on using black-box and white-box models in the context of alibi, see here.
  • 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).
  • Custom exceptions. A new public module alibi.exceptions defining the alibi exception hierarchy. This introduces two exceptions, AlibiPredictorCallException and AlibiPredictorReturnTypeError. See #520 for more details.

Changed

  • For 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.
  • Updated the minimum shap version to the latest 0.40.0 as this fixes an installation issue if alibi and shap are installed with the same command.

Fixed

  • Fix a bug with version saving being overwritten on subsequent saves (#481).
  • Fix a bug in the Integrated Gradients notebook with transformer models due to a regression in the upstream transformers library (#528).
  • Fix a bug in IntegratedGradients with forward_kwargs not always being correctly passed (#525).
  • Fix a bug resetting TreeShap predictor (#534).

Development

  • Now using 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.
  • New notebook execution testing framework via Github Actions. There are two new GA workflows, test_all_notebooks which is run once a week and can be triggered manually, and test_changed_notebooks which detects if any notebooks have been modified in a PR and executes only those. Not all notebooks are amenable to be tested automatically due to long running times or complex software/hardware dependencies. We maintain a list of notebooks to be excluded in the testing script under testing/test_notebooks.py.
  • Now using myst (a markdown superset) for more flexible documentation (#482).
  • Added a CITATION.cff file.

v0.6.1 (2021-09-02)

Full Changelog

Added

Changed

  • Future breaking change The names of 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.

Development

  • A PR checklist is available under CONTRIBUTING.md. In the future many of these may be turned into automated checks.
  • pandoc version for docs building updated to 1.19.2 which is what is used on readthedocs.
  • Citation updated to the JMLR paper.

v0.6.0 (2021-07-08)

Full Changelog

Added

  • New feature AnchorText now supports sampling according to masked language models via the transformers library. See docs and the example for using the new functionality.
  • Breaking change due to the new masked language model sampling for 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.
  • Example of using IntegratedGradients to explain transformer models.
  • Python 3.9 support.

Fixed

  • 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.

v0.5.8 (2021-04-29)

Full Changelog

Added

  • Experimental explainer serialization support using dill. See docs for more details.

Fixed

  • Handle layers which are not part of model.layers for IntegratedGradients.

Development

  • Update type hints to be compatible with numpy 1.20.
  • Separate licence build step in CI, only check licences against latest Python version.

v0.5.7 (2021-03-31)

Full Changelog

Changed

  • Support for 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 SeldonIO#376 and shap/shap#1802. We expect this to be a temporary change until changes are made upstream.

Added

  • A 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.

Fixed

  • Deprecated the alibi.datasets.fetch_imagenet function as the Imagenet API is no longer available.
  • IntegratedGradients now works with subclassed TensorFlow models.
  • Removed support for calculating attributions wrt multiple layers in IntegratedGradients as this was not working properly and is difficult to do in the general case.

Development

  • Fixed an issue with AnchorTabular tests not being picked up due to a name change of test data fixtures.

v0.5.6 (2021-02-18)

Full Changelog

Added

  • Breaking change 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 SeldonIO#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).

Fixed

  • 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/stable/api/alibi.explainers.ale.html#alibi.explainers.ale.ALE for more details.
  • Release of Spacy 3.0 broke the 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.
  • Fixed documentation to refer to the Explanation object instead of the old dict object.
  • Added warning boxes to CounterFactual, CounterFactualProto and CEM docs to explain the necessity of clearing the TensorFlow graph if switching to a new model in the same session.

Development

  • Introduced lower and upper bounds for library and development dependencies to limit the potential for breaking functionality upon new releases of dependencies.
  • Added dependabot support to automatically monitor new releases of dependencies (both library and development).
  • Switched from Travis CI to Github Actions as the former limited their free tier.
  • Removed unused CI provider configs from the repo to reduce clutter.
  • Simplified development dependencies to just two files, requirements/dev.txt and requirements/docs.txt.
  • Split out the docs building stage as a separate step on CI as it doesn't need to run on every Python version thus saving time.
  • Added .readthedocs.yml to control how user-facing docs are built directly from the repo.
  • Removed testing related entries to setup.py as the workflow is both unused and outdated.
  • Avoid shap==0.38.1 as a dependency as it assumes IPython is installed and breaks the installation.

v0.5.5 (2020-10-20)

Full Changelog

Added

  • New feature Distributed backend using ray. To use, install ray using pip install alibi[ray].
  • New feature KernelShap distributed version using the new distributed backend.
  • For anchor methods added an explanation field 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.
  • Pre-commit hook for pyupgrade via nbqa for formatting example notebooks using Python 3.6+ syntax.

Fixed

  • Flaky test for distributed anchors (note: this is the old non-batchwise implementation) by dropping the precision treshold.
  • Notebook string formatting upgraded to Python 3.6+ f-strings.

Changed

  • Breaking change For anchor methods, the returned explanation field 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.
  • Removed dependency on 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.

v0.5.4 (2020-09-03)

Full Changelog

Added

  • update_metadata method for any Explainer object to enable easy book-keeping for algorithm parameters

Fixed

  • Updated KernelShap wrapper to work with the newest shap>=0.36 library
  • Fix some missing metadata parameters in KernelShap and TreeShap

v0.5.3 (2020-09-01)

Full Changelog

Changed

  • Updated roadmap

Fixed

  • Bug in integrated gradients where incorrect layer handling led to output shape mismatch when explaining layer outputs
  • Remove tf.logging calls in example notebooks as TF 2.x API no longer supports tf.logging
  • Pin shap to 0.35.0, pending shap 0.36.0 patch release to support shap API updates/library refactoring

v0.5.2 (2020-08-05)

Full Changelog

This release changes the required TensorFlow version from <2.0 to >=2.0. This means that alibi code depends on TenorFlow>=2.0, however the explainer algorithms are compatible for models trained with both TF1.x and TF2.x.

The alibi code that depends on TensorFlow itself has not been fully migrated in the sense that the code is still not idiomatic TF2.x code just that we now use the tf.compat.v1 package provided by TF2.x internally. This does mean that for the time being to run algorithms which depend on TensorFlow (CounterFactual, CEM and CounterFactualProto) require disabling TF2.x behaviour by running tf.compat.v1.disable_v2_behavior(). This is documented in the example notebooks. Work is underway to re-write the TensorFlow dependent components in idiomatic TF2.x code so that this will not be necessary in a future release.

The upgrade to TensorFlow 2.x also enables this to be the first release with Python 3.8 support.

Finally, white-box explainers are now tested with pre-trained models from both TF1.x and TF2.x. The binaries for the models along with loading functionality and datasets used to train these are available in the alibi-testing helper package which is now a requirement for running tests.

Changed

  • Minimum required TensorFlow version is now 2.0
  • Tests depending on trained models are now run using pre-trained models hosted under the alibi-testing helper package

Fixed

  • A bug in AnchorText resulting from missing string hash entries in some spacy models (SeldonIO#276)
  • Explicitly import lazy_fixture in tests instead of relying on the deprecated usage of pytest namespace (SeldonIO#281)
  • A few bugs in example notebooks

v0.5.1 (2020-07-10)

Full Changelog

This is a bug fix release.

Fixed

  • Fix an issue with AnchorText not working on text instances with commas due to not checking for empty synonym lists
  • Enable correct behaviour of AnchorText with spacy>=2.3.0, this now requires installing spacy[lookups] as an additional dependency which contains model probability tables
  • Update the expected_value attribute of TreeSHAP which is internally updated after a call to explain
  • Fix some links in Integrated Gradients examples
  • Coverage after running tests on Travis is now correctly reported as the reports are merged for different pytest runs
  • Old Keras tests now require Keras<2.4.0 as the new release requires tensorflow>=2.2
  • Bump typing_extensions>=3.7.2 which includes the type Literal

v0.5.0 (2020-06-10)

Full Changelog

This version supports Python 3.6 and 3.7 as support for Python 3.5 is dropped.

Added

  • New feature TreeSHAP explainer for white-box, tree based model SHAP value computation
  • New feature ALE explainer for computing feature effects for black-box, tabular data models
  • New feature IntegratedGradients explainer for computing feature attributions for TensorFlow and Keras models
  • Experimental utils.visualization module currently containing visualization functions for IntegratedGradients on image datasets.The location, implementation and content of the module and functions therein are subject to change.
  • Extend datasets.fetch_imagenet to work with any class
  • Extend utils.data.gen_category_map to take a list of strings of column names

Changed

  • Internal refactoring of KernelSHAP to reuse functionality for TreeSHAP. Both SHAP wrappers are now under explainers.shap_wrappers
  • Tests are now split into two runs, one with TensorFlow in eager mode which is necessary for using IntegratedGradients
  • Added typing-extensions library as a requirement to take advantage of more precise types
  • Pinned scikit-image<0.17 due to a regression upstream
  • Pinned Sphinx<3.0 for documentation builds due to some issues with the m2r plugin

Fixed

  • Various improvements to documentation
  • Some tests were importing old keras functions instead of tensorflow.keras

v0.4.0 (2020-03-20)

Full Changelog

NB: This is the last version supporting Python 3.5.

Added

  • New feature KernelSHAP explainer for black-box model SHAP scores
  • Documentation for the LinearityMeasure algorithm

Changed

  • Breaking change New API for explainer and explanation objects. Explainer objects now inherit from Explainer base class as a minimum. When calling .explain method, an Explanation object is returned (previously a dictionary). This contains two dictionaries meta and data accessed as attributes of the object, detailing the metadata and the data of the returned explanation. The common interfaces are under api.interfaces and default return metadata and data for each explainer are under api.defaults.
  • Complete refactoring of the Anchors algorithms, many code improvements
  • Explainer tests are now more modular, utilizing scoped fixtures defined in explainers.tests.conftest and various utility functions
  • Tests are now run sequentially insted of in parallel due to overhead of launching new processes

v0.3.2 (2019-10-17)

Full Changelog

Added

  • All explanations return a metadata field meta with a name subfield which is currently the name of the class

Changed

  • Provide URL options for fetching some datasets, by default now fetches from a public Seldon bucket

v0.3.1 (2019-10-01)

Full Changelog

Fixed

  • Pin tensorflow dependency to versions 1.x as the new 2.0 release introduces breaking changes

v0.3.0 (2019-09-25)

Full Changelog

Added

  • New feature LinearityMeasure class and linearity_measure function for measuring the linearity of a classifier/regressor
  • New feature CounterFactualProto now supports categorical variables for tabular data

Changed

  • Breaking change Remove need for the user to manage TensorFlow sessions for the explanation methods that use TF internally (CEM, CounterFactual, CounterFactualProto). The session is now inferred or created depending on what is passed to predict. For finer control the sess parameter can still be passed in directly
  • Breaking change Expose low-level arguments to AnchorText to the user for finer control of the explanation algorithm, also rename some arguments for consistency
  • Various improvements to existing notebook examples

Fixed

  • CounterFactualProto and CEM bug when the class is initialized a second time it wouldn't run as the TF graph would become disconnected
  • Provide more useful error messages if external data APIs are down
  • Skip tests using external data APIs if they are down

v0.2.3 (2019-07-29)

Full Changelog

Added

  • gen_category_map utility function to facilitate using AnchorTabular explainer
  • Extend CounterFactualProto with a more flexible choice for prototypes using k closest encoded instances
  • Allow user to specify a hard target class for CounterFactualProto
  • Distributed tests usign pytest-xdist to overcome TF global session interfering with tests running in the same process

Changed

  • Sample datasets now return a Bunch object by default, bundling all necessary and optional attributes for each dataset
  • Loading sample datasets are now invoked via the fetch_ functions to indicate that a network download is being made

Fixed

  • Remove Home from docs sidebar as this was causing the sidebar logo to not show up on landing page

v0.2.2 (2019-07-05)

Full Changelog

Added

  • codecov support to CI

Fixed

  • Remove lexemes without word vectors in spacy models for AnchorTabular. This suppresses spacy warnings and also make the method (and tests) run a lot faster.

v0.2.1 (2019-07-02)

Full Changelog

Changed

  • Remove Keras and seaborn from install requirements and create optional [examples] extras_require
  • Remove python-opencv dependency in favour of PIL
  • Improve type checking with unimported modules - now requires python>3.5.1
  • Add some tests for alibi.datasets

v0.2.0 (2019-05-24)

Full Changelog

New features:

Implemented enhancements:

  • Return nearest not predicted class for trust scores #63
  • Migrate Keras dependency to tf.keras #51
  • Add warning when no anchor is found #30
  • add anchor warning #74 (arnaudvl)
  • Return closest not predicted class for trust scores #67 (arnaudvl)

Closed issues:

  • Build docs on Travis #70
  • High level documentation for 0.1 #37
  • Counterfactual explainers #12

Merged pull requests:

v0.1.0 (2019-05-03)

Closed issues:

  • Migrate CI to Travis post release #46
  • Trust scores #39
  • Make explicit Python>=3.5 requirement before release #18
  • Remove dependency on LIME #17
  • Set up CI #5
  • Set up docs #4

Merged pull requests:

* This Change Log was automatically generated by github_changelog_generator