Release notes
To see the latest changes that are due on the next release, see v0.45.0…master.
v0.45.0
Released on 2024-03-08 - GitHub - PyPI
This is a fairly significant release containing a number of breaking changes.
Thank you to a number of new contributors for their contributions to this release! We are eager to grow the pool of maintainers, so please do get in touch on #3559 if you are interested in being part of the team.
What's Changed
Breaking changes
- Dropped support for 3.8 in #3414
- Changed type and shape of returned SHAP values in some cases, to be consistent with model outputs. SHAP values for models with multiple outputs are now np.ndarray rather than list, by @CloseChoice in #3318
- Removed deprecated
feature_dependence
parameters in TreeExplainer and LinearExplainer by @thatlittleboy in #3340 - Removed deprecated alias for Coefficient by @connortann in #3511
Added
- Added support for python 3.12 by @connortann in #3414
- Added support for GPU build on recent CUDA versions by @trivialfis in #3462
- 2x import time speedup via lazy importing of pytorch by @connortann in #3533
- Added support returning the matplotlib figure in bar plots by @richarddli in #3494
- Added selu activation for tensorflow deep explainer by @CloseChoice in #3504
- Added support for special characters in catboost models by @CloseChoice in #3506
- Added ability to control marker size in
beeswarm
plots by @MonoHue in #3530
Fixed
- Fixed XGBoost model load by @trivialfis in #3462
- Fixed text masking with certain tokenizers by @costrau in #3536
- Fixed issue with KernelExplainer when explaining tensorflow models by @connortann in #3542
- Fixed force_plot contribution threshold for negative contributions by @connortann in #3547
- Removed overwrite of default warning filter or formatter by @connortann in #3514
.. plus a large number of documentation, testing and other maintenance updates by @CloseChoice , @yuanx749 , @LakshmanKishore and others.
New Contributors
- @richarddli made their first contribution in #3494
- @yuanx749 made their first contribution in #3458
- @LakshmanKishore made their first contribution in #3393
- @trivialfis made their first contribution in #3462
- @DanGolding made their first contribution in #3526
- @MonoHue made their first contribution in #3530
- @costrau made their first contribution in #3536
Full Changelog: v0.44.1...v0.45.0
v0.44.1
Released on 2024-01-25 - GitHub - PyPI
Patch release to fix an issue with the display of force plots.
Fixed
- Fixed HTML issue affecting display of force plots by @CloseChoice in #3464
- Fixed calculation of interactions values for catboost regressors by @CloseChoice in #3459
- Update XGBoost parsing to use ubjson format, replacing deprecated binary format by @CloseChoice in #3345
Other
- Further improvements to documentation
Full Changelog: v0.44.0...v0.44.1
v0.44.0
Released on 2023-12-07 - GitHub - PyPI
This release contains a number enhancements and bug fixes.
What's Changed
Added
- Faster and more stable linear solver in KernelShap by @lorentzenchr in #3271
- Enabled passing of
ax
togroup_difference()
plot by @mtlulka in #3355 - Added support for Explanation.display_data in bar plot by @fountaindive in #3386
- Improved build messages when building from source by @connortann in #3403
Fixed
- Fixed
CatboostClassifier
explanations with feature interactions on Windows by @CloseChoice in #3325 - Fixed passing of Xgboost model parameters to xgboost.DMatrix by @vancromy in #3314
- Explicit specification of xgboost>=1.4 constraint by @mtlulka in #3352
- Fixed conversion of DMatrix to CSR matrix by @thatlittleboy in #3359
- Removed deprecated
use_line_collection
independence_plot
by @CloseChoice in #3369 - Fixed bug relating to array reshaping in
scatter
plots by @SomeUserName1 in #2799
Documentation
- A large number of example notebooks fixed and updated by @connortann, @znacer , @thatlittleboy, @CloseChoice and @stompsjo
New Contributors
- @vancromy made their first contribution in #3314
- @lorentzenchr made their first contribution in #3271
- @mtlulka made their first contribution in #3352
- @fountaindive made their first contribution in #3386
- @SomeUserName1 made their first contribution in #2799
- @stompsjo made their first contribution in #3391
Full Changelog: v0.43.0...V0.44.0
v0.43.0
Released on 2023-10-09 - GitHub - PyPI
What's Changed
This release contains a number of bug fixes and improvements.
Following the NEP 29 deprecation policy, this release drops support for python 3.7.
Breaking changes
- Removed the deprecated Boston dataset by @thatlittleboy in #3316
- The shape of
Explanation.base_values
has been standardised between different TreeExplainer models to always be of shape(N,)
and not(N,1)
. By @thatlittleboy in #3121
Added
- Added additivity check to Pytorch DeepExplainer (activated by default) by @noxthot in #3265
- Added flag to allow the printing of the mean SHAP value in the legend of a multi-output bar plot. By @101AlexMartin in #3062
- Added heatmap and violin plot to top-level API by @connortann in #3157
- Replaced all tqdm imports with tqdm.auto by @owenlamont in #3199
Fixed
- Fixed segmentation faults on MacOS with lightgbm tests (with newer libomp versions) by @thatlittleboy in #3093
- Support LightGBM ensemble containing single leaf trees (stump) by @thatlittleboy in #3094
- Fixed conversion DataFrame to ndarray for Explanation.data by @danieleongari in #3131
- Fixed waterfall plot on explanations of sklearn tree models by @connortann in #3138
- Fixed pandas input for gradient explainer by @Koen-Git in #3153
- Fixed slicing of
feature_names
in Explanation objects with square.values
by @thatlittleboy in #3126 - Correct xlim in force_matplotlib in cases where the signs of force are all the same by @zaburo-ch in #2839
- Fixed ngboost explanations when col_sample < 1 by @CloseChoice in #3294
- Fixed torch additivity check in PyTorch DeepExplainer by @noxthot in #3281
- Replaced print statements with warnings in DeepExplainer by @znacer in #3264
- Replace deprecated
register_backward_hook()
by @noxthot in #3259 - Fixed deprecated use of xgboost early_stopping_rounds by @CloseChoice in #3306
- Fixed 3rd party deprecation warnings: numba, xgboost, typing, distutils by @connortann in #3084
- Updated the Javascript bundle to update deprecated dependencies by @connortann in #2974
There have also been a large number of improvements to the tutorials and examples, by @connortann, @znacer, @arshiaar, @thatlittleboy, @dsgibbons, @owenlamont and @CloseChoice
New Contributors
- @101AlexMartin made their first contribution in #3062
- @znacer made their first contribution in #3112
- @danieleongari made their first contribution in #3131
- @Koen-Git made their first contribution in #3153
- @pre-commit-ci made their first contribution in #3173
- @owenlamont made their first contribution in #3199
- @arshiaar made their first contribution in #3201
- @dsgibbons made their first contribution in #3200
- @noxthot made their first contribution in #3265
- @zaburo-ch made their first contribution in #2839
- @CloseChoice made their first contribution in #3282
Full Changelog: v0.42.1...v0.43.0
v0.42.1
Released on 2023-07-15 - GitHub - PyPI
Patch release to provide wheels for a broader range of architectures.
Added
- Added wheels for linux:aarch64 and macos:arm64 by @PrimozGodec in #3078 and @connortann in #3083.
Fixed
- Fixed circular import issues with shap.benchmark by @thatlittleboy in #3076.
- Fixed TestPyPI releases workflow by @connortann in #3068
- Fix further flaky tests by @thatlittleboy in #3073
- Fix shap.summary_plot to work with matplotlib 3.6.0 by @jklaise in #2697
- Fix benchmark top-level import by @thatlittleboy in #3076
- Fix ipython import warning from top-level shap import by @connortann in #3090
Full Changelog: v0.42.0...v0.42.1
v0.42.0
Released on 2023-07-06 - GitHub - PyPI
This release incorporates many changes that were originally contributed by the SHAP community via @dsgibbons's Community Fork, which has now been merged into the main shap repository. PRs from this origin are labelled here as fork#123
.
This will be the last release that supports python 3.7.
Added
- Added support for python 3.11 (fork#72 by @connortann).
- Added
n_points
parameter to all functions inshap.datasets
(fork#39 by @thatlittleboy). - Added
__call__
toKernelExplainer
(#2966 by @dwolfeu). - Added contributing guidelines (#2996 by @connortann).
Fixed
- Fixed
plot.waterfall
to support yticklabels with boolean features (fork#58 by @dwolfeu). - Prevent
TreeExplainer.__call__
from throwing ValueError when passed a pandas DataFrame containing Categorical columns (fork#88 by @thatlittleboy). - Fixed sampling in
shap.datasets
to sample without replacement (fork#36 by @thatlittleboy). - Fixed an
UnboundLocalError
problem arising from passing a dictionary input toshap.plots.bar
(#3001 by @thatlittleboy). - Fixed tensorflow import issue with Pyspark when using
Gradient
(#2983 by @skamdar). - Fixed the aspect ratio of the colorbar in
shap.plots.heatmap
, and use theax
matplotlib API internally for plotting (#3040 by @thatlittleboy). - Fixed deprecation warnings for
numba>=0.44
(fork#9 and fork#68 by @connortann). - Fixed deprecation warnings for
numpy>=1.24
from numpy types (fork#7 by @dsgibbons). - Fixed deprecation warnings for
Ipython>=8
fromIpython.core.display
(fork#13 by @thatlittleboy). - Fixed deprecation warnings for
tensorflow>=2.11
fromtf.optimisers
(fork#16 by @simonangerbauer). - Fixed deprecation warnings for
sklearn>=1.2
fromsklearn.linear_model
(fork#22 by @dsgibbons). - Fixed deprecation warnings for
xgboost>=1.4
fromntree_limit
in tree explainer (#2987 by @adnene-guessoum). - Fixed build on Windows and MacOS (#3015 by @PrimozGodec; #3028, #3029 and #3031 by @connortann).
- Fixed creation of ragged arrays in
shap.explainers.Exact
(#3064 by @connortann).
Changed
- Updates to docstrings of several
shap.plots
functions (#3003, #3005 by @thatlittleboy).
Removed
- Deprecated the Boston house price dataset (fork#38 by @thatlittleboy).
- Removed the unused
mimic.py
file andMimicExplainer
code (fork#53 by @thatlittleboy).
Maintenance
- Fixed failing unit tests (fork#29 by @dsgibbons, fork#20 by @simonangerbauer, #3044 and fork#24 by @connortann).
- Include CUDA GPU C extension files in the source distribution (#3009 by @jklaise).
- Fixed installation of package via setuptools (fork#51 by @thatlittleboy).
- Introduced a minimal set of
ruff
linting (fork#25, fork#26, fork#27, #2973, #2972 and #2976 by @connortann; #2968, #2986 by @thatlittleboy). - Updated project metadata to PEP 517 (#3022 by @connortann).
- Introduced more thorough testing on CI against newer dependencies (fork#61 and #3017 by @connortann)
- Reduced unit test time by ~5 mins (#3046 by @connortann).
- Introduced fixtures for reproducible fuzz testing (#3048 by @connortann).
v0.41.0
Released on 2022-06-16 - GitHub - PyPI
Lots of bugs fixes and API improvements.
- Fixed rare bug with XGBoost model loading by @TheZL @lrjball
- Fixed the beeswarm plot so it does not modify the passed explanation object, @ravwojdyla
- Automatic wheel building using GH actions by @quantumtec
- GC collection for memory in KernelExplainer by @Qingtian-Zou
- Fixed max_evals params for PartitionExplainer
- JIT optimize the PartitionExplainer
- Fix colorbar formatting issues @SleepyPepperHead
- New benchmark notebooks
- Use display_data for plotting when possible @yuuuxt
- Improved GPUTreeShap compilation and params @RAMitchell
- Fix TF API change in DeepExplainer @filusn
- Add torch tensor support for plots @alexander-pv
- Switch to Github actions for testing instead of Travis
- New California demo dataset @swalsh1123
- Fix waterfall plot bug @RichardScottOZ
- Handle missing matplotlib installation @klieret
- Add linearize link support for Additive explainer (Nandish Gupta)
- Fix exceptions to be more specific @alexisdrakopoulos @collinb9
- Add color map option for plotting @tlabarta
- Release fixed numpy version requirement @rmehyde
- And many other contributions kindly made by @WeichenXu123 @imatiach-msft @zeshengli @nkthiebaut @songololo @GiovannaNicora @joshzwiebel @Ashishbodla @navdeep-G @smathewmanuel @ycouble @anubhavmaity @adityasaini70 @ngupta20 @jckkvs @abs428 @JulesCollenne @Tiagosf00 @javirandor and @Thuener
v0.40.0
Released on 2021-10-20 - GitHub - PyPI
This release contains many bugs fixes and lots of new functionality, specifically for transformer based NLP models. Some highlights include:
- New plots, bug fixes, docs, and features for NLP model explanations (see docs for details).
- important permutation explainer performance fix by @sander-sn
- New joint scatter plots to plot many at once on the same y-scale
- better tree model memory usage by @morriskurz
- new docs by @coryroyce
- new wheel building by @PrimozGodec
- dark mode improvements for the docs by @gialmisi
- api tweaks by @c56pony @nsorros @jebarb
v0.39.0
Released on 2021-03-03 - GitHub - PyPI
Lots of new text explainer work courtesy of @ryserrao and serialization courtesy of @vivekchettiar! (will note all the other changes later)
v0.38.1
Released on 2021-01-15 - GitHub - PyPI
Fixes a version mismatch with the v0.38.0 release and serialization updates.
v0.38.0
Released on 2021-01-14 - GitHub - PyPI
This release contains improved support for explanations of transformer text models and support for the new Explanation object based API. Specific improvements include:
- Transformer model support in the Text explainer courtesy of @ryserrao
- Interventional Tree explainer GPU support courtesy of @RAMitchell
- Image captioning model support courtesy of @anusham1990
- Benchmarking improvements courtesy of @maggiewu19
- New text and image visualizations courtesy of @vivekchettiar
- New explainer serialization support courtesy of @vivekchettiar
- Bug fixes for Linear explainer and the new API courtesy of @heimengqi
- Fix for categorical plots courtesy of @jeffreyftang
- CUDA support improvements courtesy of @JohnZed
- Support for econML model courtesy of @vasilismsr
- Many other bug fixes and API improvements.
v0.37.0
Released on 2020-11-04 - GitHub - PyPI
This release contains more support for the new API, many bug fixes, and preliminary model agnostic text/image explainer support (still beta). Specific contributions include:
- Fix Sampling explainer sample counting issue courtesy of @tcbegley
- Add multi-bar plotting support.
- Preliminary support for cohorts.
- Fixed an import error courtesy of @suragnair
- Fix Tree explainer issues with isolation forests with max_features < 1 courtesy of @zhanjiezhu
- Huge documentation cleanup and update courtesy of @lrjball
- Typo fix courtesy of @anusham1990
- Added a documentation notebook for the Exact explainer.
- Text and Image explainers courtesy of @anusham1990 and Ryan Serrao
- Bug fix for shap.utils.hclust
- Initial support for InterpretML EBM models.
- Added column grouping functionality to Explainer objects.
- Fix for loop index bug in Deep explainer for PyTorch courtesy of @quentinRaq
- Initial text to text visualization concepts courtesy of @vivekchettiar
- Color conversion warning fix courtesy of @wangjoshuah
- Fix invertibility issues in Kernel explainer with the pseudoinverse courtesy of @PrimozGodec
- New benchmark code courtesy of @maggiewu19 and @vivekchettiar
- Other small bug fixes and enhancements.
v0.36.0
Released on 2020-08-27 - GitHub - PyPI
This version contains a significant refactoring of the SHAP code base into a new (cleaner) API. Full backwards compatibility should be retained, but most things are now available in locations with the new API. Note that this API is still in a beta form, so refrain from depending on it for production code until the next release. Highlights include:
- A new shap.Explainer object that auto-chooses the explainer based on the given model and masking dataset.
- A new shap.Explanation object that allows for parallel slicing of data, SHAP values, base values (expected values), and other explanation-specific elements.
- A new shap.maskers.* module that separates the various ways to mask (i.e. perturb/hide) features from the algorithms themselves.
- A new shap.explainers.Partition explainer that can explain any text or image models very quickly.
- A new shap.maskers.Partition masker that ensures tightly grouped features are perturbed in unison, so preventing "unrealistic" model inputs from inappropriately influencing the model prediction. It also allows for the exact quadratic time computation of SHAP values for the 'structured games' (with coalitions structured according to a hierarchical clustering).
- A new shap.plots.* module with revamped plot types that all support the new API. Plots are now named more directly, so
summary_plot
(default) becomesbeeswarm
, anddependent_plot
becomesscatter
. Not all the plots have been ported over to the new API, but most have. - A new notebooks/plots/* directory given examples of how to use the new plotting functions.
- A new shap.plots.bar function to directly create bar plots and also display hierarchical clustering structures to group redundant features together, and show the structure used by a Partition explainer (that relied on Owen values, which are an extension of Shapley values).
- Equally check fixes courtesy of @jameslamb
- Sparse kmeans support courtesy of @PrimozGodec
- Pytorch bug fixes courtesy of @rightx2
- NPM JS code clean up courtesy of @SachinVarghese
- Fix logit force plot bug courtesy of @ehuijzer
- Decision plot documentation updates courtesy of @floidgilbert
- sklearn GBM fix courtesy of @ChemEngDataSci
- XGBoost 1.1 fix courtesy of @lrjball
- Make SHAP spark serializable courtesy of @QuentinAmbard
- Custom summary plot color maps courtesy of @nasir-bhanpuri
- Support string inputs for KernelSHAP courtesy of @YotamElor
- Doc fixes courtesy of @imatiach-msft
- Support for GPBoost courtesy of @fabsig
- Import bug fix courtesy of @gracecarrillo and @aokeson
0.35.0
Released on 2020-02-27 - GitHub - PyPI
This release includes:
- Better support for TensorFlow 2 (thanks @imatiach-msft)
- Support for NGBoost models in TreeExplainer (thanks @zhiruiwang)
- TreeExplainer support for the new sklearn.ensemble.HistGradientBoosting model.
- New improved versions of PartitionExplainer for images and text.
- IBM zOS compatibility courtesy of @DorianCzichotzki.
- Support for XGBoost 1.0
- Many bug fixes courtesy of Ivan, Christian Paul, @RandallJEllis, and @ibuda.
0.34.0
Released on 2019-12-27 - GitHub - PyPI
This release includes:
- Many small bug fixes.
- Better matplotlib text alignment during rotation courtesy of @koomie
- Cleaned up the C++ transformer code to allow easier PRs.
- Fixed a too tight check_additivity tolerance in TreeExplainer #950
- Updated the LinearExplainer API to match TreeExplainer
- Allow custom class ordering in a summary_plot courtesy of @SimonStreicher
0.33.0
Released on 2019-12-11 - GitHub - PyPI
This release contains various bug fixes and new features including:
- Added PySpark support for TreeExplainer courtesy of @QuentinAmbard
- A new type of plot that is an alternative to the force_plot, a
waterfall_plot
- A new PermutationExplainer that is an alternative to KernelExplainer and SamplingExplainer.
- Added
return_variances
to GradientExplainer for PyTorch courtesy of @s6juncheng - Now we use exceptions rather than assertions in TreeExplainer courtesy of @ssaamm
- Fixed image_plot transpose issue courtesy of @Jimbotsai
- Fix color bar axis attachment issue courtesy of Lasse Valentini Jensen
- Fix tensor attachment issue in PyTorch courtesy of @gabrieltseng
- Fix color clipping ranges in summary_pot courtesy of @joelostblom
- Address sklearn 0.22 API changes courtesy of @lemon-yellow
- Ensure matplotlib is optional courtesy of @imatiach-msft
0.32.1
Released on 2019-11-06 - GitHub - PyPI
This release is just intended to push better auto-deploy bundles out of travis and appveyor.
0.32.0
Released on 2019-11-06 - GitHub - PyPI
This release includes:
- Support for sklearn isolation forest courtesy of @JiechengZhao
- New check_additivity tests to ensure no errors in DeepExplainer and TreeExplainer
- Fix #861, #860
- Fix missing readme example html file
- Support for spark decision tree regressor courtesy of @QuentinAmbard
- Better safe isinstance checking courtesy of @parsatorb
- Fix eager execution in TF < 2 courtesy of @bottydim
0.31.0
Released on 2019-10-21 - GitHub - PyPI
This release contains several new features and bug fixes:
- GradientExplainer now supports TensorFlow 2.0.
- We now do a lazy load of the plotting dependencies, which means a pip install no longer needs to also pull in matplotlib, skimage, and ipython. This should make installs much lighter, especially those that don't need plotting :)
- Added a new BruteForceExplainer for easy testing and comparison on small problems.
- Added a new partial_dependence_plot function. This function will be used to illustrate the close connections between partial dependence plots and SHAP values in future example notebooks.
- Handle the multiclass case with no intercept in LinearExplainer courtesy of @gabrieltseng
- Some extras_require options during the pip install courtesy of @AbdealiJK
- Other small bug fixes and updates
0.30.2
Released on 2019-10-09 - GitHub - PyPI
This release is primarily to remove a dependency on dill that was not in setup.py. It also includes:
- A typo fix in force.py courtesy of @jonlwowski012
- Test code cleanup courtesy of @jorgecarleitao
0.30.1
Released on 2019-09-09 - GitHub - PyPI
- Fix floating point rounding mismatches in recent sklearn versions of tree models
- An update to allow easier loading of custom tree ensemble models by TreeExplainer.
decision_plot
documentation updates courtesy of @floidgilbert
0.30.0
Released on 2019-08-31 - GitHub - PyPI
- New decision_plot function courtesy of @floidgilbert
- Add alpha version of the new model agnostic PartitionExplainer
- ensure data is all on the same device for pytorch in DeepExplainer courtesy of @gabrieltseng
- fix lightgbm edge case issue courtesy of @imatiach-msft
- create binder setup for shap courtesy of @jamesmyatt
- Allow for multiple inputs in the gradient explainer courtesy of @gabrieltseng
- New KernelExplainer unit tests courtesy of @jorgecarleitao
- Add python 2/3 trove classifiers courtesy of @proinsias
- support for pyspark trees courtesy of @QuentinAmbard
- many other bug fixes courtesy of @rygu, @Kylecrif, @trams, @imatiach-msft, @yunchuankong, @invokermain, @lupusomniator, @satyarta, @jotsif, @parkerzf, @jaller94, @gabrieltseng, and others
0.29.3
Released on 2019-06-19 - GitHub - PyPI
- Fixes an issue in DeepExplainer caused by a change in TensorFlow 1.14.
0.29.2
Released on 2019-06-19 - GitHub - PyPI
Various bug fixes and improvements including:
- adding SHAP values for binary classification to CatBoost courtesy of @dvpolyakov
- Integer division fix for plots courtesy of @pmeier-tiplu
- Support passing in an Axes object to dependence_plot courtesy of @mqk
- Add adaptive average pooling and conv transpose layers courtesy of of @gabrieltseng
- fix import errors on a missing matplotlib backend courtesy of @hchandola
- fix TreeExplainer GradientBoostingClassifier bug courtesy of @prempiyush
- make tqdm play nicer with notebooks courtesy of @KOLANICH
- Allow deep_pytorch to use cuda models courtesy of @juliusbierk
- Fix sklearn GradientBoostingRegressor bug courtesy of @nasir-bhanpuri
- adding sparse support to shap linear explainer courtesy of @imatiach-msft
0.29.1
Released on 2019-05-15 - GitHub - PyPI
Fixes to support changes in the most recent version of sklearn
0.29.0
Released on 2019-05-14 - GitHub - PyPI
A few contribution highlights of this release (in chronological order)
- Better testing courtesy of @jorgecarleitao
- Image plot customizations courtesy of @verdimrc
- Batch norm support for PyTorch in DeepExplainer courtesy of @JiechengZhao
- Leaky ReLU and other conv layer support for pytorch deep explainer courtesy of @gabrieltseng
- Fixed keras multi input in gradient explainer and improved random seeds courtesy of @moritzaugustin
- Support for catBoost ranker courtesy of @doramir
- Added XGBRanker and LGBMRanker to TreeExplainer courtesy of @imatiach-msft
- Fix embedding lookup with tf.keras in DeepExplainer courtesy of @andriy-nikolov
- Custom dependence_plot colors maps courtesy of @rcarneva
- Fix divide by zero issues possible with CatBoost models courtesy of @dvpolyakov
- Lots of other bug fixes/improvements!
0.28.5
Released on 2019-02-16 - GitHub - PyPI
This release is just to refresh the Windows builds on AppVeyor that didn't complete for 0.28.4
0.28.4
Released on 2019-02-16 - GitHub - PyPI
- Fixes memory corruption error from TreeExplainer (courtesy of @imatiach-msft)
- Adds support for skopt Random Forest and ExtraTrees Regressors (courtesy of @Bacoknight)
- Adds support for matplotlib forceplot with text rotation (courtesy of @vatsan)
- Adds a save_html function
0.28.3
Released on 2019-01-24 - GitHub - PyPI
- Fix some plot coloring issues introduced by 0.28 (such as #406)
0.28.2
Released on 2019-01-23 - GitHub - PyPI
- Downgrade numpy API usage to support older versions.
0.28.1
Released on 2019-01-23 - GitHub - PyPI
- Fixes a byte-alignment issue on Windows when loading XGBoost models.
- Now matches tree_limit use in XGBoost models courtesy of @HughChen
- Fix an issue with the expected_value of transformed model outputs in TreeExplainer
0.28.0
Released on 2019-01-21 - GitHub - PyPI
- Add support for rank-based feature selection in
KernelExplainer
. - Depreciate
l1_reg="auto"
inKernelExplainer
in favor of eventually defaulting tol1_reg="num_features(10)"
- New color scales based on the Lch color space.
- Better auto-color choices for multi-class summary plots.
- Better plotting of NaN values in dependence_plots
- Updates for Pytorch 1.0 courtesy of @gabrieltseng
- Fix the sklearn DecisionTreeClassifier handling to correctly normalize to a probability output
- Enable multi-output model support for
TreeExplainer
whenfeature_dependence="independent"
- Correctly load the objective of LightGBM models for use in explaining the model loss.
- Fix numerical precision mismatch with sklearn models.
- Fix numerical precision mismatch with XGBoost models by now directly loading from memory instead of JSON.
0.27.0
Released on 2019-01-01 - GitHub - PyPI
- Better hierarchal clustering orderings that now rotate subtrees to give more continuity.
- Work around XGBoost JSON issue.
- Account for NaNs when doing auto interaction detection.
- PyTorch fixes.
- Updated LinearExplainer.
0.26.0
Released on 2018-12-12 - GitHub - PyPI
- Complete refactor of TreeExplainer to support deeper C++ integration
- The ability to explain transformed outputs of tree models in TreeExplainer, including the loss. In collaboration with @HughChen
- Allow for a dynamic reference value in DeepExplainer courtesy of @AvantiShri
- Add
x_jitter
option for categorical dependence plots courtesy of @ihopethiswillfi - Added support for GradientBoostingRegressor with quantile loss courtesy of @dmilad
- Better plotting support for NaN values
- Fixes several bugs.
0.25.2
Released on 2018-11-09 - GitHub - PyPI
- Allows ordering_keys to be given to force_plot courtesy of @JasonTam
- Fixes sparse nonzero background issue with KernelExplainer courtesy of @imatiach-msft
- Fix to support tf.concat in DeepExplainer.
0.25.1
Released on 2018-11-08 - GitHub - PyPI
Fixes a problem where tree_shap.h was not included in the pip bundle.
0.25.0
Released on 2018-11-07 - GitHub - PyPI
- Support for PyTorch in GradientExplainer and preliminary support for PyTorch in DeepExplainer courtesy of @gabrieltseng.
- A matplotlib version of the single sample force_plot courtesy of @jverre.
- Support functional Keras models in GradientExplainer.
- KernelExplainer speed improvements.
- Various performance improvements and bug fixes.
0.24.0
Released on 2018-08-24 - GitHub - PyPI
New improvements include: Faster KernelExplainer execution for sparse inputs. Support for sklearn gradient boosting classifiers. DeepExplainer extended to support very deep models.
0.23.1
Released on 2018-08-16 - GitHub - PyPI
This fixes numerical stability issues with the softmax operator for DeepExplainer. It also fixes a minor alignment issue with image_plot.
0.23.0
Released on 2018-08-16 - GitHub - PyPI
This release includes a nice update courtesy of @imatiach-msft for KernelExplainer. KernelExplainer now runs faster and supports sparse data matrices!
We have also refactored DeepExplainer and made it compatible with TensorFlow 1.10. There are still a few issues to track down, but DeepExplainer is getting more complete :)
v0.22.3
Released on 2018-08-09 - GitHub - PyPI
Fixes a problem with DeepExplainer on TensorFlow >= 1.9. Fixes a bar plotting issue.
v0.22.2
Released on 2018-08-08 - GitHub - PyPI
Fix a pip packaging error with other
explainers.
v0.22.1
Released on 2018-08-08 - GitHub - PyPI
Fix an import error introduced in the last release when installing from pip.
v0.22.0
Released on 2018-08-08 - GitHub - PyPI
Integrates the JS code from iml
into shap
to simplify dependencies. Adds support for more TensorFlow components in DeepExplainer
. Refactors the plotting functions and removes some long-deprecated functions. Fixes an error in KernelExplainer when using a non-zero reference value (#192).
v0.21.0
Released on 2018-07-24 - GitHub - PyPI
A new LinearExplainer that can estimate SHAP values for linear models while accounting for correlations among the input features.
v0.20.2
Released on 2018-07-23 - GitHub - PyPI
Fixes some issues with categorical features in LightGBM. Also fixes some issues created by the v.20 API changes.
v0.20.1
Released on 2018-07-17 - GitHub - PyPI
This is just a tag to get Window's wheel files built.
v0.20.0
Released on 2018-07-17 - GitHub - PyPI
This release brings a significant interface change. The expected value of the model is no longer included as the last column of the returned shap_values array. Instead it can be accessed as explainer.expected_value
. This should make things much cleaner going forward, since the shape of the shap_values matrix will now be exactly the same as the input array to the model. This is particularly important for deep learning models with many dimensional input tensors. Note that because of this change shap.force_plot()
now requires the model's expected (base) value as the first argument.
v0.19.5
Released on 2018-07-13 - GitHub - PyPI
Adds support for embedding layers and LSTM dropout among other things.
v0.19.4
Released on 2018-07-12 - GitHub - PyPI
DeepExplainer now supports the TensorFlow components used by LSTM's.
V0.19.3: v0.19.3
Released on 2018-07-07 - GitHub - PyPI
Fully functional implementations of DeepExplainer and GradientExplainer now available.
v0.19.2
Released on 2018-06-29 - GitHub - PyPI
Fixes an important bug when explaining sklearn models (#137).
v0.19.1
Released on 2018-06-28 - GitHub - PyPI
This marks the final release before potential interface changes to support multi-dimensional inputs in DeepExplainer.