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v0.6.1
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paulbkoch committed Apr 14, 2024
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8 changes: 8 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -4,6 +4,14 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and the versioning is mostly derived from [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [v0.6.1] - 2024-04-14
### Fixed
- added compatibility with numpy 2.0 thanks to @DerWeh in PR #525
- fixed bug that was preventing SIMD from being used in python
- removed approximate division in SIMD since the approximation was too inaccurate
### Changed
- EBM fitting time reduced

## [v0.6.0] - 2024-03-16
### Added
- Documentation on recommended hyperparameters to help users optimize their models.
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2 changes: 1 addition & 1 deletion python/interpret-core/interpret/_version.py
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# Distributed under the MIT software license

# NOTE: Version is replaced by a regex script.
__version__ = "0.6.0"
__version__ = "0.6.1"
2 changes: 1 addition & 1 deletion python/interpret-core/setup.py
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from setuptools.command.sdist import sdist

# NOTE: Version is replaced by a regex script.
version = "0.6.0"
version = "0.6.1"


def _copy_native_code_to_setup():
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2 changes: 1 addition & 1 deletion python/interpret/setup.py
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name = "interpret"
# NOTE: Version is replaced by a regex script.
version = "0.6.0"
version = "0.6.1"
long_description = """
In the beginning machines learned in darkness, and data scientists struggled in the void to explain them.
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2 changes: 1 addition & 1 deletion shared/vis/package.json
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{
"name": "@interpretml/interpret-inline",
"version": "0.6.0",
"version": "0.6.1",
"description": "Interpret inline library for rendering visualizations across all notebook environments.",
"main": "index.js",
"keywords": [],
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2 comments on commit 800cbee

@sadsquirrel369
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Hi Paul,

Just wanted to know what the magnitude of the speed up will be ?

@paulbkoch
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Hi @sadsquirrel369 -- I would say marginal in most cases. I don't want to give a detailed answer because I didn't do detailed benchmarks. Best case scenario is probably 2x or 3x for classification on an intel machine installed from pypi. Worst case is probably 0% change.

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