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pyproject.toml
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pyproject.toml
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[tool.poetry]
name = "LightAutoML"
version = "0.3.8.1"
description = "Fast and customizable framework for automatic ML model creation (AutoML)"
authors = [
"Alexander Ryzhkov <[email protected]>",
"Anton Vakhrushev <[email protected]>",
"Dmitrii Simakov <[email protected]>",
"Rinchin Damdinov <[email protected]>",
"Alexander Kirilin <[email protected]>",
"Vasilii Bunakov <[email protected]>",
]
readme = "README.md"
license = "Apache-2.0"
homepage = "https://lightautoml.readthedocs.io/en/latest/"
repository = "https://github.com/AILab-MLTools/LightAutoML"
classifiers = [
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Operating System :: OS Independent",
"Intended Audience :: Science/Research",
"Development Status :: 3 - Alpha",
"Environment :: Console",
"Natural Language :: English",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Typing :: Typed"
]
[tool.poetry.dependencies]
python = ">=3.6.1, <=3.11"
poetry-core = [
{version = ">=1.0.0", python = "<3.7"},
{version = "^1.0.0", python = "^3.7"}
]
pandas = "<2.0.0"
numpy = [
{version = "<=1.24.0", python = "<3.10"},
{version = ">=1.22", python = ">=3.10"}
]
scikit-learn = ">=0.22"
lightgbm = ">=2.3, <=3.2.1"
catboost = ">=0.26.1"
optuna = "*"
torch = [
{platform = "win32", python = "3.6.1", version = "1.7.0"},
{version = ">=1.9.0, <=2.0.0"}
]
dataclasses = {version = "0.6", python = "<3.7"}
holidays = "*"
statsmodels = "<=0.14.0"
networkx = "*"
cmaes = "*"
pyyaml = "*"
tqdm = "*"
joblib = "<1.3.0"
importlib-metadata = {version = ">=1.0", python = "<3.8"}
autowoe = ">=1.2"
jinja2 = "*"
json2html = "*"
seaborn = "*"
# NLP
gensim = {version = ">=4", optional = true}
nltk = {version = "*", optional = true}
transformers = {version = ">=4", optional = true}
# CV
albumentations = {version = "<=1.0.3", optional = true}
timm = {version = ">=0.9.0", optional = true}
opencv-python = {version = "<=4.8.0.74", optional = true}
PyWavelets = {version = "*", optional = true}
torchvision = [
{platform = "win32", python = "3.6.1", version = "0.8.0", optional = true},
{platform = "*", version = "<=0.14.0", optional = true}
]
# AFG
featuretools = {version = ">=1.11.1", python = ">=3.8", optional = true}
# Report (pdf)
weasyprint = {version = "52.5", optional = true}
cffi = {version = "1.14.5", optional = true}
[tool.poetry.extras]
cv = [
"albumentations",
"timm",
"opencv-python",
"PyWavelets",
"scikit-image",
"torchvision"
]
nlp = [
"gensim",
"nltk",
"transformers"
]
report = [
"cffi",
"weasyprint"
]
afg = [
"featuretools"
]
all = [
"albumentations",
"efficientnet-pytorch",
"opencv-python",
"PyWavelets",
"scikit-image",
"torchvision",
"gensim",
"nltk",
"transformers",
"cffi",
"weasyprint",
"featuretools"
]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.black]
line-length = 120
include = '\.pyi?$'
exclude = '''
/(
\.eggs
| \.git
| \.hg
| \.mypy_cache
| \.tox
| \.venv
| _build
| buck-out
| build
| dist
| tests/.*/setup.py
)/
'''
[tool.isort]
profile = "black"
force_single_line = true
atomic = true
include_trailing_comma = true
lines_after_imports = 2
lines_between_types = 1
use_parentheses = true
filter_files = true