pyproject.toml

[tool.poetry]
name = "pydrift"
version = "0.2.15"
description = "How do we measure the degradation of a machine learning process? Why does the performance of our predictive models decrease? Maybe it is that a data source has changed (one or more variables) or maybe what changes is the relationship of these variables with the target we want to predict. `pydrift` tries to facilitate this task to the data scientist, performing this kind of checks and somehow measuring that degradation."
license="MIT"
authors = ["sergiocalde94 <sergiocalde94@gmail.com>"]
readme = "README.md"
repository = "https://github.com/sergiocalde94/pydrift"
documentation = "https://sergiocalde94.github.io/pydrift/"

[tool.poetry.dependencies]
python = ">= 3.6.1"
pandas = ">= 1.0.3"
scikit-learn = ">= 0.23.1"
typing-extensions = { version = ">= 3.7.4", python = "<3.8" }
catboost = ">= 0.23"
llvmlite = ">0.33.0"
numba = ">=0.51.2"
shap = ">= 0.35.0"
statsmodels = ">= 0.11.1"
plotly = { version = ">= 4.8.1", optional = true }

[tool.poetry.dev-dependencies]
flake8 = ">= 3.8.1"
jupyter = ">= 1.0.0"
pre-commit = ">= 2.4.0"
pytest = ">= 5.4.2"
coveralls = ">= 2.0.0"
plotly = ">= 4.8.1"

[tool.poetry.extras]
plotting = ["plotly"]

[build-system]
requires = ["poetry>= 0.12"]
build-backend = "poetry.masonry.api"