Po-Chuan Hsieh
849396bd72
Spatial modeling based on scikit-learn. The aim of the package is to provide implementations of spatially-explicit modelling. At the moment, spml provides a framework for prototyping geographically weighted extensions of regression and classification models based on scikit-learn and libpysal.graph and a subset of models implemented on top of this framework.
32 lines
1.1 KiB
Makefile
32 lines
1.1 KiB
Makefile
PORTNAME= spml
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PORTVERSION= 0.2.2
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CATEGORIES= science python
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MASTER_SITES= PYPI
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PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
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MAINTAINER= sunpoet@FreeBSD.org
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COMMENT= Spatial machine learning
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WWW= https://pysal.org/spml/stable/ \
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https://github.com/pysal/spml
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LICENSE= BSD3CLAUSE
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LICENSE_FILE= ${WRKSRC}/LICENSE
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BUILD_DEPENDS= ${PYTHON_PKGNAMEPREFIX}setuptools>=61.0:devel/py-setuptools@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}setuptools-scm>=6.2:devel/py-setuptools-scm@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}wheel>=0:devel/py-wheel@${PY_FLAVOR}
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RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}geopandas>=1.1.0:graphics/py-geopandas@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}joblib>=1.5.0:devel/py-joblib@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}libpysal>=4.12:science/py-libpysal@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}numpy>=2.0.0,1:math/py-numpy@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}pandas>=2.3.0,1:math/py-pandas@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}scikit-learn>=1.5.0:science/py-scikit-learn@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}scipy>=1.14.0,1:science/py-scipy@${PY_FLAVOR}
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USES= python:3.12+
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USE_PYTHON= autoplist concurrent pep517
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NO_ARCH= yes
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.include <bsd.port.mk>
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