recipe galaxy-ml

APIs for Galaxy machine learning tools

Homepage:

https://github.com/goeckslab/Galaxy-ML

License:

MIT

Recipe:

/galaxy-ml/meta.yaml

package galaxy-ml

versions:

0.10.0-3,  0.10.0-2,  0.10.0-1,  0.10.0-0,  0.9.1-2,  0.9.1-1,  0.9.1-0,  0.9.0-0,  0.8.3-2, 

0.10.0-3,  0.10.0-2,  0.10.0-1,  0.10.0-0,  0.9.1-2,  0.9.1-1,  0.9.1-0,  0.9.0-0,  0.8.3-2,  0.8.3-1,  0.8.3-0,  0.8.2-5,  0.8.2-4,  0.8.2-3,  0.8.2-2,  0.8.2-1,  0.8.2-0,  0.8.1-0,  0.8.0-0,  0.7.12-0,  0.7.11-0,  0.7.10-1,  0.7.10-0,  0.7.9-0,  0.7.8-0,  0.7.7-1,  0.7.7-0,  0.7.5-0,  0.7.4.1-0

depends asteval:

>=0.9.14

depends bleach:

>=3.3.0

depends graphviz:

>=2.40.1

depends h5py:

>=3.6,<3.8

depends htslib:

depends imbalanced-learn:

>=0.9,<0.10

depends joblib:

>=1.0

depends keras:

>=2.10,<2.11

depends libgcc:

>=13

depends libstdcxx:

>=13

depends matplotlib-base:

>=3.1.1

depends mlxtend:

>=0.21,<0.22

depends numpy:

>=1.22,<1.23

depends numpy:

>=1.22.4,<2.0a0

depends pandas:

>=1.0,<1.3

depends plotly:

>=4.10.0,<5.0

depends pydot:

>=1.4

depends pyfaidx:

depends pytabix:

depends python:

>=3.9,<3.10.0a0

depends python_abi:

3.9.* *_cp39

depends scikit-learn:

>=1.1,<1.2

depends scikit-optimize:

>=0.9

depends scipy:

>=1.3.1

depends six:

<=1.15.0

depends skrebate:

>=0.60,<0.70

depends tabix:

depends tensorflow:

>=2.10,<2.11

depends xgboost:

>=1.6,<1.8

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install galaxy-ml

and update with::

   mamba update galaxy-ml

To create a new environment, run:

mamba create --name myenvname galaxy-ml

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/galaxy-ml:<tag>

(see `galaxy-ml/tags`_ for valid values for ``<tag>``)

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