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4.1.0 biobb_ml-4.1.0-py3-none-any.whl

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Project: biobb-ml
Version: 4.1.0
Filename: biobb_ml-4.1.0-py3-none-any.whl
Download: [link]
Size: 193501
MD5: ad45ab7670e1aae68e6d19b762ca4428
SHA256: 3ae86faa513f1dcc2c950df3a11b12545c3ff5d300098a7c9ccf1aab20e05add
Uploaded: 2023-09-08 08:42:46 +0000

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METADATA · WHEEL · RECORD · top_level.txt · entry_points.txt

METADATA

Metadata-Version: 2.1
Name: biobb-ml
Version: 4.1.0
Summary: Biobb_ml is the Biobb module collection to perform machine learning predictions.
Author: Biobb developers
Author-Email: genis.bayarri[at]irbbarcelona.org
Home-Page: https://github.com/bioexcel/biobb_ml
Project-Url: Documentation, http://biobb_ml.readthedocs.io/en/latest/
Project-Url: Bioexcel, https://bioexcel.eu/
Keywords: Bioinformatics Workflows BioExcel Compatibility
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Requires-Python: >=3.8
Requires-Dist: biobb-common (==4.1.0)
Requires-Dist: scikit-learn (==0.24.2)
Requires-Dist: pandas (>=1.3.0)
Requires-Dist: seaborn (==0.10.1)
Requires-Dist: tensorflow (>=2.4.2)
Requires-Dist: h5py (==2.10.0)
Requires-Dist: imbalanced-learn (==0.7.0)
Description-Content-Type: text/markdown
License-File: LICENSE
[Description omitted; length: 5125 characters]

WHEEL

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Tag: py3-none-any

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biobb_ml-4.1.0.dist-info/RECORD — —

top_level.txt

biobb_ml

entry_points.txt

[console_scripts]
agglomerative_clustering = biobb_ml.clustering.agglomerative_clustering:main
agglomerative_coefficient = biobb_ml.clustering.agglomerative_coefficient:main
autoencoder_neural_network = biobb_ml.neural_networks.autoencoder_neural_network:main
classification_neural_network = biobb_ml.neural_networks.classification_neural_network:main
classification_predict = biobb_ml.classification.classification_predict:main
clustering_predict = biobb_ml.clustering.clustering_predict:main
correlation_matrix = biobb_ml.utils.correlation_matrix:main
dbscan = biobb_ml.clustering.dbscan:main
decision_tree = biobb_ml.classification.decision_tree:main
dendrogram = biobb_ml.utils.dendrogram:main
drop_columns = biobb_ml.utils.drop_columns:main
dummy_variables = biobb_ml.utils.dummy_variables:main
k_means = biobb_ml.clustering.k_means:main
k_means_coefficient = biobb_ml.clustering.k_means_coefficient:main
k_neighbors = biobb_ml.classification.k_neighbors:main
k_neighbors_coefficient = biobb_ml.classification.k_neighbors_coefficient:main
linear_regression = biobb_ml.regression.linear_regression:main
logistic_regression = biobb_ml.classification.logistic_regression:main
map_variables = biobb_ml.utils.map_variables:main
neural_network_decode = biobb_ml.neural_networks.neural_network_decode:main
neural_network_predict = biobb_ml.neural_networks.neural_network_predict:main
oversampling = biobb_ml.resampling.oversampling:main
pairwise_comparison = biobb_ml.utils.pairwise_comparison:main
pls_components = biobb_ml.dimensionality_reduction.pls_components:main
pls_regression = biobb_ml.dimensionality_reduction.pls_regression:main
polynomial_regression = biobb_ml.regression.polynomial_regression:main
principal_component = biobb_ml.dimensionality_reduction.principal_component:main
random_forest_classifier = biobb_ml.classification.random_forest_classifier:main
random_forest_regressor = biobb_ml.regression.random_forest_regressor:main
recurrent_neural_network = biobb_ml.neural_networks.recurrent_neural_network:main
regression_neural_network = biobb_ml.neural_networks.regression_neural_network:main
regression_predict = biobb_ml.regression.regression_predict:main
resampling = biobb_ml.resampling.resampling:main
scale_columns = biobb_ml.utils.scale_columns:main
spectral_clustering = biobb_ml.clustering.spectral_clustering:main
spectral_coefficient = biobb_ml.clustering.spectral_coefficient:main
support_vector_machine = biobb_ml.classification.support_vector_machine:main
undersampling = biobb_ml.resampling.undersampling:main