Cabal revisions of dataframe-learn-1.1.0.0
Hackage metadata revisions edit the .cabal file after upload; each diff below is one revision.
revision 1
-cabal-version: 2.4-name: dataframe-learn-version: 1.1.0.0-synopsis: Interpretable, expression-returning machine learning for the dataframe ecosystem.-description:- A small scikit-learn-style ML library where every model returns both an- inspectable record and dataframe @Expr@ value(s): linear/ridge/lasso/- elastic-net and logistic regression, linear and RFF-kernel SVMs, decision- trees, gradient boosting and AdaBoost, PCA and Nyström kernel PCA, k-means,- Gaussian mixtures, DBSCAN, and symbolic regression — plus cross-validation- and grid search. Pure Haskell, built on @dataframe-operations@.--bug-reports: https://github.com/mchav/dataframe/issues-license: MIT-license-file: LICENSE-author: Michael Chavinda-maintainer: mschavinda@gmail.com-copyright: (c) 2024-2026 Michael Chavinda-category: Data-tested-with: GHC ==9.4.8 || ==9.6.7 || ==9.8.4 || ==9.10.3 || ==9.12.2-extra-doc-files: README.md--common warnings- ghc-options:- -Wincomplete-patterns- -Wincomplete-uni-patterns- -Wunused-imports- -Wunused-local-binds- -Wunused-packages--library- import: warnings- exposed-modules:- DataFrame.DecisionTree- DataFrame.DecisionTree.Types- DataFrame.DecisionTree.CondVec- DataFrame.DecisionTree.Cart- DataFrame.DecisionTree.Numeric- DataFrame.DecisionTree.Prune- DataFrame.DecisionTree.Predict- DataFrame.DecisionTree.Categorical- DataFrame.DecisionTree.Pool- DataFrame.DecisionTree.Linear- DataFrame.DecisionTree.Tao- DataFrame.DecisionTree.Fit- DataFrame.LinearSolver- DataFrame.LinearSolver.Loss- DataFrame.LinearAlgebra- DataFrame.LinearAlgebra.Solve- DataFrame.LinearAlgebra.Eigen- DataFrame.Random- DataFrame.Featurize.Internal- DataFrame.Model- DataFrame.LinearModel- DataFrame.LinearModel.Regression- DataFrame.LinearModel.Logistic- DataFrame.SVM- DataFrame.DecisionTree.Regression- DataFrame.DecisionTree.Model- DataFrame.PCA- DataFrame.PCA.Kernel- DataFrame.SVM.RFF- DataFrame.KMeans- DataFrame.Transform- DataFrame.Boosting- DataFrame.Boosting.GBM- DataFrame.Boosting.AdaBoost- DataFrame.GMM- DataFrame.DBSCAN- DataFrame.Metrics- DataFrame.Metrics.Report- DataFrame.ModelSelection- DataFrame.SymbolicRegression- DataFrame.SymbolicRegression.Expr- DataFrame.SymbolicRegression.Simplify- DataFrame.SymbolicRegression.Optimize- DataFrame.SymbolicRegression.GP- DataFrame.Synthesis- build-depends: base >= 4 && < 5,- containers >= 0.6.7 && < 0.9,- parallel ^>= 3.2,- random >= 1.2 && < 2,- dataframe-core ^>= 1.1,- dataframe-operations ^>= 1.1.1,- text >= 2.0 && < 3,- vector ^>= 0.13,- vector-algorithms ^>= 0.9- hs-source-dirs: src- default-language: Haskell2010+cabal-version: 2.4 +name: dataframe-learn +version: 1.1.0.0 +x-revision: 1 +synopsis: Interpretable, expression-returning machine learning for the dataframe ecosystem. +description: + A small scikit-learn-style ML library where every model returns both an + inspectable record and dataframe @Expr@ value(s): linear/ridge/lasso/ + elastic-net and logistic regression, linear and RFF-kernel SVMs, decision + trees, gradient boosting and AdaBoost, PCA and Nyström kernel PCA, k-means, + Gaussian mixtures, DBSCAN, and symbolic regression — plus cross-validation + and grid search. Pure Haskell, built on @dataframe-operations@. + +bug-reports: https://github.com/mchav/dataframe/issues +license: MIT +license-file: LICENSE +author: Michael Chavinda +maintainer: mschavinda@gmail.com +copyright: (c) 2024-2026 Michael Chavinda +category: Data +tested-with: GHC ==9.4.8 || ==9.6.7 || ==9.8.4 || ==9.10.3 || ==9.12.2 +extra-doc-files: README.md + +common warnings + ghc-options: + -Wincomplete-patterns + -Wincomplete-uni-patterns + -Wunused-imports + -Wunused-local-binds + -Wunused-packages + +library + import: warnings + exposed-modules: + DataFrame.DecisionTree + DataFrame.DecisionTree.Types + DataFrame.DecisionTree.CondVec + DataFrame.DecisionTree.Cart + DataFrame.DecisionTree.Numeric + DataFrame.DecisionTree.Prune + DataFrame.DecisionTree.Predict + DataFrame.DecisionTree.Categorical + DataFrame.DecisionTree.Pool + DataFrame.DecisionTree.Linear + DataFrame.DecisionTree.Tao + DataFrame.DecisionTree.Fit + DataFrame.LinearSolver + DataFrame.LinearSolver.Loss + DataFrame.LinearAlgebra + DataFrame.LinearAlgebra.Solve + DataFrame.LinearAlgebra.Eigen + DataFrame.Random + DataFrame.Featurize.Internal + DataFrame.Model + DataFrame.LinearModel + DataFrame.LinearModel.Regression + DataFrame.LinearModel.Logistic + DataFrame.SVM + DataFrame.DecisionTree.Regression + DataFrame.DecisionTree.Model + DataFrame.PCA + DataFrame.PCA.Kernel + DataFrame.SVM.RFF + DataFrame.KMeans + DataFrame.Transform + DataFrame.Boosting + DataFrame.Boosting.GBM + DataFrame.Boosting.AdaBoost + DataFrame.GMM + DataFrame.DBSCAN + DataFrame.Metrics + DataFrame.Metrics.Report + DataFrame.ModelSelection + DataFrame.SymbolicRegression + DataFrame.SymbolicRegression.Expr + DataFrame.SymbolicRegression.Simplify + DataFrame.SymbolicRegression.Optimize + DataFrame.SymbolicRegression.GP + DataFrame.Synthesis + build-depends: base >= 4 && < 5, + containers >= 0.6.7 && < 0.9, + parallel ^>= 3.2, + random >= 1.2 && < 2, + dataframe-core ^>= 1.1, + dataframe-operations ^>= 1.1.1, + text >= 2.1 && < 3, + vector ^>= 0.13, + vector-algorithms ^>= 0.9 + hs-source-dirs: src + default-language: Haskell2010