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