dataframe-learn 2.1.0.1 → 2.2.0.0

raw patch · 2 files changed

+16/−14 lines, 2 filesdep ~dataframe-coredep ~dataframe-csvdep ~dataframe-expr-serializerPVP ok

version bump matches the API change (PVP)

Dependency ranges changed: dataframe-core, dataframe-csv, dataframe-expr-serializer, dataframe-operations

API changes (from Hackage documentation)

Files

• dataframe-learn.cabal +13/−13
• src/DataFrame/Model.hs +3/−1

@@ -1,6 +1,6 @@ cabal-version:      3.4 name:               dataframe-learn-version:            2.1.0.1+version:            2.2.0.0 synopsis:           Interpretable, expression-returning machine learning for the dataframe ecosystem. description:     A small scikit-learn-style ML library where every model returns both an@@ -61,8 +61,8 @@                         containers >= 0.6.7 && < 0.10,                         parallel >= 3.3 && < 4,                         random >= 1.2 && < 2,-                        dataframe-core >= 2.1 && < 2.2,-                        dataframe-operations >= 2.1 && < 2.2,+                        dataframe-core >= 2.2 && < 2.3,+                        dataframe-operations >= 2.2 && < 2.3,                         text >= 2.1 && < 3,                         vector >= 0.13 && < 0.15,                         vector-algorithms >= 0.9 && < 0.11@@ -105,11 +105,11 @@                         containers >= 0.6.7 && < 0.10,                         parallel >= 3.3 && < 4,                         random >= 1.2 && < 2,-                        dataframe-core >= 2.1 && < 2.2,-                        dataframe-core >= 2.1 && < 2.2,-                        dataframe-operations >= 2.1 && < 2.2,-                        dataframe-operations >= 2.1 && < 2.2,-                        dataframe-expr-serializer >= 1.2 && < 1.3,+                        dataframe-core >= 2.2 && < 2.3,+                        dataframe-core >= 2.2 && < 2.3,+                        dataframe-operations >= 2.2 && < 2.3,+                        dataframe-operations >= 2.2 && < 2.3,+                        dataframe-expr-serializer >= 1.2.0.1 && < 1.3,                         dataframe-learn:internal,                         text >= 2.1 && < 3,                         vector >= 0.13 && < 0.15@@ -140,13 +140,13 @@                         aeson >= 0.11.0.0 && < 3,                         bytestring >= 0.11 && < 0.14,                         containers >= 0.6.7 && < 0.10,-                        dataframe-core >= 2.1 && < 2.2,-                        dataframe-core >= 2.1 && < 2.2,-                        dataframe-csv >= 2.2 && < 2.3,+                        dataframe-core >= 2.2 && < 2.3,+                        dataframe-core >= 2.2 && < 2.3,+                        dataframe-csv >= 2.3 && < 2.4,                         dataframe-learn,                         dataframe-learn:internal,-                        dataframe-operations >= 2.1 && < 2.2,-                        dataframe-operations >= 2.1 && < 2.2,+                        dataframe-operations >= 2.2 && < 2.3,+                        dataframe-operations >= 2.2 && < 2.3,                         HUnit >= 1.6 && < 1.8,                         QuickCheck >= 2 && < 3,                         random >= 1 && < 2,

@@ -57,11 +57,13 @@ import DataFrame.Typed.Schema (AllDouble) import DataFrame.Typed.Types (AsTExpr, TExpr (..), ToTExpr (..), TypedDataFrame) +import GHC.TypeLits (Symbol)+ {- | A model trained on a typed frame, carrying the schema @cols@ as a phantom so its 'predict' yields a typed 'TExpr'. Use 'fittedModel' to recover the bare model record (coefficients, etc.). -}-newtype Fitted (cols :: [Type]) model = Fitted {fittedModel :: model}+newtype Fitted (cols :: [(Symbol, Type)]) model = Fitted {fittedModel :: model}  {- | The type 'fit' returns for a given frame source: the bare @model@ for an untyped 'DataFrame', or a schema-tagged 'Fitted' for a 'TypedDataFrame'. Training