add to ScientificTypes name change

ORGANIZATION.md

@@ -13,14 +13,14 @@ its conventional use, are marked with a ⟂ symbol:
   evaluating and tuning machine learning models. It pulls in most code
   from other repositories described below.  MLJ also hosts the [MLJ
   manual](src/docs) which documents functionality across the
-  repositories, with the exception of ScientificTypes, and
+  repositories, with the exception of ScientificTypesBase, and
   MLJScientific types which host their own documentation. (The MLJ
   manual and MLJTutorials do provide overviews of scientific types.)
 
 * [MLJModelInterface.jl](https://github.com/alan-turing-institute/MLJModelInterface.jl)
   is a lightweight package imported by packages implementing
   MLJ's interface for their machine learning models. It's *sole*
-  dependency is ScientificTypes, which is a tiny package with *no*
+  dependency is ScientificTypesBase, which is a tiny package with *no*
   dependencies. 
 
 * (⟂)
@@ -77,15 +77,15 @@ its conventional use, are marked with a ⟂ symbol:
   [Flux.jl](https://github.com/FluxML/Flux.jl), in MLJ.
 
 * (⟂)
-  [ScientificTypes.jl](https://github.com/alan-turing-institute/ScientificTypes.jl)
+  [ScientificTypesBase.jl](https://github.com/alan-turing-institute/ScientificTypesBase.jl)
   is an ultra lightweight package providing "scientific" types,
   such as `Continuous`, `OrderedFactor`, `Image` and `Table`. It's
   purpose is to formalize conventions around the scientific
   interpretation of ordinary machine types, such as `Float32` and
   `DataFrame`.
 
 * (⟂)
-  [MLJScientificTypes.jl](https://github.com/alan-turing-institute/MLJScientificTypes.jl)
+  [ScientificTypes.jl](https://github.com/alan-turing-institute/ScientificTypes.jl)
   articulates MLJ's own convention for the scientific interpretation of
   data.
 

Project.toml

@@ -14,12 +14,12 @@ MLJEnsembles = "50ed68f4-41fd-4504-931a-ed422449fee0"
 MLJIteration = "614be32b-d00c-4edb-bd02-1eb411ab5e55"
 MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
 MLJOpenML = "cbea4545-8c96-4583-ad3a-44078d60d369"
-MLJScientificTypes = "2e2323e0-db8b-457b-ae0d-bdfb3bc63afd"
 MLJSerialization = "17bed46d-0ab5-4cd4-b792-a5c4b8547c6d"
 MLJTuning = "03970b2e-30c4-11ea-3135-d1576263f10f"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
@@ -33,10 +33,10 @@ MLJEnsembles = "0.1"
 MLJIteration = "0.3"
 MLJModels = "0.14"
 MLJOpenML = "1"
-MLJScientificTypes = "0.4.1"
 MLJSerialization = "1.1"
 MLJTuning = "0.6"
 ProgressMeter = "1.1"
+ScientificTypes = "2"
 StatsBase = "0.32,0.33"
 Tables = "0.2,1.0"
 julia = "1.3"

README.md

@@ -87,8 +87,8 @@ illustrated in the dependency chart below.
   <a href="https://github.com/JuliaAI/MLJSerialization.jl">MLJSerialization</a> 
 </br>
 <br>  
-  <a href="https://github.com/JuliaAI/MLJScientificTypes.jl">MLJScientificTypes</a> &nbsp;•&nbsp;
-  <a href="https://github.com/JuliaAI/ScientificTypes.jl">ScientificTypes</a>
+  <a href="https://github.com/JuliaAI/ScientificTypes.jl">ScientificTypes</a> &nbsp;•&nbsp;
+  <a href="https://github.com/JuliaAI/ScientificTypesBase.jl">ScientificTypesBase</a>
 </p>
 <p></p>
     <br>

ROADMAP.md

@@ -152,7 +152,7 @@ list](https://github.com/alan-turing-institute/MLJ.jl/issues/673).
 
 - [x] POC for implementation of time series models classification
 	[#303](https://github.com/alan-turing-institute/MLJ.jl/issues/303),
-	[ScientificTypes #14](https://github.com/alan-turing-institute/ScientificTypes.jl/issues/14) POC is [here](https://github.com/alan-turing-institute/TimeSeriesClassification.jl)
+	[ScientificTypesBase #14](https://github.com/alan-turing-institute/ScientificTypesBase.jl/issues/14) POC is [here](https://github.com/alan-turing-institute/TimeSeriesClassification.jl)
 
 - [ ] POC for time series forecasting, along lines of sktime; probably needs [MLJBase
 	#502](https://github.com/alan-turing-institute/MLJBase.jl/issues/502)
@@ -178,7 +178,7 @@ list](https://github.com/alan-turing-institute/MLJ.jl/issues/673).
   - [x] missing value imputation using Gaussina Mixture Model. Done,
 	via addition of BetaML model, `MissingImputator`.
 
-  - [ ] improve `autotype` method (from MLJScientificTypes), perhaps by
+  - [ ] improve `autotype` method (from ScientificTypes), perhaps by
 	training on large collection of datasets with manually labelled
 	scitype schema.
 

docs/make.jl

@@ -14,9 +14,9 @@ import MLJTuning
 import MLJModels
 import MLJEnsembles
 import MLJOpenML
-import MLJScientificTypes
-import MLJModelInterface
 import ScientificTypes
+import MLJModelInterface
+import ScientificTypesBase
 import Distributions
 using CategoricalArrays # avoid types like CategoricalArrays.Categorica
 using LossFunctions
@@ -74,9 +74,9 @@ makedocs(
                 MLJTuning,
                 MLJModels,
                 MLJEnsembles,
-                MLJScientificTypes,
-                MLJModelInterface,
                 ScientificTypes,
+                MLJModelInterface,
+                ScientificTypesBase,
                 MLJIteration,
                 MLJSerialization,
                 MLJOpenML,

docs/src/adding_models_for_general_use.md

@@ -39,7 +39,7 @@ It is assumed the reader has read [Getting Started](index.md).
 To implement the API described here, some familiarity with the
 following packages is also helpful:
 
-- [MLJScientificTypes.jl](https://github.com/alan-turing-institute/MLJScientificTypes.jl)
+- [ScientificTypes.jl](https://github.com/alan-turing-institute/ScientificTypes.jl)
   (for specifying model requirements of data)
 
 - [Distributions.jl](https://github.com/JuliaStats/Distributions.jl)
@@ -672,7 +672,7 @@ attempt to use your model with inappropriately typed data.
 
 The trait functions `input_scitype` and `target_scitype` take
 scientific data types as values. We assume here familiarity with
-[MLJScientificTypes.jl](https://github.com/alan-turing-institute/MLJScientificTypes.jl)
+[ScientificTypes.jl](https://github.com/alan-turing-institute/ScientificTypes.jl)
 (see [Getting Started](index.md) for the basics).
 
 For example, to ensure that the `X` presented to the

docs/src/getting_started.md

@@ -274,9 +274,9 @@ as `Array{Float32, 2}`). Similar remarks apply to the input `X` of an
 unsupervised model.
 
 Scientific types are julia types defined in the package
-[ScientificTypes.jl](https://github.com/alan-turing-institute/ScientificTypes.jl);
+[ScientificTypesBase.jl](https://github.com/alan-turing-institute/ScientificTypesBase.jl);
 the package
-[MLJScientificTypes.jl](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/)
+[ScientificTypes.jl](https://alan-turing-institute.github.io/ScientificTypes.jl/dev/)
 implements the particular convention used in the MLJ universe for
 assigning a specific scientific type (interpretation) to each julia
 object (see the `scitype` examples below).
@@ -286,7 +286,7 @@ The basic "scalar" scientific types are `Continuous`, `Multiclass{N}`,
 scientific types](@ref) below to guarantee your scalar data is interpreted
 correctly. Tools exist to coerce the data to have the appropriate
 scientfic type; see
-[MLJScientificTypes.jl](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/)
+[ScientificTypes.jl](https://alan-turing-institute.github.io/ScientificTypes.jl/dev/)
 or run `?coerce` for details.
 
 Additionally, most data containers - such as tuples, vectors, matrices
@@ -296,7 +296,7 @@ and tables - have a scientific type.
 ![](img/scitypes.png)
 
 *Figure 1. Part of the scientific type hierarchy in*
-[ScientificTypes.jl](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/).
+[ScientificTypesBase.jl](https://alan-turing-institute.github.io/ScientificTypes.jl/dev/).
 
 ```@repl doda
 scitype(4.6)
@@ -389,7 +389,7 @@ But see also [Model Search](@ref).
 ### Scalar scientific types
 
 Models in MLJ will always apply the `MLJ` convention described in
-[MLJScientificTypes.jl](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/)
+[ScientificTypes.jl](https://alan-turing-institute.github.io/ScientificTypes.jl/dev/)
 to decide how to interpret the elements of your container types. Here
 are the key features of that convention:
 
@@ -417,7 +417,7 @@ appropriate `Finite` (categorical) scitype.  See [Working with Categorical Data]
 
 For more on scitype coercion of arrays and tables, see [`coerce`](@ref),
 [`autotype`](@ref) and [`unpack`](@ref) below and the examples at
-[MLJScientificTypes.jl](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/).
+[ScientificTypes.jl](https://alan-turing-institute.github.io/ScientificTypes.jl/dev/).
 
 
 

docs/src/preparing_data.md

@@ -1,7 +1,7 @@
 # Preparing Data
 
 As outlined in [Getting Started](@ref), it is important that the
-[scientific type](https://github.com/JuliaAI/ScientificTypes.jl) of
+[scientific type](https://github.com/JuliaAI/ScientificTypesBase.jl) of
 data matches the requirements of the model of interest. For example,
 while the majority of supervised learning models require input
 features to be `Continuous`, newcomers to MLJ are sometimes
@@ -93,7 +93,7 @@ Pipelines](@ref).
 ## Scientific type coercion
 
 Scientific type coercion is documented in detail at
-[ScientificTypes.jl](https://github.com/JuliaAI/ScientificTypes.jl). See
+[ScientificTypesBase.jl](https://github.com/JuliaAI/ScientificTypesBase.jl). See
 also the tutorial at the [this MLJ
 Workshop](https://github.com/ablaom/MachineLearningInJulia2020)
 (specifically,

docs/src/quick_start_guide_to_adding_models.md

@@ -8,7 +8,7 @@ learning models; (ii) that you would like to interface and register
 these models with MLJ; and (iii) that you have a rough understanding
 of how things work with MLJ.  In particular you are familiar with:
 
-- what [scientific types](https://github.com/alan-turing-institute/MLJScientificTypes.jl) are
+- what [scientific types](https://github.com/alan-turing-institute/ScientificTypes.jl) are
 
 - what `Probabilistic`, `Deterministic` and `Unsupervised` models are
 

docs/src/working_with_categorical_data.md

@@ -3,8 +3,8 @@
 ## Scientific types for discrete data
 
 Recall that models articulate their data requirements using scientific
-types (see [Getting Started](@ref) or the [MLJScientificTypes.jl
-documentation](https://alan-turing-institute.github.io/MLJScientificTypes.jl/dev/)). There
+types (see [Getting Started](@ref) or the [ScientificTypes.jl
+documentation](https://alan-turing-institute.github.io/ScientificTypes.jl/dev/)). There
 are three scientific types discrete data can have: `Count`,
 `OrderedFactor` and `Multiclass`.
 
@@ -62,7 +62,7 @@ above. To inspect all column scientific types in a table
 simultaneously, use `schema`. (The `scitype(X)` of a table `X`
 contains a condensed form of this information used in type dispatch;
 see
-[here](https://github.com/alan-turing-institute/ScientificTypes.jl#more-on-the-table-type).)
+[here](https://github.com/alan-turing-institute/ScientificTypesBase.jl#more-on-the-table-type).)
 
 ```@example hut
 import DataFrames.DataFrame

paper/paper.md

@@ -130,7 +130,7 @@ representation of probabilities, are avoided.
 **Scientific types** To help users focus less on data representation
 (e.g., `Float32`, `DataFrame`) and more on the intended *purpose* or
 *interpretation* of data, MLJ articulates model data requirements
-using *scientific types* [@ScientificTypes], such as "continuous",
+using *scientific types* [@ScientificTypesBase], such as "continuous",
 "ordered factor" or "table".
 
 **Connecting models directly to arbitrary data containers**. A

src/MLJ.jl

@@ -33,7 +33,7 @@ using ComputationalResources: CPUProcesses
 import MLJBase: fit, update, clean!, fit!, predict, fitted_params,
                 show_as_constructed, ==
 import MLJModels: models
-import MLJScientificTypes
+import ScientificTypes
 
 
 ## METHOD EXPORT
@@ -47,7 +47,7 @@ export MLJ_VERSION
 export pdf, logpdf, mode, median, mean, shuffle!, categorical, shuffle,
     levels, levels!, std, support, sampler
 
-# re-exports from (MLJ)ScientificTypes via MLJBase
+# re-exports from (MLJ)ScientificTypesBase via MLJBase
 export Scientific, Found, Unknown, Known, Finite, Infinite,
     OrderedFactor, Multiclass, Count, Continuous, Textual,
     Binary, ColorImage, GrayImage, Image, Table
@@ -222,6 +222,6 @@ const srcdir = dirname(@__FILE__)
 ## INCLUDE FILES
 
 include("version.jl")       # defines MLJ_VERSION constant
-include("scitypes.jl")      # extensions to ScientificTypes.scitype
+include("scitypes.jl")      # extensions to ScientificTypesBase.scitype
 
 end # module

src/scitypes.jl

@@ -2,21 +2,21 @@
 
 # This implementation of scitype for models and measures is highly experimental
 
-const MST = MLJScientificTypes # only used in this file
+const ST = ScientificTypes # only used in this file
 
 struct SupervisedScitype{input_scitype, target_scitype, prediction_type} end
 
-MST.scitype(model::Deterministic, ::MST.MLJ) =
+ST.scitype(model::Deterministic, ::ST.DefaultConvention) =
     SupervisedScitype{input_scitype(model),
                     target_scitype(model),
                     :deterministic}
 
-MST.scitype(model::Probabilistic, ::MST.MLJ) =
+ST.scitype(model::Probabilistic, ::ST.DefaultConvention) =
     SupervisedScitype{input_scitype(model),
                     target_scitype(model),
                     :probabilistic}
 
-MST.scitype(model::Interval, ::MST.MLJ) =
+ST.scitype(model::Interval, ::ST.DefaultConvention) =
     SupervisedScitype{input_scitype(model),
                     target_scitype(model),
                     :interval}
@@ -52,7 +52,7 @@ end
 
 struct UnsupervisedScitype{input_scitype, output_scitype} end
 
-MST.scitype(model::Unsupervised, ::MST.MLJ) =
+ST.scitype(model::Unsupervised, ::ST.DefaultConvention) =
     UnsupervisedScitype{input_scitype(model),
                       MLJBase.output_scitype(model)}
 
@@ -91,7 +91,7 @@ struct MeasureScitype{target_scitype,
                is_feature_dependent,
                supports_weights} end
 
-MST.scitype(measure, ::MST.MLJ, ::Val{:measure}) =
+ST.scitype(measure, ::ST.DefaultConvention, ::Val{:measure}) =
     MeasureScitype{target_scitype(measure),
                prediction_type(measure),
                orientation(measure),