✅ Replace ScientificTypes.SupportedTypes with its definition

Author: EssamWisam

src/transformers/cardinality_reducer/cardinality_reducer.jl

@@ -7,7 +7,7 @@ include("errors.jl")
 Fit a transformer that maps any level of a categorical column that occurs with
 frequency < `min_frequency` into a new level (e.g., "Other"). This is useful when some categorical columns have
 high cardinality and many levels are infrequent. This assumes that the categorical columns have raw
-types that are in `ScientificTypes.SupportedTypes` (e.g., Number, AbstractString, Char).
+types that are in `Union{Char, AbstractString, Number}`.
 
 # Arguments
 
@@ -19,7 +19,7 @@ types that are in `ScientificTypes.SupportedTypes` (e.g., Number, AbstractString
   - `min_frequency::Real=3`: Any level of a categorical column that occurs with frequency < `min_frequency` will be mapped to a new level. Could be
     an integer or a float which decides whether raw counts or normalized frequencies are used.
   - `label_for_infrequent=Dict{<:Type, <:Any}()= Dict( AbstractString => "Other", Char => 'O', )`: A
-    dictionary where the possible values for keys are the types in `ScientificTypes.SupportedTypes` and each value signifies
+    dictionary where the possible values for keys are the types in `Union{Char, AbstractString, Number}` and each value signifies
     the new level to map into given a column raw super type. By default, if the raw type of the column subtypes `AbstractString`
     then the new value is `"Other"` and if the raw type subtypes `Char` then the new value is `'O'`
     and if the raw type subtypes `Number` then the new value is the lowest value in the column - 1.
@@ -41,6 +41,7 @@ function cardinality_reducer_fit(
         Char => 'O',
     ),
 )   
+    supportedtypes = Union{Char, AbstractString, Number}
 
     # 1. Define column mapper
     function feature_mapper(col, name)
@@ -50,13 +51,13 @@ function cardinality_reducer_fit(
 
         # Ensure column type is valid (can't test because never occurs)
         # Converting array elements to strings before wrapping in a `CategoricalArray`, as...
-        if !(col_type <: ScientificTypes.SupportedTypes)
+        if !(col_type <: supportedtypes)
             throw(ArgumentError(UNSUPPORTED_COL_TYPE(col_type)))
         end
 
         # Ensure label_for_infrequent keys are valid types
         for possible_col_type in keys(label_for_infrequent)
-            if !(possible_col_type in union_types(ScientificTypes.SupportedTypes))
+            if !(possible_col_type in union_types(supportedtypes))
                 throw(ArgumentError(VALID_TYPES_NEW_VAL(possible_col_type)))
             end
         end
@@ -70,7 +71,7 @@ function cardinality_reducer_fit(
 
         # Get ancestor type of column
         elgrandtype = nothing
-        for allowed_type in union_types(ScientificTypes.SupportedTypes)
+        for allowed_type in union_types(supportedtypes)
             if col_type <: allowed_type
                 elgrandtype = allowed_type
                 break

src/transformers/cardinality_reducer/errors.jl

@@ -1,7 +1,7 @@
 UNSUPPORTED_COL_TYPE(col_type) =
-    "In CardinalityReducer, elements have type $(col_type). The supported types are $(ScientificTypes.SupportedTypes)"
+    "In CardinalityReducer, elements have type $(col_type). The supported types are `Union{Char, Number, AbstractString}`"
 VALID_TYPES_NEW_VAL(possible_col_type) =
-    "In CardinalityReducer, label_for_infrequent keys have type $(possible_col_type). The supported types are $(ScientificTypes.SupportedTypes)"
+    "In CardinalityReducer, label_for_infrequent keys have type $(possible_col_type). The supported types are `Union{Char, Number, AbstractString}`"
 COLLISION_NEW_VAL(value) =
     "In CardinalityReducer, label_for_infrequent specifies new column name $(value). However, this name already exists in one of the columns. Please respecify label_for_infrequent."
 UNSPECIFIED_COL_TYPE(col_type, label_for_infrequent) =

src/transformers/cardinality_reducer/interface_mlj.jl

@@ -87,7 +87,7 @@ $(MMI.doc_header(CardinalityReducer))
 `CardinalityReducer` maps any level of a categorical column that occurs with
 frequency < `min_frequency` into a new level (e.g., "Other"). This is useful when some categorical columns have
 high cardinality and many levels are infrequent. This assumes that the categorical columns have raw
-types that are in `ScientificTypes.SupportedTypes` (e.g., Number, AbstractString, Char).
+types that are in `Union{AbstractString, Char, Number}`.
 
 
 # Training data
@@ -112,7 +112,7 @@ Train the machine using `fit!(mach, rows=...)`.
 - `min_frequency::Real=3`: Any level of a categorical column that occurs with frequency < `min_frequency` will be mapped to a new level. Could be
 an integer or a float which decides whether raw counts or normalized frequencies are used.
 - `label_for_infrequent=Dict{<:Type, <:Any}()= Dict( AbstractString => "Other", Char => 'O', )`: A
-dictionary where the possible values for keys are the types in `ScientificTypes.SupportedTypes` and each value signifies
+dictionary where the possible values for keys are the types in `Union{Char, AbstractString, Number}` and each value signifies
 the new level to map into given a column raw super type. By default, if the raw type of the column subtypes `AbstractString`
 then the new value is `"Other"` and if the raw type subtypes `Char` then the new value is `'O'`
 and if the raw type subtypes `Number` then the new value is the lowest value in the column - 1.