See also: extract, stabilizeextractor.
JsonGrinder.stabilizeextractor — Functionstabilizeextractor(e::Extractor)Returns a new extractor with similar structure as e, containing StableExtractor in its leaves.
Examples
julia> e = (a=ScalarExtractor(), b=CategoricalExtractor(1:5)) |> DictExtractor
+ ╰── d: StableExtractor(ScalarExtractor(c=1.0, s=1.0))See also: extract, stabilizeextractor.
JsonGrinder.stabilizeextractor — Functionstabilizeextractor(e::Extractor)Returns a new extractor with similar structure as e, containing StableExtractor in its leaves.
Examples
julia> e = (a=ScalarExtractor(), b=CategoricalExtractor(1:5)) |> DictExtractor
DictExtractor
├── a: ScalarExtractor(c=0.0, s=1.0)
╰── b: CategoricalExtractor(n=6)
@@ -51,7 +51,7 @@
julia> e_stable(Dict("a" => 0))
ProductNode 1 obs, 0 bytes
├── a: ArrayNode(1×1 Array with Union{Missing, Float32} elements) 1 obs, 62 bytes
- ╰── b: ArrayNode(6×1 MaybeHotMatrix with Union{Missing, Bool} elements) 1 obs, 62 bytesSee also: suggestextractor, extract.
JsonGrinder.extract — Functionextract(e::Extractor, samples; store_input=Val(false))Efficient extraction of multiple samples at once.
Note that whereas extract expects samples to be an iterable of samples (of known length), calling the extractor directly with e(sample) works for a single sample. In other words, e(sample) is equivalent to extract(e, [sample]).
See also: suggestextractor, stabilizeextractor, schema.
Examples
julia> sample = Dict("a" => 0, "b" => "foo");
+ ╰── b: ArrayNode(6×1 MaybeHotMatrix with Union{Missing, Bool} elements) 1 obs, 62 bytesSee also: suggestextractor, extract.
JsonGrinder.extract — Functionextract(e::Extractor, samples; store_input=Val(false))Efficient extraction of multiple samples at once.
Note that whereas extract expects samples to be an iterable of samples (of known length), calling the extractor directly with e(sample) works for a single sample. In other words, e(sample) is equivalent to extract(e, [sample]).
See also: suggestextractor, stabilizeextractor, schema.
Examples
julia> sample = Dict("a" => 0, "b" => "foo");
julia> e = suggestextractor(schema([sample]))
DictExtractor
@@ -64,7 +64,7 @@
╰── b: ArrayNode(2×1 OneHotArray with Bool elements) 1 obs, 60 bytes
julia> e(sample) == extract(e, [sample])
-trueJsonGrinder.Extractor — TypeExtractorSupertype for all extractor node types.
JsonGrinder.LeafExtractor — TypeLeafExtractorSupertype for all leaf extractor node types that reside in the leafs of the hierarchy.
JsonGrinder.StableExtractor — Typestruct StableExtractor{T <: LeafExtractor} <: LeafExtractorWraps any other LeafExtractor and makes it output stable results w.r.t. missing input values.
See also: stabilizeextractor.
JsonGrinder.ScalarExtractor — TypeScalarExtractor{T} <: ExtractorExtracts a numerical value, centered by subtracting c and scaled by s.
Examples
julia> e = ScalarExtractor(2, 3)
+trueJsonGrinder.Extractor — TypeExtractorSupertype for all extractor node types.
JsonGrinder.LeafExtractor — TypeLeafExtractorSupertype for all leaf extractor node types that reside in the leafs of the hierarchy.
JsonGrinder.StableExtractor — Typestruct StableExtractor{T <: LeafExtractor} <: LeafExtractorWraps any other LeafExtractor and makes it output stable results w.r.t. missing input values.
See also: stabilizeextractor.
JsonGrinder.ScalarExtractor — TypeScalarExtractor{T} <: ExtractorExtracts a numerical value, centered by subtracting c and scaled by s.
Examples
julia> e = ScalarExtractor(2, 3)
ScalarExtractor(c=2.0, s=3.0)
julia> e(0)
@@ -73,7 +73,7 @@
julia> e(1)
1×1 ArrayNode{Matrix{Float32}, Nothing}:
- -3.0JsonGrinder.CategoricalExtractor — TypeCategoricalExtractor{V, I} <: ExtractorExtracts a single item interpreted as a categorical variable into a one-hot encoded vector.
There is always an extra category for an unknown value (and hence the displayed n is one more than the number of categories).
Examples
julia> e = CategoricalExtractor(1:3)
+ -3.0JsonGrinder.CategoricalExtractor — TypeCategoricalExtractor{V, I} <: ExtractorExtracts a single item interpreted as a categorical variable into a one-hot encoded vector.
There is always an extra category for an unknown value (and hence the displayed n is one more than the number of categories).
Examples
julia> e = CategoricalExtractor(1:3)
CategoricalExtractor(n=4)
julia> e(2)
@@ -88,13 +88,13 @@
⋅
⋅
⋅
- 1JsonGrinder.NGramExtractor — TypeNGramExtractor{T} <: ExtractorExtracts String as n-grams (Mill.NGramMatrix).
Examples
julia> e = NGramExtractor()
+ 1JsonGrinder.NGramExtractor — TypeNGramExtractor{T} <: ExtractorExtracts String as n-grams (Mill.NGramMatrix).
Examples
julia> e = NGramExtractor()
NGramExtractor(n=3, b=256, m=2053)
julia> e("foo")
2053×1 ArrayNode{NGramMatrix{String, Vector{String}, Int64}, Nothing}:
"foo"
-JsonGrinder.DictExtractor — TypeDictExtractor{S} <: ExtractorExtracts all items in a Dict and returns them as a Mill.ProductNode.
Examples
julia> e = (a=ScalarExtractor(), b=CategoricalExtractor(1:5)) |> DictExtractor
+JsonGrinder.DictExtractor — TypeDictExtractor{S} <: ExtractorExtracts all items in a Dict and returns them as a Mill.ProductNode.
Examples
julia> e = (a=ScalarExtractor(), b=CategoricalExtractor(1:5)) |> DictExtractor
DictExtractor
├── a: ScalarExtractor(c=0.0, s=1.0)
╰── b: CategoricalExtractor(n=6)
@@ -102,13 +102,13 @@
julia> e(Dict("a" => 1, "b" => 1))
ProductNode 1 obs, 0 bytes
├── a: ArrayNode(1×1 Array with Float32 elements) 1 obs, 60 bytes
- ╰── b: ArrayNode(6×1 OneHotArray with Bool elements) 1 obs, 60 bytesJsonGrinder.ArrayExtractor — TypeArrayExtractor{T}Extracts all items in an Array and returns them as a Mill.BagNode.
Examples
julia> e = ArrayExtractor(CategoricalExtractor(2:4))
+ ╰── b: ArrayNode(6×1 OneHotArray with Bool elements) 1 obs, 60 bytesJsonGrinder.ArrayExtractor — TypeArrayExtractor{T}Extracts all items in an Array and returns them as a Mill.BagNode.
Examples
julia> e = ArrayExtractor(CategoricalExtractor(2:4))
ArrayExtractor
╰── CategoricalExtractor(n=4)
julia> e([2, 3, 1, 4])
BagNode 1 obs, 64 bytes
- ╰── ArrayNode(4×4 OneHotArray with Bool elements) 4 obs, 72 bytesJsonGrinder.PolymorphExtractor — TypePolymorphExtractorExtracts to a Mill.ProductNode where each item is a result of different extractor.
Examples
julia> e = (NGramExtractor(), CategoricalExtractor(["tcp", "udp", "dhcp"])) |> PolymorphExtractor
+ ╰── ArrayNode(4×4 OneHotArray with Bool elements) 4 obs, 72 bytesJsonGrinder.PolymorphExtractor — TypePolymorphExtractorExtracts to a Mill.ProductNode where each item is a result of different extractor.
Examples
julia> e = (NGramExtractor(), CategoricalExtractor(["tcp", "udp", "dhcp"])) |> PolymorphExtractor
PolymorphExtractor
├── NGramExtractor(n=3, b=256, m=2053)
╰── CategoricalExtractor(n=4)
@@ -121,4 +121,4 @@
julia> e("http")
ProductNode 1 obs, 0 bytes
├── ArrayNode(2053×1 NGramMatrix with Int64 elements) 1 obs, 92 bytes
- ╰── ArrayNode(4×1 OneHotArray with Bool elements) 1 obs, 60 bytesSettings
This document was generated with Documenter.jl version 1.7.0 on Monday 28 October 2024. Using Julia version 1.11.1.