Classification
Encoders
LearningHorse.Classification.LabelEncoder
— TypeLabelEncoder()
LabelEncoder structure. LE(label; count=false, decode=false)
Convert labels(like string) to class numbers(encode), and convert class numbers to labels(decode).
Example
julia> label = ["Apple", "Apple", "Pear", "Pear", "Lemon", "Apple", "Pear", "Lemon"]
8-element Vector{String}
"Apple"
"Apple"
"Pear"
"Pear"
"Lemon"
"Apple"
"Pear"
"Lemon"
julia> LE = LabelEncoder()
LabelEncoder(Dict{Any, Any}())
julia> classes, count = LE(label, count=true) #Encode
([3.0 3.0 … 1.0 2.0], [3.0 2.0 3.0])
julia> LE(classes, decode=true) #Decode
1×18 Matrix{String}:
"Apple" "Apple" "Pear" "Pear" "Lemon" "Apple" "Pear" "Lemon"
LearningHorse.Classification.OneHotEncoder
— TypeOneHotEncoder()
convert data to Ont-Hot format. If you specified decode
, data
will be decoded.
Example
julia> x = [4.9 3.0; 4.6 3.1; 4.4 2.9; 4.8 3.4; 5.1 3.8; 5.4 3.4; 4.8 3.4; 5.2 4.1; 5.5 4.2; 5.5 3.5; 4.8 3.0; 5.1 3.8; 5.0 3.3; 6.4 3.2; 5.7 2.8; 6.1 2.9; 6.7 3.1; 5.6 2.5; 6.3 2.5; 5.6 3.0; 5.6 2.7; 7.6 3.0; 6.4 2.7; 6.4 3.2; 6.5 3.0; 7.7 3.8; 7.2 3.2; 7.2 3.0; 6.3 2.8; 6.1 2.6; 6.3 3.4; 6.0 3.0; 6.9 3.1; 6.7 3.1; 5.8 2.7; 6.8 3.2; 6.3 2.5];#These data are also used to explanations of other functions.
julia> t = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2];
julia> OHE = OneHotEncoder()
OneHotEncoder()
julia> ct = OHE(t)
37×3 Matrix{Float64}:
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
1.0 0.0 0.0
0.0 1.0 0.0
0.0 1.0 0.0
0.0 1.0 0.0
0.0 1.0 0.0
0.0 1.0 0.0
⋮
0.0 1.0 0.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
0.0 0.0 1.0
julia> OHE(ct, decode = true)
37-element Vector{Int64}:
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
⋮
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
Models
LearningHorse.Classification.Logistic
— TypeLogistic(; alpha = 0.01, ni = 1000)
Logistic Regression classifier.
This struct learns classifiers using multi class softmax. Parameter α
indicates the learning rate, and ni
indicates the number of learnings.
Example
julia> model = Logistic(alpha = 0.1)
Logistic(0.1, 1000, Matrix{Float64}(undef, 0, 0))
julia> fit!(model, x, ct)
3×3 Matrix{Float64}:
1.80736 1.64037 -0.447735
-1.27053 1.70026 2.57027
4.84966 -0.473835 -1.37582
julia> println(predict(model, x))
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
LearningHorse.Classification.SVC
— TypeSVC(; alpha=0.01, ni=1000)
Support Vector Machine Classifier.
This struct learns classifiers using One-Vs-Rest. One-Vs-Rest generates two-class classifiers divided into one class and the other classes using Logistic Regression, adopting the most likely one among all classifiers.
Parameter α
indicates the learning rate, and ni
indicates the number of learnings.
Example
julia> model = SVC()
SVC(0.01, 1000, Logistic[])
julia> fit!(model, ct)
3-element Vector{Logistic}:
Logistic(0.01, 1000, [0.8116709490679518 1.188329050932049; 1.7228257190036231 0.2771742809963788; -0.1519960725403138 2.1519960725403116])
Logistic(0.01, 1000, [0.9863693439936144 1.0136306560063886; 0.8838433946106077 1.11615660538939; 1.4431044559203794 0.5568955440796174])
Logistic(0.01, 1000, [1.262510641510418 0.7374893584895849; 0.5242383002319192 1.4757616997680822; 1.864635796779504 0.135364203220495])
julia> println(predict(model, x))
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
DecisionTree
Models
LearningHorse.Tree.DecisionTree
— TypeDecisionTree(; alpha = 0.01)
Normal DecisionTree. alpha
specify the complexity of the model. If it's small, it's complicated, and if it's big, it's simple.
Example
julia> tree = DecisionTree()
DecisionTree(0.01, Dict{Any, Any}(), Any[])
julia> fit!(tree, x, t)
Dict{String, Any} with 5 entries:
"left" => Dict{String, Any}("left"=>Dict{String, Union{Nothing, Vector…
"class_count" => [8, 13, 16]
"threshold" => 5.7
"right" => Dict{String, Any}("left"=>Dict{String, Union{Nothing, Vector…
"feature_id" => 1
julia> println(predict(tree, x))
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
LearningHorse.Tree.RandomForest
— TypeRandomForest(nt; alpha = 0.01)
RandomForest Model. nt
is the number of trees, and alpha
is the same as alpha
in DecisionTree.
Example
julia> model = RandomForest(10)
RandomForest(0.01, 10, DecisionTree[], Vector{Any}[], #undef)
julia> fit!(model, x, t)
10×1 Matrix{Int64}:
1
2
2
2
2
1
1
1
1
1
julia> println(predict(model, x))
Any[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
VisualTool
LearningHorse.Tree.MV
— FunctionMV(path, forest; rounded=false, bg="#ffffff", fc="#000000", label="Tree", fs="18")
Make DicisionTree and RandomForest Visual(make a dot file, see also Graphviz). The arguments are as follows:
path
: The full path of the dot file. The suffix must be.dot
.forest
: The model.rounded
: Ifrounded
istrue
, the nodes will be rounded.bg
: Background color, type of this must beString
.fc
: Font color, type of this must beString
.label
: The label of the graph.fs
: Font size, type of this must beString
.
Example
julia> MV("/home/ubuntu/test.dot", model, rounded = true)
If you make the model created in DecisionTree Example visualized, it'll be like this: