Utility package for generating, loading, partitioning, and processing Machine Learning datasets. This package serves as a end-user friendly front end to the data related JuliaML packages.
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This package has been discontinued. Most functionalities have been moved MLUtils.jl.
This package is designed to be the end-user facing frond-end to all the data related functionalty that is spread out accross the JuliaML ecosystem. Most of the following sub-categories are covered by a single back-end package that is specialized on that specific problem. Consequently, if one of the following topics is of special interest to you, make sure to check out the corresponding documentation of that package.
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Label Encodings provided by MLLabelUtils.jl
Various tools needed to deal with classification targets of arbitrary format. This includes asserting if the targets are of a desired encoding, inferring the concrete encoding the targets are in and how many classes they represent, and converting from their native encoding to the desired one.
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[docs] Infer which encoding some classification targets use.
julia> enc = labelenc([-1,1,1,-1,1]) # MLLabelUtils.LabelEnc.MarginBased{Int64}()
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[docs] Assert if some classification targets are of the encoding I need them in.
julia> islabelenc([0,1,1,0,1], LabelEnc.MarginBased) # false
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[docs] Convert targets into a specific encoding that my model requires.
julia> convertlabel(LabelEnc.OneOfK{Float32}, [-1,1,-1,1,1,-1]) # 2×6 Array{Float32,2}: # 0.0 1.0 0.0 1.0 1.0 0.0 # 1.0 0.0 1.0 0.0 0.0 1.0
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[docs] Work with matrices in which the user can choose of the rows or the columns denote the observations.
julia> convertlabel(LabelEnc.OneOfK{Float32}, Int8[-1,1,-1,1,1,-1], obsdim = 1) # 6×2 Array{Float32,2}: # 0.0 1.0 # 1.0 0.0 # 0.0 1.0 # 1.0 0.0 # 1.0 0.0 # 0.0 1.0
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[docs] Group observations according to their class-label.
julia> labelmap([0, 1, 1, 0, 0]) # Dict{Int64,Array{Int64,1}} with 2 entries: # 0 => [1,4,5] # 1 => [2,3]
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[docs] Classify model predictions into class labels appropriate for the encoding of the targets.
julia> classify(-0.3, LabelEnc.MarginBased()) # -1.0
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Data Access Pattern provided by MLDataPattern.jl
Native and generic Julia implementation for commonly used data access pattern in Machine Learning. Most notably we provide a number of pattern for shuffling, partitioning, and resampling data sets of various types and origin. At its core, the package was designed around the key requirement of allowing any user-defined type to serve as a custom data source and/or access pattern in a first class manner. That said, there was also a lot of attention focused on first class support for those types that are most commonly employed to represent the data of interest, such as
DataFrameandArray.-
[docs] Create a lazy data subset of some data.
julia> X = rand(2, 6) # 2×6 Array{Float64,2}: # 0.226582 0.933372 0.505208 0.0443222 0.812814 0.11202 # 0.504629 0.522172 0.0997825 0.722906 0.245457 0.000341996 julia> datasubset(X, 2:3) # 2×2 SubArray{Float64,2,Array{Float64,2},Tuple{Colon,UnitRange{Int64}},true}: # 0.933372 0.505208 # 0.522172 0.0997825
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[docs] Shuffle the observations of a data container.
julia> shuffleobs(X) # 2×6 SubArray{Float64,2,Array{Float64,2},Tuple{Colon,Array{Int64,1}},false}: # 0.505208 0.812814 0.11202 0.0443222 0.933372 0.226582 # 0.0997825 0.245457 0.000341996 0.722906 0.522172 0.504629
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[docs] Split data into train/test subsets.
julia> train, test = splitobs(X, at = 0.7); julia> train # 2×4 SubArray{Float64,2,Array{Float64,2},Tuple{Colon,UnitRange{Int64}},true}: # 0.226582 0.933372 0.505208 0.0443222 # 0.504629 0.522172 0.0997825 0.722906 julia> test # 2×2 SubArray{Float64,2,Array{Float64,2},Tuple{Colon,UnitRange{Int64}},true}: # 0.812814 0.11202 # 0.245457 0.000341996
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[docs] Partition data into train/test subsets using stratified sampling.
julia> train, test = stratifiedobs([:a,:a,:b,:b,:b,:b], p = 0.5) # (Symbol[:b,:b,:a],Symbol[:b,:b,:a]) julia> train # 3-element SubArray{Symbol,1,Array{Symbol,1},Tuple{Array{Int64,1}},false}: # :b # :b # :a julia> test # 3-element SubArray{Symbol,1,Array{Symbol,1},Tuple{Array{Int64,1}},false}: # :b # :b # :a
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[docs] Group multiple variables together and treat them as a single data set.
julia> shuffleobs(([1,2,3], [:a,:b,:c])) ([3,1,2],Symbol[:c,:a,:b])
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[docs] Support my own custom user-defined data container type.
julia> using DataTables, LearnBase julia> LearnBase.nobs(dt::AbstractDataTable) = nrow(dt) julia> LearnBase.getobs(dt::AbstractDataTable, idx) = dt[idx,:] julia> LearnBase.datasubset(dt::AbstractDataTable, idx, ::ObsDim.Undefined) = view(dt, idx)
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[docs] Over- or undersample an imbalanced labeled data set.
julia> undersample([:a,:b,:b,:a,:b,:b]) # 4-element SubArray{Symbol,1,Array{Symbol,1},Tuple{Array{Int64,1}},false}: # :a # :b # :b # :a
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[docs] Repartition a data container using a k-folds scheme.
julia> folds = kfolds([1,2,3,4,5,6,7,8,9,10], k = 5) # 5-fold MLDataPattern.FoldsView of 10 observations: # data: 10-element Array{Int64,1} # training: 8 observations/fold # validation: 2 observations/fold # obsdim: :last julia> folds[1] # ([3, 4, 5, 6, 7, 8, 9, 10], [1, 2])
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[docs] Iterate over my data one observation or batch at a time.
julia> obsview(([1 2 3; 4 5 6], [:a, :b, :c])) # 3-element MLDataPattern.ObsView{Tuple{SubArray{Int64,1,Array{Int64,2},Tuple{Colon,Int64},true},SubArray{Symbol,0,Array{Symbol,1},Tuple{Int64},false}},Tuple{Array{Int64,2},Array{Symbol,1}},Tuple{LearnBase.ObsDim.Last,LearnBase.ObsDim.Last}}: # ([1,4],:a) # ([2,5],:b) # ([3,6],:c)
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[docs] Prepare sequence data such as text for supervised learning.
julia> txt = split("The quick brown fox jumps over the lazy dog") # 9-element Array{SubString{String},1}: # "The" # "quick" # "brown" # ⋮ # "the" # "lazy" # "dog" julia> seq = slidingwindow(i->i+2, txt, 2, stride=1) # 7-element slidingwindow(::##9#10, ::Array{SubString{String},1}, 2, stride = 1) with element type Tuple{...}: # (["The", "quick"], "brown") # (["quick", "brown"], "fox") # (["brown", "fox"], "jumps") # (["fox", "jumps"], "over") # (["jumps", "over"], "the") # (["over", "the"], "lazy") # (["the", "lazy"], "dog") julia> seq = slidingwindow(i->[i-2:i-1; i+1:i+2], txt, 1) # 5-element slidingwindow(::##11#12, ::Array{SubString{String},1}, 1) with element type Tuple{...}: # (["brown"], ["The", "quick", "fox", "jumps"]) # (["fox"], ["quick", "brown", "jumps", "over"]) # (["jumps"], ["brown", "fox", "over", "the"]) # (["over"], ["fox", "jumps", "the", "lazy"]) # (["the"], ["jumps", "over", "lazy", "dog"])
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Data Processing: This package contains a number of simple pre-processing strategies that are often applied for ML purposes, such as feature centering and rescaling.
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Data Generators: When studying learning algorithm or other ML related functionality, it is usually of high interest to empirically test the behaviour of the system under specific conditions. Generators can provide the means to fabricate artificial data sets that observe certain attributes, which can help to deepen the understanding of the system under investigation.
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Example Datasets: We provide a small number of toy datasets. These are mainly intended for didactic and testing purposes.
Check out the latest documentation
Additionally, you can make use of Julia's native docsystem. The
following example shows how to get additional information on
kfolds within Julia's REPL:
?kfolds
This package is registered in METADATA.jl and can be installed
as usual. Just start up Julia and type the following code-snipped
into the REPL. It makes use of the native Julia package manger.
import Pkg
Pkg.add("MLDataUtils")This code is free to use under the terms of the MIT license