undersample

			undersample(data, classes; shuffle=true)

Generate a class-balanced version of data by subsampling its observations in such a way that the resulting number of observations will be the same number for every class. This way, all classes will have as many observations in the resulting data set as the smallest class has in the given (original) data.

The convenience parameter shuffle determines if the resulting data will be shuffled after its creation; if it is not shuffled then all the observations will be in their original order. Defaults to false.

			
			
			# 6 observations with 3 features each
			

			
			X
			 
			=
			 
			
			rand
			(
			3
			,
			 
			6
			)
			

			# 2 classes, severely imbalanced
			

			
			Y
			 
			=
			 
			
			[
			
			"
			a
			"
			,
			 
			
			"
			b
			"
			,
			 
			
			"
			b
			"
			,
			 
			
			"
			b
			"
			,
			 
			
			"
			b
			"
			,
			 
			
			"
			a
			"
			]
			

			

			# subsample the class "b" to match "a"
			

			
			
			X_bal
			,
			 
			Y_bal
			 
			=
			 
			

			undersample
			(
			X
			,
			 
			Y
			)
			

			

			# this results in a smaller dataset
			

			
			@
			assert
			
			 
			
			size
			(
			X_bal
			)
			 
			==
			 
			
			(
			3
			,
			4
			)
			

			
			@
			assert
			
			 
			
			length
			(
			Y_bal
			)
			 
			==
			 
			4
			

			

			# now both "a", and "b" have 2 observations each
			

			
			@
			assert
			
			 
			
			sum
			(
			
			Y_bal
			 
			.==
			 
			
			"
			a
			"
			)
			 
			==
			 
			2
			

			
			@
			assert
			
			 
			
			sum
			(
			
			Y_bal
			 
			.==
			 
			
			"
			b
			"
			)
			 
			==
			 
			2

For this function to work, the type of data must implement numobs and getobs. For example, the following code allows undersample to work on a DataFrame.

			
			
			# Make DataFrames.jl work
			

			
			
			

	
			MLUtils
			.
			

	
			getobs
			(
			
			data
			::
			DataFrame
			,
			 
			i
			)
			 
			=
			 
			
			data
			[
			i
			,
			:
			]
			

			
			
			

	
			MLUtils
			.
			

	
			numobs
			(
			
			data
			::
			DataFrame
			)
			 
			=
			 
			
			nrow
			(
			data
			)

Note that if data is a tuple, then it will be assumed that the last element of the tuple contains the targets.

			
			julia> data = DataFrame(X1=rand(6), X2=rand(6), Y=[:a,:b,:b,:b,:b,:a])
6×3 DataFrames.DataFrame
│ Row │ X1        │ X2          │ Y │
├─────┼───────────┼─────────────┼───┤
│ 1   │ 0.226582  │ 0.0443222   │ a │
│ 2   │ 0.504629  │ 0.722906    │ b │
│ 3   │ 0.933372  │ 0.812814    │ b │
│ 4   │ 0.522172  │ 0.245457    │ b │
│ 5   │ 0.505208  │ 0.11202     │ b │
│ 6   │ 0.0997825 │ 0.000341996 │ a │

julia> getobs(undersample(data, data.Y))
4×3 DataFrame
 Row │ X1        X2         Y      
     │ Float64   Float64    Symbol 
─────┼─────────────────────────────
   1 │ 0.427064  0.0648339  a
   2 │ 0.376304  0.100022   a
   3 │ 0.467095  0.185437   b
   4 │ 0.457043  0.490688   b

See ObsView for more information on data subsets. See also oversample.