[#1] - Feature/generate checkerboard

README.md

@@ -40,6 +40,7 @@ make_regression      | Generate a random regression problem.
 make_classification  | Generate a random n-class classification problem.                       | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html)
 make_low_rank_matrix | Generate a mostly low rank matrix with bell-shaped singular values.     | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_low_rank_matrix.html)
 make_swiss_roll      | Generate a swiss roll dataset.                                          | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_swiss_roll.html)
+make_checkerboard    | Generate an array with block checkerboard structure for biclustering.   | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_checkerboard.html])
 
 **Disclaimer**: SyntheticDatasets.jl borrows code and documentation from
 [scikit-learn](https://scikit-learn.org/stable/modules/classes.html#samples-generator) in the dataset module, but *it is not an official part
@@ -52,4 +53,4 @@ of that project*. It is licensed under [MIT](LICENSE).
 [codecov-url]: https://codecov.io/gh/ATISLabs/SyntheticDatasets.jl
 
 [coverage-img]: https://coveralls.io/repos/github/ATISLabs/SyntheticDatasets.jl/badge.svg?branch=master
-[coverage-url]: https://coveralls.io/github/ATISLabs/SyntheticDatasets.jl?branch=master
+[coverage-url]: https://coveralls.io/github/ATISLabs/SyntheticDatasets.jl?branch=master

src/SyntheticDatasets.jl

@@ -12,6 +12,21 @@ end
 
 include("sklearn.jl")
 
+function convert(matrix::Array{T, 2}, base_name::Union{String, Symbol} = :Column)::DataFrame where {T <: Number}
+    df = DataFrame()
+
+    for i = 1:size(matrix, 2)
+        df[!, Symbol(string(base_name) * "_$(i)")] = eltype(matrix)[]
+    end
+
+
+    for i=1:size(matrix, 1)
+        push!(df, (matrix[i, :]...,))
+    end
+
+    return df
+end
+
 function convert(features::Array{T, 2}, labels::Array{D, 1})::DataFrame where {T <: Number, D <: Number}
     df = DataFrame()
 

src/sklearn.jl

@@ -240,6 +240,45 @@ function generate_classification(;  n_samples::Int = 100,
     return convert(features, labels)
 end
 
+"""
+function generate_checkerboard(;shape::Tuple{Int, Int},
+                                n_clusters::Tuple{Int, Int},
+                                noise::Float64 = 0.0, 
+                                minval::Int = 10, 
+                                maxval::Int = 100, 
+                                shuffle::Bool = true, 
+                                random_state::Union{Nothing, Int} = nothing)
+Generate an array with block checkerboard structure for biclustering. Sklearn interface to make_checkerboard.
+#Arguments
+- `shape::Tuple{Int, Int}`: The shape of the result.
+- `n_clusters::Tuple{Int, Int}: The number of row and column clusters.
+- `noise::Float64 = 0.0`: The standard deviation of the gaussian noise.
+- `minval::Int = 10`: Minimum value of a bicluster.
+- `maxval::Int = 100`: Maximum value of a bicluster.
+- `shuffle::Bool = true`: Shuffle the samples.
+- `random_state::Union{Nothing, Int} = nothing`: Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls.
+Reference: [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_checkerboard.html)
+"""
+function generate_checkerboard(shape::Tuple{Int, Int},
+                                n_clusters::Union{Int, Tuple{Int, Int}};
+                                noise::Float64 = 0.0, 
+                                minval::Int = 3, 
+                                maxval::Int = 100, 
+                                shuffle::Bool = true, 
+                                random_state::Union{Nothing, Int} = nothing)
+
+
+    (features, rows, columns) = datasets.make_checkerboard(shape = shape,
+                                                    n_clusters = n_clusters,
+                                                    noise = noise,
+                                                    minval = minval,
+                                                    maxval = maxval,
+                                                    shuffle = shuffle,
+                                                    random_state = random_state)
+
+    return (X = convert(features, "feature"), rows = convert(rows, "row"), columns = convert(columns, "column"))
+end
+
 """
     function generate_friedman1(;   n_samples::Int = 100,
                                     n_features::Int = 10,

test/runtests.jl

@@ -78,4 +78,16 @@ using Test
 
     @test size(data)[1] == samples
     @test size(data)[2] == 4
+
+    n_clusters = 3
+    samples = 10
+    features = 4
+    data = SyntheticDatasets.generate_checkerboard((samples, features), n_clusters)
+
+    @test size(data.X)[1] == samples
+    @test size(data.X)[2] == features
+
+    @test size(data.rows)[2] == samples 
+
+    @test size(data.cols)[2] == features
 end