[#1] - Feature/generate classification function

README.md

@@ -31,6 +31,7 @@ make_blobs      | Generate isotropic Gaussian blobs for clustering.
 make_moons      | Make two interleaving half circles                                     | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html)
 make_s_curve    | Generate an S curve dataset.                                           | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_s_curve.html)
 make_regression | Generate a random regression problem.                                  | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html])
+make_classification | Generate a random n-class classification problem.                                  | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.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

src/sklearn.jl

@@ -140,4 +140,75 @@ function generate_regression(;  n_samples::Int = 100,
 
     return convert(features, labels)
 
+end
+
+"""
+function generate_classification(;  n_samples::Int = 100,
+                                    n_features::Int = 20,
+                                    n_informative::Int = 2,
+                                    n_redundant::Int = 2,
+                                    n_repeated::Int = 0,
+                                    n_classes::Int = 2,
+                                    n_clusters_per_class::Int = 2,
+                                    weights::Union{Nothing, Array{Float64,1}} = nothing,
+                                    flip_y::Float64 = 0.01,
+                                    class_sep::Float64 = 1.0,
+                                    hypercube::Bool = true, 
+                                    shift::Union{Nothing, Array{Float64,1}} = 0.0,
+                                    scale::Union{Nothing, Array{Float64,1}} = 1.0, 
+                                    shuffle::Bool = true, 
+                                    random_state::Union{Int, Nothing} = nothing)
+Generate a random n-class classification problem. Sklearn interface to make_classification.
+#Arguments
+- `n_samples::Int = 100`: The number of samples.
+- `n_features::Int = 20`: The total number of features. These comprise `n_informative` informative features, `n_redundant` redundant features, `n_repeated` duplicated features and `n_features-n_informative-n_redundant-n_repeated` useless features drawn at random.
+- `n_informative::Int = 2`: The number of informative features. Each class is composed of a number of gaussian clusters each located around the vertices of a hypercube in a subspace of dimension `n_informative`. For each cluster, informative features are drawn independently from N(0, 1) and then randomly linearly combined within each cluster in order to add covariance. The clusters are then placed on the vertices of the hypercube.
+- `n_redundant::Int = 2`: The number of redundant features. These features are generated as random linear combinations of the informative features.
+- `n_repeated::Int = 0`: The number of duplicated features, drawn randomly from the informative and the redundant features.
+- `n_classes::Int = 2`: The number of classes (or labels) of the classification problem.
+- `n_clusters_per_class::Int = 2`: The number of clusters per class.
+- `weights::Union{Nothing, Array{Float64,1}} = nothing`: 
+- `flip_y::Float64 = 0.01`: The fraction of samples whose class is assigned randomly. Larger values introduce noise in the labels and make the classification task harder. Note that the default setting flip_y > 0 might lead to less than n_classes in y in some cases.
+- `class_sep::Float64 = 1.0`: The factor multiplying the hypercube size. Larger values spread out the clusters/classes and make the classification task easier.
+- `hypercube::Bool = true`: If True, the clusters are put on the vertices of a hypercube. If False, the clusters are put on the vertices of a random polytope.
+- `shift::Union{Nothing, Array{Float64,1}} = 0.0`: Shift features by the specified value. If None, then features are shifted by a random value drawn in [-class_sep, class_sep].
+- `scale::Union{Nothing, Array{Float64,1}} = 1.0`: Multiply features by the specified value. If None, then features are scaled by a random value drawn in [1, 100]. Note that scaling happens after shifting.
+- `shuffle::Bool = true`: Shuffle the samples and the features.
+- `random_state::Union{Int, Nothing} = nothing`: Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.
+Reference: [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html)
+"""
+function generate_classification(;  n_samples::Int = 100,
+                                    n_features::Int = 20,
+                                    n_informative::Int = 2,
+                                    n_redundant::Int = 2,
+                                    n_repeated::Int = 0,
+                                    n_classes::Int = 2,
+                                    n_clusters_per_class::Int = 2,
+                                    weights::Union{Nothing, Array{Float64,1}} = nothing,
+                                    flip_y::Float64 = 0.01,
+                                    class_sep::Float64 = 1.0,
+                                    hypercube::Bool = true, 
+                                    shift::Union{Nothing, Float64, Array{Float64,1}} = 0.0,
+                                    scale::Union{Nothing, Float64, Array{Float64,1}} = 1.0, 
+                                    shuffle::Bool = true, 
+                                    random_state::Union{Int, Nothing} = nothing)
+
+
+    (features, labels) = datasets.make_classification(  n_samples = n_samples,
+                                                        n_features = n_features,
+                                                        n_informative = n_informative,
+                                                        n_redundant = n_redundant,
+                                                        n_repeated = n_repeated,
+                                                        n_classes = n_classes,
+                                                        n_clusters_per_class = n_clusters_per_class,
+                                                        weights = weights,
+                                                        flip_y = flip_y,
+                                                        class_sep = class_sep,
+                                                        hypercube = hypercube,
+                                                        shift = shift,
+                                                        scale = scale,
+                                                        shuffle = shuffle,
+                                                        random_state = random_state)
+
+    return convert(features, labels)
 end

test/runtests.jl

@@ -36,4 +36,12 @@ using Test
     @test size(data)[1] == samples
     @test size(data)[2] == features + 1
 
+    data = SyntheticDatasets.generate_classification(n_samples = samples,
+                                                    n_features = features,
+                                                    n_classes = 1)
+
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == features + 1
+
 end