Merge pull request #27 from ATISLabs/feature/generate_gaussian_quantiles

[#1]feature/generate_gaussian_quantiles

Author: filipebraida

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_gaussian_quantiles | Generate a swiss roll dataset.                                          | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_gaussian_quantiles.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/matlab.jl

@@ -30,4 +30,4 @@ function generate_twospirals(; n_samples::Int = 2000,
    labels = [zeros(Int, N1); ones(Int, N1)]
 
    return convert(features, labels);
-end
+end

src/sklearn.jl

@@ -361,3 +361,42 @@ function generate_swiss_roll(;  n_samples::Int = 100,
 
    return convert(features, labels)
 end
+
+"""
+function generate_gaussian_quantiles(; mean::Array{<:Union{Number, Nothing}, 1} = [nothing],
+                                       cov::Float64 = 1,
+                                       n_samples::Int = 100,
+                                       n_features::Int = 2,
+                                       n_classes::Int = 3,
+                                       shuffle::Bool = true,
+                                       random_state::Union{Int, Nothing} = nothing)
+
+Generate isotropic Gaussian and label samples by quantile.
+#Arguments
+- `mean::Array{<:Union{Number, Nothing}, 1} = [nothing]`: The mean of the multi-dimensional normal distribution. If None then use the origin (0, 0, …).
+- `cov::Float64 = 1`: The covariance matrix will be this value times the unit matrix.
+- `n_samples::Int = 100`: The total number of points equally divided among classes.
+- `n_features::Int = 2`: The number of features for each sample.
+- `n_classes::Int = 3`: The number of classes.
+- `shuffle::Bool = true`: Shuffle the samples.
+- `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_gaussian_quantiles.html)
+"""
+function generate_gaussian_quantiles(; mean::Union{Array{<:Number, 1}, Nothing} = nothing,
+                                       cov::Float64 = 1.0,
+                                       n_samples::Int = 100,
+                                       n_features::Int = 2,
+                                       n_classes::Int = 3,
+                                       shuffle::Bool = true,
+                                       random_state::Union{Int, Nothing} = nothing)
+
+    (features, labels) = datasets.make_gaussian_quantiles(mean = mean,
+                                                          cov = cov,
+                                                          n_samples = n_samples,
+                                                          n_features = n_features,
+                                                          n_classes = n_classes,
+                                                          shuffle = shuffle,
+                                                          random_state = random_state)
+
+      return convert(features, labels)
+end

test/runtests.jl

@@ -47,9 +47,6 @@ using Test
     @test size(data)[1] == samples
     @test size(data)[2] == features + 1
 
-    @test size(data)[1] == samples
-    @test size(data)[2] == features + 1
-
     data = SyntheticDatasets.generate_friedman1(n_samples = samples,
                                                     n_features = features)
 
@@ -74,13 +71,20 @@ using Test
 
     @test size(data)[1] == samples
     @test size(data)[2] == features
-    
+
     data = SyntheticDatasets.generate_swiss_roll(n_samples =samples,
                                                  noise = 2.2,
                                                  random_state = 5)
 
     @test size(data)[1] == samples
     @test size(data)[2] == 4
+
+    data = SyntheticDatasets.generate_gaussian_quantiles(n_samples = samples,
+                                                    n_features = features,
+                                                    random_state = 5)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == features + 1
 end
 
 @testset "Matlab Generators" begin