Merge pull request #14 from ATISLabs/feature/generate_friendman_functions

[#1] - Feature/generate friendman functions

Author: filipebraida

README.md

@@ -25,16 +25,21 @@ The package has an interface for the dataset generator of the [ScikitLearn](http
 ### ScikitLearn
 List of package datasets:
 
-Dataset         | Title                                                                  | Reference
-----------------|------------------------------------------------------------------------|--------------------------------------------------
-make_blobs      | Generate isotropic Gaussian blobs for clustering.                      | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html)
-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_circles    | Make a large circle containing a smaller circle in 2d                  | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.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)
-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)
+Dataset              | Title                                                                   | Reference
+---------------------|-------------------------------------------------------------------------|--------------------------------------------------
+make_blobs           | Generate isotropic Gaussian blobs for clustering.                       | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html)
+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])
+make_friedman1       | Generate the “Friedman #1” regression problem.                          | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman1.html)
+make_friedman2       | Generate the “Friedman #2” regression problem.                          | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman2.html)
+make_friedman3       | Generate the “Friedman #3” regression problem.                          | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman3.html)
+make_circles         | Make a large circle containing a smaller circle in 2d                   | [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.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)
+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)
 
 **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

@@ -240,6 +240,77 @@ function generate_classification(;  n_samples::Int = 100,
     return convert(features, labels)
 end
 
+"""
+    function generate_friedman1(;   n_samples::Int = 100,
+                                    n_features::Int = 10,
+                                    noise::Float64 = 0.0, 
+                                    random_state::Union{Int, Nothing} = nothing)::DataFrame
+Generate the “Friedman #1” regression problem. Sklearn interface to make_regression.
+#Arguments
+- `n_samples::Int = 100`: The number of samples.
+- `n_features::Int = 10`: The number of features. Should be at least 5.
+- `noise::Union{Nothing, Float64} = nothing`: The standard deviation of the gaussian noise applied to the output.
+- `random_state::Union{Int, Nothing} = nothing`: Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls.
+Reference: [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman1.html)
+"""
+function generate_friedman1(;   n_samples::Int = 100,
+                                n_features::Int = 10,
+                                noise::Float64 = 0.0, 
+                                random_state::Union{Int, Nothing} = nothing)::DataFrame
+
+    (features, labels) = datasets.make_friedman1(   n_samples = n_samples,
+                                                    n_features = n_features,
+                                                    noise = noise, 
+                                                    random_state = random_state)
+
+    return convert(features, labels)
+end
+
+"""
+    function generate_friedman2(;   n_samples::Int = 100,
+                                    noise::Float64 = 0.0, 
+                                    random_state::Union{Int, Nothing} = nothing)::DataFrame
+Generate the “Friedman #2” regression problem. Sklearn interface to make_friedman2.
+#Arguments
+- `n_samples::Int = 100`: The number of samples.
+- `n_features::Int = 10`: The number of features. Should be at least 5.
+- `noise::Union{Nothing, Float64} = nothing`: The standard deviation of the gaussian noise applied to the output.
+- `random_state::Union{Int, Nothing} = nothing`: Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls.
+Reference: [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman2.html)
+"""
+function generate_friedman2(;   n_samples::Int = 100,
+                                noise::Float64 = 0.0, 
+                                random_state::Union{Int, Nothing} = nothing)::DataFrame
+
+    (features, labels) = datasets.make_friedman2(   n_samples = n_samples,
+                                                    noise = noise, 
+                                                    random_state = random_state)
+
+    return convert(features, labels)
+end
+
+"""
+    function generate_friedman3(;   n_samples::Int = 100,
+                                    noise::Float64 = 0.0, 
+                                    random_state::Union{Int, Nothing} = nothing)::DataFrame
+Generate the “Friedman #3” regression problem. Sklearn interface to make_friedman3.
+#Arguments
+- `n_samples::Int = 100`: The number of samples.
+- `noise::Union{Nothing, Float64} = nothing`: The standard deviation of the gaussian noise applied to the output.
+- `random_state::Union{Int, Nothing} = nothing`: Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls.
+Reference: [link](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_friedman3.html)
+"""
+function generate_friedman3(;   n_samples::Int = 100,
+                                noise::Float64 = 0.0, 
+                                random_state::Union{Int, Nothing} = nothing)::DataFrame
+
+    (features, labels) = datasets.make_friedman3(   n_samples = n_samples,
+                                                    noise = noise, 
+                                                    random_state = random_state)
+
+    return convert(features, labels)
+end
+
 """
 function generate_low_rank_matrix(; n_samples::Int =100,
                                     n_features::Int =100,

test/runtests.jl

@@ -47,6 +47,22 @@ using Test
     @test size(data)[1] == samples
     @test size(data)[2] == features + 1
 
+    data = SyntheticDatasets.generate_friedman1(n_samples = samples,
+                                                    n_features = features)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == features + 1
+
+    data = SyntheticDatasets.generate_friedman2(n_samples = samples)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 5
+
+    data = SyntheticDatasets.generate_friedman3(n_samples = samples)
+
+    @test size(data)[1] == samples
+    @test size(data)[2] == 5
+
     data = SyntheticDatasets.generate_low_rank_matrix(n_samples = samples,
                                                     n_features = features,
                                                     effective_rank = 10,