Generating Synthetic Data

MLJ has a set of functions - make_blobs, make_circles, make_moons and make_regression (closely resembling functions in scikit-learn of the same name) - for generating synthetic data sets. These are useful for testing machine learning models (e.g., testing user-defined composite models; see Composing Models)

Generating Gaussian blobs

MLJBase.make_blobsFunction
X, y = make_blobs(n=100, p=2; kwargs...)

Generate Gaussian blobs for clustering and classification problems.

Return value

By default, a table X with p columns (features) and n rows (observations), together with a corresponding vector of n Multiclass target observations y, indicating blob membership.

Keyword arguments

  • shuffle=true: whether to shuffle the resulting points,

  • centers=3: either a number of centers or a c x p matrix with c pre-determined centers,

  • cluster_std=1.0: the standard deviation(s) of each blob,

  • center_box=(-10. => 10.): the limits of the p-dimensional cube within which the cluster centers are drawn if they are not provided,

  • eltype=Float64: machine type of points (any subtype of AbstractFloat).

  • rng=Random.GLOBAL_RNG: any AbstractRNG object, or integer to seed a MersenneTwister (for reproducibility).

  • as_table=true: whether to return the points as a table (true) or a matrix (false). If false the target y has integer element type.

Example

X, y = make_blobs(100, 3; centers=2, cluster_std=[1.0, 3.0])
using MLJ, DataFrames
X, y = make_blobs(100, 3; centers=2, cluster_std=[1.0, 3.0])
dfBlobs = DataFrame(X)
dfBlobs.y = y
first(dfBlobs, 3)

3 rows × 4 columns

x1x2x3y
Float64Float64Float64Cat…
11.70221-5.05418-7.936531
2-15.017-3.26896-5.607472
31.71833-4.24531-9.043181
using VegaLite
dfBlobs |> @vlplot(:point, x=:x1, y=:x2, color = :"y:n") 

svg

dfBlobs |> @vlplot(:point, x=:x1, y=:x3, color = :"y:n") 

svg

Generating concentric circles

MLJBase.make_circlesFunction
X, y = make_circles(n=100; kwargs...)

Generate n labeled points close to two concentric circles for classification and clustering models.

Return value

By default, a table X with 2 columns and n rows (observations), together with a corresponding vector of n Multiclass target observations y. The target is either 0 or 1, corresponding to membership to the smaller or larger circle, respectively.

Keyword arguments

  • shuffle=true: whether to shuffle the resulting points,

  • noise=0: standard deviation of the Gaussian noise added to the data,

  • factor=0.8: ratio of the smaller radius over the larger one,

  • eltype=Float64: machine type of points (any subtype of AbstractFloat).

  • rng=Random.GLOBAL_RNG: any AbstractRNG object, or integer to seed a MersenneTwister (for reproducibility).

  • as_table=true: whether to return the points as a table (true) or a matrix (false). If false the target y has integer element type.

Example

X, y = make_circles(100; noise=0.5, factor=0.3)
using MLJ, DataFrames
X, y = make_circles(100; noise=0.05, factor=0.3)
dfCircles = DataFrame(X)
dfCircles.y = y
first(dfCircles, 3)

3 rows × 3 columns

x1x2y
Float64Float64Cat…
1-0.1699590.151160
27.34162e-5-0.3247240
30.2950690.08760670
using VegaLite
dfCircles |> @vlplot(:circle, x=:x1, y=:x2, color = :"y:n") 

svg

Sampling from two interleaved half-circles

MLJBase.make_moonsFunction
    make_moons(n::Int=100; kwargs...)

Generates labeled two-dimensional points lying close to two interleaved semi-circles, for use with classification and clustering models.

Return value

By default, a table X with 2 columns and n rows (observations), together with a corresponding vector of n Multiclass target observations y. The target is either 0 or 1, corresponding to membership to the left or right semi-circle.

Keyword arguments

  • shuffle=true: whether to shuffle the resulting points,

  • noise=0.1: standard deviation of the Gaussian noise added to the data,

  • xshift=1.0: horizontal translation of the second center with respect to the first one.

  • yshift=0.3: vertical translation of the second center with respect to the first one.

  • eltype=Float64: machine type of points (any subtype of AbstractFloat).

  • rng=Random.GLOBAL_RNG: any AbstractRNG object, or integer to seed a MersenneTwister (for reproducibility).

  • as_table=true: whether to return the points as a table (true) or a matrix (false). If false the target y has integer element type.

Example

X, y = make_moons(100; noise=0.5)
using MLJ, DataFrames
X, y = make_moons(100; noise=0.05)
dfHalfCircles = DataFrame(X)
dfHalfCircles.y = y
first(dfHalfCircles, 3)

3 rows × 3 columns

x1x2y
Float64Float64Cat…
10.707411-0.6970171
2-0.5073230.8633720
31.62114-0.3989311
using VegaLite
dfHalfCircles |> @vlplot(:circle, x=:x1, y=:x2, color = :"y:n") 

svg

Regression data generated from noisy linear models

MLJBase.make_regressionFunction
make_regression(n, p; kwargs...)

Generate Gaussian input features and a linear response with Gaussian noise, for use with regression models.

Return value

By default, a tuple (X, y) where table X has p columns and n rows (observations), together with a corresponding vector of n Continuous target observations y.

Keywords

  • intercept=true: Whether to generate data from a model with intercept.

  • n_targets=1: Number of columns in the target.

  • sparse=0: Proportion of the generating weight vector that is sparse.

  • noise=0.1: Standard deviation of the Gaussian noise added to the response (target).

  • outliers=0: Proportion of the response vector to make as outliers by adding a random quantity with high variance. (Only applied if binary is false.)

  • as_table=true: Whether X (and y, if n_targets > 1) should be a table or a matrix.

  • eltype=Float64: Element type for X and y. Must subtype AbstractFloat.

  • binary=false: Whether the target should be binarized (via a sigmoid).

  • eltype=Float64: machine type of points (any subtype of AbstractFloat).

  • rng=Random.GLOBAL_RNG: any AbstractRNG object, or integer to seed a MersenneTwister (for reproducibility).

  • as_table=true: whether to return the points as a table (true) or a matrix (false).

Example

X, y = make_regression(100, 5; noise=0.5, sparse=0.2, outliers=0.1)
using MLJ, DataFrames
X, y = make_regression(100, 5; noise=0.5, sparse=0.2, outliers=0.1)
dfRegression = DataFrame(X)
dfRegression.y = y
first(dfRegression, 3)

3 rows × 6 columns

x1x2x3x4x5y
Float64Float64Float64Float64Float64Float64
1-0.219494-0.4203940.0496602-0.4978062.09631-0.871713
21.41152-0.1672891.956571.21523-0.242929-0.0372644
3-0.01834090.1119311.21685-1.63531-1.00182-0.882296