Merge pull request #52 from JuliaAI/dev

For a 0.5.0 release

Author: ablaom

Project.toml

@@ -1,19 +1,21 @@
 name = "MLJMultivariateStatsInterface"
 uuid = "1b6a4a23-ba22-4f51-9698-8599985d3728"
 authors = ["Anthony D. Blaom <[email protected]>", "Thibaut Lienart <[email protected]>", "Okon Samuel <[email protected]>"]
-version = "0.4.0"
+version = "0.5.0"
 
 [deps]
+CategoricalDistributions = "af321ab8-2d2e-40a6-b165-3d674595d28e"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
+CategoricalDistributions = "0.1.9"
 Distances = "0.9,0.10"
 MLJModelInterface = "1.4"
-MultivariateStats = "0.9"
+MultivariateStats = "0.10"
 StatsBase = "0.32, 0.33"
 julia = "1.6"
 

src/MLJMultivariateStatsInterface.jl

@@ -9,6 +9,7 @@ import MultivariateStats
 import MultivariateStats: SimpleCovariance
 import StatsBase: CovarianceEstimator
 
+using CategoricalDistributions
 using Distances
 using LinearAlgebra
 

src/models/decomposition_models.jl

@@ -284,8 +284,8 @@ MMI.fitted_params(::ICA, fr) = (projection=copy(fr.W), mean = copy(MS.mean(fr)))
 
 $(MMI.doc_header(PCA))
 
-Principal component analysis learns a linear projection onto a lower dimensional space while
-preserving most of the initial variance seen in the training data.
+Principal component analysis learns a linear projection onto a lower dimensional space 
+while preserving most of the initial variance seen in the training data.
 
 # Training data
 
@@ -303,11 +303,11 @@ Train the machine using `fit!(mach, rows=...)`.
 # Hyper-parameters
 
 - `maxoutdim=0`: Together with `variance_ratio`, controls the output dimension `outdim`
-   chosen by the model. Specifically, suppose that `k` is the smallest integer such that
-   retaining the `k` most significant principal components accounts for `variance_ratio` of
-   the total variance in the training data. Then `outdim = min(outdim, maxoutdim)`. If
-   `maxoutdim=0` (default) then the effective `maxoutdim` is `min(n, indim - 1)` where `n`
-   is the number of observations and `indim` the number of features in the training data.
+  chosen by the model. Specifically, suppose that `k` is the smallest integer such that
+  retaining the `k` most significant principal components accounts for `variance_ratio` of
+  the total variance in the training data. Then `outdim = min(outdim, maxoutdim)`. If
+  `maxoutdim=0` (default) then the effective `maxoutdim` is `min(n, indim - 1)` where `n`
+  is the number of observations and `indim` the number of features in the training data.
 
 - `variance_ratio::Float64=0.99`: The ratio of variance preserved after the transformation
 
@@ -348,7 +348,8 @@ The fields of `fitted_params(mach)` are:
 
 The fields of `report(mach)` are:
 
-- `indim`: Dimension (number of columns) of the training data and new data to be transformed.
+- `indim`: Dimension (number of columns) of the training data and new data to be 
+  transformed.
 
 - `outdim = min(n, indim, maxoutdim)` is the output dimension; here `n` is the number of
   observations.
@@ -428,14 +429,15 @@ Train the machine using `fit!(mach, rows=...)`.
 # Operations
 
 - `transform(mach, Xnew)`: Return a lower dimensional projection of the input `Xnew`, which
-  should have the same scitype as `X` above.
+    should have the same scitype as `X` above.
 
 - `inverse_transform(mach, Xsmall)`: For a dimension-reduced table `Xsmall`, such as
   returned by `transform`, reconstruct a table, having same the number of columns as the
   original training data `X`, that transforms to `Xsmall`.  Mathematically,
   `inverse_transform` is a right-inverse for the PCA projection map, whose image is
-  orthogonal to the kernel of that map. In particular, if `Xsmall = transform(mach, Xnew)`,
-  then `inverse_transform(Xsmall)` is only an approximation to `Xnew`.
+  orthogonal to the kernel of that map. In particular, if 
+  `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is only an 
+  approximation to `Xnew`.
 
 # Fitted parameters
 
@@ -502,9 +504,11 @@ Train the machine using `fit!(mach, rows=...)`.
 
 # Hyper-parameters
 
-- `outdim::Int=0`: The number of independent components to recover, set automatically if `0`.
+- `outdim::Int=0`: The number of independent components to recover, set automatically 
+  if `0`.
 
-- `alg::Symbol=:fastica`: The algorithm to use (only `:fastica` is supported at the moment).
+- `alg::Symbol=:fastica`: The algorithm to use (only `:fastica` is supported at the 
+  moment).
 
 - `fun::Symbol=:tanh`: The approximate neg-entropy function, one of `:tanh`, `:gaus`.
 
@@ -515,18 +519,18 @@ Train the machine using `fit!(mach, rows=...)`.
 - `tol::Real=1e-6`: The convergence tolerance for change in the unmixing matrix W.
 
 - `mean::Union{Nothing, Real, Vector{Float64}}=nothing`: mean to use, if nothing (default)
-  centering is computed and applied, if zero, no centering; otherwise a vector of means can
-  be passed.
+  centering is computed and applied, if zero, no centering; otherwise a vector of means 
+  can be passed.
 
-- `winit::Union{Nothing,Matrix{<:Real}}=nothing`: Initial guess for the unmixing matrix `W`:
-  either an empty matrix (for random initialization of `W`), a matrix of size `m × k` (if
-  `do_whiten` is true), or a matrix of size `m × k`. Here `m` is the number of components
-  (columns) of the input.
+- `winit::Union{Nothing,Matrix{<:Real}}=nothing`: Initial guess for the unmixing matrix 
+  `W`: either an empty matrix (for random initialization of `W`), a matrix of size 
+  `m × k` (if `do_whiten` is true), or a matrix of size `m × k`. Here `m` is the number 
+  of components (columns) of the input.
 
 # Operations
 
-- `transform(mach, Xnew)`: Return the component-separated version of input
-  `Xnew`, which should have the same scitype as `X` above.
+- `transform(mach, Xnew)`: Return the component-separated version of input `Xnew`, which 
+  should have the same scitype as `X` above.
 
 # Fitted parameters
 
@@ -540,7 +544,8 @@ The fields of `fitted_params(mach)` are:
 
 The fields of `report(mach)` are:
 
-- `indim`: Dimension (number of columns) of the training data and new data to be transformed.
+- `indim`: Dimension (number of columns) of the training data and new data to be 
+  transformed.
 
 - `outdim`: Dimension of transformed data.
 
@@ -593,9 +598,9 @@ ICA
 $(MMI.doc_header(FactorAnalysis))
 
 Factor analysis is a linear-Gaussian latent variable model that is closely related to
-probabilistic PCA. In contrast to the probabilistic PCA model, the covariance of conditional
-distribution of the observed variable given the latent variable is diagonal rather than
-isotropic.
+probabilistic PCA. In contrast to the probabilistic PCA model, the covariance of 
+conditional distribution of the observed variable given the latent variable is diagonal 
+rather than isotropic.
 
 # Training data
 
@@ -606,7 +611,7 @@ In MLJ or MLJBase, bind an instance `model` to data with
 Here:
 
 - `X` is any table of input features (eg, a `DataFrame`) whose columns
-  are of scitype `Continuous`; check column scitypes with `schema(X)`.
+    are of scitype `Continuous`; check column scitypes with `schema(X)`.
 
 Train the machine using `fit!(mach, rows=...)`.
 
@@ -615,8 +620,8 @@ Train the machine using `fit!(mach, rows=...)`.
 - `method::Symbol=:cm`: Method to use to solve the problem, one of `:ml`, `:em`, `:bayes`.
 
 - `maxoutdim=0`: Controls the the dimension (number of columns) of the output,
-  `outdim`. Specifically, `outdim = min(n, indim, maxoutdim)`, where `n` is the number of
-  observations and `indim` the input dimension.
+    `outdim`. Specifically, `outdim = min(n, indim, maxoutdim)`, where `n` is the number of
+    observations and `indim` the input dimension.
 
 - `maxiter::Int=1000`: Maximum number of iterations.
 
@@ -625,8 +630,8 @@ Train the machine using `fit!(mach, rows=...)`.
 - `eta::Real=tol`: Variance lower bound.
 
 - `mean::Union{Nothing, Real, Vector{Float64}}=nothing`: If `nothing`, centering will be
-  computed and applied; if set to `0` no centering is applied (data is assumed
-  pre-centered); if a vector, the centering is done with that vector.
+    computed and applied; if set to `0` no centering is applied (data is assumed
+    pre-centered); if a vector, the centering is done with that vector.
 
 # Operations
 
@@ -653,7 +658,8 @@ The fields of `fitted_params(mach)` are:
 
 The fields of `report(mach)` are:
 
-- `indim`: Dimension (number of columns) of the training data and new data to be transformed.
+- `indim`: Dimension (number of columns) of the training data and new data to be 
+  transformed.
 
 - `outdim`: Dimension of transformed data (number of factors).
 
@@ -737,8 +743,8 @@ Train the machine using `fit!(mach, rows=...)`.
   of columns as the original training data `X`, that transforms to `Xsmall`.
   Mathematically, `inverse_transform` is a right-inverse for the PCA projection
   map, whose image is orthogonal to the kernel of that map. In particular, if
-  `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is
-  only an approximation to `Xnew`.
+  `Xsmall = transform(mach, Xnew)`, then `inverse_transform(Xsmall)` is only an 
+  approximation to `Xnew`.
 
 # Fitted parameters
 
@@ -752,13 +758,15 @@ The fields of `fitted_params(mach)` are:
 
 The fields of `report(mach)` are:
 
-- `indim`: Dimension (number of columns) of the training data and new data to be transformed.
+- `indim`: Dimension (number of columns) of the training data and new data to be 
+  transformed.
+
 - `outdim`: Dimension of transformed data.
 
 - `tvat`: The variance of the components.
 
-- `loadings`: The models loadings, weights for each variable used when calculating principal
-  components.
+- `loadings`: The models loadings, weights for each variable used when calculating 
+  principal components.
 
 # Examples