From 429178e24791f9ec85a4ccb4f2eb4538adb5f0c9 Mon Sep 17 00:00:00 2001 From: OkonSamuel Date: Mon, 26 Oct 2020 00:52:52 +0100 Subject: [PATCH] rm clustering glue code and update registry --- Project.toml | 5 +- src/Clustering.jl | 179 ------------------------ src/MLJModels.jl | 3 +- src/registry/Metadata.toml | 276 ++++++++++++++++++------------------- src/registry/Project.toml | 3 +- test/Clustering.jl | 77 ----------- test/runtests.jl | 4 - 7 files changed, 142 insertions(+), 405 deletions(-) delete mode 100755 src/Clustering.jl delete mode 100755 test/Clustering.jl diff --git a/Project.toml b/Project.toml index 50420c19..1e6a4233 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "MLJModels" uuid = "d491faf4-2d78-11e9-2867-c94bc002c0b7" authors = ["Anthony D. Blaom "] -version = "0.12.7" +version = "0.12.8" [deps] CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" @@ -39,7 +39,6 @@ Tables = "^0.2,^1.0" julia = "1" [extras] -Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb" Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" @@ -50,4 +49,4 @@ ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [targets] -test = ["Clustering", "DecisionTree", "Distances", "GLM", "LIBSVM", "MLJMultivariateStatsInterface", "NearestNeighbors", "ScientificTypes", "Test"] +test = ["DecisionTree", "Distances", "GLM", "LIBSVM", "MLJMultivariateStatsInterface", "NearestNeighbors", "ScientificTypes", "Test"] diff --git a/src/Clustering.jl b/src/Clustering.jl deleted file mode 100755 index 0c0562ab..00000000 --- a/src/Clustering.jl +++ /dev/null @@ -1,179 +0,0 @@ -# NOTE: there's a `kmeans!` function that updates centers, maybe a candidate -# for the `update` machinery. Same for `kmedoids!` -# NOTE: if the prediction is done on the original array, just the assignment -# should be returned, unclear what's the best way of doing this. - -module Clustering_ - -import MLJModelInterface -import MLJModelInterface: @mlj_model, metadata_pkg, metadata_model, - Table, Continuous, Count, Finite, OrderedFactor, - Multiclass - -const MMI = MLJModelInterface - -import ..Clustering # strange sytax for lazy-loading - -using Distances -using LinearAlgebra: norm -using CategoricalArrays - -const C = Clustering - -const KMeansDescription = - """ - K-Means algorithm: find K centroids corresponding to K clusters in the data. - """ - -const KMedoidsDescription = - """ - K-Medoids algorithm: find K centroids corresponding to K clusters in the data. - Unlike K-Means, the centroids are found among data points themselves." - """ - -const KMFields = - """ - ## Keywords - - * `k=3` : number of centroids - * `metric` : distance metric to use - """ - -""" -KMeans(; kwargs...) - -$KMeansDescription - -$KMFields - -See also the [package documentation](http://juliastats.github.io/Clustering.jl/latest/kmeans.html). -""" -@mlj_model mutable struct KMeans <: MMI.Unsupervised - k::Int = 3::(_ ≥ 2) - metric::SemiMetric = SqEuclidean() -end - -""" -KMedoids(; kwargs...) - -$KMedoidsDescription - -$KMFields - -See also the [package documentation](http://juliastats.github.io/Clustering.jl/latest/kmedoids.html). -""" -@mlj_model mutable struct KMedoids <: MMI.Unsupervised - k::Int = 3::(_ ≥ 2) - metric::SemiMetric = SqEuclidean() -end - -function MMI.fit(model::KMeans - , verbosity::Int - , X) - # NOTE: using transpose here to get a LinearAlgebra.Transpose object which Kmeans can handle - Xarray = transpose(MMI.matrix(X)) - - result = C.kmeans(Xarray, model.k; distance=model.metric) - cluster_labels = MMI.categorical(1:model.k) - fitresult = (result.centers, cluster_labels) # centers (p x k) - cache = nothing - report = (assignments=result.assignments, # size n - cluster_labels=cluster_labels) - - return fitresult, cache, report -end - -MMI.fitted_params(::KMeans, fitresult) = (centers=fitresult[1],) - -function MMI.transform(model::KMeans - , fitresult - , X) - # pairwise distance from samples to centers - X̃ = pairwise(model.metric, transpose(MMI.matrix(X)), - fitresult[1], dims=2) - return MMI.table(X̃, prototype=X) -end - -function MMI.fit(model::KMedoids - , verbosity::Int - , X) - - # NOTE: using transpose=true will materialize the transpose (~ permutedims), KMedoids - # does not yet accept LinearAlgebra.Transpose - Xarray = MMI.matrix(X, transpose=true) - # cost matrix: all the pairwise distances - Carray = pairwise(model.metric, Xarray, dims=2) # n x n - result = C.kmedoids(Carray, model.k) - cluster_labels = MMI.categorical(1:model.k) - fitresult = (view(Xarray, :, result.medoids), cluster_labels) # medoids - cache = nothing - report = (assignments=result.assignments, # size n - cluster_labels=cluster_labels) - - return fitresult, cache, report -end - -MMI.fitted_params(::KMedoids, fitresult) = (medoids=fitresult[1],) - -function MMI.transform(model::KMedoids - , fitresult - , X) - # pairwise distance from samples to medoids - X̃ = pairwise(model.metric, MMI.matrix(X, transpose=true), - fitresult[1], dims=2) - return MMI.table(X̃, prototype=X) -end - -#### -#### Predict methods -#### - -function MMI.predict(model::Union{KMeans,KMedoids}, fitresult, Xnew) - - locations, cluster_labels = fitresult - - Xarray = MMI.matrix(Xnew) - (n, p), k = size(Xarray), model.k - - pred = zeros(Int, n) - @inbounds for i ∈ 1:n - minv = Inf - for j ∈ 1:k - curv = evaluate(model.metric, - view(Xarray, i, :), view(locations, :, j)) - P = curv < minv - pred[i] = j * P + pred[i] * !P # if P is true --> j - minv = curv * P + minv * !P # if P is true --> curvalue - end - end - return cluster_labels[pred] -end - -#### -#### METADATA -#### - -metadata_pkg.((KMeans, KMedoids), - name="Clustering", - uuid="aaaa29a8-35af-508c-8bc3-b662a17a0fe5", - url="https://github.com/JuliaStats/Clustering.jl", - julia=true, - license="MIT", - is_wrapper=false - ) - -metadata_model(KMeans, - input = Table(Continuous), - output = Table(Continuous), - weights = false, - descr = KMeansDescription - ) - -metadata_model(KMedoids, - input = Table(Continuous), - output = Table(Continuous), - weights = false, - descr = KMedoidsDescription - ) - -end # module diff --git a/src/MLJModels.jl b/src/MLJModels.jl index d7b7d6af..48d835d7 100755 --- a/src/MLJModels.jl +++ b/src/MLJModels.jl @@ -41,6 +41,7 @@ const MMI = MLJModelInterface if VERSION < v"1.3" nonmissingtype = MLJScientificTypes.ScientificTypes.nonmissing end + nonmissing = nonmissingtype include("metadata.jl") @@ -74,8 +75,6 @@ function __init__() include("DecisionTree.jl")) @require(GLM="38e38edf-8417-5370-95a0-9cbb8c7f171a", include("GLM.jl")) - @require(Clustering="aaaa29a8-35af-508c-8bc3-b662a17a0fe5", - include("Clustering.jl")) @require(LIBSVM="b1bec4e5-fd48-53fe-b0cb-9723c09d164b", include("LIBSVM.jl")) @require(NearestNeighbors="b8a86587-4115-5ab1-83bc-aa920d37bbce", diff --git a/src/registry/Metadata.toml b/src/registry/Metadata.toml index acac826f..09319c08 100644 --- a/src/registry/Metadata.toml +++ b/src/registry/Metadata.toml @@ -16,7 +16,7 @@ ":name" = "KPLSRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit"] +":implemented_methods" = [":clean!", ":fit", ":predict"] ":hyperparameters" = "`(:n_factors, :kernel, :width)`" ":hyperparameter_types" = "`(\"Integer\", \"String\", \"Real\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing)`" @@ -38,7 +38,7 @@ ":name" = "PLSRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit"] +":implemented_methods" = [":clean!", ":fit", ":predict"] ":hyperparameters" = "`(:n_factors,)`" ":hyperparameter_types" = "`(\"Int64\",)`" ":hyperparameter_ranges" = "`(nothing,)`" @@ -46,7 +46,7 @@ [NearestNeighbors.KNNClassifier] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:(AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous)`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s112,1} where _s112<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s97,1} where _s97<:ScientificTypes.Finite`" ":is_pure_julia" = "`true`" ":package_name" = "NearestNeighbors" ":package_license" = "MIT" @@ -60,7 +60,7 @@ ":name" = "KNNClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:K, :algorithm, :metric, :leafsize, :reorder, :weights)`" ":hyperparameter_types" = "`(\"Int64\", \"Symbol\", \"Distances.Metric\", \"Int64\", \"Bool\", \"Symbol\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -82,7 +82,7 @@ ":name" = "KNNRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:K, :algorithm, :metric, :leafsize, :reorder, :weights)`" ":hyperparameter_types" = "`(\"Int64\", \"Symbol\", \"Distances.Metric\", \"Int64\", \"Bool\", \"Symbol\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -104,7 +104,7 @@ ":name" = "QuantileRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:delta, :lambda, :gamma, :penalty, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"Real\", \"Real\", \"Real\", \"Union{String, Symbol}\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -112,7 +112,7 @@ [MLJLinearModels.LogisticClassifier] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:(AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous)`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s58,1} where _s58<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s57,1} where _s57<:ScientificTypes.Finite`" ":is_pure_julia" = "`true`" ":package_name" = "MLJLinearModels" ":package_license" = "MIT" @@ -126,7 +126,7 @@ ":name" = "LogisticClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:lambda, :gamma, :penalty, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"Real\", \"Real\", \"Union{String, Symbol}\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -134,7 +134,7 @@ [MLJLinearModels.MultinomialClassifier] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:(AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous)`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s58,1} where _s58<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s57,1} where _s57<:ScientificTypes.Finite`" ":is_pure_julia" = "`true`" ":package_name" = "MLJLinearModels" ":package_license" = "MIT" @@ -148,7 +148,7 @@ ":name" = "MultinomialClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:lambda, :gamma, :penalty, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"Real\", \"Real\", \"Union{String, Symbol}\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -170,7 +170,7 @@ ":name" = "LADRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:lambda, :gamma, :penalty, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"Real\", \"Real\", \"Union{String, Symbol}\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -192,7 +192,7 @@ ":name" = "RidgeRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:lambda, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"Real\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing)`" @@ -214,7 +214,7 @@ ":name" = "RobustRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:rho, :lambda, :gamma, :penalty, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"MLJLinearModels.RobustRho\", \"Real\", \"Real\", \"Union{String, Symbol}\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -236,7 +236,7 @@ ":name" = "ElasticNetRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:lambda, :gamma, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"Real\", \"Real\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing)`" @@ -258,7 +258,7 @@ ":name" = "LinearRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:fit_intercept, :solver)`" ":hyperparameter_types" = "`(\"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -280,7 +280,7 @@ ":name" = "LassoRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:lambda, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"Real\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing)`" @@ -302,7 +302,7 @@ ":name" = "HuberRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:delta, :lambda, :gamma, :penalty, :fit_intercept, :penalize_intercept, :solver)`" ":hyperparameter_types" = "`(\"Real\", \"Real\", \"Real\", \"Union{String, Symbol}\", \"Bool\", \"Bool\", \"Union{Nothing, MLJLinearModels.Solver}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -324,7 +324,7 @@ ":name" = "ProbabilisticSGDClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:loss, :penalty, :alpha, :l1_ratio, :fit_intercept, :max_iter, :tol, :shuffle, :verbose, :epsilon, :n_jobs, :random_state, :learning_rate, :eta0, :power_t, :early_stopping, :validation_fraction, :n_iter_no_change, :class_weight, :warm_start, :average)`" ":hyperparameter_types" = "`(\"String\", \"String\", \"Float64\", \"Float64\", \"Bool\", \"Int64\", \"Union{Nothing, Float64}\", \"Bool\", \"Int64\", \"Float64\", \"Union{Nothing, Int64}\", \"Any\", \"String\", \"Float64\", \"Float64\", \"Bool\", \"Float64\", \"Int64\", \"Any\", \"Bool\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -346,7 +346,7 @@ ":name" = "RidgeCVClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alphas, :fit_intercept, :normalize, :scoring, :cv, :class_weight, :store_cv_values)`" ":hyperparameter_types" = "`(\"AbstractArray{Float64,N} where N\", \"Bool\", \"Bool\", \"Any\", \"Int64\", \"Any\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -368,7 +368,7 @@ ":name" = "LogisticClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:penalty, :dual, :tol, :C, :fit_intercept, :intercept_scaling, :class_weight, :random_state, :solver, :max_iter, :multi_class, :verbose, :warm_start, :n_jobs, :l1_ratio)`" ":hyperparameter_types" = "`(\"String\", \"Bool\", \"Float64\", \"Float64\", \"Bool\", \"Float64\", \"Any\", \"Any\", \"String\", \"Int64\", \"String\", \"Int64\", \"Bool\", \"Union{Nothing, Int64}\", \"Union{Nothing, Float64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -390,7 +390,7 @@ ":name" = "RandomForestRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_estimators, :criterion, :max_depth, :min_samples_split, :min_samples_leaf, :min_weight_fraction_leaf, :max_features, :max_leaf_nodes, :min_impurity_decrease, :bootstrap, :oob_score, :n_jobs, :random_state, :verbose, :warm_start, :ccp_alpha, :max_samples)`" ":hyperparameter_types" = "`(\"Int64\", \"String\", \"Union{Nothing, Int64}\", \"Union{Float64, Int64}\", \"Union{Float64, Int64}\", \"Float64\", \"Union{Nothing, Float64, Int64, String}\", \"Union{Nothing, Int64}\", \"Float64\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\", \"Any\", \"Int64\", \"Bool\", \"Float64\", \"Union{Nothing, Float64, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -412,7 +412,7 @@ ":name" = "ElasticNetCVRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:l1_ratio, :eps, :n_alphas, :alphas, :fit_intercept, :normalize, :precompute, :max_iter, :tol, :cv, :copy_X, :verbose, :n_jobs, :positive, :random_state, :selection)`" ":hyperparameter_types" = "`(\"Union{Float64, Array{Float64,1}}\", \"Float64\", \"Int64\", \"Any\", \"Bool\", \"Bool\", \"Union{Bool, String, AbstractArray{T,2} where T}\", \"Int64\", \"Float64\", \"Any\", \"Bool\", \"Union{Bool, Int64}\", \"Union{Nothing, Int64}\", \"Bool\", \"Any\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -434,7 +434,7 @@ ":name" = "PerceptronClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:penalty, :alpha, :fit_intercept, :max_iter, :tol, :shuffle, :verbose, :eta0, :n_jobs, :random_state, :early_stopping, :validation_fraction, :n_iter_no_change, :class_weight, :warm_start)`" ":hyperparameter_types" = "`(\"Union{Nothing, String}\", \"Float64\", \"Bool\", \"Int64\", \"Union{Nothing, Float64}\", \"Bool\", \"Int64\", \"Float64\", \"Union{Nothing, Int64}\", \"Any\", \"Bool\", \"Float64\", \"Int64\", \"Any\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -456,7 +456,7 @@ ":name" = "MultiTaskLassoRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :fit_intercept, :normalize, :max_iter, :tol, :copy_X, :random_state, :selection)`" ":hyperparameter_types" = "`(\"Float64\", \"Bool\", \"Bool\", \"Int64\", \"Float64\", \"Bool\", \"Any\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -478,7 +478,7 @@ ":name" = "LinearRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:fit_intercept, :normalize, :copy_X, :n_jobs)`" ":hyperparameter_types" = "`(\"Bool\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing)`" @@ -522,7 +522,7 @@ ":name" = "RidgeRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :fit_intercept, :normalize, :copy_X, :max_iter, :tol, :solver, :random_state)`" ":hyperparameter_types" = "`(\"Union{Float64, Array{Float64,1}}\", \"Bool\", \"Bool\", \"Bool\", \"Int64\", \"Float64\", \"String\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -544,7 +544,7 @@ ":name" = "LassoLarsICRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:criterion, :fit_intercept, :verbose, :normalize, :precompute, :max_iter, :eps, :copy_X, :positive)`" ":hyperparameter_types" = "`(\"String\", \"Bool\", \"Union{Bool, Int64}\", \"Bool\", \"Union{Bool, String, AbstractArray{T,2} where T}\", \"Int64\", \"Float64\", \"Bool\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -566,7 +566,7 @@ ":name" = "ARDRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_iter, :tol, :alpha_1, :alpha_2, :lambda_1, :lambda_2, :compute_score, :threshold_lambda, :fit_intercept, :normalize, :copy_X, :verbose)`" ":hyperparameter_types" = "`(\"Int64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Bool\", \"Float64\", \"Bool\", \"Bool\", \"Bool\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -588,7 +588,7 @@ ":name" = "SVMNuRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:nu, :C, :kernel, :degree, :gamma, :coef0, :shrinking, :tol, :cache_size, :max_iter)`" ":hyperparameter_types" = "`(\"Float64\", \"Float64\", \"Union{Function, String}\", \"Int64\", \"Union{Float64, String}\", \"Float64\", \"Any\", \"Float64\", \"Int64\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -610,7 +610,7 @@ ":name" = "RidgeClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :fit_intercept, :normalize, :copy_X, :max_iter, :tol, :class_weight, :solver, :random_state)`" ":hyperparameter_types" = "`(\"Float64\", \"Bool\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\", \"Float64\", \"Any\", \"String\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -632,7 +632,7 @@ ":name" = "SGDRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:loss, :penalty, :alpha, :l1_ratio, :fit_intercept, :max_iter, :tol, :shuffle, :verbose, :epsilon, :random_state, :learning_rate, :eta0, :power_t, :early_stopping, :validation_fraction, :n_iter_no_change, :warm_start, :average)`" ":hyperparameter_types" = "`(\"String\", \"String\", \"Float64\", \"Float64\", \"Bool\", \"Int64\", \"Float64\", \"Bool\", \"Union{Bool, Int64}\", \"Float64\", \"Any\", \"String\", \"Float64\", \"Float64\", \"Bool\", \"Float64\", \"Int64\", \"Bool\", \"Union{Bool, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -654,7 +654,7 @@ ":name" = "ComplementNBClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :fit_prior, :class_prior, :norm)`" ":hyperparameter_types" = "`(\"Float64\", \"Bool\", \"Union{Nothing, AbstractArray{T,1} where T}\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing)`" @@ -676,7 +676,7 @@ ":name" = "HuberRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:epsilon, :max_iter, :alpha, :warm_start, :fit_intercept, :tol)`" ":hyperparameter_types" = "`(\"Float64\", \"Int64\", \"Float64\", \"Bool\", \"Bool\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -698,7 +698,7 @@ ":name" = "SVMNuClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:nu, :kernel, :degree, :gamma, :coef0, :shrinking, :tol, :cache_size, :max_iter, :decision_function_shape, :random_state)`" ":hyperparameter_types" = "`(\"Float64\", \"Union{Function, String}\", \"Int64\", \"Union{Float64, String}\", \"Float64\", \"Bool\", \"Float64\", \"Int64\", \"Int64\", \"String\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -720,7 +720,7 @@ ":name" = "GradientBoostingClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:loss, :learning_rate, :n_estimators, :subsample, :criterion, :min_samples_split, :min_samples_leaf, :min_weight_fraction_leaf, :max_depth, :min_impurity_decrease, :init, :random_state, :max_features, :verbose, :max_leaf_nodes, :warm_start, :validation_fraction, :n_iter_no_change, :tol)`" ":hyperparameter_types" = "`(\"String\", \"Float64\", \"Int64\", \"Float64\", \"String\", \"Union{Float64, Int64}\", \"Union{Float64, Int64}\", \"Float64\", \"Int64\", \"Float64\", \"Any\", \"Any\", \"Union{Nothing, Float64, Int64, String}\", \"Int64\", \"Union{Nothing, Int64}\", \"Bool\", \"Float64\", \"Union{Nothing, Int64}\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -742,7 +742,7 @@ ":name" = "GaussianProcessRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:kernel, :alpha, :optimizer, :n_restarts_optimizer, :normalize_y, :copy_X_train, :random_state)`" ":hyperparameter_types" = "`(\"Any\", \"Union{Float64, AbstractArray}\", \"Any\", \"Int64\", \"Bool\", \"Bool\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -764,7 +764,7 @@ ":name" = "SVMLinearRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:epsilon, :tol, :C, :loss, :fit_intercept, :intercept_scaling, :dual, :random_state, :max_iter)`" ":hyperparameter_types" = "`(\"Float64\", \"Float64\", \"Float64\", \"String\", \"Bool\", \"Float64\", \"Bool\", \"Any\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -786,7 +786,7 @@ ":name" = "LarsRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:fit_intercept, :verbose, :normalize, :precompute, :n_nonzero_coefs, :eps, :copy_X, :fit_path)`" ":hyperparameter_types" = "`(\"Bool\", \"Union{Bool, Int64}\", \"Bool\", \"Union{Bool, String, AbstractArray{T,2} where T}\", \"Int64\", \"Float64\", \"Bool\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -808,7 +808,7 @@ ":name" = "MeanShift" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:bandwidth, :seeds, :bin_seeding, :min_bin_freq, :cluster_all, :n_jobs)`" ":hyperparameter_types" = "`(\"Union{Nothing, Float64}\", \"Union{Nothing, AbstractArray}\", \"Bool\", \"Int64\", \"Bool\", \"Union{Nothing, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -830,7 +830,7 @@ ":name" = "AdaBoostRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:base_estimator, :n_estimators, :learning_rate, :loss, :random_state)`" ":hyperparameter_types" = "`(\"Any\", \"Int64\", \"Float64\", \"String\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing)`" @@ -852,7 +852,7 @@ ":name" = "AffinityPropagation" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:damping, :max_iter, :convergence_iter, :copy, :preference, :affinity, :verbose)`" ":hyperparameter_types" = "`(\"Float64\", \"Int64\", \"Int64\", \"Bool\", \"Any\", \"String\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -874,7 +874,7 @@ ":name" = "MultiTaskLassoCVRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:eps, :n_alphas, :alphas, :fit_intercept, :normalize, :max_iter, :tol, :copy_X, :cv, :verbose, :n_jobs, :random_state, :selection)`" ":hyperparameter_types" = "`(\"Float64\", \"Int64\", \"Any\", \"Bool\", \"Bool\", \"Int64\", \"Float64\", \"Bool\", \"Any\", \"Union{Bool, Int64}\", \"Union{Nothing, Int64}\", \"Any\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -896,7 +896,7 @@ ":name" = "OrthogonalMatchingPursuitRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_nonzero_coefs, :tol, :fit_intercept, :normalize, :precompute)`" ":hyperparameter_types" = "`(\"Union{Nothing, Int64}\", \"Union{Nothing, Float64}\", \"Bool\", \"Bool\", \"Union{Bool, String, AbstractArray{T,2} where T}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing)`" @@ -918,7 +918,7 @@ ":name" = "RidgeCVRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alphas, :fit_intercept, :normalize, :scoring, :cv, :gcv_mode, :store_cv_values)`" ":hyperparameter_types" = "`(\"Any\", \"Bool\", \"Bool\", \"Any\", \"Any\", \"Union{Nothing, String}\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -940,7 +940,7 @@ ":name" = "PassiveAggressiveClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:C, :fit_intercept, :max_iter, :tol, :early_stopping, :validation_fraction, :n_iter_no_change, :shuffle, :verbose, :loss, :n_jobs, :random_state, :warm_start, :class_weight, :average)`" ":hyperparameter_types" = "`(\"Float64\", \"Bool\", \"Int64\", \"Float64\", \"Bool\", \"Float64\", \"Int64\", \"Bool\", \"Int64\", \"String\", \"Union{Nothing, Int64}\", \"Any\", \"Bool\", \"Any\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -962,7 +962,7 @@ ":name" = "SVMRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:kernel, :degree, :gamma, :coef0, :tol, :C, :epsilon, :shrinking, :cache_size, :max_iter)`" ":hyperparameter_types" = "`(\"Union{Function, String}\", \"Int64\", \"Union{Float64, String}\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Any\", \"Int64\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -984,7 +984,7 @@ ":name" = "BernoulliNBClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :binarize, :fit_prior, :class_prior)`" ":hyperparameter_types" = "`(\"Float64\", \"Union{Nothing, Float64}\", \"Bool\", \"Union{Nothing, AbstractArray{T,1} where T}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing)`" @@ -1006,7 +1006,7 @@ ":name" = "GaussianNBClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:priors, :var_smoothing)`" ":hyperparameter_types" = "`(\"Union{Nothing, AbstractArray{Float64,1}}\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -1028,7 +1028,7 @@ ":name" = "ExtraTreesClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_estimators, :criterion, :max_depth, :min_samples_split, :min_samples_leaf, :min_weight_fraction_leaf, :max_features, :max_leaf_nodes, :min_impurity_decrease, :bootstrap, :oob_score, :n_jobs, :random_state, :verbose, :warm_start, :class_weight)`" ":hyperparameter_types" = "`(\"Int64\", \"String\", \"Union{Nothing, Int64}\", \"Union{Float64, Int64}\", \"Union{Float64, Int64}\", \"Float64\", \"Union{Nothing, Float64, Int64, String}\", \"Union{Nothing, Int64}\", \"Float64\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\", \"Any\", \"Int64\", \"Bool\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1050,7 +1050,7 @@ ":name" = "KMeans" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:n_clusters, :n_init, :max_iter, :tol, :verbose, :random_state, :copy_x, :n_jobs, :algorithm, :precompute_distances, :init)`" ":hyperparameter_types" = "`(\"Int64\", \"Int64\", \"Int64\", \"Float64\", \"Int64\", \"Any\", \"Bool\", \"Union{Nothing, Int64}\", \"String\", \"Union{Bool, String}\", \"Union{String, AbstractArray}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1072,7 +1072,7 @@ ":name" = "MultiTaskElasticNetCVRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:l1_ratio, :eps, :n_alphas, :alphas, :fit_intercept, :normalize, :max_iter, :tol, :cv, :copy_X, :verbose, :n_jobs, :random_state, :selection)`" ":hyperparameter_types" = "`(\"Union{Float64, Array{Float64,1}}\", \"Float64\", \"Int64\", \"Any\", \"Bool\", \"Bool\", \"Int64\", \"Float64\", \"Any\", \"Bool\", \"Union{Bool, Int64}\", \"Union{Nothing, Int64}\", \"Any\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1094,7 +1094,7 @@ ":name" = "LassoLarsCVRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:fit_intercept, :verbose, :max_iter, :normalize, :precompute, :cv, :max_n_alphas, :n_jobs, :eps, :copy_X, :positive)`" ":hyperparameter_types" = "`(\"Bool\", \"Union{Bool, Int64}\", \"Int64\", \"Bool\", \"Union{Bool, String, AbstractArray{T,2} where T}\", \"Any\", \"Int64\", \"Union{Nothing, Int64}\", \"Float64\", \"Bool\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1116,7 +1116,7 @@ ":name" = "OrthogonalMatchingPursuitCVRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:copy, :fit_intercept, :normalize, :max_iter, :cv, :n_jobs, :verbose)`" ":hyperparameter_types" = "`(\"Bool\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\", \"Any\", \"Union{Nothing, Int64}\", \"Union{Bool, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1138,7 +1138,7 @@ ":name" = "AdaBoostClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:base_estimator, :n_estimators, :learning_rate, :algorithm, :random_state)`" ":hyperparameter_types" = "`(\"Any\", \"Int64\", \"Float64\", \"String\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing)`" @@ -1160,7 +1160,7 @@ ":name" = "PassiveAggressiveRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:C, :fit_intercept, :max_iter, :tol, :early_stopping, :validation_fraction, :n_iter_no_change, :shuffle, :verbose, :loss, :epsilon, :random_state, :warm_start, :average)`" ":hyperparameter_types" = "`(\"Float64\", \"Bool\", \"Int64\", \"Float64\", \"Bool\", \"Float64\", \"Int64\", \"Bool\", \"Union{Bool, Int64}\", \"String\", \"Float64\", \"Any\", \"Bool\", \"Union{Bool, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1182,7 +1182,7 @@ ":name" = "BayesianRidgeRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_iter, :tol, :alpha_1, :alpha_2, :lambda_1, :lambda_2, :compute_score, :fit_intercept, :normalize, :copy_X, :verbose)`" ":hyperparameter_types" = "`(\"Int64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Bool\", \"Bool\", \"Bool\", \"Bool\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1204,7 +1204,7 @@ ":name" = "RANSACRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:base_estimator, :min_samples, :residual_threshold, :is_data_valid, :is_model_valid, :max_trials, :max_skips, :stop_n_inliers, :stop_score, :stop_probability, :loss, :random_state)`" ":hyperparameter_types" = "`(\"Any\", \"Union{Float64, Int64}\", \"Union{Nothing, Float64}\", \"Any\", \"Any\", \"Int64\", \"Int64\", \"Int64\", \"Float64\", \"Float64\", \"Union{Function, String}\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1226,7 +1226,7 @@ ":name" = "BaggingClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:base_estimator, :n_estimators, :max_samples, :max_features, :bootstrap, :bootstrap_features, :oob_score, :warm_start, :n_jobs, :random_state, :verbose)`" ":hyperparameter_types" = "`(\"Any\", \"Int64\", \"Union{Float64, Int64}\", \"Union{Float64, Int64}\", \"Bool\", \"Bool\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\", \"Any\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1248,7 +1248,7 @@ ":name" = "GaussianProcessClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:kernel, :optimizer, :n_restarts_optimizer, :copy_X_train, :random_state, :max_iter_predict, :warm_start, :multi_class)`" ":hyperparameter_types" = "`(\"Any\", \"Any\", \"Int64\", \"Bool\", \"Any\", \"Int64\", \"Bool\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1292,7 +1292,7 @@ ":name" = "KNeighborsRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_neighbors, :weights, :algorithm, :leaf_size, :p, :metric, :metric_params, :n_jobs)`" ":hyperparameter_types" = "`(\"Int64\", \"Union{Function, String}\", \"String\", \"Int64\", \"Int64\", \"Any\", \"Any\", \"Union{Nothing, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1314,7 +1314,7 @@ ":name" = "MiniBatchKMeans" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:n_clusters, :max_iter, :batch_size, :verbose, :compute_labels, :random_state, :tol, :max_no_improvement, :init_size, :n_init, :init, :reassignment_ratio)`" ":hyperparameter_types" = "`(\"Int64\", \"Int64\", \"Int64\", \"Int64\", \"Bool\", \"Any\", \"Float64\", \"Int64\", \"Union{Nothing, Int64}\", \"Int64\", \"Union{String, AbstractArray}\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1336,7 +1336,7 @@ ":name" = "LassoCVRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:eps, :n_alphas, :alphas, :fit_intercept, :normalize, :precompute, :max_iter, :tol, :copy_X, :cv, :verbose, :n_jobs, :positive, :random_state, :selection)`" ":hyperparameter_types" = "`(\"Float64\", \"Int64\", \"Any\", \"Bool\", \"Bool\", \"Union{Bool, String, AbstractArray{T,2} where T}\", \"Int64\", \"Float64\", \"Bool\", \"Any\", \"Union{Bool, Int64}\", \"Union{Nothing, Int64}\", \"Bool\", \"Any\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1358,7 +1358,7 @@ ":name" = "DummyRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:strategy, :constant, :quantile)`" ":hyperparameter_types" = "`(\"String\", \"Any\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing)`" @@ -1380,7 +1380,7 @@ ":name" = "LassoLarsRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :fit_intercept, :verbose, :normalize, :precompute, :max_iter, :eps, :copy_X, :fit_path, :positive)`" ":hyperparameter_types" = "`(\"Float64\", \"Bool\", \"Union{Bool, Int64}\", \"Bool\", \"Union{Bool, String, AbstractArray{T,2} where T}\", \"Int64\", \"Float64\", \"Bool\", \"Bool\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1402,7 +1402,7 @@ ":name" = "LarsCVRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:fit_intercept, :verbose, :max_iter, :normalize, :precompute, :cv, :max_n_alphas, :n_jobs, :eps, :copy_X)`" ":hyperparameter_types" = "`(\"Bool\", \"Union{Bool, Int64}\", \"Int64\", \"Bool\", \"Union{Bool, String, AbstractArray{T,2} where T}\", \"Any\", \"Int64\", \"Union{Nothing, Int64}\", \"Float64\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1424,7 +1424,7 @@ ":name" = "KNeighborsClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_neighbors, :weights, :algorithm, :leaf_size, :p, :metric, :metric_params, :n_jobs)`" ":hyperparameter_types" = "`(\"Int64\", \"Union{Function, String}\", \"String\", \"Int64\", \"Int64\", \"Any\", \"Any\", \"Union{Nothing, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1446,7 +1446,7 @@ ":name" = "SVMLinearClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:penalty, :loss, :dual, :tol, :C, :multi_class, :fit_intercept, :intercept_scaling, :random_state, :max_iter)`" ":hyperparameter_types" = "`(\"String\", \"String\", \"Bool\", \"Float64\", \"Float64\", \"String\", \"Bool\", \"Float64\", \"Any\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1490,7 +1490,7 @@ ":name" = "DummyClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:strategy, :constant, :random_state)`" ":hyperparameter_types" = "`(\"String\", \"Any\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing)`" @@ -1512,7 +1512,7 @@ ":name" = "BaggingRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:base_estimator, :n_estimators, :max_samples, :max_features, :bootstrap, :bootstrap_features, :oob_score, :warm_start, :n_jobs, :random_state, :verbose)`" ":hyperparameter_types" = "`(\"Any\", \"Int64\", \"Union{Float64, Int64}\", \"Union{Float64, Int64}\", \"Bool\", \"Bool\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\", \"Any\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1534,7 +1534,7 @@ ":name" = "BayesianQDA" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:priors, :reg_param, :store_covariance, :tol)`" ":hyperparameter_types" = "`(\"Union{Nothing, AbstractArray{T,1} where T}\", \"Float64\", \"Bool\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing)`" @@ -1556,7 +1556,7 @@ ":name" = "BayesianLDA" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:solver, :shrinkage, :priors, :n_components, :store_covariance, :tol)`" ":hyperparameter_types" = "`(\"String\", \"Union{Nothing, Float64, String}\", \"Union{Nothing, AbstractArray{T,1} where T}\", \"Union{Nothing, Int64}\", \"Bool\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1578,7 +1578,7 @@ ":name" = "SGDClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:loss, :penalty, :alpha, :l1_ratio, :fit_intercept, :max_iter, :tol, :shuffle, :verbose, :epsilon, :n_jobs, :random_state, :learning_rate, :eta0, :power_t, :early_stopping, :validation_fraction, :n_iter_no_change, :class_weight, :warm_start, :average)`" ":hyperparameter_types" = "`(\"String\", \"String\", \"Float64\", \"Float64\", \"Bool\", \"Int64\", \"Union{Nothing, Float64}\", \"Bool\", \"Int64\", \"Float64\", \"Union{Nothing, Int64}\", \"Any\", \"String\", \"Float64\", \"Float64\", \"Bool\", \"Float64\", \"Int64\", \"Any\", \"Bool\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1600,7 +1600,7 @@ ":name" = "TheilSenRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:fit_intercept, :copy_X, :max_subpopulation, :n_subsamples, :max_iter, :tol, :random_state, :n_jobs, :verbose)`" ":hyperparameter_types" = "`(\"Bool\", \"Bool\", \"Int64\", \"Union{Nothing, Int64}\", \"Int64\", \"Float64\", \"Any\", \"Union{Nothing, Int64}\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1644,7 +1644,7 @@ ":name" = "Birch" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:threshold, :branching_factor, :n_clusters, :compute_labels, :copy)`" ":hyperparameter_types" = "`(\"Float64\", \"Int64\", \"Int64\", \"Bool\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing)`" @@ -1688,7 +1688,7 @@ ":name" = "ElasticNetRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :l1_ratio, :fit_intercept, :normalize, :precompute, :max_iter, :copy_X, :tol, :warm_start, :positive, :random_state, :selection)`" ":hyperparameter_types" = "`(\"Float64\", \"Float64\", \"Bool\", \"Bool\", \"Union{Bool, AbstractArray{T,2} where T}\", \"Int64\", \"Bool\", \"Float64\", \"Bool\", \"Bool\", \"Any\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1710,7 +1710,7 @@ ":name" = "RandomForestClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_estimators, :criterion, :max_depth, :min_samples_split, :min_samples_leaf, :min_weight_fraction_leaf, :max_features, :max_leaf_nodes, :min_impurity_decrease, :bootstrap, :oob_score, :n_jobs, :random_state, :verbose, :warm_start, :class_weight, :ccp_alpha, :max_samples)`" ":hyperparameter_types" = "`(\"Int64\", \"String\", \"Union{Nothing, Int64}\", \"Union{Float64, Int64}\", \"Union{Float64, Int64}\", \"Float64\", \"Union{Nothing, Float64, Int64, String}\", \"Union{Nothing, Int64}\", \"Float64\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\", \"Any\", \"Int64\", \"Bool\", \"Any\", \"Float64\", \"Union{Nothing, Float64, Int64}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1732,7 +1732,7 @@ ":name" = "LogisticCVClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:Cs, :fit_intercept, :cv, :dual, :penalty, :scoring, :solver, :tol, :max_iter, :class_weight, :n_jobs, :verbose, :refit, :intercept_scaling, :multi_class, :random_state, :l1_ratios)`" ":hyperparameter_types" = "`(\"Union{Int64, AbstractArray{Float64,1}}\", \"Bool\", \"Any\", \"Bool\", \"String\", \"Any\", \"String\", \"Float64\", \"Int64\", \"Any\", \"Union{Nothing, Int64}\", \"Int64\", \"Bool\", \"Float64\", \"String\", \"Any\", \"Union{Nothing, AbstractArray{Float64,1}}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1754,7 +1754,7 @@ ":name" = "MultiTaskElasticNetRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :l1_ratio, :fit_intercept, :normalize, :copy_X, :max_iter, :tol, :warm_start, :random_state, :selection)`" ":hyperparameter_types" = "`(\"Float64\", \"Union{Float64, Array{Float64,1}}\", \"Bool\", \"Bool\", \"Bool\", \"Int64\", \"Float64\", \"Bool\", \"Any\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1776,7 +1776,7 @@ ":name" = "ExtraTreesRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_estimators, :criterion, :max_depth, :min_samples_split, :min_samples_leaf, :min_weight_fraction_leaf, :max_features, :max_leaf_nodes, :min_impurity_decrease, :bootstrap, :oob_score, :n_jobs, :random_state, :verbose, :warm_start)`" ":hyperparameter_types" = "`(\"Int64\", \"String\", \"Union{Nothing, Int64}\", \"Union{Float64, Int64}\", \"Union{Float64, Int64}\", \"Float64\", \"Union{Nothing, Float64, Int64, String}\", \"Union{Nothing, Int64}\", \"Float64\", \"Bool\", \"Bool\", \"Union{Nothing, Int64}\", \"Any\", \"Int64\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1798,7 +1798,7 @@ ":name" = "LassoRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :fit_intercept, :normalize, :precompute, :copy_X, :max_iter, :tol, :warm_start, :positive, :random_state, :selection)`" ":hyperparameter_types" = "`(\"Float64\", \"Bool\", \"Bool\", \"Union{Bool, AbstractArray{T,2} where T}\", \"Bool\", \"Int64\", \"Float64\", \"Bool\", \"Bool\", \"Any\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1820,7 +1820,7 @@ ":name" = "MultinomialNBClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha, :fit_prior, :class_prior)`" ":hyperparameter_types" = "`(\"Float64\", \"Bool\", \"Union{Nothing, AbstractArray{T,1} where T}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing)`" @@ -1842,7 +1842,7 @@ ":name" = "GradientBoostingRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:loss, :learning_rate, :n_estimators, :subsample, :criterion, :min_samples_split, :min_samples_leaf, :min_weight_fraction_leaf, :max_depth, :min_impurity_decrease, :init, :random_state, :max_features, :alpha, :verbose, :max_leaf_nodes, :warm_start, :validation_fraction, :n_iter_no_change, :tol)`" ":hyperparameter_types" = "`(\"String\", \"Float64\", \"Int64\", \"Float64\", \"String\", \"Union{Float64, Int64}\", \"Union{Float64, Int64}\", \"Float64\", \"Int64\", \"Float64\", \"Any\", \"Any\", \"Union{Nothing, Float64, Int64, String}\", \"Float64\", \"Int64\", \"Union{Nothing, Int64}\", \"Bool\", \"Float64\", \"Union{Nothing, Int64}\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1864,7 +1864,7 @@ ":name" = "SVMClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:C, :kernel, :degree, :gamma, :coef0, :shrinking, :tol, :cache_size, :max_iter, :decision_function_shape, :random_state)`" ":hyperparameter_types" = "`(\"Float64\", \"Union{Function, String}\", \"Int64\", \"Union{Float64, String}\", \"Float64\", \"Bool\", \"Float64\", \"Int64\", \"Int64\", \"String\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1886,7 +1886,7 @@ ":name" = "KMeans" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:algo, :k_init, :k, :tol, :max_iters, :copy, :threads, :rng, :weights, :init)`" ":hyperparameter_types" = "`(\"Union{Symbol, ParallelKMeans.AbstractKMeansAlg}\", \"String\", \"Int64\", \"Float64\", \"Int64\", \"Bool\", \"Int64\", \"Union{Int64, Random.AbstractRNG}\", \"Any\", \"Any\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -1908,7 +1908,7 @@ ":name" = "GaussianNBClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -1930,7 +1930,7 @@ ":name" = "MultinomialNBClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:alpha,)`" ":hyperparameter_types" = "`(\"Int64\",)`" ":hyperparameter_ranges" = "`(nothing,)`" @@ -1952,7 +1952,7 @@ ":name" = "DeterministicSurrogate" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fitted_params", ":inverse_transform", ":machine", ":predict_joint", ":transform"] +":implemented_methods" = [":fitted_params", ":inverse_transform", ":predict", ":predict_joint", ":transform", ":machine"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -1996,7 +1996,7 @@ ":name" = "IntervalSurrogate" ":is_supervised" = "`true`" ":prediction_type" = ":interval" -":implemented_methods" = [":predict", ":fitted_params", ":inverse_transform", ":machine", ":predict_joint", ":transform"] +":implemented_methods" = [":fitted_params", ":inverse_transform", ":predict", ":predict_joint", ":transform", ":machine"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -2018,7 +2018,7 @@ ":name" = "UnsupervisedSurrogate" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":fitted_params", ":inverse_transform", ":machine", ":predict_joint", ":transform"] +":implemented_methods" = [":fitted_params", ":inverse_transform", ":predict", ":predict_joint", ":transform", ":machine"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -2040,7 +2040,7 @@ ":name" = "JointProbabilisticSurrogate" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fitted_params", ":inverse_transform", ":machine", ":predict_joint", ":transform"] +":implemented_methods" = [":fitted_params", ":inverse_transform", ":predict", ":predict_joint", ":transform", ":machine"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -2064,7 +2064,7 @@ ":prediction_type" = ":unknown" ":implemented_methods" = [":clean!", ":evaluate", ":fit"] ":hyperparameters" = "`(:model, :resampling, :measure, :weights, :operation, :acceleration, :check_measure, :repeats)`" -":hyperparameter_types" = "`(\"Union{Nothing, MLJModelInterface.Supervised}\", \"Any\", \"Any\", \"Union{Nothing, AbstractArray{_s359,1} where _s359<:Real}\", \"Any\", \"ComputationalResources.AbstractResource\", \"Bool\", \"Int64\")`" +":hyperparameter_types" = "`(\"Union{Nothing, MLJModelInterface.Supervised}\", \"Any\", \"Any\", \"Union{Nothing, AbstractArray{_s358,1} where _s358<:Real}\", \"Any\", \"ComputationalResources.AbstractResource\", \"Bool\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" [MLJBase.StaticSurrogate] @@ -2084,7 +2084,7 @@ ":name" = "StaticSurrogate" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":fitted_params", ":inverse_transform", ":machine", ":predict_joint", ":transform"] +":implemented_methods" = [":fitted_params", ":inverse_transform", ":predict", ":predict_joint", ":transform", ":machine"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -2106,7 +2106,7 @@ ":name" = "ProbabilisticSurrogate" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fitted_params", ":inverse_transform", ":machine", ":predict_joint", ":predict_mean", ":predict_median", ":predict_mode", ":transform"] +":implemented_methods" = [":fitted_params", ":inverse_transform", ":predict", ":predict_joint", ":predict_mean", ":predict_median", ":predict_mode", ":transform", ":machine"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -2128,7 +2128,7 @@ ":name" = "LDA" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:method, :cov_w, :cov_b, :out_dim, :regcoef, :dist)`" ":hyperparameter_types" = "`(\"Symbol\", \"StatsBase.CovarianceEstimator\", \"StatsBase.CovarianceEstimator\", \"Int64\", \"Float64\", \"Distances.SemiMetric\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2150,7 +2150,7 @@ ":name" = "BayesianSubspaceLDA" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:normalize, :out_dim, :priors)`" ":hyperparameter_types" = "`(\"Bool\", \"Int64\", \"Union{Nothing, Array{Float64,1}}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing)`" @@ -2194,7 +2194,7 @@ ":name" = "LinearRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:bias,)`" ":hyperparameter_types" = "`(\"Bool\",)`" ":hyperparameter_ranges" = "`(nothing,)`" @@ -2260,7 +2260,7 @@ ":name" = "RidgeRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:lambda, :bias)`" ":hyperparameter_types" = "`(\"Union{Real, Union{AbstractArray{T,1}, AbstractArray{T,2}} where T}\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -2304,7 +2304,7 @@ ":name" = "SubspaceLDA" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:normalize, :out_dim, :dist)`" ":hyperparameter_types" = "`(\"Bool\", \"Int64\", \"Distances.SemiMetric\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing)`" @@ -2326,7 +2326,7 @@ ":name" = "BayesianLDA" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:method, :cov_w, :cov_b, :out_dim, :regcoef, :priors)`" ":hyperparameter_types" = "`(\"Symbol\", \"StatsBase.CovarianceEstimator\", \"StatsBase.CovarianceEstimator\", \"Int64\", \"Float64\", \"Union{Nothing, Array{Float64,1}}\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2356,7 +2356,7 @@ [DecisionTree.AdaBoostStumpClassifier] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:Union{AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous, AbstractArray{_s23,1} where _s23<:ScientificTypes.Count, AbstractArray{_s23,1} where _s23<:ScientificTypes.OrderedFactor}`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s112,1} where _s112<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s109,1} where _s109<:ScientificTypes.Finite`" ":is_pure_julia" = "`true`" ":package_name" = "DecisionTree" ":package_license" = "MIT" @@ -2370,7 +2370,7 @@ ":name" = "AdaBoostStumpClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:n_iter, :pdf_smoothing)`" ":hyperparameter_types" = "`(\"Int64\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -2392,7 +2392,7 @@ ":name" = "DecisionTreeRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:max_depth, :min_samples_leaf, :min_samples_split, :min_purity_increase, :n_subfeatures, :post_prune, :merge_purity_threshold)`" ":hyperparameter_types" = "`(\"Int64\", \"Int64\", \"Int64\", \"Float64\", \"Int64\", \"Bool\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2400,7 +2400,7 @@ [DecisionTree.DecisionTreeClassifier] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:Union{AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous, AbstractArray{_s23,1} where _s23<:ScientificTypes.Count, AbstractArray{_s23,1} where _s23<:ScientificTypes.OrderedFactor}`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s112,1} where _s112<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s109,1} where _s109<:ScientificTypes.Finite`" ":is_pure_julia" = "`true`" ":package_name" = "DecisionTree" ":package_license" = "MIT" @@ -2414,7 +2414,7 @@ ":name" = "DecisionTreeClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:max_depth, :min_samples_leaf, :min_samples_split, :min_purity_increase, :n_subfeatures, :post_prune, :merge_purity_threshold, :pdf_smoothing, :display_depth)`" ":hyperparameter_types" = "`(\"Int64\", \"Int64\", \"Int64\", \"Float64\", \"Int64\", \"Bool\", \"Float64\", \"Float64\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2436,7 +2436,7 @@ ":name" = "RandomForestRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:max_depth, :min_samples_leaf, :min_samples_split, :min_purity_increase, :n_subfeatures, :n_trees, :sampling_fraction, :pdf_smoothing)`" ":hyperparameter_types" = "`(\"Int64\", \"Int64\", \"Int64\", \"Float64\", \"Int64\", \"Int64\", \"Float64\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2444,7 +2444,7 @@ [DecisionTree.RandomForestClassifier] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:Union{AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous, AbstractArray{_s23,1} where _s23<:ScientificTypes.Count, AbstractArray{_s23,1} where _s23<:ScientificTypes.OrderedFactor}`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s112,1} where _s112<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s109,1} where _s109<:ScientificTypes.Finite`" ":is_pure_julia" = "`true`" ":package_name" = "DecisionTree" ":package_license" = "MIT" @@ -2458,7 +2458,7 @@ ":name" = "RandomForestClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict"] ":hyperparameters" = "`(:max_depth, :min_samples_leaf, :min_samples_split, :min_purity_increase, :n_subfeatures, :n_trees, :sampling_fraction, :pdf_smoothing)`" ":hyperparameter_types" = "`(\"Int64\", \"Int64\", \"Int64\", \"Float64\", \"Int64\", \"Int64\", \"Float64\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2470,17 +2470,17 @@ ":is_pure_julia" = "`true`" ":package_name" = "Clustering" ":package_license" = "MIT" -":load_path" = "MLJModels.Clustering_.KMeans" +":load_path" = "MLJClusteringInterface.KMeans" ":package_uuid" = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" ":package_url" = "https://github.com/JuliaStats/Clustering.jl" ":is_wrapper" = "`false`" ":supports_weights" = "`false`" ":supports_online" = "`false`" -":docstring" = "K-Means algorithm: find K centroids corresponding to K clusters in the data.\n\n→ based on [Clustering](https://github.com/JuliaStats/Clustering.jl).\n→ do `@load KMeans pkg=\"Clustering\"` to use the model.\n→ do `?KMeans` for documentation." +":docstring" = "K-Means algorithm: find K centroids corresponding to K clusters in the data. \n\n→ based on [Clustering](https://github.com/JuliaStats/Clustering.jl).\n→ do `@load KMeans pkg=\"Clustering\"` to use the model.\n→ do `?KMeans` for documentation." ":name" = "KMeans" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:k, :metric)`" ":hyperparameter_types" = "`(\"Int64\", \"Distances.SemiMetric\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -2492,17 +2492,17 @@ ":is_pure_julia" = "`true`" ":package_name" = "Clustering" ":package_license" = "MIT" -":load_path" = "MLJModels.Clustering_.KMedoids" +":load_path" = "MLJClusteringInterface.KMedoids" ":package_uuid" = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" ":package_url" = "https://github.com/JuliaStats/Clustering.jl" ":is_wrapper" = "`false`" ":supports_weights" = "`false`" ":supports_online" = "`false`" -":docstring" = "K-Medoids algorithm: find K centroids corresponding to K clusters in the data.\nUnlike K-Means, the centroids are found among data points themselves.\"\n\n→ based on [Clustering](https://github.com/JuliaStats/Clustering.jl).\n→ do `@load KMedoids pkg=\"Clustering\"` to use the model.\n→ do `?KMedoids` for documentation." +":docstring" = "K-Medoids algorithm: find K centroids corresponding to K clusters in the data.\nUnlike K-Means, the centroids are found among data points themselves.\n\n→ based on [Clustering](https://github.com/JuliaStats/Clustering.jl).\n→ do `@load KMedoids pkg=\"Clustering\"` to use the model.\n→ do `?KMedoids` for documentation." ":name" = "KMedoids" ":is_supervised" = "`false`" ":prediction_type" = ":unknown" -":implemented_methods" = [":predict", ":clean!", ":fit", ":fitted_params", ":transform"] +":implemented_methods" = [":clean!", ":fit", ":fitted_params", ":predict", ":transform"] ":hyperparameters" = "`(:k, :metric)`" ":hyperparameter_types" = "`(\"Int64\", \"Distances.SemiMetric\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -2524,7 +2524,7 @@ ":name" = "XGBoostCount" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit"] +":implemented_methods" = [":clean!", ":fit", ":predict"] ":hyperparameters" = "`(:num_round, :booster, :disable_default_eval_metric, :eta, :gamma, :max_depth, :min_child_weight, :max_delta_step, :subsample, :colsample_bytree, :colsample_bylevel, :lambda, :alpha, :tree_method, :sketch_eps, :scale_pos_weight, :updater, :refresh_leaf, :process_type, :grow_policy, :max_leaves, :max_bin, :predictor, :sample_type, :normalize_type, :rate_drop, :one_drop, :skip_drop, :feature_selector, :top_k, :tweedie_variance_power, :objective, :base_score, :eval_metric, :seed)`" ":hyperparameter_types" = "`(\"Int64\", \"String\", \"Int64\", \"Float64\", \"Float64\", \"Int64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"String\", \"Float64\", \"Float64\", \"String\", \"Union{Bool, Int64}\", \"String\", \"String\", \"Int64\", \"Int64\", \"String\", \"String\", \"String\", \"Float64\", \"Any\", \"Float64\", \"String\", \"Int64\", \"Float64\", \"Any\", \"Float64\", \"Any\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2546,7 +2546,7 @@ ":name" = "XGBoostRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit"] +":implemented_methods" = [":clean!", ":fit", ":predict"] ":hyperparameters" = "`(:num_round, :booster, :disable_default_eval_metric, :eta, :gamma, :max_depth, :min_child_weight, :max_delta_step, :subsample, :colsample_bytree, :colsample_bylevel, :lambda, :alpha, :tree_method, :sketch_eps, :scale_pos_weight, :updater, :refresh_leaf, :process_type, :grow_policy, :max_leaves, :max_bin, :predictor, :sample_type, :normalize_type, :rate_drop, :one_drop, :skip_drop, :feature_selector, :top_k, :tweedie_variance_power, :objective, :base_score, :eval_metric, :seed)`" ":hyperparameter_types" = "`(\"Int64\", \"String\", \"Int64\", \"Float64\", \"Float64\", \"Int64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"String\", \"Float64\", \"Float64\", \"String\", \"Union{Bool, Int64}\", \"String\", \"String\", \"Int64\", \"Int64\", \"String\", \"String\", \"String\", \"Float64\", \"Any\", \"Float64\", \"String\", \"Int64\", \"Float64\", \"Any\", \"Float64\", \"Any\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2568,7 +2568,7 @@ ":name" = "XGBoostClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit"] +":implemented_methods" = [":clean!", ":fit", ":predict"] ":hyperparameters" = "`(:num_round, :booster, :disable_default_eval_metric, :eta, :gamma, :max_depth, :min_child_weight, :max_delta_step, :subsample, :colsample_bytree, :colsample_bylevel, :lambda, :alpha, :tree_method, :sketch_eps, :scale_pos_weight, :updater, :refresh_leaf, :process_type, :grow_policy, :max_leaves, :max_bin, :predictor, :sample_type, :normalize_type, :rate_drop, :one_drop, :skip_drop, :feature_selector, :top_k, :tweedie_variance_power, :objective, :base_score, :eval_metric, :seed)`" ":hyperparameter_types" = "`(\"Int64\", \"String\", \"Int64\", \"Float64\", \"Float64\", \"Int64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"String\", \"Float64\", \"Float64\", \"String\", \"Union{Bool, Int64}\", \"String\", \"String\", \"Int64\", \"Int64\", \"String\", \"String\", \"String\", \"Float64\", \"Any\", \"Float64\", \"String\", \"Int64\", \"Float64\", \"Any\", \"Float64\", \"Any\", \"Int64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2590,7 +2590,7 @@ ":name" = "LGBMClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":update"] +":implemented_methods" = [":clean!", ":fit", ":predict", ":update"] ":hyperparameters" = "`(:boosting, :num_iterations, :learning_rate, :num_leaves, :max_depth, :tree_learner, :histogram_pool_size, :min_data_in_leaf, :min_sum_hessian_in_leaf, :lambda_l1, :lambda_l2, :min_gain_to_split, :feature_fraction, :feature_fraction_seed, :bagging_fraction, :bagging_freq, :bagging_seed, :early_stopping_round, :max_bin, :init_score, :drop_rate, :max_drop, :skip_drop, :xgboost_dart_mode, :uniform_drop, :drop_seed, :top_rate, :other_rate, :objective, :categorical_feature, :data_random_seed, :is_sparse, :is_unbalance, :metric, :metric_freq, :is_training_metric, :ndcg_at, :num_machines, :num_threads, :local_listen_port, :time_out, :machine_list_file, :save_binary, :device_type)`" ":hyperparameter_types" = "`(\"String\", \"Int64\", \"Float64\", \"Int64\", \"Int64\", \"String\", \"Float64\", \"Int64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Int64\", \"Float64\", \"Int64\", \"Int64\", \"Int64\", \"Int64\", \"String\", \"Float64\", \"Int64\", \"Float64\", \"Bool\", \"Bool\", \"Int64\", \"Float64\", \"Float64\", \"String\", \"Array{Int64,1}\", \"Int64\", \"Bool\", \"Bool\", \"Array{String,1}\", \"Int64\", \"Bool\", \"Array{Int64,1}\", \"Int64\", \"Int64\", \"Int64\", \"Int64\", \"String\", \"Bool\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2612,7 +2612,7 @@ ":name" = "LGBMRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":clean!", ":fit", ":update"] +":implemented_methods" = [":clean!", ":fit", ":predict", ":update"] ":hyperparameters" = "`(:boosting, :num_iterations, :learning_rate, :num_leaves, :max_depth, :tree_learner, :histogram_pool_size, :min_data_in_leaf, :min_sum_hessian_in_leaf, :lambda_l1, :lambda_l2, :min_gain_to_split, :feature_fraction, :feature_fraction_seed, :bagging_fraction, :bagging_freq, :bagging_seed, :early_stopping_round, :max_bin, :init_score, :drop_rate, :max_drop, :skip_drop, :xgboost_dart_mode, :uniform_drop, :drop_seed, :top_rate, :other_rate, :objective, :categorical_feature, :data_random_seed, :is_sparse, :is_unbalance, :metric, :metric_freq, :is_training_metric, :ndcg_at, :num_machines, :num_threads, :local_listen_port, :time_out, :machine_list_file, :save_binary, :device_type)`" ":hyperparameter_types" = "`(\"String\", \"Int64\", \"Float64\", \"Int64\", \"Int64\", \"String\", \"Float64\", \"Int64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Int64\", \"Float64\", \"Int64\", \"Int64\", \"Int64\", \"Int64\", \"String\", \"Float64\", \"Int64\", \"Float64\", \"Bool\", \"Bool\", \"Int64\", \"Float64\", \"Float64\", \"String\", \"Array{Int64,1}\", \"Int64\", \"Bool\", \"Bool\", \"Array{String,1}\", \"Int64\", \"Bool\", \"Array{Int64,1}\", \"Int64\", \"Int64\", \"Int64\", \"Int64\", \"String\", \"Bool\", \"String\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -2722,7 +2722,7 @@ ":name" = "ConstantClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit", ":fitted_params"] +":implemented_methods" = [":fit", ":fitted_params", ":predict"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -2766,7 +2766,7 @@ ":name" = "DeterministicConstantClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -2876,7 +2876,7 @@ ":name" = "ConstantRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fitted_params"] +":implemented_methods" = [":fitted_params", ":predict"] ":hyperparameters" = "`(:distribution_type,)`" ":hyperparameter_types" = "`(\"Type{D} where D<:Distributions.Sampleable\",)`" ":hyperparameter_ranges" = "`(nothing,)`" @@ -2942,7 +2942,7 @@ ":name" = "BinaryThresholdPredictor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit", ":fitted_params", ":update"] +":implemented_methods" = [":fit", ":fitted_params", ":predict", ":update"] ":hyperparameters" = "`(:wrapped_model, :threshold)`" ":hyperparameter_types" = "`(\"MLJModelInterface.Probabilistic\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -2986,7 +2986,7 @@ ":name" = "DeterministicConstantRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "``" ":hyperparameter_types" = "``" ":hyperparameter_ranges" = "``" @@ -3052,7 +3052,7 @@ ":name" = "EpsilonSVR" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "`(:kernel, :gamma, :epsilon, :cost, :cachesize, :degree, :coef0, :tolerance, :shrinking)`" ":hyperparameter_types" = "`(\"LIBSVM.Kernel.KERNEL\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Int32\", \"Float64\", \"Float64\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -3060,7 +3060,7 @@ [LIBSVM.LinearSVC] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:(AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous)`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s111,1} where _s111<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s108,1} where _s108<:ScientificTypes.Finite`" ":is_pure_julia" = "`false`" ":package_name" = "LIBSVM" ":package_license" = "unknown" @@ -3074,7 +3074,7 @@ ":name" = "LinearSVC" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "`(:solver, :weights, :tolerance, :cost, :p, :bias)`" ":hyperparameter_types" = "`(\"LIBSVM.Linearsolver.LINEARSOLVER\", \"Union{Nothing, Dict}\", \"Float64\", \"Float64\", \"Float64\", \"Float64\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -3096,7 +3096,7 @@ ":name" = "NuSVR" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "`(:kernel, :gamma, :nu, :cost, :cachesize, :degree, :coef0, :tolerance, :shrinking)`" ":hyperparameter_types" = "`(\"LIBSVM.Kernel.KERNEL\", \"Float64\", \"Float64\", \"Float64\", \"Float64\", \"Int32\", \"Float64\", \"Float64\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -3104,7 +3104,7 @@ [LIBSVM.NuSVC] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:(AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous)`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s111,1} where _s111<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s108,1} where _s108<:ScientificTypes.Finite`" ":is_pure_julia" = "`false`" ":package_name" = "LIBSVM" ":package_license" = "unknown" @@ -3118,7 +3118,7 @@ ":name" = "NuSVC" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "`(:kernel, :gamma, :weights, :nu, :cost, :cachesize, :degree, :coef0, :tolerance, :shrinking)`" ":hyperparameter_types" = "`(\"LIBSVM.Kernel.KERNEL\", \"Float64\", \"Union{Nothing, Dict}\", \"Float64\", \"Float64\", \"Float64\", \"Int32\", \"Float64\", \"Float64\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -3126,7 +3126,7 @@ [LIBSVM.SVC] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:(AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous)`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s111,1} where _s111<:ScientificTypes.Finite`" +":target_scitype" = "`AbstractArray{_s108,1} where _s108<:ScientificTypes.Finite`" ":is_pure_julia" = "`false`" ":package_name" = "LIBSVM" ":package_license" = "unknown" @@ -3140,14 +3140,14 @@ ":name" = "SVC" ":is_supervised" = "`true`" ":prediction_type" = ":deterministic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "`(:kernel, :gamma, :weights, :cost, :cachesize, :degree, :coef0, :tolerance, :shrinking, :probability)`" ":hyperparameter_types" = "`(\"LIBSVM.Kernel.KERNEL\", \"Float64\", \"Union{Nothing, Dict}\", \"Float64\", \"Float64\", \"Int32\", \"Float64\", \"Float64\", \"Bool\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" [LIBSVM.OneClassSVM] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:(AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous)`" -":output_scitype" = "`AbstractArray{_s111,1} where _s111<:ScientificTypes.Finite{2}`" +":output_scitype" = "`AbstractArray{_s108,1} where _s108<:ScientificTypes.Finite{2}`" ":target_scitype" = "`ScientificTypes.Unknown`" ":is_pure_julia" = "`false`" ":package_name" = "LIBSVM" @@ -3170,7 +3170,7 @@ [GLM.LinearBinaryClassifier] ":input_scitype" = "`ScientificTypes.Table{_s24} where _s24<:(AbstractArray{_s23,1} where _s23<:ScientificTypes.Continuous)`" ":output_scitype" = "`ScientificTypes.Unknown`" -":target_scitype" = "`AbstractArray{_s112,1} where _s112<:ScientificTypes.Finite{2}`" +":target_scitype" = "`AbstractArray{_s109,1} where _s109<:ScientificTypes.Finite{2}`" ":is_pure_julia" = "`true`" ":package_name" = "GLM" ":package_license" = "MIT" @@ -3184,7 +3184,7 @@ ":name" = "LinearBinaryClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit", ":predict_mean"] +":implemented_methods" = [":fit", ":predict", ":predict_mean"] ":hyperparameters" = "`(:fit_intercept, :link)`" ":hyperparameter_types" = "`(\"Bool\", \"GLM.Link01\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -3206,7 +3206,7 @@ ":name" = "LinearCountRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "`(:fit_intercept, :distribution, :link)`" ":hyperparameter_types" = "`(\"Bool\", \"Distributions.Distribution\", \"GLM.Link\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing)`" @@ -3228,7 +3228,7 @@ ":name" = "LinearRegressor" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit"] +":implemented_methods" = [":fit", ":predict"] ":hyperparameters" = "`(:fit_intercept, :allowrankdeficient)`" ":hyperparameter_types" = "`(\"Bool\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing)`" @@ -3272,7 +3272,7 @@ ":name" = "NeuralNetworkClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit", ":fitted_params", ":update"] +":implemented_methods" = [":fit", ":fitted_params", ":predict", ":update"] ":hyperparameters" = "`(:builder, :finaliser, :optimiser, :loss, :epochs, :batch_size, :lambda, :alpha, :optimiser_changes_trigger_retraining)`" ":hyperparameter_types" = "`(\"Any\", \"Any\", \"Any\", \"Any\", \"Int64\", \"Int64\", \"Float64\", \"Float64\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" @@ -3294,7 +3294,7 @@ ":name" = "ImageClassifier" ":is_supervised" = "`true`" ":prediction_type" = ":probabilistic" -":implemented_methods" = [":predict", ":fit", ":fitted_params", ":update"] +":implemented_methods" = [":fit", ":fitted_params", ":predict", ":update"] ":hyperparameters" = "`(:builder, :finaliser, :optimiser, :loss, :epochs, :batch_size, :lambda, :alpha, :optimiser_changes_trigger_retraining)`" ":hyperparameter_types" = "`(\"Any\", \"Any\", \"Any\", \"Any\", \"Int64\", \"Int64\", \"Float64\", \"Float64\", \"Bool\")`" ":hyperparameter_ranges" = "`(nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing, nothing)`" diff --git a/src/registry/Project.toml b/src/registry/Project.toml index b10f1eb6..bbbc5ca6 100644 --- a/src/registry/Project.toml +++ b/src/registry/Project.toml @@ -1,5 +1,4 @@ [deps] -Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb" EvoTrees = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" @@ -7,6 +6,7 @@ InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240" LIBSVM = "b1bec4e5-fd48-53fe-b0cb-9723c09d164b" LightGBM = "7acf609c-83a4-11e9-1ffb-b912bcd3b04a" MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d" +MLJClusteringInterface = "d354fa79-ed1c-40d4-88ef-b8c7bd1568af" MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845" MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692" MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea" @@ -15,7 +15,6 @@ MLJMultivariateStatsInterface = "1b6a4a23-ba22-4f51-9698-8599985d3728" MLJNaiveBayesInterface = "33e4bacb-b9e2-458e-9a13-5d9a90b235fa" MLJScikitLearnInterface = "5ae90465-5518-4432-b9d2-8a1def2f0cab" MLJXGBoostInterface = "54119dfa-1dab-4055-a167-80440f4f7a91" -MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411" NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce" ParallelKMeans = "42b8e9d4-006b-409a-8472-7f34b3fb58af" PartialLeastSquaresRegressor = "f4b1acfe-f311-436c-bb79-8483f53c17d5" diff --git a/test/Clustering.jl b/test/Clustering.jl deleted file mode 100755 index a50f4527..00000000 --- a/test/Clustering.jl +++ /dev/null @@ -1,77 +0,0 @@ -module TestClustering - -using MLJBase -using Test -using Random:seed! -import LinearAlgebra: norm -import Distances: evaluate -using CategoricalArrays - -# load code to be tested: -import MLJModels -import Clustering # MLJModels.Clustering_ now available for loading -using MLJModels.Clustering_ - -seed!(132442) - -X, y = @load_crabs - -#### -#### KMEANS -#### - -barekm = KMeans() - -fitresult, cache, report = fit(barekm, 1, X) - -R = matrix(transform(barekm, fitresult, X)) - -X_array = matrix(X) - -# distance from first point to second center -@test R[1, 2] ≈ norm(view(X_array, 1, :) .- view(fitresult[1], :, 2))^2 -@test R[10, 3] ≈ norm(view(X_array, 10, :) .- view(fitresult[1], :, 3))^2 - -p = predict(barekm, fitresult, X) - -@test argmin(R[1, :]) == p[1] -@test argmin(R[10, :]) == p[10] - -infos = info_dict(barekm) - -@test infos[:package_name] == "Clustering" -@test infos[:is_pure_julia] -@test infos[:package_license] == "MIT" - -@test infos[:input_scitype] == Table(Continuous) -@test infos[:output_scitype] == Table(Continuous) - -infos[:docstring] - -#### -#### KMEDOIDS -#### - -barekm = KMedoids() - -fitresult, cache, report = fit(barekm, 1, X) - -R = matrix(transform(barekm, fitresult, X)) - -@test R[1, 2] ≈ evaluate(barekm.metric, view(X_array, 1, :), view(fitresult[1], :, 2)) -@test R[10, 3] ≈ evaluate(barekm.metric, view(X_array, 10, :), view(fitresult[1], :, 3)) - -p = predict(barekm, fitresult, X) - -@test all(report.assignments .== p) - -# km = machine(barekm, X) -# fit!(km) - -infos = info_dict(barekm) - -@test infos[:input_scitype] == Table(Continuous) -@test infos[:output_scitype] == Table(Continuous) - -end # module -true diff --git a/test/runtests.jl b/test/runtests.jl index 5c6b66da..c96019d8 100755 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -28,10 +28,6 @@ end @test include("DecisionTree.jl") end -@testset "Clustering " begin - @test include("Clustering.jl") -end - @testset "GLM " begin @test include("GLM.jl") end