remove PartialLeastSquaresRegressor

Author: ablaom

src/registry/Metadata.toml

@@ -4103,78 +4103,6 @@
 ":reporting_operations" = "`()`"
 ":constructor" = "`IteratedModel`"
 
-[PartialLeastSquaresRegressor.KPLSRegressor]
-":input_scitype" = "`ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}`"
-":output_scitype" = "`ScientificTypesBase.Unknown`"
-":target_scitype" = "`Union{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Continuous}}`"
-":fit_data_scitype" = "`Tuple{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, Union{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Continuous}}}`"
-":predict_scitype" = "`Union{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Continuous}}`"
-":transform_scitype" = "`ScientificTypesBase.Unknown`"
-":inverse_transform_scitype" = "`ScientificTypesBase.Unknown`"
-":target_in_fit" = "`true`"
-":is_pure_julia" = "`true`"
-":package_name" = "PartialLeastSquaresRegressor"
-":package_license" = "MIT"
-":load_path" = "PartialLeastSquaresRegressor.KPLSRegressor"
-":package_uuid" = "f4b1acfe-f311-436c-bb79-8483f53c17d5"
-":package_url" = "https://github.com/lalvim/PartialLeastSquaresRegressor.jl"
-":is_wrapper" = "`false`"
-":supports_weights" = "`false`"
-":supports_class_weights" = "`false`"
-":supports_online" = "`false`"
-":docstring" = "A Kernel Partial Least Squares Regressor. A Kernel PLS2 NIPALS algorithms. Can be used mainly for regression."
-":name" = "KPLSRegressor"
-":human_name" = "kpls regressor"
-":is_supervised" = "`true`"
-":prediction_type" = ":deterministic"
-":abstract_type" = "`MLJModelInterface.Deterministic`"
-":implemented_methods" = [":clean!", ":fit", ":predict"]
-":hyperparameters" = "`(:n_factors, :kernel, :width)`"
-":hyperparameter_types" = "`(\"Integer\", \"String\", \"Real\")`"
-":hyperparameter_ranges" = "`(nothing, nothing, nothing)`"
-":iteration_parameter" = "`nothing`"
-":supports_training_losses" = "`false`"
-":reports_feature_importances" = "`false`"
-":deep_properties" = "`()`"
-":reporting_operations" = "`()`"
-":constructor" = "`nothing`"
-
-[PartialLeastSquaresRegressor.PLSRegressor]
-":input_scitype" = "`ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}`"
-":output_scitype" = "`ScientificTypesBase.Unknown`"
-":target_scitype" = "`Union{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Continuous}}`"
-":fit_data_scitype" = "`Tuple{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, Union{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Continuous}}}`"
-":predict_scitype" = "`Union{ScientificTypesBase.Table{<:AbstractVector{<:ScientificTypesBase.Continuous}}, AbstractVector{<:ScientificTypesBase.Continuous}}`"
-":transform_scitype" = "`ScientificTypesBase.Unknown`"
-":inverse_transform_scitype" = "`ScientificTypesBase.Unknown`"
-":target_in_fit" = "`true`"
-":is_pure_julia" = "`true`"
-":package_name" = "PartialLeastSquaresRegressor"
-":package_license" = "MIT"
-":load_path" = "PartialLeastSquaresRegressor.PLSRegressor"
-":package_uuid" = "f4b1acfe-f311-436c-bb79-8483f53c17d5"
-":package_url" = "https://github.com/lalvim/PartialLeastSquaresRegressor.jl"
-":is_wrapper" = "`false`"
-":supports_weights" = "`false`"
-":supports_class_weights" = "`false`"
-":supports_online" = "`false`"
-":docstring" = "A Partial Least Squares Regressor. Contains PLS1, PLS2 (multi target) algorithms. Can be used mainly for regression."
-":name" = "PLSRegressor"
-":human_name" = "pls regressor"
-":is_supervised" = "`true`"
-":prediction_type" = ":deterministic"
-":abstract_type" = "`MLJModelInterface.Deterministic`"
-":implemented_methods" = [":clean!", ":fit", ":predict"]
-":hyperparameters" = "`(:n_factors,)`"
-":hyperparameter_types" = "`(\"Int64\",)`"
-":hyperparameter_ranges" = "`(nothing,)`"
-":iteration_parameter" = "`nothing`"
-":supports_training_losses" = "`false`"
-":reports_feature_importances" = "`false`"
-":deep_properties" = "`()`"
-":reporting_operations" = "`()`"
-":constructor" = "`nothing`"
-
 [PartitionedLS.PartLS]
 ":input_scitype" = "`Union{ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}, AbstractMatrix{ScientificTypesBase.Continuous}}`"
 ":output_scitype" = "`ScientificTypesBase.Unknown`"

src/registry/Models.toml

@@ -6,7 +6,6 @@ MLJScikitLearnInterface = ["ProbabilisticSGDClassifier", "RidgeCVClassifier", "L
 OutlierDetectionNeighbors = ["ABODDetector", "DNNDetector", "LOFDetector", "KNNDetector", "COFDetector"]
 SIRUS = ["StableRulesClassifier", "StableForestClassifier", "StableRulesRegressor", "StableForestRegressor"]
 MLJIteration = ["IteratedModel"]
-PartialLeastSquaresRegressor = ["KPLSRegressor", "PLSRegressor"]
 PartitionedLS = ["PartLS"]
 MLJLinearModels = ["QuantileRegressor", "LogisticClassifier", "MultinomialClassifier", "LADRegressor", "RidgeRegressor", "RobustRegressor", "ElasticNetRegressor", "LinearRegressor", "LassoRegressor", "HuberRegressor"]
 Maxnet = ["MaxnetBinaryClassifier"]

src/registry/Project.toml

@@ -33,7 +33,6 @@ OneRule = "90484964-6d6a-4979-af09-8657dbed84ff"
 OutlierDetectionNeighbors = "51249a0a-cb36-4849-8e04-30c7f8d311bb"
 OutlierDetectionPython = "2449c660-d36c-460e-a68b-92ab3c865b3e"
 ParallelKMeans = "42b8e9d4-006b-409a-8472-7f34b3fb58af"
-PartialLeastSquaresRegressor = "f4b1acfe-f311-436c-bb79-8483f53c17d5"
 PartitionedLS = "19f41c5e-8610-11e9-2f2a-0d67e7c5027f"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 SIRUS = "cdeec39e-fb35-4959-aadb-a1dd5dede958"