simplify existin sklearn model

NAMESPACE

@@ -23,7 +23,6 @@ export(createDefaultExecuteSettings)
 export(createDefaultSplitSetting)
 export(createExecuteSettings)
 export(createExistingSplitSettings)
-export(createFeatureEngineeringMapColumnsSettings)
 export(createFeatureEngineeringSettings)
 export(createGlmModel)
 export(createLearningCurve)
@@ -34,7 +33,7 @@ export(createPreprocessSettings)
 export(createRandomForestFeatureSelection)
 export(createRestrictPlpDataSettings)
 export(createSampleSettings)
-export(createSciKitLearnModel)
+export(createSklearnModel)
 export(createSplineSettings)
 export(createStratifiedImputationSettings)
 export(createStudyPopulation)

R/ExistingSklearn.R

@@ -0,0 +1,119 @@
+# @file ExistingSklearn.R
+#
+# Copyright 2025 Observational Health Data Sciences and Informatics
+#
+# This file is part of PatientLevelPrediction
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+#' Plug an existing scikit learn python model into the
+#' PLP framework
+#'
+#' @details
+#' This function lets users add an existing scikit learn model that is saved as 
+#' model.pkl into PLP format.  covariateMap is a mapping between standard 
+#' covariateIds and the model columns. The user also needs to specify the 
+#' covariate settings and population settings as these are used to determine 
+#' the standard PLP model design.
+#'
+#' @param modelLocation  The location of the folder that contains the model as
+#' model.pkl
+#' @param covariateMap  A data.frame with the columns: columnId and covariateId. 
+#' `covariateId` from FeatureExtraction is the standard OHDSI covariateId.
+#' `columnId` is the column location the model expects that covariate to be in.
+#' For example, if you had a column called 'age' in your model and this was the 
+#' 3rd column when fitting the model, then the values for columnId would be 3, 
+#' covariateId would be 1002 (the covariateId for age in years) and
+#' @param covariateSettings  The settings for the standardized covariates
+#' @param populationSettings  The settings for the population, this includes the 
+#' time-at-risk settings and inclusion criteria.
+#' @param isPickle If the model should be saved as a pickle set this to TRUE if
+#' it should be saved as json set this to FALSE.
+#'
+#' @return
+#' An object of class plpModel, this is a list that contains: 
+#'        model (the location of the model.pkl),
+#'        preprocessing (settings for mapping the covariateIds to the model 
+#'                       column mames), 
+#'        modelDesign (specification of the model design), 
+#'        trainDetails (information about the model fitting) and 
+#'        covariateImportance.  
+#' 
+#' You can use the output as an input in PatientLevelPrediction::predictPlp to 
+#' apply the model and calculate the risk for patients.
+#'
+#' @export
+createSklearnModel <- function(
+    modelLocation = "/model", # model needs to be saved here as "model.pkl"
+    covariateMap = data.frame(
+      columnId = 1:2,
+      covariateId = c(1, 2),
+    ),
+    covariateSettings, # specify the covariates
+    populationSettings, # specify time at risk used to develop model
+    isPickle = TRUE) {
+  checkSklearn()
+  checkFileExists(modelLocation)
+  checkIsClass(covariateMap, "data.frame")
+  checkIsClass(covariateSettings, "covariateSettings")
+  checkIsClass(populationSettings, "populationSettings")
+  checkBoolean(isPickle)
+  checkDataframe(covariateMap, c("columnId", "covariateId"),
+    columnTypes = list(c("numeric", "integer"), "numeric")
+  )
+  existingModel <- list(model = "existingSklearn")
+  class(existingModel) <- "modelSettings"
+
+  plpModel <- list(
+    preprocessing = list(
+      tidyCovariates = NULL,
+      requireDenseMatrix = FALSE
+    ),
+    covariateImportance = data.frame(
+      columnId = covariateMap$columnId,
+      covariateId = covariateMap$covariateId,
+      included = TRUE
+    ),
+    modelDesign = PatientLevelPrediction::createModelDesign(
+      targetId = 1,
+      outcomeId = 2,
+      restrictPlpDataSettings = PatientLevelPrediction::createRestrictPlpDataSettings(),
+      covariateSettings = covariateSettings,
+      populationSettings = populationSettings,
+      sampleSettings = PatientLevelPrediction::createSampleSettings(),
+      preprocessSettings = PatientLevelPrediction::createPreprocessSettings(
+        minFraction = 0,
+        normalize = FALSE,
+        removeRedundancy = FALSE
+      ),
+      modelSettings = existingModel,
+      splitSettings = PatientLevelPrediction::createDefaultSplitSetting()
+    ),
+    model = modelLocation,
+    trainDetails = list(
+      analysisId = "existingSklearn",
+      developmentDatabase = "unknown",
+      developmentDatabaseId = "unknown",
+      trainingTime = -1,
+      modelName = "existingSklearn"
+    )
+  )
+
+  attr(plpModel, "modelType") <- "binary"
+  attr(plpModel, "saveType") <- "file"
+  attr(plpModel, "predictionFunction") <- "predictPythonSklearn"
+  attr(plpModel, "saveToJson") <- !isPickle
+  class(plpModel) <- "plpModel"
+  return(plpModel)
+}