Merge pull request #401 from OHDSI/develop

Develop

DESCRIPTION

@@ -2,7 +2,7 @@ Package: PatientLevelPrediction
 Type: Package
 Title: Developing patient level prediction using data in the OMOP Common Data
     Model
-Version: 6.3.2
+Version: 6.3.3
 Date: 2023-05-15
 Authors@R: c(
     person("Jenna", "Reps", email = "[email protected]", role = c("aut", "cre")),

NEWS.md

@@ -1,3 +1,8 @@
+PatientLevelPrediction 6.3.3
+======================
+- fixed bug introduced with new reticulate update in model saving to json tests
+
+
 PatientLevelPrediction 6.3.2
 ======================
 - fixed bug with database insert if result is incomplete

R/HelperFunctions.R

@@ -115,7 +115,7 @@ configurePython <- function(envname='PLP', envtype=NULL){
       ParallelLogger::logInfo(paste0('Creating virtual conda environment called ', envname))
       location <- reticulate::conda_create(envname=envname, packages = "python", conda = "auto")
     }
-    packages <- c('numpy','scipy','scikit-learn', 'pandas','pydotplus','joblib', 'sklearn-json')
+    packages <- c('numpy','scipy','scikit-learn', 'pandas','pydotplus','joblib')
     ParallelLogger::logInfo(paste0('Adding python dependancies to ', envname))
     reticulate::conda_install(envname=envname, packages = packages, forge = TRUE, pip = FALSE,
                               pip_ignore_installed = TRUE, conda = "auto")
@@ -128,7 +128,7 @@ configurePython <- function(envname='PLP', envtype=NULL){
       ParallelLogger::logInfo(paste0('Creating virtual python environment called ', envname))
       location <- reticulate::virtualenv_create(envname=envname)
     }
-    packages <- c('numpy', 'scikit-learn','scipy', 'pandas','pydotplus','sklearn-json')
+    packages <- c('numpy', 'scikit-learn','scipy', 'pandas','pydotplus')
     ParallelLogger::logInfo(paste0('Adding python dependancies to ', envname))
     reticulate::virtualenv_install(envname=envname, packages = packages, 
                                    ignore_installed = TRUE)

R/SklearnToJson.R

@@ -54,17 +54,17 @@ sklearnFromJson <- function(path) {
   with(py$open(path, "r"), as=file, {
     model <- json$load(fp=file)
   })
-  if (model["meta"] == "decision-tree") {
+  if (reticulate::py_bool(model["meta"] == "decision-tree")) {
     model <- deSerializeDecisionTree(model)
-  } else if (model["meta"] == "rf") {
+  } else if (reticulate::py_bool(model["meta"] == "rf")) {
     model <- deSerializeRandomForest(model)
-  } else if (model["meta"] == "adaboost") {
+  } else if (reticulate::py_bool(model["meta"] == "adaboost")) {
     model <- deSerializeAdaboost(model)
-  } else if (model["meta"] == "naive-bayes") {
+  } else if (reticulate::py_bool(model["meta"] == "naive-bayes")) {
     model <- deSerializeNaiveBayes(model)
-  } else if (model["meta"] == "mlp") {
+  } else if (reticulate::py_bool(model["meta"] == "mlp")) {
     model <- deSerializeMlp(model)
-  }  else if (model["meta"] == "svm") {
+  }  else if (reticulate::py_bool(model["meta"] == "svm")) {
     model <- deSerializeSVM(model)
   } else {
     stop("Unsupported model")
@@ -181,7 +181,7 @@ serializeRandomForest <- function(model) {
     "params" = model$get_params(),
     "n_classes_" = model$n_classes_)
 
-  if (model$`__dict__`["oob_score_"] != reticulate::py_none()) { 
+  if (reticulate::py_bool(model$`__dict__`["oob_score_"] != reticulate::py_none())) { 
     serialized_model["oob_score_"] <- model$oob_score_ 
     serialized_model["oob_decision_function_"] <- model$oob_decision_function_$tolist()
   }
@@ -215,7 +215,7 @@ deSerializeRandomForest <- function(model_dict) {
   model$min_impurity_split <- model_dict["min_impurity_split"]
   model$n_classes_ <- model_dict["n_classes_"]
 
-  if (model_dict$oob_score_ != reticulate::py_none()){
+  if (reticulate::py_bool(model_dict$oob_score_ != reticulate::py_none())){
     model$oob_score_ <- model_dict["oob_score_"]
     model$oob_decision_function_ <-  model_dict["oob_decision_function_"]
   }
@@ -387,23 +387,23 @@ deSerializeSVM <- function(model_dict) {
   model$`_probB` <- np$array(model_dict["probB_"])$astype(np$float64)
   model$`_intercept_` <- np$array(model_dict["_intercept_"])$astype(np$float64)
 
-  if ((model_dict$support_vectors_["meta"] != reticulate::py_none()) & 
-      (model_dict$support_vectors_["meta"] == "csr")) {
+  if (reticulate::py_bool((model_dict$support_vectors_["meta"] != reticulate::py_none())) & 
+      (reticulate::py_bool(model_dict$support_vectors_["meta"] == "csr"))) {
     model$support_vectors_ <- deSerializeCsrMatrix(model_dict$support_vectors_)
     model$`_sparse` <- TRUE
   } else {
     model$support_vectors_ <- np$array(model_dict$support_vectors_)$astype(np$float64)
     model$`_sparse` <- FALSE
   }
-  if ((model_dict$dual_coef_["meta"] != reticulate::py_none()) & 
-      (model_dict$dual_coef_["meta"] == "csr")) {
+  if (reticulate::py_bool((model_dict$dual_coef_["meta"] != reticulate::py_none())) & 
+      (reticulate::py_bool(model_dict$dual_coef_["meta"] == "csr"))) {
     model$dual_coef_ <- deSerializeCsrMatrix(model_dict$dual_coef_)
   } else {
     model$dual_coef_ <- np$array(model_dict$dual_coef_)$astype(np$float64)
   }
 
-  if ((model_dict$`_dual_coef_`["meta"] != reticulate::py_none()) & 
-      (model_dict$`_dual_coef_`["meta"] == "csr")) {
+  if (reticulate::py_bool((model_dict$`_dual_coef_`["meta"] != reticulate::py_none())) & 
+      (reticulate::py_bool(model_dict$`_dual_coef_`["meta"] == "csr"))) {
     model$`_dual_coef_` <- deSerializeCsrMatrix(model_dict$`dual_coef_`)
   } else {
     model$`_dual_coef_` <- np$array(model_dict$`_dual_coef_`)$astype(np$float64)