Running ‘testthat.R’ [47s/53s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(healthcareai)
  > 
  > test_check("healthcareai")
  1. Error: rf deploy classification pushes values to SQLite (@test-deploy-pushes-to-sqlite.R#154) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(dRF$run()) at testthat/test-deploy-pushes-to-sqlite.R:154
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: dRF$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  2. Error: rf deploy regression pushes values to SQLite (@test-deploy-pushes-to-sqlite.R#180) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(dRF$run()) at testthat/test-deploy-pushes-to-sqlite.R:180
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: dRF$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  3. Error: rf classification predicted val (w/out mtry tuning) is the same each 
            time (@test-deploy-specific-values.R#94) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(RandomForest$run()) at testthat/test-deploy-specific-values.R:94
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: RandomForest$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  4. Error: rf classification predicted val (w/ mtry tuning) is the same each 
            time (@test-deploy-specific-values.R#130) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(RandomForest$run()) at testthat/test-deploy-specific-values.R:130
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: RandomForest$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  5. Error: rf regression predicted val (w/out mtry tuning) is same each time (@test-deploy-specific-values.R#165) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(RandomForest$run()) at testthat/test-deploy-specific-values.R:165
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: RandomForest$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  6. Error: rf regression predicted val (w/mtry tuning) is same each time (@test-deploy-specific-values.R#202) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(RandomForest$run()) at testthat/test-deploy-specific-values.R:202
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: RandomForest$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  7. Error: rf predicted df doesn't have NAs (@test-deploy-to-df-has-no-nulls.R#87) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(RandomForest$run()) at testthat/test-deploy-to-df-has-no-nulls.R:87
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: RandomForest$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  8. Error: AUC_rf is the same each time the test is run (@test-develop-supervised-model.R#142) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(rf$run()) at testthat/test-develop-supervised-model.R:142
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: rf$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  9. Error: rmse_rf is the same each time the test is run non-factor column (@test-develop-supervised-model.R#155) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(rf$run()) at testthat/test-develop-supervised-model.R:155
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: rf$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  10. Error: mae_rf is the same each time the test is run non-factor column (@test-develop-supervised-model.R#168) 
  The tuning parameter grid should have columns mtry, splitrule, min.node.size
  1: capture.output(rf$run()) at testthat/test-develop-supervised-model.R:168
  2: evalVis(expr)
  3: withVisible(eval(expr, pf))
  4: eval(expr, pf)
  5: eval(expr, pf)
  6: rf$run()
  7: self$buildModel()
  8: train(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  9: train.default(x = private$dfTrain[, !(colnames(private$dfTrain) == self$params$predictedCol)], 
         y = adjustedY, method = "ranger", importance = "impurity", metric = rfTrainParams.metric, 
         num.trees = self$params$trees, tuneGrid = private$grid, trControl = train.control)
  10: stop(paste("The tuning parameter grid should have columns", paste(tuneNames, collapse = ", ", 
         sep = "")), call. = FALSE)
  
  Error: The tuning parameter grid should have columns mtry, splitrule, min.node.size
  testthat results ================================================================
  OK: 61 SKIPPED: 10 FAILED: 10
  1.  Error: rf deploy classification pushes values to SQLite (@test-deploy-pushes-to-sqlite.R#154) 
  2.  Error: rf deploy regression pushes values to SQLite (@test-deploy-pushes-to-sqlite.R#180) 
  3.  Error: rf classification predicted val (w/out mtry tuning) is the same each 
            time (@test-deploy-specific-values.R#94) 
  4.  Error: rf classification predicted val (w/ mtry tuning) is the same each 
            time (@test-deploy-specific-values.R#130) 
  5.  Error: rf regression predicted val (w/out mtry tuning) is same each time (@test-deploy-specific-values.R#165) 
  6.  Error: rf regression predicted val (w/mtry tuning) is same each time (@test-deploy-specific-values.R#202) 
  7.  Error: rf predicted df doesn't have NAs (@test-deploy-to-df-has-no-nulls.R#87) 
  8.  Error: AUC_rf is the same each time the test is run (@test-develop-supervised-model.R#142) 
  9.  Error: rmse_rf is the same each time the test is run non-factor column (@test-develop-supervised-model.R#155) 
  10. Error: mae_rf is the same each time the test is run non-factor column (@test-develop-supervised-model.R#168) 
  
  Error: testthat unit tests failed
  Execution halted