Clean up

R/Estimator.R

@@ -218,12 +218,12 @@ fitEstimator <- function(trainData,
       included = incs,
       covariateValue = 0,
       isNumeric = .data$columnId %in% cvResult$numericalIndex
-      # get mapping maybe here
     ) %>%
     left_join(cvResult$cat1Mapping %>% rename(cat1Idx = index), by = "covariateId") %>%
     left_join(cvResult$cat2Mapping %>% rename(cat2Idx = index), by = "covariateId")
 
   comp <- start - Sys.time()
+  modelSettings$estimatorSettings$initStrategy <- NULL
   result <- list(
     model = cvResult$estimator,
     preprocessing = list(
@@ -271,7 +271,6 @@ fitEstimator <- function(trainData,
       hyperParamSearch = hyperSummary
     ),
     covariateImportance = covariateRef
-    # also return mapping as part of covariateRef above, not necessary to do separately
   )
 
   class(result) <- "plpModel"
@@ -318,7 +317,7 @@ predictDeepEstimator <- function(plpModel,
     )
     data <- createDataset(mappedData, plpModel = plpModel)
   }
-  
+
   # get predictions
   prediction <- cohort
   if (is.character(plpModel$model)) {

inst/python/Dataset.py

@@ -10,8 +10,6 @@
 import json
 import os
 
-# this one needs to have a parameter to not do the mapping again
-
 class Data(Dataset):
     def __init__(self, data, labels=None, numerical_features=None,
                  in_cat_1_mapping=None, in_cat_2_mapping=None):
@@ -50,12 +48,6 @@ def __init__(self, data, labels=None, numerical_features=None,
             ).lazy()
         else:
             data = pl.scan_ipc(pathlib.Path(data).joinpath("covariates/*.arrow"))
-
-        # # Fetch only the first few rows
-        # data_head = data.limit(100).collect()
-        # print("Head of the data:")
-        # print(data_head)
-
         observations = data.select(pl.col("rowId").max()).collect()[0, 0]
         # detect features are numeric
         if numerical_features is None:
@@ -248,3 +240,7 @@ def __getitem__(self, item):
             batch["num"] = batch["num"].unsqueeze(0)
         return [batch, self.target[item].squeeze()]
 
+
+
+
+

tests/testthat/test-PoincareTransformer.R

@@ -0,0 +1,39 @@
+
+test_that("Poincare Transformer works", {
+  settings <- setCustomEmbeddingTransformer("/Users/henrikjohn/Desktop/poincare_model_dim_3.pt")
+
+  results <- PatientLevelPrediction::runPlp(
+    plpData = plpData,
+    outcomeId = 3,
+    modelSettings = settings,
+    analysisId = "Analysis_Poincare",
+    analysisName = "Testing Deep Learning",
+    populationSettings = populationSet,
+    splitSettings = PatientLevelPrediction::createDefaultSplitSetting(),
+    sampleSettings = PatientLevelPrediction::createSampleSettings(),
+    featureEngineeringSettings = PatientLevelPrediction::createFeatureEngineeringSettings(),
+    preprocessSettings = PatientLevelPrediction::createPreprocessSettings(),
+    executeSettings = PatientLevelPrediction::createExecuteSettings(
+      runSplitData = TRUE,
+      runSampleData = FALSE,
+      runfeatureEngineering = FALSE,
+      runPreprocessData = FALSE,
+      runModelDevelopment = TRUE,
+      runCovariateSummary = FALSE
+    ),
+    saveDirectory = file.path(testLoc, "Poincare")
+  )
+
+
+  params <- defaultTransformer$param[[1]]
+
+  expect_equal(params$numBlocks, 3)
+  expect_equal(params$dimToken, 192)
+  expect_equal(params$numHeads, 8)
+  expect_equal(params$resDropout, 0.0)
+  expect_equal(params$attDropout, 0.2)
+
+  settings <- attr(defaultTransformer, "settings")
+
+  expect_equal(settings$name, "defaultTransformer")
+})