diff --git a/R/Dataset.R b/R/Dataset.R index 3bba33e..98b29e6 100644 --- a/R/Dataset.R +++ b/R/Dataset.R @@ -32,10 +32,26 @@ createDataset <- function(data, labels, plpModel = NULL) { r_to_py(labels$outcomeCount), numericalIndex) } else { + cat_1_mapping <- plpModel$covariateImportance %>% + dplyr::select(covariateId, cat1Idx) %>% + dplyr::rename(index = cat1Idx) %>% + dplyr::filter(!is.na(index)) %>% + as.data.frame() %>% + r_to_py() + + cat_2_mapping <- plpModel$covariateImportance %>% + dplyr::select(covariateId, cat2Idx) %>% + dplyr::rename(index = cat2Idx) %>% + dplyr::filter(!is.na(index)) %>% + as.data.frame() %>% + r_to_py() + numericalFeatures <- r_to_py(as.array(which(plpModel$covariateImportance$isNumeric))) data <- dataset(r_to_py(normalizePath(attributes(data)$path)), - numerical_features = numericalFeatures + numerical_features = numericalFeatures, + in_cat_2_mapping = cat_2_mapping, + in_cat_1_mapping = cat_1_mapping ) } diff --git a/R/Estimator.R b/R/Estimator.R index 84e0875..d2e1164 100644 --- a/R/Estimator.R +++ b/R/Estimator.R @@ -303,20 +303,22 @@ predictDeepEstimator <- function(plpModel, if (!is.null(plpModel$covariateImportance)) { # this means that the model finished training since only in the end covariateImportance is added - browser() - - # data <- createDataset(mappedData, plpModel = plpModel) + mappedData <- PatientLevelPrediction::MapIds(data$covariateData, + cohort = cohort, + mapping = plpModel$covariateImportance %>% + dplyr::select("columnId", "covariateId") + ) + data <- createDataset(mappedData, plpModel = plpModel) } else if ("plpData" %in% class(data)) { mappedData <- PatientLevelPrediction::MapIds(data$covariateData, cohort = cohort, mapping = plpModel$covariateImportance %>% dplyr::select("columnId", "covariateId") - # check this if it is correclty passing the mapped data rather than creating a new mapping ) data <- createDataset(mappedData, plpModel = plpModel) } - + # get predictions prediction <- cohort if (is.character(plpModel$model)) { @@ -336,6 +338,7 @@ predictDeepEstimator <- function(plpModel, snakeCaseToCamelCaseNames(model$estimator_settings)) estimator$model$load_state_dict(model$model_state_dict) prediction$value <- estimator$predict_proba(data) + browser() } else { prediction$value <- plpModel$model$predict_proba(data) } diff --git a/inst/python/Dataset.py b/inst/python/Dataset.py index 04cd972..513256d 100644 --- a/inst/python/Dataset.py +++ b/inst/python/Dataset.py @@ -14,7 +14,7 @@ class Data(Dataset): def __init__(self, data, labels=None, numerical_features=None, - cat2_feature_names=None): + in_cat_1_mapping=None, in_cat_2_mapping=None): desktop_path = Path.home() / "Desktop" desktop_path = Path.home() / "Desktop" @@ -75,9 +75,7 @@ def __init__(self, data, labels=None, numerical_features=None, else: self.target = torch.zeros(size=(observations,)) - if cat2_feature_names is None: - cat2_feature_names = [] - + cat2_feature_names = [] cat2_feature_names += embed_names # filter by categorical columns, @@ -101,11 +99,19 @@ def __init__(self, data, labels=None, numerical_features=None, # Now, use 'cat2_ref' as a normal DataFrame and access "columnId" data_cat_1 = data_cat.filter( ~pl.col("covariateId").is_in(cat2_ref["covariateId"])) - self.cat_1_mapping = pl.DataFrame({ - "covariateId": data_cat_1["covariateId"].unique(), - "index": pl.Series(range(1, len(data_cat_1["covariateId"].unique()) + 1)) - }) - self.cat_1_mapping.write_json(str(desktop_path / "cat1_mapping.json")) + + self.cat_1_mapping = None + if in_cat_1_mapping is None: + self.cat_1_mapping = pl.DataFrame({ + "covariateId": data_cat_1["covariateId"].unique(), + "index": pl.Series(range(1, len(data_cat_1["covariateId"].unique()) + 1)) + }) + # self.cat_1_mapping = pl.DataFrame(self.cat_1_mapping) + self.cat_1_mapping.write_json(str(desktop_path / "cat1_mapping_train.json")) + else: + self.cat_1_mapping = pl.DataFrame(in_cat_1_mapping).with_columns(pl.col('index').cast(pl.Int64), pl.col('covariateId').cast(pl.Float64)) + self.cat_1_mapping.write_json(str(desktop_path / "cat1_mapping_test.json")) + data_cat_1 = data_cat_1.join(self.cat_1_mapping, on="covariateId", how="left") \ .select(pl.col("rowId"), pl.col("index").alias("covariateId")) @@ -128,19 +134,26 @@ def __init__(self, data, labels=None, numerical_features=None, # process cat_2 features data_cat_2 = data_cat.filter( pl.col("covariateId").is_in(cat2_ref)) - self.cat_2_mapping = pl.DataFrame({ - "covariateId": data_cat_2["covariateId"].unique(), - "index": pl.Series(range(1, len(data_cat_2["covariateId"].unique()) + 1)) - }) - self.cat_2_mapping = self.cat_2_mapping.lazy() - self.cat_2_mapping = ( - self.data_ref - .filter(pl.col("covariateId").is_in(data_cat_2["covariateId"].unique())) - .select(pl.col("conceptId"), pl.col("covariateId")) - .join(self.cat_2_mapping, on="covariateId", how="left") - .collect() - ) - self.cat_2_mapping.write_json(str(desktop_path / "cat2_mapping.json")) + + self.cat_2_mapping = None + if in_cat_2_mapping is None: + self.cat_2_mapping = pl.DataFrame({ + "covariateId": data_cat_2["covariateId"].unique(), + "index": pl.Series(range(1, len(data_cat_2["covariateId"].unique()) + 1)) + }) + self.cat_2_mapping = self.cat_2_mapping.lazy() + self.cat_2_mapping = ( + self.data_ref + .filter(pl.col("covariateId").is_in(data_cat_2["covariateId"].unique())) + .select(pl.col("conceptId"), pl.col("covariateId")) + .join(self.cat_2_mapping, on="covariateId", how="left") + .collect() + ) + self.cat_2_mapping.write_json(str(desktop_path / "cat2_mapping_train.json")) + else: + self.cat_2_mapping = pl.DataFrame(in_cat_2_mapping).with_columns(pl.col('index').cast(pl.Int64), pl.col('covariateId').cast(pl.Float64)) + self.cat_2_mapping.write_json(str(desktop_path / "cat2_mapping_test.json")) + # cat_2_mapping.write_json(str(desktop_path / "cat2_mapping.json")) data_cat_2 = data_cat_2.join(self.cat_2_mapping, on="covariateId", how="left") \ diff --git a/inst/python/InitStrategy.py b/inst/python/InitStrategy.py index c0f72ba..d5a4364 100644 --- a/inst/python/InitStrategy.py +++ b/inst/python/InitStrategy.py @@ -56,7 +56,7 @@ def initialize(self, model, model_parameters, estimator_settings): # # replace weights # cat2_concept_mapping = pl.read_json(os.path.expanduser("~/Desktop/cat2_concept_mapping.json")) - cat2_mapping = pl.read_json(os.path.expanduser("~/Desktop/cat2_mapping.json")) + cat2_mapping = pl.read_json(os.path.expanduser("~/Desktop/cat2_mapping_train.json")) # print(f"cat2_mapping: {cat2_mapping}") concept_df = pl.DataFrame({"conceptId": state['names']}).with_columns(pl.col("conceptId"))