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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,115 +1,127 @@ | ||
| #' A torch dataset | ||
| #' A torch dataset | ||
| #' @import data.table | ||
| #' @export | ||
| Dataset <- torch::dataset( | ||
| name = 'myDataset', | ||
| name = "myDataset", | ||
| #' @param data a dataframe like object with the covariates | ||
| #' @param labels a dataframe with the labels | ||
| #' @param numericalIndex in what column numeric data is in (if any) | ||
| #' @param all if True then returns all features instead of splitting num/cat | ||
| initialize = function(data, labels = NULL, numericalIndex = NULL, all=FALSE) { | ||
| initialize = function(data, labels = NULL, numericalIndex = NULL, all = FALSE) { | ||
| # determine numeric | ||
| if (is.null(numericalIndex) && all==FALSE) { | ||
| numericalIndex <- data %>% dplyr::group_by(columnId) %>% dplyr::collect() %>% | ||
| dplyr::summarise(n=dplyr::n_distinct(.data$covariateValue)) %>% dplyr::pull(n)>1 | ||
| self$numericalIndex <- numericalIndex | ||
| } | ||
| else { | ||
| if (is.null(numericalIndex) && all == FALSE) { | ||
| numericalIndex <- data %>% | ||
| dplyr::group_by(columnId) %>% | ||
| dplyr::collect() %>% | ||
| dplyr::summarise(n = dplyr::n_distinct(.data$covariateValue)) %>% | ||
| dplyr::pull(n) > 1 | ||
| self$numericalIndex <- numericalIndex | ||
| } else { | ||
| self$numericalIndex <- NULL | ||
| } | ||
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| # add labels if training (make 0 vector for prediction) | ||
| if(!is.null(labels)){ | ||
| if (!is.null(labels)) { | ||
| self$target <- torch::torch_tensor(labels) | ||
| } else{ | ||
| if (all==FALSE) { | ||
| self$target <- torch::torch_tensor(rep(0, data %>% dplyr::distinct(rowId) | ||
| %>% dplyr::collect() %>% nrow())) | ||
| } else{ | ||
| } else { | ||
| if (all == FALSE) { | ||
| self$target <- torch::torch_tensor(rep(0, data %>% dplyr::distinct(rowId) | ||
| %>% dplyr::collect() %>% nrow())) | ||
| } else { | ||
| self$target <- torch::torch_tensor(rep(0, dim(data)[[1]])) | ||
| } | ||
| } | ||
| # Weight to add in loss function to positive class | ||
| self$posWeight <- (self$target==0)$sum()/self$target$sum() | ||
| self$posWeight <- (self$target == 0)$sum() / self$target$sum() | ||
| # for DeepNNTorch | ||
| if (all) { | ||
| self$all <- torch::torch_tensor(as.matrix(data), dtype = torch::torch_float32()) | ||
| self$cat <- NULL | ||
| self$num <- NULL | ||
| return() | ||
| self$all <- torch::torch_tensor(as.matrix(data), dtype = torch::torch_float32()) | ||
| self$cat <- NULL | ||
| self$num <- NULL | ||
| return() | ||
| } | ||
| # add features | ||
| catColumns <- which(!numericalIndex) | ||
| dataCat <- dplyr::filter(data,columnId %in% catColumns) %>% | ||
| catColumns <- which(!numericalIndex) | ||
| dataCat <- dplyr::filter(data, columnId %in% catColumns) %>% | ||
| dplyr::arrange(columnId) %>% | ||
| dplyr::group_by(columnId) %>% | ||
| dplyr::collect() %>% | ||
| dplyr::mutate(newColumnId=dplyr::cur_group_id()) %>% dplyr::ungroup() %>% | ||
| dplyr::select(c('rowId', 'newColumnId')) %>% dplyr::rename(columnId=newColumnId) | ||
| dplyr::group_by(columnId) %>% | ||
| dplyr::collect() %>% | ||
| dplyr::mutate(newColumnId = dplyr::cur_group_id()) %>% | ||
| dplyr::ungroup() %>% | ||
| dplyr::select(c("rowId", "newColumnId")) %>% | ||
| dplyr::rename(columnId = newColumnId) | ||
| # the fastest way I found so far to convert data using data.table | ||
| # 1.5 min for 100k rows :( | ||
| dt <- data.table::data.table(rows=dataCat$rowId, cols=dataCat$columnId) | ||
| maxFeatures <- max(dt[, .N, by=rows][,N]) | ||
| dt <- data.table::data.table(rows = dataCat$rowId, cols = dataCat$columnId) | ||
| maxFeatures <- max(dt[, .N, by = rows][, N]) | ||
| start <- Sys.time() | ||
| tensorList <- lapply(1:max(data %>% dplyr::pull(rowId)), function(x) { | ||
| torch::torch_tensor(dt[rows==x, cols]) | ||
| }) | ||
| torch::torch_tensor(dt[rows == x, cols]) | ||
| }) | ||
| self$lengths <- lengths | ||
| self$cat <- torch::nn_utils_rnn_pad_sequence(tensorList, batch_first = T) | ||
| delta <- Sys.time() - start | ||
| ParallelLogger::logInfo("Data conversion for dataset took ", signif(delta, 3), " ", attr(delta, "units")) | ||
| if (sum(numericalIndex) == 0) { | ||
| self$num <- NULL | ||
| } else { | ||
| numericalData <- data %>% dplyr::filter(columnId %in% !! which(numericalIndex)) %>% dplyr::collect() | ||
| numericalData <-numericalData %>% dplyr::group_by(columnId) %>% dplyr::mutate(newId = dplyr::cur_group_id()) | ||
| indices <- torch::torch_tensor(cbind(numericalData$rowId, numericalData$newId), dtype=torch::torch_long())$t_() | ||
| values <- torch::torch_tensor(numericalData$covariateValue,dtype=torch::torch_float32()) | ||
| self$num <- torch::torch_sparse_coo_tensor(indices=indices, | ||
| values=values, | ||
| size=c(self$target$shape,sum(numericalIndex)))$to_dense() | ||
| } else { | ||
| numericalData <- data %>% | ||
| dplyr::filter(columnId %in% !!which(numericalIndex)) %>% | ||
| dplyr::collect() | ||
| numericalData <- numericalData %>% | ||
| dplyr::group_by(columnId) %>% | ||
| dplyr::mutate(newId = dplyr::cur_group_id()) | ||
| indices <- torch::torch_tensor(cbind(numericalData$rowId, numericalData$newId), dtype = torch::torch_long())$t_() | ||
| values <- torch::torch_tensor(numericalData$covariateValue, dtype = torch::torch_float32()) | ||
| self$num <- torch::torch_sparse_coo_tensor( | ||
| indices = indices, | ||
| values = values, | ||
| size = c(self$target$shape, sum(numericalIndex)) | ||
| )$to_dense() | ||
| } | ||
| if (self$cat$shape[1] != self$num$shape[1]) | ||
| if (self$cat$shape[1] != self$num$shape[1]) { | ||
| browser() | ||
| } | ||
| }, | ||
|
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| getNumericalIndex = function() { | ||
| return( | ||
| self$numericalIndex | ||
| ) | ||
| }, | ||
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| numCatFeatures = function() { | ||
| return ( | ||
| return( | ||
| sum(!self$numericalIndex) | ||
| ) | ||
| }, | ||
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| numNumFeatures = function() { | ||
| if (!is.null(self$num)) { | ||
| if (!is.null(self$num)) { | ||
| return(self$num$shape[2]) | ||
| } else { | ||
| } else { | ||
| return(0) | ||
| } | ||
| } | ||
| }, | ||
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| .getbatch = function(item) { | ||
| if (length(item)==1) { | ||
| if (length(item) == 1) { | ||
| # add leading singleton dimension since models expects 2d tensors | ||
| return(list(batch = list(cat = self$cat[item]$unsqueeze(1), | ||
| num = self$num[item]$unsqueeze(1)), | ||
| target = self$target[item]$unsqueeze(1))) | ||
| } | ||
| else { | ||
| return(list(batch = list(cat = self$cat[item], | ||
| num = self$num[item]), | ||
| target = self$target[item]))} | ||
| return(list( | ||
| batch = list( | ||
| cat = self$cat[item]$unsqueeze(1), | ||
| num = self$num[item]$unsqueeze(1) | ||
| ), | ||
| target = self$target[item]$unsqueeze(1) | ||
| )) | ||
| } else { | ||
| return(list( | ||
| batch = list( | ||
| cat = self$cat[item], | ||
| num = self$num[item] | ||
| ), | ||
| target = self$target[item] | ||
| )) | ||
| } | ||
| }, | ||
| .length = function() { | ||
| self$target$size()[[1]] # shape[1] | ||
| } | ||
| ) | ||
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