optimize R code, avoid double work in transform

R/model_WRMF.R

@@ -180,10 +180,15 @@ WRMF = R6::R6Class(
           RhpcBLASctl::blas_set_num_threads(blas_threads_keep)
         })
       }
-
+      logger$debug("converting input user-item matrix")
       c_ui = MatrixExtra::as.csc.matrix(x)
+      # c_ui = as(x, "CsparseMatrix")
+      logger$debug("pre-processing input")
       c_ui = private$preprocess(c_ui)
-      c_iu = MatrixExtra::t_shallow(MatrixExtra::as.csr.matrix(x))
+      logger$debug("creating item-user matrix")
+      c_iu = MatrixExtra::t_shallow(MatrixExtra::as.csr.matrix(c_ui))
+      # c_iu = t(c_ui)
+      logger$debug("created item-user matrix")
       # store item_ids in order to use them in predict method
       private$item_ids = colnames(c_ui)
 
@@ -195,7 +200,7 @@ WRMF = R6::R6Class(
       n_user = nrow(c_ui)
       n_item = ncol(c_ui)
 
-      logger$trace("initializing U")
+      logger$debug("initializing U")
       if (private$precision == "double") {
         private$U = large_rand_matrix(private$rank, n_user)
         # for item biases
@@ -210,7 +215,7 @@ WRMF = R6::R6Class(
       }
 
       if (is.null(self$components)) {
-
+        logger$debug("initializing components")
         if (private$solver_code == 1L) { ### <- cholesky
           if (private$precision == "double") {
             self$components = matrix(0, private$rank, n_item)
@@ -331,6 +336,8 @@ WRMF = R6::R6Class(
 
         loss_prev_iter = loss
       }
+      logger$debug("solver finished")
+
       if (private$precision == "double")
         data.table::setattr(self$components, "dimnames", list(NULL, colnames(x)))
       else
@@ -348,7 +355,8 @@ WRMF = R6::R6Class(
       # call extra transform to ensure results from transform() and fit_transform()
       # are the same (due to avoid_cg, etc)
       # this adds some extra computation, but not a big deal though
-      self$transform(x)
+      # self$transform(x)
+      private$transform_(c_iu, ...)
     },
     # project new users into latent user space - just make ALS step given fixed items matrix
     #' @description create user embeddings for new input
@@ -368,6 +376,41 @@ WRMF = R6::R6Class(
         x = MatrixExtra::t_shallow(x)
       }
 
+      x = private$preprocess(x)
+
+      if (self$global_bias != 0. && private$feedback == "explicit")
+        x@x = x@x - self$global_bias
+
+      private$transform_(x, ...)
+    }
+  ),
+  #### private -----
+  private = list(
+    solver_code = NULL,
+    cg_steps = NULL,
+    scorers = NULL,
+    lambda = NULL,
+    dynamic_lambda = FALSE,
+    rank = NULL,
+    non_negative = NULL,
+    cnt_u = NULL,
+    # user factor matrix = rank * n_users
+    U = NULL,
+    # item factor matrix = rank * n_items
+    I = NULL,
+    # preprocess - transformation of input matrix before passing it to ALS
+    # for example we can scale each row or apply log() to values
+    # this is essentially "confidence" transformation from WRMF article
+    preprocess = NULL,
+    feedback = NULL,
+    precision = NULL,
+    XtX = NULL,
+    solver = NULL,
+    with_user_item_bias = NULL,
+    with_global_bias = NULL,
+    init_user_item_bias = NULL,
+    transform_ = function(x, ...) {
+      logger$debug('starting transform')
       if (private$feedback == "implicit" ) {
         logger$trace("WRMF$transform(): calling `RhpcBLASctl::blas_set_num_threads(1)` (to avoid thread contention)")
         blas_threads_keep = RhpcBLASctl::blas_get_num_procs()
@@ -377,11 +420,6 @@ WRMF = R6::R6Class(
           RhpcBLASctl::blas_set_num_threads(blas_threads_keep)
         })
       }
-
-      x = private$preprocess(x)
-      if (self$global_bias != 0. && private$feedback == "explicit")
-        x@x = x@x - self$global_bias
-
       if (private$precision == "double") {
         res = matrix(0, nrow = private$rank, ncol = ncol(x))
       } else {
@@ -391,7 +429,7 @@ WRMF = R6::R6Class(
       if (private$with_user_item_bias) {
         res[1, ] = if(private$precision == "double") 1.0 else float::fl(1.0)
       }
-
+      logger$debug('starting transform solver')
       loss = private$solver(
         x,
         self$components,
@@ -401,42 +439,17 @@ WRMF = R6::R6Class(
         cnt_X = private$cnt_u,
         avoid_cg = TRUE
       )
+      logger$debug('finished transform solver')
 
       res = t(res)
 
       if (private$precision == "double")
         setattr(res, "dimnames", list(colnames(x), NULL))
       else
         setattr(res@Data, "dimnames", list(colnames(x), NULL))
-
+      logger$debug('finished transform')
       res
     }
-  ),
-  #### private -----
-  private = list(
-    solver_code = NULL,
-    cg_steps = NULL,
-    scorers = NULL,
-    lambda = NULL,
-    dynamic_lambda = FALSE,
-    rank = NULL,
-    non_negative = NULL,
-    cnt_u = NULL,
-    # user factor matrix = rank * n_users
-    U = NULL,
-    # item factor matrix = rank * n_items
-    I = NULL,
-    # preprocess - transformation of input matrix before passing it to ALS
-    # for example we can scale each row or apply log() to values
-    # this is essentially "confidence" transformation from WRMF article
-    preprocess = NULL,
-    feedback = NULL,
-    precision = NULL,
-    XtX = NULL,
-    solver = NULL,
-    with_user_item_bias = NULL,
-    with_global_bias = NULL,
-    init_user_item_bias = NULL
   )
 )