add note about failures with start_with_ALS ref #41

R/fit.R

@@ -512,6 +512,10 @@ NULL
 #' This might help to speed up the procedure by starting closer to an
 #' optimum. This option is not available when the side information is passed
 #' as sparse matrices.
+#' 
+#' Note that this option will not work (will throw an error) if there are
+#' users or items without side information, or if the input data is otherwise
+#' problematic (e.g. users/items which are duplicates of each other).
 #' @param apply_log_transf Whether to apply a logarithm transformation on the values of `X`
 #' (i.e. `X := log(X)`)
 #' @param NA_as_zero Whether to take missing entries in the `X` matrix as zeros (only

cmfrec/__init__.py

@@ -4395,7 +4395,7 @@ def from_model_matrices(A, B, glob_mean=0., precompute=True,
         if precompute:
             new_model.force_precompute_for_predictions()
         return new_model
-        
+
 
 class CMF_implicit(_CMF):
     """
@@ -7844,6 +7844,10 @@ class ContentBased(_OMF_Base):
         This might help to speed up the procedure by starting closer to an
         optimum. This option is not available when the side information is passed
         as sparse matrices.
+
+        Note that this option will not work (will throw an error) if there are
+        users or items without side information, or if the input data is otherwise
+        problematic (e.g. users/items which are duplicates of each other).
     nthreads : int
         Number of parallel threads to use. If passing a negative number, will
         use the same formula as joblib (maximum threads + 1 - nthreads).

include/cmfrec.h.in

@@ -1616,6 +1616,11 @@ CMFREC_EXPORTABLE int_t predict_X_old_most_popular
                            might reduce fitting times due to starting closer
                            to a local optimum. Sparse inputs for 'U' and 'I'
                            are not supported with this option.
+                           Note that this option will not work (will throw an
+                           error) if there are users or items without side
+                           information, or if the input data is otherwise
+                           problematic (e.g. users/items which are duplicates of
+                           each other).
                            Recommended value: true
 
     --- For prediction functionality ---