[R] change internal calls to use keyword args (#267)

* [R] Used keyword args in internal calls to estimator classes (fixes #244)

* fixed error in FastRGF_Classifier calls

* fixed error in FastRGF_Regressor calls

* updated news

R-package/NEWS.md

@@ -1,6 +1,7 @@
 ## RGF 1.0.5.1
 
 * Source files were broken up into one file per exported object as of [#266](https://github.com/RGF-team/rgf/pull/266)
+* Internal  calls to estimator constructors were changed to use keyword, instead of positional, arguments. [#267](https://github.com/RGF-team/rgf/pull/267)
 
 ## RGF 1.0.5
 

R-package/R/FastRGF_Classifier.R

@@ -151,24 +151,24 @@ FastRGF_Classifier <- R6::R6Class(
             # initialize FastRGF_Classifier
             #------------------------------
             private$rgf_init = RGF_mod$FastRGFClassifier(
-                as.integer(n_estimators)
-                , as.integer(max_depth)
-                , as.integer(max_leaf)
-                , tree_gain_ratio
-                , min_samples_leaf
-                , loss
-                , l1
-                , l2
-                , opt_algorithm
-                , learning_rate
-                , max_bin
-                , min_child_weight
-                , data_l2
-                , as.integer(sparse_max_features)
-                , as.integer(sparse_min_occurences)
-                , calc_prob
-                , as.integer(n_jobs)
-                , as.integer(verbose)
+                n_estimators = as.integer(n_estimators)
+                , max_depth = as.integer(max_depth)
+                , max_leaf = as.integer(max_leaf)
+                , tree_gain_ratio = tree_gain_ratio
+                , min_samples_leaf = min_samples_leaf
+                , loss = loss
+                , l1 = l1
+                , l2 = l2
+                , opt_algorithm = opt_algorithm
+                , learning_rate = learning_rate
+                , max_bin = max_bin
+                , min_child_weight = min_child_weight
+                , data_l2 = data_l2
+                , sparse_max_features = as.integer(sparse_max_features)
+                , sparse_min_occurences = as.integer(sparse_min_occurences)
+                , calc_prob = calc_prob
+                , n_jobs = as.integer(n_jobs)
+                , verbose = as.integer(verbose)
             )
         }
     )

R-package/R/FastRGF_Regressor.R

@@ -139,22 +139,22 @@ FastRGF_Regressor <- R6::R6Class(
             # initialize FastRGF_Regressor
             #-----------------------------
             private$rgf_init <- RGF_mod$FastRGFRegressor(
-                as.integer(n_estimators)
-                , as.integer(max_depth)
-                , as.integer(max_leaf)
-                , tree_gain_ratio
-                , min_samples_leaf
-                , l1
-                , l2
-                , opt_algorithm
-                , learning_rate
-                , max_bin
-                , min_child_weight
-                , data_l2
-                , as.integer(sparse_max_features)
-                , as.integer(sparse_min_occurences)
-                , as.integer(n_jobs)
-                , as.integer(verbose)
+                n_estimators = as.integer(n_estimators)
+                , max_depth = as.integer(max_depth)
+                , max_leaf = as.integer(max_leaf)
+                , tree_gain_ratio = tree_gain_ratio
+                , min_samples_leaf = min_samples_leaf
+                , l1 = l1
+                , l2 = l2
+                , opt_algorithm = opt_algorithm
+                , learning_rate = learning_rate
+                , max_bin = max_bin
+                , min_child_weight = min_child_weight
+                , data_l2 = data_l2
+                , sparse_max_features = as.integer(sparse_max_features)
+                , sparse_min_occurences = as.integer(sparse_min_occurences)
+                , n_jobs = as.integer(n_jobs)
+                , verbose = as.integer(verbose)
             )
         }
     )

R-package/R/RGF_Classifier.R

@@ -158,23 +158,23 @@ RGF_Classifier <- R6::R6Class(
             # initialize RGF_Classifier
             #--------------------------
             private$rgf_init <- RGF_mod$RGFClassifier(
-                as.integer(max_leaf)
-                , as.integer(test_interval)
-                , algorithm
-                , loss
-                , reg_depth
-                , l2
-                , sl2
-                , normalize
-                , min_samples_leaf
-                , n_iter
-                , as.integer(n_tree_search)
-                , as.integer(opt_interval)
-                , learning_rate
-                , calc_prob
-                , as.integer(n_jobs)
-                , memory_policy
-                , as.integer(verbose)
+                max_leaf = as.integer(max_leaf)
+                , test_interval = as.integer(test_interval)
+                , algorithm = algorithm
+                , loss = loss
+                , reg_depth = reg_depth
+                , l2 = l2
+                , sl2 = sl2
+                , normalize = normalize
+                , min_samples_leaf = min_samples_leaf
+                , n_iter = n_iter
+                , n_tree_search = as.integer(n_tree_search)
+                , opt_interval = as.integer(opt_interval)
+                , learning_rate = learning_rate
+                , calc_prob = calc_prob
+                , n_jobs = as.integer(n_jobs)
+                , memory_policy = memory_policy
+                , verbose = as.integer(verbose)
             )
         }
     )

R-package/R/RGF_Regressor.R

@@ -145,21 +145,21 @@ RGF_Regressor <- R6::R6Class(
             # initialize RGF_Regressor
             #------------------------
             private$rgf_init <- RGF_mod$RGFRegressor(
-                as.integer(max_leaf)
-                , as.integer(test_interval)
-                , algorithm
-                , loss
-                , reg_depth
-                , l2
-                , sl2
-                , normalize
-                , min_samples_leaf
-                , n_iter
-                , as.integer(n_tree_search)
-                , as.integer(opt_interval)
-                , learning_rate
-                , memory_policy
-                , as.integer(verbose)
+                max_leaf = as.integer(max_leaf)
+                , test_interval = as.integer(test_interval)
+                , algorithm = algorithm
+                , loss = loss
+                , reg_depth = reg_depth
+                , l2 = l2
+                , sl2 = sl2
+                , normalize = normalize
+                , min_samples_leaf = min_samples_leaf
+                , n_iter = n_iter
+                , n_tree_search = as.integer(n_tree_search)
+                , opt_interval = as.integer(opt_interval)
+                , learning_rate = learning_rate
+                , memory_policy = memory_policy
+                , verbose = as.integer(verbose)
             )
         }
     )