Renamed verse to verse_pagerank

R/RcppExports.R

 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
-embeddings_verse <- function(dgrmatrix_p, dgrmatrix_j, n_epochs = 100000L) {
-    .Call('_deepwalker_embeddings_verse', PACKAGE = 'deepwalker', dgrmatrix_p, dgrmatrix_j, n_epochs)
+embeddings_verse_pagerank <- function(dgrmatrix_p, dgrmatrix_j, dimension = 128L, epochs = 100000L, learning_rate = 0.0025, samples = 3L, alpha = 0.85, threads = 1L) {
+    .Call('_deepwalker_embeddings_verse_pagerank', PACKAGE = 'deepwalker', dgrmatrix_p, dgrmatrix_j, dimension, epochs, learning_rate, samples, alpha, threads)
 }
 

R/deepwalker_verse.R

@@ -2,7 +2,19 @@
 #' @description This function calculates node embeddings in a graph by using 
 #' VERSE (Versatile Graph Embeddings from Similarity Measures)
 #' @param x an object of class dgRMatrix
-#' @return TODO
+#' @param dimension integer with the dimension of the embedding
+#' @param epochs number of epochs
+#' @param learning_rate the learning rate of the algorithm
+#' @param samples integer
+#' @param alpha the alpha parameter of the algorithm
+#' @return 
+#' a list with elements 
+#' \enumerate{
+#' \item{vertices: }{the number of vertices in the graph}
+#' \item{edges: }{the number of edges (links) between the vertices in the graph}
+#' \item{embedding: }{the embeddings of the vertices}
+#' \item{edges_data and offsets_data: }{for debugging purposes now}
+#' }
 #' @export
 #' @examples
 #' library(igraphdata)
@@ -13,8 +25,28 @@
 #' x <- as_adj(karate)
 #' x <- as(x, "RsparseMatrix")
 #' 
-#' embeddings <- deepwalker_verse(x)
-deepwalker_verse <- function(x){
-  emb <- embeddings_verse(x@p, x@j, n_epochs = 10)
+#' embeddings <- deepwalker_verse(x, dimension = 64, epochs = 1000)
+deepwalker_verse <- function(x, 
+                             similarity = c("pagerank", "adjacency", "simrank"),
+                             dimension = 128,
+                             epochs = 100000, 
+                             learning_rate = 0.0025,
+                             samples = 3,
+                             alpha = 0.85){
+  stopifnot(inherits(x, "dgRMatrix"))
+  similarity <- match.arg(similarity)
+  if(similarity == "pagerank"){
+    emb <- embeddings_verse_pagerank(x@p, x@j, 
+                            dimension = dimension, 
+                            epochs = epochs,
+                            learning_rate = learning_rate,
+                            samples = samples,
+                            alpha = alpha)
+  }else if(similarity == "adjacency"){
+    
+  }else if(similarity == "simrank"){
+    
+  }
+  
   emb
 }
\ No newline at end of file

man/deepwalker_verse.Rd

@@ -4,13 +4,31 @@
 \alias{deepwalker_verse}
 \title{Versatile Graph Embeddings from Similarity Measures}
 \usage{
-deepwalker_verse(x)
+deepwalker_verse(x, similarity = c("pagerank", "adjacency", "simrank"),
+  dimension = 128, epochs = 1e+05, learning_rate = 0.0025, samples = 3,
+  alpha = 0.85)
 }
 \arguments{
 \item{x}{an object of class dgRMatrix}
+
+\item{dimension}{integer with the dimension of the embedding}
+
+\item{epochs}{number of epochs}
+
+\item{learning_rate}{the learning rate of the algorithm}
+
+\item{samples}{integer}
+
+\item{alpha}{the alpha parameter of the algorithm}
 }
 \value{
-TODO
+a list with elements 
+\enumerate{
+\item{vertices: }{the number of vertices in the graph}
+\item{edges: }{the number of edges (links) between the vertices in the graph}
+\item{embedding: }{the embeddings of the vertices}
+\item{edges_data and offsets_data: }{for debugging purposes now}
+}
 }
 \description{
 This function calculates node embeddings in a graph by using 
@@ -25,5 +43,5 @@ data(karate, package = "igraphdata")
 x <- as_adj(karate)
 x <- as(x, "RsparseMatrix")
 
-embeddings <- deepwalker_verse(x)
+embeddings <- deepwalker_verse(x, dimension = 64, epochs = 1000)
 }

src/RcppExports.cpp

@@ -5,22 +5,27 @@
 
 using namespace Rcpp;
 
-// embeddings_verse
-SEXP embeddings_verse(Rcpp::IntegerVector dgrmatrix_p, Rcpp::IntegerVector dgrmatrix_j, int n_epochs);
-RcppExport SEXP _deepwalker_embeddings_verse(SEXP dgrmatrix_pSEXP, SEXP dgrmatrix_jSEXP, SEXP n_epochsSEXP) {
+// embeddings_verse_pagerank
+SEXP embeddings_verse_pagerank(Rcpp::IntegerVector dgrmatrix_p, Rcpp::IntegerVector dgrmatrix_j, int dimension, int epochs, float learning_rate, int samples, float alpha, int threads);
+RcppExport SEXP _deepwalker_embeddings_verse_pagerank(SEXP dgrmatrix_pSEXP, SEXP dgrmatrix_jSEXP, SEXP dimensionSEXP, SEXP epochsSEXP, SEXP learning_rateSEXP, SEXP samplesSEXP, SEXP alphaSEXP, SEXP threadsSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
     Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type dgrmatrix_p(dgrmatrix_pSEXP);
     Rcpp::traits::input_parameter< Rcpp::IntegerVector >::type dgrmatrix_j(dgrmatrix_jSEXP);
-    Rcpp::traits::input_parameter< int >::type n_epochs(n_epochsSEXP);
-    rcpp_result_gen = Rcpp::wrap(embeddings_verse(dgrmatrix_p, dgrmatrix_j, n_epochs));
+    Rcpp::traits::input_parameter< int >::type dimension(dimensionSEXP);
+    Rcpp::traits::input_parameter< int >::type epochs(epochsSEXP);
+    Rcpp::traits::input_parameter< float >::type learning_rate(learning_rateSEXP);
+    Rcpp::traits::input_parameter< int >::type samples(samplesSEXP);
+    Rcpp::traits::input_parameter< float >::type alpha(alphaSEXP);
+    Rcpp::traits::input_parameter< int >::type threads(threadsSEXP);
+    rcpp_result_gen = Rcpp::wrap(embeddings_verse_pagerank(dgrmatrix_p, dgrmatrix_j, dimension, epochs, learning_rate, samples, alpha, threads));
     return rcpp_result_gen;
 END_RCPP
 }
 
 static const R_CallMethodDef CallEntries[] = {
-    {"_deepwalker_embeddings_verse", (DL_FUNC) &_deepwalker_embeddings_verse, 3},
+    {"_deepwalker_embeddings_verse_pagerank", (DL_FUNC) &_deepwalker_embeddings_verse_pagerank, 8},
     {NULL, NULL, 0}
 };
 

src/makefile

-TARGETS = verse verse-neigh verse-simrank
-
-CXXFLAGS = -std=c++11 -march=native
-
-ifeq ($(CXX), g++)
-	CXXFLAGS += -fopenmp -Ofast
-else ifeq ($(CXX), icpc)
-	CXXFLAGS += -qopenmp -O3 -no-prec-div -ansi-alias -ip -static-intel
-else ifeq ($(CXX), clang++)
-	CXXFLAGS += -fopenmp=libomp -O3 -Wno-shift-op-parentheses
-else
-	# use g++ by default
-	CXX = g++
-	CXXFLAGS += -fopenmp -Ofast
-endif
-
-all: $(TARGETS)
-
-$(TARGETS):
-	$(CXX) $(CXXFLAGS) $(LDFLAGS) -o $@ $@.cpp
-
-clean:
-	rm -rfv $(TARGETS)
-
-.PHONY: all clean
\ No newline at end of file

src/verse.cpp

@@ -137,17 +137,6 @@ inline int sample_rw(int node) {
   return n2;
 }
 
-int ArgPos(char *str, int argc, char **argv) {
-  for (int a = 1; a < argc; a++)
-    if (!strcmp(str, argv[a])) {
-      if (a == argc - 1) {
-        cout << "Argument missing for " << str << endl;
-        exit(1);
-      }
-      return a;
-    }
-  return -1;
-}
 
 inline void update(float *w_s, float *w_t, int label, const float bias) {
   float score = -bias;
@@ -182,7 +171,7 @@ void Train() {
           break;
         if (tid == 0)
           if (!silent)
-            cout << fixed << "\r Progress " << std::setprecision(2)
+            Rcpp::Rcout << fixed << "\r Progress " << std::setprecision(2)
                  << step / (float)(total_steps + 1) * 100 << "%";
         last_ncount = ncount;
       }
@@ -203,16 +192,21 @@ void Train() {
 
 
 // [[Rcpp::export]]
-SEXP embeddings_verse(Rcpp::IntegerVector dgrmatrix_p, Rcpp::IntegerVector dgrmatrix_j,
-                      int n_epochs = 100000) {
+SEXP embeddings_verse_pagerank(Rcpp::IntegerVector dgrmatrix_p, Rcpp::IntegerVector dgrmatrix_j,
+                      int dimension = 128,
+                      int epochs = 100000, 
+                      float learning_rate = 0.0025,
+                      int samples = 3,
+                      float alpha = 0.85,
+                      int threads = 1) {
   step = 0;
   silent = false;
-  n_threads = 1;
-  global_lr = 0.0025f;
-  //n_epochs = ;
-  n_hidden = 128;
-  n_samples = 3;
-  ppralpha = 0.85f;
+  n_threads = threads;
+  global_lr = learning_rate;
+  n_epochs = epochs;
+  n_hidden = dimension;
+  n_samples = samples;
+  ppralpha = alpha;
   
   // 
   // Set random nr
@@ -249,12 +243,12 @@ SEXP embeddings_verse(Rcpp::IntegerVector dgrmatrix_p, Rcpp::IntegerVector dgrma
   for (int i = 0; i < (int)nv; i++)
     degrees[i] = offsets[i + 1] - offsets[i];
   total_steps = n_epochs * (long long)nv;
-  cout << "Total steps (mil): " << total_steps / 1000000. << endl;
+  Rcpp::Rcout << "Total steps (mil): " << total_steps / 1000000. << endl;
   chrono::steady_clock::time_point begin = chrono::steady_clock::now();
   Train();
   chrono::steady_clock::time_point end = chrono::steady_clock::now();
   
-  cout << endl
+  Rcpp::Rcout << endl
        << "Calculations took "
        << chrono::duration_cast<std::chrono::duration<float>>(end - begin)
   .count()
@@ -277,8 +271,8 @@ SEXP embeddings_verse(Rcpp::IntegerVector dgrmatrix_p, Rcpp::IntegerVector dgrma
   }
    */
   Rcpp::NumericMatrix embeddings(nv, n_hidden);
-  for (int i = 0; i < nv; i++){
-    for (int j = 0; j < n_hidden; j++){
+  for (int i = 0; i < (int)nv; i++){
+    for (int j = 0; j < (int)n_hidden; j++){
       embeddings(i, j) = w0[i*nv + j];
     }
   }