Another round of bugfixes

src/conceptnet.jl

@@ -10,7 +10,7 @@ function init(::Type{ConceptNet})
              DataDeps.unpack,
              :multi),
             ("English",
-             "https://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-17.06.txt.gz",
+             "https://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-en-17.06.txt.gz",
              "numberbatch-en-17.06.txt",
              "72faf0a487c61b9a6a8c9ff0a1440d2f4936bb19102bddf27a833c2567620f2d",
              DataDeps.unpack,
@@ -47,15 +47,10 @@ end
 
 function _load_embeddings(::Type{<:ConceptNet}, embedding_file, max_vocab_size, keep_words)
     local LL, indexed_words, index
-    if any(endswith.(embedding_file, [".h5", ".hdf5"]))
-        LL, indexed_words = _load_hdf5_embeddings(embedding_file,
-                                                  max_vocab_size=max_vocab_size,
-                                                  keep_words=keep_words)
-    else
-        LL, indexed_words = _load_txt_embeddings(embedding_file,
-                                                 max_vocab_size=max_vocab_size,
-                                                 keep_words=keep_words)
-    end
+    _loader = ifelse(any(endswith.(embedding_file, [".h5", ".hdf5"])),
+                     _load_hdf5_embeddings,
+                     _load_txt_embeddings)
+    LL, indexed_words = _loader(embedding_file, max_vocab_size, keep_words)
     return LL, indexed_words
 end
 
@@ -69,6 +64,7 @@ end
 
 
 function _load_txt_embeddings(file::AbstractString, max_vocab_size, keep_words)
+    local LL, indexed_words
     open(file, "r") do fid
         vocab_size, vector_size = map(x->parse(Int,x), split(readline(fid)))
         max_stored_vocab_size = _get_vocab_size(vocab_size, max_vocab_size)
@@ -84,18 +80,23 @@ function _load_txt_embeddings(file::AbstractString, max_vocab_size, keep_words)
             return word, embedding
         end
 
+        # TODO Improve performance of this bit
         cnt = 0
+        indices = Int[]
         for (index, row) in enumerate(data)
             word, embedding = _parseline(row)
+            LL[:, index] = embedding
+            indexed_words[index] = word
             if length(keep_words)==0 || word in keep_words
-                LL[:, index] = embedding
-                idexed_words[index] = word
+                push!(indices, index)
                 cnt+=1
-                if cnt > max_stored_vocab_size
+                if cnt > max_stored_vocab_size-1
                     break
                 end
             end
         end
+        LL = LL[:, indices]
+        indexed_words = indexed_words[indices]
     end
     return LL, indexed_words
 end
@@ -106,14 +107,15 @@ function _load_hdf5_embeddings(file::AbstractString, max_vocab_size, keep_words)
     payload = h5open(read, file)["mat"]
     words = payload["axis1"]
     vectors = payload["block0_values"]
-    max_vocab_size = _get_vocab_size(length(words), max_vocab_size)
+    max_stored_vocab_size = _get_vocab_size(length(words), max_vocab_size)
 
     indices = Int[]
+    cnt = 0
     for (index, word) in enumerate(words)
         if length(keep_words)==0 || word in keep_words
             push!(indices, index)
             cnt+=1
-            if cnt > max_stored_vocab_size
+            if cnt > max_stored_vocab_size-1
                 break
             end
         end

test/runtests.jl

@@ -191,9 +191,9 @@ end
         @test embs_mini.vocab == embs_full.vocab[1:100]
 
         embs_specific =  load_embeddings(ConceptNet{:compressed};
-                                         keep_words=Set(["red", "green", "blue"]))
+                                         keep_words=Set(["/c/en/red", "/c/en/green", "/c/en/blue"]))
 
         @test size(embs_specific.embeddings) == (300, 3)
-        @test Set(embs_specific.vocab) == Set(["red", "green", "blue"])
+        @test Set(embs_specific.vocab) == Set(["/c/en/red", "/c/en/green", "/c/en/blue"])
     end
 end