WordEmbedding Tests added plus added dimension check for the first row

src/Microsoft.ML.Transforms/Text/WordEmbeddingsTransform.cs

@@ -426,13 +426,14 @@ private Model GetVocabularyDictionary()
                         dimension = wordsInFirstLine.Length - 1;
                         if (model == null)
                             model = new Model(dimension);
-                        float temp;
-                        string firstKey = wordsInFirstLine[0];
-                        float[] firstValue = wordsInFirstLine.Skip(1).Select(x => float.TryParse(x, out temp) ? temp : Single.NaN).ToArray();
-                        if (!firstValue.Contains(Single.NaN))
-                            model.AddWordVector(ch, firstKey, firstValue);
-                        else
-                            ch.Warning($"Parsing error while reading model file: '{_modelFileNameWithPath}', line number 1");
+                        if (model.Dimension == dimension)
+                        {
+                            float temp;
+                            string firstKey = wordsInFirstLine[0];
+                            float[] firstValue = wordsInFirstLine.Skip(1).Select(x => float.TryParse(x, out temp) ? temp : Single.NaN).ToArray();
+                            if (!firstValue.Contains(Single.NaN))
+                                model.AddWordVector(ch, firstKey, firstValue);
+                        }
                         pch.Checkpoint(lineNumber);
                     }
                 }

test/Microsoft.ML.Benchmarks/BigramAndTrigramBenchMark.cs → test/Microsoft.ML.Benchmarks/Text/MultiClassClassification.cs

@@ -20,14 +20,16 @@ internal class EmptyWriter : TextWriter
         public override Encoding Encoding => null;
     }
 
-    public class BigramAndTrigramBenchmark
+    public class MultiClassClassification
     {
         private string _dataPath_Wiki;
         private string _modelPath_Wiki;
 
         [GlobalSetup(Targets = new string[] {
             nameof(CV_Multiclass_WikiDetox_BigramsAndTrichar_OVAAveragedPerceptron),
-            nameof(CV_Multiclass_WikiDetox_BigramsAndTrichar_LightGBMMulticlass) })]
+            nameof(CV_Multiclass_WikiDetox_BigramsAndTrichar_LightGBMMulticlass),
+            nameof(CV_Multiclass_WikiDetox_WordEmbeddings_OVAAveragedPerceptron),
+            nameof(CV_Multiclass_WikiDetox_WordEmbeddings_SDCAMC)})]
         public void SetupTrainingSpeedTests()
         {
             _dataPath_Wiki = Path.GetFullPath(TestDatasets.WikiDetox.trainFilename);
@@ -81,5 +83,25 @@ public void Test_Multiclass_WikiDetox_BigramsAndTrichar_OVAAveragedPerceptron()
                 Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false);
             }
         }
+
+        [Benchmark]
+        public void CV_Multiclass_WikiDetox_WordEmbeddings_OVAAveragedPerceptron()
+        {
+            string cmd = @"CV tr=OVA{p=AveragedPerceptron{iter=10}} k=5 loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} data=" + _dataPath_Wiki + " xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor=NGramExtractorTransform{ngram=2}} xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D} xf=Concat{col=Features:FeaturesText,FeaturesWordEmbedding,logged_in,ns}";
+            using (var tlc = new TlcEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
+            {
+                Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false);
+            }
+        }
+
+        [Benchmark]
+        public void CV_Multiclass_WikiDetox_WordEmbeddings_SDCAMC()
+        {
+            string cmd = @"CV tr=SDCAMC k=5 loader=TextLoader{quote=- sparse=- col=Label:R4:0 col=rev_id:TX:1 col=comment:TX:2 col=logged_in:BL:4 col=ns:TX:5 col=sample:TX:6 col=split:TX:7 col=year:R4:3 header=+} data=" + _dataPath_Wiki + " xf=Convert{col=logged_in type=R4} xf=CategoricalTransform{col=ns} xf=TextTransform{col=FeaturesText:comment tokens=+ wordExtractor={} charExtractor={}} xf=WordEmbeddingsTransform{col=FeaturesWordEmbedding:FeaturesText_TransformedText model=FastTextWikipedia300D} xf=Concat{col=Features:FeaturesWordEmbedding,logged_in,ns}";
+            using (var tlc = new TlcEnvironment(verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
+            {
+                Maml.MainCore(tlc, cmd, alwaysPrintStacktrace: false);
+            }
+        }
     }
 }