including signed native nuget package. Null fix

Author: michaelgsharp

src/Microsoft.ML.Featurizers/TimeSeriesImputer.cs

@@ -107,14 +107,14 @@ internal TimeSeriesImputerEstimator(IHostEnvironment env, string timeSeriesColum
             _host = Contracts.CheckRef(env, nameof(env)).Register("TimeSeriesImputerEstimator");
             _host.CheckValue(timeSeriesColumn, nameof(timeSeriesColumn), "TimePoint column should not be null.");
             _host.CheckNonEmpty(grainColumns, nameof(grainColumns), "Need at least one grain column.");
-            if(filterMode == FilterMode.Include)
+            if (filterMode == FilterMode.Include)
                 _host.CheckNonEmpty(filterColumns, nameof(filterColumns), "Need at least 1 filter column if a FilterMode is specified");
 
             _options = new Options
             {
                 TimeSeriesColumn = timeSeriesColumn,
                 GrainColumns = grainColumns,
-                FilterColumns = filterColumns == null? new string[] { } : filterColumns,
+                FilterColumns = filterColumns == null ? new string[] { } : filterColumns,
                 FilterMode = filterMode,
                 ImputeMode = imputeMode,
                 SupressTypeErrors = supressTypeErrors
@@ -262,7 +262,7 @@ private unsafe TransformerEstimatorSafeHandle CreateTransformerFromSavedData(byt
                 if (!result)
                     throw new Exception(CommonExtensions.GetErrorDetailsAndFreeNativeMemory(errorHandle));
 
-               return new TransformerEstimatorSafeHandle(transformer, DestroyTransformerNative);
+                return new TransformerEstimatorSafeHandle(transformer, DestroyTransformerNative);
             }
         }
 

temp-nuget-folder/MicrosoftMLFeaturizers.0.1.0.nupkg

@@ -257,17 +257,6 @@ public void HolidayTest()
             Assert.Equal("Christmas Day", row[20].Value.ToString());  // HolidayName
             Assert.Equal((byte)0, row[21].Value);                       // IsPaidTimeOff
 
-            mlContext.Model.Save(model, data.Schema, "D:/Repos/machinelearning/temp-nuget-folder/model.zip");
-            var loadedModel = mlContext.Model.Load("D:/Repos/machinelearning/temp-nuget-folder/model.zip", out DataViewSchema loadedSchema);
-
-            var loadedOutput = loadedModel.Transform(data);
-            var loadedRow = loadedOutput.Preview(1).RowView[0].Values;
-
-            // Assert the data from the first row from loaded data for holidays is what we expect
-            Assert.Equal("Christmas Day", loadedRow[20].Value.ToString());  // HolidayName
-            Assert.Equal((byte)0, loadedRow[21].Value);                       // IsPaidTimeOff
-
-
             TestEstimatorCore(pipeline, data);
             Done();
         }

test/Microsoft.ML.Tests/Transformers/DateTimeTransformerTests.cs

@@ -60,10 +60,8 @@ public void DropOneColumn()
             var model = pipeline.Fit(data);
             var output = model.Transform(data);
             var schema = output.Schema;
-            var outputSchema = model.GetOutputSchema(data.Schema);
-            output.Preview();
 
-            //Assert.Equal(6, schema.Count);
+            Assert.Equal(6, schema.Count);
 
             TestEstimatorCore(pipeline, data);
             Done();
@@ -141,12 +139,11 @@ public void Median()
             var pipeline = mlContext.Transforms.TimeSeriesImputer("date", new string[] { "grainA" }, imputeMode: TimeSeriesImputerEstimator.ImputationStrategy.Median, filterColumns: null, suppressTypeErrors: true);
             var model = pipeline.Fit(data);
 
-            dataList = new[] { new TimeSeriesOneGrainFloatInput() { date = 0, grainA = "B", dataA = float.NaN },
-            new TimeSeriesOneGrainFloatInput() { date = 1, grainA = "B", dataA = 2 }};
+            var dataListNewGrain = new[] { new TimeSeriesOneGrainFloatInput() { date = 0, grainA = "B", dataA = float.NaN } };
 
-            data = mlContext.Data.LoadFromEnumerable(dataList);
+            var dataNewGrain = mlContext.Data.LoadFromEnumerable(dataListNewGrain);
 
-            var output = model.Transform(data);
+            var output = model.Transform(dataNewGrain);
 
             var prev = output.Preview();