updates with correct native nuget package:

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.NoFilter)
+            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,
+                FilterColumns = filterColumns == null? new string[] { } : filterColumns,
                 FilterMode = filterMode,
                 ImputeMode = imputeMode,
                 SupressTypeErrors = supressTypeErrors

temp-nuget-folder/MicrosoftMLFeaturizers.0.1.0.nupkg

@@ -19,15 +19,15 @@ public TimeSeriesImputerTests(ITestOutputHelper output) : base(output)
         {
         }
 
-        //private class TimeSeriesTwoGrainInput
-        //{
-        //    public long date;
-        //    public string grainA;
-        //    public string grainB;
-        //    public int dataA;
-        //    public float dataB;
-        //    public uint dataC;
-        //}
+        private class TimeSeriesTwoGrainInput
+        {
+            public long date;
+            public string grainA;
+            public string grainB;
+            public int dataA;
+            public float dataB;
+            public uint dataC;
+        }
 
         private class TimeSeriesOneGrainInput
         {
@@ -138,18 +138,17 @@ public void Median()
             var data = mlContext.Data.LoadFromEnumerable(dataList);
 
             // Build the pipeline, fit, and transform it.
-            var pipeline = mlContext.Transforms.TimeSeriesImputer("date", new string[] { "grainA" }, imputeMode: TimeSeriesImputerEstimator.ImputationStrategy.ForwardFill);
+            var pipeline = mlContext.Transforms.TimeSeriesImputer("date", new string[] { "grainA" }, imputeMode: TimeSeriesImputerEstimator.ImputationStrategy.Median, filterColumns: null, suppressTypeErrors: true);
             var model = pipeline.Fit(data);
-            mlContext.Model.Save(model, data.Schema, "D:/Repos/machinelearning/temp-nuget-folder/SaveNoTransform.zip");
-            var loadedModel = mlContext.Model.Load("D:/Repos/machinelearning/temp-nuget-folder/SaveNoTransform.zip", out DataViewSchema loadedSchema);
+
+            dataList = new[] { new TimeSeriesOneGrainFloatInput() { date = 0, grainA = "B", dataA = float.NaN },
+            new TimeSeriesOneGrainFloatInput() { date = 1, grainA = "B", dataA = 2 }};
+
+            data = mlContext.Data.LoadFromEnumerable(dataList);
 
             var output = model.Transform(data);
-            var loadedOutput = loadedModel.Transform(data);
 
             var prev = output.Preview();
-            var loadedPrev = loadedOutput.Preview();
-
-            Assert.Equal(prev.ColumnView.Length, loadedPrev.ColumnView.Length);
 
             TestEstimatorCore(pipeline, data);
             Done();
@@ -177,5 +176,51 @@ public void Backfill()
             TestEstimatorCore(pipeline, data);
             Done();
         }
+
+        [Fact]
+        public void BackfillTwoGrain()
+        {
+            MLContext mlContext = new MLContext(1);
+            var dataList = new[] { new TimeSeriesTwoGrainInput() { date = 0, grainA = "A", grainB = "B", dataA = 0, dataB = 0.0f, dataC = 0 },
+                new TimeSeriesTwoGrainInput() { date = 1, grainA = "A", grainB = "B", dataA = 0, dataB = float.NaN, dataC = 0 },
+                new TimeSeriesTwoGrainInput() { date = 3, grainA = "A", grainB = "B", dataA = 0, dataB = 1.0f, dataC = 0 },
+                new TimeSeriesTwoGrainInput() { date = 5, grainA = "A", grainB = "B", dataA = 0, dataB = float.NaN, dataC = 0 },
+                new TimeSeriesTwoGrainInput() { date = 7, grainA = "A", grainB = "B", dataA = 0, dataB = 2.0f, dataC = 0 }};
+            var data = mlContext.Data.LoadFromEnumerable(dataList);
+
+            // Build the pipeline, fit, and transform it.
+            var pipeline = mlContext.Transforms.TimeSeriesImputer("date", new string[] { "grainA", "grainB" }, TimeSeriesImputerEstimator.ImputationStrategy.BackFill);
+            var model = pipeline.Fit(data);
+            var output = model.Transform(data);
+            var schema = output.Schema;
+            var outputSchema = model.GetOutputSchema(data.Schema);
+            var prev = output.Preview();
+
+            TestEstimatorCore(pipeline, data);
+            Done();
+        }
+        
+        [Fact]
+        public void ForwardFillTwoGrain()
+        {
+            MLContext mlContext = new MLContext(1);
+            var dataList = new[] { new TimeSeriesTwoGrainInput() { date = 0, grainA = "A", grainB = "B", dataA = 0, dataB = 0.0f, dataC = 0 },
+                new TimeSeriesTwoGrainInput() { date = 1, grainA = "A", grainB = "B", dataA = 0, dataB = float.NaN, dataC = 0 },
+                new TimeSeriesTwoGrainInput() { date = 3, grainA = "A", grainB = "B", dataA = 0, dataB = 1.0f, dataC = 0 },
+                new TimeSeriesTwoGrainInput() { date = 5, grainA = "A", grainB = "B", dataA = 0, dataB = float.NaN, dataC = 0 },
+                new TimeSeriesTwoGrainInput() { date = 7, grainA = "A", grainB = "B", dataA = 0, dataB = 2.0f, dataC = 0 }};
+            var data = mlContext.Data.LoadFromEnumerable(dataList);
+
+            // Build the pipeline, fit, and transform it.
+            var pipeline = mlContext.Transforms.TimeSeriesImputer("date", new string[] { "grainA", "grainB" }, TimeSeriesImputerEstimator.ImputationStrategy.ForwardFill);
+            var model = pipeline.Fit(data);
+            var output = model.Transform(data);
+            var schema = output.Schema;
+            var outputSchema = model.GetOutputSchema(data.Schema);
+            var prev = output.Preview();
+
+            TestEstimatorCore(pipeline, data);
+            Done();
+        }
     }
 }