Cherry-pick bug fixes and samples from master to release/1.0

THIRD-PARTY-NOTICES.TXT

@@ -42,4 +42,27 @@ PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+License notice for SharpZipLib
+------------------------------
+
+https://github.com/icsharpcode/SharpZipLib
+
+Copyright © 2000-2018 SharpZipLib Contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this
+software and associated documentation files (the "Software"), to deal in the Software
+without restriction, including without limitation the rights to use, copy, modify, merge,
+publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons
+to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
+PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
+FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs

@@ -1,7 +1,8 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class BootstrapSample
     {
@@ -12,7 +13,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
-            IEnumerable<SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample> enumerableOfData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(5);
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample> enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(5);
             var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Look at the original dataset
@@ -43,7 +44,7 @@ public static void Example()
             {
                 var resample = mlContext.Data.BootstrapSample(data, seed: i);
 
-                var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample>(resample, reuseRowObject: false);
+                var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.BinaryLabelFloatFeatureVectorFloatWeightSample>(resample, reuseRowObject: false);
                 Console.WriteLine($"Label\tFeatures[0]");
                 foreach (var row in enumerable)
                 {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/Cache.cs

@@ -1,7 +1,8 @@
 using System;
+using Microsoft.ML;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class Cache
     {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/CrossValidationSplit.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using static Microsoft.ML.DataOperationsCatalog;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use CrossValidationSplit.

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use ShuffleRows.
@@ -53,8 +54,6 @@ public static void Example()
             //  1/4/2012        34      0
             //  1/5/2012        35      0
             //  1/6/2012        35      0
-
-            Console.ReadLine();
         }
     }
 }

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs

@@ -1,7 +1,8 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use FilterRowsByColumn.
@@ -15,7 +16,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
-            IEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData> enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData> enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
             var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
@@ -42,7 +43,7 @@ public static void Example()
             var filteredData = mlContext.Data.FilterRowsByColumn(data, columnName: "Temperature", lowerBound: 34, upperBound: 37);
 
             // Look at the filtered data and observe that values outside [34,37) have been dropped.
-            var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
+            var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
             Console.WriteLine($"Date\tTemperature");
             foreach (var row in enumerable)
             {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs

@@ -1,9 +1,10 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     using MulticlassClassificationExample = DatasetUtils.MulticlassClassificationExample;
 

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs

@@ -1,7 +1,8 @@
 using System;
+using Microsoft.ML;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public class FilterRowsByMissingValues
     {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/ShuffleRows.cs

@@ -1,7 +1,8 @@
 using System;
+using Microsoft.ML;
 using Microsoft.ML.SamplesUtils;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use ShuffleRows.

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SkipRows.cs

@@ -1,5 +1,7 @@
 using System;
-namespace Microsoft.ML.Samples.Dynamic
+using Microsoft.ML;
+
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use Skip.
@@ -13,7 +15,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
-            var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
+            var enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
             var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
@@ -40,7 +42,7 @@ public static void Example()
             var filteredData = mlContext.Data.SkipRows(data, 5);
 
             // Look at the filtered data and observe that the first 5 rows have been dropped
-            var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
+            var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
             Console.WriteLine($"Date\tTemperature");
             foreach (var row in enumerable)
             {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TakeRows.cs

@@ -1,5 +1,7 @@
 using System;
-namespace Microsoft.ML.Samples.Dynamic
+using Microsoft.ML;
+
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use Take.
@@ -13,7 +15,7 @@ public static void Example()
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
-            var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
+            var enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10);
             var data = mlContext.Data.LoadFromEnumerable(enumerableOfData);
 
             // Before we apply a filter, examine all the records in the dataset.
@@ -40,7 +42,7 @@ public static void Example()
             var filteredData = mlContext.Data.TakeRows(data, 5);
 
             // Look at the filtered data and observe that only the first 5 rows are in the resulting dataset.
-            var enumerable = mlContext.Data.CreateEnumerable<SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
+            var enumerable = mlContext.Data.CreateEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleTemperatureData>(filteredData, reuseRowObject: true);
             Console.WriteLine($"Date\tTemperature");
             foreach (var row in enumerable)
             {

docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TrainTestSplit.cs

@@ -1,11 +1,9 @@
 using System;
 using System.Collections.Generic;
-using System.Collections.Immutable;
-using System.Linq;
-using Microsoft.ML.Data;
+using Microsoft.ML;
 using static Microsoft.ML.DataOperationsCatalog;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     /// <summary>
     /// Sample class showing how to use TrainTestSplit.

docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs

@@ -1,9 +1,9 @@
 using System;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.SamplesUtils;
-using Microsoft.ML.Trainers;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class FeatureContributionCalculationTransform
     {
@@ -76,7 +76,6 @@ public static void Example()
 
                 index++;
             }
-            Console.ReadLine();
 
             // The output of the above code is:
             // Label Score   BiggestFeature Value   Weight Contribution

docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs

@@ -1,19 +1,20 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static partial class TransformSamples
     {
-        public static void NgramTransform()
+        public static void Example()
         {
             // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
             // as well as the source of randomness.
             var ml = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert to IDataView.
-            IEnumerable<SamplesUtils.DatasetUtils.SampleSentimentData> data = SamplesUtils.DatasetUtils.GetSentimentData();
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleSentimentData> data = Microsoft.ML.SamplesUtils.DatasetUtils.GetSentimentData();
             var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.

docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class NormalizerTransform
     {
@@ -13,7 +14,7 @@ public static void Example()
             var ml = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
-            IEnumerable<SamplesUtils.DatasetUtils.SampleInfertData> data = SamplesUtils.DatasetUtils.GetInfertData();
+            IEnumerable<Microsoft.ML.SamplesUtils.DatasetUtils.SampleInfertData> data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
             var trainData = ml.Data.LoadFromEnumerable(data);
 
             // Preview of the data.
@@ -56,8 +57,8 @@ public static void Example()
 
             // Composing a different pipeline if we wanted to normalize more than one column at a time. 
             // Using log scale as the normalization mode. 
-            var multiColPipeline = ml.Transforms.NormalizeMinMax("LogInduced", "Induced")
-                .Append(ml.Transforms.NormalizeMinMax("LogSpontaneous", "Spontaneous"));
+            var multiColPipeline = ml.Transforms.NormalizeLogMeanVariance(new[] { new InputOutputColumnPair("LogInduced", "Induced"), new InputOutputColumnPair("LogSpontaneous", "Spontaneous") });
+
             // The transformed data.
             var multiColtransformer = multiColPipeline.Fit(trainData);
             var multiColtransformedData = multiColtransformer.Transform(trainData);

docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs

@@ -1,9 +1,10 @@
 using System;
 using System.Linq;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.OnnxRuntime;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class OnnxTransformExample
     {
@@ -13,8 +14,9 @@ public static class OnnxTransformExample
         public static void Example()
         {
             // Download the squeeznet image model from ONNX model zoo, version 1.2
-            // https://github.com/onnx/models/tree/master/squeezenet
-            var modelPath = @"squeezenet\model.onnx";
+            // https://github.com/onnx/models/tree/master/squeezenet or use
+            // Microsoft.ML.Onnx.TestModels nuget.
+            var modelPath = @"squeezenet\00000001\model.onnx";
 
             // Inspect the model's inputs and outputs
             var session = new InferenceSession(modelPath);

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs

@@ -2,8 +2,9 @@
 using System.Linq;
 using Microsoft.ML.Trainers;
 using Microsoft.ML.SamplesUtils;
+using Microsoft.ML;
 
-namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
+namespace Samples.Dynamic.PermutationFeatureImportance
 {
     public static class PfiHelper
     {

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIRegressionExample.cs

@@ -1,7 +1,8 @@
 using System;
 using System.Linq;
+using Microsoft.ML;
 
-namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
+namespace Samples.Dynamic.PermutationFeatureImportance
 {
     public static class PfiRegression
     {

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs

@@ -1,8 +1,9 @@
 using System;
 using System.Linq;
+using Microsoft.ML;
 using Microsoft.ML.Trainers;
 
-namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
+namespace Samples.Dynamic.PermutationFeatureImportance
 {
     public static class PfiBinaryClassification
     {