Merge branch 'master' into scrub-hash-ngrams

Author: wschin

docs/code/MlNetCookBook.md

@@ -512,7 +512,7 @@ var pipeline =
     // Convert each categorical feature into one-hot encoding independently.
     mlContext.Transforms.Categorical.OneHotEncoding("CategoricalOneHot", "CategoricalFeatures")
     // Convert all categorical features into indices, and build a 'word bag' of these.
-    .Append(mlContext.Transforms.Categorical.OneHotEncoding("CategoricalBag", "CategoricalFeatures", OneHotEncodingTransformer.OutputKind.Bag))
+    .Append(mlContext.Transforms.Categorical.OneHotEncoding("CategoricalBag", "CategoricalFeatures", OneHotEncodingEstimator.OutputKind.Bag))
     // One-hot encode the workclass column, then drop all the categories that have fewer than 10 instances in the train set.
     .Append(mlContext.Transforms.Categorical.OneHotEncoding("WorkclassOneHot", "Workclass"))
     .Append(mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount("WorkclassOneHotTrimmed", "WorkclassOneHot", count: 10));

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

@@ -34,12 +34,12 @@ public static void Example()
                 .Append(ml.Transforms.Conversion.MapValueToKey(defaultColumnName, "Review"));
 
             // Another pipeline, that customizes the advanced settings of the ValueToKeyMappingEstimator.
-            // We can change the maxNumTerm to limit how many keys will get generated out of the set of words, 
-            // and condition the order in which they get evaluated by changing sort from the default Occurence (order in which they get encountered) 
+            // We can change the maximumNumberOfKeys to limit how many keys will get generated out of the set of words, 
+            // and condition the order in which they get evaluated by changing keyOrdinality from the default ByOccurence (order in which they get encountered) 
             // to value/alphabetically.
             string customizedColumnName = "CustomizedKeys";
             var customized_pipeline = ml.Transforms.Text.TokenizeWords("Review")
-                .Append(ml.Transforms.Conversion.MapValueToKey(customizedColumnName, "Review", maxNumKeys: 10, sort: ValueToKeyMappingEstimator.SortOrder.Value));
+                .Append(ml.Transforms.Conversion.MapValueToKey(customizedColumnName, "Review", maximumNumberOfKeys: 10, keyOrdinality: ValueToKeyMappingEstimator.KeyOrdinality.ByValue));
 
             // The transformed data.
             var transformedData_default = default_pipeline.Fit(trainData).Transform(trainData);

docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs

@@ -69,7 +69,7 @@ public static void Example()
             };
 
             var engine = mlContext.Transforms.Text.TokenizeWords("TokenizedWords", "Sentiment_Text")
-                .Append(mlContext.Transforms.Conversion.ValueMap(lookupMap, "Words", "Ids", new ColumnOptions[] { ("VariableLenghtFeatures", "TokenizedWords") }))
+                .Append(mlContext.Transforms.Conversion.MapValue(lookupMap, "Words", "Ids", new ColumnOptions[] { ("VariableLenghtFeatures", "TokenizedWords") }))
                 .Append(mlContext.Transforms.CustomMapping(ResizeFeaturesAction, "Resize"))
                 .Append(tensorFlowModel.ScoreTensorFlowModel(new[] { "Prediction/Softmax" }, new[] { "Features" }))
                 .Append(mlContext.Transforms.CopyColumns(("Prediction", "Prediction/Softmax")))

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

@@ -36,7 +36,7 @@ public static void Example()
                 KeepPunctuations = false,
                 KeepNumbers = false,
                 OutputTokens = true,
-                TextLanguage = TextFeaturizingEstimator.Language.English, // supports  English, French, German, Dutch, Italian, Spanish, Japanese
+                Language = TextFeaturizingEstimator.Language.English, // supports  English, French, German, Dutch, Italian, Spanish, Japanese
             }, "SentimentText");
 
             // The transformed data for both pipelines.

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

@@ -55,7 +55,7 @@ public static void Example()
             };
 
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.ValueMap(educationKeys, educationValues, ("EducationCategory", "Education"));
+            var pipeline = mlContext.Transforms.Conversion.MapValue(educationKeys, educationValues, ("EducationCategory", "Education"));
 
             // Fits the ValueMappingEstimator and transforms the data converting the Education to EducationCategory.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);

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

@@ -46,7 +46,7 @@ public static void Example()
             };
 
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.ValueMap(temperatureKeys, classificationValues, ("TemperatureCategory", "Temperature"));
+            var pipeline = mlContext.Transforms.Conversion.MapValue(temperatureKeys, classificationValues, ("TemperatureCategory", "Temperature"));
 
             // Fits the ValueMappingEstimator and transforms the data adding the TemperatureCategory column.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);

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

@@ -49,7 +49,7 @@ public static void Example()
             };
 
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.ValueMap<string, int>(educationKeys, educationValues, ("EducationFeature", "Education"));
+            var pipeline = mlContext.Transforms.Conversion.MapValue<string, int>(educationKeys, educationValues, ("EducationFeature", "Education"));
 
             // Fits the ValueMappingEstimator and transforms the data adding the EducationFeature column.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);

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

@@ -55,7 +55,7 @@ public static void Example()
             // Generate the ValueMappingEstimator that will output KeyTypes even though our values are strings.
             // The KeyToValueMappingEstimator is added to provide a reverse lookup of the KeyType, converting the KeyType value back
             // to the original value.
-            var pipeline = mlContext.Transforms.Conversion.ValueMap<string, string>(educationKeys, educationValues, true, ("EducationKeyType", "Education"))
+            var pipeline = mlContext.Transforms.Conversion.MapValue<string, string>(educationKeys, educationValues, true, ("EducationKeyType", "Education"))
                               .Append(mlContext.Transforms.Conversion.MapKeyToValue(("EducationCategory", "EducationKeyType")));
 
             // Fits the ValueMappingEstimator and transforms the data adding the EducationKeyType column.