Implement ML/CountVectorizer and ML/CountVectorizerModel

src/csharp/Microsoft.Spark.E2ETest/IpcTests/ML/Feature/CountVectorizerModelTests.cs

@@ -0,0 +1,73 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using System.Collections.Generic;
+using System.IO;
+using Microsoft.Spark.ML.Feature;
+using Microsoft.Spark.Sql;
+using Microsoft.Spark.UnitTest.TestUtils;
+using Xunit;
+
+namespace Microsoft.Spark.E2ETest.IpcTests.ML.Feature
+{
+    [Collection("Spark E2E Tests")]
+    public class CountVectorizerModelTests
+    {
+        private readonly SparkSession _spark;
+
+        public CountVectorizerModelTests(SparkFixture fixture)
+        {
+            _spark = fixture.Spark;
+        }
+
+        /// <summary>
+        /// Test that we can create a CountVectorizerModel, pass in a specifc vocabulary to use
+        /// when creating the model. Verify the standard features methods as well as load/save.
+        /// </summary>
+        [Fact]
+        public void TestCountVectorizerModel()
+        {
+            const string inputColumn = "input";
+            const string outputColumn = "output";
+            const double minTf = 10.0;
+            const bool binary = false;
+
+            var vocabulary = new List<string>()
+            {
+                "hello",
+                "I",
+                "AM",
+                "TO",
+                "TOKENIZE"
+            };
+
+            var countVectorizerModel = new CountVectorizerModel(vocabulary);
+
+            Assert.IsType<CountVectorizerModel>(new CountVectorizerModel("my-uid", vocabulary));
+
+            countVectorizerModel = countVectorizerModel
+                .SetInputCol(inputColumn)
+                .SetOutputCol(outputColumn)
+                .SetMinTF(minTf)
+                .SetBinary(binary);
+
+            Assert.Equal(inputColumn, countVectorizerModel.GetInputCol());
+            Assert.Equal(outputColumn, countVectorizerModel.GetOutputCol());
+            Assert.Equal(minTf, countVectorizerModel.GetMinTF());
+            Assert.Equal(binary, countVectorizerModel.GetBinary());
+            using (var tempDirectory = new TemporaryDirectory())
+            {
+                string savePath = Path.Join(tempDirectory.Path, "countVectorizerModel");
+                countVectorizerModel.Save(savePath);
+
+                CountVectorizerModel loadedModel = CountVectorizerModel.Load(savePath);
+                Assert.Equal(countVectorizerModel.Uid(), loadedModel.Uid());
+            }
+
+            Assert.IsType<int>(countVectorizerModel.GetVocabSize());
+            Assert.NotEmpty(countVectorizerModel.ExplainParams());
+            Assert.NotEmpty(countVectorizerModel.ToString());
+        } 
+    }
+}

src/csharp/Microsoft.Spark.E2ETest/IpcTests/ML/Feature/CountVectorizerTests.cs

@@ -0,0 +1,83 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using System;
+using System.IO;
+using Microsoft.Spark.E2ETest.Utils;
+using Microsoft.Spark.ML.Feature;
+using Microsoft.Spark.Sql;
+using Microsoft.Spark.UnitTest.TestUtils;
+using Xunit;
+
+namespace Microsoft.Spark.E2ETest.IpcTests.ML.Feature
+{
+    [Collection("Spark E2E Tests")]
+    public class CountVectorizerTests
+    {
+        private readonly SparkSession _spark;
+
+        public CountVectorizerTests(SparkFixture fixture)
+        {
+            _spark = fixture.Spark;
+        }
+
+        /// <summary>
+        /// Test that we can create a CountVectorizer. Verify the standard features methods as well
+        /// as load/save. 
+        /// </summary>
+        [Fact]
+        public void TestCountVectorizer()
+        {
+            DataFrame input = _spark.Sql("SELECT array('hello', 'I', 'AM', 'a', 'string', 'TO', " +
+                "'TOKENIZE') as input from range(100)");
+
+            const string inputColumn = "input";
+            const string outputColumn = "output";
+            const double minDf = 1;
+            const double minTf = 10;
+            const int vocabSize = 10000;
+            const bool binary = false;
+
+            var countVectorizer = new CountVectorizer();
+
+            countVectorizer
+                .SetInputCol(inputColumn)
+                .SetOutputCol(outputColumn)
+                .SetMinDF(minDf)
+                .SetMinTF(minTf)
+                .SetVocabSize(vocabSize);
+
+            Assert.IsType<CountVectorizerModel>(countVectorizer.Fit(input));
+            Assert.Equal(inputColumn, countVectorizer.GetInputCol());
+            Assert.Equal(outputColumn, countVectorizer.GetOutputCol());
+            Assert.Equal(minDf, countVectorizer.GetMinDF());
+            Assert.Equal(minTf, countVectorizer.GetMinTF());
+            Assert.Equal(vocabSize, countVectorizer.GetVocabSize());
+            Assert.Equal(binary, countVectorizer.GetBinary());
+
+            using (var tempDirectory = new TemporaryDirectory())
+            {
+                string savePath = Path.Join(tempDirectory.Path, "countVectorizer");
+                countVectorizer.Save(savePath);
+
+                CountVectorizer loadedVectorizer = CountVectorizer.Load(savePath);
+                Assert.Equal(countVectorizer.Uid(), loadedVectorizer.Uid());
+            }
+
+            Assert.NotEmpty(countVectorizer.ExplainParams());
+            Assert.NotEmpty(countVectorizer.ToString());
+        }
+
+        /// <summary>
+        /// Test signatures for APIs introduced in Spark 2.4.*.
+        /// </summary>
+        [SkipIfSparkVersionIsLessThan(Versions.V2_4_0)]
+        public void TestSignaturesV2_4_X()
+        {
+            const double maxDf = 100;
+            CountVectorizer countVectorizer = new CountVectorizer().SetMaxDF(maxDf);
+            Assert.Equal(maxDf, countVectorizer.GetMaxDF());
+        }
+    }
+}

src/csharp/Microsoft.Spark/ML/Feature/CountVectorizer.cs

@@ -0,0 +1,198 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using Microsoft.Spark.Interop;
+using Microsoft.Spark.Interop.Ipc;
+using Microsoft.Spark.Sql;
+
+namespace Microsoft.Spark.ML.Feature
+{
+    public class CountVectorizer : FeatureBase<CountVectorizer>, IJvmObjectReferenceProvider
+    {
+        private static readonly string s_countVectorizerClassName = 
+            "org.apache.spark.ml.feature.CountVectorizer";
+
+        /// <summary>
+        /// Creates a <see cref="CountVectorizer"/> without any parameters.
+        /// </summary>
+        public CountVectorizer() : base(s_countVectorizerClassName)
+        {
+        }
+
+        /// <summary>
+        /// Creates a <see cref="CountVectorizer"/> with a UID that is used to give the
+        /// <see cref="CountVectorizer"/> a unique ID.
+        /// </summary>
+        /// <param name="uid">An immutable unique ID for the object and its derivatives.</param>
+        public CountVectorizer(string uid) : base(s_countVectorizerClassName, uid)
+        {
+        }
+
+        internal CountVectorizer(JvmObjectReference jvmObject) : base(jvmObject)
+        {
+        }
+
+        JvmObjectReference IJvmObjectReferenceProvider.Reference => _jvmObject;
+
+        /// <summary>Fits a model to the input data.</summary>
+        /// <param name="dataFrame">The <see cref="DataFrame"/> to fit the model to.</param>
+        /// <returns><see cref="CountVectorizerModel"/></returns>
+        public CountVectorizerModel Fit(DataFrame dataFrame) => 
+            new CountVectorizerModel((JvmObjectReference)_jvmObject.Invoke("fit", dataFrame));
+
+        /// <summary>
+        /// Loads the <see cref="CountVectorizer"/> that was previously saved using Save.
+        /// </summary>
+        /// <param name="path">
+        /// The path the previous <see cref="CountVectorizer"/> was saved to.
+        /// </param>
+        /// <returns>New <see cref="CountVectorizer"/> object</returns>
+        public static CountVectorizer Load(string path) =>
+            WrapAsCountVectorizer((JvmObjectReference)
+                SparkEnvironment.JvmBridge.CallStaticJavaMethod(
+                    s_countVectorizerClassName,"load", path));
+
+        /// <summary>
+        /// Gets the binary toggle to control the output vector values. If True, all nonzero counts
+        /// (after minTF filter applied) are set to 1. This is useful for discrete probabilistic
+        /// models that model binary events rather than integer counts. Default: false
+        /// </summary>
+        /// <returns>boolean</returns>
+        public bool GetBinary() => (bool)_jvmObject.Invoke("getBinary");
+
+        /// <summary>
+        /// Sets the binary toggle to control the output vector values. If True, all nonzero counts
+        /// (after minTF filter applied) are set to 1. This is useful for discrete probabilistic
+        /// models that model binary events rather than integer counts. Default: false
+        /// </summary>
+        /// <param name="value">Turn the binary toggle on or off</param>
+        /// <returns><see cref="CountVectorizer"/> with the new binary toggle value set</returns>
+        public CountVectorizer SetBinary(bool value) =>
+            WrapAsCountVectorizer((JvmObjectReference)_jvmObject.Invoke("setBinary", value));
+
+        /// <summary>
+        /// Gets the column that the <see cref="CountVectorizer"/> should read from and convert
+        /// into buckets. This would have been set by SetInputCol.
+        /// </summary>
+        /// <returns>The input column of type string</returns>
+        public string GetInputCol() => (string)_jvmObject.Invoke("getInputCol");
+
+        /// <summary>
+        /// Sets the column that the <see cref="CountVectorizer"/> should read from.
+        /// </summary>
+        /// <param name="value">The name of the column to use as the source.</param>
+        /// <returns><see cref="CountVectorizer"/> with the input column set</returns>
+        public CountVectorizer SetInputCol(string value) =>
+            WrapAsCountVectorizer((JvmObjectReference)_jvmObject.Invoke("setInputCol", value));
+
+        /// <summary>
+        /// Gets the name of the new column the <see cref="CountVectorizer"/> creates in the
+        /// DataFrame.
+        /// </summary>
+        /// <returns>The name of the output column.</returns>
+        public string GetOutputCol() => (string)_jvmObject.Invoke("getOutputCol");
+
+        /// <summary>
+        /// Sets the name of the new column the <see cref="CountVectorizer"/> creates in the
+        /// DataFrame.
+        /// </summary>
+        /// <param name="value">The name of the output column which will be created.</param>
+        /// <returns>New <see cref="CountVectorizer"/> with the output column set</returns>
+        public CountVectorizer SetOutputCol(string value) =>
+            WrapAsCountVectorizer((JvmObjectReference)_jvmObject.Invoke("setOutputCol", value));
+
+        /// <summary>
+        /// Gets the maximum number of different documents a term could appear in to be included in
+        /// the vocabulary. A term that appears more than the threshold will be ignored. If this is
+        /// an integer greater than or equal to 1, this specifies the maximum number of documents
+        /// the term could appear in; if this is a double in [0,1), then this specifies the maximum
+        /// fraction of documents the term could appear in.
+        /// </summary>
+        /// <returns>The maximum document term frequency</returns>
+        [Since(Versions.V2_4_0)]
+        public double GetMaxDF() => (double)_jvmObject.Invoke("getMaxDF");
+
+        /// <summary>
+        /// Sets the maximum number of different documents a term could appear in to be included in
+        /// the vocabulary. A term that appears more than the threshold will be ignored. If this is
+        /// an integer greater than or equal to 1, this specifies the maximum number of documents
+        /// the term could appear in; if this is a double in [0,1), then this specifies the maximum
+        /// fraction of documents the term could appear in.
+        /// </summary>
+        /// <param name="value">The maximum document term frequency</param>
+        /// <returns>New <see cref="CountVectorizer"/> with the max df value set</returns>
+        [Since(Versions.V2_4_0)]
+        public CountVectorizer SetMaxDF(double value) =>
+            WrapAsCountVectorizer((JvmObjectReference)_jvmObject.Invoke("setMaxDF", value));
+
+        /// <summary>
+        /// Gets the minimum number of different documents a term must appear in to be included in
+        /// the vocabulary. If this is an integer greater than or equal to 1, this specifies the
+        /// number of documents the term must appear in; if this is a double in [0,1), then this
+        /// specifies the fraction of documents.
+        /// </summary>
+        /// <returns>The minimum document term frequency</returns>
+        public double GetMinDF() => (double)_jvmObject.Invoke("getMinDF");
+
+        /// <summary>
+        /// Sets the minimum number of different documents a term must appear in to be included in
+        /// the vocabulary. If this is an integer greater than or equal to 1, this specifies the
+        /// number of documents the term must appear in; if this is a double in [0,1), then this
+        /// specifies the fraction of documents.
+        /// </summary>
+        /// <param name="value">The minimum document term frequency</param>
+        /// <returns>New <see cref="CountVectorizer"/> with the min df value set</returns>
+        public CountVectorizer SetMinDF(double value) =>
+            WrapAsCountVectorizer((JvmObjectReference)_jvmObject.Invoke("setMinDF", value));
+
+        /// <summary>
+        /// Gets the filter to ignore rare words in a document. For each document, terms with
+        /// frequency/count less than the given threshold are ignored. If this is an integer
+        /// greater than or equal to 1, then this specifies a count (of times the term must appear
+        /// in the document); if this is a double in [0,1), then this specifies a fraction (out of
+        /// the document's token count).
+        ///
+        /// Note that the parameter is only used in transform of CountVectorizerModel and does not
+        /// affect fitting.
+        /// </summary>
+        /// <returns>Minimum term frequency</returns>
+        public double GetMinTF() => (double)_jvmObject.Invoke("getMinTF");
+
+        /// <summary>
+        /// Sets the filter to ignore rare words in a document. For each document, terms with
+        /// frequency/count less than the given threshold are ignored. If this is an integer
+        /// greater than or equal to 1, then this specifies a count (of times the term must appear
+        /// in the document); if this is a double in [0,1), then this specifies a fraction (out of
+        /// the document's token count).
+        ///
+        /// Note that the parameter is only used in transform of CountVectorizerModel and does not
+        /// affect fitting.
+        /// </summary>
+        /// <param name="value">Minimum term frequency</param>
+        /// <returns>New <see cref="CountVectorizer"/> with the min term frequency set</returns>
+        public CountVectorizer SetMinTF(double value) =>
+            WrapAsCountVectorizer((JvmObjectReference)_jvmObject.Invoke("setMinTF", value));
+
+        /// <summary>
+        /// Gets the max size of the vocabulary. <see cref="CountVectorizer"/> will build a
+        /// vocabulary that only considers the top vocabSize terms ordered by term frequency across
+        /// the corpus.
+        /// </summary>
+        /// <returns>The max size of the vocabulary of type int.</returns>
+        public int GetVocabSize() => (int)_jvmObject.Invoke("getVocabSize");
+
+        /// <summary>
+        /// Sets the max size of the vocabulary. <see cref="CountVectorizer"/> will build a
+        /// vocabulary that only considers the top vocabSize terms ordered by term frequency across
+        /// the corpus.
+        /// </summary>
+        /// <param name="value">The max vocabulary size</param>
+        /// <returns><see cref="CountVectorizer"/> with the max vocab value set</returns>
+        public CountVectorizer SetVocabSize(int value) => 
+            WrapAsCountVectorizer(_jvmObject.Invoke("setVocabSize", value));
+
+        private static CountVectorizer WrapAsCountVectorizer(object obj) => 
+            new CountVectorizer((JvmObjectReference)obj);
+    }
+}