private static DataFrame GetSearchTermTFIDF(SparkSession spark, string searchTerm,
Tokenizer tokenizer, HashingTF hashingTF, IDFModel idfModel)
{
var searchTermDataFrame = spark.CreateDataFrame(new List <string>()
{
searchTerm
})
.WithColumnRenamed("_1", "Content");
var searchWords = tokenizer.Transform(searchTermDataFrame);
var featurizedSeachTerm = hashingTF.Transform(searchWords);
var search = idfModel.Transform(featurizedSeachTerm).WithColumnRenamed("features", "features2")
.WithColumn("norm2", udfCalcNorm(Col("features2")));
return(search);
}
public void TestIDFModel()
{
int expectedDocFrequency = 1980;
string expectedInputCol = "rawFeatures";
string expectedOutputCol = "features";
DataFrame sentenceData =
_spark.Sql("SELECT 0.0 as label, 'Hi I heard about Spark' as sentence");
Tokenizer tokenizer = new Tokenizer()
.SetInputCol("sentence")
.SetOutputCol("words");
DataFrame wordsData = tokenizer.Transform(sentenceData);
HashingTF hashingTF = new HashingTF()
.SetInputCol("words")
.SetOutputCol(expectedInputCol)
.SetNumFeatures(20);
DataFrame featurizedData = hashingTF.Transform(wordsData);
IDF idf = new IDF()
.SetInputCol(expectedInputCol)
.SetOutputCol(expectedOutputCol)
.SetMinDocFreq(expectedDocFrequency);
IDFModel idfModel = idf.Fit(featurizedData);
DataFrame rescaledData = idfModel.Transform(featurizedData);
Assert.Contains(expectedOutputCol, rescaledData.Columns());
Assert.Equal(expectedInputCol, idfModel.GetInputCol());
Assert.Equal(expectedOutputCol, idfModel.GetOutputCol());
Assert.Equal(expectedDocFrequency, idfModel.GetMinDocFreq());
using (var tempDirectory = new TemporaryDirectory())
{
string modelPath = Path.Join(tempDirectory.Path, "idfModel");
idfModel.Save(modelPath);
IDFModel loadedModel = IDFModel.Load(modelPath);
Assert.Equal(idfModel.Uid(), loadedModel.Uid());
}
}