/// This example demonstrates the use of KeyTypes using both the ValueMappingEstimator and KeyToValueEstimator. Using a KeyType
/// instead of the actual value provides a unique integer representation of the value. When the treatValueAsKeyTypes is true,
/// the ValueMappingEstimator will generate a KeyType for each unique value.
///
/// In this example, the education data is mapped to a grouping of 'Undergraduate' and 'Postgraduate'. Because KeyTypes are used, the
/// ValueMappingEstimator will output the KeyType value rather than string value of 'Undergraduate' or 'Postgraduate'.
///
/// The KeyToValueEstimator is added to the pipeline to convert the KeyType back to the original value. Therefore the output of this example
/// results in the string value of 'Undergraduate' and 'Postgraduate'.
public static void Run()
{
// 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 mlContext = new MLContext();
// Get a small dataset as an IEnumerable.
IEnumerable <SamplesUtils.DatasetUtils.SampleInfertData> data = SamplesUtils.DatasetUtils.GetInfertData();
IDataView trainData = mlContext.Data.ReadFromEnumerable(data);
// Creating a list of keys based on the Education values from the dataset
// These lists are created by hand for the demonstration, but the ValueMappingEstimator does take an IEnumerable.
var educationKeys = new List <string>()
{
"0-5yrs",
"6-11yrs",
"12+yrs"
};
// Creating a list of values that are sample strings. These will be converted to KeyTypes
var educationValues = new List <string>()
{
"Undergraduate",
"Postgraduate",
"Postgraduate"
};
// 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 = new ValueMappingEstimator <string, string>(mlContext, educationKeys, educationValues, true, ("EducationKeyType", "Education"))
.Append(new KeyToValueMappingEstimator(mlContext, ("EducationCategory", "EducationKeyType")));
// Fits the ValueMappingEstimator and transforms the data adding the EducationKeyType column.
IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);
// Getting the resulting data as an IEnumerable of SampleInfertDataWithFeatures.
IEnumerable <SampleInfertDataWithFeatures> featureRows = mlContext.CreateEnumerable <SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
Console.WriteLine($"Example of mapping string->keytype");
Console.WriteLine($"Age\tEducation\tEducationCategory");
foreach (var featureRow in featureRows)
{
Console.WriteLine($"{featureRow.Age}\t{featureRow.Education} \t{featureRow.EducationCategory}");
}
// Features column obtained post-transformation.
//
// Age Education EducationCategory
// 26 0-5yrs Undergraduate
// 42 0-5yrs Undergraduate
// 39 12+yrs Postgraduate
// 34 0-5yrs Undergraduate
// 35 6-11yrs Postgraduate
}
public void KeyToValueWorkout()
{
string dataPath = GetDataPath("iris.txt");
var reader = new TextLoader(Env, new TextLoader.Arguments
{
Column = new[]
{
new TextLoader.Column("ScalarString", DataKind.TX, 1),
new TextLoader.Column("VectorString", DataKind.TX, new[] { new TextLoader.Range(1, 4) }),
new TextLoader.Column
{
Name = "BareKey",
Source = new[] { new TextLoader.Range(0) },
Type = DataKind.U4,
KeyCount = new KeyCount(6),
}
}
});
var data = reader.Read(dataPath);
data = new ValueToKeyMappingEstimator(Env, new[] {
new ValueToKeyMappingTransformer.ColumnInfo("A", "ScalarString"),
new ValueToKeyMappingTransformer.ColumnInfo("B", "VectorString")
}).Fit(data).Transform(data);
var badData1 = new ColumnCopyingTransformer(Env, ("A", "BareKey")).Transform(data);
var badData2 = new ColumnCopyingTransformer(Env, ("B", "VectorString")).Transform(data);
var est = new KeyToValueMappingEstimator(Env, ("A_back", "A"), ("B_back", "B"));
TestEstimatorCore(est, data, invalidInput: badData1);
TestEstimatorCore(est, data, invalidInput: badData2);
var outputPath = GetOutputPath("KeyToValue", "featurized.tsv");
using (var ch = Env.Start("save"))
{
var saver = new TextSaver(Env, new TextSaver.Arguments {
Silent = true
});
IDataView savedData = est.Fit(data).Transform(data);
using (var fs = File.Create(outputPath))
DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true);
}
CheckEquality("KeyToValue", "featurized.tsv");
Done();
}