/// 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(mlContext.Transforms.Conversion.MapKeyToValue(("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
}
/// This example demonstrates the use of the ValueMappingEstimator by mapping string-to-array values which allows for mapping string data
/// to numeric arrays that can then be used as a feature set for a trainer. In this example, we are mapping the education data to
/// arbitrary integer arrays with the following association:
/// 0-5yrs -> 1, 2, 3
/// 6-11yrs -> 5, 6, 7
/// 12+yrs -> 42,32,64
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);
// If the list of keys and values are known, they can be passed to the API. The ValueMappingEstimator can also get the mapping through an IDataView
// Creating a list of keys based on the Education values from the dataset
var educationKeys = new List <string>()
{
"0-5yrs",
"6-11yrs",
"12+yrs"
};
// Sample list of associated array values
var educationValues = new List <int[]>()
{
new int[] { 1, 2, 3 },
new int[] { 5, 6, 7 },
new int[] { 42, 32, 64 }
};
// Constructs the ValueMappingEstimator making the ML.net pipeline
var pipeline = new ValueMappingEstimator <string, int>(mlContext, educationKeys, educationValues, ("EducationFeature", "Education"));
// Fits the ValueMappingEstimator and transforms the data adding the EducationFeature column.
IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);
// Getting the resulting data as an IEnumerable of SampleInfertDataWithIntArray. This will contain the newly created column EducationCategory
IEnumerable <SampleInfertDataWithIntArray> featuresColumn = mlContext.CreateEnumerable <SampleInfertDataWithIntArray>(transformedData, reuseRowObject: false);
Console.WriteLine($"Example of mapping string->array");
Console.WriteLine($"Age\tEducation\tEducationFeature");
foreach (var featureRow in featuresColumn)
{
Console.WriteLine($"{featureRow.Age}\t{featureRow.Education} \t{string.Join(",", featureRow.EducationFeature)}");
}
// Features column obtained post-transformation.
//
// Example of mapping string->array
// Age Education EducationFeature
// 26 0 - 5yrs 1,2,3
// 42 0 - 5yrs 1,2,3
// 39 12 + yrs 42,32,64
// 34 0 - 5yrs 1,2,3
// 35 6 - 11yrs 5,6,7
}
