/// <summary>
/// Creates an instance of <see cref="PredictionFunctionDataFrame"/>.
/// </summary>
/// <param name="env">The host environment.</param>
/// <param name="transformer">The model (transformer) to use for prediction.</param>
/// <param name="inputSchema">Input schema.</param>
/// <param name="conc">Number of threads.</param>
public PredictionFunctionDataFrame(IHostEnvironment env, ITransformer transformer, DataViewSchema inputSchema)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(transformer, nameof(transformer));
var df = new DataFrame(transformer.GetOutputSchema(inputSchema), 0);
var tr = transformer.Transform(df) as IDataTransform;
_fastValueMapperObject = new ValueMapperDataFrameFromTransform(env, tr);
_fastValueMapper = _fastValueMapperObject.GetMapper <DataFrame, DataFrame>();
}
private static void CallMachineLearningDotNet()
{
sample_ml = new SomatotypeInputData()
{
Height = 191.7,
Mass = 82.0,
BreadthHumerus = 7.3,
BreadthFemur = 10.1,
GirthArmUpper = 33.2,
GirthCalfStanding = 36,
SkinfoldTriceps = 7,
SkinfoldSubscapular = 6,
SkinfoldMedialCalf = 4,
SkinfoldSupraspinale = 9
};
mlContext = new MLContext(seed: 0);
// Train/learn
model = TrainLearnMLdotnet(mlContext, _trainDataPath);
EvaluateTestMLdotnet(mlContext, model);
EvaluateTestSinglePredictionMLdotnet(mlContext, model);
}
public static ITransformer TrainLearnMLdotnet(MLContext mlContext, string path)
{
IDataView dataView = null;
dataView = mlContext
.Data
.LoadFromTextFile <SomatotypeInputData>
(
path,
hasHeader: true,
separatorChar: ','
);
DataOperationsCatalog.TrainTestData dataSplit;
dataSplit = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.25);
trainData = dataSplit.TrainSet;
testData = dataSplit.TestSet;
IEnumerable <SomatotypeInputData> list = null;
list = mlContext
.Data
.CreateEnumerable <SomatotypeInputData>(dataView, reuseRowObject: false)
.ToList();
for (int i = 0; i < list.Count(); i++)
{
Console.WriteLine($" Id = {list.ElementAt(i).Id}");
Console.WriteLine($" EndomorphicComponent = {list.ElementAt(i).EndomorphicComponent}");
Console.WriteLine($" MesomorphicComponent = {list.ElementAt(i).MesomorphicComponent}");
Console.WriteLine($" EctomorphicComponent = {list.ElementAt(i).EctomorphicComponent}");
}
Microsoft.ML.Transforms.ColumnCopyingEstimator pipeline = null;
pipeline = mlContext.Transforms.CopyColumns
(
outputColumnName: "Label",
inputColumnName: "EndomorphicComponent"
);
pipeline.Append
(
mlContext.Transforms.Concatenate
(
"Features",
"Height",
"Mass",
"BreadthHumerus",
"BreadthFemur",
"GirthArmUpper",
"GirthCalfStanding",
"SkinfoldSubscapular",
"SkinfoldTriceps",
"SkinfoldSupraspinale",
"SkinfoldMedialCalf"
)
);
pipeline.Append(mlContext.Regression.Trainers.FastTree());
Microsoft.ML.Transforms.ColumnCopyingTransformer model_1 = pipeline.Fit(trainData);
return(model = model_1);
}
/// <summary> /// Create an instance of the 'prediction function', or 'prediction machine', from a model /// denoted by <paramref name="transformer"/>. /// It will be accepting instances of <typeparamref name="TSrc"/> as input, and produce /// instances of <typeparamref name="TDst"/> as output. /// </summary> public static PredictionFunctionDataFrame MakePredictionFunctionDataFrame(this ITransformer transformer, IHostEnvironment env, DataViewSchema inputSchema) => new PredictionFunctionDataFrame(env, transformer, inputSchema);