static void Main(string[] args)
{
string dataset =
"SalaryData.csv"
//MLNetUtilities.GetDataPathByDatasetName("SalaryData.csv")
;
string testDataset =
"SalaryData-test.csv"
//MLNetUtilities.GetDataPathByDatasetName("SalaryData-test.csv")
;
LocalEnvironment env = new LocalEnvironment();
Microsoft.ML.StaticPipe.DataReader
<
IMultiStreamSource,
(
Microsoft.ML.StaticPipe.Scalar <float> YearsExperience,
Microsoft.ML.StaticPipe.Scalar <float> Target
)
> reader = TextLoader.CreateReader
(
env,
ctx =>
(
YearsExperience: ctx.LoadFloat(0),
Target: ctx.LoadFloat(1)
),
hasHeader: true,
separator: ','
);
Microsoft.ML.StaticPipe.DataView
<
(
Microsoft.ML.StaticPipe.Scalar <float> YearsExperience,
Microsoft.ML.StaticPipe.Scalar <float> Target
)
> data = reader.Read(new MultiFileSource(dataset));
var regression = new RegressionContext(env);
var pipeline = reader.MakeNewEstimator()
.Append(r => (
r.Target,
Prediction: regression.Trainers.FastTree(label: r.Target, features: r.YearsExperience.AsVector())
));
Microsoft.ML.Core.Data.ITransformer model = pipeline.Fit(data).AsDynamic;
var predictionFunc = model.MakePredictionFunction <SalaryData, SalaryPrediction>(env);
var prediction = predictionFunc.Predict(new SalaryData {
YearsExperience = 8
});
Console.WriteLine($"Predicted salary - {String.Format("{0:C}", prediction.PredictedSalary)}");
Console.Read();
}
/// <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, Schema inputSchema, int conc = 1)
{
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, conc: conc);
_fastValueMapper = _fastValueMapperObject.GetMapper <DataFrame, DataFrame>();
}
public PredictionViewModel()
{
_home = new Home();
Title = "Emlak Tahmin";
var context = new MLContext();
var assembly = IntrospectionExtensions.GetTypeInfo(typeof(PredictionViewModel)).Assembly;
using (var stream = assembly.GetManifestResourceStream("TeknolotEmlak.MLModels.model.zip"))
{
_model = context.Model.Load(stream);
_predictionEngine = _model.CreatePredictionEngine <Home, HousePricePrediction>(context);
}
this.PropertyChanged += (s, e) => {
if (e.PropertyName == nameof(this.Price))
{
return;
}
Price = (decimal)_predictionEngine.Predict(_home).Price;
};
}
/// <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, Schema inputSchema, int conc = 1) => new PredictionFunctionDataFrame(env, transformer, inputSchema, conc);
