public async void PredictHousePriceModelTest()
{
string modelFilePath = GetOutputPath("PredictHousePriceModelTest.zip");
ModelHelper.WriteKcHousePriceModel(GetDataPath("kc_house_data.csv"), modelFilePath);
PredictionModel <HousePriceData, HousePricePrediction> model = await PredictionModel.ReadAsync <HousePriceData, HousePricePrediction>(modelFilePath);
HousePricePrediction prediction = model.Predict(new HousePriceData()
{
Bedrooms = 3,
Bathrooms = 2,
SqftLiving = 1710,
SqftLot = 4697,
Floors = 1.5f,
Waterfront = 0,
View = 0,
Condition = 5,
Grade = 6,
SqftAbove = 1710,
SqftBasement = 0,
YearBuilt = 1941,
YearRenovated = 0,
Zipcode = 98002,
Lat = 47.3048f,
Long = -122.218f,
SqftLiving15 = 1030,
SqftLot15 = 4705
});
Assert.InRange(prediction.Price, 260_000, 330_000);
}
public void TrainAndPredictHousePriceModelTest()
{
string dataPath = GetDataPath("kc_house_data.csv");
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader <HousePriceData>(dataPath, useHeader: true, separator: ","));
pipeline.Add(new ColumnConcatenator(outputColumn: "NumericalFeatures",
"SqftLiving", "SqftLot", "SqftAbove", "SqftBasement", "Lat", "Long", "SqftLiving15", "SqftLot15"));
pipeline.Add(new ColumnConcatenator(outputColumn: "CategoryFeatures",
"Bedrooms", "Bathrooms", "Floors", "Waterfront", "View", "Condition", "Grade", "YearBuilt", "YearRenovated", "Zipcode"));
pipeline.Add(new CategoricalOneHotVectorizer("CategoryFeatures"));
pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
"NumericalFeatures", "CategoryFeatures"));
pipeline.Add(new StochasticDualCoordinateAscentRegressor());
PredictionModel <HousePriceData, HousePricePrediction> model = pipeline.Train <HousePriceData, HousePricePrediction>();
HousePricePrediction prediction = model.Predict(new HousePriceData()
{
Bedrooms = 3,
Bathrooms = 2,
SqftLiving = 1710,
SqftLot = 4697,
Floors = 1.5f,
Waterfront = 0,
View = 0,
Condition = 5,
Grade = 6,
SqftAbove = 1710,
SqftBasement = 0,
YearBuilt = 1941,
YearRenovated = 0,
Zipcode = 98002,
Lat = 47.3048f,
Long = -122.218f,
SqftLiving15 = 1030,
SqftLot15 = 4705
});
Assert.InRange(prediction.Price, 260_000, 330_000);
string testDataPath = GetDataPath("kc_house_test.csv");
var testData = new TextLoader <HousePriceData>(testDataPath, useHeader: true, separator: ",");
var evaluator = new RegressionEvaluator();
RegressionMetrics metrics = evaluator.Evaluate(model, testData);
Assert.InRange(metrics.L1, 85_000, 89_000);
Assert.InRange(metrics.L2, 17_000_000_000, 19_000_000_000);
Assert.InRange(metrics.Rms, 130_500, 135_000);
Assert.InRange(metrics.LossFn, 17_000_000_000, 19_000_000_000);
Assert.Equal(.8, metrics.RSquared, 1);
}
public ActionResult <IEnumerable <string> > Get(string area, float approxSquFeet, float rooms, float bedrooms, float fullbath, float halfbath, string garageType, float garageSpaces)
{
var housePriceSample1 = new HouseData()
{
Area = area, BedRooms = bedrooms, BedRoomsBsmt = 0, FullBath = fullbath, HalfBath = halfbath, Rooms = rooms, ApproxSquFeet = approxSquFeet, GarageType = garageType, GarageSpaces = garageSpaces
};
var price = HousePricePrediction.PredictSinglePrice(housePriceSample1);
return(new string[] { price });
}
public ActionResult <IEnumerable <string> > Get()
{
var housePriceSample1 = new HouseData()
{
Area = "76", BedRooms = 3, BedRoomsBsmt = 0, FullBath = 2, HalfBath = 0, Rooms = 7, ApproxSquFeet = 1300, GarageType = "Attached", GarageSpaces = 2
};
//var price = HousePricePrediction.PredictSinglePrice(housePriceSample1, @"MLNETModels\housePriceModel.zip");
var price = HousePricePrediction.PredictSinglePrice(housePriceSample1);
return(new string[] { price });
}
public ActionResult <float> Post([FromBody] HousePriceData data)
{
if (!ModelState.IsValid)
{
return(BadRequest());
}
Console.WriteLine(data.Surface);
HousePricePrediction predictedValue = _predictionEnginePool.Predict(modelName: "HousePriceModel", example: data);
return(Ok(System.Text.Json.JsonSerializer.Serialize(predictedValue)));
}
public ActionResult <IEnumerable <string> > Get()
{
var housePriceSample1 = new HouseData()
{
Area = "76", BedRooms = 3, BedRoomsBsmt = 0, FullBath = 2, HalfBath = 0, Rooms = 7, ApproxSquFeet = 1300, GarageType = "Attached", GarageSpaces = 2
};
HouseData[] hd = new HouseData[] { housePriceSample1 };
var results = HousePricePrediction.PredictSinglePriceSet(hd, _dataTransformModelPath, _modelPath);
return(new string[] { results[0].ToString() });
}
public ActionResult <IEnumerable <string> > Get([FromQuery] string area, [FromQuery] float approxSquFeet, [FromQuery] float rooms, [FromQuery] float bedrooms, [FromQuery] float fullbath,
[FromQuery] float halfbath, [FromQuery] string garageType, [FromQuery] float garageSpaces)
{
var requestHousePriceFeatures = new HouseData()
{
Area = area, BedRooms = bedrooms, BedRoomsBsmt = 0, FullBath = fullbath, HalfBath = halfbath, Rooms = rooms, ApproxSquFeet = approxSquFeet, GarageType = garageType, GarageSpaces = garageSpaces
};
HouseData[] hd = new HouseData[] { requestHousePriceFeatures };
var results = HousePricePrediction.PredictSinglePriceSet(hd, _dataTransformModelPath, _modelPath);
return(new string[] { results[0].ToString() });
}
public async Task ReadStrongTypeModelFromStream()
{
using (var memoryStream = new MemoryStream())
{
ModelHelper.WriteKcHousePriceModel(GetDataPath("kc_house_data.csv"), memoryStream);
memoryStream.Position = 0;
var model = await Legacy.PredictionModel.ReadAsync <HousePriceData, HousePricePrediction>(memoryStream);
HousePricePrediction prediction = model.Predict(new HousePriceData()
{
Bedrooms = 3,
Bathrooms = 1.75f,
SqftLiving = 2450,
SqftLot = 2691,
Floors = 2,
Waterfront = 0,
View = 0,
Condition = 3,
Grade = 8,
SqftAbove = 1750,
SqftBasement = 700,
YearBuilt = 1915,
YearRenovated = 0,
Zipcode = 98119,
Lat = 47.6386f,
Long = -122.36f,
SqftLiving15 = 1760,
SqftLot15 = 3573
});
Assert.InRange(prediction.Price, 790_000, 850_000);
var dataView = model.Predict(ModelHelper.GetKcHouseDataView(GetDataPath("kc_house_data.csv")));
dataView.Schema.TryGetColumnIndex("Score", out int scoreColumn);
using (var cursor = dataView.GetRowCursor((int col) => col == scoreColumn))
{
var scoreGetter = cursor.GetGetter <float>(scoreColumn);
float score = 0;
cursor.MoveNext();
scoreGetter(ref score);
Assert.InRange(score, 100_000, 200_000);
}
Legacy.PredictionModel nonGenericModel;
using (var anotherStream = new MemoryStream())
{
await model.WriteAsync(anotherStream);
nonGenericModel = await Legacy.PredictionModel.ReadAsync(anotherStream);
}
dataView = nonGenericModel.Predict(ModelHelper.GetKcHouseDataView(GetDataPath("kc_house_data.csv")));
using (var cursor = dataView.GetRowCursor((int col) => col == scoreColumn))
{
var scoreGetter = cursor.GetGetter <float>(scoreColumn);
float score = 0;
cursor.MoveNext();
scoreGetter(ref score);
Assert.InRange(score, 100_000, 200_000);
}
}
}
