public void ArbitraryPrediction_Test_With_Named_Iterator()
{
var data = ArbitraryPrediction.GetDataUsingNamedIterator();
var description = Descriptor.Create<ArbitraryPrediction>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
ArbitraryPrediction minimumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 10.0m,
ThirdTestFeature = 1.2m
};
ArbitraryPrediction maximumPredictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
SecondTestFeature = 57.0m,
ThirdTestFeature = 1.2m
};
var expectedMinimum = model.Predict<ArbitraryPrediction>(minimumPredictionValue).OutcomeLabel;
var expectedMaximum = model.Predict<ArbitraryPrediction>(maximumPredictionValue).OutcomeLabel;
//The value should be minimum, however, maximum is returned
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Minimum, expectedMinimum);
//Maximum is returned as expected
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedMaximum);
}
public DecisionTreeGenerator CreateDecisionTreeGenerator()
{
var generator = new DecisionTreeGenerator(50)
{
Descriptor = Descriptor.Create<Passenger>(),
Hint = 0
};
return generator;
}
public void Save_And_Load_Iris_DT()
{
var data = Iris.Load();
var description = Descriptor.Create<Iris>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data) as DecisionTreeModel;
Serialize(model);
var lmodel = Deserialize<DecisionTreeModel>();
Assert.AreEqual(model.Hint, lmodel.Hint);
AreEqual(model.Tree, lmodel.Tree, false);
}
static void Main(string[] args)
{
Tennis[] data = Tennis.GetData();
var d = Descriptor.Create<Tennis>();
var g = new DecisionTreeGenerator(d);
g.SetHint(false);
var model = Learner.Learn(data, 0.8, 1000, g);
Console.WriteLine(model);
Console.ReadKey();
}
public void Save_And_Load_HouseDT()
{
var data = House.GetData();
var description = Descriptor.Create<House>();
var generator = new DecisionTreeGenerator { Depth = 50 };
var model = generator.Generate(description, data) as DecisionTreeModel;
Serialize(model);
var lmodel = Deserialize<DecisionTreeModel>();
Assert.AreEqual(model.Hint, lmodel.Hint);
AreEqual(model.Tree, lmodel.Tree, false);
}
public void ArbitraryPrediction_Test_With_Feature_Value_Greater_Than_Trained_Instances()
{
//TODO Seth, this test has an error
var data = ArbitraryPrediction.GetData();
var description = Descriptor.Create<ArbitraryPrediction>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
ArbitraryPrediction predictionValue = new ArbitraryPrediction
{
FirstTestFeature = 1.0m,
//This value is greater than any of the trained instances
SecondTestFeature = 106.0m,
ThirdTestFeature = 1.2m
};
var expectedValue = model.Predict<ArbitraryPrediction>(predictionValue).OutcomeLabel;
Assert.AreEqual(ArbitraryPrediction.PredictionLabel.Maximum, expectedValue);
}
public void ValueObject_Test_With_Yield_Enumerator()
{
var data = ValueObject.GetData();
var generator = new DecisionTreeGenerator()
{
Descriptor = Descriptor.Create<ValueObject>()
};
var model = generator.Generate(data);
var s = model.ToString();
var o = new ValueObject() { V1 = 1, V2 = 60 };
var os = model.Predict<ValueObject>(o).R;
Assert.AreEqual("l".Sanitize(), os);
}
public void Titanic_Passenger_Survival_Test_Success_Percentage_Should_Be_Seventy_Five_Percent()
{
var generator = new DecisionTreeGenerator(50)
{
Descriptor = Descriptor.Create<Passenger>(),
Hint = 0
};
var data = Passenger.LoadData(@"D:\Development\machinelearning\numl-fork\numl\numl.TestPredictions\Data\Titanic_Training_Data.csv", true);
var model = generator.Generate(data);
//var model = Learner.Learn(data, .90, 1000, generator);
int incorrectPredictions = 0;
int correctPredictions = 0;
for (int passengerIndex = 0; passengerIndex < 800; passengerIndex++)
{
var passenger = data[passengerIndex];
var survived = passenger.PassengerSurvived;
var passengerPredicted =
model.Predict<Passenger>(passenger);
if (survived != passengerPredicted.PassengerSurvived)
incorrectPredictions++;
else
correctPredictions++;
}
var percentageCorrect = (correctPredictions / 800.0) * 100;
var percentageIncorrect = (incorrectPredictions / 800.0) * 100;
Debug.WriteLine(string.Format("Out of 800 predictions, the engine predicted {0}% correctly and {1}% incorrectly", percentageCorrect, percentageIncorrect));
Assert.IsTrue(percentageCorrect >= 70.0);
}
public void Tennis_DT_and_Prediction()
{
var data = Tennis.GetData();
var description = Descriptor.Create<Tennis>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
Tennis t = new Tennis
{
Humidity = Humidity.Normal,
Outlook = Outlook.Overcast,
Temperature = Temperature.Cool,
Windy = true
};
model.Predict<Tennis>(t);
Assert.IsTrue(t.Play);
}
public void Iris_DT_and_Prediction_with_Learner()
{
var data = Iris.Load();
var generator = new DecisionTreeGenerator(50)
{
Descriptor = Descriptor.Create<Iris>(),
Hint = 0
};
var lmodel = Learner.Learn(data, .80, 1000, generator);
// should be Iris-Setosa
Iris iris = new Iris
{
PetalWidth = 0.5m,
PetalLength = 2.3m,
SepalLength = 2.1m,
SepalWidth = 2.1m
};
iris = lmodel.Model.Predict<Iris>(iris);
Assert.AreEqual("Iris-setosa".Sanitize(), iris.Class);
}
public void Iris_DT_and_Prediction()
{
var data = Iris.Load();
var description = Descriptor.Create<Iris>();
var generator = new DecisionTreeGenerator(50);
var model = generator.Generate(description, data);
// should be Iris-Setosa
Iris iris = new Iris
{
PetalWidth = 0.5m,
PetalLength = 2.3m,
SepalLength = 2.1m,
SepalWidth = 2.1m
};
iris = model.Predict<Iris>(iris);
Assert.AreEqual("Iris-setosa".Sanitize(), iris.Class);
}
public void House_DT_and_Prediction()
{
var data = House.GetData();
var description = Descriptor.Create<House>();
var generator = new DecisionTreeGenerator { Depth = 50 };
var model = generator.Generate(description, data);
House h = new House
{
District = District.Rural,
HouseType = HouseType.Detached,
Income = Income.High,
PreviousCustomer = false
};
var prediction = model.Predict(h);
Assert.IsTrue(prediction.Response);
}