public void RunPipelineTest()
{
// Creates learning api object
LearningApi api = new LearningApi(TestHelpers.GetDescriptor());
// Initialize data provider
api.UseCsvDataProvider(m_iris_data_path, ',', 1);
// Use mapper for data, which will extract (map) required columns
api.UseDefaultDataMapper();
// Use MinMax data normalizer
//api.UseMinMaxNormalizer(m_stats.Select(x => x.Min).ToArray(), m_stats.Select(x => x.Max).ToArray());
// We could also use some other normalizer like Gaus data normalizer
//api.UseGaussNormalizer(m_stats.Select(x => x.Mean).ToArray(), m_stats.Select(x => x.StDev).ToArray());
// Prepares the ML Algoritm and setup parameters
api.UseBackPropagation(1, 0.2, 1.0, null);
//start process of learning
api.Run();
// api.Train();
// api.TrainSample();
IScore status = api.GetScore();
//api.Train(vector)
return;
}
public bool InitNeuralBackPropagationTest()
{
// InitIrisMapperInJsonFormat_helper();
// Creates learning api object
LearningApi api = new LearningApi(TestHelpers.GetDescriptor());
// Initialize data provider
api.UseCsvDataProvider(m_IrisDataPath, ',', false, 1);
// Use mapper for data, which will extract (map) required columns
api.UseDefaultDataMapper();
// Use MinMax data normalizer
//api.UseMinMaxNormalizer();
// We could also use some other normalizer like Gaus data normalizer
//api.UseGaussNormalizer(m_stats.Select(x => x.Mean).ToArray(), m_stats.Select(x => x.StDev).ToArray());
// Prepares the ML Algoritm and setup parameters
api.UseBackPropagation(1, 0.2, 1.0, null);
api.Run();
IScore status = api.GetScore();
//api.Train(vector)
return(true);
}
public void LogisticRegression_Test_iterations_20_learningrate_015()
{
var desc = loadMetaData();
LearningApi api = new LearningApi(desc);
//Real dataset must be defined as object type, because data can be numeric, binary and classification
api.UseActionModule <object[][], object[][]>((input, ctx) =>
{
return(loadRealDataSample());
});
// Use mapper for data, which will extract (map) required columns
api.UseDefaultDataMapper();
api.UseMinMaxNormalizer();
//run logistic regression for 10 iteration with learningRate=0.15
api.UseLogisticRegression(0.15, 20);
api.Run();
IScore score = api.GetScore();
//Errors during each iteration
Assert.Equal(Math.Round(score.Errors[0], 5), 0.24236);
Assert.Equal(Math.Round(score.Errors[1], 5), 0.23707);
Assert.Equal(Math.Round(score.Errors[2], 5), 0.23358);
Assert.Equal(Math.Round(score.Errors[3], 5), 0.23001);
Assert.Equal(Math.Round(score.Errors[4], 5), 0.22806);
Assert.Equal(Math.Round(score.Errors[5], 5), 0.22506);
Assert.Equal(Math.Round(score.Errors[6], 5), 0.22409);
Assert.Equal(Math.Round(score.Errors[7], 5), 0.22134);
Assert.Equal(Math.Round(score.Errors[8], 5), 0.22105);
Assert.Equal(Math.Round(score.Errors[9], 5), 0.21840);
Assert.Equal(Math.Round(score.Errors[10], 5), 0.21857);
Assert.Equal(Math.Round(score.Errors[11], 5), 0.21595);
Assert.Equal(Math.Round(score.Errors[12], 5), 0.21640);
Assert.Equal(Math.Round(score.Errors[13], 5), 0.21381);
Assert.Equal(Math.Round(score.Errors[14], 5), 0.21439);
Assert.Equal(Math.Round(score.Errors[15], 5), 0.21189);
Assert.Equal(Math.Round(score.Errors[16], 5), 0.21251);
Assert.Equal(Math.Round(score.Errors[17], 5), 0.21015);
Assert.Equal(Math.Round(score.Errors[18], 5), 0.21076);
Assert.Equal(Math.Round(score.Errors[19], 5), 0.20860);
//LG Model Best Found model in 20 iteration
Assert.Equal(Math.Round(score.Weights[0], 5), 0.28363);
Assert.Equal(Math.Round(score.Weights[1], 5), 0.37424);
Assert.Equal(Math.Round(score.Weights[2], 5), 1.41890);
Assert.Equal(Math.Round(score.Weights[3], 5), 1.01207);
Assert.Equal(Math.Round(score.Weights[4], 5), -0.33841);
Assert.Equal(Math.Round(score.Weights[5], 5), -0.33841);
Assert.Equal(Math.Round(score.Weights[6], 5), -1.62489);
}
public void LogisticsRegression_Test_iterations_10_learningrate_013()
{
var desc = loadMetaData();
LearningApi api = new LearningApi(desc);
//Real dataset must be defined as object type, because data can be numeric, binary and classification
api.UseActionModule <object[][], object[][]>((input, ctx) =>
{
return(loadRealDataSample());
});
// Use mapper for data, which will extract (map) required columns
api.UseDefaultDataMapper();
api.UseMinMaxNormalizer();
//run logistic regression for 10 iterations with learningRate=0.13
api.UseLogisticRegression(0.13, 10);
api.Run();
IScore score = api.GetScore();
//Errors during each iteration. IF the learningRate is suitable erros is descrising for every next iteration
Assert.Equal(Math.Round(score.Errors[0], 5), 0.24278);
Assert.Equal(Math.Round(score.Errors[1], 5), 0.23749);
Assert.Equal(Math.Round(score.Errors[2], 5), 0.23359);
Assert.Equal(Math.Round(score.Errors[3], 5), 0.23010);
Assert.Equal(Math.Round(score.Errors[4], 5), 0.22740);
Assert.Equal(Math.Round(score.Errors[5], 5), 0.22476);
Assert.Equal(Math.Round(score.Errors[6], 5), 0.22271);
Assert.Equal(Math.Round(score.Errors[7], 5), 0.22065);
Assert.Equal(Math.Round(score.Errors[8], 5), 0.21902);
Assert.Equal(Math.Round(score.Errors[9], 5), 0.21739);
//LG Model Best Found model in 10 iteration
Assert.Equal(Math.Round(score.Weights[0], 5), 0.06494);
Assert.Equal(Math.Round(score.Weights[1], 5), 0.21584);
Assert.Equal(Math.Round(score.Weights[2], 5), 0.89901);
Assert.Equal(Math.Round(score.Weights[3], 5), 0.51497);
Assert.Equal(Math.Round(score.Weights[4], 5), -0.30213);
Assert.Equal(Math.Round(score.Weights[5], 5), -0.30213);
Assert.Equal(Math.Round(score.Weights[6], 5), -0.85624);
}
public void LogisticsRegression_Test_iterations_10_learningrate_013()
{
var desc = loadMetaData();
LearningApi api = new LearningApi(desc);
//Real dataset must be defined as object type, because data can be numeric, binary and classification
api.UseActionModule <object[][], object[][]>((input, ctx) =>
{
return(loadRealDataSample());
});
// Use mapper for data, which will extract (map) required columns
api.UseDefaultDataMapper();
api.UseMinMaxNormalizer();
//run logistic regression for 10 iterations with learningRate=0.13
api.UseLogisticRegression(0.13, 10);
api.Run();
LogisticRegressionScore score = api.GetScore() as LogisticRegressionScore;
//Errors during each iteration. IF the learningRate is suitable errors is describing for every next iteration
Assert.Equal(Math.Round(score.Errors[0], 5), 0.24278);
Assert.Equal(Math.Round(score.Errors[1], 5), 0.23749);
Assert.Equal(Math.Round(score.Errors[2], 5), 0.23359);
Assert.Equal(Math.Round(score.Errors[3], 5), 0.23010);
Assert.Equal(Math.Round(score.Errors[4], 5), 0.22740);
Assert.Equal(Math.Round(score.Errors[5], 5), 0.22476);
Assert.Equal(Math.Round(score.Errors[6], 5), 0.22271);
Assert.Equal(Math.Round(score.Errors[7], 5), 0.22065);
Assert.Equal(Math.Round(score.Errors[8], 5), 0.21902);
Assert.Equal(Math.Round(score.Errors[9], 5), 0.21739);
//LG Model Best Found model in 10 iteration
Assert.Equal(Math.Round(score.Weights[0], 5), 0.06494);
Assert.Equal(Math.Round(score.Weights[1], 5), 0.21584);
Assert.Equal(Math.Round(score.Weights[2], 5), 0.89901);
Assert.Equal(Math.Round(score.Weights[3], 5), 0.51497);
Assert.Equal(Math.Round(score.Weights[4], 5), -0.30213);
Assert.Equal(Math.Round(score.Weights[5], 5), -0.30213);
Assert.Equal(Math.Round(score.Weights[6], 5), -0.85624);
//define data for testing (prediction)
LearningApi apiPrediction = new LearningApi(desc);
//Real dataset must be defined as object type, because data can be numeric, binary and classification
apiPrediction.UseActionModule <object[][], object[][]>((input, ctx) =>
{
var data = new object[4][]
{
new object[] { 0.202, "blue", "male", 13, "yes" },
new object[] { 0.447, "green", "female", 37, "no" },
new object[] { 0.120, "red", "male", "21", "yes" },
new object[] { 0.313, "green", "male", 22, "yes" },
};
return(data);
});
// Use mapper for data, which will extract (map) required columns
apiPrediction.UseDefaultDataMapper();
var testData = apiPrediction.Run();
//use previous trained model
var result = api.Algorithm.Predict(testData as double[][], api.Context) as LogisticRegressionResult;
Assert.Equal(Math.Round(result.PredictedValues[0], 5), 1E-05);
Assert.Equal(Math.Round(result.PredictedValues[1], 5), 0);
Assert.Equal(Math.Round(result.PredictedValues[2], 5), 0);
Assert.Equal(Math.Round(result.PredictedValues[3], 5), 0);
}