public void RunTest()
{
// context is null
{
string parameterName = "context";
string innerMessage =
ArgumentExceptionAssert.NullPartialMessage +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var sample = DoubleMatrix.Dense(1, 1);
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "Run",
methodArgs: new object[] { null, 1, .01 });
},
expectedInnerType: typeof(ArgumentNullException),
expectedInnerMessage: innerMessage);
}
}
public void RunTest()
{
// Valid input - Minimization
{
var optimizer = new SystemPerformanceOptimizer();
// Create the context.
var testableContext =
TestablePartitionOptimizationContext00.Get();
var context = testableContext.Context;
// Set optimization parameters.
int sampleSize = 2000;
double rarity = 0.01;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
var expectedPartition = IndexPartition.Create(
testableContext.OptimalState);
var actualPartition = IndexPartition.Create(
results.OptimalState);
IndexPartitionAssert.HaveEqualIdentifiers(
expected: expectedPartition,
actual: actualPartition);
IndexPartitionAssert.HaveEqualParts(
expected: expectedPartition,
actual: actualPartition);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
}
public void Main()
{
// Create the context.
var context = new RosenbrockFunctionMinimizationContext();
// Create the optimizer.
var optimizer = new SystemPerformanceOptimizer()
{
PerformanceEvaluationParallelOptions = { MaxDegreeOfParallelism = -1 },
SampleGenerationParallelOptions = { MaxDegreeOfParallelism = -1 }
};
// Set optimization parameters.
double rarity = 0.1;
int sampleSize = 1000;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
// Show the results.
Console.WriteLine(
"The Cross-Entropy optimizer has converged: {0}.",
results.HasConverged);
Console.WriteLine();
Console.WriteLine("Initial guess parameter:");
Console.WriteLine(context.InitialParameter);
Console.WriteLine();
Console.WriteLine("The minimizer of the performance is:");
Console.WriteLine(results.OptimalState);
Console.WriteLine();
Console.WriteLine("The minimum performance is:");
Console.WriteLine(results.OptimalPerformance);
}
/// <summary>
/// Initializes a new instance of the
/// <see cref="CategoricalEntailmentEnsembleClassifier" /> class
/// by exploiting the specified <paramref name="trainer"/>
/// to select the given
/// <paramref name="numberOfTrainedCategoricalEntailments"/>.
/// </summary>
/// <param name="trainer">
/// An object whose state contains the information needed to
/// train the classifier.
/// </param>
/// <param name="numberOfTrainedCategoricalEntailments">
/// The number of categorical entailments to be trained.
/// </param>
/// <returns>
/// A classifier whose ensemble contains the trained
/// categorical entailments.
/// </returns>
/// <remarks>
/// <para>
/// The <paramref name="trainer"/> can be equipped with a
/// nonempty initial ensemble of categorical entailments.
/// This method always trains a classifier by adding
/// to such ensemble further entailments, whose number
/// is specified by parameter
/// <paramref name="numberOfTrainedCategoricalEntailments"/>.
/// However, the method returns the partial classifier whose
/// ensemble contains the additional trained entailments only
/// (no initial entailments).
/// </para>
/// </remarks>
private static CategoricalEntailmentEnsembleClassifier Train(
CategoricalEntailmentEnsembleTrainer trainer,
int numberOfTrainedCategoricalEntailments)
{
var optimizer = new SystemPerformanceOptimizer();
int numberOfResponseCategories =
trainer.ResponseVariable.NumberOfCategories;
var context =
new CategoricalEntailmentEnsembleOptimizationContext(
objectiveFunction: trainer.Performance,
trainer.featureCategoryCounts,
numberOfResponseCategories,
numberOfTrainedCategoricalEntailments,
trainer.allowEntailmentPartialTruthValues,
probabilitySmoothingCoefficient: .9,
optimizationGoal: OptimizationGoal.Maximization,
minimumNumberOfIterations: 10,
maximumNumberOfIterations: 1000);
int numberOfParameters = numberOfTrainedCategoricalEntailments * (
trainer.featureCategoryCounts.Sum() + numberOfResponseCategories);
int sampleSize = 100 * numberOfParameters;
double rarity = .01;
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
var partialClassifier = context.GetCategoricalEntailmentEnsembleClassifier(
results.OptimalState,
new List <CategoricalVariable>(trainer.FeatureVariables),
trainer.ResponseVariable);
return(partialClassifier);
}
public void SampleGenerationParallelOptionsTest()
{
// value is null
{
string parameterName = "value";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.SampleGenerationParallelOptions
= null;
},
expectedType: typeof(ArgumentNullException),
expectedPartialMessage:
ArgumentExceptionAssert.NullPartialMessage,
expectedParameterName: parameterName);
}
// value is valid
{
int maxDegreeOfParallelism = 2;
var optimizer = new SystemPerformanceOptimizer
{
SampleGenerationParallelOptions
= new System.Threading.Tasks.ParallelOptions()
{
MaxDegreeOfParallelism = maxDegreeOfParallelism
}
};
Assert.AreEqual(
expected: 2,
actual: optimizer
.SampleGenerationParallelOptions.MaxDegreeOfParallelism);
}
}
public void RunTest()
{
var optimizer = new SystemPerformanceOptimizer();
// Create the context.
var testableContext =
TestableCategoricalEntailmentEnsembleOptimizationContext01.Get();
var context = testableContext.Context;
context.TraceExecution = true;
// Set optimization parameters.
int sampleSize = 3600;
double rarity = 0.01;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
var expectedClassifier =
new CategoricalEntailmentEnsembleClassifier(
featureVariables: testableContext.FeatureVariables,
responseVariable: testableContext.ResponseVariable);
expectedClassifier.Add(
featurePremises: new List <SortedSet <double> >(1)
{
new SortedSet <double>()
{
0.0, 1.0, 2.0
}
},
responseConclusion: 0.0,
truthValue: 1.0);
expectedClassifier.Add(
featurePremises: new List <SortedSet <double> >(1)
{
new SortedSet <double>()
{
3.0, 4.0, 5.0
}
},
responseConclusion: 1.0,
truthValue: 1.0);
var actualClassifier =
new CategoricalEntailmentEnsembleClassifier(
featureVariables: testableContext.FeatureVariables,
responseVariable: testableContext.ResponseVariable);
actualClassifier.Add(
featurePremises: new List <SortedSet <double> >(1)
{
new SortedSet <double>()
{
0.0, 1.0, 2.0
}
},
responseConclusion: 0.0,
truthValue: 1.0);
actualClassifier.Add(
context.GetCategoricalEntailmentEnsembleClassifier(
state: results.OptimalState,
featureVariables: testableContext.FeatureVariables,
responseVariable: testableContext.ResponseVariable)
.Entailments[0]);
var expectedTrainedEntailment = expectedClassifier.Entailments[1];
var actualTrainedEntailment = actualClassifier.Entailments[1];
Assert.IsTrue(
expectedTrainedEntailment.FeaturePremises[0]
.IsSubsetOf(
actualTrainedEntailment.FeaturePremises[0]));
Assert.AreEqual(
expected: expectedTrainedEntailment.ResponseConclusion,
actual: actualTrainedEntailment.ResponseConclusion,
DoubleMatrixTest.Accuracy);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
public void Main()
{
// Set the number of items and features under study.
const int numberOfItems = 12;
int numberOfFeatures = 7;
// Create a matrix that will represent
// an artificial data set,
// having 12 items (rows) and 7 features (columns).
// This will store the observations which
// partition discovery will be based on.
var data = DoubleMatrix.Dense(
numberOfRows: numberOfItems,
numberOfColumns: numberOfFeatures);
// Fill the data rows by sampling from a different
// distribution while, respectively, drawing observations
// for items 0 to 3, 4 to 7, and 8 to 11: these will be the
// three different parts expected to be included in the
// optimal partition.
double mu = 1.0;
var g = new GaussianDistribution(mu: mu, sigma: .01);
IndexCollection range = IndexCollection.Range(0, 3);
for (int j = 0; j < numberOfFeatures; j++)
{
data[range, j] = g.Sample(sampleSize: range.Count);
}
mu += 5.0;
g.Mu = mu;
range = IndexCollection.Range(4, 7);
for (int j = 0; j < numberOfFeatures; j++)
{
data[range, j] = g.Sample(sampleSize: range.Count);
}
mu += 5.0;
g.Mu = mu;
range = IndexCollection.Range(8, 11);
for (int j = 0; j < numberOfFeatures; j++)
{
data[range, j] = g.Sample(sampleSize: range.Count);
}
Console.WriteLine("The data set:");
Console.WriteLine(data);
// Define the optimization problem as
// the minimization of the Davies-Bouldin Index
// of a candidate partition.
double objectiveFunction(DoubleMatrix x)
{
// An argument x has 12 entries, each belonging
// to the set {0,...,k-1}, where k is the
// maximum number of allowed parts, so
// x[j]==i signals that, at x, item j
// has been assigned to part i.
IndexPartition <double> selected =
IndexPartition.Create(x);
var performance = IndexPartition.DaviesBouldinIndex(
data: data,
partition: selected);
return(performance);
}
var optimizationGoal = OptimizationGoal.Minimization;
// Define the maximum number of parts allowed in the
// partition to be discovered.
int maximumNumberOfParts = 3;
// Create the required context.
var context = new PartitionOptimizationContext(
objectiveFunction: objectiveFunction,
stateDimension: numberOfItems,
partitionDimension: maximumNumberOfParts,
probabilitySmoothingCoefficient: .8,
optimizationGoal: optimizationGoal,
minimumNumberOfIterations: 3,
maximumNumberOfIterations: 1000);
// Create the optimizer.
var optimizer = new SystemPerformanceOptimizer()
{
PerformanceEvaluationParallelOptions = { MaxDegreeOfParallelism = -1 },
SampleGenerationParallelOptions = { MaxDegreeOfParallelism = -1 }
};
// Set optimization parameters.
double rarity = 0.01;
int sampleSize = 2000;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
IndexPartition <double> optimalPartition =
IndexPartition.Create(results.OptimalState);
// Show the results.
Console.WriteLine(
"The Cross-Entropy optimizer has converged: {0}.",
results.HasConverged);
Console.WriteLine();
Console.WriteLine("Initial guess parameter:");
Console.WriteLine(context.InitialParameter);
Console.WriteLine();
Console.WriteLine("The minimizer of the performance is:");
Console.WriteLine(results.OptimalState);
Console.WriteLine();
Console.WriteLine(
"The optimal partition is:");
Console.WriteLine(optimalPartition);
Console.WriteLine();
Console.WriteLine("The minimum performance is:");
Console.WriteLine(results.OptimalPerformance);
Console.WriteLine();
Console.WriteLine("The Dunn Index for the optimal partition is:");
var di = IndexPartition.DunnIndex(
data,
optimalPartition);
Console.WriteLine(di);
}
public void Main()
{
// Set the number of items and features under study.
const int numberOfItems = 12;
int numberOfFeatures = 7;
// Define a partition that must be explained.
// Three parts (clusters) are included,
// containing, respectively, items 0 to 3,
// 4 to 7, and 8 to 11.
var partition = IndexPartition.Create(
new double[numberOfItems]
{
0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2
});
// Create a matrix that will represent
// an artificial data set,
// having 12 items (rows) and 7 features (columns).
// This will store the observations which
// explanation will be based on.
var data = DoubleMatrix.Dense(
numberOfRows: numberOfItems,
numberOfColumns: numberOfFeatures);
// The first 5 features are built to be almost
// surely non informative, since they result
// as samples drawn from a same distribution.
var g = new GaussianDistribution(mu: 0, sigma: .01);
for (int j = 0; j < 5; j++)
{
data[":", j] = g.Sample(sampleSize: numberOfItems);
}
// Features 5 to 6 are instead built to be informative,
// since they are sampled from different distributions
// while filling rows whose indexes are in different parts
// of the partition to be explained.
var partIdentifiers = partition.Identifiers;
double mu = 1.0;
for (int i = 0; i < partIdentifiers.Count; i++)
{
var part = partition[partIdentifiers[i]];
int partSize = part.Count;
g.Mu = mu;
data[part, 5] = g.Sample(sampleSize: partSize);
mu += 2.0;
g.Mu = mu;
data[part, 6] = g.Sample(sampleSize: partSize);
mu += 2.0;
}
Console.WriteLine("The data set:");
Console.WriteLine(data);
// Define the selection problem as
// the maximization of the Dunn Index.
double objectiveFunction(DoubleMatrix x)
{
// An argument x has entries equal to one,
// signaling that the corresponding features
// are selected at x. Otherwise, the entries
// are zero.
IndexCollection selected = x.FindNonzero();
double performance =
IndexPartition.DunnIndex(
data: data[":", selected],
partition: partition);
return(performance);
}
var optimizationGoal = OptimizationGoal.Maximization;
// Define how many features must be selected
// for explanation.
int numberOfExplanatoryFeatures = 2;
// Create the required context.
var context = new CombinationOptimizationContext(
objectiveFunction: objectiveFunction,
stateDimension: numberOfFeatures,
combinationDimension: numberOfExplanatoryFeatures,
probabilitySmoothingCoefficient: .8,
optimizationGoal: optimizationGoal,
minimumNumberOfIterations: 3,
maximumNumberOfIterations: 1000);
// Create the optimizer.
var optimizer = new SystemPerformanceOptimizer()
{
PerformanceEvaluationParallelOptions = { MaxDegreeOfParallelism = -1 },
SampleGenerationParallelOptions = { MaxDegreeOfParallelism = -1 }
};
// Set optimization parameters.
double rarity = 0.01;
int sampleSize = 1000;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
IndexCollection optimalExplanatoryFeatureIndexes =
results.OptimalState.FindNonzero();
// Show the results.
Console.WriteLine(
"The Cross-Entropy optimizer has converged: {0}.",
results.HasConverged);
Console.WriteLine();
Console.WriteLine("Initial guess parameter:");
Console.WriteLine(context.InitialParameter);
Console.WriteLine();
Console.WriteLine("The maximizer of the performance is:");
Console.WriteLine(results.OptimalState);
Console.WriteLine();
Console.WriteLine(
"The {0} features best explaining the given partition have column indexes:",
numberOfExplanatoryFeatures);
Console.WriteLine(optimalExplanatoryFeatureIndexes);
Console.WriteLine();
Console.WriteLine("The maximum performance is:");
Console.WriteLine(results.OptimalPerformance);
Console.WriteLine();
Console.WriteLine("This is the Dunn Index for the selected features:");
var di = IndexPartition.DunnIndex(
data[":", optimalExplanatoryFeatureIndexes],
partition);
Console.WriteLine(di);
}
public void RunTest()
{
// Valid input - Minimization
{
var optimizer = new SystemPerformanceOptimizer();
// Create the context.
var testableContext =
TestableSystemPerformanceOptimizationContext00.Get();
var context = testableContext.Context;
// Set optimization parameters.
double rarity = 0.05;
int sampleSize = 1000;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
DoubleMatrixAssert.AreEqual(
expected: testableContext.OptimalState,
actual: results.OptimalState,
delta: .03);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
// Valid input - Maximization - with overrides
{
var optimizer = new SystemPerformanceOptimizer()
{
PerformanceEvaluationParallelOptions = { MaxDegreeOfParallelism = 1 },
SampleGenerationParallelOptions = { MaxDegreeOfParallelism = 1 }
};
// Create the context.
var testableContext =
TestableSystemPerformanceOptimizationContext01.Get();
var context = testableContext.Context;
// Set optimization parameters.
double rarity = 0.1;
int sampleSize = 1000;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
DoubleMatrixAssert.AreEqual(
expected: testableContext.OptimalState,
actual: results.OptimalState,
DoubleMatrixTest.Accuracy);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
// Valid input - Maximization - without overrides
{
var optimizer = new SystemPerformanceOptimizer()
{
PerformanceEvaluationParallelOptions = { MaxDegreeOfParallelism = 1 },
SampleGenerationParallelOptions = { MaxDegreeOfParallelism = 1 }
};
// Create the context.
var testableContext =
TestableSystemPerformanceOptimizationContext02.Get();
var context = testableContext.Context;
// Set optimization parameters.
double rarity = 0.1;
int sampleSize = 1000;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
DoubleMatrixAssert.AreEqual(
expected: testableContext.OptimalState,
actual: results.OptimalState,
DoubleMatrixTest.Accuracy);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
}
public void RunTest()
{
// Valid input - Minimization
{
var optimizer = new SystemPerformanceOptimizer();
// Create the context.
var testableContext =
TestableContinuousOptimizationContext00.Get();
var context = testableContext.Context;
// Set optimization parameters.
int sampleSize = 100;
double rarity = 0.09;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
DoubleMatrixAssert.AreEqual(
expected: testableContext.OptimalState,
actual: results.OptimalState,
delta: .03);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
// Valid input - Maximization
{
var optimizer = new SystemPerformanceOptimizer();
// Create the context.
var testableContext =
TestableContinuousOptimizationContext01.Get();
var context = testableContext.Context;
// Set optimization parameters.
int sampleSize = 100;
double rarity = 0.1;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
DoubleMatrixAssert.AreEqual(
expected: testableContext.OptimalState,
actual: results.OptimalState,
DoubleMatrixTest.Accuracy);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
// Valid input - Maximization - not converging
{
var optimizer = new SystemPerformanceOptimizer();
// Create the context.
var testableContext =
TestableContinuousOptimizationContext01.Get();
var context = new ContinuousOptimizationContext(
objectiveFunction: testableContext.ObjectiveFunction,
initialArgument: testableContext.InitialArgument,
meanSmoothingCoefficient: testableContext.MeanSmoothingCoefficient,
standardDeviationSmoothingCoefficient: testableContext.StandardDeviationSmoothingCoefficient,
standardDeviationSmoothingExponent: testableContext.StandardDeviationSmoothingExponent,
initialStandardDeviation: testableContext.InitialStandardDeviation,
terminationTolerance: testableContext.TerminationTolerance,
optimizationGoal: testableContext.OptimizationGoal,
minimumNumberOfIterations: testableContext.MinimumNumberOfIterations,
maximumNumberOfIterations: testableContext.MinimumNumberOfIterations + 1);
// Set optimization parameters.
int sampleSize = 100;
double rarity = 0.1;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: false,
actual: results.HasConverged);
}
}
/// <summary>
/// Finds the maximum of the specified
/// parametric objective function.
/// </summary>
/// <param name="objectiveFunction">
/// The objective function to be maximized.
/// </param>
/// <param name="initialArgument">
/// The argument at which the method starts the search
/// for optimality.
/// </param>
/// <param name="functionParameter">
/// The function parameter.
/// </param>
/// <typeparam name="TFunctionParameter">
/// The type of the function parameter.
/// </typeparam>
/// <returns>
/// The argument at which the function is maximized.
/// </returns>
/// <remarks>
/// <para>
/// It is assumed that the <paramref name="objectiveFunction"/>
/// will accept row
/// vectors as valid representations of an argument.
/// As a consequence, <paramref name="initialArgument"/> is
/// expected to be a row vector.
/// </para>
/// </remarks>
/// <example>
/// <para>
/// In the following example, function
/// <latex mode='display'>
/// f\round{x,\alpha}=\exp\round{-\round{x-2}^2}
/// + \round{\alpha} \exp\round{-\round{x+2}^2}
/// </latex>
/// is maximized.
/// </para>
/// <para>
/// The search for the maximizer starts from
/// the initial argument <latex>-6</latex>.
/// </para>
/// <para>
/// <code title="Maximizing a parametric function."
/// source="..\Novacta.Analytics.CodeExamples\ContinuousOptimizationExample1.cs.txt"
/// language="cs" />
/// </para>
/// </example>
/// <exception cref="ArgumentNullException">
/// <paramref name="objectiveFunction"/> is
/// <b>null</b>.<br/>
/// -or-<br/>
/// <paramref name="initialArgument"/> is <b>null</b>.
/// </exception>
/// <exception cref="ArgumentException">
/// <paramref name="initialArgument"/> is not a row
/// vector.
/// </exception>
public static DoubleMatrix Maximize <TFunctionParameter>(
Func <DoubleMatrix, TFunctionParameter, double> objectiveFunction,
DoubleMatrix initialArgument,
TFunctionParameter functionParameter)
{
#region Input validation
if (objectiveFunction is null)
{
throw new ArgumentNullException(nameof(objectiveFunction));
}
if (initialArgument is null)
{
throw new ArgumentNullException(nameof(initialArgument));
}
if (!initialArgument.IsRowVector)
{
throw new ArgumentException(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_ROW_VECTOR"),
nameof(initialArgument));
}
#endregion
double func(DoubleMatrix x) => objectiveFunction(x, functionParameter);
int numberOfArguments = initialArgument.Count;
var initialParameter = DoubleMatrix.Dense(
2,
numberOfArguments);
initialParameter[0, ":"] = initialArgument;
initialParameter[1, ":"] += 1.0e2;
var optimizer =
new SystemPerformanceOptimizer();
var context = new ContinuousOptimizationContext(
objectiveFunction: func,
initialArgument: initialArgument,
meanSmoothingCoefficient: .8,
standardDeviationSmoothingCoefficient: .7,
standardDeviationSmoothingExponent: 6,
initialStandardDeviation: 100.0,
optimizationGoal: OptimizationGoal.Maximization,
terminationTolerance: 1.0e-3,
minimumNumberOfIterations: 3,
maximumNumberOfIterations: 1000);
int sampleSize = 100 * numberOfArguments;
double rarity = .01;
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
return(results.OptimalState);
}
public void EvauatePerformancesTest()
{
// context is null
{
string parameterName = "context";
string innerMessage =
ArgumentExceptionAssert.NullPartialMessage +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var sample = DoubleMatrix.Dense(1, 1);
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "EvaluatePerformances",
methodArgs: new object[] { null, sample });
},
expectedInnerType: typeof(ArgumentNullException),
expectedInnerMessage: innerMessage);
}
// sample is null
{
string parameterName = "sample";
string innerMessage =
ArgumentExceptionAssert.NullPartialMessage +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var context = TestableSystemPerformanceOptimizationContext00.Get().Context;
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "EvaluatePerformances",
methodArgs: new object[] { context, null });
},
expectedInnerType: typeof(ArgumentNullException),
expectedInnerMessage: innerMessage);
}
// sample is not context compatible
{
string parameterName = "sample";
string innerMessage =
ImplementationServices.GetResourceString(
"STR_EXCEPT_CEP_SAMPLE_IS_CONTEXT_INCOMPATIBLE") +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var context = TestableSystemPerformanceOptimizationContext00.Get().Context;
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "EvaluatePerformances",
methodArgs: new object[] {
context,
DoubleMatrix.Dense(1, context.StateDimension + 1)
});
},
expectedInnerType: typeof(ArgumentException),
expectedInnerMessage: innerMessage);
}
}
public void SampleTest()
{
// context is null
{
string parameterName = "context";
string innerMessage =
ArgumentExceptionAssert.NullPartialMessage +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var parameter = DoubleMatrix.Dense(1, 1);
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "Sample",
methodArgs: new object[] { null, 1, parameter });
},
expectedInnerType: typeof(ArgumentNullException),
expectedInnerMessage: innerMessage);
}
// parameter is null
{
string parameterName = "parameter";
string innerMessage =
ArgumentExceptionAssert.NullPartialMessage +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var context = TestableSystemPerformanceOptimizationContext00.Get().Context;
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "Sample",
methodArgs: new object[] { context, 1, null });
},
expectedInnerType: typeof(ArgumentNullException),
expectedInnerMessage: innerMessage);
}
// sampleSize is zero
{
string parameterName = "sampleSize";
string innerMessage =
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_POSITIVE") +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var context = TestableSystemPerformanceOptimizationContext00.Get().Context;
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "Sample",
methodArgs: new object[] {
context,
0,
context.InitialParameter
});
},
expectedInnerType: typeof(ArgumentOutOfRangeException),
expectedInnerMessage: innerMessage);
}
// sampleSize is negative
{
string parameterName = "sampleSize";
string innerMessage =
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_POSITIVE") +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var context = TestableSystemPerformanceOptimizationContext00.Get().Context;
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "Sample",
methodArgs: new object[] {
context,
-1,
context.InitialParameter
});
},
expectedInnerType: typeof(ArgumentOutOfRangeException),
expectedInnerMessage: innerMessage);
}
// parameter is not context compatible: wrong number of rows
{
string parameterName = "parameter";
string innerMessage =
ImplementationServices.GetResourceString(
"STR_EXCEPT_CEP_PARAMETER_IS_CONTEXT_INCOMPATIBLE") +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var context = TestableSystemPerformanceOptimizationContext00.Get().Context;
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "Sample",
methodArgs: new object[] {
context,
1,
DoubleMatrix.Dense(
context.InitialParameter.NumberOfRows + 1,
context.InitialParameter.NumberOfColumns)
});
},
expectedInnerType: typeof(ArgumentException),
expectedInnerMessage: innerMessage);
}
// parameter is not context compatible: wrong number of columns
{
string parameterName = "parameter";
string innerMessage =
ImplementationServices.GetResourceString(
"STR_EXCEPT_CEP_PARAMETER_IS_CONTEXT_INCOMPATIBLE") +
" (Parameter '" + parameterName + "')";
var optimizer = new SystemPerformanceOptimizer();
var context = TestableSystemPerformanceOptimizationContext00.Get().Context;
ExceptionAssert.InnerThrow(
() =>
{
Reflector.ExecuteBaseMember(
obj: optimizer,
methodName: "Sample",
methodArgs: new object[] {
context,
1,
DoubleMatrix.Dense(
context.InitialParameter.NumberOfRows,
context.InitialParameter.NumberOfColumns + 1)
});
},
expectedInnerType: typeof(ArgumentException),
expectedInnerMessage: innerMessage);
}
}
/// <summary>
/// Explains existing clusters by selecting
/// a number of features from the specified corresponding data set.
/// </summary>
/// <param name="data">
/// The matrix whose columns contain the features observed at the
/// items under study.
/// </param>
/// <param name="partition">
/// A partition of the row indexes valid for <paramref name="data"/>.
/// </param>
/// <param name="numberOfExplanatoryFeatures">
/// The number of features to be selected.
/// </param>
/// <remarks>
/// <para>
/// Method <see cref="Explain(
/// DoubleMatrix, IndexPartition{double}, int)"/>
/// selects the specified <paramref name="numberOfExplanatoryFeatures"/>
/// from the given
/// <paramref name="data"/>, by minimizing the Davies-Bouldin
/// Index corresponding to
/// the <paramref name="partition"/> of the items under study.
/// </para>
/// <para>
/// This method uses a default Cross-Entropy context of
/// type <see cref="CombinationOptimizationContext"/> to identify the
/// optimal features.
/// If different selection criteria need to be applied,
/// or extra control on the
/// parameters of the underlying algorithm is required,
/// a specialized <see cref="CombinationOptimizationContext"/> can be
/// can be instantiated and hence exploited executing
/// method <see cref="SystemPerformanceOptimizer.Optimize(
/// SystemPerformanceOptimizationContext, double, int)">Optimize</see>
/// on a <see cref="SystemPerformanceOptimizer"/> object.
/// See the documentation about <see cref="CombinationOptimizationContext"/>
/// for additional examples.
/// </para>
/// </remarks>
/// <example>
/// <para>
/// In the following example, an existing partition of 12 items is explained
/// by selecting 2 features out of the seven ones available in
/// an artificial data set regarding the items under study.
/// </para>
/// <para>
/// <code title="Selecting features from a data set to explain a given partition."
/// source="..\Novacta.Analytics.CodeExamples\ClustersExplainExample0.cs.txt"
/// language="cs" />
/// </para>
/// </example>
/// <returns>
/// The collection of column indexes, valid for <paramref name="data"/>, that
/// correspond to the features selected to explain the
/// given <paramref name="partition"/> of row indexes.
/// </returns>
/// <exception cref="ArgumentNullException">
/// <paramref name="data"/> is <b>null</b>.<br/>
/// -or-<br/>
/// <paramref name="partition"/> is <b>null</b>.
/// </exception>
/// <exception cref="ArgumentOutOfRangeException">
/// <paramref name="numberOfExplanatoryFeatures"/> is not positive.
/// </exception>
/// <exception cref="ArgumentException">
/// <paramref name="numberOfExplanatoryFeatures"/> is not less than
/// the number of columns in <paramref name="data"/>.<br/>
/// -or-<br/>
/// A part in <paramref name="partition"/> contains a position
/// which is not valid as a row index of <paramref name="data"/>.
/// </exception>
/// <seealso cref="IndexPartition.DaviesBouldinIndex(
/// DoubleMatrix, IndexPartition{double})"/>
/// <seealso cref="CombinationOptimizationContext"/>
/// <seealso cref="SystemPerformanceOptimizer"/>
public static IndexCollection Explain(
DoubleMatrix data,
IndexPartition <double> partition,
int numberOfExplanatoryFeatures)
{
#region Input validation
if (data is null)
{
throw new ArgumentNullException(nameof(data));
}
if (numberOfExplanatoryFeatures < 1)
{
throw new ArgumentOutOfRangeException(
nameof(numberOfExplanatoryFeatures),
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_POSITIVE"));
}
int stateDimension = data.NumberOfColumns;
if (stateDimension <= numberOfExplanatoryFeatures)
{
throw new ArgumentException(
string.Format(
CultureInfo.InvariantCulture,
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_COLUMNS"),
nameof(numberOfExplanatoryFeatures),
nameof(data)),
nameof(numberOfExplanatoryFeatures)
);
}
if (partition is null)
{
throw new ArgumentNullException(nameof(partition));
}
#endregion
double objectiveFunction(DoubleMatrix x)
{
IndexCollection selected = x.FindNonzero();
double performance =
IndexPartition.DaviesBouldinIndex(
data: data[":", selected],
partition: partition);
return(performance);
}
var optimizer =
new SystemPerformanceOptimizer();
var context = new CombinationOptimizationContext(
objectiveFunction: objectiveFunction,
stateDimension: stateDimension,
combinationDimension: numberOfExplanatoryFeatures,
probabilitySmoothingCoefficient: .8,
optimizationGoal: OptimizationGoal.Minimization,
minimumNumberOfIterations: 3,
maximumNumberOfIterations: 1000);
double rarity = .01;
int sampleSize = 1000 * stateDimension;
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
var optimalState = results.OptimalState;
return(optimalState.FindNonzero());
}
public void RunTest()
{
// Valid input - Minimization
{
var optimizer = new SystemPerformanceOptimizer();
// Create the context.
var testableContext =
TestableCombinationOptimizationContext00.Get();
var context = testableContext.Context;
// Set optimization parameters.
int sampleSize = 300;
double rarity = 0.01;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
DoubleMatrixAssert.AreEqual(
expected: testableContext.OptimalState,
actual: results.OptimalState,
delta: .03);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
// Valid input - Maximization
{
var optimizer = new SystemPerformanceOptimizer();
// Create the context.
var testableContext =
TestableCombinationOptimizationContext01.Get();
var context = testableContext.Context;
// Set optimization parameters.
int sampleSize = 300;
double rarity = 0.01;
// Solve the problem.
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
Assert.AreEqual(
expected: true,
actual: results.HasConverged);
DoubleMatrixAssert.AreEqual(
expected: testableContext.OptimalState,
actual: results.OptimalState,
DoubleMatrixTest.Accuracy);
Assert.AreEqual(
expected: testableContext.OptimalPerformance,
actual: results.OptimalPerformance,
DoubleMatrixTest.Accuracy);
}
}
/// <summary>
/// Discovers optimal clusters
/// in a data set.
/// </summary>
/// <param name="maximumNumberOfParts">
/// The maximum number of parts allowed in the optimal
/// partition.
/// </param>
/// <param name="data">
/// The matrix whose rows contain the observations for the
/// items under study.
/// </param>
/// <remarks>
/// <para>
/// Method <see cref="Discover(DoubleMatrix, int)"/> partitions
/// a collection of items in no more than the specified
/// <paramref name="maximumNumberOfParts"/>,
/// given the specified <paramref name="data"/>, by minimizing the sum of
/// intra-cluster squared deviations.
/// </para>
/// <para>
/// This method uses a default Cross-Entropy context of
/// type <see cref="PartitionOptimizationContext"/> to identify the
/// optimal partition.
/// If different partitioning criteria need to be applied,
/// or extra control on the
/// parameters of the underlying algorithm is required,
/// a specialized <see cref="PartitionOptimizationContext"/> can be
/// can be instantiated and hence exploited executing
/// method <see cref="SystemPerformanceOptimizer.Optimize(
/// SystemPerformanceOptimizationContext, double, int)">Optimize</see>
/// on a <see cref="SystemPerformanceOptimizer"/> object.
/// See the documentation about <see cref="PartitionOptimizationContext"/>
/// for additional examples.
/// </para>
/// </remarks>
/// <example>
/// <para>
/// In the following example, a partition that optimally split 12 items
/// is discovered
/// given
/// an artificial data set regarding the items under study.
/// </para>
/// <para>
/// <code title="Optimal partitioning of a data set."
/// source="..\Novacta.Analytics.CodeExamples\ClustersDiscoverExample0.cs.txt"
/// language="cs" />
/// </para>
/// </example>
/// <returns>
/// A partition of the row indexes valid for <paramref name="data"/>.
/// </returns>
/// <exception cref="ArgumentNullException">
/// <paramref name="data"/> is <b>null</b>.
/// </exception>
/// <exception cref="ArgumentOutOfRangeException">
/// <paramref name="maximumNumberOfParts"/> is not greater than one.
/// </exception>
/// <exception cref="ArgumentException">
/// <paramref name="maximumNumberOfParts"/> is not less than
/// the number of rows in <paramref name="data"/>.
/// </exception>
/// <seealso cref="PartitionOptimizationContext"/>
/// <seealso cref="SystemPerformanceOptimizer"/>
public static IndexPartition <double> Discover(
DoubleMatrix data,
int maximumNumberOfParts)
{
#region Input validation
if (data is null)
{
throw new ArgumentNullException(nameof(data));
}
if (maximumNumberOfParts < 2)
{
throw new ArgumentOutOfRangeException(
nameof(maximumNumberOfParts),
string.Format(
CultureInfo.InvariantCulture,
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_GREATER_THAN_VALUE"),
"1"));
}
int stateDimension = data.NumberOfRows;
if (stateDimension <= maximumNumberOfParts)
{
throw new ArgumentException(
string.Format(
CultureInfo.InvariantCulture,
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_LESS_THAN_OTHER_ROWS"),
nameof(maximumNumberOfParts),
nameof(data)),
nameof(maximumNumberOfParts)
);
}
#endregion
double objectiveFunction(DoubleMatrix x)
{
double performance = 0.0;
var partition = IndexPartition.Create(x);
foreach (double category in partition.Identifiers)
{
performance += Stat.Sum(
Stat.SumOfSquaredDeviations(
data[partition[category], ":"],
DataOperation.OnColumns));
}
return(performance);
}
var optimizer =
new SystemPerformanceOptimizer();
var context = new PartitionOptimizationContext(
objectiveFunction: objectiveFunction,
stateDimension: stateDimension,
partitionDimension: maximumNumberOfParts,
probabilitySmoothingCoefficient: .8,
optimizationGoal: OptimizationGoal.Minimization,
minimumNumberOfIterations: 3,
maximumNumberOfIterations: 1000);
double rarity = .01;
int sampleSize = 500 * maximumNumberOfParts;
var results = optimizer.Optimize(
context,
rarity,
sampleSize);
return(IndexPartition.Create(results.OptimalState));
}
public void OptimizeTest()
{
// context is null
{
string parameterName = "context";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.Optimize(
context: null,
rarity: 0.1,
sampleSize: 1000);
},
expectedType: typeof(ArgumentNullException),
expectedPartialMessage:
ArgumentExceptionAssert.NullPartialMessage,
expectedParameterName: parameterName);
}
// rarity is less than 0
{
var STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL =
String.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL"), 0.0, 1.0);
string parameterName = "rarity";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.Optimize(
context:
TestableSystemPerformanceOptimizationContext00
.Get().Context,
rarity: -0.1,
sampleSize: 1000);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL,
expectedParameterName: parameterName);
}
// rarity is equal to 0
{
var STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL =
String.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL"), 0.0, 1.0);
string parameterName = "rarity";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.Optimize(
context:
TestableSystemPerformanceOptimizationContext00
.Get().Context,
rarity: 0.0,
sampleSize: 1000);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL,
expectedParameterName: parameterName);
}
// rarity is greater than 1
{
var STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL =
String.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL"), 0.0, 1.0);
string parameterName = "rarity";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.Optimize(
context:
TestableSystemPerformanceOptimizationContext00
.Get().Context,
rarity: 1.1,
sampleSize: 1000);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL,
expectedParameterName: parameterName);
}
// rarity is equal to 1
{
var STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL =
String.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL"), 0.0, 1.0);
string parameterName = "rarity";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.Optimize(
context:
TestableSystemPerformanceOptimizationContext00
.Get().Context,
rarity: 1.0,
sampleSize: 1000);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_NOT_IN_OPEN_INTERVAL,
expectedParameterName: parameterName);
}
// rarity is too low given context and sample size
{
var STR_EXCEPT_CEM_TOO_LOW_RARITY =
String.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_CEM_TOO_LOW_RARITY"), "sampleSize");
string parameterName = "rarity";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.Optimize(
context:
TestableSystemPerformanceOptimizationContext02
.Get().Context,
rarity: .01,
sampleSize: 50);
},
expectedType: typeof(ArgumentException),
expectedPartialMessage:
STR_EXCEPT_CEM_TOO_LOW_RARITY,
expectedParameterName: parameterName);
}
// rarity is too high given context and sample size
{
var STR_EXCEPT_CEM_TOO_HIGH_RARITY =
String.Format(
ImplementationServices.GetResourceString(
"STR_EXCEPT_CEM_TOO_HIGH_RARITY"), "sampleSize");
string parameterName = "rarity";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.Optimize(
context:
TestableSystemPerformanceOptimizationContext00
.Get().Context,
rarity: .99,
sampleSize: 50);
},
expectedType: typeof(ArgumentException),
expectedPartialMessage:
STR_EXCEPT_CEM_TOO_HIGH_RARITY,
expectedParameterName: parameterName);
}
// sampleSize is not positive
{
var STR_EXCEPT_PAR_MUST_BE_POSITIVE =
ImplementationServices.GetResourceString(
"STR_EXCEPT_PAR_MUST_BE_POSITIVE");
string parameterName = "sampleSize";
var optimizer = new SystemPerformanceOptimizer();
ArgumentExceptionAssert.Throw(
() =>
{
optimizer.Optimize(
context:
TestableSystemPerformanceOptimizationContext00
.Get().Context,
rarity: 0.1,
sampleSize: 0);
},
expectedType: typeof(ArgumentOutOfRangeException),
expectedPartialMessage:
STR_EXCEPT_PAR_MUST_BE_POSITIVE,
expectedParameterName: parameterName);
}
}