CombinationOptimizationContext Class |
Namespace: Novacta.Analytics.Advanced
The CombinationOptimizationContext type exposes the following members.
Name | Description | |
---|---|---|
CombinationOptimizationContext |
Initializes a new instance of the
CombinationOptimizationContext class
aimed to optimize the specified
objective function,
with the given optimization goal,
range of iterations, and probability smoothing coefficient.
|
Name | Description | |
---|---|---|
CombinationDimension |
Gets the dimension of a combination represented by a
system's state
when
a CrossEntropyProgram executes in this
context.
| |
EliteSampleDefinition |
Gets the elite sample definition for this context.
(Inherited from SystemPerformanceOptimizationContext.) | |
InitialParameter |
Gets the parameter initially exploited to sample from
the state-space of the system defined by this context.
(Inherited from CrossEntropyContext.) | |
MaximumNumberOfIterations |
Gets the maximum number of iterations
allowed by this context.
(Inherited from SystemPerformanceOptimizationContext.) | |
MinimumNumberOfIterations |
Gets the minimum number of iterations
required by this context.
(Inherited from SystemPerformanceOptimizationContext.) | |
OptimizationGoal |
Gets a constant specifying if the performance function
in this context must be minimized or maximized.
(Inherited from SystemPerformanceOptimizationContext.) | |
ProbabilitySmoothingCoefficient |
Gets the coefficient that defines the smoothing scheme
for the probabilities of the Cross-Entropy parameters
exploited by this context.
| |
StateDimension |
Gets or sets the dimension of a vector representing a
system's state
when
a CrossEntropyProgram executes in this
context.
(Inherited from CrossEntropyContext.) | |
TraceExecution |
Gets or sets a value indicating whether the
execution of this context must be traced.
(Inherited from CrossEntropyContext.) |
Name | Description | |
---|---|---|
Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) | |
Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) | |
GetHashCode | Serves as the default hash function. (Inherited from Object.) | |
GetOptimalState |
Gets the argument that optimizes the objective function
in this context, according to the specified
Cross-Entropy sampling parameter.
(Overrides SystemPerformanceOptimizationContextGetOptimalState(DoubleMatrix).) | |
GetType | Gets the Type of the current instance. (Inherited from Object.) | |
MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) | |
OnExecutedIteration |
Called after completion of each iteration of
a CrossEntropyProgram executing in this
context.
(Overrides SystemPerformanceOptimizationContextOnExecutedIteration(Int32, DoubleMatrix, LinkedListDouble, LinkedListDoubleMatrix).) | |
PartialSample |
Draws the specified subset of a sample from a distribution
characterized by the given parameter, using the stated
random number generator. Used when executing the sampling
step of a CrossEntropyProgram running
in this context.
(Overrides CrossEntropyContextPartialSample(Double, TupleInt32, Int32, RandomNumberGenerator, DoubleMatrix, Int32).) | |
Performance |
Computes the objective function at a specified argument
as the performance defined in this context.
(Overrides CrossEntropyContextPerformance(DoubleMatrix).) | |
SmoothParameter |
Provides the smoothing of the updated sampling parameter
of a SystemPerformanceOptimizer
executing in this context.
(Overrides SystemPerformanceOptimizationContextSmoothParameter(LinkedListDoubleMatrix).) | |
StopAtIntermediateIteration |
Specifies conditions
under which
a SystemPerformanceOptimizer executing in
this context should be considered as terminated after
completing an intermediate iteration.
(Overrides SystemPerformanceOptimizationContextStopAtIntermediateIteration(Int32, LinkedListDouble, LinkedListDoubleMatrix).) | |
StopExecution |
Specifies conditions
under which
a CrossEntropyProgram executing in this
context should be considered
as terminated.
(Inherited from SystemPerformanceOptimizationContext.) | |
ToString | Returns a string that represents the current object. (Inherited from Object.) | |
UpdateLevel |
Updates the performance level for the current iteration
of a CrossEntropyProgram executing in
this context
and determines the corresponding elite sample.
(Inherited from SystemPerformanceOptimizationContext.) | |
UpdateParameter |
Updates the
sampling parameter attending the generation
of the sample in the next iteration of a
CrossEntropyProgram executing in
this context.
(Overrides CrossEntropyContextUpdateParameter(LinkedListDoubleMatrix, DoubleMatrix).) |
Class CombinationOptimizationContext derives from SystemPerformanceOptimizationContext, and defines a Cross-Entropy context able to solve combinatorial optimization problems regarding the selection of fixed size subsets from a collection of items.
Class SystemPerformanceOptimizationContext thoroughly defines a system whose performance must be optimized. Class CombinationOptimizationContext specializes that system by assuming that its performance, say , is defined on the set of combinations, having the specified size, available from a given collection of items.
The system's state-space , i.e. the domain of , can thus be represented as the Cartesian product of copies of the set , where is the number of available items. An argument defines a combination by signaling that the -th item is included in the combination if , otherwise setting .
If the combinations under study have fixed size equal to , then each argument will have exactly nonzero entries.
A Cross-Entropy optimizer is designed to identify the
optimal arguments at which the performance function of a
complex system reaches
its minimum or maximum value.
To get the optimal state, the system's state-space
, i.e. the domain of
, is traversed iteratively
by sampling, at each iteration, from
a specific density function, member of a parametric
family
where is
a possible argument of ,
and is the set of
allowable values for parameter .
The parameter exploited at a given iteration
is referred to
as the reference parameter of such iteration and indicated
as .
A minimum number
of iterations, say , must be executed, while a
number of them up to a maximum, say , is allowed.
Implementing a context for optimizing on combinations
The Cross-Entropy method provides an iterative multi step procedure. In the context of combinatorial optimization, at each iteration a sampling step is executed in order to generate diverse candidate arguments of the objective function, sampled from a distribution characterized by the reference parameter of the iteration, say . Such sample is thus exploited in the updating step in which a new reference parameter is identified to modify the distribution from which the samples will be obtained in the next iteration: such modification is executed in order to improve the probability of sampling relevant arguments, i.e. those arguments corresponding to the function values of interest (See the documentation of class CrossEntropyProgram for a thorough discussion of the Cross-Entropy method).
When the Cross-Entropy method is applied in an optimization context, a final optimizing step is executed, in which the argument corresponding to the searched extremum is effectively identified.
These steps have been implemented as follows.
Sampling step
In a CombinationOptimizationContext, the parametric family is outlined as follows. Each component of an argument of is attached to a independent Bernoulli distribution having parameter , and is defined as the distribution of the sum of the corresponding Bernoulli trials, conditional to having exactly successes. The Cross-Entropy sampling parameter can thus be represented as the vector .
The parametric space should include a parameter under which all possible states must have a real chance of being selected: this parameter is specified as the initial reference parameter . A CombinationOptimizationContext defines as a constant vector whose entries are all equal to .
Updating step
At iteration , let us represent the sample drawn
as , where is the
Cross-Entropy sample size, and the -th sample point
is the sequence .
The parameter's
updating formula is,
for ,
where
is the elite sample in this context, i.e. the set of sample
points having the lowest performances observed during the -th
iteration, if minimizing, the highest ones, otherwise, while
is its indicator function.
Applying a smoothing scheme to updated parameters
In a CombinationOptimizationContext,
the sampling parameter
is smoothed applying the following formula
(See Rubinstein and Kroese,
Remark 5.2, p. 189[1]
):
where .
Optimizing step
The optimizing step is executed after that the underlying Cross-Entropy program has converged. In a specified context, it is expected that, given a reference parameter , a corresponding reasonable value could be guessed for the optimizing argument of , say , with a function from to . Function is defined by overriding method GetOptimalState(DoubleMatrix) that should return given a specific reference parameter .
Given the optimal parameter (the parameter corresponding to the
last iteration executed by the algorithm before
stopping),
the argument at which the searched extremum is considered
as reached according to the Cross-Entropy method will be
returned as follows.
The probabilities are sorted in increasing order,
say obtaining the
following ordering:
and the corresponding sequence of indexes
such that
is taken into account by defining the set
and returning
where
is one if ;
zero otherwise.
This is equivalent to include in the optimal combination
those items having the greatest probabilities in
parameter .
Stopping criterion
A CombinationOptimizationContext never stops before executing a number of iterations less than MinimumNumberOfIterations, and always stops if such number is greater than or equal to MaximumNumberOfIterations.
For intermediate iterations, method StopAtIntermediateIteration(Int32, LinkedListDouble, LinkedListDoubleMatrix) is called to check if a Cross-Entropy program executing in this context should stop or not.
In a CombinationOptimizationContext, the method
analyzes the currently updated reference parameter,
say as follows.
If condition
can be verified,
the method returns true; otherwise false is returned.
Equivalently, the algorithm converges if the indexes of
the largest probabilities coincide
times in a row of iterations.
Instantiating a context for optimizing on combinations
At instantiation, the constructor of a CombinationOptimizationContext object will receive information about the optimization under study by means of parameters representing the objective function , the combination constants and , the extremes of the allowed range of intermediate iterations, and , and a constant stating if the optimization goal is a maximization or a minimization. In addition, the smoothing parameter is also passed to the constructor.
After construction, and can be inspected, respectively, via properties MinimumNumberOfIterations and MaximumNumberOfIterations. The smoothing coefficient is also available via property ProbabilitySmoothingCoefficient. Combination constants and are returned by StateDimension and CombinationDimension, respectively. In addition, property OptimizationGoal signals that the performance function must be maximized if it evaluates to the constant Maximization, or that a minimization is requested if it evaluates to the constant Minimization.
To evaluate the objective function at a specific argument, one can call method Performance(DoubleMatrix) passing the argument as a parameter. It is expected that the objective function will accept a row vector having binary entries as a valid representation of an argument.
In the following example, an existing partition of twelve items is explained by selecting two features out of the seven ones available in an artificial data set regarding the items under study.
The selection criterion is defined as the maximization of the Dunn Index in the domain of selectable sub data sets.
using System; using Novacta.Analytics.Advanced; namespace Novacta.Analytics.CodeExamples.Advanced { public class CombinationOptimizationContextExample0 { 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); } } } // Executing method Main() produces the following output: // // The data set: // 0.00443412891 0.00269053161 0.00413587912 -0.00765022961 -0.00516230961 1.00663787 3.01053155 // -0.00206677161 0.0208840727 -0.00323082941 -0.00939014629 0.00144991289 0.999318094 3.01264231 // 0.0115714825 0.00980880513 0.00490173372 0.00327885751 0.0157818959 0.990821676 3.01207396 // -0.0156854205 -0.00757566326 -0.00972832587 -0.00217925897 0.0107421304 0.992541729 2.99695621 // 0.0022067431 -0.00321077809 -0.00611898592 0.00720305793 0.0128767272 4.99440474 6.99892958 // -0.00637438188 0.00505242911 -0.0040927039 0.00210944391 -0.0152463979 4.9974367 6.99460151 // -0.00662648185 -0.0149292848 0.00236975765 0.0103282087 -0.0108846478 4.99249371 6.98860335 // -0.0219354054 0.012282089 0.01095691 -0.0108910034 0.00275269084 5.02268395 6.99732006 // -0.00172760645 0.000890969089 -0.0121749937 -0.0060896535 -0.0125774475 9.00956698 10.9938497 // 0.0157657881 0.00840849213 0.00295384061 -0.00358519597 0.00447359706 8.98856241 11.0013196 // 0.0129253424 -0.000948574239 0.00235032211 -0.0135124598 -0.023309088 9.00738398 10.9891406 // -0.00848136345 -0.00459883434 -0.0148632861 0.0223964956 -0.00259506386 9.00721897 11.005672 // // // The Cross-Entropy optimizer has converged: True. // // Initial guess parameter: // 0.5 0.5 0.5 0.5 0.5 0.5 0.5 // // // // The maximizer of the performance is: // 0 0 0 0 0 1 1 // // // // The 2 features best explaining the given partition have column indexes: // 5, 6 // // The maximum performance is: // 179.2086694228848 // // This is the Dunn Index for the selected features: // 179.2086694228848