static void Main(string[] args)
{
var random = new Random(3_14159265);
Func <double> vertexAttributeGenerator = () =>
{
return(random.NextDouble());
};
Func <double> edgeAttributeGenerator = () =>
{
return(random.NextDouble());
};
double vertexBound = 2;
double edgeBound = 3;
double vertexDecay = 1;
double edgeDecay = 1;
Func <double, double, double, double> boundMetric = (a, bound, decay) => bound * a / (1 / decay + a);
Func <double, double, double> vertexRelabel = (a1, a2) =>
{
return(boundMetric(Math.Abs(a1 - a2), vertexBound, vertexDecay));
};
Func <double, double> vertexAdd = a => boundMetric(Math.Abs(a), vertexBound, vertexDecay);
Func <double, double> vertexRemove = vertexAdd;
Func <double, double, double> edgeRelabel = (a1, a2) =>
{
return(boundMetric(Math.Abs(a1 - a2), edgeBound, edgeDecay));
};
Func <double, double> edgeAdd = a => boundMetric(Math.Abs(a), edgeBound, edgeDecay);
Func <double, double> edgeRemove = edgeAdd;
// Func<double, double, double> vertexRelabel = (a1, a2) =>
// {
// return Math.Abs(a1 - a2);
// };
// Func<double, double> vertexAdd = a => Math.Abs(a);
// Func<double, double> vertexRemove = vertexAdd;
// Func<double, double, double> edgeRelabel = (a1, a2) =>
// {
// return Math.Abs(a1 - a2);
// };
// Func<double, double> edgeAdd = a => Math.Abs(a);
// Func<double, double> edgeRemove = edgeAdd;
var a = new List <double>()
{
0, 1, .5, 1d / 3, 2d / 3, -1, 2, 10, -10, 100, -100, 1000, -1000
};
var b = new List <double>()
{
0, 1, .5, 1d / 3, 2d / 3, -1, 2, 10, -10, 100, -100, 1000, -1000
};
var RiesenBunke2009AB = (1, 1);
for (int gVertices = 8; gVertices < 12; gVertices++)
{
var hVertices = gVertices;
// for (int hVertices = gVertices; hVertices > 0; hVertices-=1)
{
var measurements = 0;
var results = new Dictionary <(double, double), double>();
foreach (var aElement in a)
{
foreach (var bElement in b)
{
results.Add((aElement, bElement), 0);
}
}
for (double gDensity = 1.0; gDensity > 0; gDensity -= 0.1)
{
for (double hDensity = gDensity; hDensity > 0; hDensity -= 0.1)
{
for (int iter = 0; iter < 5; iter++)
{
var G = RandomGraphFactory.GenerateRandomInstance(
vertices: gVertices,
density: gDensity,
directed: true,
vertexAttributeGenerator: vertexAttributeGenerator,
edgeAttributeGenerator: edgeAttributeGenerator
);
var H = RandomGraphFactory.GenerateRandomInstance(
vertices: hVertices,
density: hDensity,
directed: true,
vertexAttributeGenerator: vertexAttributeGenerator,
edgeAttributeGenerator: edgeAttributeGenerator
);
var matchingParameters = new GraphMatchingParameters <int, double, double>
{
aCollection = a,
bCollection = b,
edgeAdd = edgeAdd,
vertexAdd = vertexAdd,
edgeRelabel = edgeRelabel,
edgeRemove = edgeRemove,
vertexRemove = vertexRemove,
vertexRelabel = vertexRelabel,
encodingMethod = GraphEncodingMethod.Wojciechowski
};
var matching = new VertexPartialMatchingNode <int, double, double>(
G,
H,
matchingParameters
);
var myRelativeError = (matching.BestUpperBound - matching.BestLowerBound) / matching.BestLowerBound;
var theirRelativeError = (matching.BestUpperBound - matching.abLowerBounds[RiesenBunke2009AB]) / matching.abLowerBounds[RiesenBunke2009AB];
var eps = 1e-12;
// if (theirRelativeError > eps)
// {
// System.Console.WriteLine($"|Vg|={gVertices}, |Eg|={gDensity:f2}, |Vh|={hVertices}, |Eh|={hDensity:f2}. My estimate / theirs {matching.LowerBound / matching.abLowerBounds[RiesenBunke2009AB]:f2}.");
// }
// var theirLowerBound = matching.abLowerBounds[(2d/3, .5)];
// if (theirLowerBound > eps)
{
measurements += 1;
foreach (var kvp in matching.abLowerBounds)
{
// var score = (matching.UpperBound - kvp.Value) / matching.UpperBound;
var score = kvp.Value > Math.Max(Math.Max(
matching.abLowerBounds[(.5, .5)],
static void Main(string[] args)
{
using var context = new EnronContext();
Console.WriteLine($"{context.Emails.Count()} emails in total.");
// var resultsFilePath = "./results.txt";
var topResultsFilePath = "./top_results.txt";
var topTopResultsFilePath = "./top_top_results.txt";
var matchingFeatureSelectors = new List <(string, Func <VertexPartialMatchingNode <string, double, double>, double>)>()
{
("Upper bound", matching => matching.BestUpperBound),
("Lower bound", matching => matching.BestLowerBound)
};
var edgeCostTypes = new List <(string, CostType, List <double>, List <double>)>()
{
// ("Unit cost Wojciechowski .5", CostType.UnitCost, new List<double>(){.5}, new List<double>(){.5}),
// ("Unit cost Wojciechowski (1.) .5", CostType.UnitCost, new List<double>(){1}, new List<double>(){.5}),
// ("Unit cost Wojciechowski .5 Riesen Bunke 1.", CostType.UnitCost, new List<double>(){.5, 1}, new List<double>(){1, .5}),
// ("Unit cost Riesen Bunke 1.", CostType.UnitCost, new List<double>(){1}, new List<double>(){1}),
("Absolute value Wojciechowski .5", CostType.AbsoluteValue, new List <double>()
{
.5
}, new List <double>()
{
.5
}),
// ("Absolute value Wojciechowski (1.) .5", CostType.AbsoluteValue, new List<double>(){1}, new List<double>(){.5}),
// ("Absolute value Wojciechowski .5 Riesen Bunke 1.", CostType.AbsoluteValue, new List<double>(){.5, 1}, new List<double>(){1, .5}),
("Absolute value Riesen Bunke 1.", CostType.AbsoluteValue, new List <double>()
{
1
}, new List <double>()
{
1
}),
// ("Absolute value bounded Wojciechowski .5", CostType.AbsoluteValueBounded, new List<double>(){.5}, new List<double>(){.5}),
// ("Absolute value bounded Wojciechowski (1.) .5", CostType.AbsoluteValueBounded, new List<double>(){1}, new List<double>(){.5}),
// ("Absolute value bounded Wojciechowski .5 Riesen Bunke 1.", CostType.AbsoluteValueBounded, new List<double>(){.5, 1}, new List<double>(){1, .5}),
// ("Absolute value bounded Riesen Bunke 1.", CostType.AbsoluteValueBounded, new List<double>(){1}, new List<double>(){1}),
};
var results = new Dictionary <(int vertexUpperBound, int k, string distanceScorerName, string edgeCostType, string matchingFeatureSelectorName), (double testAccuracy, double validationAccuracy)>();
var topResults = new Dictionary <(int vertexUpperBound, int k, string distanceScorerName, string edgeCostType, string matchingFeatureSelectorName), (double testAccuracy, double validationAccuracy)>();
for (int iteration = 0; iteration < 10; iteration++)
{
for (int vertexUpperBound = 3; vertexUpperBound < 8; vertexUpperBound++)
{
var dataset = GenerateDataSet(
context,
trainingProportion: 8,
validatingProportion: 1,
testingProportion: 1,
vertexUpperBound: vertexUpperBound,
randomSeed: iteration
);
foreach (var(matchingFeatureSelectorName, matchingFeatureSelector) in matchingFeatureSelectors)
{
foreach (var(edgeCostTypeName, edgeCostType, aCollection, bCollection) in edgeCostTypes)
{
var matchingParameters = GraphMatchingParameters <string, double, double> .DoubleCostComposer(
CostType.UnitCost,
edgeCostType
);
matchingParameters.aCollection = aCollection;
matchingParameters.bCollection = bCollection;
// determine closest neighbours for each test and validation graph
var testMatchingClassPairsList = new List <(List <(VertexPartialMatchingNode <string, double, double>, bool)>, bool)>();
var validationMatchingClassPairsList = new List <(List <(VertexPartialMatchingNode <string, double, double>, bool)>, bool)>();
foreach (var(pair, i) in dataset.testSet.Zip(Enumerable.Range(0, int.MaxValue)))
{
var pairToAdd = (
KNNClassifier.KNNClassifier.FindClosest(pair.Item1, dataset.trainingSet, matchingParameters, matchingFeatureSelector),
pair.Item2
);
testMatchingClassPairsList.Add(pairToAdd);
// System.Console.WriteLine($"Test set: computed distance: {i * 100d / dataset.testSet.Count:f2}%.");
}
foreach (var(pair, i) in dataset.validationSet.Zip(Enumerable.Range(0, int.MaxValue)))
{
var pairToAdd = (
KNNClassifier.KNNClassifier.FindClosest(pair.Item1, dataset.trainingSet, matchingParameters, matchingFeatureSelector),
pair.Item2
);
validationMatchingClassPairsList.Add(pairToAdd);
// System.Console.WriteLine($"Validation set: computed distance: {i * 100d / dataset.validationSet.Count:f2}%.");
}
Func <int, Func <int, VertexPartialMatchingNode <string, double, double>, double>, List <(List <(VertexPartialMatchingNode <string, double, double>, bool)>, bool)>, double> getAccuracy = (k, distanceScorer, matchingClassPairsList) =>
{
var truePositives = 0;
var falsePositives = 0;
var trueNegatives = 0;
var falseNegatives = 0;
foreach (var(matchingClassPairs, testGraphLabel) in matchingClassPairsList)
{
var classificationResult = KNNClassifier.KNNClassifier.Classify <string, double, double, bool>(
matchingClassPairs,
distanceScorer,
k: k
);
var result = (expected : testGraphLabel, received : classificationResult.graphClass);
if (result.expected)
{
if (result.received)
{
truePositives += 1;
}
else
{
falseNegatives += 1;
}
}
else
{
if (result.received)
{
falsePositives += 1;
}
else
{
trueNegatives += 1;
}
}
}
return((truePositives + trueNegatives) * 1d / matchingClassPairsList.Count);
};
var ks = new int[]
{
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
-1
};
var distanceScorers = new (string, Func <int, VertexPartialMatchingNode <string, double, double>, double>)[]
