def testGetWeights(self):
try:
import sklearn
except ImportError as error:
return
numExamples = 6
X = numpy.array([[-3], [-2], [-1], [1], [2], [3]], numpy.float64)
#X = numpy.random.rand(numExamples, 10)
y = numpy.array([[-1], [-1], [-1], [1], [1], [1]])
svm = LibSVM()
svm.learnModel(X, y.ravel())
weights, b = svm.getWeights()
#Let's see if we can compute the decision values
y, decisions = svm.predict(X, True)
decisions2 = numpy.zeros(numExamples)
decisions2 = numpy.dot(X, weights) - b
self.assertTrue((decisions == decisions2).all())
predY = numpy.sign(decisions2)
self.assertTrue((y.ravel() == predY).all())
#Do the same test on a random datasets
numExamples = 50
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
y = numpy.sign(numpy.random.rand(numExamples) - 0.5)
svm = LibSVM()
svm.learnModel(X, y.ravel())
weights, b = svm.getWeights()
#Let's see if we can compute the decision values
y, decisions = svm.predict(X, True)
decisions2 = numpy.dot(X, weights) + b
tol = 10**-6
self.assertTrue(numpy.linalg.norm(decisions - decisions2) < tol)
predY = numpy.sign(decisions2)
self.assertTrue((y.ravel() == predY).all())
def testGetWeights(self):
try:
import sklearn
except ImportError as error:
return
numExamples = 6
X = numpy.array([[-3], [-2], [-1], [1], [2] ,[3]], numpy.float64)
#X = numpy.random.rand(numExamples, 10)
y = numpy.array([[-1], [-1], [-1], [1], [1] ,[1]])
svm = LibSVM()
svm.learnModel(X, y.ravel())
weights, b = svm.getWeights()
#Let's see if we can compute the decision values
y, decisions = svm.predict(X, True)
decisions2 = numpy.zeros(numExamples)
decisions2 = numpy.dot(X, weights) - b
self.assertTrue((decisions == decisions2).all())
predY = numpy.sign(decisions2)
self.assertTrue((y.ravel() == predY).all())
#Do the same test on a random datasets
numExamples = 50
numFeatures = 10
X = numpy.random.rand(numExamples, numFeatures)
y = numpy.sign(numpy.random.rand(numExamples)-0.5)
svm = LibSVM()
svm.learnModel(X, y.ravel())
weights, b = svm.getWeights()
#Let's see if we can compute the decision values
y, decisions = svm.predict(X, True)
decisions2 = numpy.dot(X, weights) + b
tol = 10**-6
self.assertTrue(numpy.linalg.norm(decisions - decisions2) < tol)
predY = numpy.sign(decisions2)
self.assertTrue((y.ravel() == predY).all())
def testGetModel(self):
try:
import sklearn
except ImportError as error:
return
numExamples = 50
numFeatures = 3
eg = ExamplesGenerator()
X, y = eg.generateBinaryExamples(numExamples, numFeatures)
svm = LibSVM()
svm.learnModel(X, y)
weights, b = svm.getWeights()
def testGetModel(self):
try:
import sklearn
except ImportError as error:
return
numExamples = 50
numFeatures = 3
eg = ExamplesGenerator()
X, y = eg.generateBinaryExamples(numExamples, numFeatures)
svm = LibSVM()
svm.learnModel(X, y)
weights, b = svm.getWeights()
class SvmEgoSimulator(AbstractDiffusionSimulator):
"""
A class which combines SVM classification with the EgoSimulation. There are methods
to run modelSelection, train the SVM and then run the simulation. The simulation itself
is run using EgoSimulator.
"""
def __init__(self, examplesFileName):
"""
Create the class by reading examples from a Matlab file. Instantiate the SVM
and create a preprocesor to standarise examples to have zero mean and unit variance.
"""
self.examplesList = ExamplesList.readFromFile(examplesFileName)
self.examplesList.setDefaultExamplesName("X")
self.examplesList.setLabelsName("y")
(freqs, items) = Util.histogram(self.examplesList.getSampledDataField("y").ravel())
logging.info("Distribution of labels: " + str((freqs, items)))
logging.info("The base error rate is " + str(float(min(freqs))/self.examplesList.getNumExamples()))
self.classifier = LibSVM()
self.errorMethod = Evaluator.balancedError
self.preprocessor = Standardiser()
X = self.preprocessor.standardiseArray(self.examplesList.getDataField(self.examplesList.getDefaultExamplesName()))
self.examplesList.overwriteDataField(self.examplesList.getDefaultExamplesName(), X)
def getPreprocessor(self):
"""
Returns the preprocessor
"""
return self.preprocessor
def sampleExamples(self, sampleSize):
"""
This function exists so that we can sample the same examples used in model
selection and exclude them when running evaluateClassifier.
"""
self.examplesList.randomSubData(sampleSize)
def modelSelection(self, Cs, kernel, kernelParams, errorCosts, folds, sampleSize):
"""
Perform model selection using an SVM
"""
Parameter.checkInt(sampleSize, 0, self.examplesList.getNumExamples())
Parameter.checkInt(folds, 0, sampleSize)
Parameter.checkString(kernel, ["linear", "polynomial", "gaussian"])
Parameter.checkList(Cs, Parameter.checkFloat, [0.0, float("inf")])
Parameter.checkList(errorCosts, Parameter.checkFloat, [0.0, float("inf")])
#Perform model selection
self.examplesList.randomSubData(sampleSize)
(freqs, items) = Util.histogram(self.examplesList.getSampledDataField("y").ravel())
logging.info("Using " + str(sampleSize) + " examples for model selection")
logging.info("Distribution of labels: " + str((freqs, items)))
logging.info("List of Cs " + str(Cs))
logging.info("List of kernels " + str(kernel))
logging.info("List of kernelParams " + str(kernelParams))
logging.info("List of errorCosts " + str(errorCosts))
CVal, kernelParamVal, errorCost, error = self.classifier.cvModelSelection(self.examplesList, Cs, kernelParams, kernel, folds, errorCosts, self.errorMethod)
logging.info("Model selection returned C = " + str(CVal) + " kernelParam = " + str(kernelParamVal) + " errorCost = " + str(errorCost) + " with error " + str(error))
return CVal, kernelParamVal, errorCost, error
def evaluateClassifier(self, CVal, kernel, kernelParamVal, errorCost, folds, sampleSize, invert=True):
"""
Evaluate the SVM with the given parameters. Often model selection is done before this step
and in that case, invert=True uses a sample excluding those used for model selection.
"""
Parameter.checkFloat(CVal, 0.0, float('inf'))
Parameter.checkFloat(errorCost, 0.0, float('inf'))
Parameter.checkString(kernel, ["linear", "polynomial", "gaussian"])
if kernel == "gaussian":
Parameter.checkFloat(kernelParamVal, 0.0, float('inf'))
elif kernel == "polynomial":
Parameter.checkInt(kernelParamVal, 2, float('inf'))
Parameter.checkInt(folds, 0, sampleSize)
Parameter.checkInt(sampleSize, 0, self.examplesList.getNumExamples())
if invert:
allIndices = numpy.array(list(range(0, self.examplesList.getNumExamples())))
testIndices = numpy.setdiff1d(allIndices, self.examplesList.getPermutationIndices())
testIndices = numpy.random.permutation(testIndices)[0:sampleSize]
else:
testIndices = Util.sampleWithoutReplacement(sampleSize, self.examplesList.getNumExamples())
logging.info("Using " + str(testIndices.shape[0]) + " examples for SVM evaluation")
self.examplesList.setPermutationIndices(testIndices)
self.classifier.setParams(C=CVal, kernel=kernel, kernelParam=kernelParamVal)
self.classifier.setErrorCost(errorCost)
(means, vars) = self.classifier.evaluateCv(self.examplesList, folds)
logging.info("--- Classification evaluation ---")
logging.info("Error on " + str(testIndices.shape[0]) + " examples is " + str(means[0]) + "(" + str(vars[0]) + ")")
logging.info("Sensitivity (recall = TP/(TP+FN)): " + str(means[1]) + "(" + str(vars[1]) + ")")
logging.info("Specificity (TN/TN+FP): " + str(means[2]) + "(" + str(vars[2]) + ")")
logging.info("Error on positives: " + str(means[3]) + "(" + str(vars[3]) + ")")
logging.info("Error on negatives: " + str(means[4]) + "(" + str(vars[4]) + ")")
logging.info("Balanced error: " + str(means[5]) + "(" + str(vars[5]) + ")")
return (means, vars)
def trainClassifier(self, CVal, kernel, kernelParamVal, errorCost, sampleSize):
Parameter.checkFloat(CVal, 0.0, float('inf'))
Parameter.checkString(kernel, ["linear", "gaussian", "polynomial"])
Parameter.checkFloat(kernelParamVal, 0.0, float('inf'))
Parameter.checkFloat(errorCost, 0.0, float('inf'))
Parameter.checkInt(sampleSize, 0, self.examplesList.getNumExamples())
logging.info("Training SVM with C=" + str(CVal) + ", " + kernel + " kernel" + ", param=" + str(kernelParamVal) + ", sampleSize=" + str(sampleSize) + ", errorCost=" + str(errorCost))
self.examplesList.randomSubData(sampleSize)
self.classifier.setC(C=CVal)
self.classifier.setKernel(kernel=kernel, kernelParam=kernelParamVal)
self.classifier.setErrorCost(errorCost)
X = self.examplesList.getSampledDataField(self.examplesList.getDefaultExamplesName())
y = self.examplesList.getSampledDataField(self.examplesList.getLabelsName())
y = y.ravel()
self.classifier.learnModel(X, y)
return self.classifier
def getWeights(self):
return self.classifier.getWeights()
def runSimulation(self, maxIterations):
Parameter.checkInt(maxIterations, 1, float('inf'))
#Notice that the data is preprocessed in the same way as the survey data
egoSimulator = EgoSimulator(self.graph, self.classifier, self.preprocessor)
totalInfo = numpy.zeros(maxIterations+1)
totalInfo[0] = EgoUtils.getTotalInformation(self.graph)
logging.info("Total number of people with information: " + str(totalInfo[0]))
logging.info("--- Simulation Started ---")
for i in range(0, maxIterations):
logging.info("--- Iteration " + str(i) + " ---")
self.graph = egoSimulator.advanceGraph()
totalInfo[i+1] = EgoUtils.getTotalInformation(self.graph)
logging.info("Total number of people with information: " + str(totalInfo[i+1]))
#Compute distribution of ages etc. in alters
alterIndices = egoSimulator.getAlters(i)
alterAges = numpy.zeros(len(alterIndices))
alterGenders = numpy.zeros(len(alterIndices))
for j in range(0, len(alterIndices)):
currentVertex = self.graph.getVertex(alterIndices[j])
alterAges[j] = currentVertex[self.egoQuestionIds.index(("Q5X", 0))]
alterGenders[j] = currentVertex[self.egoQuestionIds.index(("Q4", 0))]
(freqs, items) = Util.histogram(alterAges)
logging.info("Distribution of ages " + str(freqs) + " " + str(items))
(freqs, items) = Util.histogram(alterGenders)
logging.info("Distribution of genders " + str(freqs) + " " + str(items))
logging.info("--- Simulation Finished ---")
return totalInfo, egoSimulator.getTransmissions()
def getVertexFeatureDistribution(self, fIndex, vIndices=None):
return self.graph.getVertexFeatureDistribution(fIndex, vIndices)
def getPreProcessor(self):
return self.preprocessor
def getClassifier(self):
return self.classifier
preprocessor = None
examplesList = None
classifier = None
graph = None
edgeWeight = 1