def runExperiment():
with open("data_banknote_authentication.txt", "r") as dataset:
bankNoteData = removeAttrsRandom(np.asarray([line.split(",") for line in dataset.readlines()], dtype=np.float64))
numFolds = 10
crossVal = CrossValidation(bankNoteData, numFolds)
for fold in range(crossVal.folds):
print("\nRunning Fold : " + str(fold + 1))
trainingData = crossVal.getTrainingData(fold)
testingData = crossVal.getTestingData(fold)
attrIndices = [i for i in range(bankNoteData.shape[1] - 1)]
decisionTree = DecisionTree(bankNoteData, trainingData, attrIndices)
makeDecisionTree(bankNoteData, decisionTree.root, decisionTree.root.allowedAttrs, None)
outputs = [int(bankNoteData[item, 4]) for item in testingData]
crossVal.addOutputs(outputs)
queries = [bankNoteData[item, :4] for item in testingData]
predictedOutputs = [classifyQuery([query], decisionTree.root) for query in queries]
crossVal.addPredictedOutputs(predictedOutputs)
print("OUTPUTS = " + str(outputs))
print("PREDICTED OUTPUTS = " + str(predictedOutputs))
crossVal.printConfusionMatrix()
print("ACCURACY = " + str(crossVal.getAverageAccuracy() * 100) + "%")
print("PRECISION = " + str(crossVal.getAveragePrecision() * 100) + "%")
print("RECALL = " + str(crossVal.getAverageRecall() * 100) + "%")
def runExperiment():
with open("data_banknote_authentication.txt", "r") as dataset:
bankNoteData = np.asarray([line.split(",") for line in dataset.readlines()], dtype=np.float64)
numFolds = 10
crossVal = CrossValidation(bankNoteData, numFolds)
for fold in range(crossVal.folds):
print("\nRunning Fold : " + str(fold + 1))
trainingData = crossVal.getTrainingData(fold)
testingData = crossVal.getTestingData(fold)
outputs = [int(bankNoteData[item, 4]) for item in testingData]
crossVal.addOutputs(outputs)
randomForest = buildRandomForest(bankNoteData, trainingData)
queries = [bankNoteData[item, :4] for item in testingData]
predictedOutputs = [classifyQuery(randomForest, query) for query in queries]
crossVal.addPredictedOutputs(predictedOutputs)
print("OUTPUTS = " + str(outputs))
print("PREDICTED OUTPUTS = " + str(predictedOutputs))
crossVal.printConfusionMatrix()
print("ACCURACY = " + str(crossVal.getAverageAccuracy() * 100) + "%")
print("PRECISION = " + str(crossVal.getAveragePrecision() * 100) + "%")
print("RECALL = " + str(crossVal.getAverageRecall() * 100) + "%")