def main():
trainData = pd.read_csv('CrimeClassification/Dataset/train-2.csv')
classesMap = dm.mapClasses(trainData)
print trainData.info()
print(classesMap)
cleanedTrainData,normalizationValues = dm.cleanTrainData(trainData,classesMap)
print(cleanedTrainData.info())
data = cleanedTrainData.values
np.random.shuffle(data.astype(np.float64))
Ytrain = binarizeLabels(data[0:,0])
Xtrain = data[0:,1:]
model = trainModel(Xtrain,Ytrain)
output = testProbaModel(model,Xtrain)
testData = pd.read_csv('CrimeClassification/Dataset/test-2.csv')
cleanedTestData = dm.cleanTestData(testData,normalizationValues)
print(cleanedTestData.info())
output = testProbaModel(model,cleanedTestData.values[:,1:])
result = np.c_[cleanedTestData.values[:,0].astype(int),output]
outputVec = sorted(classesMap, key=classesMap.__getitem__)
outputVec.insert(0,'Id')
dataFrameResults = pd.DataFrame(result,columns=outputVec)
dataFrameResults['Id']=dataFrameResults['Id'].astype(int)
dm.saveResults(dataFrameResults)
def main():
trainData = pd.read_csv('CrimeClassification/Dataset/train-2.csv')
classesMap = dm.mapClasses(trainData)
print trainData.info()
print(classesMap)
cleanedTrainData,normalizationValues = dm.cleanTrainData(trainData,classesMap)
print(cleanedTrainData.info())
[Xtrain, Ytrain, Xtest,Ytest]=splitData(cleanedTrainData.values)
model = trainModel(Xtrain,Ytrain)
Ypred = testModel(model,Xtest)
confMatrix = da.confusionMatrix(Ypred,Ytest)
titleCM = da.orderClassesMapKeys(classesMap)
da.plotConfusionMatrix(confMatrix,titleCM)
print (da.f1Score(Ypred,Ytest))
