def validation_core(i, x, y, model, feature_count):
(foldTrainX, foldTrainY, foldValidationX, foldValidationY) = self.__splitDataFold(x, y, i)
mutualInformationTable = FeatureSelection.byMutualInformation(foldTrainX, foldTrainY)
words = [word for word,_ in mutualInformationTable[:feature_count]]
(xNewTrain, xNewValidation) = FeatureSelection.Featurize(foldTrainX, foldValidationX, words)
model.fit(xNewTrain, foldTrainY)
return self.__countCorrect(model.predict(xNewValidation), foldValidationY)
def validateByMutualInformation(self, x, y, model):
totalCorrect = 0
for i in range(self.k):
(foldTrainX, foldTrainY, foldValidationX,
foldValidationY) = self.__splitDataFold(x, y, i)
mutualInformationTable = FeatureSelection.byMutualInformation(
foldTrainX, foldTrainY)
words = [word for word, _ in mutualInformationTable[:10]]
print('For fold %d/%d, choose words:' % (i + 1, self.k))
print(words)
(xNewTrain, xNewValidation) = FeatureSelection.Featurize(
foldTrainX, foldValidationX, words)
model.fit(xNewTrain, foldTrainY)
totalCorrect += self.__countCorrect(model.predict(xNewValidation),
foldValidationY)
accuracy = totalCorrect / len(x)
return accuracy
############################
import FeatureSelection
print('### Get the Frequency Table')
frequencyTable = FeatureSelection.byFrequency(xTrainRaw)
print('Top 10')
for i in range(10):
print(frequencyTable[i])
#############################
print('### Get the Mutual Information Table')
mutualInformationTable = FeatureSelection.byMutualInformation(
xTrainRaw, yTrain)
print('Top 10')
for i in range(10):
print(mutualInformationTable[i])
#############################
print('### Run Gradient Descent with the Top 10 Words by Frequency')
words = [word for word, _ in frequencyTable[:10]]
print(words)
(xNewTrain, xNewTest) = FeatureSelection.Featurize(xTrainRaw, xTestRaw, words)
model.fit(xNewTrain, yTrain, iterations=50000, step=0.01)
yTestPredicted = model.predict(xNewTest)
testAccuracy = EvaluationsStub.Accuracy(yTest, yTestPredicted)
print("Test Set Accuracy is %f" % (testAccuracy))
print("Train is %f percent spam." % (sum(yTrainRaw)/len(yTrainRaw)))
print("Test is %f percent spam." % (sum(yTestRaw)/len(yTestRaw)))
(xTrain, xTest) = Assignment1Support.Featurize(xTrainRaw, xTestRaw)
yTrain = yTrainRaw
yTest = yTestRaw
import LogisticRegressionModel
model = LogisticRegressionModel.LogisticRegressionModel()
#############################
import FeatureSelection
print('### Get the Mutual Information Table')
mutualInformationTable = FeatureSelection.byMutualInformation(xRaw, yRaw)
#############################
import EvaluationsStub
print('### Get the Confusion Matrix')
words = [word for word,_ in mutualInformationTable[:10]]
print(words)
(xNewTrain, xNewTest) = FeatureSelection.Featurize(xTrainRaw, xTestRaw, words)
model.fit(xNewTrain, yTrain, iterations=50000, step=0.01)
yTestPredicted = model.predict(xNewTest)
EvaluationsStub.ExecuteAll(yTest, yTestPredicted)
