def ExecuteEvaluationRun(runSpecification, xTrainRaw, yTrain, numberOfFolds=2):
print("runSpecification: ", runSpecification)
startTime = time.time()
# HERE upgrade this to use crossvalidation
featurizer = SMSSpamFeaturize.SMSSpamFeaturize()
featurizer.CreateVocabulary(
xTrainRaw,
yTrain,
numFrequentWords=runSpecification['numFrequentWords'],
numMutualInformationWords=runSpecification['numMutualInformationWords']
)
xTrain = featurizer.Featurize(xTrainRaw)
xValidate = featurizer.Featurize(xValidateRaw)
if numberOfFolds > 1:
crossValidationAccuracy = []
for i in range(numberOfFolds):
xTrainI, yTrainI, xEvaluateI, yEvaluateI = CrossValidation.CrossValidation(
xTrain, yTrain, numberOfFolds, i)
model = LogisticRegression.LogisticRegression()
model.fit(xTrainI,
yTrainI,
convergence=runSpecification['convergence'],
stepSize=runSpecification['stepSize'],
verbose=False)
crossValidationAccuracy.append(
EvaluateBinaryClassification.Accuracy(
yEvaluateI, model.predict(xEvaluateI)))
mean = np.mean(crossValidationAccuracy)
runSpecification['crossValidationMean'] = mean
lower, _ = ErrorBounds.GetAccuracyBounds(
np.mean(crossValidationAccuracy), len(yEvaluateI), .5)
runSpecification['crossValidationErrorBound'] = mean - lower
if numberOfFolds == 1:
model = LogisticRegression.LogisticRegression()
model.fit(xTrain,
yTrain,
convergence=runSpecification['convergence'],
stepSize=runSpecification['stepSize'],
verbose=False)
validationSetAccuracy = EvaluateBinaryClassification.Accuracy(
yValidate, model.predict(xValidate))
runSpecification['accuracy'] = validationSetAccuracy
lower, _ = ErrorBounds.GetAccuracyBounds(validationSetAccuracy,
len(yValidate), .5)
runSpecification['accuracyErrorBound'] = validationSetAccuracy - lower
endTime = time.time()
if numberOfFolds > 1:
runSpecification['runtime'] = endTime - startTime
return runSpecification
def ExecuteEvaluationRun(runSpecification, xTrainRaw, yTrain, numberOfFolds=5):
startTime = time.time()
# HERE upgrade this to use crossvalidation
featurizer = SMSSpamFeaturize.SMSSpamFeaturize()
featurizer.CreateVocabulary(
xTrainRaw,
yTrain,
numFrequentWords=runSpecification['numFrequentWords'],
numMutualInformationWords=runSpecification['numMutualInformationWords']
)
xTrain = featurizer.Featurize(xTrainRaw)
xValidate = featurizer.Featurize(xValidateRaw)
model = LogisticRegression.LogisticRegression()
model.fit(xTrain,
yTrain,
convergence=runSpecification['convergence'],
stepSize=runSpecification['stepSize'],
verbose=True)
validationSetAccuracy = EvaluateBinaryClassification.Accuracy(
yValidate, model.predict(xValidate))
runSpecification['accuracy'] = validationSetAccuracy
# HERE upgrade this to calculate and save some error bounds...
endTime = time.time()
runSpecification['runtime'] = endTime - startTime
return runSpecification
def ExecuteEvaluationRun(runSpecification, xTrain, yTrain, numberOfFolds=2):
print("runSpecification: ", runSpecification)
startTime = time.time()
if numberOfFolds > 1:
crossValidationAccuracy = []
for i in range(numberOfFolds):
xTrainI, yTrainI, xEvaluateI, yEvaluateI = CrossValidation.CrossValidation(
xTrain, yTrain, numberOfFolds, i)
model = LogisticRegression.LogisticRegression()
model.fit(xTrainI,
yTrainI,
convergence=runSpecification['convergence'],
stepSize=runSpecification['stepSize'],
verbose=False)
crossValidationAccuracy.append(
EvaluateBinaryClassification.Accuracy(
yEvaluateI, model.predict(xEvaluateI)))
mean = np.mean(crossValidationAccuracy)
runSpecification['crossValidationMean'] = mean
lower, _ = ErrorBounds.GetAccuracyBounds(
np.mean(crossValidationAccuracy), len(yEvaluateI), .5)
runSpecification['crossValidationErrorBound'] = mean - lower
if numberOfFolds == 1:
model = LogisticRegression.LogisticRegression()
model.fit(xTrain,
yTrain,
convergence=runSpecification['convergence'],
stepSize=runSpecification['stepSize'],
verbose=False)
validationSetAccuracy = EvaluateBinaryClassification.Accuracy(
yValidate, model.predict(xValidate))
runSpecification['accuracy'] = validationSetAccuracy
lower, _ = ErrorBounds.GetAccuracyBounds(validationSetAccuracy,
len(yValidate), .5)
runSpecification['accuracyErrorBound'] = validationSetAccuracy - lower
endTime = time.time()
if numberOfFolds > 1:
runSpecification['runtime'] = endTime - startTime
return runSpecification
init['stepSize'] = 1.0
init['convergence'] = 0.005
init['numFrequentWords'] = 0
init['numMutualInformationWords'] = 20
featurizer = SMSSpamFeaturize.SMSSpamFeaturize()
featurizer.CreateVocabulary(
xTrainRaw,
yTrain,
numFrequentWords=init['numFrequentWords'],
numMutualInformationWords=init['numMutualInformationWords'])
xTrain = featurizer.Featurize(xTrainRaw)
xTest = featurizer.Featurize(xTestRaw)
model = LogisticRegression.LogisticRegression()
model.fit(xTrain,
yTrain,
convergence=init['convergence'],
stepSize=init['stepSize'],
verbose=False)
(modelFPRs, modelFNRs,
thresholds) = TabulateModelPerformanceForROC(model, xTest, yTest)
seriesFPRs.append(modelFPRs)
seriesFNRs.append(modelFNRs)
seriesLabels.append('initial parameters')
featurizer = SMSSpamFeaturize.SMSSpamFeaturize()
featurizer.CreateVocabulary(
xTrainRaw,
if doModelEvaluation:
######
### Build a model and evaluate on validation data
stepSize = 1.0
convergence = 0.001
featurizer = SMSSpamFeaturize.SMSSpamFeaturize()
featurizer.CreateVocabulary(xTrainRaw,
yTrain,
numMutualInformationWords=25)
xTrain = featurizer.Featurize(xTrainRaw)
xValidate = featurizer.Featurize(xValidateRaw)
xTest = featurizer.Featurize(xTestRaw)
frequentModel = LogisticRegression.LogisticRegression()
frequentModel.fit(xTrain,
yTrain,
convergence=convergence,
stepSize=stepSize,
verbose=True)
######
### Use equation 5.1 from Mitchell to bound the validation set error and the true error
import MachineLearningCourse.MLUtilities.Evaluations.ErrorBounds as ErrorBounds
print("Logistic regression with 25 features by mutual information:")
validationSetAccuracy = EvaluateBinaryClassification.Accuracy(
yValidate, frequentModel.predict(xValidate))
print("Validation set accuracy: %.4f." % (validationSetAccuracy))
for confidence in [.5, .8, .9, .95, .99]:
kOutputDirectory = "C:\\temp\\visualize"
runUnitTest = True
if runUnitTest:
# Little synthetic dataset to help with implementation. 2 features, 8 samples.
xTrain = [[.1, .1], [.2, .2], [.2, .1], [.1, .2], [.95, .95], [.9, .8],
[.8, .9], [.7, .6]]
yTrain = [0, 0, 0, 0, 1, 1, 1, 1]
# create a linear model with the right number of weights initialized
import MachineLearningCourse.MLUtilities.Learners.LogisticRegression as LogisticRegression
model = LogisticRegression.LogisticRegression(featureCount=len(xTrain[0]))
# To use this visualizer you need to install the PIL imaging library. Instructions are in the lecture notes.
import MachineLearningCourse.MLUtilities.Visualizations.Visualize2D as Visualize2D
while not model.converged:
# do 10 iterations of training
model.incrementalFit(xTrain,
yTrain,
maxSteps=10,
stepSize=1.0,
convergence=0.005)
# then look at the models weights
model.visualize()
# then look at how training set loss is converging
print(" fit for %d iterations, train set loss is %.4f" %
(model.totalGradientDescentSteps, model.loss(xTrain, yTrain)))
# Remember to create a new featurizer object/vocabulary for each part of the assignment
featurizer = SMSSpamFeaturize.SMSSpamFeaturize(
useHandCraftedFeatures=False)
featurizer.CreateVocabulary(xTrainRaw,
yTrain,
numMutualInformationWords=10)
print(featurizer.vocabulary)
# Remember to reprocess the raw data whenever you change the featurizer
xTrain = featurizer.Featurize(xTrainRaw)
xValidate = featurizer.Featurize(xValidateRaw)
xTest = featurizer.Featurize(xTestRaw)
## Good luck!
print("Learning the logistic regression model:")
import MachineLearningCourse.MLUtilities.Learners.LogisticRegression as LogisticRegression
logisticRegressionModel = LogisticRegression.LogisticRegression()
logisticRegressionModel.fit(xTrain,
yTrain,
stepSize=stepSize,
convergence=convergence)
import MachineLearningCourse.MLUtilities.Evaluations.EvaluateBinaryClassification as EvaluateBinaryClassification
print("\nLogistic regression model:")
logisticRegressionModel.visualize()
#EvaluateBinaryClassification.ExecuteAll(yTrain, logisticRegressionModel.predict(xTrain, classificationThreshold=0.5))
EvaluateBinaryClassification.ExecuteAll(
yValidate,
logisticRegressionModel.predict(xValidate,
classificationThreshold=0.5))