def test():
"""
Run tests on the implementation of the naive Bayes classifier.
The tests are going to be ran on instances 20-25 from both the train and test sets of the contest agent.
Passing this test is a very good (however not a perfect) indicator that your code is correct.
"""
train_path = os.path.join('classifier_data', 'contest_training.tsv')
test_path = os.path.join('classifier_data', 'contest_test.tsv')
smoothing = [0, 1]
logtransform = {
0: [True, False],
1: [True]
}
trainData, trainLabels, trainFeatures, = loadDataset(train_path)
testData, testLabels, testFeatures = loadDataset(test_path)
labels = set(trainLabels) | set(testLabels)
for s in smoothing:
for lt in logtransform[s]:
classifierArgs = {'smoothing':s, 'logTransform':lt}
classifierArgs['legalLabels'] = labels
if s:
featureValues = mergeFeatureValues(trainFeatures, testFeatures)
classifierArgs['featureValues'] = featureValues
# train on train set
classifier = NaiveBayesClassifier(**classifierArgs)
classifier.fit(trainData, trainLabels)
# evaluate on train set
trainPredictions = classifier.predict(trainData)
evaluateClassifier(trainPredictions, trainLabels, 'train', classifier.k)
staticOutputCheck(train_path, s, lt, classifier.posteriors[20:25])
# evaluate on test set
testPredictions = classifier.predict(testData)
evaluateClassifier(testPredictions, testLabels, 'test', classifier.k)
staticOutputCheck(test_path, s, lt, classifier.posteriors[20:25])
def test():
"""
Run tests on the implementation of the naive Bayes classifier.
The tests are going to be ran on instances 20-25 from both the train and test sets of the contest agent.
Passing this test is a very good (however not a perfect) indicator that your code is correct.
"""
train_path = os.path.join('classifier_data', 'contest_training.tsv')
test_path = os.path.join('classifier_data', 'contest_test.tsv')
smoothing = [0, 1]
logtransform = {
0: [True, False],
1: [True]
}
trainData, trainLabels, trainFeatures, = loadDataset(train_path)
testData, testLabels, testFeatures = loadDataset(test_path)
labels = set(trainLabels) | set(testLabels)
for s in smoothing:
for lt in logtransform[s]:
classifierArgs = {'smoothing':s, 'logTransform':lt}
classifierArgs['legalLabels'] = labels
if s:
featureValues = mergeFeatureValues(trainFeatures, testFeatures)
classifierArgs['featureValues'] = featureValues
# train on train set
classifier = NaiveBayesClassifier(**classifierArgs)
classifier.fit(trainData, trainLabels)
# evaluate on train set
trainPredictions = classifier.predict(trainData)
evaluateClassifier(trainPredictions, trainLabels, 'train', classifier.k)
staticOutputCheck(train_path, s, lt, classifier.posteriors[20:25])
# evaluate on test set
testPredictions = classifier.predict(testData)
evaluateClassifier(testPredictions, testLabels, 'test', classifier.k)
staticOutputCheck(test_path, s, lt, classifier.posteriors[20:25])
def runClassifier(train_path, test_path, smoothing, logtransform):
classifierArgs = {'smoothing':smoothing, 'logTransform':logtransform}
trainData, trainLabels, trainFeatures, = loadDataset(train_path)
testData, testLabels, testFeatures = loadDataset(test_path)
labels = set(trainLabels) | set(testLabels)
classifierArgs['legalLabels'] = labels
if smoothing:
featureValues = mergeFeatureValues(trainFeatures, testFeatures)
classifierArgs['featureValues'] = featureValues
# train the actual model
classifier = NaiveBayesClassifier(**classifierArgs)
classifier.fit(trainData, trainLabels)
trainPredictions = classifier.predict(trainData)
evaluateClassifier(trainPredictions, trainLabels, 'train', classifier.k)
testPredictions = classifier.predict(testData)
evaluateClassifier(testPredictions, testLabels, 'test', classifier.k)
def runClassifier(train_path, test_path, smoothing, logtransform):
classifierArgs = {'smoothing':smoothing, 'logTransform':logtransform}
trainData, trainLabels, trainFeatures, = loadDataset(train_path)
testData, testLabels, testFeatures = loadDataset(test_path)
labels = set(trainLabels) | set(testLabels)
classifierArgs['legalLabels'] = labels
if smoothing:
featureValues = mergeFeatureValues(trainFeatures, testFeatures)
classifierArgs['featureValues'] = featureValues
# train the actual model
classifier = NaiveBayesClassifier(**classifierArgs)
classifier.fit(trainData, trainLabels)
trainPredictions = classifier.predict(trainData)
evaluateClassifier(trainPredictions, trainLabels, 'train', classifier.k)
testPredictions = classifier.predict(testData)
evaluateClassifier(testPredictions, testLabels, 'test', classifier.k)
