def main(dataset_name, testset_name, new_emails=False):
'''Runs the knn classifier for a training set dataset_name and test set testset_name'''
current_path = os.path.dirname(os.path.abspath(__file__)) + "\\"
trainingset_path = current_path + dataset_name + "\\"
testset_path = current_path + testset_name + "\\"
results_path = testset_path + "results\\"
if not os.path.exists(results_path):
os.mkdir(results_path)
folder_names = next(os.walk(testset_path + "."))[1]
if 'results' in folder_names:
folder_names.remove('results')
if new_emails:
folder_names = [""]
workfilename = 'mergedworkfile.csv'
wordfilename = 'wordfile.csv'
# klist = [1, 3, 7, 15, 24, 33, 42, 50]
klist = [1, 3]
acc = []
ks = []
trainingSet = []
print("Loading Training Set...")
wordsd, subd, digramsd, trigramsd = ex.loadTrainingset(
trainingset_path, workfilename, wordfilename, trainingSet)
print("Training Set loaded.")
print('Collecting ' + 'New' * new_emails + 'Test' * (not new_emails) +
' Emails...')
testSet, all_files = ex.loadTestset(testset_path, folder_names, wordsd,
subd, digramsd, trigramsd)
print('New' * new_emails + 'Test' * (not new_emails) +
' Emails Collected.')
assert (len(trainingSet[0]) == len(testSet[0]))
list_of_predictions = knn.classify(klist, trainingSet, testSet,
results_path)
if not new_emails:
#Finds the predictions and accuracy for new test mails given the predictions for these mails
for i in range(len(klist)):
predictions = []
for x in range(len(testSet)):
predictions.append(list_of_predictions[x][i])
accuracy = knn.getAccuracy(testSet, predictions)
acc.append(accuracy)
ks.append(klist[i])
print('K: ' + repr(klist[i]))
print('Accuracy: ' + repr(accuracy) + '%')
print('Overall Accuracy: ' + str(sum(acc) / len(acc)) + "%")
plt.plot(ks, acc)
plt.xlabel('K')
plt.ylabel('Accuracy')
plt.show()
print('Find the results at: ' + results_path)
def main(dataset_name, testset_name, new_emails = False):
if platform.system() == 'Windows':
current_path = os.path.dirname(os.path.abspath(__file__)) + "\\"
trainingset_path = current_path + dataset_name + "\\"
testset_path = current_path + testset_name + "\\"
results_path = testset_path + "results\\"
elif platform.system() == 'Linux':
current_path = os.path.dirname(os.path.abspath(__file__)) + "/"
trainingset_path = current_path + dataset_name + "/"
testset_path = current_path + testset_name + "/"
results_path = testset_path + "results/"
if not os.path.exists(results_path):
os.mkdir(results_path)
folder_names = next(os.walk(testset_path + "."))[1]
if 'results' in folder_names:
folder_names.remove('results')
# folder_names = ["calendar"]
if new_emails:
folder_names = [""]
workfilename = 'mergedworkfile.csv'
wordfilename = 'wordfile.csv'
trainingSet = []
predicted_folders = []
print("Loading Training Set...")
wordsd, subd, digramsd, trigramsd = ex.loadTrainingset(trainingset_path, workfilename, wordfilename, trainingSet)
print("Training Set loaded.")
print('Collecting ' + 'New'*new_emails + 'Test'*(not new_emails) + ' Emails...')
testSet, all_files = ex.loadTestset(testset_path, folder_names, wordsd, subd, digramsd, trigramsd)
print('New'*new_emails + 'Test'*(not new_emails) + ' Emails Collected.')
assert(len(trainingSet[0]) == len(testSet[0]))
# prepare model
summaries, classproirprobabilities = mnb.summarizeByClass(trainingSet)
# test model
predictions = mnb.getPredictions(summaries, classproirprobabilities, testSet, results_path)
folder_names = next(os.walk(trainingset_path + "."))[1]
if 'results' in folder_names:
folder_names.remove('results')
for fname in folder_names:
if not os.path.exists(results_path + fname):
os.mkdir(results_path + fname)
for i in range(len(predictions)):
shutil.copy2(all_files[i], results_path + folder_names[predictions[i]])
predicted_folders.append(folder_names[predictions[i]])
if not new_emails:
accuracy = mnb.getAccuracy(testSet, predictions)
print('Accuracy: {0}%'.format(accuracy))
print('Find the results at: ' + results_path)
return predicted_folders
def main(dataset_name, testset_name, new_emails = False):
'''Runs the mnb classifier for a training set dataset_name and test set testset_name'''
current_path = os.path.dirname(os.path.abspath(__file__)) + "\\"
trainingset_path = current_path + dataset_name + "\\"
testset_path = current_path + testset_name + "\\"
results_path = testset_path + "results\\"
if not os.path.exists(results_path):
os.mkdir(results_path)
folder_names = next(os.walk(testset_path + "."))[1]
if 'results' in folder_names:
folder_names.remove('results')
# folder_names = ["calendar"]
if new_emails:
folder_names = [""]
workfilename = 'mergedworkfile.csv'
wordfilename = 'wordfile.csv'
trainingSet = []
predicted_folders = []
print("Loading Training Set...")
wordsd, subd, digramsd, trigramsd = ex.loadTrainingset(trainingset_path, workfilename, wordfilename, trainingSet)
print("Training Set loaded.")
print('Collecting ' + 'New'*new_emails + 'Test'*(not new_emails) + ' Emails...')
testSet, all_files = ex.loadTestset(testset_path, folder_names, wordsd, subd, digramsd, trigramsd)
print('New'*new_emails + 'Test'*(not new_emails) + ' Emails Collected.')
assert(len(trainingSet[0]) == len(testSet[0]))
# prepare model
summaries, classpriorprobabilities = mnb.summarizeByClass(trainingSet)
# test model
predictions = mnb.getPredictions(summaries, classpriorprobabilities, testSet, results_path)
folder_names = next(os.walk(trainingset_path + "."))[1]
if 'results' in folder_names:
folder_names.remove('results')
for fname in folder_names:
if not os.path.exists(results_path + fname):
os.mkdir(results_path + fname)
for i in range(len(predictions)):
shutil.copy2(all_files[i], results_path + folder_names[predictions[i]])
predicted_folders.append(folder_names[predictions[i]])
if not new_emails:
#Finds the accuracy for new test mails given the predictions for these mails
accuracy = mnb.getAccuracy(testSet, predictions)
print('Accuracy: {0}%'.format(accuracy))
print('Find the results at: ' + results_path)
return predicted_folders
def main(dataset_name, testset_name, new_emails = False):
'''Runs the knn classifier for a training set dataset_name and test set testset_name'''
current_path = os.path.dirname(os.path.abspath(__file__)) + "\\"
trainingset_path = current_path + dataset_name + "\\"
testset_path = current_path + testset_name + "\\"
results_path = testset_path + "results\\"
if not os.path.exists(results_path):
os.mkdir(results_path)
folder_names = next(os.walk(testset_path + "."))[1]
if 'results' in folder_names:
folder_names.remove('results')
if new_emails:
folder_names = [""]
workfilename = 'mergedworkfile.csv'
wordfilename = 'wordfile.csv'
# klist = [1, 3, 7, 15, 24, 33, 42, 50]
klist = [1, 3]
acc = []
ks = []
trainingSet=[]
print("Loading Training Set...")
wordsd, subd, digramsd, trigramsd = ex.loadTrainingset(trainingset_path, workfilename, wordfilename, trainingSet)
print("Training Set loaded.")
print('Collecting ' + 'New'*new_emails + 'Test'*(not new_emails) + ' Emails...')
testSet, all_files = ex.loadTestset(testset_path, folder_names, wordsd, subd, digramsd, trigramsd)
print('New'*new_emails + 'Test'*(not new_emails) + ' Emails Collected.')
assert(len(trainingSet[0]) == len(testSet[0]))
list_of_predictions = knn.classify(klist, trainingSet, testSet, results_path)
if not new_emails:
#Finds the predictions and accuracy for new test mails given the predictions for these mails
for i in range(len(klist)):
predictions = []
for x in range(len(testSet)):
predictions.append(list_of_predictions[x][i])
accuracy = knn.getAccuracy(testSet, predictions)
acc.append(accuracy)
ks.append(klist[i])
print('K: ' + repr(klist[i]))
print('Accuracy: ' + repr(accuracy) + '%')
print('Overall Accuracy: '+ str(sum(acc)/len(acc)) + "%")
plt.plot(ks, acc)
plt.xlabel('K')
plt.ylabel('Accuracy')
plt.show()
print('Find the results at: ' + results_path)
