def main(args):
#Modify stemming and lower case below. Note that our test cases may use both settings of the two parameters
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(args.training_dir,args.development_dir,stemming=False,lower_case=False)
#predicted_labels = nb.naiveBayes(train_set, train_labels, dev_set)
predicted_labels = nb.bigramBayes(train_set, train_labels, dev_set)
accuracy, false_positive, false_negative, true_positive, true_negative = compute_accuracies(predicted_labels,dev_labels)
print("Accuracy:",accuracy)
print("False Positive", false_positive)
print("Fale Negative", false_negative)
print("True Positive", true_positive)
print("True Negative", true_negative)
def main(args):
#Modify stemming and lower case below. Note that our test cases may use both settings of the two parameters
max_iterations = 10
accuracy_limit = 0.87
min_accuracy = 0
max_accuracy = 0
unigram_smoothing_parameter = 0.0625
bigram_smoothing_parameter = 0.125
bigram_lambda = 0.05
# unigram smoothing parameter tuning domain
min_unigram_smoothing_parameter = 0.0000001
max_unigram_smoothing_parameter = 1.0
# bigram smoothing parameter tuning domain
min_bigram_smoothing_parameter = 0.0000001
max_bigram_smoothing_parameter = 1.0
# bigram_lambda tuning domain
min_bigram_lambda = 0.0000001
max_bigram_lambda = 1.0
#bigram_lambda tuner
iteration = 0
while min_accuracy < accuracy_limit or max_accuracy < accuracy_limit:
if iteration > max_iterations:
break
# min_bigram_lambda
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir,
args.development_dir,
stemming=False,
lower_case=False)
predicted_labels = nb.bigramBayes(train_set, train_labels, dev_set,
unigram_smoothing_parameter,
bigram_smoothing_parameter,
min_bigram_lambda)
min_accuracy, false_positive, false_negative, true_positive, true_negative = compute_accuracies(
predicted_labels, dev_labels)
# max_bigram_lambda
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir,
args.development_dir,
stemming=False,
lower_case=False)
predicted_labels = nb.bigramBayes(train_set, train_labels, dev_set,
unigram_smoothing_parameter,
bigram_smoothing_parameter,
max_bigram_lambda)
max_accuracy, false_positive, false_negative, true_positive, true_negative = compute_accuracies(
predicted_labels, dev_labels)
print("Iteration:", iteration)
print("unigram_smoothing_parameter:", unigram_smoothing_parameter)
print("bigram_smoothing_parameter:", bigram_smoothing_parameter)
print("min_bigram_lambda:", min_bigram_lambda)
print("max_bigram_lambda:", max_bigram_lambda)
print("min_Accuracy:", min_accuracy)
print("max_Accuracy:", max_accuracy)
print("False Positive:", false_positive)
print("False Negative:", false_negative)
print("True Positive:", true_positive)
print("True Negative:", true_negative)
if (min_accuracy < max_accuracy):
min_bigram_lambda += (max_bigram_lambda - min_bigram_lambda) / 2
bigram_lambda = max_bigram_lambda
else:
max_bigram_lambda -= (max_bigram_lambda - min_bigram_lambda) / 2
bigram_lambda = min_bigram_lambda
iteration += 1
# unigram_smoothing_parameter tuner
iteration = 0
while min_accuracy < accuracy_limit or max_accuracy < accuracy_limit:
if iteration > max_iterations:
break
# min_unigram_smoothing_parameter
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir,
args.development_dir,
stemming=False,
lower_case=False)
predicted_labels = nb.bigramBayes(train_set, train_labels, dev_set,
min_unigram_smoothing_parameter,
bigram_smoothing_parameter,
bigram_lambda)
min_accuracy, false_positive, false_negative, true_positive, true_negative = compute_accuracies(
predicted_labels, dev_labels)
# max_unigram_smoothing_parameter
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir,
args.development_dir,
stemming=False,
lower_case=False)
predicted_labels = nb.bigramBayes(train_set, train_labels, dev_set,
max_unigram_smoothing_parameter,
bigram_smoothing_parameter,
bigram_lambda)
max_accuracy, false_positive, false_negative, true_positive, true_negative = compute_accuracies(
predicted_labels, dev_labels)
print("Iteration:", iteration)
print("min_unigram_smoothing_parameter:",
min_unigram_smoothing_parameter)
print("max_unigram_smoothing_parameter:",
max_unigram_smoothing_parameter)
print("bigram_smoothing_parameter:", bigram_smoothing_parameter)
print("bigram_lambda:", bigram_lambda)
print("min_Accuracy:", min_accuracy)
print("max_Accuracy:", max_accuracy)
print("False Positive:", false_positive)
print("False Negative:", false_negative)
print("True Positive:", true_positive)
print("True Negative:", true_negative)
if (min_accuracy < max_accuracy):
min_unigram_smoothing_parameter += (
max_unigram_smoothing_parameter -
min_unigram_smoothing_parameter) / 2
unigram_smoothing_parameter = max_unigram_smoothing_parameter
else:
max_unigram_smoothing_parameter -= (
max_unigram_smoothing_parameter -
min_unigram_smoothing_parameter) / 2
unigram_smoothing_parameter = min_unigram_smoothing_parameter
iteration += 1
# bigram_smoothing_parameter tuner
iteration = 0
while min_accuracy < accuracy_limit or max_accuracy < accuracy_limit:
if iteration > max_iterations:
break
# min_bigram_smoothing_parameter
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir,
args.development_dir,
stemming=False,
lower_case=False)
predicted_labels = nb.bigramBayes(train_set, train_labels, dev_set,
unigram_smoothing_parameter,
min_bigram_smoothing_parameter,
bigram_lambda)
min_accuracy, false_positive, false_negative, true_positive, true_negative = compute_accuracies(
predicted_labels, dev_labels)
# max_bigram_smoothing_parameter
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir,
args.development_dir,
stemming=False,
lower_case=False)
predicted_labels = nb.bigramBayes(train_set, train_labels, dev_set,
unigram_smoothing_parameter,
max_bigram_smoothing_parameter,
bigram_lambda)
max_accuracy, false_positive, false_negative, true_positive, true_negative = compute_accuracies(
predicted_labels, dev_labels)
print("Iteration:", iteration)
print("unigram_smoothing_parameter:", unigram_smoothing_parameter)
print("min_bigram_smoothing_parameter:",
min_bigram_smoothing_parameter)
print("max_bigram_smoothing_parameter:",
max_bigram_smoothing_parameter)
print("bigram_lambda:", bigram_lambda)
print("min_Accuracy:", min_accuracy)
print("max_Accuracy:", max_accuracy)
print("False Positive:", false_positive)
print("False Negative:", false_negative)
print("True Positive:", true_positive)
print("True Negative:", true_negative)
if (min_accuracy < max_accuracy):
min_bigram_smoothing_parameter += (
max_bigram_smoothing_parameter -
min_bigram_smoothing_parameter) / 2
bigram_smoothing_parameter = max_bigram_smoothing_parameter
else:
max_bigram_smoothing_parameter -= (
max_bigram_smoothing_parameter -
min_bigram_smoothing_parameter) / 2
bigram_smoothing_parameter = min_bigram_smoothing_parameter
iteration += 1