def main(args):
if not args.extra:
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.dataset_file)
pred_p = c.classifyPerceptron(train_set, train_labels, dev_set,
args.lrate, args.max_iter)
print("Perceptron")
accuracy, f1, precision, recall = compute_accuracies(
pred_p, dev_set, dev_labels)
pred_lr = c.classifyLR(train_set, train_labels, dev_set, args.lrate,
args.max_iter)
print("\nLogistic Regression")
accuracy, f1, precision, recall = compute_accuracies(
pred_lr, dev_set, dev_labels)
else:
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.dataset_file, extra=True)
predicted_labels = c.classifyEC(train_set, train_labels, dev_set,
args.k)
print("kNN, k = {}".format(args.k))
accuracy, f1, precision, recall = compute_accuracies(
predicted_labels, dev_set, dev_labels)
def main(args):
laplace_number_set = np.linspace(0, 0.2, 2001)
print(laplace_number_set)
laplace_performance = np.zeros(2000)
for number in range(2000):
if (number == 0):
laplace_performance[number] = 0
else:
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir, args.development_dir, args.stemming)
predicted_labels = nb.naiveBayes(train_set, train_labels, dev_set,
laplace_number_set[number])
accuracy, f1, precision, recall = compute_accuracies(
predicted_labels, dev_set, dev_labels)
laplace_performance[number] = accuracy
best_laplace = np.argmax(laplace_performance)
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir, args.development_dir, args.stemming)
predicted_labels = nb.naiveBayes(train_set, train_labels, dev_set,
best_laplace)
accuracy, f1, precision, recall = compute_accuracies(
predicted_labels, dev_set, dev_labels)
laplace_performance[number] = accuracy
print("best laplace parameter:", best_laplace)
print("Accuracy:", accuracy)
print("F1-Score:", f1)
print("Precision:", precision)
print("Recall:", recall)
def main(args):
### Part 1 ###
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.dataset_file, part=1)
train_set = torch.tensor(train_set, dtype=torch.float32)
train_labels = torch.tensor(train_labels, dtype=torch.int64)
dev_set = torch.tensor(dev_set, dtype=torch.float32)
losses, predicted_labels, net = p1.fit(train_set, train_labels, dev_set,
args.max_iter)
accuracy, f1, precision, recall = compute_accuracies(predicted_labels,
dev_set,
dev_labels,
N_class=3)
print(' ##### Part 1 results #####')
print("Accuracy:", accuracy)
print("F1-Score:", f1)
print("Precision:", precision)
print("Recall:", recall)
print("num_parameters:",
sum([np.prod(w.shape) for w in net.get_parameters()]))
torch.save(net, "net_p1.model")
### Part 2 ###
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.dataset_file, part=2)
train_set = torch.tensor(train_set, dtype=torch.float32)
train_labels = torch.tensor(train_labels, dtype=torch.int64)
dev_set = torch.tensor(dev_set, dtype=torch.float32)
_, predicted_labels, net = p2.fit(train_set, train_labels, dev_set,
10 * args.max_iter)
accuracy, f1, precision, recall = compute_accuracies(predicted_labels,
dev_set,
dev_labels,
N_class=5)
print(' ##### Part 2 results #####')
print("Accuracy:", accuracy)
print("F1-Score:", f1)
print("Precision:", precision)
print("Recall:", recall)
print("num_parameters:",
sum([np.prod(w.shape) for w in net.get_parameters()]))
torch.save(net, "net_p2.model")
### Part 3 ###
# input provided will be normalized to 0.0 - 1.0
train_set, _, dev_set, _ = reader.load_dataset(
args.dataset_file, part=2) # use same data as part 2
train_set = torch.tensor(train_set, dtype=torch.float32) / 255.0
dev_set = torch.tensor(dev_set, dtype=torch.float32) / 255.0
_, x_recon, net = p3.fit(train_set, dev_set, args.max_iter * 10)
diff = (x_recon - dev_set.numpy())**2
MSE = diff.mean()
print(' ##### Part 3 results #####')
print("MSE:", MSE)
print("num_parameters:",
sum([np.prod(w.shape) for w in net.get_parameters()]))
torch.save(net, "net_p3.model")
def main(args):
train_set = load_dataset(args.training_file, args.case_sensitive)
test_set = load_dataset(args.test_file, args.case_sensitive)
if args.baseline:
print("You are running the baseline algorithm!")
accuracy = compute_accuracies(test_set, baseline(train_set, strip_tags(test_set)))
else:
print("You are running the Viterbi algorithm!")
accuracy = compute_accuracies(test_set, viterbi(train_set, strip_tags(test_set)))
print("Accuracy:",accuracy)
def main(args):
if args.method == 'perceptron':
train_set, train_labels, dev_set,dev_labels = reader.load_dataset(args.dataset_file)
pred_p = c.classifyPerceptron(train_set, train_labels, dev_set, args.lrate, args.max_iter)
print("Perceptron")
accuracy,f1,precision,recall = compute_accuracies(pred_p, dev_labels)
elif args.method == 'knn':
train_set, train_labels, dev_set,dev_labels = reader.load_dataset(args.dataset_file, extra=True)
predicted_labels = c.classifyKNN(train_set, train_labels, dev_set, args.k)
print("kNN, k = {}".format(args.k))
accuracy,f1,precision,recall = compute_accuracies(predicted_labels, dev_labels)
else:
print("Method must be either perceptron or knn!")
def test_unigram_dev_stem_false_lower_false():
print("Running unigram test..."+'\n')
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
"data/spam_data/train",
"data/spam_data/dev",
stemming=False,
lower_case=False,
use_tqdm=False
)
predicted_labels = nb.naiveBayes(
train_set, train_labels, dev_set, smoothing_parameter=1.0, pos_prior=0.5)
if len(predicted_labels) != len(dev_labels):
print("The length of the list of predictions is not equivalent to the length of the list of development labels.")
errorDict = {
'name': 'Unigram test on dev set without stemming and without lowercase',
'score': 0,
'max_score': 20,
'visibility': 'visible'
}
return json.dumps(errorDict, indent=1)
(
accuracy,
f1,
precision,
recall,
) = mp2.compute_accuracies(predicted_labels, dev_set, dev_labels)
print("Accuracy:",accuracy)
print("F1-Score:",f1)
print("Precision:",precision)
print("Recall:",recall)
total_score = 0
if accuracy >= 0.81:
total_score += 5
print("+ 5 points for accuracy above " + str(0.81))
else:
print("Accuracy needs to be above " + str(0.81))
if accuracy >= 0.86:
total_score += 5
print("+ 5 points for accuracy above " + str(0.86))
else:
print("Accuracy needs to be above " + str(0.86))
if accuracy >= 0.91:
total_score += 5
print("+ 5 points for accuracy above " + str(0.91))
else:
print("Accuracy needs to be above " + str(0.91))
if accuracy >= 0.95:
total_score += 5
print("+ 5 points for accuracy above " + str(0.95))
else:
print("Accuracy needs to be above " + str(0.95))
resultDict = {
'name': 'Unigram test on dev set without stemming and without lowercase',
'score': total_score,
'max_score': 20,
'visibility': 'visible'
}
return json.dumps(resultDict, indent=1)
def main(season):
train_set, train_labels, rec_set, url_to_jpg = reader.load_dataset()
seasons = {'summer': 0, 'fall': 1, 'winter': 2, 'spring': 3}
res = knn(train_set, train_labels, rec_set, url_to_jpg, seasons[season])
print(res)
def main(args):
train_set, train_labels, dev_set,dev_labels = reader.load_dataset(args.training_dir,args.development_dir,args.stemming)
predicted_labels = nb.naiveBayes(train_set,train_labels, dev_set, args.laplace)
accuracy,f1,precision,recall = compute_accuracies(predicted_labels,dev_set,dev_labels)
print("Accuracy:",accuracy)
print("F1-Score:",f1)
print("Precision:",precision)
print("Recall:",recall)
def main(args):
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir,
args.development_dir,
stemming=False,
lower_case=True)
best_tf_idf_words = tf_idf.compute_tf_idf(train_set, train_labels, dev_set)
print("Finished executing compute_tf_idf()")
def main(args):
# ### Part 1 ###
# train_set, train_labels, dev_set,dev_labels = reader.load_dataset(args.dataset_file, part=1)
# train_set = torch.tensor(train_set,dtype=torch.float32)
# train_labels = torch.tensor(train_labels,dtype=torch.int64)
# dev_set = torch.tensor(dev_set,dtype=torch.float32)
# losses,predicted_labels,net = p1.fit(train_set,train_labels, dev_set,args.max_iter)
# accuracy,f1,precision,recall = compute_accuracies(predicted_labels,dev_set,dev_labels, N_class=3)
# print(' ##### Part 1 results #####')
# print("Accuracy:",accuracy)
# print("F1-Score:",f1)
# print("Precision:",precision)
# print("Recall:",recall)
# print("num_parameters:", sum([ np.prod(w.shape) for w in net.get_parameters()]))
# torch.save(net, "net_p1.model")
# ### Part 2 ###
# train_set, train_labels, dev_set,dev_labels = reader.load_dataset(args.dataset_file, part=2)
# train_set = torch.tensor(train_set,dtype=torch.float32)
# train_labels = torch.tensor(train_labels,dtype=torch.int64)
# dev_set = torch.tensor(dev_set,dtype=torch.float32)
# _,predicted_labels,net = p2.fit(train_set,train_labels, dev_set,10*args.max_iter)
# accuracy,f1,precision,recall = compute_accuracies(predicted_labels,dev_set,dev_labels, N_class=5)
# print(' ##### Part 2 results #####')
# print("Accuracy:",accuracy)
# print("F1-Score:",f1)
# print("Precision:",precision)
# print("Recall:",recall)
# print("num_parameters:", sum([ np.prod(w.shape) for w in net.get_parameters()]))
# torch.save(net, "net_p2.model")
### Part 3 ###
# input provided will be normalized to 0.0 - 1.0
train_set, _, dev_set, _ = reader.load_dataset(
args.dataset_file, part=2) # use same data as part 2
train_set = torch.tensor(train_set, dtype=torch.float32) / 255.0
dev_set = torch.tensor(dev_set, dtype=torch.float32) / 255.0
_, x_recon, net = p3.fit(train_set, dev_set, args.max_iter * 10)
diff = (x_recon - dev_set.numpy())**2
MSE = diff.mean()
print(' ##### Part 3 results #####')
print("MSE:", MSE)
print("num_parameters:",
sum([np.prod(w.shape) for w in net.get_parameters()]))
torch.save(net, "net_p3.model")
# Show some original images in 1st row & reconstructed images in 2nd row
# For debug only
import matplotlib.pyplot as plt
idx = np.random.choice(x_recon.shape[0], 10)
fig, axes = plt.subplots(2, 10)
for i in range(10):
axes[0, i].imshow(dev_set[idx[i]].reshape(28, 28), cmap='gray')
axes[1, i].imshow(x_recon[idx[i]].reshape(28, 28), cmap='gray')
plt.show()
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)
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):
train_set, train_labels, dev_set,dev_labels = reader.load_dataset(args.dataset_file)
train_set = torch.tensor(train_set,dtype=torch.float32)
train_labels = torch.tensor(train_labels,dtype=torch.int64)
dev_set = torch.tensor(dev_set,dtype=torch.float32)
_,predicted_labels,net = p.fit(train_set,train_labels, dev_set,args.max_iter)
accuracy,f1,precision,recall = compute_accuracies(predicted_labels,dev_set,dev_labels)
print("Accuracy:",accuracy)
print("F1-Score:",f1)
print("Precision:",precision)
print("Recall:",recall)
torch.save(net, "net.model")
def main(args):
train_set = load_dataset(args.training_file, args.case_sensitive)
test_set = load_dataset(args.test_file, args.case_sensitive)
if args.baseline:
print("You are running the baseline algorithm!")
predTags, unseenIdx, seenIdx = baseline(train_set,
strip_tags(test_set))
check_seen_accuracy = True
accuracy = compute_accuracies(test_set, predTags, unseenIdx, seenIdx,
check_seen_accuracy)
else:
print("You are running the Viterbi algorithm!")
predTags, unseenIdx, seenIdx = viterbi(train_set, strip_tags(test_set))
check_seen_accuracy = False
# check_seen_accuracy = True
accuracy = compute_accuracies(test_set, predTags, unseenIdx, seenIdx,
check_seen_accuracy)
if check_seen_accuracy:
print("Accuracy of sentences with no unseen words")
else:
print("accuracy of sentences with unseen words")
print("Accuracy:", accuracy)
def main(args):
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.dataset_file)
if not args.extra:
predicted_labels = p.classify(train_set, train_labels, dev_set,
args.lrate, args.max_iter)
else:
predicted_labels = p.classifyEC(train_set, train_labels, dev_set,
args.lrate, args.max_iter)
accuracy, f1, precision, recall = compute_accuracies(
predicted_labels, dev_set, dev_labels)
print("Accuracy:", accuracy)
print("F1-Score:", f1)
print("Precision:", precision)
print("Recall:", recall)
def main(args):
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir, args.development_dir, args.stemming)
predicted_labels = nb.naiveBayes(train_set, train_labels, dev_set,
args.laplace)
answer = open('answer.txt', 'w')
test = open('./data/test.jsonl', 'r').readlines()
for i in range(len(test)):
answer.write(json.loads(test[i])['id'])
answer.write(",")
if predicted_labels[i] == 0:
answer.write("SARCASM")
else:
answer.write("NOT_SARCASM")
answer.write("\n")
def main(args):
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
args.training_dir, args.development_dir, args.stemming,
args.lower_case)
predicted_labels = nb.naiveBayesMixture(train_set, train_labels, dev_set,
args.bigram_lambda,
args.unigram_smoothing,
args.bigram_smoothing,
args.pos_prior)
accuracy, f1, precision, recall = compute_accuracies(
predicted_labels, dev_set, dev_labels)
print("Accuracy:", accuracy)
print("F1-Score:", f1)
print("Precision:", precision)
print("Recall:", recall)
def bigram_check():
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
"data/bigram_check/train",
"data/bigram_check/dev",
stemming=False,
lower_case=False,
use_tqdm=False,
)
predicted_labels = nbm.naiveBayesMixture(
train_set,
train_labels,
dev_set,
unigram_smoothing_parameter=1.0,
bigram_smoothing_parameter=1.0,
bigram_lambda=1.0,
pos_prior=0.5,
)
if isinstance(predicted_labels, np.ndarray):
predicted_labels = list(predicted_labels.reshape(-1))
if predicted_labels == [1, 1]:
return BIGRAM_PENALTY
else:
return 1.0
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
def test_bigram_dev_stem_false_lower_false():
print("Running mixture model test..."+'\n')
train_set, train_labels, dev_set, dev_labels = reader.load_dataset(
"data/spam_data/train",
"data/spam_data/dev",
stemming=False,
lower_case=False,
use_tqdm=False
)
predicted_labels = nbm.naiveBayesMixture(train_set, train_labels, dev_set, bigram_lambda=0.05, unigram_smoothing_parameter=1,
bigram_smoothing_parameter=0.005, pos_prior=0.5)
if len(predicted_labels) != len(dev_labels):
print("The length of the list of predictions is not equivalent to the length of the list of development labels.")
errorDict = {
'name': 'Mixture model test on dev set without stemming and without lowercase',
'score': 0,
'max_score': 5,
'visibility': 'visible'
}
return json.dumps(errorDict, indent=1)
(
accuracy,
f1,
precision,
recall
) = mp2.compute_accuracies(predicted_labels, dev_set, dev_labels)
print("Accuracy:",accuracy)
print("F1-Score:",f1)
print("Precision:",precision)
print("Recall:",recall)
total_score = 0
if accuracy >= 0.80:
total_score += 1.25
print("+ 1.25 points for accuracy above " + str(0.80))
else:
print("Accuracy needs to be above " + str(0.80))
if accuracy >= 0.85:
total_score += 1.25
print("+ 1.25 points for accuracy above " + str(0.85))
else:
print("Accuracy needs to be above " + str(0.85))
if accuracy >= 0.90:
total_score += 1.25
print("+ 1.25 points for accuracy above " + str(0.90))
else:
print("Accuracy needs to be above " + str(0.90))
if accuracy >= 0.95:
total_score += 1.25
print("+ 1.25 points for accuracy above " + str(0.95))
else:
print("Accuracy needs to be above " + str(0.95))
if bigram_check() == BIGRAM_PENALTY:
print(f"We hypothesize that your implementation of naiveBayesMixture is not correct. "
f"Therefore, we applied a penalty multiplier of {BIGRAM_PENALTY} to your score.")
total_score *= BIGRAM_PENALTY
resultDict = {
'name': 'Mixture test on dev set without stemming and without lowercase',
'score': total_score,
'max_score': 5,
'visibility': 'visible'
}
return json.dumps(resultDict, indent = 1)
