params: list of parameters (initial values)
"""
for e_i in range(num_iterations):
cum_loss = 0.0 # total loss in this iteration.
random.shuffle(train_data)
for label, features in train_data:
x = feats_to_vec(features) # convert features to a vector.
y = L2I[label] # convert the label to number if needed.
loss, grads = ll.loss_and_gradients(x, y, params)
cum_loss += loss
params[0] -= (learning_rate * grads[0])
params[1] -= (learning_rate * grads[1])
train_loss = cum_loss / len(train_data)
train_accuracy = accuracy_on_dataset(train_data, params)
dev_accuracy = accuracy_on_dataset(dev_data, params)
print(e_i, train_loss, train_accuracy, dev_accuracy)
return params
if __name__ == '__main__':
train_data = TRAIN
dev_data = DEV
params = ll.create_classifier(len(F2I), len(L2I))
num_iterations = 100
learning_rate = 10**-4
trained_params = train_classifier(train_data, dev_data, num_iterations,
learning_rate, params)
return params
if __name__ == '__main__':
# YOUR CODE HERE
# write code to load the train and dev sets, set up whatever you need,
# and call train_classifier.
TRAIN = [(l, text_to_bigrams(t)) for l, t in read_data(open('train', 'r'))]
train_vocab = vocabu(TRAIN)
L2I = {l: i for i, l in enumerate(list(sorted(set([l for l, t in TRAIN]))))}
F2I = {f: i for i, f in enumerate(list(sorted(vocabu(TRAIN))))}
features=[]
for idx in range(len(TRAIN)): #take the features out of TRAIN
features.append(TRAIN[idx][-1])
##print(F2I[features[1][3]])
features_vec=feats_to_vec(features,F2I)
y=labels_to_y(TRAIN,L2I)
in_dim = len(F2I)
out_dim = len(L2I)
params = create_classifier(in_dim, out_dim)
# y=y.reshape(-1)
features_vec=np.array(features_vec)
train_data=np.concatenate((y,features_vec),axis=1)
#p=random.shuffle(train_data);
# train_classifier
# ...
params = create_classifier(in_dim, out_dim)
trained_params = train_classifier(train_data, 5, 0.01, params)
test classifier with test data - no labels
params - the trained params
"""
fd = open("test.pred.ll", 'w')
counter = 0
test_ans = ''
test_data = ut.read_data('test')
for label, feature in test_data:
pred = ll.predict(feats_to_vec(feature), parameters)
for l, i in ut.L2I.items():
if i == pred:
test_ans = l
counter += 1
fd.write(test_ans + "\n")
fd.close()
if __name__ == '__main__':
# write code to load the train and dev sets, set up whatever you need,
# and call train_classifier.
train_data = ut.read_data('train')
dev_data = ut.read_data('dev')
params = ll.create_classifier(len(ut.F2I), len(ut.L2I))
trained_params = train_classifier(train_data, dev_data, EPOCH, LR, params)
print 'the final params are:\nW =\n', trained_params[
0], '\nb =\n', trained_params[1]
#test(trained_params)
params: list of parameters (initial values)
"""
for I in xrange(num_iterations):
cum_loss = 0.0 # total loss in this iteration.
random.shuffle(train_data)
for label, features in train_data:
x = feats_to_vec(features) # convert features to a vector.
y = label # convert the label to number if needed.
loss, grads = ll.loss_and_gradients(x, y, params)
cum_loss += loss
params = params - learning_rate * grads
train_loss = cum_loss / len(train_data)
train_accuracy = accuracy_on_dataset(train_data, params)
dev_accuracy = accuracy_on_dataset(dev_data, params)
print I, train_loss, train_accuracy, dev_accuracy
return params
if __name__ == '__main__':
TRAIN = [(l, ut.text_to_bigrams(t)) for l, t in ut.read_data("train")]
DEV = [(l, ut.text_to_bigrams(t)) for l, t in ut.read_data("dev")]
from collections import Counter
fc = Counter()
for l, feats in TRAIN:
fc.update(feats)
params = ll.create_classifier(ut.vocab, 12)
trained_params = train_classifier(TRAIN, DEV, 10000, 0.5, params)
train_loss = cum_loss / len(train_data)
train_accuracy = accuracy_on_dataset(train_data, params)
dev_accuracy = accuracy_on_dataset(dev_data, params)
print(I, train_loss, train_accuracy, dev_accuracy)
return params
if __name__ == '__main__':
from utils import TRAIN as train_data
from utils import DEV as dev_data
from utils import TEST as test_data
from utils import I2L
num_iterations = 10
learning_rate = 1e-3
in_dim = len(F2I)
out_dim = len(L2I)
params = ll.create_classifier(in_dim, out_dim)
trained_params = train_classifier(train_data, dev_data, num_iterations, learning_rate, params)
preds = []
for features in test_data:
x = feats_to_vec(features)
preds.append(ll.predict(x, trained_params))
# with open('test.pred', 'w') as f:
# for y_hat in preds:
# f.write(f'{I2L[y_hat]}\n')
