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main.py
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import project1 as p1
import utils
import numpy as np
#-------------------------------------------------------------------------------
# Data loading. There is no need to edit code in this section.
#-------------------------------------------------------------------------------
train_data = utils.load_data('reviews_train.tsv')
val_data = utils.load_data('reviews_val.tsv')
test_data = utils.load_data('reviews_test.tsv')
train_texts, train_labels = zip(*((sample['text'], sample['sentiment']) for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment']) for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment']) for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#-------------------------------------------------------------------------------
# Problem 5
#-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
T = 10
L = 0.2
thetas_perceptron = p1.perceptron(toy_features, toy_labels, T)
thetas_avg_perceptron = p1.average_perceptron(toy_features, toy_labels, T)
thetas_pegasos = p1.pegasos(toy_features, toy_labels, T, L)
def plot_toy_results(algo_name, thetas):
print('theta for', algo_name, 'is', ', '.join(map(str,list(thetas[0]))))
print('theta_0 for', algo_name, 'is', str(thetas[1]))
#utils.plot_toy_data(algo_name, toy_features, toy_labels, thetas)
plot_toy_results('Perceptron', thetas_perceptron)
plot_toy_results('Average Perceptron', thetas_avg_perceptron)
plot_toy_results('Pegasos', thetas_pegasos)
#-------------------------------------------------------------------------------
# Problem 7
#-------------------------------------------------------------------------------
T = 10
L = 0.01
pct_train_accuracy, pct_val_accuracy = \
p1.classifier_accuracy(p1.perceptron, train_bow_features,val_bow_features,train_labels,val_labels,T=T)
print("{:35} {:.4f}".format("Training accuracy for perceptron:", pct_train_accuracy))
print("{:35} {:.4f}".format("Validation accuracy for perceptron:", pct_val_accuracy))
avg_pct_train_accuracy, avg_pct_val_accuracy = \
p1.classifier_accuracy(p1.average_perceptron, train_bow_features,val_bow_features,train_labels,val_labels,T=T)
print("{:43} {:.4f}".format("Training accuracy for average perceptron:", avg_pct_train_accuracy))
print("{:43} {:.4f}".format("Validation accuracy for average perceptron:", avg_pct_val_accuracy))
avg_peg_train_accuracy, avg_peg_val_accuracy = \
p1.classifier_accuracy(p1.pegasos, train_bow_features,val_bow_features,train_labels,val_labels,T=T,L=L)
print("{:50} {:.4f}".format("Training accuracy for Pegasos:", avg_peg_train_accuracy))
print("{:50} {:.4f}".format("Validation accuracy for Pegasos:", avg_peg_val_accuracy))
#-------------------------------------------------------------------------------
# Problem 8
#-------------------------------------------------------------------------------
data = (train_bow_features, train_labels, val_bow_features, val_labels)
# values of T and lambda to try
Ts = [1, 5, 10, 15, 25, 50]
Ls = [0.001, 0.01, 0.1, 1, 10]
pct_tune_results = utils.tune_perceptron(Ts, *data)
print('perceptron valid:', list(zip(Ts, pct_tune_results[1])))
print('best = {:.4f}, T={:.4f}'.format(np.max(pct_tune_results[1]), Ts[np.argmax(pct_tune_results[1])]))
avg_pct_tune_results = utils.tune_avg_perceptron(Ts, *data)
print('avg perceptron valid:', list(zip(Ts, avg_pct_tune_results[1])))
print('best = {:.4f}, T={:.4f}'.format(np.max(avg_pct_tune_results[1]), Ts[np.argmax(avg_pct_tune_results[1])]))
# fix values for L and T while tuning Pegasos T and L, respective
fix_L = 0.01
peg_tune_results_T = utils.tune_pegasos_T(fix_L, Ts, *data)
print('Pegasos valid: tune T', list(zip(Ts, peg_tune_results_T[1])))
print('best = {:.4f}, T={:.4f}'.format(np.max(peg_tune_results_T[1]), Ts[np.argmax(peg_tune_results_T[1])]))
fix_T = Ts[np.argmax(peg_tune_results_T[1])]
peg_tune_results_L = utils.tune_pegasos_L(fix_T, Ls, *data)
print('Pegasos valid: tune L', list(zip(Ls, peg_tune_results_L[1])))
print('best = {:.4f}, L={:.4f}'.format(np.max(peg_tune_results_L[1]), Ls[np.argmax(peg_tune_results_L[1])]))
utils.plot_tune_results('Perceptron', 'T', Ts, *pct_tune_results)
utils.plot_tune_results('Avg Perceptron', 'T', Ts, *avg_pct_tune_results)
utils.plot_tune_results('Pegasos', 'T', Ts, *peg_tune_results_T)
utils.plot_tune_results('Pegasos', 'L', Ls, *peg_tune_results_L)
#-------------------------------------------------------------------------------
# Use the best method (perceptron, average perceptron or Pegasos) along with
# the optimal hyperparameters according to validation accuracies to test
# against the test dataset. The test data has been provided as
# test_bow_features and test_labels.
#-------------------------------------------------------------------------------
T = 25
L = 0.01
peg_train_accuracy, peg_test_accuracy = \
p1.classifier_accuracy(p1.pegasos, train_bow_features,test_bow_features,train_labels,test_labels,T=T, L=L)
print('making prediction using Pegasos, T =', T, 'L =', L)
print("{:43} {:.4f}".format("Training accuracy for Pegasos:", peg_train_accuracy))
print("{:43} {:.4f}".format("Testing accuracy for Pegasos:", peg_test_accuracy))
#-------------------------------------------------------------------------------
# Assign to best_theta, the weights (and not the bias!) learned by your most
# accurate algorithm with the optimal choice of hyperparameters.
#-------------------------------------------------------------------------------
best_theta, best_theta_0 = p1.pegasos(train_bow_features,train_labels,T=T, L=L)
wordlist = [word for (idx, word) in sorted(zip(dictionary.values(), dictionary.keys()))]
sorted_word_features = utils.most_explanatory_word(best_theta, wordlist)
print("Most Explanatory Word Features")
print(sorted_word_features[:10])
#-------------------------------------------------------------------------------
print('len(dictionary) before:', len(dictionary))
train_final_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_final_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_final_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
T = 25
L = 0.01
peg_train_accuracy, peg_test_accuracy = \
p1.classifier_accuracy(p1.pegasos, train_final_features,test_final_features,train_labels,test_labels,T=T, L=L)
print('[normal feataures]: making prediction using PEGASOS, T =', T)
print("{:43} {:.4f}".format("Training accuracy for PEGASOS:", peg_train_accuracy))
print("{:43} {:.4f}".format("Testing accuracy for PEGASOS:", peg_test_accuracy))
dictionary = p1.bag_of_words(train_texts, remove_stopword=True)
print('len(dictionary) after:', len(dictionary))
train_final_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_final_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_final_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
peg_train_accuracy, peg_test_accuracy = \
p1.classifier_accuracy(p1.pegasos, train_final_features,test_final_features,train_labels,test_labels,T=T, L=L)
print('[Stopword feataures]: making prediction using PEGASOS, T =', T)
print("{:43} {:.4f}".format("Training accuracy for PEGASOS:", peg_train_accuracy))
print("{:43} {:.4f}".format("Testing accuracy for PEGASOS:", peg_test_accuracy))
train_final_features = p1.extract_bow_feature_vectors(train_texts, dictionary, binarize=False)
val_final_features = p1.extract_bow_feature_vectors(val_texts, dictionary, binarize=False)
test_final_features = p1.extract_bow_feature_vectors(test_texts, dictionary, binarize=False)
peg_train_accuracy, peg_test_accuracy = \
p1.classifier_accuracy(p1.pegasos, train_final_features,test_final_features,train_labels,test_labels,T=T, L=L)
print('[Stopword w/o binarize feataures]: making prediction using PEGASOS, T =', T)
print("{:43} {:.4f}".format("Training accuracy for PEGASOS:", peg_train_accuracy))
print("{:43} {:.4f}".format("Testing accuracy for PEGASOS:", peg_test_accuracy))