def problem8():
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)
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)
# print("{:50} {:.4f}".format("Validation accuracy for Pegasos:", avg_peg_val_accuracy))
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, 100]
Ls = [0.01, 0.1, 0.2, 0.5, 1]
pct_tune_results = utils.tune_perceptron(Ts, *data)
avg_pct_tune_results = utils.tune_avg_perceptron(Ts, *data)
# fix values for L and T while tuning Pegasos T and L, respective
best_L = 0.01
best_T = 15
avg_peg_tune_results_T = utils.tune_pegasos_T(best_L, Ts, *data)
avg_peg_tune_results_L = utils.tune_pegasos_L(best_T, Ls, *data)
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, *avg_peg_tune_results_T)
utils.plot_tune_results('Pegasos', 'L', Ls, *avg_peg_tune_results_L)
#-------------------------------------------------------------------------------
# Section 3.13
#
# Modify the code below to extract your best features from the submission data
# and then classify it using your most accurate classifier.
#-------------------------------------------------------------------------------
# submit_texts = [sample['text'] for sample in utils.load_data('reviews_submit.tsv')]
#
