Aaa

.~lock.Tabela de simiilaridade.xlsx#

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+,raime,raime-K45A,10.01.2022 01:54,file:///home/raime/.config/libreoffice/4;

200.txt

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+131,181,22,172,144,92,97,187,58,93,6,70,106,68,153,168,179,199,29,46,9,142,134,88,193,110,26,32,117,112,17,39,166,13,94,138,109,147,51,101,59,188,116,5,170,99,100,167,180,146,65,1,104,43,38,184,123,171,137,162,71,44,95,174,12,7,54,152,21,47,28,176,34,2,132,118,42,189,150,14,165,41,192,45,82,128,63,57,197,160,53,75,108,135,121,159,183,67,169,50,87,69,89,196,115,19,148,96,86,11,8,60,33,173,78,4,119,105,182,127,177,30,186,40,49,178,76,157,161,73,164,151,31,74,191,27,125,198,81,20,155,114,139,36,61,56,145,48,16,83,62,85,126,0,102,23,3,140,15,195,133,113,190,141,52,163,156,80,111,90,175,143,120,84,18,25,79,37,154,136,64,158,24,185,72,35,129,55,149,91,122,77,103,124,130,66,10,107,194,98

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.
+#-------------------------------------------------------------------------------
+
+# Your code here
+
+#-------------------------------------------------------------------------------
+# Assign to best_theta, the weights (and not the bias!) learned by your most
+# accurate algorithm with the optimal choice of hyperparameters.
+#-------------------------------------------------------------------------------
+
+# best_theta = None # Your code here
+# 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])