From 7e009c5d7e1d28f879ed4274f72ab300139964ae Mon Sep 17 00:00:00 2001 From: Evgeniya Ovchinnikova Date: Tue, 22 May 2018 19:36:11 +0200 Subject: [PATCH] to amend --- data/Mohler/processed/doc2vecbow.pickle | Bin 0 -> 170992 bytes .../grading_correlation3skipvsROUGE.txt | 29 + .../grading_correlationBLEUvsROUGE.txt | 22 + .../grading_correlationdoc2vec PV-DBOW.txt | 1 + .../grading_correlationdoc2vec PV-DM.txt | 1 + .../processed/grading_correlationdoc2vec.txt | 1141 +++++++++++++++++ img/all.png | Bin 0 -> 191188 bytes img/all_heuristics-3.png | Bin 0 -> 181169 bytes img/all_heuristics-4.png | Bin 0 -> 181187 bytes img/all_heuristics-4_kskip.png | Bin 0 -> 180920 bytes img/all_heuristics-5.png | Bin 0 -> 181621 bytes img/all_heuristics-5_kskip.png | Bin 0 -> 179810 bytes img/all_heuristics3.png | Bin 0 -> 182042 bytes img/all_heuristics3_kskip.png | Bin 0 -> 180073 bytes img/all_heuristics4.png | Bin 0 -> 182042 bytes img/all_heuristics4_kskip.png | Bin 0 -> 180073 bytes img/all_heuristics5.png | Bin 0 -> 179198 bytes img/all_heuristics5_kskip.png | Bin 0 -> 180364 bytes .../doc2vecbow_confusion_nonnorm.png | Bin 0 -> 31370 bytes img/confusions/doc2vecbow_confusion_norm.png | Bin 0 -> 35659 bytes src/doc2vec_vectorization.py | 2 +- src/main.py | 118 +- 22 files changed, 1257 insertions(+), 57 deletions(-) create mode 100644 data/Mohler/processed/doc2vecbow.pickle create mode 100644 data/Mohler/processed/grading_correlation3skipvsROUGE.txt create mode 100644 data/Mohler/processed/grading_correlationBLEUvsROUGE.txt create mode 100644 data/Mohler/processed/grading_correlationdoc2vec PV-DBOW.txt create mode 100644 data/Mohler/processed/grading_correlationdoc2vec PV-DM.txt create mode 100644 img/all.png create mode 100644 img/all_heuristics-3.png create mode 100644 img/all_heuristics-4.png create mode 100644 img/all_heuristics-4_kskip.png create mode 100644 img/all_heuristics-5.png create mode 100644 img/all_heuristics-5_kskip.png create mode 100644 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z5}nj2bVD3wM`3RRERIOX$7*kf%+m1^+Y*QAQ@av^S13#qhEOlxNwRMQ7nA+d(#vE? z{Qr 5): + features_to_train = features_kskip[question_index][0:split_position] + else: + features_to_train = features[question_index][0:split_position] #print(features_to_train) grades_to_classify = answer_array[question_index][1][0:split_position] # for SVMs only since they need at least several classes @@ -358,34 +364,35 @@ def main(): grades_to_classify.append(596) clf.fit(features_to_train, grades_to_classify) - features_confidence = get_inconfidence_level( - features[question_index][split_position:len(answer_array[question_index][1])], - features[question_index][0:split_position], names[feature_number]) - - test_features = [] - test_grades = [] - if(names[feature_number] == "combined ROUGE KPCA"): - for conf in range(len(features_confidence)): - if features_confidence[conf] < 0.005: - test_features.append(features[question_index][split_position:len(answer_array[question_index][1])][ conf]) - test_grades.append(answer_array[question_index][1][ - split_position:len(answer_array[question_index][1])][conf]) - else: - print("discarded") - if (test_features == []): - score = 1 - else: - score = clf.score(test_features, test_grades) + # features_confidence = get_inconfidence_level( + # features[question_index][split_position:len(answer_array[question_index][1])], + # features[question_index][0:split_position], names[feature_number]) + + # test_features = [] + # test_grades = [] + # if(names[feature_number] == "combined ROUGE KPCA"): + # for conf in range(len(features_confidence)): + # if features_confidence[conf] < 0.005: + # test_features.append(features[question_index][split_position:len(answer_array[question_index][1])][ conf]) + # test_grades.append(answer_array[question_index][1][ + # split_position:len(answer_array[question_index][1])][conf]) + # else: + # print("discarded") + # if (test_features == []): + # score = 1 + # else: + # score = clf.score(test_features, test_grades) + # else: + if (names[feature_number] == "ROUGE-1" and len(correct_answers[question_index]) > 5): + score = clf.score(features_kskip[question_index][split_position:len(answer_array[question_index][1])], answer_array[question_index][1][split_position:len(answer_array[question_index][1])]) else: - score = clf.score(features[question_index][split_position:len(answer_array[question_index][1])], - answer_array[question_index][1][split_position:len(answer_array[question_index][1])]) - + score = clf.score(features[question_index][split_position:len(answer_array[question_index][1])],answer_array[question_index][1][split_position:len(answer_array[question_index][1])]) # if (split_position == 13): # prediction = clf.predict(features[question_index][split_position:len(answer_array[ question_index][1])]) - # for item in range(len(prediction)): - # all_predictions.append(prediction[item]) - # all_grades.append(answer_array[question_index][1][split_position:len(answer_array[ question_index][1])][item]) - # + # for item in range(len(prediction)): + # all_predictions.append(prediction[item]) + # all_grades.append(answer_array[question_index][1][split_position:len(answer_array[ question_index][1])][item]) + # grading_correlation_file.writelines("correct answer and question\n") # grading_correlation_file.writelines(questions[question_index] + "\n") # grading_correlation_file.writelines(str(correct_answers_raw[question_index]) + "\n") @@ -410,26 +417,25 @@ def main(): scores_for_position.append(scores_for_question) average_score = sum(scores) / len(scores) average_scores.append(average_score) - if (names[feature_number] == "combined ROUGE KPCA"): - print(average_scores) # plt.boxplot(scores_for_position, positions=split_positions, sym='', widths=0.6) average_feature_scores.append(average_scores) grading_correlation_file.close() - # all_predictions = np.asarray(all_predictions) - # all_grades = np.asarray(all_grades) - # cnf_matrix = confusion_matrix(np.asarray(all_predictions).flatten(), np.asarray(all_grades).flatten(), labels=['0','1', '2', '3', '4', '5']) - # np.set_printoptions(precision=2) + # if (names[feature_number] == "doc2vec PV-DBOW"): + # all_predictions = np.asarray(all_predictions) + # all_grades = np.asarray(all_grades) + # cnf_matrix = confusion_matrix(np.asarray(all_predictions).flatten(), np.asarray(all_grades).flatten(), labels=class_names) + # np.set_printoptions(precision=2) # - # # Plot non-normalized confusion matrix - # plt.figure() - # plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix, without normalization for ' + names[feature_number]) + # # Plot non-normalized confusion matrix + # plt.figure() + # plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix, without normalization for ' + names[feature_number]) # - # # Plot normalized confusion matrix - # plt.figure() - # plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True, - # title='Normalized confusion matrix for ' + names[feature_number]) + # # Plot normalized confusion matrix + # plt.figure() + # plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True, + # title='Normalized confusion matrix for ' + names[feature_number]) - plt.show() + #plt.show() np.set_printoptions(precision=2) plt.ylabel('HCC') plt.xlabel('Split position')