diff --git a/classification/per_label_results.csv b/classification/per_label_results.csv index d52062d..503c095 100644 --- a/classification/per_label_results.csv +++ b/classification/per_label_results.csv @@ -134,3 +134,106 @@ experiment,label,precision,recall,f1 6,"Name calling/Labeling",0.5180722891566265,0.3706896551724138,0.43216080402010054 6,"Loaded Language",0.5520833333333334,0.3925925925925926,0.4588744588744589 6,"Misrepresentation of Someone's Position (Straw Man)",0.0,0.0,0.0 +7,"Ad Hominem",0.6986899563318777,0.6530612244897959,0.6751054852320675 +7,"Appeal to authority",0.75,0.7142857142857143,0.7317073170731706 +7,"Appeal to fear/prejudice",0.26666666666666666,0.14814814814814814,0.19047619047619047 +7,"Bandwagon",0.6,0.42857142857142855,0.5 +7,"Black-and-white Fallacy/Dictatorship",0.36,0.16981132075471697,0.23076923076923075 +7,"Causal Oversimplification",0.1111111111111111,0.047619047619047616,0.06666666666666667 +7,"Distraction",0.38461538461538464,0.17857142857142858,0.24390243902439027 +7,"Doubt",0.375,0.125,0.1875 +7,"Exaggeration/Minimisation",0.375,0.1111111111111111,0.17142857142857143 +7,"Flag-waving",0.5,0.3333333333333333,0.4 +7,"Glittering generalities (Virtue)",0.44,0.3055555555555556,0.36065573770491804 +7,"Loaded Language",0.5520833333333334,0.3925925925925926,0.4588744588744589 +7,"Logos",0.7307692307692307,0.6653696498054474,0.6965376782077393 +7,"Misrepresentation of Someone's Position (Straw Man)",0.0,0.0,0.0 +7,"Name calling/Labeling",0.5180722891566265,0.3706896551724138,0.43216080402010054 +7,"Obfuscation, Intentional vagueness, Confusion",0.0,0.0,0.0 +7,"Presenting Irrelevant Data (Red Herring)",0.0,0.0,0.0 +7,"Reductio ad hitlerum",0.0,0.0,0.0 +7,"Repetition",0.5,0.21739130434782608,0.30303030303030304 +7,"Slogans",0.48484848484848486,0.32,0.38554216867469876 +7,"Smears",0.5508474576271186,0.45774647887323944,0.5 +7,"Thought-terminating cliché",0.3888888888888889,0.18421052631578946,0.25 +7,"Whataboutism",0.1111111111111111,0.047619047619047616,0.06666666666666667 +8,"Appeal to authority",0.75,0.7142857142857143,0.7317073170731706 +8,"Appeal to fear/prejudice",0.26666666666666666,0.14814814814814814,0.19047619047619047 +8,"Bandwagon",0.6,0.42857142857142855,0.5 +8,"Black-and-white Fallacy/Dictatorship",0.36,0.16981132075471697,0.23076923076923075 +8,"Causal Oversimplification",0.1111111111111111,0.047619047619047616,0.06666666666666667 +8,"Doubt",0.375,0.125,0.1875 +8,"Exaggeration/Minimisation",0.375,0.1111111111111111,0.17142857142857143 +8,"Flag-waving",0.5,0.3333333333333333,0.4 +8,"Glittering generalities (Virtue)",0.44,0.3055555555555556,0.36065573770491804 +8,"Loaded Language",0.5520833333333334,0.3925925925925926,0.4588744588744589 +8,"Misrepresentation of Someone's Position (Straw Man)",0.0,0.0,0.0 +8,"Name calling/Labeling",0.5180722891566265,0.3706896551724138,0.43216080402010054 +8,"Obfuscation, Intentional vagueness, Confusion",0.0,0.0,0.0 +8,"Presenting Irrelevant Data (Red Herring)",0.0,0.0,0.0 +8,"Reductio ad hitlerum",0.0,0.0,0.0 +8,"Repetition",0.5,0.21739130434782608,0.30303030303030304 +8,"Slogans",0.48484848484848486,0.32,0.38554216867469876 +8,"Smears",0.5508474576271186,0.45774647887323944,0.5 +8,"Thought-terminating cliché",0.3888888888888889,0.18421052631578946,0.25 +8,"Whataboutism",0.1111111111111111,0.047619047619047616,0.06666666666666667 +9,"Appeal to authority",0.7288135593220338,0.6825396825396826,0.7049180327868851 +9,"Appeal to fear/prejudice",0.23076923076923078,0.1111111111111111,0.15 +9,"Bandwagon",0.75,0.42857142857142855,0.5454545454545454 +9,"Black-and-white Fallacy/Dictatorship",0.34782608695652173,0.1509433962264151,0.2105263157894737 +9,"Causal Oversimplification",0.1111111111111111,0.047619047619047616,0.06666666666666667 +9,"Doubt",0.5714285714285714,0.16666666666666666,0.25806451612903225 +9,"Exaggeration/Minimisation",0.375,0.1111111111111111,0.17142857142857143 +9,"Flag-waving",0.4482758620689655,0.30952380952380953,0.3661971830985915 +9,"Glittering generalities (Virtue)",0.5,0.2777777777777778,0.35714285714285715 +9,"Loaded Language",0.5408163265306123,0.3925925925925926,0.4549356223175966 +9,"Misrepresentation of Someone's Position (Straw Man)",0.0,0.0,0.0 +9,"Name calling/Labeling",0.5443037974683544,0.3706896551724138,0.441025641025641 +9,"Obfuscation, Intentional vagueness, Confusion",0.0,0.0,0.0 +9,"Presenting Irrelevant Data (Red Herring)",0.0,0.0,0.0 +9,"Reductio ad hitlerum",0.0,0.0,0.0 +9,"Repetition",0.5,0.21739130434782608,0.30303030303030304 +9,"Slogans",0.5172413793103449,0.3,0.379746835443038 +9,"Smears",0.5652173913043478,0.45774647887323944,0.5058365758754865 +9,"Thought-terminating cliché",0.3888888888888889,0.18421052631578946,0.25 +9,"Whataboutism",0.14285714285714285,0.047619047619047616,0.07142857142857142 +10,"Appeal to authority",0.7068965517241379,0.6507936507936508,0.6776859504132232 +10,"Appeal to fear/prejudice",0.16666666666666666,0.037037037037037035,0.06060606060606061 +10,"Bandwagon",0.75,0.42857142857142855,0.5454545454545454 +10,"Black-and-white Fallacy/Dictatorship",0.3,0.11320754716981132,0.1643835616438356 +10,"Causal Oversimplification",0.0,0.0,0.0 +10,"Doubt",0.0,0.0,0.0 +10,"Exaggeration/Minimisation",0.5,0.1111111111111111,0.1818181818181818 +10,"Flag-waving",0.5217391304347826,0.2857142857142857,0.3692307692307692 +10,"Glittering generalities (Virtue)",0.5,0.19444444444444445,0.28 +10,"Loaded Language",0.5568181818181818,0.362962962962963,0.4394618834080717 +10,"Misrepresentation of Someone's Position (Straw Man)",0.0,0.0,0.0 +10,"Name calling/Labeling",0.5396825396825397,0.29310344827586204,0.37988826815642457 +10,"Obfuscation, Intentional vagueness, Confusion",0.0,0.0,0.0 +10,"Presenting Irrelevant Data (Red Herring)",0.0,0.0,0.0 +10,"Reductio ad hitlerum",0.0,0.0,0.0 +10,"Repetition",0.5,0.13043478260869565,0.20689655172413793 +10,"Slogans",0.5263157894736842,0.2,0.28985507246376807 +10,"Smears",0.5420560747663551,0.4084507042253521,0.465863453815261 +10,"Thought-terminating cliché",0.5,0.10526315789473684,0.17391304347826086 +10,"Whataboutism",0.3333333333333333,0.047619047619047616,0.08333333333333333 +11,"Appeal to authority",0.7857142857142857,0.6984126984126984,0.7394957983193275 +11,"Appeal to fear/prejudice",0.2727272727272727,0.1111111111111111,0.15789473684210525 +11,"Bandwagon",0.6666666666666666,0.2857142857142857,0.4 +11,"Black-and-white Fallacy/Dictatorship",0.2631578947368421,0.09433962264150944,0.13888888888888887 +11,"Causal Oversimplification",0.125,0.047619047619047616,0.06896551724137931 +11,"Doubt",0.2857142857142857,0.08333333333333333,0.12903225806451613 +11,"Exaggeration/Minimisation",0.6,0.1111111111111111,0.18750000000000003 +11,"Flag-waving",0.48,0.2857142857142857,0.3582089552238806 +11,"Glittering generalities (Virtue)",0.5,0.2777777777777778,0.35714285714285715 +11,"Loaded Language",0.5698924731182796,0.3925925925925926,0.4649122807017544 +11,"Misrepresentation of Someone's Position (Straw Man)",0.0,0.0,0.0 +11,"Name calling/Labeling",0.5606060606060606,0.31896551724137934,0.4065934065934066 +11,"Obfuscation, Intentional vagueness, Confusion",0.0,0.0,0.0 +11,"Presenting Irrelevant Data (Red Herring)",0.0,0.0,0.0 +11,"Reductio ad hitlerum",0.0,0.0,0.0 +11,"Repetition",0.6666666666666666,0.2608695652173913,0.37500000000000006 +11,"Slogans",0.5909090909090909,0.26,0.36111111111111116 +11,"Smears",0.5686274509803921,0.4084507042253521,0.47540983606557374 +11,"Thought-terminating cliché",0.35714285714285715,0.13157894736842105,0.1923076923076923 +11,"Whataboutism",0.14285714285714285,0.047619047619047616,0.07142857142857142 diff --git a/classification/results.csv b/classification/results.csv index c95f82d..b0343fd 100644 --- a/classification/results.csv +++ b/classification/results.csv @@ -5,3 +5,8 @@ 4,jhu-clsp/bernice,cls,,-,semeval_internal,dev_set,LogisticRegression,0.5415415415415415,0.622554660529344,0.479185119574845,"Black-and-white Fallacy/Dictatorship 431 16 44 9 Glittering generalities (Virtue) 450 14 25 11 Repetition 472 5 18 5 Slogans 433 17 34 16 Doubt 471 5 21 3 Name calling/Labeling 344 40 73 43 Exaggeration/Minimisation 468 5 24 3 Ad Hominem 186 69 85 160 Causal Oversimplification 471 8 20 1 Distraction 464 8 23 5 Misrepresentation of Someone's Position (Straw Man) 494 2 4 0 Flag-waving 444 14 28 14 Thought-terminating cliché 451 11 31 7 Logos 180 63 86 171 Presenting Irrelevant Data (Red Herring) 496 0 4 0 Smears 305 53 77 65 Reductio ad hitlerum 494 2 4 0 Obfuscation, Intentional vagueness, Confusion 498 0 2 0 Appeal to fear/prejudice 462 11 23 4 Loaded Language 322 43 82 53 Whataboutism 471 8 20 1 Appeal to authority 422 15 18 45 Bandwagon 491 2 4 3 ",1707070176 5,jhu-clsp/bernice,cls,,-,semeval_internal,dev_set,ClassifierChain,0.5312419645152995,0.6333537706928265,0.4574844995571302,"Exaggeration/Minimisation 470 3 24 3 Whataboutism 475 4 20 1 Bandwagon 492 1 4 3 Presenting Irrelevant Data (Red Herring) 495 1 4 0 Smears 307 51 71 71 Distraction 468 4 27 1 Thought-terminating cliché 455 7 31 7 Loaded Language 328 37 81 54 Logos 194 49 106 151 Appeal to fear/prejudice 461 12 20 7 Causal Oversimplification 475 4 20 1 Reductio ad hitlerum 496 0 4 0 Doubt 465 11 18 6 Repetition 475 2 20 3 Name calling/Labeling 348 36 76 40 Glittering generalities (Virtue) 455 9 27 9 Black-and-white Fallacy/Dictatorship 405 42 36 17 Flag-waving 442 16 23 19 Ad Hominem 207 48 114 131 Misrepresentation of Someone's Position (Straw Man) 496 0 4 0 Slogans 420 30 34 16 Appeal to authority 418 19 19 44 Obfuscation, Intentional vagueness, Confusion 498 0 2 0 ",1707077292 6,jhu-clsp/bernice,cls,,-,semeval2024,dev_set,LogisticRegression,0.4996013818761626,0.6245847176079734,0.4162976085031001,"Doubt 471 5 21 3 Presenting Irrelevant Data (Red Herring) 496 0 4 0 Slogans 433 17 34 16 Appeal to fear/prejudice 462 11 23 4 Exaggeration/Minimisation 468 5 24 3 Obfuscation, Intentional vagueness, Confusion 498 0 2 0 Causal Oversimplification 471 8 20 1 Black-and-white Fallacy/Dictatorship 431 16 44 9 Reductio ad hitlerum 494 2 4 0 Glittering generalities (Virtue) 450 14 25 11 Repetition 472 5 18 5 Thought-terminating cliché 451 11 31 7 Smears 305 53 77 65 Appeal to authority 422 15 18 45 Flag-waving 444 14 28 14 Bandwagon 491 2 4 3 Whataboutism 471 8 20 1 Name calling/Labeling 344 40 73 43 Loaded Language 322 43 82 53 Misrepresentation of Someone's Position (Straw Man) 494 2 4 0 ",1707080779 +7,jhu-clsp/bernice,cls,,-,semeval_internal,dev_set,LogisticRegression,0.5415415415415415,0.622554660529344,0.479185119574845,"Ad Hominem 186 69 85 160 Appeal to authority 422 15 18 45 Appeal to fear/prejudice 462 11 23 4 Bandwagon 491 2 4 3 Black-and-white Fallacy/Dictatorship 431 16 44 9 Causal Oversimplification 471 8 20 1 Distraction 464 8 23 5 Doubt 471 5 21 3 Exaggeration/Minimisation 468 5 24 3 Flag-waving 444 14 28 14 Glittering generalities (Virtue) 450 14 25 11 Loaded Language 322 43 82 53 Logos 180 63 86 171 Misrepresentation of Someone's Position (Straw Man) 494 2 4 0 Name calling/Labeling 344 40 73 43 Obfuscation, Intentional vagueness, Confusion 498 0 2 0 Presenting Irrelevant Data (Red Herring) 496 0 4 0 Reductio ad hitlerum 494 2 4 0 Repetition 472 5 18 5 Slogans 433 17 34 16 Smears 305 53 77 65 Thought-terminating cliché 451 11 31 7 Whataboutism 471 8 20 1 ",1713202515 +8,jhu-clsp/bernice,cls,,-,semeval2024,dev_set,LogisticRegression,0.4996013818761626,0.6245847176079734,0.4162976085031001,"Appeal to authority 422 15 18 45 Appeal to fear/prejudice 462 11 23 4 Bandwagon 491 2 4 3 Black-and-white Fallacy/Dictatorship 431 16 44 9 Causal Oversimplification 471 8 20 1 Doubt 471 5 21 3 Exaggeration/Minimisation 468 5 24 3 Flag-waving 444 14 28 14 Glittering generalities (Virtue) 450 14 25 11 Loaded Language 322 43 82 53 Misrepresentation of Someone's Position (Straw Man) 494 2 4 0 Name calling/Labeling 344 40 73 43 Obfuscation, Intentional vagueness, Confusion 498 0 2 0 Presenting Irrelevant Data (Red Herring) 496 0 4 0 Reductio ad hitlerum 494 2 4 0 Repetition 472 5 18 5 Slogans 433 17 34 16 Smears 305 53 77 65 Thought-terminating cliché 451 11 31 7 Whataboutism 471 8 20 1 ",1713204359 +9,jhu-clsp/bernice,cls,,-,semeval2024,dev_set,LogisticRegression,0.4948731786292499,0.6332872928176796,0.4061116031886625,"Appeal to authority 421 16 20 43 Appeal to fear/prejudice 463 10 24 3 Bandwagon 492 1 4 3 Black-and-white Fallacy/Dictatorship 432 15 45 8 Causal Oversimplification 471 8 20 1 Doubt 473 3 20 4 Exaggeration/Minimisation 468 5 24 3 Flag-waving 442 16 29 13 Glittering generalities (Virtue) 454 10 26 10 Loaded Language 320 45 82 53 Misrepresentation of Someone's Position (Straw Man) 495 1 4 0 Name calling/Labeling 348 36 73 43 Obfuscation, Intentional vagueness, Confusion 498 0 2 0 Presenting Irrelevant Data (Red Herring) 496 0 4 0 Reductio ad hitlerum 494 2 4 0 Repetition 472 5 18 5 Slogans 436 14 35 15 Smears 308 50 77 65 Thought-terminating cliché 451 11 31 7 Whataboutism 473 6 20 1 ",1713212203 +10,jhu-clsp/bernice,cls,,-,semeval2024,dev_set,LogisticRegression,0.4425036390101892,0.6457094307561597,0.3365810451727192,"Appeal to authority 420 17 22 41 Appeal to fear/prejudice 468 5 26 1 Bandwagon 492 1 4 3 Black-and-white Fallacy/Dictatorship 433 14 47 6 Causal Oversimplification 474 5 21 0 Doubt 474 2 24 0 Exaggeration/Minimisation 470 3 24 3 Flag-waving 447 11 30 12 Glittering generalities (Virtue) 457 7 29 7 Loaded Language 326 39 86 49 Misrepresentation of Someone's Position (Straw Man) 495 1 4 0 Name calling/Labeling 355 29 82 34 Obfuscation, Intentional vagueness, Confusion 498 0 2 0 Presenting Irrelevant Data (Red Herring) 495 1 4 0 Reductio ad hitlerum 495 1 4 0 Repetition 474 3 20 3 Slogans 441 9 40 10 Smears 309 49 84 58 Thought-terminating cliché 458 4 34 4 Whataboutism 477 2 20 1 ",1713212586 +11,jhu-clsp/bernice,cls,,-,semeval2024,dev_set,LogisticRegression,0.4709203839638622,0.6495327102803738,0.36935341009743133,"Appeal to authority 425 12 19 44 Appeal to fear/prejudice 465 8 24 3 Bandwagon 492 1 5 2 Black-and-white Fallacy/Dictatorship 433 14 48 5 Causal Oversimplification 472 7 20 1 Doubt 471 5 22 2 Exaggeration/Minimisation 471 2 24 3 Flag-waving 445 13 30 12 Glittering generalities (Virtue) 454 10 26 10 Loaded Language 325 40 82 53 Misrepresentation of Someone's Position (Straw Man) 494 2 4 0 Name calling/Labeling 355 29 79 37 Obfuscation, Intentional vagueness, Confusion 498 0 2 0 Presenting Irrelevant Data (Red Herring) 496 0 4 0 Reductio ad hitlerum 495 1 4 0 Repetition 474 3 17 6 Slogans 441 9 37 13 Smears 314 44 84 58 Thought-terminating cliché 453 9 33 5 Whataboutism 473 6 20 1 ",1713213167 diff --git a/commands.sh b/commands.sh index 4c4b330..65370b9 100644 --- a/commands.sh +++ b/commands.sh @@ -1,12 +1,6 @@ #!/bin/bash -# ft extraction using semeval base -python -m src.feature_extraction \ - --dataset semeval_internal \ - --model jhu-clsp/bernice \ - --extraction_method cls - -# semeval_internal +# semeval python -m src.classification \ --dataset semeval2024 \ --train_features feature_extraction/semeval2024/bernice/1707079469_jhu-clsp-bernice_train_features.json \ @@ -17,86 +11,7 @@ python -m src.classification \ # semeval_internal trained with train test dev python -m src.classification \ --dataset semeval_internal \ - --train_features feature_extraction/1706826974_jhu-clsp-bernice_train_features.json \ - --test_features feature_extraction/1706826974_jhu-clsp-bernice_test_features.json \ - --dev_features feature_extraction/1706826974_jhu-clsp-bernice_dev_features.json \ - --classifier LogisticRegression - -# ft extraction semeval augmented -python -m src.feature_extraction \ - --dataset semeval_augmented \ - --model xlm-roberta-base \ - --extraction_method cls - - -# semeval2024 -python -m src.classification \ - --dataset semeval2024 \ - --train_features feature_extraction/1701981179_xlm-roberta-base_train_features.json \ - --test_features feature_extraction/1701981179_xlm-roberta-base_test_features.json \ - --dev_features feature_extraction/1701981179_xlm-roberta-base_dev_features.json \ - --classifier LogisticRegression - -# using ft from bernice -python -m src.classification \ - --dataset semeval2024 \ - --train_features feature_extraction/1706662916_jhu-clsp-bernice_train_features.json \ - --test_features feature_extraction/1706662916_jhu-clsp-bernice_test_features.json \ - --dev_features feature_extraction/1706662916_jhu-clsp-bernice_dev_features.json \ - --classifier LogisticRegression - -# using ft from bertweet-base -python -m src.classification \ - --dataset semeval2024 \ - --train_features feature_extraction/1706660251_vinai-bertweet-base_train_features.json \ - --test_features feature_extraction/1706660251_vinai-bertweet-base_test_features.json \ - --dev_features feature_extraction/1706660251_vinai-bertweet-base_dev_features.json \ - --classifier LogisticRegression - -# augmented from ptc reductio -python -m src.classification \ - --dataset semeval_augmented \ - --train_features feature_extraction/1706560674_xlm-roberta-base_train_features.json \ - --test_features feature_extraction/1706560674_xlm-roberta-base_test_features.json \ - --dev_features feature_extraction/1706560674_xlm-roberta-base_dev_features.json \ - --classifier LogisticRegression - -# augmented from 2301 -python -m src.classification \ - --dataset semeval_augmented \ - --train_features feature_extraction/1706492805_xlm-roberta-base_train_features.json \ - --test_features feature_extraction/1706492805_xlm-roberta-base_test_features.json \ - --dev_features feature_extraction/1706492805_xlm-roberta-base_dev_features.json \ - --classifier LogisticRegression - -# augmented label preserving Reductio ad Hitlerum -python -m src.classification \ - --dataset semeval_augmented \ - --train_features feature_extraction/1706486057_xlm-roberta-base_train_features.json \ - --test_features feature_extraction/1706486057_xlm-roberta-base_test_features.json \ - --dev_features feature_extraction/1706486057_xlm-roberta-base_dev_features.json \ - --classifier LogisticRegression - -# augmented Reductio ad Hitlerum -python -m src.classification \ - --dataset semeval_augmented \ - --train_features feature_extraction/1706482884_xlm-roberta-base_train_features.json \ - --test_features feature_extraction/1706482884_xlm-roberta-base_test_features.json \ - --dev_features feature_extraction/1706482884_xlm-roberta-base_dev_features.json \ - --classifier LogisticRegression - -# augmented label preserving Smears -python -m src.classification \ - --dataset semeval_augmented \ - --train_features feature_extraction/1706474866_xlm-roberta-base_train_features.json \ - --test_features feature_extraction/1706474866_xlm-roberta-base_test_features.json \ - --dev_features feature_extraction/1706474866_xlm-roberta-base_dev_features.json \ - --classifier LogisticRegression - -# augmented Smears -python -m src.classification \ - --dataset semeval_augmented \ - --train_features feature_extraction/1706466400_xlm-roberta-base_train_features.json \ - --test_features feature_extraction/1706466400_xlm-roberta-base_test_features.json \ - --dev_features feature_extraction/1706466400_xlm-roberta-base_dev_features.json \ - --classifier LogisticRegression + --train_features feature_extraction/semeval_internal/bernice/1707069752_jhu-clsp-bernice_train_features.json \ + --test_features feature_extraction/semeval_internal/bernice/1707069752_jhu-clsp-bernice_test_features.json \ + --dev_features feature_extraction/semeval_internal/bernice/1707069752_jhu-clsp-bernice_dev_features.json \ + --classifier LogisticRegression \ No newline at end of file diff --git a/multilabel_test_set_submission_1707956219_dev_labeled_predictions.json b/multilabel_test_set_submission_1707956219_dev_labeled_predictions.json new file mode 100644 index 0000000..c473619 --- /dev/null +++ b/multilabel_test_set_submission_1707956219_dev_labeled_predictions.json @@ -0,0 +1,8729 @@ +[ + { + "id": "79056", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "79095", + "labels": [ + "Loaded Language", + "Smears" + ] + }, + { + "id": "63268", + "labels": [ + "Appeal to authority", + "Flag-waving" + ] + }, + { + "id": "67985", + "labels": [] + }, + { + "id": "64747", + "labels": [] + }, + { + "id": "79845", + "labels": [ + "Flag-waving" + ] + }, + { + "id": "65953", + "labels": [] + }, + { + "id": "67431", + "labels": [ + "Repetition" + ] + }, + { + "id": "66132", + "labels": [ + "Loaded Language" + ] + }, + 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Fallacy/Dictatorship" + ] + }, + { + "id": "70433", + "labels": [ + "Appeal to authority", + "Thought-terminating clich\u00e9" + ] + }, + { + "id": "79821", + "labels": [] + }, + { + "id": "76038", + "labels": [] + }, + { + "id": "64251", + "labels": [] + }, + { + "id": "68462", + "labels": [] + }, + { + "id": "68471", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "78300", + "labels": [ + "Smears" + ] + }, + { + "id": "67560", + "labels": [] + }, + { + "id": "70485", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "78292", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "70507", + "labels": [ + "Smears" + ] + }, + { + "id": "71106", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "67090", + "labels": [ + "Loaded Language", + "Smears" + ] + }, + { + "id": "78272", + "labels": [ + "Flag-waving", + "Loaded Language", + "Name calling/Labeling" + ] + }, + { + "id": "68474", + "labels": [] + }, + { + "id": "67069", + "labels": [ + "Smears" + ] + }, + { + "id": "68453", + "labels": [ + "Slogans" + ] + }, + { + "id": "65934", + "labels": [ + "Slogans" + ] + }, + { + "id": "69057", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "76957", + "labels": [ + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "67473", + "labels": [] + }, + { + "id": "79113", + "labels": [] + }, + { + "id": "69079", + "labels": [ + "Name calling/Labeling", + "Repetition", + "Smears", + "Thought-terminating clich\u00e9" + ] + }, + { + "id": "70479", + "labels": [ + "Appeal to authority", + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "67083", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "76911", + "labels": [ + "Smears" + ] + }, + { + "id": "64171", + "labels": [ + "Slogans" + ] + }, + { + "id": "74952", + "labels": [ + "Appeal to authority", + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "77906", + "labels": [ + "Appeal to authority", + "Glittering generalities (Virtue)" + ] + }, + { + "id": "69053", + "labels": [] + }, + { + "id": "67059", + "labels": [] + }, + { + "id": "71360", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "76963", + "labels": [] + }, + { + "id": "71135", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "74969", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "71385", + "labels": [ + "Glittering generalities (Virtue)" + ] + }, + { + "id": "68510", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "74956", + "labels": [ + "Appeal to authority", + "Loaded Language" + ] + }, + { + "id": "71333", + "labels": [] + }, + { + "id": "68517", + "labels": [ + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "79117", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "78824", + "labels": [] + }, + { + "id": "78746", + "labels": [ + "Flag-waving", + "Slogans" + ] + }, + { + "id": "79764", + "labels": [ + "Smears" + ] + }, + { + "id": "78289", + "labels": [] + }, + { + "id": "67079", + "labels": [] + }, + { + "id": "69077", + "labels": [] + }, + { + "id": "71388", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "70427", + "labels": [ + "Appeal to authority", + "Loaded Language", + "Smears", + "Thought-terminating clich\u00e9" + ] + }, + { + "id": "79136", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "71377", + "labels": [] + }, + { + "id": "76097", + "labels": [] + }, + { + "id": "76978", + "labels": [ + "Loaded Language", + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "78301", + "labels": [ + "Smears" + ] + }, + { + "id": "78353", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "67527", + "labels": [] + }, + { + "id": "64231", + "labels": [] + }, + { + "id": "70482", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "68465", + "labels": [ + "Loaded Language", + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "71066", + "labels": [ + "Smears" + ] + }, + { + "id": "67133", + "labels": [] + }, + { + "id": "64247", + "labels": [] + }, + { + "id": "78823", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "67487", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "64191", + "labels": [ + "Appeal to authority", + "Smears" + ] + }, + { + "id": "74970", + "labels": [ + "Appeal to authority", + "Smears" + ] + }, + { + "id": "67504", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "75019", + "labels": [] + }, + { + "id": "76035", + "labels": [ + "Slogans" + ] + }, + { + "id": "67507", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "79830", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "74978", + "labels": [ + "Smears" + ] + }, + { + "id": "69019", + "labels": [] + }, + { + "id": "77967", + "labels": [ + "Glittering generalities (Virtue)" + ] + }, + { + "id": "78781", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "76058", + "labels": [ + "Loaded Language", + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "71382", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "79797", + "labels": [] + }, + { + "id": "67057", + "labels": [ + "Loaded Language", + "Name calling/Labeling", + "Repetition", + "Smears" + ] + }, + { + "id": "71313", + "labels": [ + "Smears" + ] + }, + { + "id": "69054", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "69024", + "labels": [] + }, + { + "id": "69033", + "labels": [ + "Smears" + ] + }, + { + "id": "79776", + "labels": [ + "Smears" + ] + }, + { + "id": "76927", + "labels": [ + "Appeal to authority", + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "67512", + "labels": [] + }, + { + "id": "67089", + "labels": [ + "Smears" + ] + }, + { + "id": "79161", + "labels": [ + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "65909", + "labels": [ + "Slogans" + ] + }, + { + "id": "67547", + "labels": [ + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "71389", + "labels": [] + }, + { + "id": "69114", + "labels": [] + }, + { + "id": "67500", + "labels": [ + "Appeal to authority", + "Loaded Language", + "Thought-terminating clich\u00e9" + ] + }, + { + "id": "70493", + "labels": [ + "Loaded Language", + "Name calling/Labeling" + ] + }, + { + "id": "76929", + "labels": [ + "Loaded Language", + "Repetition" + ] + }, + { + "id": "67092", + "labels": [ + "Repetition" + ] + }, + { + "id": "64232", + "labels": [] + }, + { + "id": "71112", + "labels": [ + "Smears" + ] + }, + { + "id": "67555", + "labels": [ + "Loaded Language", + "Smears" + ] + }, + { + "id": "67147", + "labels": [ + "Flag-waving", + "Loaded Language" + ] + }, + { + "id": "71393", + "labels": [ + "Name calling/Labeling", + "Smears" + ] + }, + { + "id": "68515", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "68487", + "labels": [ + "Glittering generalities (Virtue)" + ] + }, + { + "id": "71043", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "76920", + "labels": [ + "Smears" + ] + }, + { + "id": "78293", + "labels": [ + "Appeal to authority", + "Loaded Language" + ] + }, + { + "id": "76013", + "labels": [ + "Appeal to authority", + "Loaded Language" + ] + }, + { + "id": "79133", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "64227", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "65855", + "labels": [ + "Smears" + ] + }, + { + "id": "69088", + "labels": [] + }, + { + "id": "65918", + "labels": [] + }, + { + "id": "68443", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "78756", + "labels": [ + "Glittering generalities (Virtue)" + ] + }, + { + "id": "76934", + "labels": [ + "Flag-waving", + "Glittering generalities (Virtue)" + ] + }, + { + "id": "71084", + "labels": [] + }, + { + "id": "79142", + "labels": [] + }, + { + "id": "71089", + "labels": [ + "Loaded Language" + ] + }, + { + "id": "68484", + "labels": [] + }, + { + "id": "65323", + "labels": [ + "Smears" + ] + }, + { + "id": "71081", + "labels": [ + "Name calling/Labeling" + ] + }, + { + "id": "64258", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "77878", + "labels": [ + "Appeal to authority", + "Flag-waving", + "Loaded Language" + ] + }, + { + "id": "64216", + "labels": [ + "Appeal to authority" + ] + }, + { + "id": "76078", + "labels": [] + }, + { + "id": "67087", + "labels": [ + "Loaded Language", + "Repetition", + "Thought-terminating clich\u00e9" + ] + }, + { + "id": "67485", + "labels": [] + }, + { + "id": "71054", + "labels": [ + "Name calling/Labeling", + "Smears" + ] + } +] \ No newline at end of file diff --git a/src/classification.py b/src/classification.py index 0a0a78c..5608012 100644 --- a/src/classification.py +++ b/src/classification.py @@ -1,3 +1,4 @@ +#%% import argparse from argparse import Namespace import json @@ -23,6 +24,7 @@ from src.subtask_1_2a import get_dag, get_dag_labels, get_dag_parents, get_leaf_parents from imblearn.over_sampling import RandomOverSampler, SMOTE from copy import deepcopy +from src.mlsmote import MLSMOTE OUTPUT_DIR = f"{get_workdir()}/classification" GOLD_DIR = f"{get_workdir()}/dataset/semeval2024/subtask1" @@ -113,8 +115,16 @@ def classify(args: Namespace): train_ft, test_ft, dev_ft = map( load_features_array, [ train_ft_info["features"], test_ft_info["features"], dev_ft_info["features"]]) - print("Features Lengths", len(train_ft), len(test_ft), len(dev_ft)) + + #%% + # Using MLSMOTE to oversample the training data + train_ft, train_labels = MLSMOTE(pd.DataFrame(train_ft), pd.DataFrame(train_labels), n_sample=1000) + train_ft, train_labels = train_ft.to_numpy(), train_labels.to_numpy() + print("Features Lengths", len(train_ft), len(test_ft), len(dev_ft)) + print(train_ft) + print(type(train_labels)) + #%% if args.concat_train_dev: print("Concatenating train and dev features arrays") train_ft = np.concatenate((train_ft, dev_ft)) @@ -221,7 +231,7 @@ def classify(args: Namespace): max_iter=600, multi_class="multinomial"), labels=labels[0], - oversampler=oversamplers + oversampler= None # oversamplers ) elif args.classifier == "GradientBoostingClassifier": clf = BinaryRelevance( diff --git a/src/data.py b/src/data.py index be0eb52..f911503 100644 --- a/src/data.py +++ b/src/data.py @@ -95,7 +95,7 @@ def _load_semeval_internal() -> List[pd.DataFrame]: train = pd.DataFrame().from_records(json.load(f)) with open(f"{DATASET_DIR}/semeval2024/subtask1/validation.json", "r") as f: validation = pd.DataFrame().from_records(json.load(f)) - with open(f"{DATASET_DIR}/semeval2024/subtask1/test_unlabeled.json", "r") as f: + with open(f"{DATASET_DIR}/semeval2024/subtask1/dev_unlabeled.json", "r") as f: dev_unlabeled = pd.DataFrame().from_records(json.load(f)) # with open(f"{DATASET_DIR}/semeval2024/subtask1/test_unlabeled.json", "r") as f: # test_unlabeled = pd.DataFrame().from_records(json.load(f)) diff --git a/src/mlsmote.py b/src/mlsmote.py new file mode 100644 index 0000000..a11f442 --- /dev/null +++ b/src/mlsmote.py @@ -0,0 +1,142 @@ +# -*- coding: utf-8 -*- +# Importing required Library + +# Code adapted from +# https://github.com/niteshsukhwani/MLSMOTE + + +import numpy as np +import pandas as pd +import random +from sklearn.datasets import make_classification +from sklearn.neighbors import NearestNeighbors + +def create_dataset(n_sample=1000): + ''' + Create a unevenly distributed sample data set multilabel + classification using make_classification function + + args + nsample: int, Number of sample to be created + + return + X: pandas.DataFrame, feature vector dataframe with 10 features + y: pandas.DataFrame, target vector dataframe with 5 labels + ''' + X, y = make_classification(n_classes=5, class_sep=2, + weights=[0.1,0.025, 0.205, 0.008, 0.9], n_informative=3, n_redundant=1, flip_y=0, + n_features=10, n_clusters_per_class=1, n_samples=1000, random_state=10) + y = pd.get_dummies(y, prefix='class') + return pd.DataFrame(X), y + +def get_tail_label(df): + """ + Give tail label colums of the given target dataframe + + args + df: pandas.DataFrame, target label df whose tail label has to identified + + return + tail_label: list, a list containing column name of all the tail label + """ + columns = df.columns + n = len(columns) + irpl = np.zeros(n) + for column in range(n): + irpl[column] = df[columns[column]].value_counts()[1] + irpl = max(irpl)/irpl + mir = np.average(irpl) + tail_label = [] + for i in range(n): + if irpl[i] > mir: + tail_label.append(columns[i]) + return tail_label + +def get_index(df): + """ + give the index of all tail_label rows + args + df: pandas.DataFrame, target label df from which index for tail label has to identified + + return + index: list, a list containing index number of all the tail label + """ + tail_labels = get_tail_label(df) + index = set() + for tail_label in tail_labels: + sub_index = set(df[df[tail_label]==1].index) + index = index.union(sub_index) + return list(index) + +def get_minority_instace(X, y): + """ + Give minority dataframe containing all the tail labels + + args + X: pandas.DataFrame, the feature vector dataframe + y: pandas.DataFrame, the target vector dataframe + + return + X_sub: pandas.DataFrame, the feature vector minority dataframe + y_sub: pandas.DataFrame, the target vector minority dataframe + """ + index = get_index(y) + X_sub = X[X.index.isin(index)].reset_index(drop = True) + y_sub = y[y.index.isin(index)].reset_index(drop = True) + return X_sub, y_sub + +def nearest_neighbour(X): + """ + Give index of 5 nearest neighbor of all the instance + + args + X: np.array, array whose nearest neighbor has to find + + return + indices: list of list, index of 5 NN of each element in X + """ + nbs=NearestNeighbors(n_neighbors=5,metric='euclidean',algorithm='kd_tree').fit(X) + euclidean,indices= nbs.kneighbors(X) + return indices + +def MLSMOTE(X,y, n_sample): + """ + Give the augmented data using MLSMOTE algorithm + + args + X: pandas.DataFrame, input vector DataFrame + y: pandas.DataFrame, feature vector dataframe + n_sample: int, number of newly generated sample + + return + new_X: pandas.DataFrame, augmented feature vector data + target: pandas.DataFrame, augmented target vector data + """ + indices2 = nearest_neighbour(X) + n = len(indices2) + new_X = np.zeros((n_sample, X.shape[1])) + target = np.zeros((n_sample, y.shape[1])) + for i in range(n_sample): + reference = random.randint(0,n-1) + neighbour = random.choice(indices2[reference,1:]) + all_point = indices2[reference] + nn_df = y[y.index.isin(all_point)] + ser = nn_df.sum(axis = 0, skipna = True) + target[i] = np.array([1 if val>2 else 0 for val in ser]) + ratio = random.random() + gap = X.loc[reference,:] - X.loc[neighbour,:] + new_X[i] = np.array(X.loc[reference,:] + ratio * gap) + new_X = pd.DataFrame(new_X, columns=X.columns) + target = pd.DataFrame(target, columns=y.columns) + new_X = pd.concat([X, new_X], axis=0) + target = pd.concat([y, target], axis=0) + return new_X, target + +if __name__=='__main__': + """ + main function to use the MLSMOTE + """ + X, y = create_dataset() #Creating a Dataframe + X_sub, y_sub = get_minority_instace(X, y) #Getting minority instance of that datframe + X_res,y_res =MLSMOTE(X_sub, y_sub, 100) #Applying MLSMOTE to augment the dataframe + print("hi") \ No newline at end of file diff --git a/src/utils/br.py b/src/utils/br.py index 4e539ab..14966d9 100644 --- a/src/utils/br.py +++ b/src/utils/br.py @@ -109,7 +109,7 @@ def evaluate_per_label(y_true:np.ndarray, y_pred:np.ndarray, labels:list): # Evaluate prediction for each label for idx in range(num_labels): preds_for_label = y_pred.transpose()[idx] - precision, recall, f1, _ = precision_recall_fscore_support(y_true.transpose()[idx], preds_for_label) + precision, recall, f1, _ = precision_recall_fscore_support(y_true.transpose()[idx], preds_for_label, zero_division=0) _, precision = precision _, recall = recall _, f1 = f1