From dd6b378aefa53606b9c055a312d189da5304ddf3 Mon Sep 17 00:00:00 2001 From: gicraveiro Date: Sat, 5 Feb 2022 16:05:23 +0100 Subject: [PATCH] rerun unigram adaboost to confirm results were aligned --- AIclassifier.py | 38 +++++++++--------- .../1Label_confusion_matrix_NonNorm.png | Bin 47048 -> 47714 bytes .../1Label_confusion_matrix_NormTrue.png | Bin 51868 -> 53518 bytes .../1labelPredictionsStatsTest.txt | 18 ++++----- .../1labelRidgePredictionsStatsDev.txt | 14 ------- .../1labelSGDPredictionsStatsDev.txt | 14 ------- .../1labelSGDPredictionsStatsTest.txt | 14 ------- 7 files changed, 28 insertions(+), 70 deletions(-) delete mode 100644 output/AI Classifier/1labelRidgePredictionsStatsDev.txt delete mode 100644 output/AI Classifier/1labelSGDPredictionsStatsDev.txt delete mode 100644 output/AI Classifier/1labelSGDPredictionsStatsTest.txt diff --git a/AIclassifier.py b/AIclassifier.py index fa3d36f..39acbab 100644 --- a/AIclassifier.py +++ b/AIclassifier.py @@ -121,9 +121,9 @@ def create_vectors_list(sents, conversion_dict): bigrams_vector.append(sent_bigrams_vector) mixed_vector.append(sent_mixed_vector) - #return vectors_list + return vectors_list # unigrams #return bigrams_vector - return mixed_vector + #return mixed_vector # def create_word_embedding(partition): @@ -195,9 +195,9 @@ def create_vectors_list(sents, conversion_dict): #train_word_embedding_features = create_word_embedding(sents_train) #dev_word_embedding_features = create_word_embedding(sents_dev) #test_word_embedding_features = numpy.asarray(create_word_embedding(sents_test)) -#print("Length of the dictionary of word representations:",len(words_to_numbers)) +print("Length of the dictionary of word representations:",len(words_to_numbers)) # print("Length of the dictionary of word representations:",len(bigrams_to_numbers)) -print("Length of the dictionary of word representations:",len(mixed_to_numbers)) +#print("Length of the dictionary of word representations:",len(mixed_to_numbers)) # FLAG - CHECK IF DICTIONARY IS BUILT CORRECTLY # SHOULD PUNCTUATION BE UNKNOWN? BECAUSE RIGHT NOW IT IS -NOPE, FIXED @@ -205,33 +205,33 @@ def create_vectors_list(sents, conversion_dict): # count frequency before and after removing unknown words - ??? - ASK GABRIEL!! # checked that it seems ok -# train_vectors_list = create_vectors_list(sents_train, words_to_numbers) -# dev_vectors_list = create_vectors_list(sents_dev, words_to_numbers) -# test_vectors_list = create_vectors_list(sents_test, words_to_numbers) +train_vectors_list = create_vectors_list(sents_train, words_to_numbers) +dev_vectors_list = create_vectors_list(sents_dev, words_to_numbers) +test_vectors_list = create_vectors_list(sents_test, words_to_numbers) # train_vectors_list = create_vectors_list(sents_train, bigrams_to_numbers) # dev_vectors_list = create_vectors_list(sents_dev, bigrams_to_numbers) # test_vectors_list = create_vectors_list(sents_test, bigrams_to_numbers) -train_vectors_list = create_vectors_list(sents_train, mixed_to_numbers) -dev_vectors_list = create_vectors_list(sents_dev, mixed_to_numbers) -test_vectors_list = create_vectors_list(sents_test, mixed_to_numbers) +# train_vectors_list = create_vectors_list(sents_train, mixed_to_numbers) +# dev_vectors_list = create_vectors_list(sents_dev, mixed_to_numbers) +# test_vectors_list = create_vectors_list(sents_test, mixed_to_numbers) # COUNT STATISTICS - HOW MANY WORDS WERE CONSIDERED UNK, AND HOW MANY OF EACH WORD # FLAG - CHECK IF SENTENCE REPRESENTATIONS WERE DONE CORRECTLY -# train_matrix_array = format_sentVector_to_SparseMatrix(train_vectors_list, words_to_numbers) -# dev_matrix_array = format_sentVector_to_SparseMatrix(dev_vectors_list, words_to_numbers) -# test_matrix_array = format_sentVector_to_SparseMatrix(test_vectors_list, words_to_numbers) +train_matrix_array = format_sentVector_to_SparseMatrix(train_vectors_list, words_to_numbers) +dev_matrix_array = format_sentVector_to_SparseMatrix(dev_vectors_list, words_to_numbers) +test_matrix_array = format_sentVector_to_SparseMatrix(test_vectors_list, words_to_numbers) # train_matrix_array = format_sentVector_to_SparseMatrix(train_vectors_list, bigrams_to_numbers) # dev_matrix_array = format_sentVector_to_SparseMatrix(dev_vectors_list, bigrams_to_numbers) # test_matrix_array = format_sentVector_to_SparseMatrix(test_vectors_list, bigrams_to_numbers) -train_matrix_array = format_sentVector_to_SparseMatrix(train_vectors_list, mixed_to_numbers) -dev_matrix_array = format_sentVector_to_SparseMatrix(dev_vectors_list, mixed_to_numbers) -test_matrix_array = format_sentVector_to_SparseMatrix(test_vectors_list, mixed_to_numbers) +# train_matrix_array = format_sentVector_to_SparseMatrix(train_vectors_list, mixed_to_numbers) +# dev_matrix_array = format_sentVector_to_SparseMatrix(dev_vectors_list, mixed_to_numbers) +# test_matrix_array = format_sentVector_to_SparseMatrix(test_vectors_list, mixed_to_numbers) # FLAG - CHECK IF SPARSE MATRIX REPRESENTATION WAS DONE CORRECTLY @@ -283,8 +283,8 @@ def create_vectors_list(sents, conversion_dict): importances = model.feature_importances_ features = {} -# for i,(token,value) in enumerate(zip(words_to_numbers, importances)): -for i,(token,value) in enumerate(zip(mixed_to_numbers, importances)): +for i,(token,value) in enumerate(zip(words_to_numbers, importances)): +#for i,(token,value) in enumerate(zip(mixed_to_numbers, importances)): #for i,(token,value) in enumerate(zip(bigrams_to_numbers, importances)): # IMPORTANTO TO CHANGE TO ADEQUATE DICT if (value != 0): features[token] = value @@ -324,7 +324,7 @@ def create_vectors_list(sents, conversion_dict): os.makedirs(os.path.dirname(path), exist_ok=True) with open(path, 'w') as file: #print("Performance measures - Unigram Dictionary - MLP Word Embeddings\n", file=file) - print("Performance measures - Mixed Dictionary - Adaboost\n", file=file) + print("Performance measures - Unigram Dictionary - Adaboost\n", file=file) #write_output_stats_file(path, "Dev", dev_labels_primary, predictions, labels) write_output_stats_file(path, "Test", 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b/output/AI Classifier/1labelPredictionsStatsTest.txt index 4e03ab3..e6800f9 100644 --- a/output/AI Classifier/1labelPredictionsStatsTest.txt +++ b/output/AI Classifier/1labelPredictionsStatsTest.txt @@ -1,14 +1,14 @@ -Performance measures - Mixed Dictionary - Adaboost +Performance measures - Unigram Dictionary - Adaboost Test set: -Precision macro: 0.246 -Precision Individually: [0.364 0. 0.615 0. 0.25 ] -Recall macro: 0.289 -Recall Individually: [0.571 0. 0.774 0. 0.1 ] -F1 Score micro: 0.493 -F1 Score macro: 0.255 -F1 Score weighted: 0.431 -F1 Score Individually: [0.444 0. 0.686 0. 0.143] +Precision macro: 0.368 +Precision Individually: [0.556 0. 0.551 0.4 0.333] +Recall macro: 0.302 +Recall Individually: [0.357 0. 0.871 0.182 0.1 ] +F1 Score micro: 0.522 +F1 Score macro: 0.303 +F1 Score weighted: 0.467 +F1 Score Individually: [0.435 0. 0.675 0.25 0.154] diff --git a/output/AI Classifier/1labelRidgePredictionsStatsDev.txt b/output/AI Classifier/1labelRidgePredictionsStatsDev.txt deleted file mode 100644 index a295ad9..0000000 --- a/output/AI Classifier/1labelRidgePredictionsStatsDev.txt +++ /dev/null @@ -1,14 +0,0 @@ -Performance measures - -Test set: - -Precision macro: 0.436 -Precision Individually: [0.667 0. 0.611 0.4 0.5 ] -Recall macro: 0.328 -Recall Individually: [0.286 0. 0.971 0.182 0.2 ] -F1 Score micro: 0.586 -F1 Score macro: 0.337 -F1 Score weighted: 0.524 -F1 Score Individually: [0.4 0. 0.75 0.25 0.286] - - diff --git a/output/AI Classifier/1labelSGDPredictionsStatsDev.txt b/output/AI Classifier/1labelSGDPredictionsStatsDev.txt deleted file mode 100644 index 874d72a..0000000 --- a/output/AI Classifier/1labelSGDPredictionsStatsDev.txt +++ /dev/null @@ -1,14 +0,0 @@ -Performance measures - Unigram Dictionary - -Dev set: - -Precision macro: 0.472 -Precision Individually: [0.778 0. 0.696 0.333 0.556] -Recall macro: 0.438 -Recall Individually: [0.538 0. 0.914 0.182 0.556] -F1 Score micro: 0.657 -F1 Score macro: 0.443 -F1 Score weighted: 0.622 -F1 Score Individually: [0.636 0. 0.79 0.235 0.556] - - diff --git a/output/AI Classifier/1labelSGDPredictionsStatsTest.txt b/output/AI Classifier/1labelSGDPredictionsStatsTest.txt deleted file mode 100644 index 4bba9dd..0000000 --- a/output/AI Classifier/1labelSGDPredictionsStatsTest.txt +++ /dev/null @@ -1,14 +0,0 @@ -Performance measures - Unigram Dictionary - -Test set: - -Precision macro: 0.483 -Precision Individually: [0.778 0. 0.681 0.333 0.625] -Recall macro: 0.425 -Recall Individually: [0.5 0. 0.941 0.182 0.5 ] -F1 Score micro: 0.657 -F1 Score macro: 0.438 -F1 Score weighted: 0.622 -F1 Score Individually: [0.609 0. 0.79 0.235 0.556] - -