75 lines
4.1 KiB
Python
75 lines
4.1 KiB
Python
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import pandas as pd
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.metrics import pairwise_distances
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import os
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import numpy as np
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# Initialize overall accuracy results
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k_accuracies = []
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for k in range(1, 53): # k를 1부터 52까지 수행
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total_s_correct_count = 0
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total_mdm_true_count = 0
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for group_number in range(1, 6):
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# test_csv = pd.read_csv(f'post_process/tfidf_class/0.class_document/{group_number}/test_p_c.csv', low_memory=False)
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test_csv = pd.read_csv(f'translation/0.result/{group_number}/test_p.csv', low_memory=False)
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train_all_csv = pd.read_csv(f'data_preprocess/dataset/{group_number}/train_all.csv', low_memory=False)
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test_csv['s_score'], test_csv['s_thing'], test_csv['s_property'], test_csv['s_correct'] = -1, '', '', False
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duplicate_filtered = test_csv[test_csv['p_MDM']].copy()
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train_all_csv['tag_description'] = train_all_csv['tag_description'].fillna('')
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duplicate_filtered['tag_description'] = duplicate_filtered['tag_description'].fillna('')
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for ships_idx, group in duplicate_filtered.groupby('ships_idx'):
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for (p_thing, p_property), sub_group in group.groupby(['p_thing', 'p_property']):
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matching_train_data = train_all_csv[(train_all_csv['thing'] == p_thing) & (train_all_csv['property'] == p_property)]
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if not matching_train_data.empty:
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combined_descriptions = sub_group['tag_description'].tolist() + matching_train_data['tag_description'].tolist()
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# BoW 벡터화를 위한 CountVectorizer 사용
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vectorizer = CountVectorizer(token_pattern=r'\S+')
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bow_matrix = vectorizer.fit_transform(combined_descriptions).toarray()
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test_bow_matrix = bow_matrix[:len(sub_group)]
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train_bow_matrix = bow_matrix[len(sub_group):]
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# 코사인 거리를 계산하고, 유사도로 변환 (1 - 거리)
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distance_matrix = pairwise_distances(test_bow_matrix, train_bow_matrix, metric='euclidean')
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similarity_matrix = 1 - distance_matrix
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for i, row in enumerate(similarity_matrix):
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top_k_indices = np.argsort(row)[-k:] # 가장 가까운 k개의 인덱스 (유사도 기준, 내림차순)
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sub_group.iloc[i, sub_group.columns.get_loc('s_score')] = row[top_k_indices].mean() # 유사도를 s_score에 저장
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else:
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sub_group['s_score'] = 0
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duplicate_filtered.loc[sub_group.index, 's_score'] = sub_group['s_score']
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for ships_idx, group in duplicate_filtered.groupby('ships_idx'):
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for (p_thing, p_property), sub_group in group.groupby(['p_thing', 'p_property']):
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best_index = sub_group.index.min() if (sub_group['s_score'] == -1).any() else sub_group['s_score'].idxmax()
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duplicate_filtered.at[best_index, 's_thing'] = sub_group.at[best_index, 'p_thing']
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duplicate_filtered.at[best_index, 's_property'] = sub_group.at[best_index, 'p_property']
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duplicate_filtered = duplicate_filtered.drop(sub_group.index.difference([best_index]))
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test_csv.update(duplicate_filtered[['s_thing', 's_property', 's_score']])
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test_csv['s_correct'] = ((test_csv['thing'] == test_csv['s_thing']) &
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(test_csv['property'] == test_csv['s_property']) &
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(test_csv['MDM']))
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mdm_true_count = test_csv['MDM'].sum()
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s_correct_count = test_csv['s_correct'].sum()
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total_s_correct_count += s_correct_count
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total_mdm_true_count += mdm_true_count
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if total_mdm_true_count > 0:
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average_s_correct_percentage = (total_s_correct_count / total_mdm_true_count) * 100
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k_accuracies.append(average_s_correct_percentage)
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print(f"k={k}, Average s_correct percentage: {average_s_correct_percentage:.2f}%")
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# k의 평균 정확도 출력
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overall_average_accuracy = np.mean(k_accuracies)
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print(f"Overall average s_correct percentage across all k values: {overall_average_accuracy:.2f}%")
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