148 lines
6.1 KiB
Plaintext
148 lines
6.1 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running SVM with C=1000\n",
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"Average Accuracy (MDM=True) across all groups with C=1000: 89.36%\n",
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"Running SVM with C=10000\n",
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"Average Accuracy (MDM=True) across all groups with C=10000: 89.36%\n",
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"Running SVM with C=100000\n",
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"Average Accuracy (MDM=True) across all groups with C=100000: 89.36%\n",
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"Running SVM with C=1000000\n",
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"Average Accuracy (MDM=True) across all groups with C=1000000: 89.36%\n",
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"\n",
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"Final Results for each C value:\n",
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"C=1000, Average Accuracy: 89.36%\n",
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"C=10000, Average Accuracy: 89.36%\n",
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"C=100000, Average Accuracy: 89.36%\n",
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"C=1000000, Average Accuracy: 89.36%\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"from sklearn.feature_extraction.text import CountVectorizer\n",
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"from sklearn.svm import SVC\n",
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"import os\n",
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"import numpy as np\n",
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"from joblib import Parallel, delayed\n",
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"\n",
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"# Initialize variables to store overall accuracy results\n",
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"average_accuracies = {}\n",
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"\n",
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"# Function to process each group (parallelized later)\n",
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"def process_group(C_value, group_number):\n",
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" train_all_path = f'../../data_preprocess/dataset/{group_number}/train_all.csv'\n",
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" test_path = f'../../translation/0.result/{group_number}/test_p.csv'\n",
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"\n",
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" if not os.path.exists(test_path):\n",
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" print(f\"Test file for Group {group_number} does not exist. Skipping...\")\n",
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" return None\n",
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"\n",
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" # Load the train_all and test CSVs\n",
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" train_all_csv = pd.read_csv(train_all_path, low_memory=False)\n",
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" test_csv = pd.read_csv(test_path, low_memory=False)\n",
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"\n",
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" train_all_csv['tag_description'] = train_all_csv['tag_description'].fillna('')\n",
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" test_csv['tag_description'] = test_csv['tag_description'].fillna('')\n",
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"\n",
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" test_csv['c_thing'] = ''\n",
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" test_csv['c_property'] = ''\n",
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" test_csv['c_score'] = ''\n",
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" test_csv['c_duplicate'] = 0\n",
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"\n",
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" combined_tag_descriptions = train_all_csv['tag_description'].tolist()\n",
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"\n",
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" # BoW를 Boolean 방식으로 변환\n",
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" vectorizer = CountVectorizer(token_pattern=r'\\S+', binary=True)\n",
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" vectorizer.fit(combined_tag_descriptions)\n",
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"\n",
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" train_all_bow_matrix = vectorizer.transform(train_all_csv['tag_description']).toarray().astype(bool) # bool로 변환\n",
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" test_bow_matrix = vectorizer.transform(test_csv['tag_description']).toarray().astype(bool)\n",
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"\n",
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" # SVM 모델 학습 및 예측\n",
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" svm_model_thing = SVC(kernel='linear', probability=True, C=C_value)\n",
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" svm_model_property = SVC(kernel='linear', probability=True, C=C_value)\n",
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"\n",
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" # SVM을 이용하여 'thing' 및 'property' 예측 모델 학습\n",
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" svm_model_thing.fit(train_all_bow_matrix, train_all_csv['thing'])\n",
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" svm_model_property.fit(train_all_bow_matrix, train_all_csv['property'])\n",
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"\n",
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" # 'thing' 및 'property' 예측\n",
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" predicted_things = svm_model_thing.predict(test_bow_matrix)\n",
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" predicted_properties = svm_model_property.predict(test_bow_matrix)\n",
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" \n",
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" predicted_scores_thing = svm_model_thing.predict_proba(test_bow_matrix)[:, 1] # 'thing'의 예측 확률 점수\n",
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" predicted_scores_property = svm_model_property.predict_proba(test_bow_matrix)[:, 1] # 'property'의 예측 확률 점수\n",
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"\n",
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" predicted_scores = (predicted_scores_thing + predicted_scores_property) / 2 # 평균 점수로 결합\n",
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"\n",
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" test_csv['c_thing'] = predicted_things\n",
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" test_csv['c_property'] = predicted_properties\n",
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" test_csv['c_score'] = predicted_scores\n",
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"\n",
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" test_csv['cthing_correct'] = test_csv['thing'] == test_csv['c_thing']\n",
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" test_csv['cproperty_correct'] = test_csv['property'] == test_csv['c_property']\n",
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" test_csv['ctp_correct'] = test_csv['cthing_correct'] & test_csv['cproperty_correct']\n",
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"\n",
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" mdm_true_count = len(test_csv[test_csv['MDM'] == True])\n",
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" accuracy = (test_csv['ctp_correct'].sum() / mdm_true_count) * 100 if mdm_true_count > 0 else 0\n",
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" return accuracy\n",
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"\n",
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"# C 값들에 대해 실험할 값 설정 (log 스케일)\n",
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"C_values = [0.01, 0.1, 1, 10, 100]\n",
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"C_values = [1000, 10000, 100000, 1000000]\n",
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"# 각 C 값에 대해 실험\n",
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"for C_value in C_values:\n",
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" print(f\"Running SVM with C={C_value}\")\n",
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" average_accuracies[C_value] = []\n",
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"\n",
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" # Parallel processing for groups\n",
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" results = Parallel(n_jobs=-1)(delayed(process_group)(C_value, group_number) for group_number in range(1, 6))\n",
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"\n",
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" # Filter out None results (in case of missing files)\n",
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" accuracies = [result for result in results if result is not None]\n",
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"\n",
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" if accuracies:\n",
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" average_accuracy = sum(accuracies) / len(accuracies)\n",
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" average_accuracies[C_value].append(average_accuracy)\n",
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" print(f\"Average Accuracy (MDM=True) across all groups with C={C_value}: {average_accuracy:.2f}%\")\n",
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"\n",
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"# Print overall results for all C values\n",
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"print(\"\\nFinal Results for each C value:\")\n",
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"for C_value, accuracies in average_accuracies.items():\n",
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" avg_acc = np.mean(accuracies)\n",
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" print(f\"C={C_value}, Average Accuracy: {avg_acc:.2f}%\")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "torch",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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