hipom_data_mapping/post_process/tfidf_class/2.classify_by_tfidf.ipynb

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     "text": [
      "Accuracy (MDM=True) for Group 1: 79.41%\n",
      "Accuracy (MDM=True) for Group 2: 79.32%\n",
      "Accuracy (MDM=True) for Group 3: 82.49%\n",
      "Accuracy (MDM=True) for Group 4: 85.61%\n",
      "Accuracy (MDM=True) for Group 5: 79.72%\n",
      "Average Accuracy (MDM=True) across all groups: 81.31%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "\n",
    "# Initialize a list to store the accuracies for each group\n",
    "accuracies = []\n",
    "\n",
    "# Loop through group numbers from 1 to 5\n",
    "for group_number in range(1, 6):\n",
    "    \n",
    "    # Load the CSV files from the specified group\n",
    "    sdl_class_rdoc_path = f'0.class_document/{group_number}/sdl_class_rdoc.csv'\n",
    "    test_path = f'../../data_preprocess/dataset/{group_number}/test.csv'\n",
    "    \n",
    "    # Check if test file exists, if not, skip this iteration\n",
    "    if not os.path.exists(test_path):\n",
    "        print(f\"test file for Group {group_number} does not exist. Skipping...\")\n",
    "        continue\n",
    "    \n",
    "    sdl_class_rdoc_csv = pd.read_csv(sdl_class_rdoc_path, low_memory=False)\n",
    "    test_csv = pd.read_csv(test_path, low_memory=False)\n",
    "    \n",
    "    # Replace NaN values with empty strings in relevant columns\n",
    "    sdl_class_rdoc_csv['tag_description'] = sdl_class_rdoc_csv['tag_description'].fillna('')\n",
    "    test_csv['tag_description'] = test_csv['tag_description'].fillna('')\n",
    "    \n",
    "    # Initialize new columns in test_csv\n",
    "    test_csv['c_thing'] = ''\n",
    "    test_csv['c_property'] = ''\n",
    "    test_csv['c_score'] = ''\n",
    "    test_csv['c_duplicate'] = 0  # Initialize c_duplicate to store duplicate counts\n",
    "    \n",
    "    # Combine both sdl_class_rdoc and test CSVs tag_descriptions for TF-IDF Vectorizer training\n",
    "    combined_tag_descriptions = sdl_class_rdoc_csv['tag_description'].tolist() + test_csv['tag_description'].tolist()\n",
    "    \n",
    "    # Create a TF-IDF Vectorizer\n",
    "    vectorizer = TfidfVectorizer(\n",
    "        token_pattern=r'\\S+',\n",
    "        ngram_range=(1, 6),  # Use ngrams from 1 to 6\n",
    "    )\n",
    "    \n",
    "    # Fit the TF-IDF vectorizer on the combined tag_descriptions\n",
    "    vectorizer.fit(combined_tag_descriptions)\n",
    "    \n",
    "    # Transform both sdl_class_rdoc and test CSVs into TF-IDF matrices\n",
    "    sdl_class_rdoc_tfidf_matrix = vectorizer.transform(sdl_class_rdoc_csv['tag_description'])\n",
    "    test_tfidf_matrix = vectorizer.transform(test_csv['tag_description'])\n",
    "    \n",
    "    # Calculate cosine similarity between test and class-level sdl_class_rdoc vectors\n",
    "    similarity_matrix = cosine_similarity(test_tfidf_matrix, sdl_class_rdoc_tfidf_matrix)\n",
    "    \n",
    "    # Find the most similar class-level tag_description for each test description\n",
    "    most_similar_indices = similarity_matrix.argmax(axis=1)\n",
    "    most_similar_scores = similarity_matrix.max(axis=1)\n",
    "    \n",
    "    # Assign the corresponding thing, property, and similarity score to the test CSV\n",
    "    test_csv['c_thing'] = sdl_class_rdoc_csv.iloc[most_similar_indices]['thing'].values\n",
    "    test_csv['c_property'] = sdl_class_rdoc_csv.iloc[most_similar_indices]['property'].values\n",
    "    test_csv['c_score'] = most_similar_scores\n",
    "    \n",
    "    # Check if the predicted 'c_thing' and 'c_property' match the actual 'thing' and 'property'\n",
    "    test_csv['cthing_correct'] = test_csv['thing'] == test_csv['c_thing']\n",
    "    test_csv['cproperty_correct'] = test_csv['property'] == test_csv['c_property']\n",
    "    test_csv['ctp_correct'] = test_csv['cthing_correct'] & test_csv['cproperty_correct']\n",
    "    \n",
    "    # Calculate accuracy based only on MDM = True\n",
    "    mdm_true_count = len(test_csv[test_csv['MDM'] == True])\n",
    "    accuracy = (test_csv['ctp_correct'].sum() / mdm_true_count) * 100\n",
    "    accuracies.append(accuracy)\n",
    "    \n",
    "    print(f\"Accuracy (MDM=True) for Group {group_number}: {accuracy:.2f}%\")\n",
    "    \n",
    "    # Specify output file paths\n",
    "    output_path = f'0.class_document/{group_number}/test_p_c.csv'\n",
    "    test_csv.to_csv(output_path, index=False, encoding='utf-8-sig')\n",
    "    \n",
    "    # Filter for rows where MDM is True and ctp_correct is False\n",
    "    false_positive_rows = test_csv[(test_csv['MDM'] == True) & (test_csv['ctp_correct'] == False)]\n",
    "    \n",
    "    # Save false positives to a separate file\n",
    "    fp_output_path = f'0.class_document/{group_number}/fp_class.csv'\n",
    "    false_positive_rows.to_csv(fp_output_path, index=False, encoding='utf-8-sig')\n",
    "\n",
    "# Calculate and print the average accuracy across all groups\n",
    "average_accuracy = sum(accuracies) / len(accuracies)\n",
    "print(f\"Average Accuracy (MDM=True) across all groups: {average_accuracy:.2f}%\")\n"
   ]
  }
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