981 lines
41 KiB
Python
981 lines
41 KiB
Python
import os
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import json
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import pandas as pd
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from glob import glob
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from tqdm import tqdm
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import time
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import fitz
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import re
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from io import BytesIO
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from traceback import print_exc
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from utils.logger import logger
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from utils.pdf_download import download_pdf_from_documents_warehouse
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from utils.sql_query_util import query_document_fund_mapping
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from utils.pdf_util import PDFUtil
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from utils.biz_utils import add_slash_to_text_as_regex
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from core.page_filter import FilterPages
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from core.data_extraction import DataExtraction
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from core.data_mapping import DataMapping
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from core.metrics import Metrics
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class EMEA_AR_Parsing:
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def __init__(
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self,
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doc_id: str,
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pdf_folder: str = r"/data/emea_ar/pdf/",
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output_extract_data_folder: str = r"/data/emea_ar/output/extract_data/docs/",
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output_mapping_data_folder: str = r"/data/emea_ar/output/mapping_data/docs/",
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extract_way: str = "text",
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drilldown_folder: str = r"/data/emea_ar/output/drilldown/",
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) -> None:
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self.doc_id = doc_id
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self.pdf_folder = pdf_folder
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os.makedirs(self.pdf_folder, exist_ok=True)
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self.pdf_file = self.download_pdf()
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self.document_mapping_info_df = query_document_fund_mapping(doc_id, rerun=False)
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if extract_way is None or len(extract_way) == 0:
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extract_way = "text"
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self.extract_way = extract_way
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self.output_extract_image_folder = None
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if self.extract_way == "image":
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self.output_extract_image_folder = (
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r"/data/emea_ar/output/extract_data/images/"
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)
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os.makedirs(self.output_extract_image_folder, exist_ok=True)
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if output_extract_data_folder is None or len(output_extract_data_folder) == 0:
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output_extract_data_folder = r"/data/emea_ar/output/extract_data/docs/"
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if not output_extract_data_folder.endswith("/"):
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output_extract_data_folder = f"{output_extract_data_folder}/"
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if extract_way is not None and len(extract_way) > 0:
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output_extract_data_folder = (
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f"{output_extract_data_folder}by_{extract_way}/"
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)
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self.output_extract_data_folder = output_extract_data_folder
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os.makedirs(self.output_extract_data_folder, exist_ok=True)
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if output_mapping_data_folder is None or len(output_mapping_data_folder) == 0:
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output_mapping_data_folder = r"/data/emea_ar/output/mapping_data/docs/"
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if not output_mapping_data_folder.endswith("/"):
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output_mapping_data_folder = f"{output_mapping_data_folder}/"
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if extract_way is not None and len(extract_way) > 0:
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output_mapping_data_folder = (
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f"{output_mapping_data_folder}by_{extract_way}/"
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)
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self.output_mapping_data_folder = output_mapping_data_folder
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os.makedirs(self.output_mapping_data_folder, exist_ok=True)
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self.filter_pages = FilterPages(
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self.doc_id, self.pdf_file, self.document_mapping_info_df
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)
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self.page_text_dict = self.filter_pages.page_text_dict
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self.datapoint_page_info, self.result_details = self.get_datapoint_page_info()
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self.datapoints = self.get_datapoints_from_datapoint_page_info()
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if drilldown_folder is None or len(drilldown_folder) == 0:
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drilldown_folder = r"/data/emea_ar/output/drilldown/"
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os.makedirs(drilldown_folder, exist_ok=True)
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self.drilldown_folder = drilldown_folder
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def download_pdf(self) -> str:
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pdf_file = download_pdf_from_documents_warehouse(self.pdf_folder, self.doc_id)
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return pdf_file
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def get_datapoint_page_info(self) -> tuple:
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datapoint_page_info, result_details = self.filter_pages.start_job()
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return datapoint_page_info, result_details
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def get_datapoints_from_datapoint_page_info(self) -> list:
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datapoints = list(self.datapoint_page_info.keys())
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if "doc_id" in datapoints:
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datapoints.remove("doc_id")
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return datapoints
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def extract_data(
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self,
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re_run: bool = False,
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) -> list:
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found_data = False
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if not re_run:
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output_data_json_folder = os.path.join(
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self.output_extract_data_folder, "json/"
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)
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os.makedirs(output_data_json_folder, exist_ok=True)
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json_file = os.path.join(output_data_json_folder, f"{self.doc_id}.json")
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if os.path.exists(json_file):
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logger.info(
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f"The document: {self.doc_id} has been parsed, loading data from {json_file}"
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)
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with open(json_file, "r", encoding="utf-8") as f:
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data_from_gpt = json.load(f)
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found_data = True
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if not found_data:
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try:
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data_extraction = DataExtraction(
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self.doc_id,
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self.pdf_file,
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self.output_extract_data_folder,
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self.page_text_dict,
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self.datapoint_page_info,
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self.datapoints,
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self.document_mapping_info_df,
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extract_way=self.extract_way,
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output_image_folder=self.output_extract_image_folder,
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)
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data_from_gpt = data_extraction.extract_data()
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except Exception as e:
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logger.error(f"Error: {e}")
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data_from_gpt = {"data": []}
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# Drilldown data to relevant PDF document
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annotation_list = []
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try:
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annotation_list = self.drilldown_pdf_document(data_from_gpt)
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except Exception as e:
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logger.error(f"Error: {e}")
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return data_from_gpt, annotation_list
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def drilldown_pdf_document(self, data_from_gpt: list) -> list:
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logger.info(f"Drilldown PDF document for doc_id: {self.doc_id}")
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pdf_util = PDFUtil(self.pdf_file)
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drilldown_data_list = []
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for data in data_from_gpt:
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doc_id = str(data.get("doc_id", ""))
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page_index = data.get("page_index", -1)
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if page_index == -1:
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continue
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extract_data_list = data.get("extract_data", {}).get("data", [])
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dp_reported_name_dict = data.get("extract_data", {}).get("dp_reported_name", {})
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highlighted_value_list = []
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for extract_data in extract_data_list:
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for data_point, value in extract_data.items():
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if value in highlighted_value_list:
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continue
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if data_point in ["ter", "ogc", "performance_fee"]:
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continue
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drilldown_data = {
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"doc_id": doc_id,
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"page_index": page_index,
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"data_point": data_point,
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"parent_text_block": None,
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"value": value,
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"annotation_attribute": {}
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}
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drilldown_data_list.append(drilldown_data)
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highlighted_value_list.append(value)
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for data_point, reported_name in dp_reported_name_dict.items():
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if reported_name in highlighted_value_list:
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continue
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data_point = f"{data_point}_reported_name"
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drilldown_data = {
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"doc_id": doc_id,
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"page_index": page_index,
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"data_point": data_point,
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"parent_text_block": None,
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"value": reported_name,
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"annotation_attribute": {}
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}
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drilldown_data_list.append(drilldown_data)
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highlighted_value_list.append(reported_name)
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drilldown_result = pdf_util.batch_drilldown(drilldown_data_list=drilldown_data_list,
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output_pdf_folder=self.drilldown_folder)
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annotation_list = []
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if len(drilldown_result) > 0:
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logger.info(f"Drilldown PDF document for doc_id: {doc_id} successfully")
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annotation_list = drilldown_result.get("annotation_list", [])
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for annotation in annotation_list:
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annotation["doc_id"] = doc_id
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if self.drilldown_folder is not None and len(self.drilldown_folder) > 0:
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drilldown_data_folder = os.path.join(self.drilldown_folder, "data/")
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os.makedirs(drilldown_data_folder, exist_ok=True)
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drilldown_file = os.path.join(drilldown_data_folder, f"{doc_id}_drilldown.xlsx")
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drilldown_source_df = pd.DataFrame(drilldown_data_list)
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annotation_list_df = pd.DataFrame(annotation_list)
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# set drilldown_result_df column order as doc_id, pdf_file, page_index,
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# data_point, value, matching_val_area, normalized_bbox
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try:
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annotation_list_df = annotation_list_df[["doc_id", "pdf_file", "page_index",
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"data_point", "value", "matching_val_area",
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"normalized_bbox"]]
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except Exception as e:
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logger.error(f"Error: {e}")
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logger.info(f"Writing drilldown data to {drilldown_file}")
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try:
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with pd.ExcelWriter(drilldown_file) as writer:
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drilldown_source_df.to_excel(writer, index=False, sheet_name="source_data")
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annotation_list_df.to_excel(writer, index=False, sheet_name="drilldown_data")
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except Exception as e:
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logger.error(f"Error: {e}")
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annotation_list = annotation_list_df.to_dict(orient="records")
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return annotation_list
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def mapping_data(self, data_from_gpt: list, re_run: bool = False) -> list:
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if not re_run:
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output_data_json_folder = os.path.join(
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self.output_mapping_data_folder, "json/"
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)
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os.makedirs(output_data_json_folder, exist_ok=True)
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json_file = os.path.join(output_data_json_folder, f"{self.doc_id}.json")
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if os.path.exists(json_file):
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logger.info(
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f"The fund/ share of this document: {self.doc_id} has been mapped, loading data from {json_file}"
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)
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with open(json_file, "r", encoding="utf-8") as f:
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doc_mapping_data = json.load(f)
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return doc_mapping_data
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"""
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doc_id,
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datapoints: list,
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raw_document_data_list: list,
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document_mapping_info_df: pd.DataFrame,
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output_data_folder: str,
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"""
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data_mapping = DataMapping(
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self.doc_id,
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self.datapoints,
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data_from_gpt,
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self.document_mapping_info_df,
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self.output_mapping_data_folder,
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)
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return data_mapping.mapping_raw_data()
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def filter_pages(doc_id: str, pdf_folder: str) -> None:
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logger.info(f"Filter EMEA AR PDF pages for doc_id: {doc_id}")
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emea_ar_parsing = EMEA_AR_Parsing(doc_id, pdf_folder)
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datapoint_page_info, result_details = emea_ar_parsing.get_datapoint_page_info()
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return datapoint_page_info, result_details
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|
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def extract_data(
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doc_id: str,
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pdf_folder: str,
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output_data_folder: str,
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extract_way: str = "text",
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re_run: bool = False,
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) -> None:
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logger.info(f"Extract EMEA AR data for doc_id: {doc_id}")
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emea_ar_parsing = EMEA_AR_Parsing(
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doc_id,
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pdf_folder,
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output_extract_data_folder=output_data_folder,
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extract_way=extract_way,
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)
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data_from_gpt, annotation_list = emea_ar_parsing.extract_data(re_run)
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return data_from_gpt, annotation_list
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def mapping_data(
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doc_id: str,
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pdf_folder: str,
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output_extract_data_folder: str,
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output_mapping_folder: str,
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extract_way: str = "text",
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re_run_extract_data: bool = False,
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re_run_mapping_data: bool = False,
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) -> None:
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logger.info(f"Extract EMEA AR data for doc_id: {doc_id}")
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emea_ar_parsing = EMEA_AR_Parsing(
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doc_id,
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pdf_folder,
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output_extract_data_folder=output_extract_data_folder,
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output_mapping_data_folder=output_mapping_folder,
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extract_way=extract_way,
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)
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doc_data_from_gpt, annotation_list = emea_ar_parsing.extract_data(re_run=re_run_extract_data)
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doc_mapping_data = emea_ar_parsing.mapping_data(
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data_from_gpt=doc_data_from_gpt, re_run=re_run_mapping_data
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)
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return doc_data_from_gpt, annotation_list, doc_mapping_data
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|
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def batch_extract_data(
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pdf_folder: str,
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doc_data_excel_file: str = None,
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output_child_folder: str = r"/data/emea_ar/output/extract_data/docs/",
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output_total_folder: str = r"/data/emea_ar/output/extract_data/total/",
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extract_way: str = "text",
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special_doc_id_list: list = None,
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re_run: bool = False,
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) -> None:
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pdf_files = glob(pdf_folder + "*.pdf")
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doc_list = []
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if special_doc_id_list is not None and len(special_doc_id_list) > 0:
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doc_list = special_doc_id_list
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if (
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len(doc_list) == 0
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and doc_data_excel_file is not None
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and len(doc_data_excel_file) > 0
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and os.path.exists(doc_data_excel_file)
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):
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doc_data_df = pd.read_excel(doc_data_excel_file)
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doc_data_df = doc_data_df[doc_data_df["Checked"] == 1]
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doc_list = [str(doc_id) for doc_id in doc_data_df["doc_id"].tolist()]
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result_list = []
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for pdf_file in tqdm(pdf_files):
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pdf_base_name = os.path.basename(pdf_file)
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doc_id = pdf_base_name.split(".")[0]
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if doc_list is not None and doc_id not in doc_list:
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continue
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data_from_gpt = extract_data(
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doc_id=doc_id,
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pdf_folder=pdf_folder,
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output_data_folder=output_child_folder,
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extract_way=extract_way,
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re_run=re_run,
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)
|
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result_list.extend(data_from_gpt)
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if special_doc_id_list is None or len(special_doc_id_list) == 0:
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result_df = pd.DataFrame(result_list)
|
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result_df.reset_index(drop=True, inplace=True)
|
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logger.info(f"Saving the result to {output_total_folder}")
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os.makedirs(output_total_folder, exist_ok=True)
|
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time_stamp = time.strftime("%Y%m%d%H%M%S", time.localtime())
|
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output_file = os.path.join(
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output_total_folder,
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f"extract_data_info_{len(pdf_files)}_documents_{time_stamp}.xlsx",
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)
|
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with pd.ExcelWriter(output_file) as writer:
|
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result_df.to_excel(writer, index=False, sheet_name="extract_data_info")
|
|
|
|
|
|
def batch_start_job(
|
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pdf_folder: str,
|
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doc_data_excel_file: str = None,
|
|
output_extract_data_child_folder: str = r"/data/emea_ar/output/extract_data/docs/",
|
|
output_mapping_child_folder: str = r"/data/emea_ar/output/mapping_data/docs/",
|
|
output_extract_data_total_folder: str = r"/data/emea_ar/output/extract_data/total/",
|
|
output_mapping_total_folder: str = r"/data/emea_ar/output/mapping_data/total/",
|
|
extract_way: str = "text",
|
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special_doc_id_list: list = None,
|
|
re_run_extract_data: bool = False,
|
|
re_run_mapping_data: bool = False,
|
|
force_save_total_data: bool = False,
|
|
calculate_metrics: bool = False,
|
|
total_data_prefix: str = None
|
|
):
|
|
pdf_files = glob(pdf_folder + "*.pdf")
|
|
doc_list = []
|
|
for pdf_file in tqdm(pdf_files):
|
|
pdf_base_name = os.path.basename(pdf_file)
|
|
doc_id = pdf_base_name.split(".")[0]
|
|
doc_list.append(doc_id)
|
|
|
|
if special_doc_id_list is not None and len(special_doc_id_list) > 0:
|
|
doc_list = special_doc_id_list
|
|
if (
|
|
len(doc_list) == 0
|
|
and doc_data_excel_file is not None
|
|
and len(doc_data_excel_file) > 0
|
|
and os.path.exists(doc_data_excel_file)
|
|
):
|
|
doc_data_df = pd.read_excel(doc_data_excel_file)
|
|
doc_data_df = doc_data_df[doc_data_df["Checked"] == 1]
|
|
doc_list = [str(doc_id) for doc_id in doc_data_df["doc_id"].tolist()]
|
|
|
|
result_extract_data_list = []
|
|
result_mapping_data_list = []
|
|
for doc_id in tqdm(doc_list):
|
|
try:
|
|
doc_data_from_gpt, annotation_list, doc_mapping_data_list = mapping_data(
|
|
doc_id=doc_id,
|
|
pdf_folder=pdf_folder,
|
|
output_extract_data_folder=output_extract_data_child_folder,
|
|
output_mapping_folder=output_mapping_child_folder,
|
|
extract_way=extract_way,
|
|
re_run_extract_data=re_run_extract_data,
|
|
re_run_mapping_data=re_run_mapping_data,
|
|
)
|
|
result_extract_data_list.extend(doc_data_from_gpt)
|
|
result_mapping_data_list.extend(doc_mapping_data_list)
|
|
except Exception as e:
|
|
logger.error(f"Document: {doc_id} met error: {e}")
|
|
print_exc()
|
|
|
|
if force_save_total_data or (
|
|
special_doc_id_list is None or len(special_doc_id_list) == 0
|
|
):
|
|
result_extract_data_df = pd.DataFrame(result_extract_data_list)
|
|
result_extract_data_df.reset_index(drop=True, inplace=True)
|
|
|
|
result_mappingdata_df = pd.DataFrame(result_mapping_data_list)
|
|
result_mappingdata_df.reset_index(drop=True, inplace=True)
|
|
|
|
logger.info(f"Saving extract data to {output_extract_data_total_folder}")
|
|
unique_doc_ids = result_extract_data_df["doc_id"].unique().tolist()
|
|
os.makedirs(output_extract_data_total_folder, exist_ok=True)
|
|
time_stamp = time.strftime("%Y%m%d%H%M%S", time.localtime())
|
|
file_name = f"extract_data_info_{len(unique_doc_ids)}_documents_by_{extract_way}_{time_stamp}.xlsx"
|
|
if total_data_prefix is not None and len(total_data_prefix) > 0:
|
|
file_name = f"{total_data_prefix}_{file_name}"
|
|
output_file = os.path.join(output_extract_data_total_folder, file_name)
|
|
with pd.ExcelWriter(output_file) as writer:
|
|
result_extract_data_df.to_excel(
|
|
writer, index=False, sheet_name="extract_data_info"
|
|
)
|
|
|
|
logger.info(f"Saving mapping data to {output_mapping_total_folder}")
|
|
unique_doc_ids = result_mappingdata_df["doc_id"].unique().tolist()
|
|
os.makedirs(output_mapping_total_folder, exist_ok=True)
|
|
time_stamp = time.strftime("%Y%m%d%H%M%S", time.localtime())
|
|
file_name = f"mapping_data_info_{len(unique_doc_ids)}_documents_by_{extract_way}_{time_stamp}.xlsx"
|
|
if total_data_prefix is not None and len(total_data_prefix) > 0:
|
|
file_name = f"{total_data_prefix}_{file_name}"
|
|
output_file = os.path.join(output_mapping_total_folder, file_name)
|
|
|
|
doc_mapping_data_in_db = only_output_mapping_data_in_db(result_mappingdata_df)
|
|
with pd.ExcelWriter(output_file) as writer:
|
|
doc_mapping_data_in_db.to_excel(
|
|
writer, index=False, sheet_name="data_in_doc_mapping"
|
|
)
|
|
result_mappingdata_df.to_excel(
|
|
writer, index=False, sheet_name="total_mapping_data"
|
|
)
|
|
result_extract_data_df.to_excel(
|
|
writer, index=False, sheet_name="extract_data"
|
|
)
|
|
|
|
if calculate_metrics:
|
|
prediction_sheet_name = "total_mapping_data"
|
|
ground_truth_file = r"/data/emea_ar/ground_truth/data_extraction/mapping_data_info_73_documents.xlsx"
|
|
ground_truth_sheet_name = "mapping_data"
|
|
metrics_output_folder = r"/data/emea_ar/output/metrics/"
|
|
|
|
# logger.info(f"Calculating metrics for data extraction")
|
|
# missing_error_list, metrics_list, metrics_file = get_metrics(
|
|
# "data_extraction",
|
|
# output_file,
|
|
# prediction_sheet_name,
|
|
# ground_truth_file,
|
|
# ground_truth_sheet_name,
|
|
# metrics_output_folder,
|
|
# )
|
|
|
|
# logger.info(f"Calculating metrics for investment mapping by actual document mapping")
|
|
# missing_error_list, metrics_list, metrics_file = get_metrics(
|
|
# "investment_mapping",
|
|
# output_file,
|
|
# prediction_sheet_name,
|
|
# ground_truth_file,
|
|
# ground_truth_sheet_name,
|
|
# metrics_output_folder,
|
|
# )
|
|
|
|
logger.info(f"Calculating metrics for investment mapping by database document mapping")
|
|
missing_error_list, metrics_list, metrics_file = get_metrics(
|
|
"document_mapping_in_db",
|
|
output_file,
|
|
prediction_sheet_name,
|
|
ground_truth_file,
|
|
ground_truth_sheet_name,
|
|
metrics_output_folder,
|
|
)
|
|
|
|
|
|
def only_output_mapping_data_in_db(mapping_data: pd.DataFrame) -> None:
|
|
doc_id_list = mapping_data["doc_id"].unique().tolist()
|
|
data_in_mapping_df_list = []
|
|
for doc_id in doc_id_list:
|
|
doc_mapping_data = mapping_data[mapping_data["doc_id"] == doc_id]
|
|
|
|
document_mapping = query_document_fund_mapping(doc_id, rerun=False)
|
|
fund_id_list = document_mapping["FundId"].unique().tolist()
|
|
sec_id_list = document_mapping["SecId"].unique().tolist()
|
|
id_list = fund_id_list + sec_id_list
|
|
# filter doc_mapping_data by id_list or empty id
|
|
filter_doc_mapping_data = doc_mapping_data[(doc_mapping_data["investment_id"].isin(id_list)) | (doc_mapping_data["investment_id"] == "")]
|
|
data_in_mapping_df_list.append(filter_doc_mapping_data)
|
|
result_mapping_data_df = pd.concat(data_in_mapping_df_list)
|
|
result_mapping_data_df.reset_index(drop=True, inplace=True)
|
|
|
|
return result_mapping_data_df
|
|
|
|
|
|
def batch_filter_pdf_files(
|
|
pdf_folder: str,
|
|
doc_data_excel_file: str = None,
|
|
output_folder: str = r"/data/emea_ar/output/filter_pages/",
|
|
special_doc_id_list: list = None,
|
|
) -> None:
|
|
pdf_files = glob(pdf_folder + "*.pdf")
|
|
doc_list = []
|
|
if special_doc_id_list is not None and len(special_doc_id_list) > 0:
|
|
doc_list = special_doc_id_list
|
|
if (
|
|
len(doc_list) == 0
|
|
and doc_data_excel_file is not None
|
|
and len(doc_data_excel_file) > 0
|
|
and os.path.exists(doc_data_excel_file)
|
|
):
|
|
doc_data_df = pd.read_excel(doc_data_excel_file)
|
|
doc_data_df = doc_data_df[doc_data_df["Checked"] == 1]
|
|
doc_list = [str(doc_id) for doc_id in doc_data_df["doc_id"].tolist()]
|
|
result_list = []
|
|
result_details = []
|
|
for pdf_file in tqdm(pdf_files):
|
|
pdf_base_name = os.path.basename(pdf_file)
|
|
doc_id = pdf_base_name.split(".")[0]
|
|
if doc_list is not None and doc_id not in doc_list:
|
|
continue
|
|
doc_datapoint_page_info, doc_result_details = filter_pages(
|
|
doc_id=doc_id, pdf_folder=pdf_folder
|
|
)
|
|
result_list.append(doc_datapoint_page_info)
|
|
result_details.extend(doc_result_details)
|
|
|
|
result_df = pd.DataFrame(result_list)
|
|
result_df.reset_index(drop=True, inplace=True)
|
|
|
|
result_details_df = pd.DataFrame(result_details)
|
|
result_details_df.reset_index(drop=True, inplace=True)
|
|
|
|
logger.info(f"Saving the result to {output_folder}")
|
|
os.makedirs(output_folder, exist_ok=True)
|
|
time_stamp = time.strftime("%Y%m%d%H%M%S", time.localtime())
|
|
output_file = os.path.join(
|
|
output_folder,
|
|
f"datapoint_page_info_{len(result_df)}_documents_{time_stamp}.xlsx",
|
|
)
|
|
with pd.ExcelWriter(output_file) as writer:
|
|
result_df.to_excel(writer, index=False, sheet_name="dp_page_info")
|
|
result_details_df.to_excel(
|
|
writer, index=False, sheet_name="dp_page_info_details"
|
|
)
|
|
|
|
if len(special_doc_id_list) == 0:
|
|
logger.info(f"Calculating metrics for {output_file}")
|
|
metrics_output_folder = r"/data/emea_ar/output/metrics/"
|
|
missing_error_list, metrics_list, metrics_file = get_metrics(
|
|
data_type="page_filter",
|
|
prediction_file=output_file,
|
|
prediction_sheet_name="dp_page_info",
|
|
ground_truth_file=doc_data_excel_file,
|
|
output_folder=metrics_output_folder,
|
|
)
|
|
return missing_error_list, metrics_list, metrics_file
|
|
|
|
|
|
def get_metrics(
|
|
data_type: str,
|
|
prediction_file: str,
|
|
prediction_sheet_name: str,
|
|
ground_truth_file: str,
|
|
ground_truth_sheet_name: str = None,
|
|
output_folder: str = None,
|
|
) -> None:
|
|
metrics = Metrics(
|
|
data_type=data_type,
|
|
prediction_file=prediction_file,
|
|
prediction_sheet_name=prediction_sheet_name,
|
|
ground_truth_file=ground_truth_file,
|
|
ground_truth_sheet_name=ground_truth_sheet_name,
|
|
output_folder=output_folder,
|
|
)
|
|
missing_error_list, metrics_list, metrics_file = metrics.get_metrics(strict_model=False)
|
|
return missing_error_list, metrics_list, metrics_file
|
|
|
|
|
|
def test_auto_generate_instructions():
|
|
"""
|
|
doc_id: str,
|
|
pdf_file: str,
|
|
page_text_dict: dict,
|
|
datapoint_page_info: dict,
|
|
document_mapping_info_df: pd.DataFrame
|
|
"""
|
|
doc_id = "402397014"
|
|
pdf_file = f"/data/emea_ar/small_pdf/{doc_id}.pdf"
|
|
document_mapping_info_df = query_document_fund_mapping(doc_id, rerun=False)
|
|
filter_pages = FilterPages(doc_id, pdf_file, document_mapping_info_df)
|
|
page_text_dict = filter_pages.page_text_dict
|
|
datapoint_page_info, datapoint_page_info_details = filter_pages.start_job()
|
|
datapoint_list = list(datapoint_page_info.keys())
|
|
datapoint_list.remove("doc_id")
|
|
|
|
data_extraction = DataExtraction(
|
|
doc_id, pdf_file, page_text_dict, datapoint_page_info, document_mapping_info_df
|
|
)
|
|
page_index_list = list(page_text_dict.keys())
|
|
if len(page_index_list) > 0:
|
|
page_text = ""
|
|
for datapoint in datapoint_list:
|
|
if len(datapoint_page_info[datapoint]) > 0:
|
|
page_index_list = datapoint_page_info[datapoint]
|
|
page_text = page_text_dict[page_index_list[0]]
|
|
break
|
|
|
|
output_folder = (
|
|
r"/data/emea_ar/basic_information/prompts_example/generate_by_config/"
|
|
)
|
|
os.makedirs(output_folder, exist_ok=True)
|
|
tor_instructions_text = data_extraction.get_instructions_by_datapoints(
|
|
page_text, ["tor"]
|
|
)
|
|
with open(
|
|
os.path.join(output_folder, "tor_instructions.txt"), "w", encoding="utf-8"
|
|
) as f:
|
|
f.write(tor_instructions_text)
|
|
|
|
ter_instructions_text = data_extraction.get_instructions_by_datapoints(
|
|
page_text, ["ter"]
|
|
)
|
|
with open(
|
|
os.path.join(output_folder, "ter_instructions.txt"), "w", encoding="utf-8"
|
|
) as f:
|
|
f.write(ter_instructions_text)
|
|
|
|
ogc_instructions_text = data_extraction.get_instructions_by_datapoints(
|
|
page_text, ["ogc"]
|
|
)
|
|
with open(
|
|
os.path.join(output_folder, "ogc_instructions.txt"), "w", encoding="utf-8"
|
|
) as f:
|
|
f.write(ogc_instructions_text)
|
|
|
|
performance_fee_instructions_text = (
|
|
data_extraction.get_instructions_by_datapoints(
|
|
page_text, ["performance_fee"]
|
|
)
|
|
)
|
|
with open(
|
|
os.path.join(output_folder, "performance_fee_instructions.txt"),
|
|
"w",
|
|
encoding="utf-8",
|
|
) as f:
|
|
f.write(performance_fee_instructions_text)
|
|
|
|
ter_ogc_instructions_text = data_extraction.get_instructions_by_datapoints(
|
|
page_text, ["ter", "ogc"]
|
|
)
|
|
with open(
|
|
os.path.join(output_folder, "ter_ogc_instructions.txt"),
|
|
"w",
|
|
encoding="utf-8",
|
|
) as f:
|
|
f.write(ter_ogc_instructions_text)
|
|
|
|
ter_performance_fee_instructions_text = (
|
|
data_extraction.get_instructions_by_datapoints(
|
|
page_text, ["ter", "performance_fee"]
|
|
)
|
|
)
|
|
with open(
|
|
os.path.join(output_folder, "ter_performance_fee_instructions.txt"),
|
|
"w",
|
|
encoding="utf-8",
|
|
) as f:
|
|
f.write(ter_performance_fee_instructions_text)
|
|
|
|
ogc_ter_performance_fee_instructions_text = (
|
|
data_extraction.get_instructions_by_datapoints(
|
|
page_text, ["ogc", "ter", "performance_fee"]
|
|
)
|
|
)
|
|
with open(
|
|
os.path.join(output_folder, "ogc_ter_performance_fee_instructions.txt"),
|
|
"w",
|
|
encoding="utf-8",
|
|
) as f:
|
|
f.write(ogc_ter_performance_fee_instructions_text)
|
|
|
|
|
|
def test_data_extraction_metrics():
|
|
data_type = "data_extraction"
|
|
# prediction_file = r"/data/emea_ar/output/mapping_data/total/mapping_data_info_88_documents_by_image_20240920033929.xlsx"
|
|
prediction_file = r"/data/emea_ar/output/mapping_data/total/mapping_data_info_88_documents_by_text_20240922152517.xlsx"
|
|
# prediction_file = r"/data/emea_ar/output/mapping_data/docs/by_text/excel/481475385.xlsx"
|
|
prediction_sheet_name = "mapping_data"
|
|
ground_truth_file = r"/data/emea_ar/ground_truth/data_extraction/mapping_data_info_73_documents.xlsx"
|
|
ground_truth_sheet_name = "mapping_data"
|
|
metrics_output_folder = r"/data/emea_ar/output/metrics/"
|
|
missing_error_list, metrics_list, metrics_file = get_metrics(
|
|
data_type,
|
|
prediction_file,
|
|
prediction_sheet_name,
|
|
ground_truth_file,
|
|
ground_truth_sheet_name,
|
|
metrics_output_folder,
|
|
)
|
|
|
|
|
|
def test_mapping_raw_name():
|
|
doc_id = "337293427"
|
|
# KBC Bonds Inflation-Linked Bonds Distribution Shares
|
|
# KBC Bonds Inflation-Linked Bonds Institutional B Shares
|
|
raw_name = "KBC Bonds Inflation-Linked Bonds Institutional B Shares"
|
|
raw_share_name = "Institutional B Shares"
|
|
output_folder = r"/data/emea_ar/output/mapping_data/docs/by_text/"
|
|
data_mapping = DataMapping(
|
|
doc_id,
|
|
datapoints=None,
|
|
raw_document_data_list=None,
|
|
document_mapping_info_df=None,
|
|
output_data_folder=output_folder,
|
|
)
|
|
process_cache = {}
|
|
mapping_info = data_mapping.matching_with_database(
|
|
raw_name=raw_name,
|
|
raw_share_name=raw_share_name,
|
|
parent_id="FSGBR051XK",
|
|
matching_type="share",
|
|
process_cache=process_cache
|
|
)
|
|
print(mapping_info)
|
|
|
|
|
|
def test_translate_pdf():
|
|
from core.data_translate import Translate_PDF
|
|
pdf_file = r"/data/emea_ar/pdf/451063582.pdf"
|
|
output_folder = r"/data/translate/output/"
|
|
translate_pdf = Translate_PDF(pdf_file, output_folder)
|
|
translate_pdf.start_job()
|
|
|
|
|
|
def test_replace_abbrevation():
|
|
from utils.biz_utils import replace_abbrevation
|
|
text_list= ["M&G European Credit Investment Fund A CHFH Acc",
|
|
"M&G European Credit Investment Fund A CHFHInc",
|
|
"M&G European Credit Investment Fund A USDHAcc",
|
|
"M&G European High Yield Credit Investment Fund E GBPHedgedAcc",
|
|
"M&G Sustainable European Credit Investment Fd Cl L GBPH Acc",
|
|
"M&G Sustainable Total Return Credit Investment Fd AI HGBPInc",
|
|
"M&G Total Return Credit Investment Fund Class WI GBPHedgedInc",
|
|
"M&G Total Return Credit Investment Fund Class W GBP HedgedInc",
|
|
"M&G Total Return Credit Investment Fund Class P CHF H Acc",
|
|
"M&G Total Return Credit Investment Fund P EUR Inc"]
|
|
for text in text_list:
|
|
result = replace_abbrevation(text)
|
|
logger.info(f"Original text: {text}, replaced text: {result}")
|
|
|
|
|
|
def test_calculate_metrics():
|
|
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
|
|
data_file = r"/data/emea_ar/ground_truth/data_extraction/verify/mapping_data_info_30_documents_all_4_datapoints_20241106_verify_mapping.xlsx"
|
|
mapping_file = r"/data/emea_ar/basic_information/English/sample_doc/emea_doc_with_all_4_dp/doc_ar_data_with_all_4_dp.xlsx"
|
|
|
|
data_df = pd.read_excel(data_file, sheet_name="data_in_doc_mapping")
|
|
data_df = data_df[data_df["check"].isin([0, 1])]
|
|
data_df.fillna("", inplace=True)
|
|
data_df.reset_index(drop=True, inplace=True)
|
|
|
|
mapping_df = pd.read_excel(mapping_file, sheet_name="doc_ar_data_in_db")
|
|
mapping_fund_id = mapping_df["FundId"].unique().tolist()
|
|
mapping_share_id = mapping_df["FundClassId"].unique().tolist()
|
|
mapping_id_list = mapping_fund_id + mapping_share_id
|
|
# filter data_df whether investment_id in mapping_id_list
|
|
filter_data_df = data_df[(data_df["investment_id"].isin(mapping_id_list)) | (data_df["investment_id"] == "")]
|
|
|
|
# Investment mapping data
|
|
mapping_metrics = get_sub_metrics(filter_data_df, "investment_mapping")
|
|
logger.info(f"Investment mapping metrics: {mapping_metrics}")
|
|
|
|
# tor data
|
|
tor_data_df = filter_data_df[filter_data_df["datapoint"] == "tor"]
|
|
tor_metrics = get_sub_metrics(tor_data_df, "tor")
|
|
logger.info(f"TOR metrics: {tor_metrics}")
|
|
|
|
# ter data
|
|
ter_data_df = filter_data_df[filter_data_df["datapoint"] == "ter"]
|
|
ter_metrics = get_sub_metrics(ter_data_df, "ter")
|
|
logger.info(f"TER metrics: {ter_metrics}")
|
|
|
|
# ogc data
|
|
ogc_data_df = filter_data_df[filter_data_df["datapoint"] == "ogc"]
|
|
ogc_metrics = get_sub_metrics(ogc_data_df, "ogc")
|
|
logger.info(f"OGC metrics: {ogc_metrics}")
|
|
|
|
# performance_fee data
|
|
performance_fee_data_df = filter_data_df[filter_data_df["datapoint"] == "performance_fee"]
|
|
performance_fee_metrics = get_sub_metrics(performance_fee_data_df, "performance_fee")
|
|
logger.info(f"Performance fee metrics: {performance_fee_metrics}")
|
|
|
|
metrics_df = pd.DataFrame([mapping_metrics, tor_metrics, ter_metrics, ogc_metrics, performance_fee_metrics])
|
|
metrics_df.reset_index(drop=True, inplace=True)
|
|
output_folder = r"/data/emea_ar/ground_truth/data_extraction/verify/"
|
|
output_metrics_file = os.path.join(output_folder,
|
|
r"mapping_data_info_30_documents_all_4_datapoints_roughly_metrics.xlsx")
|
|
with pd.ExcelWriter(output_metrics_file) as writer:
|
|
metrics_df.to_excel(writer, index=False, sheet_name="metrics")
|
|
|
|
|
|
def get_sub_metrics(data_df: pd.DataFrame, data_point: str) -> dict:
|
|
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
|
|
gt_list = [1] * len(data_df)
|
|
pre_list = data_df["check"].tolist()
|
|
# convert pre_list member to be integer
|
|
pre_list = [int(pre) for pre in pre_list]
|
|
|
|
for index, row in data_df.iterrows():
|
|
if row["check"] == 0 and len(row["investment_id"].strip()) > 0:
|
|
pre_list.append(1)
|
|
gt_list.append(0)
|
|
# calculate metrics
|
|
accuracy = accuracy_score(gt_list, pre_list)
|
|
precision = precision_score(gt_list, pre_list)
|
|
recall = recall_score(gt_list, pre_list)
|
|
f1 = f1_score(gt_list, pre_list)
|
|
support = len(data_df)
|
|
|
|
metrics = {
|
|
"DataPoint": data_point,
|
|
"F1": f1,
|
|
"Precision": precision,
|
|
"Recall": recall,
|
|
"Accuracy": accuracy,
|
|
"Support": support
|
|
}
|
|
return metrics
|
|
|
|
def replace_rerun_data(new_data_file: str, original_data_file: str):
|
|
data_in_doc_mapping_sheet = "data_in_doc_mapping"
|
|
total_mapping_data_sheet = "total_mapping_data"
|
|
extract_data_sheet = "extract_data"
|
|
|
|
new_data_in_doc_mapping = pd.read_excel(new_data_file, sheet_name=data_in_doc_mapping_sheet)
|
|
new_total_mapping_data = pd.read_excel(new_data_file, sheet_name=total_mapping_data_sheet)
|
|
new_extract_data = pd.read_excel(new_data_file, sheet_name=extract_data_sheet)
|
|
|
|
document_list = new_data_in_doc_mapping["doc_id"].unique().tolist()
|
|
|
|
original_data_in_doc_mapping = pd.read_excel(original_data_file, sheet_name=data_in_doc_mapping_sheet)
|
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original_total_mapping_data = pd.read_excel(original_data_file, sheet_name=total_mapping_data_sheet)
|
|
original_extract_data = pd.read_excel(original_data_file, sheet_name=extract_data_sheet)
|
|
|
|
# remove data in original data by document_list
|
|
original_data_in_doc_mapping = original_data_in_doc_mapping[~original_data_in_doc_mapping["doc_id"].isin(document_list)]
|
|
original_total_mapping_data = original_total_mapping_data[~original_total_mapping_data["doc_id"].isin(document_list)]
|
|
original_extract_data = original_extract_data[~original_extract_data["doc_id"].isin(document_list)]
|
|
|
|
# merge new data to original data
|
|
new_data_in_doc_mapping = pd.concat([original_data_in_doc_mapping, new_data_in_doc_mapping])
|
|
new_data_in_doc_mapping.reset_index(drop=True, inplace=True)
|
|
new_total_mapping_data = pd.concat([original_total_mapping_data, new_total_mapping_data])
|
|
new_total_mapping_data.reset_index(drop=True, inplace=True)
|
|
new_extract_data = pd.concat([original_extract_data, new_extract_data])
|
|
new_extract_data.reset_index(drop=True, inplace=True)
|
|
|
|
with pd.ExcelWriter(original_data_file) as writer:
|
|
new_data_in_doc_mapping.to_excel(writer, index=False, sheet_name=data_in_doc_mapping_sheet)
|
|
new_total_mapping_data.to_excel(writer, index=False, sheet_name=total_mapping_data_sheet)
|
|
new_extract_data.to_excel(writer, index=False, sheet_name=extract_data_sheet)
|
|
|
|
|
|
def batch_run_documents():
|
|
sample_document_list_folder = r'./sample_documents/'
|
|
document_list_files = glob(sample_document_list_folder + "*.txt")
|
|
|
|
pdf_folder = r"/data/emea_ar/pdf/"
|
|
page_filter_ground_truth_file = (
|
|
r"/data/emea_ar/ground_truth/page_filter/datapoint_page_info_88_documents.xlsx"
|
|
)
|
|
output_extract_data_child_folder = r"/data/emea_ar/output/extract_data/docs/"
|
|
output_extract_data_total_folder = r"/data/emea_ar/output/extract_data/total/"
|
|
output_mapping_child_folder = r"/data/emea_ar/output/mapping_data/docs/"
|
|
output_mapping_total_folder = r"/data/emea_ar/output/mapping_data/total/"
|
|
re_run_extract_data = True
|
|
re_run_mapping_data = True
|
|
force_save_total_data = False
|
|
calculate_metrics = False
|
|
|
|
extract_way = "text"
|
|
special_doc_id_list = ["435128656"]
|
|
if len(special_doc_id_list) == 0:
|
|
force_save_total_data = True
|
|
# file_base_name_candidates = ["sample_document_complex", "emea_case_from_word_complex"]
|
|
file_base_name_candidates = ["sample_document_complex"]
|
|
for document_list_file in document_list_files:
|
|
file_base_name = os.path.basename(document_list_file).replace(".txt", "")
|
|
if (file_base_name_candidates is not None and
|
|
len(file_base_name_candidates) > 0 and
|
|
file_base_name not in file_base_name_candidates):
|
|
continue
|
|
with open(document_list_file, "r", encoding="utf-8") as f:
|
|
doc_id_list = f.readlines()
|
|
doc_id_list = [doc_id.strip() for doc_id in doc_id_list]
|
|
batch_start_job(
|
|
pdf_folder,
|
|
page_filter_ground_truth_file,
|
|
output_extract_data_child_folder,
|
|
output_mapping_child_folder,
|
|
output_extract_data_total_folder,
|
|
output_mapping_total_folder,
|
|
extract_way,
|
|
doc_id_list,
|
|
re_run_extract_data,
|
|
re_run_mapping_data,
|
|
force_save_total_data=force_save_total_data,
|
|
calculate_metrics=calculate_metrics,
|
|
total_data_prefix=file_base_name
|
|
)
|
|
else:
|
|
batch_start_job(
|
|
pdf_folder,
|
|
page_filter_ground_truth_file,
|
|
output_extract_data_child_folder,
|
|
output_mapping_child_folder,
|
|
output_extract_data_total_folder,
|
|
output_mapping_total_folder,
|
|
extract_way,
|
|
special_doc_id_list,
|
|
re_run_extract_data,
|
|
re_run_mapping_data,
|
|
force_save_total_data=force_save_total_data,
|
|
calculate_metrics=calculate_metrics,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
batch_run_documents()
|
|
|
|
# new_data_file = r"/data/emea_ar/output/mapping_data/total/mapping_data_info_15_documents_by_text_20241121154243.xlsx"
|
|
# original_data_file = r"/data/emea_ar/ground_truth/data_extraction/verify/mapping_data_info_30_documents_all_4_datapoints_20241106_verify_mapping.xlsx"
|
|
# replace_rerun_data(new_data_file, original_data_file)
|
|
# test_calculate_metrics()
|
|
# test_replace_abbrevation()
|
|
# test_translate_pdf()
|
|
# test_mapping_raw_name()
|
|
# test_data_extraction_metrics()
|
|
|
|
# batch_filter_pdf_files(
|
|
# pdf_folder, page_filter_ground_truth_file, prediction_output_folder, special_doc_id_list
|
|
# )
|
|
|
|
# data_type = "page_filter"
|
|
# prediction_file = r"/data/emea_ar/output/filter_pages/datapoint_page_info_73_documents_20240903145002.xlsx"
|
|
# missing_error_list, metrics_list, metrics_file = get_metrics(
|
|
# data_type, prediction_file, page_filter_ground_truth_file, metrics_output_folder
|
|
# )
|
|
|
|
# test_auto_generate_instructions()
|
|
|
|
# batch_extract_data(
|
|
# pdf_folder,
|
|
# page_filter_ground_truth_file,
|
|
# output_extract_data_child_folder,
|
|
# output_extract_data_total_folder,
|
|
# special_doc_id_list,
|
|
# re_run,
|
|
# )
|
|
|
|
# doc_id = "476492237"
|
|
# extract_way = "image"
|
|
# extract_data(doc_id,
|
|
# pdf_folder,
|
|
# output_extract_data_child_folder,
|
|
# extract_way,
|
|
# re_run_extract_data)
|
|
|
|
|
|
|
|
|
|
|