import os import json import pandas as pd from glob import glob from tqdm import tqdm import time import fitz import re from io import BytesIO from traceback import print_exc from utils.logger import logger from utils.pdf_download import download_pdf_from_documents_warehouse from utils.sql_query_util import query_document_fund_mapping from utils.pdf_util import PDFUtil from utils.biz_utils import add_slash_to_text_as_regex from core.page_filter import FilterPages from core.data_extraction import DataExtraction from core.data_mapping import DataMapping from core.metrics import Metrics class EMEA_AR_Parsing: def __init__( self, doc_id: str, pdf_folder: str = r"/data/emea_ar/pdf/", output_extract_data_folder: str = r"/data/emea_ar/output/extract_data/docs/", output_mapping_data_folder: str = r"/data/emea_ar/output/mapping_data/docs/", extract_way: str = "text", drilldown_folder: str = r"/data/emea_ar/output/drilldown/", ) -> None: self.doc_id = doc_id self.pdf_folder = pdf_folder os.makedirs(self.pdf_folder, exist_ok=True) self.pdf_file = self.download_pdf() self.document_mapping_info_df = query_document_fund_mapping(doc_id, rerun=False) if extract_way is None or len(extract_way) == 0: extract_way = "text" self.extract_way = extract_way self.output_extract_image_folder = None if self.extract_way == "image": self.output_extract_image_folder = ( r"/data/emea_ar/output/extract_data/images/" ) os.makedirs(self.output_extract_image_folder, exist_ok=True) if output_extract_data_folder is None or len(output_extract_data_folder) == 0: output_extract_data_folder = r"/data/emea_ar/output/extract_data/docs/" if not output_extract_data_folder.endswith("/"): output_extract_data_folder = f"{output_extract_data_folder}/" if extract_way is not None and len(extract_way) > 0: output_extract_data_folder = ( f"{output_extract_data_folder}by_{extract_way}/" ) self.output_extract_data_folder = output_extract_data_folder os.makedirs(self.output_extract_data_folder, exist_ok=True) if output_mapping_data_folder is None or len(output_mapping_data_folder) == 0: output_mapping_data_folder = r"/data/emea_ar/output/mapping_data/docs/" if not output_mapping_data_folder.endswith("/"): output_mapping_data_folder = f"{output_mapping_data_folder}/" if extract_way is not None and len(extract_way) > 0: output_mapping_data_folder = ( f"{output_mapping_data_folder}by_{extract_way}/" ) self.output_mapping_data_folder = output_mapping_data_folder os.makedirs(self.output_mapping_data_folder, exist_ok=True) self.filter_pages = FilterPages( self.doc_id, self.pdf_file, self.document_mapping_info_df ) self.page_text_dict = self.filter_pages.page_text_dict self.datapoint_page_info, self.result_details = self.get_datapoint_page_info() self.datapoints = self.get_datapoints_from_datapoint_page_info() if drilldown_folder is None or len(drilldown_folder) == 0: drilldown_folder = r"/data/emea_ar/output/drilldown/" os.makedirs(drilldown_folder, exist_ok=True) self.drilldown_folder = drilldown_folder def download_pdf(self) -> str: pdf_file = download_pdf_from_documents_warehouse(self.pdf_folder, self.doc_id) return pdf_file def get_datapoint_page_info(self) -> tuple: datapoint_page_info, result_details = self.filter_pages.start_job() return datapoint_page_info, result_details def get_datapoints_from_datapoint_page_info(self) -> list: datapoints = list(self.datapoint_page_info.keys()) if "doc_id" in datapoints: datapoints.remove("doc_id") return datapoints def extract_data( self, re_run: bool = False, ) -> list: found_data = False if not re_run: output_data_json_folder = os.path.join( self.output_extract_data_folder, "json/" ) os.makedirs(output_data_json_folder, exist_ok=True) json_file = os.path.join(output_data_json_folder, f"{self.doc_id}.json") if os.path.exists(json_file): logger.info( f"The document: {self.doc_id} has been parsed, loading data from {json_file}" ) with open(json_file, "r", encoding="utf-8") as f: data_from_gpt = json.load(f) found_data = True if not found_data: try: data_extraction = DataExtraction( self.doc_id, self.pdf_file, self.output_extract_data_folder, self.page_text_dict, self.datapoint_page_info, self.datapoints, self.document_mapping_info_df, extract_way=self.extract_way, output_image_folder=self.output_extract_image_folder, ) data_from_gpt = data_extraction.extract_data() except Exception as e: logger.error(f"Error: {e}") data_from_gpt = {"data": []} # Drilldown data to relevant PDF document annotation_list = [] try: annotation_list = self.drilldown_pdf_document(data_from_gpt) except Exception as e: logger.error(f"Error: {e}") return data_from_gpt, annotation_list def drilldown_pdf_document(self, data_from_gpt: list) -> list: logger.info(f"Drilldown PDF document for doc_id: {self.doc_id}") pdf_util = PDFUtil(self.pdf_file) drilldown_data_list = [] for data in data_from_gpt: doc_id = str(data.get("doc_id", "")) page_index = data.get("page_index", -1) if page_index == -1: continue extract_data_list = data.get("extract_data", {}).get("data", []) dp_reported_name_dict = data.get("extract_data", {}).get("dp_reported_name", {}) highlighted_value_list = [] for extract_data in extract_data_list: for data_point, value in extract_data.items(): if value in highlighted_value_list: continue if data_point in ["ter", "ogc", "performance_fee"]: continue drilldown_data = { "doc_id": doc_id, "page_index": page_index, "data_point": data_point, "parent_text_block": None, "value": value, "annotation_attribute": {} } drilldown_data_list.append(drilldown_data) highlighted_value_list.append(value) for data_point, reported_name in dp_reported_name_dict.items(): if reported_name in highlighted_value_list: continue data_point = f"{data_point}_reported_name" drilldown_data = { "doc_id": doc_id, "page_index": page_index, "data_point": data_point, "parent_text_block": None, "value": reported_name, "annotation_attribute": {} } drilldown_data_list.append(drilldown_data) highlighted_value_list.append(reported_name) drilldown_result = pdf_util.batch_drilldown(drilldown_data_list=drilldown_data_list, output_pdf_folder=self.drilldown_folder) annotation_list = [] if len(drilldown_result) > 0: logger.info(f"Drilldown PDF document for doc_id: {doc_id} successfully") annotation_list = drilldown_result.get("annotation_list", []) for annotation in annotation_list: annotation["doc_id"] = doc_id if self.drilldown_folder is not None and len(self.drilldown_folder) > 0: drilldown_data_folder = os.path.join(self.drilldown_folder, "data/") os.makedirs(drilldown_data_folder, exist_ok=True) drilldown_file = os.path.join(drilldown_data_folder, f"{doc_id}_drilldown.xlsx") drilldown_source_df = pd.DataFrame(drilldown_data_list) annotation_list_df = pd.DataFrame(annotation_list) # set drilldown_result_df column order as doc_id, pdf_file, page_index, # data_point, value, matching_val_area, normalized_bbox try: annotation_list_df = annotation_list_df[["doc_id", "pdf_file", "page_index", "data_point", "value", "matching_val_area", "normalized_bbox"]] except Exception as e: logger.error(f"Error: {e}") logger.info(f"Writing drilldown data to {drilldown_file}") try: with pd.ExcelWriter(drilldown_file) as writer: drilldown_source_df.to_excel(writer, index=False, sheet_name="source_data") annotation_list_df.to_excel(writer, index=False, sheet_name="drilldown_data") except Exception as e: logger.error(f"Error: {e}") annotation_list = annotation_list_df.to_dict(orient="records") return annotation_list def mapping_data(self, data_from_gpt: list, re_run: bool = False) -> list: if not re_run: output_data_json_folder = os.path.join( self.output_mapping_data_folder, "json/" ) os.makedirs(output_data_json_folder, exist_ok=True) json_file = os.path.join(output_data_json_folder, f"{self.doc_id}.json") if os.path.exists(json_file): logger.info( f"The fund/ share of this document: {self.doc_id} has been mapped, loading data from {json_file}" ) with open(json_file, "r", encoding="utf-8") as f: doc_mapping_data = json.load(f) return doc_mapping_data """ doc_id, datapoints: list, raw_document_data_list: list, document_mapping_info_df: pd.DataFrame, output_data_folder: str, """ data_mapping = DataMapping( self.doc_id, self.datapoints, data_from_gpt, self.document_mapping_info_df, self.output_mapping_data_folder, ) return data_mapping.mapping_raw_data() def filter_pages(doc_id: str, pdf_folder: str) -> None: logger.info(f"Filter EMEA AR PDF pages for doc_id: {doc_id}") emea_ar_parsing = EMEA_AR_Parsing(doc_id, pdf_folder) datapoint_page_info, result_details = emea_ar_parsing.get_datapoint_page_info() return datapoint_page_info, result_details def extract_data( doc_id: str, pdf_folder: str, output_data_folder: str, extract_way: str = "text", re_run: bool = False, ) -> None: logger.info(f"Extract EMEA AR data for doc_id: {doc_id}") emea_ar_parsing = EMEA_AR_Parsing( doc_id, pdf_folder, output_extract_data_folder=output_data_folder, extract_way=extract_way, ) data_from_gpt, annotation_list = emea_ar_parsing.extract_data(re_run) return data_from_gpt, annotation_list def mapping_data( doc_id: str, pdf_folder: str, output_extract_data_folder: str, output_mapping_folder: str, extract_way: str = "text", re_run_extract_data: bool = False, re_run_mapping_data: bool = False, ) -> None: logger.info(f"Extract EMEA AR data for doc_id: {doc_id}") emea_ar_parsing = EMEA_AR_Parsing( doc_id, pdf_folder, output_extract_data_folder=output_extract_data_folder, output_mapping_data_folder=output_mapping_folder, extract_way=extract_way, ) doc_data_from_gpt, annotation_list = emea_ar_parsing.extract_data(re_run=re_run_extract_data) doc_mapping_data = emea_ar_parsing.mapping_data( data_from_gpt=doc_data_from_gpt, re_run=re_run_mapping_data ) return doc_data_from_gpt, annotation_list, doc_mapping_data def batch_extract_data( pdf_folder: str, doc_data_excel_file: str = None, output_child_folder: str = r"/data/emea_ar/output/extract_data/docs/", output_total_folder: str = r"/data/emea_ar/output/extract_data/total/", extract_way: str = "text", special_doc_id_list: list = None, re_run: bool = False, ) -> 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 = [] 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 data_from_gpt = extract_data( doc_id=doc_id, pdf_folder=pdf_folder, output_data_folder=output_child_folder, extract_way=extract_way, re_run=re_run, ) result_list.extend(data_from_gpt) if special_doc_id_list is None or len(special_doc_id_list) == 0: result_df = pd.DataFrame(result_list) result_df.reset_index(drop=True, inplace=True) logger.info(f"Saving the result to {output_total_folder}") os.makedirs(output_total_folder, exist_ok=True) time_stamp = time.strftime("%Y%m%d%H%M%S", time.localtime()) output_file = os.path.join( output_total_folder, f"extract_data_info_{len(pdf_files)}_documents_{time_stamp}.xlsx", ) with pd.ExcelWriter(output_file) as writer: result_df.to_excel(writer, index=False, sheet_name="extract_data_info") def batch_start_job( pdf_folder: str, 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", 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) 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(): # special_doc_id_list = ["505174428", "510326848", "349679479"] # check_mapping_doc_id_list = [ # "327956364", # "391456740", # "391736837", # "458359181", # "486383912", # "497497599", # "529925114", # "321733631", # "334718372", # "344636875", # "362246081", # "445256897", # "449623976", # "458291624", # "478585901", # "492121213", # "502821436", # "507967525", # "481475385", # "508854243", # "520879048", # "402181770", # "463081566", # "502693599", # "509845549", # "389171486", # "323390570", # "366179419", # "486378555", # "506559375", # "479793787", # "471641628", # ] # English documents with ground truth # check_db_mapping_doc_id_list = [ # "292989214", # "316237292", # "321733631", # "323390570", # "327956364", # "332223498", # "333207452", # "334718372", # "344636875", # "362246081", # "366179419", # "380945052", # "382366116", # "387202452", # "389171486", # "391456740", # "391736837", # "394778487", # "401684600", # "402113224", # "402181770", # "402397014", # "405803396", # "445102363", # "445256897", # "448265376", # "449555622", # "449623976", # "458291624", # "458359181", # "463081566", # "469138353", # "471641628", # "476492237", # "478585901", # "478586066", # "479042264", # "479042269", # "479793787", # "481475385", # "483617247", # "486378555", # "486383912", # "492121213", # "497497599", # "502693599" # ] # Documents in EMEA Case 1.docx # check_db_mapping_doc_id_list = [ # "424976833", # "425480144", # "427637151", # "429564034", # "429950833", # "430240853", # "431073795", # "434710819", # "434851173", # "434902020", # "434924914", # "435128656", # "440029306", # "466371135", # "466528487", # "466859621", # "466860852", # "467595142", # "467788879", # "470515549" # ] # documents in New EMEA Documents sample.xlsx as typical documents # """ # Below 9 documents can't get data by keywords or ChatGPT # 526747539, # 534112077, # 535798742, # 536299372, # 539566148, # 541343431, # 541923319, # 543243585, # 543243654 # """ # check_db_mapping_doc_id_list = [ # "511052670", # "520733219", # "524306810", # "526747539", # "528783089", # "532422720", # "532438210", # "534112077", # "534538571", # "534538682", # "535798742", # "536299372", # "539566148", # "539604165", # "540056900", # "541343431", # "541669780", # "541669996", # "541670397", # "541923319", # "542335994", # "543243585", # "543243654", # "543244170", # "543519140", # "543519615", # "543628379", # "543809340", # "543944737" # ] # documents in Final list of EMEA documents.xlsx as typical documents # check_db_mapping_doc_id_list = [ # "532500349", # "535324239", # "532442891", # "543243650", # "528588598", # "532437639", # "527525440", # "534987291", # "534112055", # "533482585", # "544208174", # "534547266", # "544713166", # "526463547", # "534535569", # "534106067", # "532486560", # "532781760", # "533727067", # "527256381", # "533392425", # "532179676", # "534300608", # "539233950", # # "533727908", # "532438414", # "533681744", # "537654645", # "533594905", # "537926443", # "533499655", # "533862814", # "544918611", # "539087870", # "536343790" # ] # document samples 2024-11-06 # check_db_mapping_doc_id_list = ["546483469", # "546375582", # "546375575", # "546375576", # "546375577", # "546375568", # "546371033", # "546632761", # "546632544", # "546632464", # "546724583", # "546724552", # "546694677", # "546660422", # "546638908", # "546632845", # "546105299", # "546085481", # "546078693", # "546078650", # "546289930", # "546289910", # "542967371", # "542798238", # "546048730", # "546048143", # "546047619", # "546047528", # "546046730", # "546919329"] # document samples: 30 documents, all with 4 data points # check_db_mapping_doc_id_list = ["479742284", # "501380497", # "501380553", # "501380775", # "501380801", # "501600428", # "501600429", # "501600541", # "501600549", # "503659548", # "506326520", # "507720522", # "507928179", # "508981020", # "509133771", # "509743502", # "514636951", # "514636952", # "514636953", # "514636954", # "514636955", # "514636957", # "514636958", # "514636959", # "514636985", # "514636988", # "514636990", # "514636993", # "514636994", # "539794746", # ] # Sample documents with special cases check_db_mapping_doc_id_list = [ "334584772", "406913630", "407275419", "337937633", "337293427", "404712928", "451063582", "451878128", "425595958", "536344026", "532422548", "423418540", "423418395", "532998065", "540307575", "423395975", "508704368", "481482392", "466580448", "423365707", "423364758", "422761666", "422760156", "422760148", "422686965", "492029971", "510300817", "512745032", "514213638", "527525440", "534535767" ] # total_data_prefix = "complex_doc_" # documents from EMEA Case 1.docx check_db_mapping_doc_id_list = [ "510483464", "525412280", "528208797", "435128656", "425480144", "466528487", "434902020", "440029306", "431073795", "430240853", "427637151", "434924914", "467595142", "466859621", "429564034", "424976833", "466860852", "466371135", "470515549", "434851173", "434710819", "429950833", "467788879" ] total_data_prefix = "complex_doc_from_word" special_doc_id_list = check_db_mapping_doc_id_list special_doc_id_list = ["334584772", "435128656"] special_doc_id_list = ["514213638"] 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_ways = ["text"] for extract_way in extract_ways: 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, total_data_prefix=total_data_prefix ) 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)