realize merge_output_data function, whether to output as this format, depends on confirmation with data/ developer teams
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Blade He
parent
309bb714f6
commit
65e752e25a
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@ -6,3 +6,4 @@
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/test_calc_metrics.py
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/test_metrics
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/data
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/sample_documents/japan_prospectus.txt
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@ -10,11 +10,7 @@ from utils.logger import logger
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from utils.pdf_util import PDFUtil
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def drilldown_documents():
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# doc_id: str,
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pdf_folder = r"/data/emea_ar/pdf/"
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drilldown_folder = r"/data/emea_ar/output/drilldown/"
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extract_data_folder = r'/data/emea_ar/output/extract_data/docs/by_text/json/'
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def drilldown_documents(pdf_folder: str, extract_data_folder: str, drilldown_folder: str):
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extract_files = glob(extract_data_folder + '*.json')
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for index, json_file in enumerate(tqdm(extract_files)):
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@ -156,5 +152,8 @@ def calculate_metrics():
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if __name__ == "__main__":
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pdf_folder = r"/data/emea_ar/pdf/"
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drilldown_folder = r"/data/emea_ar/output/drilldown/"
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extract_data_folder = r'/data/emea_ar/output/extract_data/docs/by_text/json/'
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drilldown_documents()
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# calculate_metrics()
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85
main.py
85
main.py
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@ -935,11 +935,11 @@ def batch_run_documents():
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def batch_initial_document():
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sample_document_list_folder = r'./sample_documents/'
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document_list_file = os.path.join(sample_document_list_folder, "sample_documents_12_11.txt")
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document_list_file = os.path.join(sample_document_list_folder, "japan_prospectus.txt")
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with open(document_list_file, "r", encoding="utf-8") as f:
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doc_id_list = f.readlines()
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doc_id_list = [doc_id.strip() for doc_id in doc_id_list]
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pdf_folder = r"/data/emea_ar/pdf/"
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pdf_folder = r"/data/illume/japan_prospectus/pdf/"
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page_filter_ground_truth_file = (
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r"/data/emea_ar/ground_truth/page_filter/datapoint_page_info_88_documents.xlsx"
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)
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@ -952,9 +952,88 @@ def batch_initial_document():
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output_extract_data_folder=output_extract_data_child_folder,
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output_mapping_data_folder=output_mapping_child_folder)
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def merge_output_data(data_file_path: str,
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document_mapping_file: str,
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output_data_file_path: str):
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data_df = pd.read_excel(data_file_path, sheet_name="total_mapping_data")
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document_mapping_df = pd.read_excel(document_mapping_file, sheet_name="doc_date")
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# set doc_id to be string type
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data_df["doc_id"] = data_df["doc_id"].astype(str)
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document_mapping_df["DocumentId"] = document_mapping_df["DocumentId"].astype(str)
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"""
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doc_id page_index raw_name datapoint value raw_check comment investment_type investment_id investment_name similarity
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553242368 344 Deutsche MSCI World Index Fund tor 61 33 FS0000AY1Y Xtrackers MSCI World Index Fund 0.75
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553242368 344 db x-trackers EUR Liquid Corporate 12.5 UCITS ETF - Klasse 1C ter 0.35 1 F000018PY1 Xtrackers EUR Corporate Green Bond UCITS ETF 1C 0.462
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"""
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doc_id_list = data_df["doc_id"].unique().tolist()
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data_point_dict = {
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"tor": "TurnoverRatio",
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"ter": "NetExpenseRatio",
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"ogc": "OngoingCharge",
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"performance_fee": "PerformanceFee"
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}
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total_data_list = []
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for doc_id in tqdm(doc_id_list):
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doc_data_list = []
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doc_data_df = data_df[data_df["doc_id"] == doc_id]
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doc_date = str(document_mapping_df[document_mapping_df["DocumentId"] == doc_id]["EffectiveDate"].values[0])[0:10]
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exist_raw_name_list = []
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for index, row in doc_data_df.iterrows():
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doc_id = str(row["doc_id"])
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page_index = int(row["page_index"])
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raw_name = str(row["raw_name"])
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datapoint = str(row["datapoint"])
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value = row["value"]
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investment_type = row["investment_type"]
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investment_id = row["investment_id"]
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investment_name = row["investment_name"]
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exist = False
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for exist_raw_name_info in exist_raw_name_list:
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exist_raw_name = exist_raw_name_info["raw_name"]
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exist_investment_type = exist_raw_name_info["investment_type"]
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if exist_raw_name == raw_name and exist_investment_type == investment_type:
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exist = True
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break
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if not exist:
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data = {
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"DocumentId": doc_id,
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"investment_type": investment_type,
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"investment_id": investment_id,
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"investment_name": investment_name,
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"EffectiveDate": doc_date,
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"page_index": [],
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"RawName": raw_name,
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"NetExpenseRatio": "",
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"OngoingCharge": "",
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"TurnoverRatio": "",
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"PerformanceFee": ""
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}
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exist_raw_name_list.append({"raw_name": raw_name, "investment_type": investment_type})
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doc_data_list.append(data)
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# find data from total_data_list by raw_name
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for data in doc_data_list:
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if data["RawName"] == raw_name and data["investment_type"] == investment_type:
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update_key = data_point_dict[datapoint]
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data[update_key] = value
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if page_index not in data["page_index"]:
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data["page_index"].append(page_index)
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break
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total_data_list.extend(doc_data_list)
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total_data_df = pd.DataFrame(total_data_list)
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total_data_df.fillna("", inplace=True)
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with pd.ExcelWriter(output_data_file_path) as writer:
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total_data_df.to_excel(writer, index=False, sheet_name="total_data")
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if __name__ == "__main__":
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data_file_path = r"/data/emea_ar/ground_truth/data_extraction/verify/20241211/sample_documents_12_11_mapping_data_info_44_documents_by_text_20241211185546.xlsx"
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document_mapping_file_path = r"/data/emea_ar/basic_information/sample_documents/sample_doc/sample_documents_12_11/doc_mapping_12_11.xlsx"
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output_data_file_path = r"/data/emea_ar/ground_truth/data_extraction/verify/20241211/sample_documents_12_11_merged_data_info.xlsx"
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merge_output_data(data_file_path, document_mapping_file_path, output_data_file_path)
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# batch_initial_document()
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batch_run_documents()
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# batch_run_documents()
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# new_data_file = r"/data/emea_ar/output/mapping_data/total/mapping_data_info_15_documents_by_text_20241121154243.xlsx"
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# original_data_file = r"/data/emea_ar/ground_truth/data_extraction/verify/mapping_data_info_30_documents_all_4_datapoints_20241106_verify_mapping.xlsx"
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@ -276,6 +276,8 @@ def statistics_document(
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describe_stat_df = pd.concat(describe_stat_df_list)
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describe_stat_df.reset_index(drop=True, inplace=True)
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doc_dp_data_df = None
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if doc_ar_data_file_path is not None and os.path.exists(doc_ar_data_file_path):
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doc_ar_data = pd.read_excel(doc_ar_data_file_path, sheet_name=ar_data_sheet_name)
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doc_dp_result = get_document_with_all_4_data_points(None, None, doc_ar_data)
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doc_dp_data_list = []
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@ -299,11 +301,13 @@ def statistics_document(
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doc_page_num_df["DocumentId"] = doc_page_num_df["DocumentId"].astype(str)
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doc_fund_count["DocumentId"] = doc_fund_count["DocumentId"].astype(str)
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doc_share_class_count["DocumentId"] = doc_share_class_count["DocumentId"].astype(str)
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if doc_dp_data_df is not None:
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doc_dp_data_df["DocumentId"] = doc_dp_data_df["DocumentId"].astype(str)
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# merge statistics data for doc_page_num_df, doc_dp_data_df, doc_fund_count, doc_share_class_count based on DocumentId
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doc_page_num_df = doc_page_num_df.merge(doc_fund_count, on="DocumentId", how="left")
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doc_page_num_df = doc_page_num_df.merge(doc_share_class_count, on="DocumentId", how="left")
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if doc_dp_data_df is not None:
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doc_page_num_df = doc_page_num_df.merge(doc_dp_data_df, on="DocumentId", how="left")
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# save statistics data to excel
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@ -1395,7 +1399,10 @@ if __name__ == "__main__":
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# sheet_name="Sheet1",
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# doc_id_column="Document Id",
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# pdf_path=pdf_folder)
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# output_pdf_page_text(pdf_folder, output_folder)
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pdf_folder = r"/data/illume/japan_prospectus/pdf/"
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output_folder = r"/data/illume/japan_prospectus/pdf_txt/"
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output_pdf_page_text(pdf_folder, output_folder)
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# extract_pdf_table(pdf_folder, output_folder)
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# analyze_json_error()
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@ -1409,13 +1416,20 @@ if __name__ == "__main__":
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doc_ar_data_file_path = r"/data/emea_ar/basic_information/sample_documents/sample_doc/sample_documents_12_11/doc_ar_data_12_11.xlsx"
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doc_mapping_data_file_path = r"/data/emea_ar/basic_information/sample_documents/sample_doc/sample_documents_12_11/doc_mapping_12_11.xlsx"
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output_data_folder = r"/data/emea_ar/basic_information/sample_documents/sample_doc/sample_documents_12_11/"
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statistics_document(pdf_folder=pdf_folder,
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doc_mapping_file_path=doc_mapping_data_file_path,
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doc_ar_data_file_path=doc_ar_data_file_path,
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mapping_sheet_name="Sheet1",
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ar_data_sheet_name="doc_ar_data_in_db",
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output_folder=output_data_folder,
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output_file="doc_ar_data_sample_documents_12_11_statistics.xlsx")
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output_file="doc_ar_data_sample_documents_12_11_statistics.xlsx"
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pdf_folder = r"/data/illume/japan_prospectus/pdf/"
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doc_ar_data_file_path = None
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doc_mapping_data_file_path = r"/data/illume/japan_prospectus/materials/document_mapping.xlsx"
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output_data_folder = r"/data/illume/japan_prospectus/materials/"
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output_file = "japan_prospectus_statistics.xlsx"
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# statistics_document(pdf_folder=pdf_folder,
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# doc_mapping_file_path=doc_mapping_data_file_path,
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# doc_ar_data_file_path=doc_ar_data_file_path,
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# mapping_sheet_name="Sheet1",
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# ar_data_sheet_name="doc_ar_data_in_db",
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# output_folder=output_data_folder,
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# output_file=output_file)
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# get_document_extracted_share_diff_by_db()
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# statistics_provider_mapping(
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# provider_mapping_data_file=provider_mapping_data_file,
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