1. metrics's key should be share class name: sec_name
2. support output metrics data as Excel file 3. Optimize instructions for performance_fee_costs
This commit is contained in:
Blade He
parent
1f6b781b12
commit
a090b5cc9e
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@ -567,11 +567,11 @@ def calculate_metrics_based_db_data_file(audit_file_path: str = r"/data/aus_pros
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verify_data_df = pd.DataFrame()
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audit_fields = [
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"DocumentId",
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"FundLegalName",
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"FundId",
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"FundClassLegalName",
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"FundClassId",
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"doc_id",
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"fund_name",
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"fund_id",
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"sec_name",
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"sec_id",
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"management_fee_and_costs",
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"management_fee",
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"administration_fees",
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@ -590,11 +590,11 @@ def calculate_metrics_based_db_data_file(audit_file_path: str = r"/data/aus_pros
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audit_data_df = pd.read_excel(audit_file_path, sheet_name=audit_data_sheet)
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audit_data_df = audit_data_df[audit_fields]
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audit_data_df = audit_data_df.drop_duplicates()
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audit_data_df = audit_data_df.rename(columns={"DocumentId": "doc_id",
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"FundLegalName": "fund_name",
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"FundId": "fund_id",
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"FundClassLegalName": "sec_name",
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"FundClassId": "sec_id"})
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# audit_data_df = audit_data_df.rename(columns={"DocumentId": "doc_id",
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# "FundLegalName": "fund_name",
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# "FundId": "fund_id",
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# "FundClassLegalName": "sec_name",
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# "FundClassId": "sec_id"})
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audit_data_df.fillna("", inplace=True)
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audit_data_df.reset_index(drop=True, inplace=True)
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@ -403,14 +403,15 @@
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"---Example 1 End---",
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"The relevant values: 0.00 and 2.18, are in the range, so the output should be:",
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"{\"data\": []}",
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"B If with pure performance fee in table, please extract relevant values",
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"B. If with pure performance fee in table, please extract relevant values",
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"---Example Start---",
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"\n\nFees and costs summary \nPlatinum Trust Funds \nType of fee or cost Amount How and when paid \nC Class and E Class* -\nStandard Fee Option \nP Class - Performance \nFee Option \nOngoing annual fees and costs \nPerformance fees \nAmounts deducted from your investment in \nrelation to the performance of the product. \nPlatinum International Fund Nil 0.15%\nPlatinum Global Fund (Long Only) Nil 0.24%\n",
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"---Example End---",
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"a. For this example, there is pure \"Performance fees\", please extract relevant values as performance_fee_costs.",
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"b. This example mentioned share classes, please output according to share class.",
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"The output should be",
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"{\"data\": [{\"fund name\": \"Platinum International Fund\", \"share name\": \"C Class\", \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum International Fund\", \"share name\": \"E Class\", \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum International Fund\", \"share name\": \"P Class\", \"performance_fee_costs\": 0.15}, {\"fund name\": \"Platinum Global Fund (Long Only)\", \"share name\": \"C Class\", \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum Global Fund (Long Only)\", \"share name\": \"E Class\", \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum Global Fund (Long Only)\", \"share name\": \"P Class\", \"performance_fee_costs\": 0.24}]}"
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"{\"data\": [{\"fund name\": \"Platinum International Fund\", \"share name\": \"C Class\", \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum International Fund\", \"share name\": \"E Class\", \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum International Fund\", \"share name\": \"P Class\", \"performance_fee_costs\": 0.15}, {\"fund name\": \"Platinum Global Fund (Long Only)\", \"share name\": \"C Class\", \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum Global Fund (Long Only)\", \"share name\": \"E Class\", \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum Global Fund (Long Only)\", \"share name\": \"P Class\", \"performance_fee_costs\": 0.24}]}",
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"C. Identify the value of performance fee and if it is written 0% or 0.00% or 0 or 0.00 then extract the same as 0 do not assume nil for the same and return its values as 0"
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],
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"minimum_initial_investment": [
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@ -518,6 +519,7 @@
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"For main fund: Platinum Asia with values: 2.14 2.99 0.02 0.00 0.21 2.37 3.22, ",
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"the fund: Platinum Asia Entry Fee, both of management_fee and management_fee_and_costs should be 2.16 = 2.14 (the column 1 number) + 0.02 (the column 3 number), performance_fee_costs is 0 (the column 4 number)",
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"the fund: Platinum Asia Nil Entry, both of management_fee and management_fee_and_costs should be 3.01 = 2.99 (the column 2 number) + 0.02 (the column 3 number), performance_fee_costs is 0 (the column 4 number)",
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"Identify the value of the column \"Estimated Performance fees\" and if it is written 0.00 then extract the same as 0 do not assume nil for the same and return its values as 0",
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"Therefore, the output should be:",
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"{\"data\": [{\"fund name\": \"OnePath International Shares Index (Hedged) Entry Fee\", \"share name\": \"OnePath International Shares Index (Hedged) Entry Fee\", \"management_fee_and_costs\": 0.47, \"management_fee\": 0.47, \"performance_fee_costs\": 0},{\"fund name\": \"OnePath International Shares Index (Hedged) Nil Entry\", \"share name\": \"OnePath International Shares Index (Hedged) Nil Entry\", \"management_fee_and_costs\": 1.32, \"management_fee\": 1.32, \"performance_fee_costs\": 0}, {\"fund name\": \"Pendal Concentrated Global Shares Hedged II Entry Fee\", \"share name\": \"Pendal Concentrated Global Shares Hedged II Entry Fee\", \"management_fee_and_costs\": 1.44, \"management_fee\": 1.44, \"performance_fee_costs\": 0}]}, {\"fund name\": \"Pendal Concentrated Global Shares Hedged II Nil Entry\", \"share name\": \"Pendal Concentrated Global Shares Hedged II Nil Entry\", \"management_fee_and_costs\": 2.29, \"management_fee\": 2.29, \"performance_fee_costs\": 0}]}, {\"fund name\": \"Platinum Asia Entry Fee\", \"share name\": \"Platinum Asia Entry Fee\", \"management_fee_and_costs\": 2.16, \"management_fee\": 2.16, \"performance_fee_costs\": 0}, {\"fund name\": \"Platinum Asia Nil Entry\", \"share name\": \"Platinum Asia Nil Entry\", \"management_fee_and_costs\": 3.01, \"management_fee\": 3.01, \"performance_fee_costs\": 0}"
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]
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@ -597,6 +599,7 @@
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"---Example Start---",
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"Performance fee \nPlus other investment fees and costs \nEquals investment fees and costs \nTransaction costs(net) \nBuy-sell spreads \nTransaction costs(gross) \nMLC multi-asset portfolios\nMLC Inflation Plus\nConservative Portfolio\nSuper & Pension \npre-retirement phase \n0.18 \n0.77 \n0.95 \n0.01 \n0.10 / 0.10 \n0.09 \nRetirement Phase \n0.18 \n0.77 \n0.95 \n0.04 \n0.10 / 0.10 \n0.09 \n",
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"---Example End---",
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"Identify the value of the 1st column \"Performance fee\" and if it is written 0.00 then extract the same as 0 do not assume nil for the same and return its values as 0",
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"Please ignore the 3rd column: \"Equals investment fees and costs\" values!!",
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"Please read context carefully, don't miss any data row!!",
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"The output should be:",
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2
main.py
2
main.py
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@ -1560,7 +1560,7 @@ if __name__ == "__main__":
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# "544886057",
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# "550769189",
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# "553449663"]
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# special_doc_id_list = ["446324179"]
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special_doc_id_list = ["420339794", "401212184"]
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# special_doc_id_list = ["391080133", "391080140", "401212184", "412778803", "420339794", "454036250", "414751292"]
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pdf_folder: str = r"/data/aus_prospectus/pdf/"
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output_pdf_text_folder: str = r"/data/aus_prospectus/output/pdf_text/"
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -30,16 +30,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"path_ground_truth = r\"C:\\data\\aus_prospectus\\output\\Performance\\46_documents_ground_truth_with_mapping.xlsx\"\n",
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"path_generated_results = r\"C:\\data\\aus_prospectus\\output\\Performance\\mapping_data_info_46_documents_by_text_20250313024715.xlsx\"\n",
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"provider_mapping_file_path = r\"C:\\Users\\rmahesh\\OneDrive - MORNINGSTAR INC\\Desktop\\NLP Transitions\\Project\\Exprs\\INO71\\dc-ml-dataextraction-llm-aus-nz-pro-AUS_NZ_EXE_COMBINED_PHASE1_PHASE2\\output_files\\ground_truth\\TopProvidersBiz.xlsx\"\n",
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"\n",
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"\n"
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"path_ground_truth = r\"/data/aus_prospectus/ground_truth/phase2_file/46_documents/46_documents_ground_truth_with_mapping.xlsx\"\n",
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"path_generated_results = r\"/data/aus_prospectus/output/mapping_data/total/mapping_data_info_46_documents_by_text_20250313024715.xlsx\"\n",
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"provider_mapping_file_path = r\"/data/aus_prospectus/ground_truth/phase2_file/46_documents/TopProvidersBiz.xlsx\"\n"
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]
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},
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{
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@ -353,7 +351,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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@ -365,132 +363,55 @@
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"\n",
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"\n",
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"All Providers Results: \n",
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"Performance fee and cost - 377377369 truth is null and generated - 0 SPDR® S&P Emerging Markets Carbon Control Fund\n",
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"Performance fee and cost - 397107472 truth is null and generated - 0 AMP Capital Specialist Diversified Fixed Income Fund\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.11 OnePath OneAnswer Frontier Investment Portfolio-OnePath Multi Asset Income Trust\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.07 OA Frontier IP-OnePath Australian Share Trust\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.33 OA Frontier Investment Portfolio- BlackRock Tactical Growth\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.02 OA Frontier Investment Portfolio- Pendal Monthly Income Plus\n",
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"Performance fee and cost - 401212184 truth - 0.41 and generated - 0.13 OnePath Alternatives Growth Trust\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.03 OA Frontier IP-Ausbil Australian Emerging Leaders Trust\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.15 OA Frontier IP-Perpetual Balanced Growth\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.03 OA Frontier IP-Perpetual Conservative Growth\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.06 OA Frontier IP-Platinum International\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.15 OnePath OneAnswer Investment Portfolio - BlackRock Diversified ESG Growth\n",
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"Performance fee and cost - 401212184 truth - 0 and generated - 0.01 ANZ OneAnswer Investment Portfolio - OnePath Balanced Index\n",
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"Performance fee and cost - 401212184 generated is null and truth is - 0 ANZ OneAnswer Investment Portfolio - OnePath Growth Index\n",
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"Performance fee and cost - 409723592 truth is null and generated - 0 Vanguard Index Diversified Bond\n",
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"Performance fee and cost - 409723592 truth is null and generated - 0 Vanguard International Shares Index\n",
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"Performance fee and cost - 409723592 truth is null and generated - 0 Vanguard Investor Short Term Fixed Interest Fund\n",
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"Performance fee and cost - 409723592 truth is null and generated - 0 Vanguard Index Hedged International Shares Fund\n",
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"Performance fee and cost - 409723592 truth is null and generated - 0 Vanguard LifeStrategy Growth\n",
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"Performance fee and cost - 409723592 truth is null and generated - 0 Vanguard LifeStrategy Conservative\n",
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"Performance fee and cost - 409723592 truth is null and generated - 0 Vanguard LifeStrategy High Growth\n",
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"Performance fee and cost - 411062815 truth is null and generated - 13.98 Perpetual WFP-Perpetual Share Plus L/S\n",
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"Performance fee and cost - 411062815 truth - 0 and generated - 0.01 WFP Schroder Fixed Income\n",
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"Performance fee and cost - 411062815 truth - 0 and generated - 15.38 Perpetual Ausbil Australian Emerg Ldrs\n",
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"Performance fee and cost - 411062815 truth - 0.03 and generated - 0.12 WFP Macquarie Income Opportunities\n",
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"Performance fee and cost - 411062815 generated is null and truth is - 0 WFP Diversified Income\n",
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"Performance fee and cost - 412778803 generated is null and truth is - 0.14 \n",
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"Performance fee and cost - 412778803 generated is null and truth is - 0.67 Telstra Property Pension\n",
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"Performance fee and cost - 412778803 generated is null and truth is - 0.01 Telstra Cash Pension\n",
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"Performance fee and cost - 412778803 generated is null and truth is - 0.01 Telstra Australian shares Pension\n",
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"Performance fee and cost - 412778803 generated is null and truth is - 0.14 Telstra Defensive growth Pension\n",
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"Performance fee and cost - 412778803 generated is null and truth is - 0.01 Telstra International shares Pension\n",
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"Performance fee and cost - 414751292 truth - 0.24 and generated - 0 Platinum Global Fund (Long Only)\n",
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"Performance fee and cost - 414751292 truth - 0.15 and generated - 0 \n",
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"Performance fee and cost - 414751292 truth - 0.03 and generated - 0 Platinum International Brands Fund\n",
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"Performance fee and cost - 414751292 truth - 0.86 and generated - 0 Platinum International Healthcare\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 \n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPFPR - Ausbil Aus. Emrging Leaders\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPFPR - Investors Mutual Aus. Shre\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPFPR - Macquarie Inc Opportunities\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MasterKey Pension Fundamentals (Pre Retirement) - MLC Cash\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPFPR - Global Share Fund\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPF - Hedged Global Share Fund\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPFPR - Hedged Global Share Fund\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPFPR - IncomeBuilder\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPF - PIMCO Div. Fixed Interest\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPF - PIMCO Global Bond Fund\n",
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"Performance fee and cost - 420339794 generated is null and truth is - 0 MLC MKPFPR - PIMCO Global Bond Fund\n",
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"Performance fee and cost - 446324179 generated is null and truth is - 0.28 Lifeplan Investment Bond - Allan Gray Australian Equity Fund\n",
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"Performance fee and cost - 446324179 generated is null and truth is - 0.05 Lifeplan MLC Horizon 2-Capital Stable Open\n",
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"Performance fee and cost - 454036250 generated is null and truth is - \n",
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"Performance fee and cost - 530101994 truth is null and generated - 0 Dimensional Global Value Trust -Active ETF\n",
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"Performance fee and cost - 530101994 truth is null and generated - 0 Dimensional Australia Core Equity Trust - Active ETF\n",
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"Performance fee and cost - 530101994 truth is null and generated - 0 Dimensional Australian Value Trust - Active ETF\n",
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"Performance fee and cost - 530101994 truth is null and generated - 0 Dimensional Global Core Equity Trust (Unhedged Class) - Active ETF\n",
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"Performance fee and cost - 530101994 truth is null and generated - 0 Dimensional Global Core Equity Tr\n",
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"Performance fee and cost - 550769189 truth is null and generated - 0 Acadian Global Managed Volatility Equity - Class A\n",
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"Performance fee and cost - 550522985 truth is null and generated - 0 RQI Global Value – Class A\n",
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"Performance fee and cost - 539266893 generated is null and truth is - AMP - Generations - BlackRock Australian Fixed Interest Index\n",
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"Performance fee and cost - 539266893 generated is null and truth is - AMP - Generations - BlackRock Australian Equity Index\n",
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"Performance fee and cost - 539266893 generated is null and truth is - AMP Generations - Alliance Capital Cash Management\n",
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"Performance fee and cost - 539266893 generated is null and truth is - AMP - Generations - BlackRock Property Securities Index\n",
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"Performance fee and cost - 539266893 generated is null and truth is - AMP - Generations - BlackRock International Equity Index (Unhedged)\n",
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"Performance fee and cost - 539266893 generated is null and truth is - AMP - Generations - BlackRock International Equity Index (Hedged)\n",
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"Performance fee and cost - 539241700 truth - 0.08 and generated - 0.05 North Professional Balanced\n",
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"Performance fee and cost - 539241700 truth - 0.06 and generated - 0 North Professional High Growth\n",
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"Performance fee and cost - 539241700 truth - 0.08 and generated - 0 North Professional Conservative\n",
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"Performance fee and cost - 539241700 truth - 0.08 and generated - 0 North Professional Growth\n",
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"Performance fee and cost - 539241700 truth - 0.09 and generated - 0 North Professional Moderately Conservative\n",
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"Performance fee and cost - 539261734 truth - 0.01 and generated - 0 ipac life choices Income Generator\n",
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"Performance fee and cost - 539261734 truth - 0.06 and generated - 0 ipac life choices Active 100\n",
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"Performance fee and cost - 539261734 truth - 0.08 and generated - 0 ipac life choices Active 85\n",
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"Performance fee and cost - 539261734 truth - 0.01 and generated - 0 ipac life choices Index 50\n",
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"Performance fee and cost - 539261734 truth - 0.09 and generated - 0 ipac life choices Active 50\n",
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"Performance fee and cost - 539261734 truth - 0.08 and generated - 0 ipac life choices Active 70\n",
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"Performance fee and cost - 506913190 generated is null and truth is - 0.03 FC W Pen-CFS TTR Moderate\n",
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"Performance fee and cost - 506913190 generated is null and truth is - 0.04 FC W Pen-CFS TTR Growth\n",
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"Performance fee and cost - 506913190 generated is null and truth is - 0.47 \n",
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"Performance fee and cost - 553449663 truth - 0 and generated - 0.07 AMP Capital Specialist International Share (Hedged) Fund\n",
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"Performance fee and cost - 539266874 truth - 0.03 and generated - 0 SUMMIT Select - Active High Growth Units\n",
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"Performance fee and cost - 539266874 truth - 0.05 and generated - 0 SUMMIT Select - Active Moderately Defensive\n",
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"Performance fee and cost - 539266874 truth - 0.05 and generated - 0 SUMMIT Select - Active Growth Units\n",
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"Performance fee and cost - 539266874 truth - 0.05 and generated - 0 SUMMIT Select - Active Balanced\n",
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"Performance fee and cost - 539266874 truth - 0.06 and generated - 0 SUMMIT Select - Active Defensive Units\n",
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"Performance fee and cost - 539266880 truth - 0.01 and generated - 0 North Multi-manager Active High Growth\n",
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"Performance fee and cost - 539266880 truth - 0.01 and generated - 0 North Multi-manager Active Moderately Defensive\n",
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"Performance fee and cost - 539266880 truth - 0.01 and generated - 0 North Multi-manager Active Growth\n",
|
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"Performance fee and cost - 539266880 truth - 0.01 and generated - 0 North Multi-manager Balanced\n",
|
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"Performance fee and cost - 526200514 generated is null and truth is - 0 BT Future Goals BTFM\n",
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"Performance fee and cost - 526200514 generated is null and truth is - 0 BTFM Asian Share\n",
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"Performance fee and cost - 526200514 generated is null and truth is - 0 BT International Share BTFM\n",
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"Performance fee and cost - 526200514 generated is null and truth is - 0 BT Smaller Companies BTFM\n",
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"Performance fee and cost - 526200514 generated is null and truth is - 0 BT Investment Funds - BT TIME Fund\n",
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"Performance fee and cost - 526200514 generated is null and truth is - 0 BT European Share Growth\n",
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"Performance fee and cost - 526200514 generated is null and truth is - 0 BT American Share Growth\n",
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"Performance fee and cost - 526200514 generated is null and truth is - 0 BT Imputation Share BTFM\n",
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"Performance fee and cost - 526200514 generated is null and truth is - 0 \n",
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"Performance fee and cost - 521606755 truth is null and generated - 0 CFS Index Diversified\n",
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"Performance fee and cost - 557526129 truth is null and generated - 0 Fortlake Real-Income Fund\n",
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"Performance fee and cost - 540028470 truth is null and generated - 0 CFS Wholesale Index Australian Share\n",
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"Performance fee and cost - 531373053 truth is null and generated - 0 Dimensional Global Core Equity Trust (Unhedged Class) - Active ETF\n",
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"Performance fee and cost - 531373053 truth is null and generated - 0 Dimensional Australian Value Trust - Active ETF\n",
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"Performance fee and cost - 531373053 truth is null and generated - 0 Dimensional Global Value Trust -Active ETF\n",
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"Performance fee and cost - 531373053 truth is null and generated - 0 Dimensional Australia Core Equity Trust - Active ETF\n",
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"Performance fee and cost - 531373053 truth is null and generated - 0 Dimensional Global Small Company Trust\n",
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"Performance fee and cost - 557362553 truth is null and generated - 0 JPMorgan Global Select Equity Fund\n",
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"Performance fee and cost - 527969661 truth is null and generated - 0 JPMorgan Global Equity Premium Income (Hedged) Complex ETF\n",
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"Performance fee and cost - 384508026 generated is null and truth is - 0 Mercer Multi-manager High Growth Fund\n",
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"Performance fee and cost - 384508026 generated is null and truth is - 0 Mercer Multi-manager Growth Fund\n",
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"Performance fee and cost - 384508026 generated is null and truth is - 0 \n",
|
||||
"total - 452.72727272727275\n",
|
||||
"Document List File - None\n",
|
||||
"Metric \tPrecision \tRecall \tAccuracy \tF1-Score \tSUPPORT \tTP \tTN \tFP \tFN \n",
|
||||
"Management Fee and Costs \t0.8790 \t0.9250 \t0.8213 \t0.9014 \t494 \t407 \t2 \t56 \t33 \n",
|
||||
"Management Fee \t0.8985 \t0.9265 \t0.8394 \t0.9123 \t494 \t416 \t2 \t47 \t33 \n",
|
||||
"Performance fee and cost \t0.7871 \t0.8472 \t0.7791 \t0.8161 \t327 \t244 \t144 \t66 \t44 \n",
|
||||
"Interposed vehicle Performance fee and Costs \t0.5000 \t1.0000 \t0.9237 \t0.6667 \t39 \t38 \t422 \t38 \t0 \n",
|
||||
"Administration Fee and costs \t0.9787 \t0.9388 \t0.9839 \t0.9583 \t98 \t92 \t398 \t2 \t6 \n",
|
||||
"Total Annual Dollar Based Charges \t0.8165 \t1.0000 \t0.9598 \t0.8990 \t90 \t89 \t389 \t20 \t0 \n",
|
||||
"Buy Spread \t0.8957 \t0.8910 \t0.8394 \t0.8933 \t405 \t335 \t83 \t39 \t41 \n",
|
||||
"Sell Spread \t0.9064 \t0.8921 \t0.8474 \t0.8992 \t405 \t339 \t83 \t35 \t41 \n",
|
||||
"Minimum Initial Investment \t0.8571 \t0.9671 \t0.8815 \t0.9088 \t310 \t294 \t145 \t49 \t10 \n",
|
||||
"Benchmark \t0.6402 \t0.8582 \t0.8233 \t0.7333 \t173 \t121 \t289 \t68 \t20 \n",
|
||||
"TOTAL \t0.8159 \t0.9246 \t0.8699 \t0.8588 \t2835 \t2375 \t1957 \t420 \t228 \n",
|
||||
"Total Funds Matched - 498\n",
|
||||
"Total Funds Not Matched - 28\n",
|
||||
"Percentage of Funds Matched - 94.67680608365019\n"
|
||||
"management_fee_and_costs \t0.8907 \t0.9513 \t0.8525 \t0.9200 \t457 \t391 \t2 \t48 \t20 \n",
|
||||
"management_fee \t0.9043 \t0.9520 \t0.8655 \t0.9276 \t457 \t397 \t2 \t42 \t20 \n",
|
||||
"performance_fee_costs \t0.8408 \t0.8556 \t0.8113 \t0.8482 \t303 \t243 \t131 \t46 \t41 \n",
|
||||
"interposed_vehicle_performance_fee_cost \t0.6316 \t1.0000 \t0.9393 \t0.7742 \t49 \t48 \t385 \t28 \t0 \n",
|
||||
"administration_fees \t0.9767 \t0.9655 \t0.9892 \t0.9711 \t87 \t84 \t372 \t2 \t3 \n",
|
||||
"total_annual_dollar_based_charges \t0.8350 \t1.0000 \t0.9631 \t0.9101 \t87 \t86 \t358 \t17 \t0 \n",
|
||||
"buy_spread \t0.9059 \t0.9258 \t0.8655 \t0.9158 \t391 \t337 \t62 \t35 \t27 \n",
|
||||
"sell_spread \t0.9113 \t0.9262 \t0.8698 \t0.9187 \t391 \t339 \t62 \t33 \t27 \n",
|
||||
"minimum_initial_investment \t0.9463 \t0.9814 \t0.9479 \t0.9635 \t329 \t317 \t120 \t18 \t6 \n",
|
||||
"benchmark_name \t0.7444 \t0.8701 \t0.8568 \t0.8024 \t172 \t134 \t261 \t46 \t20 \n",
|
||||
"TOTAL \t0.8587 \t0.9428 \t0.8961 \t0.8951 \t2723 \t2376 \t1755 \t315 \t164 \n",
|
||||
"Total Funds Matched - 461\n",
|
||||
"Total Funds Not Matched - 125\n",
|
||||
"Percentage of Funds Matched - 78.66894197952219\n",
|
||||
"All Providers Results: \n",
|
||||
"Document List File - ./sample_documents/aus_prospectus_29_documents_sample.txt\n",
|
||||
"Metric \tPrecision \tRecall \tAccuracy \tF1-Score \tSUPPORT \tTP \tTN \tFP \tFN \n",
|
||||
"management_fee_and_costs \t0.8960 \t0.9451 \t0.8516 \t0.9199 \t180 \t155 \t0 \t18 \t9 \n",
|
||||
"management_fee \t0.9017 \t0.9455 \t0.8571 \t0.9231 \t180 \t156 \t0 \t17 \t9 \n",
|
||||
"performance_fee_costs \t0.8000 \t0.8261 \t0.8077 \t0.8128 \t94 \t76 \t71 \t19 \t16 \n",
|
||||
"interposed_vehicle_performance_fee_cost \t0.5273 \t1.0000 \t0.8571 \t0.6905 \t30 \t29 \t127 \t26 \t0 \n",
|
||||
"administration_fees \t1.0000 \t0.3333 \t0.9890 \t0.5000 \t3 \t1 \t179 \t0 \t2 \n",
|
||||
"buy_spread \t0.9643 \t0.9419 \t0.9121 \t0.9529 \t176 \t162 \t4 \t6 \t10 \n",
|
||||
"sell_spread \t0.9702 \t0.9422 \t0.9176 \t0.9560 \t176 \t163 \t4 \t5 \t10 \n",
|
||||
"minimum_initial_investment \t0.9137 \t0.9549 \t0.9011 \t0.9338 \t139 \t127 \t37 \t12 \t6 \n",
|
||||
"benchmark_name \t0.7188 \t0.8734 \t0.7967 \t0.7886 \t91 \t69 \t76 \t27 \t10 \n",
|
||||
"TOTAL \t0.7692 \t0.7762 \t0.8885 \t0.7478 \t1069 \t938 \t679 \t131 \t236 \n",
|
||||
"Total Funds Matched - 182\n",
|
||||
"Total Funds Not Matched - 24\n",
|
||||
"Percentage of Funds Matched - 88.3495145631068\n",
|
||||
"All Providers Results: \n",
|
||||
"Document List File - ./sample_documents/aus_prospectus_17_documents_sample.txt\n",
|
||||
"Metric \tPrecision \tRecall \tAccuracy \tF1-Score \tSUPPORT \tTP \tTN \tFP \tFN \n",
|
||||
"management_fee_and_costs \t0.8872 \t0.9555 \t0.8530 \t0.9201 \t277 \t236 \t2 \t30 \t11 \n",
|
||||
"management_fee \t0.9060 \t0.9563 \t0.8710 \t0.9305 \t277 \t241 \t2 \t25 \t11 \n",
|
||||
"performance_fee_costs \t0.8608 \t0.8698 \t0.8136 \t0.8653 \t209 \t167 \t60 \t27 \t25 \n",
|
||||
"interposed_vehicle_performance_fee_cost \t0.9048 \t1.0000 \t0.9928 \t0.9500 \t19 \t19 \t258 \t2 \t0 \n",
|
||||
"administration_fees \t0.9765 \t0.9881 \t0.9892 \t0.9822 \t84 \t83 \t193 \t2 \t1 \n",
|
||||
"total_annual_dollar_based_charges \t0.8431 \t1.0000 \t0.9427 \t0.9149 \t87 \t86 \t177 \t16 \t0 \n",
|
||||
"buy_spread \t0.8578 \t0.9115 \t0.8351 \t0.8838 \t215 \t175 \t58 \t29 \t17 \n",
|
||||
"sell_spread \t0.8627 \t0.9119 \t0.8387 \t0.8866 \t215 \t176 \t58 \t28 \t17 \n",
|
||||
"minimum_initial_investment \t0.9694 \t1.0000 \t0.9785 \t0.9845 \t190 \t190 \t83 \t6 \t0 \n",
|
||||
"benchmark_name \t0.7738 \t0.8667 \t0.8961 \t0.8176 \t81 \t65 \t185 \t19 \t10 \n",
|
||||
"TOTAL \t0.8842 \t0.9460 \t0.9011 \t0.9136 \t1654 \t1438 \t1076 \t184 \t328 \n",
|
||||
"Total Funds Matched - 279\n",
|
||||
"Total Funds Not Matched - 101\n",
|
||||
"Percentage of Funds Matched - 73.42105263157895\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -499,7 +420,9 @@
|
|||
"from collections import defaultdict\n",
|
||||
"import pandas as pd\n",
|
||||
"import statistics\n",
|
||||
"\n",
|
||||
"import os\n",
|
||||
"import re\n",
|
||||
"from utils.similarity import Similarity\n",
|
||||
"\n",
|
||||
"funds_matched = 0\n",
|
||||
"funds_not_matched = 0\n",
|
||||
|
|
@ -519,7 +442,7 @@
|
|||
" return headers, data\n",
|
||||
"\n",
|
||||
"def index_data_by_key(data, key_index, secondary_key_index, header):\n",
|
||||
" \"\"\"Index data by primary and secondary keys (doc_id and fund_name).\"\"\"\n",
|
||||
" \"\"\"Index data by primary and secondary keys (doc_id and sec_name).\"\"\"\n",
|
||||
" indexed_data = defaultdict(dict)\n",
|
||||
" \n",
|
||||
" for row in data:\n",
|
||||
|
|
@ -528,7 +451,8 @@
|
|||
" for i in range(len(row)):\n",
|
||||
" if header[i] == \"doc_id\":\n",
|
||||
" primary_key = int(row[i])\n",
|
||||
" elif header[i] == \"fund_name\":\n",
|
||||
" elif header[i] == \"sec_name\":\n",
|
||||
" # share class should be the comparison level and key\n",
|
||||
" secondary_key = str(row[i])\n",
|
||||
" else:\n",
|
||||
" row_data[header[i]] = convert_if_number(row[i])\n",
|
||||
|
|
@ -549,7 +473,7 @@
|
|||
" value1 = convert_if_number(value1)\n",
|
||||
" value2 = convert_if_number(value2)\n",
|
||||
" return value1 == value2\n",
|
||||
"def compare_data(ground_truth, generated_results, headers, doc_id_index, fund_name_index, intersection_list, funds_matched, funds_not_matched):\n",
|
||||
"def compare_data(ground_truth, generated_results, headers, doc_id_index, fund_name_index, intersection_list, funds_matched, funds_not_matched, document_list):\n",
|
||||
" \"\"\"Compare data from two indexed sets, with the focus on matching generated results against ground truth.\"\"\"\n",
|
||||
" results = {}\n",
|
||||
" funds_matched, funds_not_matched = 0, 0\n",
|
||||
|
|
@ -566,11 +490,15 @@
|
|||
" # Iterate over the generated results instead of the ground truth\n",
|
||||
" \n",
|
||||
" total = 0\n",
|
||||
" for doc_id, funds in ground_truth.items():\n",
|
||||
" message_list = []\n",
|
||||
" # print(document_list)\n",
|
||||
" for doc_id, secs in ground_truth.items():\n",
|
||||
" if document_list is not None and str(doc_id) not in document_list:\n",
|
||||
" continue\n",
|
||||
" if doc_id in generated_results:\n",
|
||||
" for fund_name, truth_values in funds.items():\n",
|
||||
" if fund_name in generated_results[doc_id]:\n",
|
||||
" generated_values = generated_results[doc_id][fund_name]\n",
|
||||
" for sec_name, truth_values in secs.items():\n",
|
||||
" if sec_name in generated_results[doc_id]:\n",
|
||||
" generated_values = generated_results[doc_id][sec_name]\n",
|
||||
" # Compare all other columns\n",
|
||||
" for i in intersection_list:\n",
|
||||
" for keys in imp_datapoints:\n",
|
||||
|
|
@ -581,52 +509,56 @@
|
|||
" results[i][\"TN\"] = results[i][\"TN\"] + 1\n",
|
||||
" else:\n",
|
||||
" results[i][\"FP\"] = results[i][\"FP\"] + 1\n",
|
||||
" if \"Performance fee and cost\" in keys:\n",
|
||||
" debug = 0\n",
|
||||
" print(keys, \" - \" , doc_id, \" truth is null and generated - \", generated_values[i], fund_name) \n",
|
||||
" # if \"Performance fee and cost\" in keys:\n",
|
||||
" debug = 0\n",
|
||||
" # print(keys, \" - \" , doc_id, \" truth is null and generated - \", generated_values[i], sec_name) \n",
|
||||
" message = {\"data_point\": i, \"doc_id\": doc_id, \"sec_name\": sec_name, \"truth\": truth_values[i], \"generated\": generated_values[i], \"error\": \"Truth is null and generated is not null\"}\n",
|
||||
" message_list.append(message) \n",
|
||||
" else:\n",
|
||||
" if truth_values[i] == generated_values[i]:\n",
|
||||
" results[i][\"TP\"] = results[i][\"TP\"] + 1\n",
|
||||
" elif generated_values[i] != \"\":\n",
|
||||
" results[i][\"FP\"] = results[i][\"FP\"] + 1\n",
|
||||
" if \"Performance fee and cost\" in keys:\n",
|
||||
" if i == \"benchmark_name\" and compare_text(truth_values[i], generated_values[i]):\n",
|
||||
" results[i][\"TP\"] = results[i][\"TP\"] + 1\n",
|
||||
" else:\n",
|
||||
" results[i][\"FP\"] = results[i][\"FP\"] + 1\n",
|
||||
" # if \"Performance fee and cost\" in keys:\n",
|
||||
" debug = 0\n",
|
||||
" print(keys, \" - \" , doc_id, \" truth - \", truth_values[i], \" and generated - \", generated_values[i], \" \", fund_name)\n",
|
||||
" # print(keys, \" - \" , doc_id, \" truth - \", truth_values[i], \" and generated - \", generated_values[i], \" \", sec_name)\n",
|
||||
" message = {\"data_point\": i, \"doc_id\": doc_id, \"sec_name\": sec_name, \"truth\": truth_values[i], \"generated\": generated_values[i], \"error\": \"Truth is not equal with generated\"}\n",
|
||||
" message_list.append(message)\n",
|
||||
" else:\n",
|
||||
" results[i][\"FN\"] = results[i][\"FN\"] + 1\n",
|
||||
" if \"Performance fee and cost\" in keys:\n",
|
||||
" debug = 0\n",
|
||||
" print(keys, \" - \" , doc_id, \" generated is null and truth is - \", truth_values[i], fund_name)\n",
|
||||
" # if \"Performance fee and cost\" in keys:\n",
|
||||
" debug = 0\n",
|
||||
" # print(keys, \" - \" , doc_id, \" generated is null and truth is - \", truth_values[i], sec_name)\n",
|
||||
" message = {\"data_point\": i, \"doc_id\": doc_id, \"sec_name\": sec_name, \"truth\": truth_values[i], \"generated\": generated_values[i], \"error\": \"Generated is null and truth is not null\"}\n",
|
||||
" message_list.append(message)\n",
|
||||
" results[i][\"SUPPORT\"] = results[i][\"SUPPORT\"] + 1\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # if truth_values[i] == generated_values[i] and truth_values[i] == \"\":\n",
|
||||
" # results[i][\"TN\"] = results[i][\"TN\"] + 1\n",
|
||||
" # elif truth_values[i] == generated_values[i]:\n",
|
||||
" # results[i][\"TP\"] = results[i][\"TP\"] + 1\n",
|
||||
" # elif truth_values[i] != \"\" and generated_values[i] == \"\":\n",
|
||||
" # results[i][\"FN\"] = results[i][\"FN\"] + 1\n",
|
||||
" # elif truth_values[i] == \"\" and generated_values[i] != \"\":\n",
|
||||
" # results[i][\"FP\"] = results[i][\"FP\"] + 1\n",
|
||||
" # else:\n",
|
||||
" # results[i][\"FP\"] = results[i][\"FP\"] + 1\n",
|
||||
" # if truth_values[i] != \"\":\n",
|
||||
" # results[i][\"SUPPORT\"] = results[i][\"SUPPORT\"] + 1\n",
|
||||
" funds_matched += 1\n",
|
||||
" else:\n",
|
||||
" funds_not_matched += 1\n",
|
||||
" # for keys in headers:\n",
|
||||
" # if keys != \"doc_id\":\n",
|
||||
" # results[keys][\"FN\"] = results[keys][\"FN\"] + 1\n",
|
||||
" else:\n",
|
||||
" # If the entire document is not found, count all funds as not matched\n",
|
||||
" funds_not_matched += len(funds)\n",
|
||||
" # for fund_name in funds:\n",
|
||||
" # for keys in headers:\n",
|
||||
" # if keys != \"doc_id\":\n",
|
||||
" # results[keys][\"FN\"] = results[keys][\"FN\"] + 1\n",
|
||||
" return results, funds_matched, funds_not_matched\n",
|
||||
" funds_not_matched += len(secs)\n",
|
||||
" return results, message_list, funds_matched, funds_not_matched\n",
|
||||
"\n",
|
||||
"def clean_text(text: str):\n",
|
||||
" if text is None or len(text) == 0:\n",
|
||||
" return text\n",
|
||||
" text = re.sub(r\"\\W\", \" \", text)\n",
|
||||
" text = re.sub(r\"\\s+\", \" \", text)\n",
|
||||
" return text\n",
|
||||
"\n",
|
||||
"def compare_text(source_text, target_text):\n",
|
||||
" source_text = clean_text(source_text)\n",
|
||||
" target_text = clean_text(target_text)\n",
|
||||
" if source_text == target_text or source_text in target_text or target_text in source_text:\n",
|
||||
" return True\n",
|
||||
" similarity = Similarity()\n",
|
||||
" jacard_score = similarity.jaccard_similarity(source_text.lower().split(), target_text.lower().split())\n",
|
||||
" if jacard_score > 0.8:\n",
|
||||
" return True\n",
|
||||
"\n",
|
||||
"# Load the files\n",
|
||||
"headers_gt, ground_truth_data = load_excel(path_ground_truth, 0)\n",
|
||||
|
|
@ -664,12 +596,15 @@
|
|||
" total_fp = []\n",
|
||||
" #total_fn = []\n",
|
||||
" # Calculate and print metrics for each item\n",
|
||||
" metrics_list = []\n",
|
||||
" for keys in imp_datapoints:\n",
|
||||
" try:\n",
|
||||
" key = imp_datapoints_mapping[keys]\n",
|
||||
" values = data[key]\n",
|
||||
" tp, tn, fp, fn = values['TP'], values['TN'], values['FP'], values['FN']\n",
|
||||
" precision, recall, accuracy, f1_score = calculate_metrics(tp, tn, fp, fn)\n",
|
||||
" metrics = {\"Datapoint\": keys, \"F1-Score\": f1_score, \"Precision\": precision, \"Recall\": recall, \"Accuracy\": accuracy, \"SUPPORT\": values[\"SUPPORT\"], \"TP\": tp, \"TN\": tn, \"FP\": fp, \"FN\": fn}\n",
|
||||
" metrics_list.append(metrics)\n",
|
||||
" total_precision.append(precision)\n",
|
||||
" total_recall.append(recall)\n",
|
||||
" total_accuracy.append(accuracy)\n",
|
||||
|
|
@ -681,10 +616,22 @@
|
|||
" total_fn.append(fn)\n",
|
||||
"\n",
|
||||
" if values[\"SUPPORT\"] > 0 and key > \"\":\n",
|
||||
" print(\"{:<50}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\".format(keys, precision, recall, accuracy, f1_score, values[\"SUPPORT\"], tp, tn, fp, fn))\n",
|
||||
" print(\"{:<50}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\".format(key, precision, recall, accuracy, f1_score, values[\"SUPPORT\"], tp, tn, fp, fn))\n",
|
||||
" except:\n",
|
||||
" pass\n",
|
||||
" print(\"{:<50}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\".format(\"TOTAL\", statistics.mean(total_precision), statistics.mean(total_recall), statistics.mean(total_accuracy), statistics.mean(total_f1_score), sum(total_support), sum(total_tp), sum(total_tn), sum(total_fp), sum(total_fn)))\n",
|
||||
" total_mean_precision = statistics.mean(total_precision)\n",
|
||||
" total_mean_recall = statistics.mean(total_recall)\n",
|
||||
" total_mean_accuracy = statistics.mean(total_accuracy)\n",
|
||||
" total_mean_f1_score = statistics.mean(total_f1_score)\n",
|
||||
" total_sum_support = sum(total_support)\n",
|
||||
" total_sum_tp = sum(total_tp)\n",
|
||||
" total_sum_tn = sum(total_tn)\n",
|
||||
" total_sum_fp = sum(total_fp)\n",
|
||||
" total_sum_fn = sum(total_fn)\n",
|
||||
" total_metrics = {\"Datapoint\": \"TOTAL\", \"F1-Score\": total_mean_f1_score, \"Precision\": total_mean_precision, \"Recall\": total_mean_recall, \"Accuracy\": total_mean_accuracy, \"SUPPORT\": total_sum_support, \"TP\": total_sum_tp, \"TN\": total_sum_tn, \"FP\": total_sum_fp, \"FN\": total_sum_fn}\n",
|
||||
" metrics_list.append(total_metrics)\n",
|
||||
" print(\"{:<50}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.4f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\\t{:<10.0f}\".format(\"TOTAL\", total_mean_precision, total_mean_recall, total_mean_accuracy, total_mean_f1_score, total_sum_support, total_sum_tp, total_sum_tn, total_sum_fp, total_sum_fn))\n",
|
||||
" return metrics_list\n",
|
||||
" \n",
|
||||
"def create_metrics_df(data):\n",
|
||||
" # Define a list to hold data for DataFrame\n",
|
||||
|
|
@ -771,14 +718,45 @@
|
|||
"\n",
|
||||
"print(\"\\n\")\n",
|
||||
"print(\"\\n\")\n",
|
||||
"print(\"All Providers Results: \")\n",
|
||||
"comparison_results, funds_matched, funds_not_matched = compare_data(ground_truth_indexed, generated_results_indexed, headers_gt, doc_id_index, fund_name_index, intersection_list,funds_matched, funds_not_matched)\n",
|
||||
"document_list_file_list = [None, \n",
|
||||
" \"./sample_documents/aus_prospectus_29_documents_sample.txt\", \n",
|
||||
" \"./sample_documents/aus_prospectus_17_documents_sample.txt\"]\n",
|
||||
"for document_list_file in document_list_file_list:\n",
|
||||
" document_list = None\n",
|
||||
" if document_list_file is not None:\n",
|
||||
" with open(document_list_file, \"r\", encoding=\"utf-8\") as f:\n",
|
||||
" document_list = f.readlines()\n",
|
||||
" document_list = [doc_id.strip() for doc_id in document_list]\n",
|
||||
" \n",
|
||||
" print(\"All Providers Results: \")\n",
|
||||
" print(\"Document List File - \", document_list_file)\n",
|
||||
" comparison_results, message_list, funds_matched, funds_not_matched = compare_data(ground_truth_indexed, \n",
|
||||
" generated_results_indexed, \n",
|
||||
" headers_gt, doc_id_index, \n",
|
||||
" fund_name_index, \n",
|
||||
" intersection_list,\n",
|
||||
" funds_matched, \n",
|
||||
" funds_not_matched,\n",
|
||||
" document_list)\n",
|
||||
" metrics_list = print_metrics_table(comparison_results)\n",
|
||||
" print(\"Total Funds Matched - \" + str(funds_matched) + \"\\nTotal Funds Not Matched - \" + str(funds_not_matched))\n",
|
||||
" print(\"Percentage of Funds Matched - \" + str((funds_matched/(funds_matched + funds_not_matched))*100))\n",
|
||||
"\n",
|
||||
"print_metrics_table(comparison_results)\n",
|
||||
"print(\"Total Funds Matched - \" + str(funds_matched) + \"\\nTotal Funds Not Matched - \" + str(funds_not_matched))\n",
|
||||
"print(\"Percentage of Funds Matched - \" + str((funds_matched/(funds_matched + funds_not_matched))*100))\n",
|
||||
" metrics_df = pd.DataFrame(metrics_list)\n",
|
||||
" message_df = pd.DataFrame(message_list)\n",
|
||||
"\n",
|
||||
"\n"
|
||||
" output_metrics_folder = r\"/data/aus_prospectus/output/metrics_data/\"\n",
|
||||
" if os.path.exists(output_metrics_folder):\n",
|
||||
" generated_file_base_name = os.path.basename(path_generated_results).replace(\".xlsx\", \"\")\n",
|
||||
" metrics_file_name = f\"metrics_{generated_file_base_name}\"\n",
|
||||
" if document_list_file is not None:\n",
|
||||
" metrics_file_name = f\"{metrics_file_name}_{len(document_list)}_documents.xlsx\"\n",
|
||||
" else:\n",
|
||||
" metrics_file_name = f\"{metrics_file_name}_all_documents.xlsx\"\n",
|
||||
" metrics_file_path = os.path.join(output_metrics_folder, metrics_file_name)\n",
|
||||
" with pd.ExcelWriter(metrics_file_path) as writer:\n",
|
||||
" metrics_df.to_excel(writer, sheet_name=\"metrics_data\", index=False)\n",
|
||||
" message_df.to_excel(writer, sheet_name=\"message_data\", index=False)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -833,7 +811,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.4"
|
||||
"version": "3.12.6"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
|
|
|
|||
|
|
@ -41,11 +41,17 @@ def download_pdf_from_documents_warehouse(pdf_directory: str, doc_id: str):
|
|||
ACCESS_KEY = os.getenv('ACCESS_KEY')
|
||||
SECRET_KEY = os.getenv('SECRET_KEY')
|
||||
AWS_SESSION_TOKEN = os.getenv('AWS_SESSION_TOKEN')
|
||||
s3 = boto3.client("s3", region_name="us-east-1", verify=certifi.where(),
|
||||
if AWS_SESSION_TOKEN:
|
||||
s3 = boto3.client("s3", region_name="us-east-1", verify=certifi.where(),
|
||||
aws_access_key_id=ACCESS_KEY,
|
||||
aws_secret_access_key=SECRET_KEY,
|
||||
aws_session_token=AWS_SESSION_TOKEN
|
||||
)
|
||||
else:
|
||||
s3 = boto3.client("s3", region_name="us-east-1", verify=certifi.where(),
|
||||
aws_access_key_id=ACCESS_KEY,
|
||||
aws_secret_access_key=SECRET_KEY
|
||||
)
|
||||
else:
|
||||
s3 = boto3.client('s3')
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue