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{
"cells": [
{
"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
"outputs": [],
"source": [
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"import openpyxl\n",
"from collections import defaultdict\n",
"import pandas as pd\n",
"import statistics\n",
"import os\n",
"import re\n",
"from utils.similarity import Similarity\n",
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"\n",
"\n",
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"imp_datapoints = [\"Management Fee and Costs\", \"Management Fee\", \"Performance fee and cost\", \"Interposed vehicle Performance fee and Costs\",\n",
" \"Administration Fee and costs\", \"Total Annual Dollar Based Charges\", \"Buy Spread\", \"Sell Spread\", \"Performance Fee\",\n",
" \"Minimum Initial Investment\", \"Benchmark\"]\n",
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"\n",
"\n",
"imp_datapoints_mapping = {\n",
" \"Management Fee and Costs\": \"management_fee_and_costs\",\n",
" \"Management Fee\": \"management_fee\",\n",
" \"Performance fee and cost\": \"performance_fee_costs\",\n",
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" \"Interposed vehicle Performance fee and Costs\": \"interposed_vehicle_performance_fee_cost\",\n",
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" \"Administration Fee and costs\": \"administration_fees\",\n",
" \"Total Annual Dollar Based Charges\": \"total_annual_dollar_based_charges\",\n",
" \"Buy Spread\": \"buy_spread\",\n",
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" \"Sell Spread\": \"sell_spread\",\n",
" \"Performance Fee\": \"PerformanceFeeCharged\",\n",
" \"Minimum Initial Investment\": \"minimum_initial_investment\",\n",
" \"Benchmark\": \"benchmark_name\"\n",
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"}\n",
"\n",
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"# imp_datapoints = [\"Management Fee and Costs\", \"Management Fee\", \"Performance fee and cost\",\n",
"# \"Administration Fee and costs\", \"Total Annual Dollar Based Charges\", \"Buy Spread\", \"Sell Spread\"]\n",
"\n",
"\n",
"# imp_datapoints_mapping = {\n",
"# \"Management Fee and Costs\": \"management_fee_and_costs\",\n",
"# \"Management Fee\": \"management_fee\",\n",
"# \"Performance fee and cost\": \"performance_fee_costs\",\n",
"# \"Administration Fee and costs\": \"administration_fees\",\n",
"# \"Total Annual Dollar Based Charges\": \"total_annual_dollar_based_charges\",\n",
"# \"Buy Spread\": \"buy_spread\",\n",
"# \"Sell Spread\": \"sell_spread\"\n",
"# }\n",
"\n",
"path_ground_truth = r\"/data/aus_prospectus/ground_truth/phase2_file/46_documents/46_documents_ground_truth_with_mapping.xlsx\"\n",
"# path_ground_truth = r\"/data/aus_prospectus/ground_truth/phase2_file/next_round/next_round_6_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_20250331220152.xlsx\"\n",
"# path_generated_results = r\"/data/aus_prospectus/output/mapping_data/total/mapping_data_info_46_documents_by_text_20250328035602.xlsx\"\n",
"# path_generated_results = r\"/data/aus_prospectus/output/mapping_data/total/mapping_data_info_6_documents_by_text_20250331180753.xlsx\"\n",
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"provider_mapping_file_path = r\"/data/aus_prospectus/ground_truth/phase2_file/46_documents/TopProvidersBiz.xlsx\"\n",
"\n"
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]
},
{
"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
"\n",
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"message_list = []\n",
"total_fn = []\n",
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"def load_excel(filepath, header_row_index):\n",
" \"\"\"Load an Excel file and use the specified row as the header.\"\"\"\n",
" wb = openpyxl.load_workbook(filepath, data_only=True)\n",
" sheet = wb.active\n",
" headers = []\n",
" data = []\n",
"\n",
" for index, row in enumerate(sheet.iter_rows(values_only=True)):\n",
" if index == header_row_index:\n",
" headers = [cell if cell is not None else \"\" for cell in row]\n",
" elif index > header_row_index:\n",
" data.append([cell if cell is not None else \"\" for cell in row])\n",
"\n",
" return headers, data\n",
"\n",
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"def index_data_by_key(data, header):\n",
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" \"\"\"Index data by primary and secondary keys (doc_id and sec_name).\"\"\"\n",
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" indexed_data = defaultdict(dict)\n",
" \n",
" for row in data:\n",
" row_data = {}\n",
" # Store the entire row, which will be useful for full row comparison\n",
" for i in range(len(row)):\n",
" if header[i] == \"doc_id\":\n",
" primary_key = int(row[i])\n",
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" elif header[i] == \"sec_name\":\n",
" # share class should be the comparison level and key\n",
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" secondary_key = str(row[i])\n",
" else:\n",
" row_data[header[i]] = convert_if_number(row[i])\n",
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" if secondary_key is None or (isinstance(secondary_key, str) and len(secondary_key) == 0):\n",
" continue\n",
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" indexed_data[primary_key][secondary_key] = row_data\n",
" return indexed_data\n",
"\n",
"def convert_if_number(value):\n",
" \"\"\"Attempt to convert value to a float or int, otherwise return as string.\"\"\"\n",
" try:\n",
" float_value = round(float(value), 2)\n",
" int_value = int(float_value)\n",
" return int_value if int_value == float_value else float_value\n",
" except (ValueError, TypeError):\n",
" return value\n",
"\n",
"def compare_values(value1, value2):\n",
" \"\"\"Convert values to numbers if possible and compare, otherwise compare as strings.\"\"\"\n",
" value1 = convert_if_number(value1)\n",
" value2 = convert_if_number(value2)\n",
" return value1 == value2\n",
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"\n",
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"def compare_data(ground_truth, generated_results, headers, intersection_list, document_list):\n",
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" \"\"\"Compare data from two indexed sets, with the focus on matching generated results against ground truth.\"\"\"\n",
" results = {}\n",
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" share_name_list = []\n",
" not_matched_share_name_list = []\n",
" share_matched, share_not_matched = 0, 0\n",
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" # Initialize result dictionaries for each column except 'doc_id'\n",
" for keys in headers:\n",
" if keys != \"doc_id\":\n",
" results[keys] = {}\n",
" results[keys][\"TP\"] = 0\n",
" results[keys][\"TN\"] = 0\n",
" results[keys][\"FP\"] = 0\n",
" results[keys][\"FN\"] = 0\n",
" results[keys][\"SUPPORT\"] = 0\n",
" \n",
" # Iterate over the generated results instead of the ground truth\n",
" \n",
" total = 0\n",
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" # 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",
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" if doc_id in generated_results:\n",
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" 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",
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" # Compare all other columns\n",
" for i in intersection_list:\n",
" for keys in imp_datapoints:\n",
" if i == imp_datapoints_mapping[keys]:\n",
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" truth = str(truth_values[i]).strip()\n",
" generated = str(generated_values[i]).strip()\n",
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" total = total +1\n",
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" if truth == \"\":\n",
" if truth == generated:\n",
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" results[i][\"TN\"] = results[i][\"TN\"] + 1\n",
" else:\n",
" results[i][\"FP\"] = results[i][\"FP\"] + 1\n",
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" # 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",
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" message = {\"data_point\": i, \"doc_id\": doc_id, \"sec_name\": sec_name, \n",
" \"truth\": truth, \"generated\": generated, \"error\": \"Truth is null and generated is not null\"}\n",
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" message_list.append(message) \n",
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" else:\n",
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" if truth == generated:\n",
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" results[i][\"TP\"] = results[i][\"TP\"] + 1\n",
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" elif generated != \"\":\n",
" if i == \"benchmark_name\" and compare_text(truth, generated):\n",
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" 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",
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" debug = 0\n",
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" # print(keys, \" - \" , doc_id, \" truth - \", truth_values[i], \" and generated - \", generated_values[i], \" \", sec_name)\n",
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" message = {\"data_point\": i, \"doc_id\": doc_id, \"sec_name\": sec_name, \n",
" \"truth\": truth, \"generated\": generated, \"error\": \"Truth is not equal with generated\"}\n",
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" message_list.append(message)\n",
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" else:\n",
" results[i][\"FN\"] = results[i][\"FN\"] + 1\n",
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" # 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",
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" message = {\"data_point\": i, \"doc_id\": doc_id, \"sec_name\": sec_name, \n",
" \"truth\": truth, \"generated\": generated, \"error\": \"Generated is null and truth is not null\"}\n",
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" message_list.append(message)\n",
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" results[i][\"SUPPORT\"] = results[i][\"SUPPORT\"] + 1\n",
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" if sec_name not in share_name_list:\n",
" share_name_list.append(sec_name)\n",
" share_matched += 1\n",
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" else:\n",
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" if sec_name not in share_name_list:\n",
" share_name_list.append(sec_name)\n",
" if sec_name not in not_matched_share_name_list:\n",
" # If the share class is not found in the generated results, count it as not matched\n",
" # print(\"Share class not matched - \", sec_name, doc_id)\n",
" message = {\"data_point\": \"Share Class\", \"doc_id\": doc_id, \"sec_name\": sec_name, \n",
" \"truth\": \"\", \"generated\": \"\", \"error\": \"Share class not found in generated results\"}\n",
" message_list.append(message)\n",
" share_not_matched += 1\n",
" not_matched_share_name_list.append(sec_name)\n",
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" else:\n",
" # If the entire document is not found, count all funds as not matched\n",
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" message = {\"data_point\": \"Document\", \"doc_id\": doc_id, \"sec_name\": \"\",\n",
" \"truth\": \"\", \"generated\": \"\", \"error\": \"Document not found in generated results\"}\n",
" message_list.append(message)\n",
" \n",
" # share_not_matched += len(secs)\n",
" return results, message_list, share_matched, share_not_matched, not_matched_share_name_list\n",
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"\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",
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" \n",
" \n",
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"def calculate_metrics(tp, tn, fp, fn):\n",
" \"\"\"Calculate precision, recall, accuracy, and F1-score.\"\"\"\n",
" precision = tp / (tp + fp) if (tp + fp) != 0 else 0\n",
" recall = tp / (tp + fn) if (tp + fn) != 0 else 0\n",
" accuracy = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) != 0 else 0\n",
" f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) != 0 else 0\n",
" return precision, recall, accuracy, f1_score\n",
"\n",
"def print_metrics_table(data):\n",
" # Print table headers\n",
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" print(\"{:<50}\\t{:<10}\\t{:<10}\\t{:<10}\\t{:<10}\\t{:<10}\\t{:<10}\\t{:<10}\\t{:<10}\\t{:<10}\".format(\"Metric\", \"F1-Score\", \"Precision\", \"Recall\", \"Accuracy\", \"SUPPORT\", \"TP\", \"TN\", \"FP\", \"FN\"))\n",
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" total_precision, total_recall, total_accuracy, total_f1_score, total_support= [],[],[],[],[]\n",
" \n",
" total_tp = []\n",
" total_tn = []\n",
" total_fp = []\n",
" #total_fn = []\n",
" # Calculate and print metrics for each item\n",
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" metrics_list = []\n",
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" for keys in imp_datapoints:\n",
" try:\n",
" key = imp_datapoints_mapping[keys]\n",
" values = data[key]\n",
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" if values[\"SUPPORT\"] == 0:\n",
" continue\n",
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" 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",
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" metrics = {\"Datapoint\": key, \"F1-Score\": f1_score, \"Precision\": precision, \"Recall\": recall, \"Accuracy\": accuracy, \"SUPPORT\": values[\"SUPPORT\"], \"TP\": tp, \"TN\": tn, \"FP\": fp, \"FN\": fn}\n",
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" metrics_list.append(metrics)\n",
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" total_precision.append(precision)\n",
" total_recall.append(recall)\n",
" total_accuracy.append(accuracy)\n",
" total_f1_score.append(f1_score)\n",
" total_support.append(values[\"SUPPORT\"])\n",
" total_tp.append(tp)\n",
" total_tn.append(tn)\n",
" total_fp.append(fp)\n",
" total_fn.append(fn)\n",
"\n",
" if values[\"SUPPORT\"] > 0 and key > \"\":\n",
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" 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, f1_score, precision, recall, accuracy, values[\"SUPPORT\"], tp, tn, fp, fn))\n",
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" except:\n",
" pass\n",
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" 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",
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" 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_f1_score, total_mean_precision, total_mean_recall, total_mean_accuracy, total_sum_support, total_sum_tp, total_sum_tn, total_sum_fp, total_sum_fn))\n",
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" return metrics_list\n",
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" \n",
"def create_metrics_df(data):\n",
" # Define a list to hold data for DataFrame\n",
" rows = []\n",
" \n",
" # Iterate through each metric item\n",
" for key in imp_datapoints:\n",
" try:\n",
" mapped_key = imp_datapoints_mapping[key]\n",
" values = data[mapped_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",
" \n",
" # Only add rows where SUPPORT > 0\n",
" if values[\"SUPPORT\"] > 0:\n",
" row = {\n",
" \"Metric\": key,\n",
" \"Precision\": precision,\n",
" \"Recall\": recall,\n",
" \"Accuracy\": accuracy,\n",
" \"F1-Score\": f1_score,\n",
" \"SUPPORT\": values[\"SUPPORT\"]\n",
" }\n",
" rows.append(row)\n",
" except KeyError as e:\n",
" continue\n",
"\n",
" # Create a DataFrame from the list of rows\n",
" df_metrics = pd.DataFrame(rows)\n",
" df_metrics.reset_index(inplace=True)\n",
" df_metrics.drop(columns=[\"index\"], inplace=True)\n",
" print(df_metrics)\n",
" return df_metrics\n",
"\n",
"\n",
"\n",
"def get_provider_mapping(file_path):\n",
" df = pd.read_excel(file_path)\n",
" df = (df.groupby([\"Docid\", \"ProviderName\"]).first())\n",
" df.reset_index(inplace = True)\n",
" return df[[\"Docid\", \"ProviderName\"]]\n",
"\n",
"\n",
"def get_provider_names(generated_results_indexed, df_provider_mapping):\n",
" providers_dict = {}\n",
" for doc_id in generated_results_indexed:\n",
" try:\n",
" provider_name = (df_provider_mapping[df_provider_mapping[\"Docid\"] == doc_id][\"ProviderName\"].values)[0]\n",
" if provider_name in providers_dict:\n",
" providers_dict[provider_name].append(doc_id)\n",
" else:\n",
" providers_dict[provider_name] = []\n",
" providers_dict[provider_name].append(doc_id)\n",
"\n",
" except:\n",
" pass\n",
" return providers_dict\n",
"\n",
"def get_specified_doc_data(results, doc_list):\n",
" provider_res = {}\n",
" for doc_id in doc_list:\n",
" if doc_id in results:\n",
" provider_res[doc_id] = results[doc_id]\n",
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" return provider_res\n"
]
},
{
"cell_type": "code",
2025-04-01 03:04:31 +00:00
"execution_count": 31,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n",
"All Providers Results: \n",
"Document List File - None\n",
"Metric \tF1-Score \tPrecision \tRecall \tAccuracy \tSUPPORT \tTP \tTN \tFP \tFN \n",
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"management_fee_and_costs \t0.9369 \t0.8988 \t0.9785 \t0.8814 \t413 \t364 \t0 \t41 \t8 \n",
"management_fee \t0.9478 \t0.9185 \t0.9789 \t0.9007 \t413 \t372 \t0 \t33 \t8 \n",
"performance_fee_costs \t0.9160 \t0.9231 \t0.9091 \t0.8935 \t273 \t240 \t129 \t20 \t24 \n",
"interposed_vehicle_performance_fee_cost \t0.9114 \t0.8372 \t1.0000 \t0.9661 \t73 \t72 \t327 \t14 \t0 \n",
"administration_fees \t1.0000 \t1.0000 \t1.0000 \t1.0000 \t52 \t52 \t361 \t0 \t0 \n",
"total_annual_dollar_based_charges \t1.0000 \t1.0000 \t1.0000 \t1.0000 \t52 \t52 \t361 \t0 \t0 \n",
"buy_spread \t0.9290 \t0.8920 \t0.9691 \t0.8838 \t359 \t314 \t51 \t38 \t10 \n",
"sell_spread \t0.9258 \t0.8864 \t0.9689 \t0.8789 \t359 \t312 \t51 \t40 \t10 \n",
"minimum_initial_investment \t0.9733 \t0.9799 \t0.9669 \t0.9613 \t302 \t292 \t105 \t6 \t10 \n",
"benchmark_name \t0.9109 \t0.8790 \t0.9452 \t0.9346 \t155 \t138 \t248 \t19 \t8 \n",
"TOTAL \t0.9451 \t0.9215 \t0.9717 \t0.9300 \t2451 \t2208 \t1633 \t211 \t78 \n",
"Total Shares Matched - 365\n",
"Total Shares Not Matched - 142\n",
"Percentage of Shares Matched - 71.99211045364892\n",
"Not Matched Shares Name List - ['SPDR® S&P World ex Australia Carbon Control Fund', 'Mercer Multi-manager Growth Fund – –
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"All Providers Results: \n",
"Document List File - ./sample_documents/aus_prospectus_29_documents_sample.txt\n",
"Metric \tF1-Score \tPrecision \tRecall \tAccuracy \tSUPPORT \tTP \tTN \tFP \tFN \n",
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"management_fee_and_costs \t0.9529 \t0.9101 \t1.0000 \t0.9101 \t178 \t162 \t0 \t16 \t0 \n",
"management_fee \t0.9829 \t0.9663 \t1.0000 \t0.9663 \t178 \t172 \t0 \t6 \t0 \n",
"performance_fee_costs \t0.8830 \t0.8646 \t0.9022 \t0.8764 \t95 \t83 \t73 \t13 \t9 \n",
"interposed_vehicle_performance_fee_cost \t0.8814 \t0.7879 \t1.0000 \t0.9213 \t53 \t52 \t112 \t14 \t0 \n",
"administration_fees \t1.0000 \t1.0000 \t1.0000 \t1.0000 \t1 \t1 \t177 \t0 \t0 \n",
"buy_spread \t0.9856 \t0.9716 \t1.0000 \t0.9719 \t176 \t171 \t2 \t5 \t0 \n",
"sell_spread \t0.9767 \t0.9545 \t1.0000 \t0.9551 \t176 \t168 \t2 \t8 \t0 \n",
"minimum_initial_investment \t0.9611 \t0.9577 \t0.9645 \t0.9382 \t141 \t136 \t31 \t6 \t5 \n",
"benchmark_name \t0.9184 \t0.8654 \t0.9783 \t0.9101 \t100 \t90 \t72 \t14 \t2 \n",
"TOTAL \t0.9491 \t0.9198 \t0.9828 \t0.9388 \t1098 \t1035 \t469 \t82 \t94 \n",
"Total Shares Matched - 173\n",
"Total Shares Not Matched - 18\n",
"Percentage of Shares Matched - 90.57591623036649\n",
"Not Matched Shares Name List - ['Dimensional Australian Core Equity Trust', 'CFS FC ESup-CFS Diversified Fix Int', 'FC W Pen-CFS TTR Conservative', 'FC W Pen-CFS TTR Diversified', 'FC W Pen-CFS TTR High Growth', 'FC W Pen-CFS TTR Australian Share', 'FC W Pen-CFS TTR Property Securities', 'FC W Pen-CFS TTR Moderate', 'FC W Pen-CFS TTR Balanced', 'FC W Pen-CFS TTR Growth', 'FC W Pen-CFS TTR Australian Small Companies', 'FC W Pen-CFS TTR Global Infrastructure Securities', 'FC W Pen-CFS TTR Fixed Interest', 'FC W Pen-CFS TTR Global Share', 'FC W Pen-CFS TTR Emerging Markets', 'FC W Pen-CFS TTR Defensive', 'CFS MIF-Geared Share NEF', 'Dimensional Australia Core Equity Trust - Active ETF']\n",
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"All Providers Results: \n",
"Document List File - ./sample_documents/aus_prospectus_17_documents_sample.txt\n",
"Metric \tF1-Score \tPrecision \tRecall \tAccuracy \tSUPPORT \tTP \tTN \tFP \tFN \n",
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"management_fee_and_costs \t0.9245 \t0.8899 \t0.9619 \t0.8596 \t235 \t202 \t0 \t25 \t8 \n",
"management_fee \t0.9195 \t0.8811 \t0.9615 \t0.8511 \t235 \t200 \t0 \t27 \t8 \n",
"performance_fee_costs \t0.9345 \t0.9573 \t0.9128 \t0.9064 \t178 \t157 \t56 \t7 \t15 \n",
"interposed_vehicle_performance_fee_cost \t1.0000 \t1.0000 \t1.0000 \t1.0000 \t20 \t20 \t215 \t0 \t0 \n",
"administration_fees \t1.0000 \t1.0000 \t1.0000 \t1.0000 \t51 \t51 \t184 \t0 \t0 \n",
"total_annual_dollar_based_charges \t1.0000 \t1.0000 \t1.0000 \t1.0000 \t52 \t52 \t183 \t0 \t0 \n",
"buy_spread \t0.8693 \t0.8125 \t0.9346 \t0.8170 \t183 \t143 \t49 \t33 \t10 \n",
"sell_spread \t0.8727 \t0.8182 \t0.9351 \t0.8213 \t183 \t144 \t49 \t32 \t10 \n",
"minimum_initial_investment \t0.9842 \t1.0000 \t0.9689 \t0.9787 \t161 \t156 \t74 \t0 \t5 \n",
"benchmark_name \t0.8972 \t0.9057 \t0.8889 \t0.9532 \t55 \t48 \t176 \t5 \t6 \n",
"TOTAL \t0.9402 \t0.9265 \t0.9564 \t0.9187 \t1353 \t1173 \t986 \t129 \t156 \n",
"Total Shares Matched - 235\n",
"Total Shares Not Matched - 124\n",
"Percentage of Shares Matched - 65.45961002785515\n",
"Not Matched Shares Name List - ['SPDR® S&P World ex Australia Carbon Control Fund', 'Mercer Multi-manager Growth Fund – –
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]
}
],
"source": [
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"\n",
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"\"\"\"\n",
"Blade's updates\n",
"1. Set the secondary key to be the share class name, instead of the fund name\n",
"2. Remove the data point which support is 0 to calculate the metrics\n",
"3. Add the message list to store the error message\n",
"4. Support save metrics/ error message to excel file\n",
"5. Support statistics for different document list\n",
"6. Set F1-Score to the first column in the metrics table\n",
"\"\"\"\n",
"\n",
"# Load the files\n",
"headers_gt, ground_truth_data = load_excel(path_ground_truth, 0)\n",
"headers_gen, generated_results_data = load_excel(path_generated_results, 0)\n",
"\n",
"# Assuming doc_id is the first column and fund_name is the second column\n",
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"# doc_id_index = 0\n",
"# fund_name_index = 1\n",
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"\n",
"# Index the data\n",
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"ground_truth_indexed = index_data_by_key(ground_truth_data, headers_gt)\n",
"generated_results_indexed = index_data_by_key(generated_results_data, headers_gen)\n",
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"\n",
"intersection = set(headers_gen).intersection(headers_gt)\n",
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"\n",
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"# Convert the result back to a list (if you need it as a list)\n",
"intersection_list = list(intersection)\n",
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"\n",
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"total_fn = []\n",
"\n",
"# df_provider_mapping = get_provider_mapping(provider_mapping_file_path)\n",
"\n",
"# all_provider_dict = get_provider_names(generated_results_indexed, df_provider_mapping)\n",
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"\n",
"\n",
"# for provider_name in all_provider_dict:\n",
"# provider_vise_generated_results = get_specified_doc_data(generated_results_indexed, all_provider_dict[provider_name])\n",
"# comparison_results, funds_matched, funds_not_matched = compare_data(ground_truth_indexed, provider_vise_generated_results, headers_gt, doc_id_index, fund_name_index, intersection_list,funds_matched, funds_not_matched)\n",
"# print(\"\\n\")\n",
"# print(\"\\n\")\n",
"# print(\"Provider Name - \" + provider_name + \"\\t Number of Docs - \" + str(len(all_provider_dict[provider_name])))\n",
"# #create_metrics_df(comparison_results)\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",
"\n",
"\n",
"\n",
"print(\"\\n\")\n",
"print(\"\\n\")\n",
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"document_list_file_list = [None, \n",
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" \"./sample_documents/aus_prospectus_29_documents_sample.txt\", \n",
" \"./sample_documents/aus_prospectus_17_documents_sample.txt\"\n",
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" ]\n",
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"# document_list_file_list = [None]\n",
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"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",
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" comparison_results, message_list, share_matched, \\\n",
" share_not_matched, not_matched_share_name_list = compare_data(ground_truth_indexed, \n",
" generated_results_indexed,\n",
" headers_gt,\n",
" intersection_list,\n",
" document_list)\n",
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" metrics_list = print_metrics_table(comparison_results)\n",
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" print(\"Total Shares Matched - \" + str(share_matched) + \"\\nTotal Shares Not Matched - \" + str(share_not_matched))\n",
" print(\"Percentage of Shares Matched - \" + str((share_matched/(share_matched + share_not_matched))*100))\n",
" print(\"Not Matched Shares Name List - \", not_matched_share_name_list)\n",
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"\n",
" metrics_df = pd.DataFrame(metrics_list)\n",
" message_df = pd.DataFrame(message_list)\n",
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" share_matched_data = {\"share_matched\": share_matched, \"share_not_matched\": share_not_matched, \"not_matched_share_name_list\": not_matched_share_name_list}\n",
" share_matched_df = pd.DataFrame([share_matched_data])\n",
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"\n",
" output_metrics_folder = r\"/data/aus_prospectus/output/metrics_data/\"\n",
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" os.makedirs(output_metrics_folder, exist_ok=True)\n",
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" 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",
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" message_df.to_excel(writer, sheet_name=\"message_data\", index=False)\n",
" share_matched_df.to_excel(writer, sheet_name=\"share_matched_data\", index=False)\n"
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]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"for message_list_element in message_list:\n",
" if message_list_element[\"data_point\"] == \"performance_fee_costs\":\n",
" print(message_list_element)"
]
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},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"import pandas as pd\n",
"\n",
"\n",
"# Convert data to DataFrame\n",
"df = pd.DataFrame(message_list)\n",
"\n",
"# Sort DataFrame by 'doc_id'\n",
"df_sorted = df.sort_values(by=['doc_id'])\n",
"\n",
"# Save DataFrame to Excel file\n",
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"os.makedirs(\"/data/aus_prospectus/output/error_analysis/\", exist_ok=True)\n",
"output_filename = r\"/data/aus_prospectus/output/error_analysis/anomalies_found.xlsx\"\n",
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"df_sorted.to_excel(output_filename, index=False)\n",
"\n",
"print(f\"Excel file '{output_filename}' has been created successfully.\")\n"
]
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},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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"display_name": "emea_ar_test",
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"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.12.6"
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},
"orig_nbformat": 4
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"nbformat": 4,
"nbformat_minor": 2
}
