├── figures ├── general_framework.png └── imputation_framework.png ├── requirements.txt ├── main.py ├── preprocess.py ├── README.md ├── postprocess.py ├── llm_imputation.py ├── graph_search.py └── data └── house.csv /figures/general_framework.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xinrui17/LLM-Forest/HEAD/figures/general_framework.png -------------------------------------------------------------------------------- /figures/imputation_framework.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xinrui17/LLM-Forest/HEAD/figures/imputation_framework.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | python==3.9.12 2 | numpy==1.22.4 3 | pandas==1.4.2 4 | networkx==3.2.1 5 | torch==2.2.1 6 | openai==1.10.0 7 | mistralai==1.0.2 8 | 9 | lightgbm==4.6.0 10 | xgboost==2.0.2 11 | missforest==1.1.3 12 | missingpy==0.2.0 13 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import pickle 4 | import argparse 5 | import inspect 6 | import logging 7 | import sys 8 | import random 9 | import torch 10 | from graph_search import neighbor_search 11 | from llm_imputation import llm_imputation 12 | from preprocess import preprocess 13 | from postprocess import postprocess 14 | 15 | 16 | def parse_global_args(parser): 17 | parser.add_argument("--data_path", type = str, default = "/data/", help="path to dataset") 18 | parser.add_argument("--model_name", type = str, default="gpt", help="gpt or mixtral") 19 | parser.add_argument("--dataset", type=str, default="house", help="dataset name") 20 | parser.add_argument("--num_round", type=int, default=3, help="number of trees in the forest") 21 | parser.add_argument("--group_size", type=2, help="size to merge graphs") 22 | parser.add_argument("--num_neighbors", type=str, help="number of neighbors in the context") 23 | return parser 24 | 25 | if __name__ == "__main__": 26 | parser = argparse.ArgumentParser() 27 | parser = parse_global_args(parser) 28 | args = parser.parse_args() 29 | preprocess(args.dataset, args.data_path) 30 | neighbor_search(args) 31 | for round_id in range(args.num_round): 32 | llm_imputation(args.model_name, args.dataset, round_id) 33 | postprocess(args.dataset, args.data_path, args.num_round) 34 | 35 | -------------------------------------------------------------------------------- /preprocess.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import json 3 | import os 4 | 5 | def generate_description_from_row(row): 6 | description = [] 7 | missing_features = [] 8 | 9 | for col in row.index: 10 | value = row[col] 11 | if pd.isna(value): 12 | missing_features.append(col) # Track missing features 13 | else: 14 | description.append(f"{col}: {value}") 15 | 16 | # Add information about missing features if any 17 | if missing_features: 18 | description.append(f"the house has missing features: {', '.join(missing_features)}") 19 | 20 | return ",".join(description) 21 | 22 | def preprocess(dataset, data_path): 23 | df = pd.read_csv(os.path.join(data_path,f"{dataset}", ".csv")) 24 | with open(os.path.join(data_path,f'{dataset}_descriptions.json'), 'w') as outfile: 25 | outfile.write('[') 26 | 27 | first_entry = True 28 | for index, row in df.iterrows(): 29 | try: 30 | description = generate_description_from_row(row) 31 | description_entry = {"user": index, "description": description} 32 | 33 | if not first_entry: 34 | outfile.write(',\n') # Add comma and newline before the next entry, except for the first 35 | else: 36 | first_entry = False 37 | 38 | json.dump(description_entry, outfile) 39 | print("Generated Description for index", index, ":\n", description, "\n---\n") 40 | 41 | except Exception as e: 42 | print(f"An error occurred at index {index}: {e}") 43 | 44 | outfile.write('\n]') # Close the list with a newline 45 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🌲 LLM-Forest 2 | 3 | This repository contains the implementation of [**LLM-Forest**](https://arxiv.org/abs/2410.21520), our ACL 2025 Findings paper. 4 | LLM-Forest is a **task-agnostic framework** that ensembles the outputs of multiple large language models (LLMs) based on diverse prompt generations, inspired by the idea of random forests. 5 | 6 | --- 7 | 8 | ## General Framework 9 | 10 | LLM-Forest constructs diverse prompts for a given task (e.g., Question Answering, Text Generation, Classification), obtains multiple outputs from LLMs, and aggregates them through self-evaluated confidence-weighted voting. 11 | 12 |

13 | 14 |

15 | --- 16 | 17 | ## LLM-Forest for Data Imputation 18 | 19 | We instantiate LLM-Forest for **tabular data imputation**, where the goal is to fill in missing values in health datasets. 20 | The process includes graph construction, prompt generation, LLM-based imputation, and ensemble aggregation to yield an **imputed table** for downstream tasks (e.g., classification). 21 | 22 |

23 | 24 |

25 | --- 26 | 27 | ## Reproducing the Results 28 | 29 | There are **no strict environment requirements** for this project and it can run under most standard Python setups. 30 | You can simply install the minimal dependencies with: 31 | 32 | ```bash 33 | pip install -r requirements.txt 34 | ``` 35 | 36 | To reproduce the results in our paper, here is an example for Diabetes dataset with GPT-4: 37 | 38 | ```bash 39 | python main.py \ 40 | --data_path ./data/diabetes_train4.csv \ 41 | --dataset diabetes \ 42 | --model_name gpt \ 43 | --num_round 3 \ 44 | --group_size 2 \ 45 | --num_neighbors 5 46 | ``` 47 | --- 48 | 49 | ## Citation 50 | If you find this work useful, please cite our paper: 51 | 52 | ``` 53 | @inproceedings{he-etal-2025-llm, 54 | title = "{LLM}-Forest: Ensemble Learning of {LLM}s with Graph-Augmented Prompts for Data Imputation", 55 | author = "He, Xinrui and 56 | Ban, Yikun and 57 | Zou, Jiaru and 58 | Wei, Tianxin and 59 | Cook, Curtiss and 60 | He, Jingrui", 61 | editor = "Che, Wanxiang and 62 | Nabende, Joyce and 63 | Shutova, Ekaterina and 64 | Pilehvar, Mohammad Taher", 65 | booktitle = "Findings of the Association for Computational Linguistics: ACL 2025", 66 | month = jul, 67 | year = "2025", 68 | address = "Vienna, Austria", 69 | publisher = "Association for Computational Linguistics", 70 | url = "https://aclanthology.org/2025.findings-acl.361/", 71 | doi = "10.18653/v1/2025.findings-acl.361", 72 | pages = "6921--6936", 73 | ISBN = "979-8-89176-256-5" 74 | } 75 | ``` 76 | 77 | -------------------------------------------------------------------------------- /postprocess.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import json 3 | import re 4 | import os 5 | 6 | def extract_json_blocks(record_str): 7 | json_matches = re.findall(r'```json\s*(\{.*?\})\s*```', record_str, re.DOTALL) 8 | inferred_values = {} 9 | confidence_levels = {} 10 | 11 | if json_matches: 12 | try: 13 | # Assume the first JSON block contains inferred values 14 | inferred_values = json.loads(json_matches[0]) 15 | # Assume the second JSON block contains confidence levels, if present 16 | if len(json_matches) > 1: 17 | confidence_levels = json.loads(json_matches[1]) 18 | except json.JSONDecodeError as e: 19 | print(f"JSON decoding error: {e}") 20 | 21 | return inferred_values, confidence_levels 22 | 23 | def parse_free_text_records(record_str): 24 | inferred_values = {} 25 | confidence_levels = {} 26 | 27 | try: 28 | inferred_matches = re.findall(r'"([^"]+)":\s*([\d.]+)', record_str) 29 | for key, value in inferred_matches: 30 | inferred_values[key.strip()] = float(value) 31 | 32 | confidence_matches = re.findall(r'"([^"]+)":\s*"([^"]+)"', record_str) 33 | for key, value in confidence_matches: 34 | confidence_levels[key.strip()] = value.strip() 35 | 36 | except Exception as e: 37 | print(f"Error parsing free-text record: {e}") 38 | 39 | return inferred_values, confidence_levels 40 | 41 | def post_process(dataset, data_path, num_round): 42 | imputed_data_list = [] 43 | confidence_data_list = [] 44 | confidence_rank = {"high": 1, "medium": 0.4, "low": 0.3} 45 | for round_id in range(num_round): 46 | data = pd.read_csv(os.path.join(data_path, f"{dataset}", ".csv")) 47 | original_columns = data.columns.tolist() 48 | 49 | column_mapping = {col[:9].lower(): col for col in original_columns} # Map shortened column names to original names 50 | 51 | json_path = os.path.join(data_path, f'{dataset}_imputation_{round_id}.json') 52 | with open(json_path, 'r') as file: 53 | imputed_data = json.load(file) 54 | 55 | inferred_data = data.copy() 56 | confidence_data = data.copy() 57 | 58 | for entry in imputed_data: 59 | patient_id = entry['patient'] 60 | updated_records_str = entry['description'] 61 | 62 | inferred_values, confidence_levels = extract_json_blocks(updated_records_str) 63 | 64 | # If JSON extraction fails, fall back to free-text parsing 65 | if not inferred_values and not confidence_levels: 66 | inferred_values, confidence_levels = parse_free_text_records(updated_records_str) 67 | 68 | # Skip the record if both methods fail 69 | if not inferred_values: 70 | print(f"No valid data found for patient {patient_id}. Skipping.") 71 | continue 72 | 73 | for key, value in inferred_values.items(): 74 | normalized_key = key[:9].lower() 75 | if normalized_key in column_mapping: 76 | original_key = column_mapping[normalized_key] 77 | inferred_data.at[patient_id, original_key] = value 78 | else: 79 | print(f"Unknown key in inferred values for patient {patient_id}: {key}") 80 | 81 | for key, value in confidence_levels.items(): 82 | normalized_key = key[:9].lower() 83 | if normalized_key in column_mapping: 84 | original_key = column_mapping[normalized_key] 85 | confidence_data.at[patient_id, original_key] = value 86 | else: 87 | print(f"Unknown key in confidence levels for patient {patient_id}: {key}") 88 | 89 | inferred_csv_path = os.path.join(data_path, f'{dataset}_{round_id}_values.csv') 90 | confidence_csv_path = os.path.join(data_path, f'{dataset}_{round_id}_confidence.csv') 91 | imputed_data_list.append(inferred_csv_path) 92 | confidence_data_list.append(confidence_data) 93 | inferred_data.to_csv(inferred_csv_path, index=False) 94 | confidence_data.to_csv(confidence_csv_path, index=False) 95 | 96 | print(f"Inferred values saved to {inferred_csv_path}") 97 | print(f"Confidence levels saved to {confidence_csv_path}") 98 | 99 | for patient_id in data.index: 100 | for column in data.columns: 101 | normalized_key = column.lower() 102 | 103 | if normalized_key not in column_mapping: 104 | print("unknown normalized_key") 105 | continue 106 | 107 | if pd.isna(data.at[patient_id, column]): 108 | imputed_values = [] 109 | confidences = [] 110 | 111 | for i in range(len(imputed_data_list)): 112 | imputed_data = imputed_data_list[i] 113 | confidence_data = confidence_data_list[i] 114 | 115 | if patient_id < len(imputed_data): 116 | imputed_value = imputed_data.at[patient_id, column_mapping[normalized_key]] 117 | confidence = confidence_data.at[patient_id, column_mapping[normalized_key]] 118 | 119 | if pd.notna(imputed_value) and pd.notna(confidence): 120 | confidence_score = confidence_rank.get(str(confidence).strip().lower(), 0) # Default to 0 if unrecognized 121 | imputed_values.append(imputed_value) 122 | confidences.append(confidence_score) 123 | 124 | if len(imputed_values) > 0: 125 | max_confidence = max(confidences) 126 | max_confidence_indices = [i for i, conf in enumerate(confidences) if conf == max_confidence] 127 | 128 | if len(max_confidence_indices) > 1: 129 | final_value = sum(imputed_values[i] for i in max_confidence_indices) / len(max_confidence_indices) # Use average 130 | else: 131 | final_value = imputed_values[max_confidence_indices[0]] 132 | 133 | 134 | data.at[patient_id, column] = final_value 135 | 136 | updated_csv_path = os.path.join(data_path, f'{dataset}_result.csv') 137 | data.to_csv(updated_csv_path, index=False) 138 | 139 | print(f"Updated data saved to {updated_csv_path}") 140 | 141 | -------------------------------------------------------------------------------- /llm_imputation.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | from openai import OpenAI 4 | from mistralai import Mistral 5 | import time 6 | 7 | 8 | descriptions = """ 9 | ## Below are the detailed information about the house's feature description. Its follws the format of 10 | Name - Description 11 | 12 | # crim - per capita crime rate by town 13 | # zn - proportion of residential land zoned for lots over 25,000 sq.ft. 14 | # indus - proportion of non-retail business acres per town. 15 | # chas - Charles River dummy variable (1 if tract bounds river; 0 otherwise) 16 | # nox - nitric oxides concentration (parts per 10 million) 17 | # rm - average number of rooms per dwelling 18 | # age - proportion of owner-occupied units built prior to 1940 19 | # dis - weighted distances to five Boston employment centres 20 | # rad - index of accessibility to radial highways 21 | # tax - full-value property-tax rate per $10,000 22 | # ptratio - pupil-teacher ratio by town 23 | # b - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 24 | # lstat - %% lower status of the population 25 | # medvgpt - Median value of owner-occupied homes in $1000's 26 | """ 27 | 28 | def read_json_file(file_path): 29 | """Reads a JSON file and returns the data.""" 30 | with open(file_path, 'r') as file: 31 | data = json.load(file) 32 | return data 33 | 34 | def append_to_json_file(data, file_path): 35 | """Appends data to a JSON file.""" 36 | try: 37 | with open(file_path, 'r+') as file: 38 | file_data = json.load(file) 39 | file_data.append(data) 40 | file.seek(0) 41 | json.dump(file_data, file, indent=4) 42 | except json.JSONDecodeError: 43 | with open(file_path, 'w') as file: 44 | json.dump([data], file, indent=4) 45 | 46 | def imputation_missing_value(client, model_name, patinet_id, records): 47 | # print(records) 48 | """Generates a concise description for patient records using OpenAI's Chat Completion.""" 49 | try: 50 | if model_name == 'gpt': 51 | # print(records) 52 | response = client.chat.completions.create( 53 | model="gpt-4o", 54 | temperature=0.001, 55 | messages=[ 56 | {"role": "system","content": f"You are a helpful assistant tasked with filling the missing values for hosue {patinet_id} in the Boston Housing Dataset. You must give the results for all the missing value. You have the flexibility to determine the best approach for each missing feature by analyzing both the house's own records and the data of other houses. Possible methods may include in no particular sequence (1) using the most common values of the similar houses (2) using the most similar houses' values (3) uncovering patterns and relationships between the features (4) applying your knowledge in the related domains (5)... After performing the imputation, ensure your inferred results are consistent with domain knowledge and validate them using common sense and reasonable assumptions within the related domains."}, 57 | {"role":"user", "content": "Here are some patterns observed based on the correlations and feature distributions: crim and rad have a strong positive correlation of 0.814, indicating that higher crime rates (crim) are associated with higher accessibility to radial highways (rad). indus and nox have strong positive correlation of 0.780, showing that areas with higher proportions of non-retail business acres (indus) also tend to have higher nitric oxide concentrations (nox). rad and tax have a positive correlation (0.69) showing that towns with better highway accessibility (rad) tend to have higher property tax rates (tax). This could reflect higher infrastructure investment in these areas. indus and rad have moderate positive correlation of 0.564, suggesting that industrial areas are more likely to have better access to radial highways (rad). indus and zn have a moderate negative correlation of -0.537, implying that regions with higher industrial activity (indus) are less likely to have a higher proportion of residential land zoned for lots over 25,000 sq. ft. (zn). For crim, most values cluster around 0.63. The majority of zn values are concentrated at 0, reflecting that many towns have minimal or no large residential zoning. Few towns show much higher values, indicating skewness. For rm, the mean is around 6.24, with a moderate spread, indicating a relatively consistent distribution of housing room sizes. For ptratio, the majority of values are centered around 18.5, showing minimal variability and suggesting uniformity in educational resources. For b, most values cluster around 356.0, with a relatively narrow spread and a few extreme outliers showing significant deviation. For chas,the majority of values are concentrated at 0, indicating that most towns do not bound the Charles River. A small portion of the towns has a value of 1."}, 58 | {"role":"user", "content": f'''The data is from Boston Housing dataset, containing information collected by the U.S Census Service concerning housing in the area of Boston Mass and aiming to predict the price, in which the median value of a home is to be predicted. Some values are missing in the orginal records due to various reasons. Given feature descriptionn {descriptions} and house's records {records}. 59 | Imagine that your are working as an analyst to complete this dataset for further research purpose. Provide your understanding of the missing feature and your explanations for choosing the appropriate value before the final inference. Give the imputation results in a succinct JSON format strictly starting with ```json with the following structure: \"feature name\": \"inferred value\". Output your confidence level for the imputation results in a JSON format starting with ```json with the following structure: \"feature name\": \" confidence level\" at the end without any explanations. In the results, use the exact feature names as those in the feature description.'''} 60 | ] 61 | ) 62 | 63 | print(response.choices[0].message.content) 64 | return response.choices[0].message.content 65 | elif model_name == 'mixtral': 66 | retry_count = 0 67 | max_retries = 50 68 | backoff_factor = 2 # Exponential backoff factor 69 | while retry_count < max_retries: 70 | try: 71 | chat_response = client.chat.complete( 72 | model="open-mixtral-8x22b", 73 | temperature=0.001, 74 | messages=[ 75 | {"role": "system","content": f"You are a helpful assistant tasked with filling the missing values for hosue {patinet_id} in the Boston Housing Dataset. You must give the results for all the missing value. You have the flexibility to determine the best approach for each missing feature by analyzing both the house's own records and the data of other houses. Possible methods may include in no particular sequence (1) using the most common values of the similar houses (2) using the most similar houses' values (3) uncovering patterns and relationships between the features (4) applying your knowledge in the related domains (5)... After performing the imputation, ensure your inferred results are consistent with domain knowledge and validate them using common sense and reasonable assumptions within the related domains."}, 76 | {"role":"user", "content": "Here are some patterns observed based on the correlations and feature distributions: crim and rad have a strong positive correlation of 0.814, indicating that higher crime rates (crim) are associated with higher accessibility to radial highways (rad). indus and nox have strong positive correlation of 0.780, showing that areas with higher proportions of non-retail business acres (indus) also tend to have higher nitric oxide concentrations (nox). rad and tax have a positive correlation (0.69) showing that towns with better highway accessibility (rad) tend to have higher property tax rates (tax). This could reflect higher infrastructure investment in these areas. indus and rad have moderate positive correlation of 0.564, suggesting that industrial areas are more likely to have better access to radial highways (rad). indus and zn have a moderate negative correlation of -0.537, implying that regions with higher industrial activity (indus) are less likely to have a higher proportion of residential land zoned for lots over 25,000 sq. ft. (zn). For crim, most values cluster around 0.63. The majority of zn values are concentrated at 0, reflecting that many towns have minimal or no large residential zoning. Few towns show much higher values, indicating skewness. For rm, the mean is around 6.24, with a moderate spread, indicating a relatively consistent distribution of housing room sizes. For ptratio, the majority of values are centered around 18.5, showing minimal variability and suggesting uniformity in educational resources. For b, most values cluster around 356.0, with a relatively narrow spread and a few extreme outliers showing significant deviation. For chas,the majority of values are concentrated at 0, indicating that most towns do not bound the Charles River. A small portion of the towns has a value of 1."}, 77 | {"role":"user", "content": f'''The data is from Boston Housing dataset, containing information collected by the U.S Census Service concerning housing in the area of Boston Mass and aiming to predict the price, in which the median value of a home is to be predicted. Some values are missing in the orginal records due to various reasons. Given feature descriptionn {descriptions} and house's records {records}. 78 | Imagine that your are working as an analyst to complete this dataset for further research purpose. Provide your understanding of the missing feature and your explanations for choosing the appropriate value before the final inference. Give the imputation results in a succinct JSON format strictly starting with ```json with the following structure: \"feature name\": \"inferred value\". Output your confidence level for the imputation results in a JSON format starting with ```json with the following structure: \"feature name\": \" confidence level\" at the end without any explanations. In the results, use the exact feature names as those in the feature description.'''} 79 | ] 80 | ) 81 | 82 | print(chat_response.choices[0].message.content) 83 | return chat_response.choices[0].message.content 84 | 85 | except Exception as e: 86 | if "Status 429" in str(e) or "Status 403" in str(e): 87 | retry_count += 1 88 | wait_time = backoff_factor ** retry_count 89 | print(f"Rate limit exceeded. Retrying in {wait_time} seconds...") 90 | time.sleep(wait_time) 91 | else: 92 | print(f"An error occurred: {e}") 93 | return "Failed to generate description due to an error." 94 | 95 | except Exception as e: 96 | print(f"An error occurred: {e}") 97 | return "Failed to generate description due to an error." 98 | print("Maximum retries reached. Skipping this patient.") 99 | return "Failed to generate description after retries." 100 | 101 | 102 | def process_and_write_patient_record(client, model_name, round_id, patient, output_file_path): 103 | """Generates concise description for a single patient and appends to JSON file.""" 104 | imputation_results = imputation_missing_value(client, model_name, patient["user"], patient[f"UpdatedRecords_Round_{round_id}"]) 105 | 106 | updated_patient_data = { 107 | "patient": patient["user"], 108 | "UpdatedRecords": imputation_results 109 | } 110 | append_to_json_file(updated_patient_data, output_file_path) 111 | 112 | def llm_imputation(model_name, data_path, dataset, round_id): 113 | if model_name == "gpt": 114 | client = OpenAI(api_key = 'put_your_key_here') 115 | elif model_name == "mixtral": 116 | client = Mistral(api_key="put_your_key_here") 117 | 118 | input_file_path = os.path.join(data_path,f'{dataset}_neighbors_{round_id}.json') 119 | output_file_path = os.path.join(data_path, f'{dataset}_imputation_{round_id}.json') 120 | open(output_file_path, 'w').close() 121 | patient_data = read_json_file(input_file_path) 122 | 123 | for patient in patient_data: 124 | process_and_write_patient_record(client, model_name, patient, output_file_path) 125 | 126 | print(f"Updated User data has been written to {output_file_path}") 127 | -------------------------------------------------------------------------------- /graph_search.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import networkx as nx 3 | import numpy as np 4 | import random 5 | import json 6 | from collections import Counter 7 | import time 8 | import os 9 | 10 | 11 | def sigmoid(x): 12 | return 1 / (1 + np.exp(-x)) 13 | 14 | 15 | def load_neighbors_from_txt(file_path): 16 | round_neighbors = [] 17 | current_round_neighbors = {} 18 | current_round = None 19 | 20 | with open(file_path, 'r') as file: 21 | for line in file: 22 | if 'Neighbors found in Round' in line: 23 | if current_round_neighbors: 24 | round_neighbors.append(current_round_neighbors) 25 | current_round_neighbors = {} 26 | current_round = int(line.strip().split()[-1][:-1]) # Extract round number 27 | elif line.strip(): 28 | parts = line.split(":") 29 | patient_id = int(parts[0].split()[1]) 30 | neighbors = eval(parts[1].strip()) 31 | current_round_neighbors[patient_id] = neighbors 32 | # Append the last round 33 | if current_round_neighbors: 34 | round_neighbors.append(current_round_neighbors) 35 | 36 | return round_neighbors 37 | 38 | 39 | def dynamic_binning(data, feature, num_bins=10, min_patients_per_bin=5): 40 | """ 41 | Dynamically bins a feature to ensure each bin has at least min_patients_per_bin patients. 42 | Handles cases where distinct values are fewer than the number of bins. 43 | """ 44 | unique_values = sorted(data[feature].dropna().unique()) 45 | if len(unique_values) <= num_bins: 46 | bins_assigned = pd.cut(data[feature], bins=len(unique_values), labels=False, include_lowest=True) 47 | bin_edges = unique_values + [unique_values[-1] + 1] # Extend to cover the last value 48 | return bins_assigned, bin_edges 49 | 50 | _, bin_edges = pd.cut(data[feature], bins=num_bins, retbins=True, labels=False, include_lowest=True) 51 | bins_assigned = pd.cut(data[feature], bins=bin_edges, labels=False, include_lowest=True) 52 | bin_counts = bins_assigned.value_counts().sort_index() 53 | 54 | # Adjust bins dynamically 55 | max_iterations = 100 56 | iterations = 0 57 | 58 | while bin_counts.min() < min_patients_per_bin: 59 | if iterations >= max_iterations: 60 | print(f"Warning: Max iterations reached for {feature}.") 61 | break 62 | 63 | new_bin_edges = [bin_edges[0]] # Always include the first bin edge 64 | 65 | i = 0 66 | while i < len(bin_counts) - 1: # Iterate through bins 67 | current_count = bin_counts.iloc[i] 68 | next_count = bin_counts.iloc[i + 1] 69 | 70 | if current_count < min_patients_per_bin: 71 | merged_count = current_count + next_count 72 | if merged_count >= min_patients_per_bin: 73 | bin_counts.iloc[i + 1] = merged_count 74 | bin_counts.iloc[i] = 0 # Mark the current bin as merged 75 | new_bin_edges.append(bin_edges[i + 2]) # Skip the next edge 76 | i += 2 77 | else: 78 | bin_counts.iloc[i + 1] = merged_count 79 | bin_counts.iloc[i] = 0 80 | i += 1 81 | else: 82 | new_bin_edges.append(bin_edges[i + 1]) 83 | i += 1 84 | 85 | if bin_counts.iloc[-1] < min_patients_per_bin: 86 | bin_counts.iloc[-2] += bin_counts.iloc[-1] 87 | bin_counts.iloc[-1] = 0 88 | new_bin_edges = new_bin_edges[:-1] # Remove the second-to-last edge 89 | 90 | new_bin_edges.append(bin_edges[-1]) 91 | new_bin_edges = sorted(set(new_bin_edges)) 92 | bin_edges = new_bin_edges 93 | bins_assigned = pd.cut(data[feature], bins=bin_edges, labels=False, include_lowest=True) 94 | bin_counts = bins_assigned.value_counts().sort_index() 95 | iterations += 1 96 | 97 | return bins_assigned, bin_edges 98 | 99 | 100 | def create_bipartite_graph_for_binned(binned_data, data, feature, bin_edges): 101 | G = nx.Graph() 102 | patients = data.index.tolist() 103 | bin_edges = np.array(bin_edges) 104 | 105 | # Compute bin centers 106 | bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2 107 | bin_nodes = [f'{feature}_bin_{i}' for i in range(len(bin_centers))] 108 | 109 | G.add_nodes_from(patients, bipartite=0) 110 | G.add_nodes_from(bin_nodes, bipartite=1) 111 | 112 | # Connect each patient to their respective bin 113 | for patient in patients: 114 | bin_index = binned_data.at[patient, feature] 115 | if pd.notna(bin_index): 116 | bin_node = f'{feature}_bin_{bin_index}' 117 | bin_center = bin_centers[int(bin_index)] 118 | value = data.at[patient, feature] 119 | 120 | # Calculate the relative distance 121 | if bin_center != 0: 122 | distance_to_center = abs(value - bin_center) / bin_center 123 | else: 124 | distance_to_center = abs(value - bin_center) 125 | 126 | # Calculate weight 127 | weight = 1 / (2 + distance_to_center) 128 | G.add_edge(patient, bin_node, weight=weight) 129 | 130 | return G 131 | 132 | 133 | def bin_continuous_features(data, num_bins=10, min_patients_per_bin=4): 134 | """ 135 | Bins all continuous features dynamically in the dataset. 136 | Handles cases with fewer distinct values than the number of bins. 137 | """ 138 | binned_data = data.copy() 139 | bin_edges_dict = {} 140 | 141 | for col in data.columns: 142 | if pd.api.types.is_numeric_dtype(data[col]): 143 | bins_assigned, bin_edges = dynamic_binning(data, col, num_bins=num_bins, min_patients_per_bin=min_patients_per_bin) 144 | binned_data[col] = bins_assigned 145 | bin_edges_dict[col] = bin_edges 146 | 147 | return binned_data, bin_edges_dict 148 | 149 | 150 | def group_graphs_randomly(graphs, group_size=3): 151 | remaining_features = list(graphs.keys()) 152 | graph_groups = [] 153 | 154 | while len(remaining_features) >= group_size: 155 | group = random.sample(remaining_features, group_size) 156 | graph_groups.append(group) 157 | for g in group: 158 | remaining_features.remove(g) 159 | 160 | # Handle any remaining graphs (less than group_size) 161 | if len(remaining_features) > 0: 162 | graph_groups.append(remaining_features) 163 | 164 | return graph_groups 165 | 166 | # Function to randomly pair graphs for merging 167 | def pair_graphs_randomly(graphs): 168 | remaining_features = list(graphs.keys()) 169 | graph_pairs = [] 170 | 171 | while len(remaining_features) > 1: 172 | pair = random.sample(remaining_features, 2) 173 | graph_pairs.append(pair) 174 | remaining_features.remove(pair[0]) 175 | remaining_features.remove(pair[1]) 176 | 177 | if len(remaining_features) == 1: 178 | graph_pairs.append([remaining_features[0]]) 179 | 180 | return graph_pairs 181 | 182 | def merge_three_graphs_by_patient_connections(G1, G2, G3, threshold=3): 183 | merged_graph = nx.Graph() 184 | 185 | patients = [n for n, d in G1.nodes(data=True) if d.get('bipartite') == 0] 186 | values1 = [n for n, d in G1.nodes(data=True) if d.get('bipartite') == 1] 187 | values2 = [n for n, d in G2.nodes(data=True) if d.get('bipartite') == 1] 188 | values3 = [n for n, d in G3.nodes(data=True) if d.get('bipartite') == 1] 189 | 190 | # Calculate common patients between every pair of value nodes across three graphs 191 | value_pairs_common_patients = [] 192 | for v1 in values1: 193 | for v2 in values2: 194 | for v3 in values3: 195 | common_patients = set(G1.neighbors(v1)) & set(G2.neighbors(v2)) & set(G3.neighbors(v3)) 196 | if len(common_patients) >= threshold: 197 | value_pairs_common_patients.append((v1, v2, v3, len(common_patients))) 198 | 199 | value_pairs_common_patients.sort(key=lambda x: x[3], reverse=True) 200 | 201 | # Merge nodes based on common patient connections 202 | merged_values = set() 203 | for v1, v2, v3, common_count in value_pairs_common_patients: 204 | if v1 in merged_values or v2 in merged_values or v3 in merged_values: 205 | continue # Skip if any value has already been merged 206 | 207 | merged_graph.add_node((v1, v2, v3), bipartite=1) 208 | merged_values.add(v1) 209 | merged_values.add(v2) 210 | merged_values.add(v3) 211 | 212 | # Add edges between patients and merged value nodes 213 | for patient in patients: 214 | merged_graph.add_node(patient, bipartite=0) 215 | for v1, v2, v3, _ in value_pairs_common_patients: 216 | weight1 = G1[patient][v1]['weight'] if G1.has_edge(patient, v1) else 0 217 | weight2 = G2[patient][v2]['weight'] if G2.has_edge(patient, v2) else 0 218 | weight3 = G3[patient][v3]['weight'] if G3.has_edge(patient, v3) else 0 219 | if weight1 > 0 or weight2 > 0 or weight3 > 0: 220 | if merged_graph.has_node((v1, v2, v3)): 221 | merged_graph.add_edge(patient, (v1, v2, v3), weight=weight1 + weight2 + weight3) 222 | 223 | # Add remaining unmerged value nodes to the graph 224 | for v in set(values1 + values2 + values3) - merged_values: 225 | bipartite_value = 1 if v in values1 or v in values2 or v in values3 else 0 # Determine if it's a patient or value node 226 | merged_graph.add_node(v, bipartite=bipartite_value) 227 | for patient in G1.neighbors(v) if v in values1 else (G2.neighbors(v) if v in values2 else G3.neighbors(v)): 228 | weight = G1[patient][v]['weight'] if G1.has_edge(patient, v) else (G2[patient][v]['weight'] if G2.has_edge(patient, v) else G3[patient][v]['weight']) 229 | merged_graph.add_edge(patient, v, weight=weight) 230 | 231 | return merged_graph 232 | 233 | # Function to merge two graphs by computing common patients between value nodes 234 | def merge_two_graphs_by_patient_connections(G1, G2, threshold=3): 235 | merged_graph = nx.Graph() 236 | 237 | patients = [n for n, d in G1.nodes(data=True) if d.get('bipartite') == 0] 238 | values1 = [n for n, d in G1.nodes(data=True) if d.get('bipartite') == 1] 239 | values2 = [n for n, d in G2.nodes(data=True) if d.get('bipartite') == 1] 240 | 241 | # Calculate common patients between every pair of value nodes across three graphs 242 | value_pairs_common_patients = [] 243 | for v1 in values1: 244 | for v2 in values2: 245 | 246 | common_patients = set(G1.neighbors(v1)) & set(G2.neighbors(v2)) 247 | if len(common_patients) >= threshold: 248 | value_pairs_common_patients.append((v1, v2, len(common_patients))) 249 | 250 | # Sort value pairs by the number of common patients (descending order) 251 | value_pairs_common_patients.sort(key=lambda x: x[3], reverse=True) 252 | 253 | # Merge nodes based on common patient connections 254 | merged_values = set() 255 | for v1, v2, common_count in value_pairs_common_patients: 256 | if v1 in merged_values or v2 in merged_values: 257 | continue # Skip if any value has already been merged 258 | 259 | merged_graph.add_node((v1, v2), bipartite=1) 260 | merged_values.add(v1) 261 | merged_values.add(v2) 262 | 263 | # Add edges between patients and merged value nodes 264 | for patient in patients: 265 | merged_graph.add_node(patient, bipartite=0) 266 | for v1, v2, _ in value_pairs_common_patients: 267 | weight1 = G1[patient][v1]['weight'] if G1.has_edge(patient, v1) else 0 268 | weight2 = G2[patient][v2]['weight'] if G2.has_edge(patient, v2) else 0 269 | if weight1 > 0 or weight2 > 0: 270 | if merged_graph.has_node((v1, v2)): 271 | merged_graph.add_edge(patient, (v1, v2), weight=weight1 + weight2) 272 | 273 | # Add remaining unmerged value nodes to the graph 274 | for v in set(values1 + values2 ) - merged_values: 275 | bipartite_value = 1 if v in values1 or v in values2 else 0 # Determine if it's a patient or value node 276 | merged_graph.add_node(v, bipartite=bipartite_value) 277 | for patient in G1.neighbors(v) if v in values1 else G2.neighbors(v): 278 | weight = G1[patient][v]['weight'] if G1.has_edge(patient, v) else G2[patient][v]['weight'] 279 | merged_graph.add_edge(patient, v, weight=weight) 280 | 281 | return merged_graph 282 | 283 | 284 | def merge_and_process_graphs(graphs, group_size=2): 285 | graph_groups = group_graphs_randomly(graphs, group_size=group_size) 286 | merged_graphs = [] 287 | 288 | for group in graph_groups: 289 | if len(group) == 3: 290 | g1, g2, g3 = group 291 | merged_graph = merge_three_graphs_by_patient_connections(graphs[g1], graphs[g2], graphs[g3], threshold=10) 292 | merged_graphs.append(merged_graph) 293 | elif len(group) == 2: 294 | g1, g2 = group 295 | merged_graph = merge_two_graphs_by_patient_connections(graphs[g1], graphs[g2], threshold=5) 296 | merged_graphs.append(merged_graph) 297 | else: 298 | merged_graphs.append(graphs[group[0]]) 299 | 300 | return merged_graphs 301 | 302 | def list_depth(lst): 303 | if isinstance(lst, list): 304 | return 1 + max(list_depth(item) for item in lst) if lst else 1 305 | return 0 306 | 307 | def flatten_node(node): 308 | if isinstance(node, tuple): 309 | return '_'.join(map(str, node)) 310 | return node 311 | 312 | def weighted_random_walk(graph, start_node, steps=5): 313 | if start_node not in graph: 314 | raise ValueError(f"The node {start_node} is not present in the graph.") 315 | 316 | current_node = start_node 317 | for _ in range(steps): 318 | neighbors = list(graph.neighbors(current_node)) 319 | if not neighbors: 320 | break 321 | weights = np.array([graph[current_node][neighbor]['weight'] for neighbor in neighbors]) 322 | sigmoid_weights = sigmoid(weights) 323 | probabilities = sigmoid_weights / sigmoid_weights.sum() 324 | neighbors_flat = [flatten_node(neighbor) for neighbor in neighbors] 325 | next_node_flat = np.random.choice(neighbors_flat, p=probabilities) 326 | next_node = neighbors[neighbors_flat.index(next_node_flat)] 327 | current_node = next_node 328 | yield current_node 329 | 330 | # Function to get top neighbors by weight using random walk 331 | def top_neighbors_by_weight(graph, start_node, steps=10, max_neighbors=10, num_walks=50, feature_value_count=1): 332 | neighbors_weights = Counter() 333 | for _ in range(num_walks): 334 | for neighbor in weighted_random_walk(graph, start_node, steps): 335 | neighbors_weights[neighbor] += 1 336 | 337 | sorted_neighbors = neighbors_weights.most_common(max_neighbors) 338 | top_patient_nodes_with_weights = { 339 | neighbor: neighbors_weights[neighbor] for neighbor, _ in sorted_neighbors if graph.nodes[neighbor].get('bipartite') == 0 340 | } 341 | 342 | return top_patient_nodes_with_weights 343 | 344 | def save_neighbors_to_txt(all_neighbors, file_path='house_neighbors_list.txt'): 345 | with open(file_path, 'w') as file: 346 | for round_num, neighbors in enumerate(all_neighbors, 1): 347 | file.write(f"Neighbors found in Round {round_num}:\n") 348 | for patient, neighbor_dict in neighbors.items(): 349 | file.write(f"Patient {patient}: {list(neighbor_dict.keys())}\n") 350 | file.write("\n") 351 | 352 | def load_json_data(file_path): 353 | with open(file_path, 'r') as file: 354 | data = json.load(file) 355 | return data 356 | 357 | def save_json_data(data, file_path): 358 | with open(file_path, 'w') as file: 359 | json.dump(data, file, indent=4) 360 | 361 | 362 | def update_patient_records_with_neighbors(patients_data, final_neighbors, round_num): 363 | for patient in patients_data: 364 | patient_id = patient['user'] 365 | if patient_id in final_neighbors: 366 | neighbor_records = [] 367 | i = 1 368 | for neighbor_id in final_neighbors[patient_id].keys(): 369 | neighbor_data = next((item for item in patients_data if item['user'] == neighbor_id), None) 370 | if neighbor_data: 371 | neighbor_record = neighbor_data.get('description', 'No record available') 372 | neighbor_records.append(f"Similar house records {i}: {neighbor_record}") 373 | i += 1 374 | 375 | original_record = patient.get('description', 'No original record available') 376 | neighbors_record = ' '.join(neighbor_records) 377 | updated_record = ( 378 | f"the information of the similar houses for house {patient_id} are : {neighbors_record}\n" 379 | f"Only infer the missing values in house {patient_id}'s records: {original_record} " 380 | # f"Provide only the imputation results using the following succinct output format in JSON without any extra explanations: \"feature name\": \"inferred value\"" 381 | ) 382 | new_round_num = round_num+3 383 | patient[f'UpdatedRecords_Round_{round_num}'] = updated_record 384 | 385 | def find_neighbors_and_update_json(data, patients_data, graphs, dataset, data_path, rounds, group_size, num_neighbors): 386 | all_neighbors = [] # To store neighbors from all rounds 387 | 388 | previous_neighbors = {patient: set() for patient in data.index.tolist()} 389 | for round_num in range(1, rounds + 1): 390 | print(f"Starting round {round_num}") 391 | current_neighbors = {} 392 | merged_graphs = merge_and_process_graphs(graphs, group_size=group_size) 393 | 394 | for patient in data.index.tolist(): 395 | aggregated_neighbors = Counter() 396 | for merged_graph in merged_graphs: 397 | feature_value_count = len(set(n for n, d in merged_graph.nodes(data=True) if d.get('bipartite') == 1)) 398 | neighbors = top_neighbors_by_weight(merged_graph, patient, steps=2, max_neighbors=100, num_walks=100, feature_value_count=feature_value_count) 399 | 400 | for neighbor, weight in neighbors.items(): 401 | aggregated_neighbors[neighbor] += weight * feature_value_count 402 | sorted_aggregated_neighbors = aggregated_neighbors.most_common(num_neighbors) # Get more neighbors to account for filtering 403 | filtered_neighbors = sorted_aggregated_neighbors[:num_neighbors] 404 | 405 | current_neighbors[patient] = {neighbor: weight for neighbor, weight in filtered_neighbors} 406 | 407 | previous_neighbors[patient].update(current_neighbors[patient].keys()) 408 | 409 | all_neighbors.append(current_neighbors) 410 | 411 | update_patient_records_with_neighbors(patients_data, current_neighbors, round_num) 412 | json_file_path = os.path.join(data_path, f'{dataset}_neighbors_{round_num}.json') 413 | save_json_data(patients_data, json_file_path) 414 | 415 | return all_neighbors 416 | 417 | 418 | def neighbor_search(args): 419 | graphs = {} 420 | e = 0.001 421 | data = pd.read_csv(os.path.join(args.data_path, args.dataset, '.csv')) 422 | binned_data, bin_edges_dict = bin_continuous_features(data, num_bins=args.num_neighbors) 423 | 424 | graphs = {} 425 | for feature in binned_data.columns: 426 | if feature in bin_edges_dict: 427 | graphs[feature] = create_bipartite_graph_for_binned(binned_data, data, feature, bin_edges_dict[feature]) 428 | 429 | original_file_path = os.path.join(args.data_path, f'{args.dataset}_descriptions.json') # Replace with your actual file path 430 | patients_data = load_json_data(original_file_path) 431 | 432 | all_neighbors = find_neighbors_and_update_json(data, patients_data, graphs, dataset = args.dataset, data_path=args.data_path, rounds=args.num_round, group_size=args.group_size) 433 | 434 | save_neighbors_to_txt(all_neighbors, file_path=os.path.join(args.data_path, f'{args.dataset}_neighbors.txt')) 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0.21161,,,,,6.137,87.4,2.7147,5.0,384.0,20.9,,13.44,19.3 149 | 0.04462,,,0.0,0.426,,70.4,5.4007,,281.0,,,7.22,23.9 150 | 0.17505,,5.96,0.0,0.499,5.966,,3.8473,5.0,,19.2,,10.13,24.7 151 | 0.24522,,9.9,0.0,0.544,5.782,71.7,,4.0,304.0,18.4,396.9,,19.8 152 | 1.80028,,19.58,0.0,,,79.2,2.4259,,403.0,,227.61,12.14,23.8 153 | 6.39312,,18.1,,0.584,6.162,97.4,2.206,24.0,666.0,20.2,302.76,24.1,13.3 154 | 0.05561,70.0,2.24,0.0,,,,7.8278,5.0,358.0,,,4.74,29.0 155 | 0.05372,,13.92,0.0,0.437,,,,4.0,,,,7.39,27.1 156 | ,,1.52,,,,38.3,,2.0,,,392.2,,34.6 157 | ,0.0,18.1,0.0,0.671,6.794,98.8,,24.0,666.0,,396.9,21.24,13.3 158 | ,0.0,,0.0,,5.628,,1.5166,5.0,,14.7,169.27,16.65,15.6 159 | ,0.0,18.1,,0.693,6.405,96.0,1.6768,24.0,,20.2,,19.37,12.5 160 | ,,,,0.871,4.926,95.7,1.4608,,403.0,,,29.53,14.6 161 | 7.36711,,18.1,0.0,0.679,,,1.9356,,,20.2,96.73,21.52,11.0 162 | ,52.5,5.32,,0.405,,,7.3172,,,16.6,371.72,,24.8 163 | 0.15038,,25.65,0.0,0.581,,,1.9444,,,19.1,370.31,25.41,17.3 164 | 0.20746,0.0,27.74,0.0,,,98.0,1.8226,,,20.1,,,8.1 165 | ,0.0,2.89,0.0,0.445,,,3.4952,2.0,276.0,,,11.34,21.4 166 | 4.0974,0.0,,0.0,0.871,,,1.4118,5.0,,14.7,,26.42,15.6 167 | 0.09252,30.0,4.93,0.0,0.428,6.606,42.2,,,,16.6,383.78,7.37,23.3 168 | ,40.0,,,0.447,6.854,42.8,4.2673,4.0,254.0,,396.9,2.98,32.0 169 | 0.12083,,2.89,0.0,0.445,8.069,76.0,3.4952,,276.0,,,4.21,38.7 170 | 0.01709,,2.02,0.0,,,36.1,,5.0,,17.0,384.46,4.5,30.1 171 | 0.09299,,25.65,0.0,,,92.9,2.0869,2.0,,19.1,,,20.5 172 | ,0.0,,0.0,,,95.6,2.847,,,17.8,396.9,5.68,32.5 173 | 0.02177,,2.03,0.0,0.415,7.61,15.7,,,348.0,14.7,,3.11,42.3 174 | 0.33983,,,0.0,,6.108,,8.0555,7.0,,19.1,390.18,9.16,24.3 175 | 2.37857,0.0,18.1,,0.583,5.871,,3.724,24.0,,20.2,370.73,,20.6 176 | ,34.0,6.09,0.0,0.433,6.59,,,7.0,,,395.75,9.5,22.0 177 | ,,1.91,0.0,,,,,,334.0,22.0,,8.05,18.2 178 | ,0.0,,,,5.757,,1.413,,666.0,,2.6,10.11,15.0 179 | 9.91655,,,0.0,,5.852,77.8,,24.0,666.0,20.2,,29.97,6.3 180 | 0.01965,80.0,1.76,0.0,,6.23,31.5,9.0892,1.0,241.0,18.2,341.6,,20.1 181 | 0.16902,0.0,,0.0,,5.986,,,2.0,188.0,,385.02,14.81,21.4 182 | 0.05479,33.0,,0.0,0.472,6.616,58.1,,7.0,,18.4,,,28.4 183 | 0.6147,,6.2,0.0,0.507,,80.8,3.2721,8.0,307.0,17.4,396.9,7.6,30.1 184 | 12.0482,,18.1,0.0,,,87.6,1.9512,,666.0,20.2,,14.1,20.8 185 | 0.11425,0.0,,1.0,,,92.4,3.3633,5.0,276.0,16.4,393.74,10.5,23.0 186 | 0.88125,0.0,21.89,,,5.637,94.7,,4.0,,21.2,396.9,,14.3 187 | 8.79212,0.0,,0.0,0.584,5.565,70.6,2.0635,24.0,666.0,20.2,3.65,17.16,11.7 188 | ,,,0.0,,7.148,27.7,,4.0,245.0,19.2,396.9,3.56,37.3 189 | 0.05023,35.0,6.06,0.0,,5.706,28.4,6.6407,,304.0,16.9,394.02,12.43,17.1 190 | 88.9762,0.0,18.1,,0.671,6.968,,,,666.0,20.2,,17.21,10.4 191 | 5.82401,0.0,18.1,,,6.242,64.7,3.4242,,,,396.9,10.74,23.0 192 | 5.20177,,,1.0,0.77,6.127,,2.7227,24.0,666.0,,395.43,,22.7 193 | 0.14103,0.0,13.92,0.0,,,58.0,6.32,4.0,289.0,,,,20.3 194 | ,0.0,13.92,0.0,0.437,6.009,42.3,5.5027,4.0,,16.0,,10.4,21.7 195 | 6.53876,,,1.0,0.631,7.016,,1.2024,24.0,666.0,20.2,392.05,,50.0 196 | 13.6781,,,0.0,,,,1.8206,24.0,666.0,,,34.02,8.4 197 | 0.12329,0.0,10.01,,0.547,5.913,92.9,2.3534,6.0,432.0,,394.95,,18.8 198 | 0.0578,0.0,,0.0,0.488,6.98,58.4,,3.0,193.0,17.8,396.9,5.04,37.2 199 | 2.63548,,9.9,0.0,0.544,,37.8,,4.0,304.0,,,,16.1 200 | 0.02498,0.0,1.89,0.0,,6.54,59.7,,1.0,422.0,,389.96,,16.5 201 | ,,,0.0,,6.316,38.1,6.4584,5.0,224.0,20.2,389.71,,22.2 202 | ,0.0,,0.0,0.583,,53.2,,,666.0,,,11.45,20.6 203 | 8.20058,0.0,18.1,0.0,0.713,5.936,80.3,2.7792,,666.0,20.2,,,13.5 204 | ,0.0,6.2,0.0,,8.247,70.4,,,,17.4,,,48.3 205 | 0.3692,,9.9,0.0,,6.567,87.3,,4.0,304.0,,,,23.8 206 | 2.24236,,,,,5.854,,2.422,5.0,403.0,,395.11,11.64,22.7 207 | 0.32264,0.0,21.89,0.0,,5.942,,1.9669,,437.0,21.2,,,17.4 208 | 0.04666,,,,,7.107,36.6,7.309,,,12.6,354.31,,30.3 209 | 0.66351,20.0,,0.0,0.647,7.333,,1.8946,5.0,,13.0,383.29,,36.0 210 | ,,,0.0,,,73.3,3.8384,8.0,,,385.91,,41.7 211 | 0.17134,0.0,10.01,0.0,,5.928,88.2,,6.0,,17.8,,15.76,18.3 212 | 0.06899,0.0,25.65,,0.581,5.87,69.7,2.2577,2.0,188.0,,,,22.0 213 | 0.07244,,1.69,,0.411,,,10.7103,,,,392.33,7.79,18.6 214 | 0.31533,0.0,6.2,,0.504,8.266,,2.8944,,307.0,17.4,,4.14,44.8 215 | 20.7162,,,0.0,0.659,4.138,100.0,,24.0,,20.2,,,11.9 216 | 0.06151,0.0,5.19,,,5.968,58.5,,,,20.2,396.9,,18.7 217 | 0.25915,0.0,,0.0,0.624,5.693,,1.7883,,,21.2,,17.19,16.2 218 | 0.01096,55.0,2.25,,0.389,,,,1.0,300.0,15.3,394.72,8.23,22.0 219 | 18.0846,0.0,18.1,,,6.434,100.0,,24.0,666.0,,27.25,,7.2 220 | ,0.0,8.56,0.0,0.52,6.127,85.2,,5.0,384.0,20.9,,,20.4 221 | ,0.0,18.1,0.0,,4.138,100.0,,,666.0,20.2,396.9,37.97,13.8 222 | ,0.0,,,0.713,,98.3,,24.0,,,304.21,19.31,13.0 223 | 0.32982,0.0,21.89,0.0,0.624,,95.4,2.4699,,437.0,,388.69,,18.4 224 | 13.5222,0.0,,0.0,0.631,,100.0,1.5106,24.0,,20.2,,,23.1 225 | ,0.0,,0.0,,,40.0,,3.0,233.0,17.9,389.39,9.55,21.2 226 | ,0.0,9.69,0.0,,,,2.7986,,391.0,,393.29,17.6,23.1 227 | 0.03584,80.0,3.37,0.0,0.398,,17.8,6.6115,4.0,,,,4.67,23.5 228 | ,90.0,,1.0,0.401,7.923,,5.885,1.0,198.0,13.6,,3.16,50.0 229 | 0.05735,,4.49,0.0,,6.63,56.1,4.4377,3.0,247.0,18.5,,6.53,26.6 230 | 0.1029,30.0,,,0.428,,,7.0355,6.0,300.0,16.6,372.75,11.22,22.2 231 | ,,,,0.488,7.831,53.6,,3.0,193.0,17.8,392.63,4.45,50.0 232 | ,,18.1,,,,,1.9096,,666.0,20.2,,24.39,8.3 233 | ,,19.58,,,6.51,100.0,1.7659,5.0,403.0,,,,23.3 234 | ,12.5,,,,5.889,,,5.0,,,390.5,,21.7 235 | 0.06417,,,0.0,0.499,,68.2,,,279.0,19.2,,,18.9 236 | 0.77299,0.0,,0.0,0.538,6.495,,,4.0,307.0,21.0,,,18.4 237 | 1.20742,0.0,19.58,,0.605,5.875,94.6,,5.0,,14.7,292.29,14.43,17.4 238 | 3.32105,0.0,19.58,1.0,,5.403,,1.3216,,403.0,14.7,,26.82,13.4 239 | ,,,,0.693,6.404,100.0,,,666.0,20.2,,20.31,12.1 240 | ,,,0.0,0.429,6.939,34.5,8.7921,1.0,,,,,26.6 241 | 0.40771,,,1.0,,6.164,,3.048,,307.0,17.4,395.24,21.46,21.7 242 | ,,,,,,57.8,3.4952,2.0,,,357.98,,28.4 243 | 0.04337,21.0,5.64,0.0,0.439,,63.0,,,243.0,,393.97,9.43,20.5 244 | 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0.21977,,,0.0,,5.602,62.0,6.0877,3.0,233.0,17.9,396.9,,19.4 328 | ,0.0,,,,6.824,76.5,,,666.0,20.2,48.45,22.74,8.4 329 | 0.04113,25.0,,0.0,,6.727,33.5,5.4007,4.0,281.0,19.0,,5.29,28.0 330 | 0.13642,0.0,10.59,,0.489,5.891,,,,277.0,,,,22.6 331 | 1.61282,0.0,8.14,0.0,,6.096,96.9,,4.0,,21.0,248.31,20.34,13.5 332 | ,0.0,18.1,,0.584,6.348,,,24.0,666.0,,,,14.5 333 | 0.82526,20.0,3.97,0.0,,7.327,94.5,,5.0,264.0,,,,31.0 334 | ,0.0,,0.0,0.679,6.202,,1.8629,24.0,666.0,20.2,18.82,,10.9 335 | 3.69695,0.0,18.1,,,,,1.7523,24.0,666.0,,316.03,14.0,21.9 336 | ,,,0.0,0.489,6.405,73.9,3.0921,,270.0,17.8,393.55,8.2,22.0 337 | 0.05497,,5.19,,,,45.4,4.8122,5.0,224.0,20.2,396.9,9.74,19.0 338 | 14.3337,0.0,18.1,,,6.229,88.0,1.9512,24.0,666.0,20.2,383.32,,21.4 339 | 0.0536,21.0,,,0.439,6.511,21.1,6.8147,4.0,,16.8,,5.28,25.0 340 | 0.03113,,,,,6.014,48.5,8.0136,3.0,352.0,18.8,385.64,10.53,17.5 341 | ,,3.97,0.0,0.647,7.206,91.6,1.9301,5.0,264.0,13.0,,,36.5 342 | ,,,,,6.095,65.1,,,,16.6,394.62,,20.1 343 | 0.62976,,8.14,0.0,,,,,,307.0,21.0,396.9,8.26,20.4 344 | ,0.0,,,0.544,,77.7,3.945,4.0,,18.4,396.42,,16.2 345 | ,0.0,3.41,,0.489,7.007,,3.4217,2.0,,17.8,,5.5,23.6 346 | ,0.0,,0.0,0.7,,89.5,1.5184,24.0,666.0,,396.9,31.99,7.4 347 | ,20.0,6.96,1.0,0.464,,,4.3665,,223.0,18.6,,,35.2 348 | 2.01019,0.0,19.58,,0.605,7.929,96.2,,5.0,403.0,,369.3,,50.0 349 | ,0.0,,,0.46,,,,4.0,,16.9,,,19.3 350 | ,0.0,,0.0,0.585,6.019,65.3,2.4091,6.0,,19.2,396.9,12.92,21.2 351 | 0.97617,0.0,21.89,0.0,0.624,5.757,,2.346,,437.0,21.2,262.76,17.31,15.6 352 | ,33.0,2.18,,,,71.9,3.0992,,,18.4,396.9,6.47,33.4 353 | ,0.0,18.1,0.0,0.583,6.114,,3.5459,,,20.2,,14.98,19.1 354 | 0.47547,0.0,,,0.544,6.113,58.8,4.0019,4.0,,18.4,,12.73,21.0 355 | 0.12757,30.0,4.93,0.0,0.428,6.393,7.8,7.0355,6.0,,,374.71,,23.7 356 | 0.0136,,,,,,,,3.0,,21.1,,,18.9 357 | 4.22239,,18.1,,0.77,5.803,,,,666.0,,,,16.8 358 | 0.08873,21.0,,,0.439,5.963,45.7,,4.0,243.0,,395.56,13.45,19.7 359 | 3.69311,0.0,18.1,,0.713,6.376,,,,,,391.43,14.65,17.7 360 | 0.08447,0.0,,0.0,0.51,5.859,,2.7019,,296.0,16.6,393.23,,22.6 361 | ,,18.1,0.0,0.74,,,,24.0,666.0,20.2,43.06,,11.8 362 | 0.0837,45.0,3.44,0.0,,,38.9,,5.0,398.0,,396.9,5.39,34.9 363 | ,0.0,,,,6.12,,2.2875,1.0,273.0,21.0,396.9,9.08,20.6 364 | 5.82115,0.0,,0.0,0.713,,89.9,2.8016,24.0,,20.2,,10.29,20.2 365 | 0.07875,,3.44,0.0,0.437,6.782,41.1,,,,15.2,393.87,6.68,32.0 366 | 2.44953,,,0.0,0.605,,95.2,,5.0,403.0,,,,22.3 367 | ,,5.13,0.0,,,,,8.0,284.0,19.7,,6.86,23.3 368 | 0.25387,0.0,,,0.448,,95.3,5.87,,,17.9,396.9,,14.4 369 | 0.03049,55.0,3.78,0.0,0.484,6.874,,,5.0,370.0,17.6,,4.61,31.2 370 | ,0.0,6.2,0.0,,,61.5,,8.0,,,376.75,,24.0 371 | ,,5.86,0.0,0.431,6.957,6.8,,,330.0,19.1,,,29.6 372 | 0.85204,,,0.0,0.538,5.965,89.2,,,307.0,,,13.83,19.6 373 | ,0.0,,,,,82.8,3.2628,4.0,,,,,21.6 374 | 6.80117,0.0,,0.0,0.713,6.081,84.4,2.7175,,666.0,20.2,,,20.0 375 | ,,,1.0,0.605,6.25,92.6,1.7984,,403.0,,338.92,5.5,27.0 376 | ,40.0,,1.0,,7.267,,4.7872,,,,,6.05,33.2 377 | 9.96654,0.0,,0.0,0.74,,100.0,1.9784,,666.0,20.2,,,15.4 378 | ,45.0,3.44,0.0,,6.739,30.8,6.4798,5.0,,,,,30.5 379 | ,,18.1,0.0,0.7,,,,24.0,,20.2,396.9,,7.2 380 | 0.08265,,13.92,,0.437,6.127,18.4,5.5027,4.0,,,396.9,8.58,23.9 381 | ,,18.1,0.0,0.597,5.155,100.0,1.5894,,666.0,20.2,210.97,20.08,16.3 382 | 0.02543,55.0,,0.0,,6.696,,,5.0,,,,,23.9 383 | ,20.0,,0.0,0.647,8.704,86.9,1.801,5.0,264.0,,389.7,5.12,50.0 384 | 0.49298,0.0,,,,6.635,82.5,,4.0,304.0,18.4,,,22.8 385 | 2.73397,0.0,,0.0,0.871,,,1.5257,5.0,403.0,,351.85,21.45,15.4 386 | 0.34006,0.0,,,0.624,6.458,,,4.0,437.0,21.2,395.04,12.6,19.2 387 | ,0.0,19.58,0.0,,5.404,,1.5916,,403.0,,341.6,13.28,19.6 388 | ,,,,0.77,,81.3,,24.0,666.0,20.2,390.74,12.67,22.6 389 | 0.0686,0.0,2.89,,,7.416,62.5,3.4952,,276.0,,396.9,,33.2 390 | 8.26725,,,,,5.875,,,24.0,,20.2,347.88,,50.0 391 | 0.07151,,4.49,0.0,,,,3.7476,3.0,247.0,18.5,395.15,8.44,22.2 392 | 7.75223,,18.1,0.0,,6.301,83.7,,24.0,666.0,20.2,272.21,,14.9 393 | 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