Merged in cnc_fl_main (pull request #258)
Cnc fl main * prov type moved * comment out invalid prov2 bc it's overly restrictive * Update prompt template to ensure N/A rather than blanks * Update AC multi-prompt template. Update prompts to ensure all keys are present and N/As are filled * Update AC postprocessing * Update clean_tin() to add N/A for values other than 10 digits * update tin postprocessing * Update npi postprocessing * Add 'Filename:' to the start of all filenames * Update group TIN/NPI postprocessing * Update parent agreement code * fix health plan state mapping * Update B postprocessing * Updated indicators, re-ordering * Update postprocessing - all columns included and reordered * Ensure Pages is included in AC-only output * Remove excess print statements * remove example_test to pass pipeline * Fix parent agreement code for AC * Updated postprocessing for NPI/TIN other * Update prompts for N explicitely on AC prompt-based indicators * Updated main and utils for more intuitive filtering * Updated file_counting to match new main process * update test * Add example_test Approved-by: Michael McGuinness
This commit is contained in:
Katon Minhas
@@ -0,0 +1,459 @@
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import pandas as pd
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import numpy as np
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import re
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import difflib
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import config
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import prompts
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import valid
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from valid import DERIVED_INDICATOR_FIELDS
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import claude_funcs
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from utils import is_empty
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import logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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def sanitize_value(value):
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try:
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if isinstance(value, list):
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return ', '.join(str(v) for v in value)
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elif isinstance(value, str):
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value = value.strip('[]')
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return ', '.join([item.strip(" '") for item in value.split(',')])
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elif pd.isna(value):
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return "N/A"
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except:
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return value
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def exact_match(val, valid_values):
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val = val.strip().upper()
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for valid_val in valid_values:
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if val == valid_val.upper():
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return valid_val
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return None
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def get_closest_match(val, valid_values, similarity_threshold=0.7):
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if pd.isna(val):
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return None
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val = val.strip().upper()
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matches = difflib.get_close_matches(val, [v.upper() for v in valid_values], n=1, cutoff=similarity_threshold)
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return matches[0] if matches else None
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def correct_misplaced_values(df, columns, valid_values_dict):
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for index, row in df.iterrows():
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for col in columns:
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if pd.notna(row[col]):
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terms = row[col].split(',')
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for term in terms:
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term = term.strip()
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for target_col, valid_values in valid_values_dict.items():
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if target_col != col:
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match = exact_match(term, valid_values)
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if match:
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if pd.isna(row[target_col]) or not row[target_col].strip():
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df.at[index, target_col] = match
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df.at[index, col] = None
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else:
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current_value = row[target_col].strip()
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if get_closest_match(match, [current_value], config.FUZZY_MATCH_THRESHOLD) is None:
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df.at[index, 'Corrected_' + target_col] = f"Found {term} in {col} cell"
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df.at[index, col] = None
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return df
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def filter_service_column(answer_dicts):
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filtered_list = []
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for d in answer_dicts:
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if 'FULL_SERVICE' not in d:
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continue # Skip this dictionary if it doesn't have FULL_SERVICE
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clean_dict = True
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for keyword in valid.SERVICE_FILTER:
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if keyword.upper() in d['FULL_SERVICE'].upper() or d['FULL_SERVICE'].upper() in keyword.upper():
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clean_dict = False
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break
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if clean_dict:
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filtered_list.append(d)
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return filtered_list
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def filter_methodology_column(answer_dicts):
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filtered_list = []
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for d in answer_dicts:
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if 'FULL_METHODOLOGY' not in d:
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continue # Skip this dictionary if it doesn't have FULL_METHODOLOGY
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clean_dict = True
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for keyword in valid.METHODOLOGY_FILTER:
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if keyword.upper() in d['FULL_METHODOLOGY'].upper() or d['FULL_METHODOLOGY'].upper() in keyword.upper():
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clean_dict = False
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break
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if clean_dict:
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filtered_list.append(d)
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return filtered_list
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def clean_td(td):
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td_clean = []
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for d in td:
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new_d = {}
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for k, v in d.items():
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if 'DATE' in k:
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new_d[k] = v if isinstance(v, list) else [v]
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elif k not in ['page_num', 'Filename']:
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if isinstance(v, str) and ',' in v:
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new_d[k] = [item.strip() for item in v.split(',')]
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elif v == 'N/A':
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new_d[k] = []
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else:
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new_d[k] = [v] if isinstance(v, str) else v
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else:
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new_d[k] = v
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td_clean.append(new_d)
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return td_clean
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def get_parent_agreement_code(filename):
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try:
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filename = filename.split('.txt')[0]
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match = re.search(r'([^\sa-zA-Z]+)(?=\.\w+$|$)', filename)
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end = [i for i in match.group(1).split('_') if i]
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return end[0]
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except:
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return 'N/A'
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def consolidate_subheader(dict_list):
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modified_list = []
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for d in dict_list:
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if 'FULL_SERVICE' in d and 'SUBHEADER' in d:
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if d['SUBHEADER'] != 'N/A':
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d['FULL_SERVICE'] = d['SUBHEADER'] + ' - ' + d['FULL_SERVICE']
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# Remove the 'SUBHEADER' key
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del d['SUBHEADER']
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modified_list.append(d)
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return modified_list
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def clean_msr_lesser(df, ls=valid.VALID_MSR):
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pattern = '|'.join(re.escape(item) for item in ls)
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mask = df['FULL_SERVICE'].str.contains(pattern, case=False, na=False)
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target_rows = df[mask]
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# Iterate over these rows
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for index, row in target_rows.iterrows():
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# Get all rows with the same 'EXHIBIT' value
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exhibit_rows = df[df['EXHIBIT'] == row['EXHIBIT']]
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# Check the 'LESSER' values of these rows
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if (exhibit_rows['LESSER'] == 'Y').any():
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# If any row has 'LESSER' == 'Y', set the 'LESSER' value of the original row to 'Y'
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df.at[index, 'LESSER'] = 'Y'
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# Move first LESSER_RATE where LESSER==Y to MSR row
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lesser_rate = exhibit_rows[exhibit_rows['LESSER'] == 'Y']['LESSER_RATE'].iloc[0]
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df.at[index, 'LESSER_RATE'] = lesser_rate
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return df
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def clean_default_term(df, ls=valid.INVALID_DEFAULT):
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# Create a case-insensitive regex pattern that matches any of the values in ls
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pattern = '|'.join(re.escape(term) for term in ls)
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# Find rows where the DEFAULT_TERM column contains any of the terms from ls
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mask = df['DEFAULT_TERM'].str.contains(pattern, case=False, na=False)
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# Replace these values with 'N/A'
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df.loc[mask, 'DEFAULT_TERM'] = 'N/A'
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df.loc[mask, 'DEFAULT_RATE'] = 'N/A'
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return df
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def clean_lesser_rate(df):
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for index, row in df.iterrows():
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if 'Y' in row['LESSER'] and is_empty(row['LESSER_RATE']) and not is_empty(row['RATE_STANDARD']):
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df.at[index, 'LESSER_RATE'] = row['RATE_STANDARD']
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df.at[index, 'RATE_STANDARD'] = 'N/A'
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return df
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def clean_prov_2(df):
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valid_types = valid.select_valid_prov_2(df['PROV_TYPE'])
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target_rows = df[df['PROV_TYPE_LEVEL_2'].apply(is_empty)]
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def find_exact_match(text):
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if pd.isna(text) or text == '':
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return None
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words = re.findall(r'\b[\w/]+(?:[-\s][\w/]+)*\b', text)
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for i in range(len(words)):
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for j in range(i+1, len(words)+1):
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phrase = ' '.join(words[i:j])
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if phrase in valid_types: # Case-sensitive matching
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return phrase
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return None
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for index, row in target_rows.iterrows():
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match = None
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if not is_empty(row["FULL_SERVICE"]):
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match = find_exact_match(str(row["FULL_SERVICE"]))
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if match:
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logging.info(f"Row {index}: Matched in FULL_SERVICE: {match}")
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df.at[index, 'PROV_TYPE_LEVEL_2'] = match
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continue
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if not is_empty(row["EXHIBIT"]):
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match = find_exact_match(str(row["EXHIBIT"]))
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if match:
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logging.info(f"Row {index}: Matched in EXHIBIT: {match}")
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df.at[index, 'PROV_TYPE_LEVEL_2'] = match
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continue
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exhibit_rows = df[df['EXHIBIT'] == row['EXHIBIT']]
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if not exhibit_rows.empty:
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for _, exhibit_row in exhibit_rows.iterrows():
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if not is_empty(exhibit_row['PROV_TYPE_LEVEL_2']):
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match = find_exact_match(str(exhibit_row['PROV_TYPE_LEVEL_2']))
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if match:
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logging.info(f"Row {index}: Matched in other row's PROV_TYPE_LEVEL_2: {match}")
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df.at[index, 'PROV_TYPE_LEVEL_2'] = match
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break
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elif not is_empty(exhibit_row['FULL_SERVICE']):
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match = find_exact_match(str(exhibit_row['FULL_SERVICE']))
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if match:
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logging.info(f"Row {index}: Matched in other row's FULL_SERVICE: {match}")
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df.at[index, 'PROV_TYPE_LEVEL_2'] = match
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break
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if match is None:
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logging.warning(f"Row {index}: No valid match found")
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# Final check to ensure no invalid values were assigned
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invalid_assignments = df[
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(~df['PROV_TYPE_LEVEL_2'].isin(valid_types)) &
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(~df['PROV_TYPE_LEVEL_2'].apply(is_empty))
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]
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if not invalid_assignments.empty:
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df.loc[invalid_assignments.index, 'PROV_TYPE_LEVEL_2'] = ''
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return df
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def clean_ac_fields(final_df):
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# additional post-processing
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final_df = final_df.fillna('')
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# final_df.loc[final_df['CREDENTIALING_APP_IND'] != 'Yes', 'CREDENTIALING_APP_IND'] = "No"
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# final_df.loc[final_df['AFFILIATION_CLAUSE_IND'] != 'Yes', 'AFFILIATION_CLAUSE_IND'] = "No"
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# final_df.loc[final_df['ASSIGNMENTS_CLAUSE_IND'] != 'Yes', 'ASSIGNMENTS_CLAUSE_IND'] = "No"
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if 'TERM_CLAUSE' in final_df:
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final_df.loc[final_df['TERM_CLAUSE'].str.startswith('IL-4 Termination'), 'TERM_CLAUSE'] = "N/A"
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final_df.loc[final_df['TERM_CLAUSE'].str.startswith('bonus payment shall be effective'), 'TERM_CLAUSE'] = "N/A"
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final_df.loc[final_df['TERM_CLAUSE'].str.contains('does not contain'), 'TERM_CLAUSE'] = "N/A"
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final_df.loc[final_df['TERM_CLAUSE'].str.contains('No term or termination'), 'TERM_CLAUSE'] = "N/A"
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# if 'CONTRACT_SIGNATORY_IND' in final_df and 'PROV_PARTICIPATION_STATUS' in final_df:
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# final_df.loc[final_df['CONTRACT_SIGNATORY_IND'] == 'Yes', 'PROV_PARTICIPATION_STATUS'] = "Yes"
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if 'CONTRACT_AUTO_RENEWAL_IND' in final_df and 'CONTRACT_TERMINATION_DT' in final_df:
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final_df.loc[final_df['CONTRACT_AUTO_RENEWAL_IND'] == 'Yes', 'CONTRACT_TERMINATION_DT'] = np.nan
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# check if NPI has 10 digits
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if 'PROV_GROUP_NPI' in final_df:
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final_df['PROV_GROUP_NPI'] = final_df['PROV_GROUP_NPI'].map(lambda x: x if sum(c.isdigit() for c in x+' ') == 10 else '')
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# NETWORK_ACCESS_FEES_IND
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if 'NETWORK_ACCESS_FEES_IND' in final_df:
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final_df.loc[~final_df['NETWORK_ACCESS_FEES_IND'].isin(['N/A', 'No', '', ' ']), 'NETWORK_ACCESS_FEES_IND'] = "Yes"
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if 'PAYER_NAME' in final_df and 'HEALTH_PLAN_STATE' in final_df:
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final_df.loc[final_df['PAYER_NAME'].str.startswith('Illini'), 'HEALTH_PLAN_STATE'] = "Illinois"
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if 'HEALTH_PLAN_STATE' in final_df:
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final_df['HEALTH_PLAN_STATE'] = final_df['HEALTH_PLAN_STATE'].map(valid.STATE_MAP).fillna(final_df['HEALTH_PLAN_STATE'])
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if 'NOTICE_PROVIDER_NAME' in final_df and 'NOTICE_PROVIDER_ADDRESS' in final_df:
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final_df.loc[final_df['NOTICE_PROVIDER_NAME'].str.contains('Superior HealthPlan', na=False), 'NOTICE_PROVIDER_NAME'] = np.nan
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final_df.loc[final_df['NOTICE_PROVIDER_NAME'].str.contains('Superior HealthPlan', na=False), 'NOTICE_PROVIDER_ADDRESS'] = np.nan
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final_df['temp_filename'] = final_df['Contract Name'].str[:10].str.replace('-','')
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if 'PROV_GROUP_TIN' in final_df and 'Contract Name' in final_df:
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final_df.loc[(final_df['PROV_GROUP_TIN'].isin([' ', '']))&(final_df['temp_filename'].str.isnumeric()), 'PROV_GROUP_TIN'] = final_df['temp_filename']
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final_df.drop(columns=['temp_filename'], inplace=True)
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# For POLICIES_AND_PROCEDURES, filter out anything without either "policies" or "procedures".
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if 'POLICIES_AND_PROCEDURES' in final_df:
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final_df.loc[~final_df['POLICIES_AND_PROCEDURES'].str.contains('policies', flags=re.IGNORECASE) &
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~final_df['POLICIES_AND_PROCEDURES'].str.contains('procedures', flags=re.IGNORECASE), 'POLICIES_AND_PROCEDURES'] = "N/A"
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final_df = final_df.apply(lambda x: x.map(replace_null_terms))
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final_df = final_df.apply(lambda x: x.map(replace_quotes))
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return final_df
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def replace_quotes(value):
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try:
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return str(value).replace(r'\"', '"')
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except:
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return value
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def replace_null_terms(value):
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# Convert value to string and check if any term from NULL_ANSWER_TERMS is in the value
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if any(term.lower() in str(value).lower() for term in valid.NULL_ANSWER_TERMS):
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return 'N/A'
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return value
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def filter_dict(d, pattern):
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final_dict = {}
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for page_num, answer in d.items():
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if not pattern.search(answer):
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final_dict[page_num] = answer
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return final_dict
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def filter_add_ons(df):
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pattern = r"(in no event).+(includ.?)|(forward).+(payments)"
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mask = df['ADD_ON_REIMBURSEMENT_LANGUAGE'].str.contains(pattern, case=False, na=False, regex=True)
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df.loc[mask, 'ADD_ON_REIMBURSEMENT_LANGUAGE'] = 'N/A'
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df.loc[mask, "ADD_ON_REIMBURSEMENT_IND"] = 'N'
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def check_add_on(group):
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# Check if any 'SERVICE_TYPE' contains 'Add-on' or 'Add On'
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if group['FULL_SERVICE'].str.contains('Add-on|Add On', regex=True, case=False, na=False).any():
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group['ADD_ON_REIMBURSEMENT_LANGUAGE'] = 'N/A' # Set 'ADD_ON' to 'N/A' for the whole group
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group["ADD_ON_REIMBURSEMENT_IND"] = 'N'
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return group
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# Group by 'Contract Name' and 'EXHIBIT', then apply the check_add_on function
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df = df.groupby(['Contract Name', 'EXHIBIT']).apply(check_add_on)
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# TODO : If original add on value is actually an Exclusion, and the Exclusion value for the row is invalid, then move the Add On value to the Exclusions column
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return df
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import pandas as pd
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import re
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def clean_process_AWP_FLATFEE_cols(df):
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# Function to extract dollar amount after "NET INVOICE PRICE" or "FLAT FEE"
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def extract_dollar_amount(text, pattern):
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if pd.isna(text):
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return None
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match = re.search(pattern, str(text), re.IGNORECASE)
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if match:
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return match.group(1).replace(',', '')
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return None
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# Apply changes based on conditions
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def apply_changes(row):
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if 'FULL_METHODOLOGY' in row and isinstance(row['FULL_METHODOLOGY'], str) and 'AWP' in row['FULL_METHODOLOGY'].upper():
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full_methodology = row['FULL_METHODOLOGY'].upper()
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dollar_amount_found = False
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# Check FLAT_FEE_STANDARD first
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if 'FLAT_FEE_STANDARD' in row and row['FLAT_FEE_STANDARD'] != 'N/A' and pd.notna(row['FLAT_FEE_STANDARD']) and row['FLAT_FEE_STANDARD'] != '':
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row['SHORT_METHODOLOGY'] = 'Flat Fee'
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dollar_amount_found = True
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else:
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# Then check for FLAT FEE in FULL_METHODOLOGY
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flat_fee = extract_dollar_amount(full_methodology, r'FLAT FEE\s*:\s*\$?([\d,]+(?:\.\d{2})?)')
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if flat_fee:
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row['FLAT_FEE_STANDARD'] = flat_fee
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row['SHORT_METHODOLOGY'] = 'Flat Fee'
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dollar_amount_found = True
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else:
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# Finally, check for NET INVOICE PRICE
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net_invoice_price = extract_dollar_amount(full_methodology, r'NET INVOICE PRICE\s*:\s*\$?([\d,]+(?:\.\d{2})?)')
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if net_invoice_price:
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row['FLAT_FEE_STANDARD'] = net_invoice_price
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row['SHORT_METHODOLOGY'] = 'Flat Fee'
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dollar_amount_found = True
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# Set RATE_STANDARD and RATE_SHORT to 'N/A' if a dollar amount was found
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if dollar_amount_found:
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row['RATE_STANDARD'] = 'N/A'
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row['RATE_SHORT'] = 'N/A'
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return row
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|
||||
# Apply the changes to the DataFrame
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df = df.apply(apply_changes, axis=1)
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return df
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||||
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|
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|
||||
def add_scmr(df):
|
||||
def count_dollar_values(s):
|
||||
return str(s).count('$')
|
||||
|
||||
df['dollar_count'] = df['FLAT_FEE_STANDARD'].apply(count_dollar_values)
|
||||
|
||||
df['Single Code Multiple Rates (Language)'] = df.apply(
|
||||
lambda row: row['FLAT_FEE_STANDARD'] if row['dollar_count'] > 1 else 'N/A', axis=1)
|
||||
|
||||
df['Single Code Multiple Rates (Y/N)'] = df['dollar_count'].apply(
|
||||
lambda x: 'Y' if x > 1 else 'N')
|
||||
|
||||
df.drop('dollar_count', axis=1, inplace=True)
|
||||
|
||||
return df
|
||||
|
||||
def clean_lob(df, filename):
|
||||
def update_contract_lob(row):
|
||||
# Check if more than one valid lob is present in the 'FULL_SERVICE' column
|
||||
if sum(val.lower() in row['FULL_SERVICE'].lower() for val in valid.VALID_LOBS) > 1:
|
||||
prompt = prompts.LOB_SWEEPER(row['EXHIBIT'])
|
||||
answer = claude_funcs.invoke_claude(prompt, config.MODEL_ID_CLAUDE35_SONNET, filename, 256)
|
||||
return answer # Return the value from 'EXHIBIT' if condition is met
|
||||
return row['CONTRACT_LOB']
|
||||
df['CONTRACT_LOB'] = df.apply(update_contract_lob, axis=1)
|
||||
return df
|
||||
|
||||
|
||||
def derive_indicators(results):
|
||||
def is_populated(value):
|
||||
if not value: # Handles None and empty string
|
||||
return False
|
||||
|
||||
# Convert to string and lowercase for comparison
|
||||
str_value = str(value).lower().strip()
|
||||
|
||||
# Check if the value is any variation of N/A
|
||||
na_values = ['n/a', 'na', 'not applicable', '']
|
||||
if str_value in na_values:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
# Process standard derived indicator fields
|
||||
for field in DERIVED_INDICATOR_FIELDS:
|
||||
root_field = field
|
||||
indicator_field = field.rsplit('_', 1)[0] + '_IND'
|
||||
|
||||
if root_field in results and is_populated(results[root_field]):
|
||||
results[indicator_field] = 'Y'
|
||||
else:
|
||||
results[indicator_field] = 'N'
|
||||
|
||||
# Handle special cases
|
||||
special_mappings = {
|
||||
'DELEGATED_TERMS': 'DELEGATED_FUNCTION_IND',
|
||||
'SEQUESTRATION_REDUCTIONS': 'SEQUESTRATION_REDUCTIONS_IND'
|
||||
}
|
||||
|
||||
for root_field, indicator_field in special_mappings.items():
|
||||
if root_field in results and is_populated(results[root_field]):
|
||||
results[indicator_field] = 'Y'
|
||||
else:
|
||||
results[indicator_field] = 'N'
|
||||
|
||||
return results
|
||||
Reference in New Issue
Block a user