khushalps
9e8db857f4
git-subtree-dir: utils_v2 git-subtree-split: 3be5145c7a4cfede04d753324dfae31ace913c98
169 lines
5.9 KiB
Python
169 lines
5.9 KiB
Python
"""
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AUTHOR:
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Khushal P Soonderji
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DATE:
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Saturday, 18th May, 2024
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OBJECTIVE:
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To provide a quick set of functions to work with fuzzy logic.
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REFERENCES:
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1) https://www.w3schools.com/python/python_json.asp
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DOWNLOADS:
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N/A
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"""
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# *****************************************************************************************************************
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# ***** ****
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# *** IMPORT ***
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# ***** ****
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# *****************************************************************************************************************
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# To make sibling directories accessible for imports:
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import sys
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sys.path.append(".")
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sys.path.append("..")
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# To apply fuzzy logic:
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from thefuzz import fuzz, process
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# To work with tabulated data:
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import pandas as pd
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# *****************************************************************************************************************
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# ***** ****
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# *** MACROS / ONE-TIME INIT ***
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# ***** ****
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# *****************************************************************************************************************
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** VARIABLES ***
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# ***** ****
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# *****************************************************************************************************************
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** FUNCTIONS ***
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# ***** ****
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# *****************************************************************************************************************
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def get_best_match(
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target,
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choices,
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threshold = 0.70,
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partial = False
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):
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if partial: scorer = fuzz.partial_token_sort_ratio
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else: scorer = fuzz.ratio
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result = process.extractOne(
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target,
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choices,
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score_cutoff = threshold * 100,
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scorer = scorer
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)
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try: return result[0]
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except: return None
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# ---------------------------------------------------------------------------------------------------------------------
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def rank(target, choices, partial = True):
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if partial: scorer = fuzz.partial_token_sort_ratio
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else: scorer = fuzz.ratio
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result = process.extract(
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target,
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choices,
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limit = len(choices),
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scorer = scorer
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)
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result = pd.DataFrame(result, columns = ["choice", "closeness"])
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result["closeness"] = result["closeness"] / 100.0
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return result
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# ---------------------------------------------------------------------------------------------------------------------
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def match(targets, choices, threshold = 0.7, partial = False, allow_null = False):
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all_matches_df = None
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all_matches = {target: None for target in targets}
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something_is_null = False
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for target in targets:
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match_df = rank(target, choices, partial = partial)
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match_df["target"] = target
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if all_matches_df is None: all_matches_df = match_df
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else: all_matches_df = pd.concat([all_matches_df, match_df])
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all_matches_df = all_matches_df.sort_values(by = ["closeness"], ascending = False).reset_index(drop = True)
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for target in targets:
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target_df = all_matches_df[all_matches_df["target"] == target].reset_index(drop = True)
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if target_df.empty: continue
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if target_df.at[0, "closeness"] >= threshold:
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choice = target_df.at[0, "choice"]
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all_matches[target] = choice
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all_matches_df = all_matches_df[all_matches_df["choice"] != choice]
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else:
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all_matches[target] = None
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something_is_null = True
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# print(all_matches)
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if something_is_null and not allow_null: return None
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else: return all_matches
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# *****************************************************************************************************************
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# ***** ****
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# *** MAIN PROGRAM ***
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# ***** ****
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# *****************************************************************************************************************
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if __name__ == "__main__":
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import async_json_utils
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awb_numbers = [
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"SF1111BIC",
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"SF2222BIC",
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"SF3333BIC",
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"SF4444BIC",
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]
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chat_text = "SF1112BIC"
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# print(chat_text == names[0])
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best_match = get_best_match(chat_text, awb_numbers, threshold = 0.60, partial = False)
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print(f"Best match for '{chat_text}' is '{best_match}'")
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