233 lines
8.6 KiB
Python
233 lines
8.6 KiB
Python
from __future__ import annotations
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import re
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from typing import Iterable, List, TypeVar
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import pandas as pd
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T = TypeVar("T")
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CLASSES = ("d", "c", "t", "s", "h", "b", "l", "r", "w")
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DISTRICTS_PREFIXES = ("мо ", "р-н")
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COUNTRYSIDE_PREFIXES = (
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" г", " п", " д", " гп", " рп", " кп", " пгт", " c", "хутор", " урочище"
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"г.", "п.", "д.", "гп.", "рп.", "кп.", "пгт.", "c.")
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TERRITORY_PREFIXES =("тер.", " тер", "снт ", "ст ", "дск ", "днп ", "дпк ", "нп ", "пдк ", "т/б ", "садоводство", "массив", "хоз","сад-во","с-во")
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STREET_PREFIXES = (
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" ул", " бул", " пр", " ш", " пер", " дор", " маг", " наб", " пл", " просп", " туп", "шоссе","линия","аллея", "мост", " парк", "кольцо","проезд", "съезд",
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"ул.", "бул.", "пр.", "ш.", "пер.", "дор.", "маг.", "наб.", "пл.", "просп.", "туп.")
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HOUSES_PREFIXES = ("д.", "уч.", "участок","мкд","тп")
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BUILDING_PREFIXES = ("к.", "корп", 'стр.', "строение","корпус")
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LETTER = ("лит.", "литера"," л.")
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def unfold_house_ranges(address: str, token: str) -> List[str]:
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adresses = []
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pairs_strings = re.findall(r"([\d]+-[\d]+)", token)
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for pair_string in pairs_strings:
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a, b = pair_string.split("-")
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a, b = int(a), int(b)
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if b > a:
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token = token.replace(pair_string, "")
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adresses += [address + " " + token + number for number in map(str, range(a, b + 1))]
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else:
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token = token.replace("-", "/")
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adresses += address + " " + token
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if not adresses:
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adresses.append(address + " " + token)
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return adresses
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def unfold_houses_list(token: str) -> List[str]:
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token = unfold_house_ranges(token)
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reg = re.compile(r"(д|д\.)? ?\d+[а-яА-Я\/]*\d*(,|$| )")
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if len(re.findall(reg, token)) > 1:
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tokens = token.split(",")
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return [*[tokens[0] + " " + house_token for house_token in tokens[1:]]]
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return [token]
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def any_of_in(substrings: Iterable[str], string: str) -> bool:
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return any(map(lambda substring: substring in string, substrings))
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def flatten(arr: Iterable[List[T]]) -> List[T]:
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return sum(arr, [])
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def find_room(token: pd.Series, pre_token: pd.Series) -> str:
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if re.search(r"пом\.?", token['obj']):
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return "r"
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return ""
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def find_litera(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(LETTER, token['obj'].lower()) \
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or re.search(r"\d{1,3}([А-Я]|[а-я])( |$)", token['obj']):
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return "l"
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if (re.search(r"\b([А-Я]|[а-я]){1}$", token['obj']) \
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and ("l" in pre_token['class'] or "h" in pre_token['class'])) \
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and not (" ш" in token["obj"]) \
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and not find_countryside(token,pre_token):
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return "l"
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return ""
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def find_building(token: pd.Series, pre_token: pd.Series) -> str:
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if re.search(r"\d", token['obj']):
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if any_of_in(BUILDING_PREFIXES, token['obj'].lower()) \
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or "b" in pre_token['class'] and not ("h" in token['class'])\
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or re.search(r"к\.* ?\d", token['obj']):
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return "b"
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return ""
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def find_house(token: pd.Series, pre_token: pd.Series) -> str:
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if re.search(r"\d{1,4}", token['obj']):
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if any_of_in(HOUSES_PREFIXES, token['obj'].lower()):
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return "h"
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if re.search(r"(д|д\.) ?\d{1,4} ?\/*\d* ?", token['obj']):
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return "h"
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if ("s" in pre_token['class'] or "h" in pre_token['class'] or "s" in token['class']) \
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and not any_of_in(("-я", "-й", "-Я"), token['obj'])\
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and not find_building(token,pre_token):
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return "h"
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return ""
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def find_street(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(STREET_PREFIXES, token['obj'].lower()) \
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or re.search(r"[А-Я]{1}[а-я]+ая", token['obj']):
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return "s"
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return ""
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def find_territory(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(TERRITORY_PREFIXES, token['obj'].lower()):
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return "t"
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return ""
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def find_countryside(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(COUNTRYSIDE_PREFIXES, token['obj'].lower()) \
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and not find_house(token,pre_token) \
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and not find_street(token,pre_token):
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return "c"
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return ""
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def find_district(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(DISTRICTS_PREFIXES, token['obj'].lower()):
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return "d"
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return ""
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# TODO: переработать систему из if в нормальный вид и классификация чисел/букв
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def split_address(address: str) -> List[str]:
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if ";" in address:
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address = address.replace(";", ",")
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if "," in address:
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tokens = address.split(",")
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t = list(map(str.strip, filter(lambda token: token != "", tokens)))
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# токены в датафрэйм
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tokens = pd.DataFrame()
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tokens['obj'] = t
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tokens.insert(len(tokens.columns), "class", "")
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res = []
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accumulator = ""
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for i in range(len(tokens)):
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# TODO: напселённые пункты
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# if any_of_in(SETTLEMENTS_PREFIXES, tokens[i].lower())
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# accumulator += tokens[i]
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cur_tk = tokens.iloc[i]
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if i == 0:
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pre_token = pd.Series(data=["", ""], index=['obj', 'class'])
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else:
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pre_token = tokens.iloc[i - 1]
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obj_class = find_district(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "d" in pre_token['class']:
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res.append(accumulator)
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accumulator = ""
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accumulator += cur_tk["obj"]
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obj_class = find_countryside(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "c" in pre_token['class']:
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res.append(accumulator)
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accumulator = ""
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accumulator += cur_tk["obj"]
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obj_class = find_territory(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "t" in pre_token['class']:
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res.append(accumulator)
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accumulator = ""
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accumulator +=cur_tk["obj"]
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obj_class = find_street(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "s" in pre_token['class']:
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res.append(accumulator)
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accumulator = ""
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accumulator += cur_tk["obj"]
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obj_class = find_house(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "h" in pre_token["class"]:
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res.append(accumulator)
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num = re.findall("\d{1,4}", cur_tk['obj'])[-1]
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accumulator = re.sub(r"\d{1,4} ?\/*\d* ?", num, accumulator)
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else:
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accumulator += cur_tk["obj"]
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obj_class = find_building(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "b" in pre_token["class"]:
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res.append(accumulator)
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num = re.findall("\d", tokens['obj'].iloc[i])[-1]
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accumulator = re.sub(r"\d$", num, accumulator)
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else:
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accumulator += pre_token["obj"]
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obj_class = find_litera(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "l" in pre_token["class"]:
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res.append(accumulator)
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num = re.findall("[А-яа-я]", cur_tk["obj"].strip())[-1]
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accumulator = re.sub(r"[А-яа-я]$", num, accumulator)
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else:
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accumulator += cur_tk["obj"]
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if cur_tk['class'] == "":
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cur_tk['class'] = "w"
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tokens.iloc[i] = cur_tk
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print(tokens.iloc[i])
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# print(cur_tk)
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return res
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return [address]
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def process_row(row: pd.Series[str]) -> pd.Series[str]:
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row = row.copy()
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if pd.isnull(row["Улица"]):
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row["Улица"] = [None]
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else:
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addresses = split_address(row["Улица"])
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row["Улица"] = addresses
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return row
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def split_addresses(df: pd.DataFrame) -> pd.DataFrame:
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merged_df = df.apply(process_row, axis=1).reset_index()
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return merged_df.explode("Улица", ignore_index=True)
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