350 lines
12 KiB
Python
350 lines
12 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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from collections.abc import Sequence
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import pandas as pd
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T = TypeVar("T")
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CLASSES = ("w", "d", "c", "t", "s", "h", "b", "l", "r")
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DISTRICTS_PREFIXES = ("мо ", "р-н","городское","лесхоз")
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COUNTRYSIDE_PREFIXES = (
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"г", "п", "д", "гп", "рп", "кп", "пгт", "c", "хутор", " урочище")
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TERRITORY_PREFIXES = (
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"тер.", " тер", "снт ", "ст ", "дск ", "днп ", "дпк ", "нп ", "пдк ", "т/б ", "садоводство", "массив", "хозя", "сад-во")
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STREET_PREFIXES = (
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" ул", " бул", " пр", " ш", " пер", " дор", " маг", " наб", " пл", " просп", " туп", "шоссе", "лини", "аллея",
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"мост", " парк", "кольцо", "проезд", "съезд","переулок",
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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","дом")
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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(token: str) -> List[str]:
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addresses = []
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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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addresses += [re.sub(r"([\d]+-[\d]+)", number, token) 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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if not addresses:
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addresses.append(token)
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return addresses
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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]+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 re.search(r"\b[гпдрпктc]{1,3}(\b|\. )", token['obj']) \
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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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def address_classification(token: pd.Series, pre_token: pd.Series) -> pd.Series:
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brackets = re.search(r"\(.+\)", token["obj"])
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if brackets:
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token["obj"] = re.sub(r"\(.+\)", "()", token["obj"])
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token["class"] += find_district(token, pre_token)
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token["class"] += find_countryside(token, pre_token)
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token["class"] += find_territory(token, pre_token)
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token["class"] += find_street(token, pre_token)
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token["class"] += find_house(token, pre_token)
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token["class"] += find_building(token, pre_token)
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token["class"] += find_litera(token, pre_token)
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if token['class'] == "":
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token['class'] = "w"
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if brackets:
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token["obj"] = re.sub(r"\(\)", brackets.group(), token["obj"])
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return token
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def cut_address(ad: pd.Series, cl: str) -> pd.Series:
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while ad["class"] and CLASSES.index(ad["class"][-1]) > CLASSES.index(cl[0]):
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if ad["class"][-1] == "h":
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ad["address"] = re.sub(r"[мкдтпучасток]*\.? ?\d{1,4} ?\/*\d* ?", "",
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ad["address"].lower())
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elif ad["class"][-1] == "b":
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num = re.findall("к{0,1}\.? ?\d", ad["address"])[-1]
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ad["address"] = re.sub(num, "", ad["address"])
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elif ad["class"][-1] == "l":
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ad["address"] = re.sub(r"[литера]*\.? ?[А-Яа-я]{1}$", "", ad["address"])
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elif ad["class"][-1] == "r":
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ad["address"] = re.sub(r"пом\.? ?\d+", "", ad["address"])
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ad["class"] = ad["class"][:-1]
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return ad
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def is_nonempty_str(string: str) -> bool:
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return string != ""
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def create_token(obj: str = "", token_class: str = ""):
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return pd.Series(
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{
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"obj": obj,
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"class": token_class,
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}
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)
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class AddressSplitter(Sequence):
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addresses: list[str]
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tokens: list[pd.Series]
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def __init__(self, address: str):
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self.input = address
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self.addresses = self.split()
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if len(self.addresses) == 0:
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self.addresses = [address]
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# Sequence abstract methods implementation
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def __getitem__(self, key: int):
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if key < len(self.addresses):
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return self.addresses[key]
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else:
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raise IndexError()
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def __len__(self):
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return len(self.addresses)
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# Address token class manipulations
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def next_class(self) -> str:
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return self.token["class"][0]
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def correct_order(self) -> bool:
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prev_class = self.accumulator["class"][-1]
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return (
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CLASSES.index(prev_class) < CLASSES.index(self.next_class())
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and self.accumulator["class"] != "w"
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)
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def next_class_is(self, comparing_class: str) -> bool:
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return len(self.token["class"]) > 0 and self.next_class() == comparing_class[0]
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def pop_token_class(self):
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self.token["class"] = self.token["class"][1:]
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def has_no_class(self, comparing_class: str) -> bool:
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return comparing_class[0] not in self.accumulator["class"]
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def next_is_street_or_upper(self) -> bool:
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is_unknown_class = self.accumulator["class"] in ("", "w")
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return (
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CLASSES.index(self.next_class()) <= CLASSES.index("s") or is_unknown_class
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)
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# Accumulator manipulation
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def substitue_house(self) -> str:
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num = re.findall(r"\d{1,4} ?\/?\d* ?", self.token["obj"])[0]
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return re.sub(r"\d{1,4} ?\/*\d* ?", num, self.accumulator["address"])
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def append_building(self, num: int) -> pd.Series:
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self.accumulator["class"] += "b"
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self.accumulator["address"] += "к." + num
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return self.accumulator
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def substitue_building(self, num: int) -> str:
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return re.sub(r"\d$", num, self.accumulator["address"])
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def insert_building(self):
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number = re.findall(r"\d", self.token["obj"])[-1]
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if number and self.has_no_class("building"):
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self.accumulator = self.append_building(number)
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else:
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self.accumulator["address"] = self.substitue_building(number)
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def without_letter(self) -> str:
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return re.sub(r"[А-Яа-я]$", "", self.accumulator["address"].strip())
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def substitue_letter(self, letter: str) -> str:
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address_without_letter = self.without_letter()
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return address_without_letter + letter
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def insert_letter(self):
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letter = re.findall(r"[А-Яа-я]", self.token["obj"].strip())[-1]
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self.accumulator["address"] = self.substitue_letter(letter)
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if letter and self.has_no_class("litera"):
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self.accumulator["class"] += "l"
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def has_letter_in(self) -> bool:
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return (
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re.search(r"\d{1,3}([А-Я]|[а-я])( |$)", self.accumulator["address"])
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is not None
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)
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# Data preprocessing
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def split_tokens(self) -> list[pd.Series]:
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address = self.input.replace(";", ",")
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parts = address.split(",")
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parts = map(str.strip, parts)
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parts = filter(is_nonempty_str, parts)
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tokens = map(lambda part: create_token(part, ""), parts)
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return list(tokens)
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def split(self):
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self.tokens = self.split_tokens()
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result = []
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self.accumulator = pd.Series({"address": "", "class": ""})
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prev_token = create_token()
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for cursor in self.tokens:
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self.token = address_classification(cursor, prev_token)
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prev_token = self.token.copy()
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if self.accumulator["class"] == "":
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self.accumulator = self.token.rename({"obj": "address"})
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continue
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if self.correct_order():
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self.accumulator["address"] += " "
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self.accumulator += self.token.rename({"obj": "address"})
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else:
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unfolded_address = unfold_house_ranges(self.accumulator["address"])
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self.accumulator["address"] = unfolded_address[-1]
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result.extend(unfolded_address)
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self.accumulator = cut_address(self.accumulator, self.token["class"])
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if self.next_is_street_or_upper():
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self.accumulator = self.token.rename({"obj": "address"})
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if self.next_class_is("house"):
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self.accumulator["address"] = self.substitue_house()
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self.pop_token_class()
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if self.next_class_is("building"):
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self.insert_building()
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self.pop_token_class()
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if self.next_class_is("letter"):
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self.insert_letter()
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elif self.has_letter_in():
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self.accumulator["address"] = self.without_letter()
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result.extend(unfold_house_ranges(self.accumulator["address"]))
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return result
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def split_pesoch_res(address: str) -> List[str]:
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t = re.sub(r",", " ", address)
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t = re.split(r"(Санкт-Петербург|Ленинградская обл|Л\.О)", t)
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t = list(map(str.strip, filter(lambda token: token != "", t)))
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tokens = [t[i] + " " + t[i+1] for i in range(0, len(t)-1, 2)]
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if tokens:
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return list(set(tokens))
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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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if row["РЭС"] == "Песочинский РЭС":
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addresses = split_pesoch_res(row["Улица"])
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else:
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addresses = AddressSplitter(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) |