254 lines
10 KiB
Python
254 lines
10 KiB
Python
from __future__ import annotations
|
||
|
||
import re
|
||
from typing import Iterable, List, TypeVar
|
||
|
||
import pandas as pd
|
||
|
||
T = TypeVar("T")
|
||
|
||
CLASSES = ("w", "d", "c", "t", "s", "h", "b", "l", "r")
|
||
|
||
DISTRICTS_PREFIXES = ("мо ", "р-н","городское","лесхоз")
|
||
COUNTRYSIDE_PREFIXES = (
|
||
"г", "п", "д", "гп", "рп", "кп", "пгт", "c", "хутор", " урочище")
|
||
TERRITORY_PREFIXES = (
|
||
"тер.", " тер", "снт ", "ст ", "дск ", "днп ", "дпк ", "нп ", "пдк ", "т/б ", "садоводство", "массив", "хозя", "сад-во")
|
||
STREET_PREFIXES = (
|
||
" ул", " бул", " пр", " ш", " пер", " дор", " маг", " наб", " пл", " просп", " туп", "шоссе", "лини", "аллея",
|
||
"мост", " парк", "кольцо", "проезд", "съезд","переулок",
|
||
"ул.", "бул.", "пр.", "ш.", "пер.", "дор.", "маг.", "наб.", "пл.", "просп.", "туп.")
|
||
HOUSES_PREFIXES = ("д.", "уч.", "участок", "мкд", "тп","дом")
|
||
BUILDING_PREFIXES = ("к.", "корп", 'стр.', "строение", "корпус")
|
||
LETTER = ("лит.", "литера", " л.")
|
||
|
||
|
||
def unfold_house_ranges(token: str) -> List[str]:
|
||
addresses = []
|
||
pairs_strings = re.findall(r"([\d]+-[\d]+)", token)
|
||
for pair_string in pairs_strings:
|
||
a, b = pair_string.split("-")
|
||
a, b = int(a), int(b)
|
||
|
||
if b > a:
|
||
addresses += [re.sub(r"([\d]+-[\d]+)", number, token) for number in map(str, range(a, b + 1))]
|
||
else:
|
||
token = token.replace("-", "/")
|
||
if not addresses:
|
||
addresses.append(token)
|
||
return addresses
|
||
|
||
def any_of_in(substrings: Iterable[str], string: str) -> bool:
|
||
return any(map(lambda substring: substring in string, substrings))
|
||
|
||
|
||
def flatten(arr: Iterable[List[T]]) -> List[T]:
|
||
return sum(arr, [])
|
||
|
||
|
||
def find_room(token: pd.Series, pre_token: pd.Series) -> str:
|
||
if re.search(r"пом\.?", token['obj']):
|
||
return "r"
|
||
return ""
|
||
|
||
|
||
def find_litera(token: pd.Series, pre_token: pd.Series) -> str:
|
||
if any_of_in(LETTER, token['obj'].lower()) \
|
||
or re.search(r"\d{1,3}([А-Я]|[а-я])( |$)", token['obj']):
|
||
return "l"
|
||
if (re.search(r"\b([А-Я]|[а-я]){1}$", token['obj']) \
|
||
and ("l" in pre_token['class'] or "h" in pre_token['class'])) \
|
||
and not (" ш" in token["obj"]) \
|
||
and not find_countryside(token, pre_token):
|
||
return "l"
|
||
return ""
|
||
|
||
|
||
def find_building(token: pd.Series, pre_token: pd.Series) -> str:
|
||
if re.search(r"\d", token['obj']):
|
||
if any_of_in(BUILDING_PREFIXES, token['obj'].lower()) \
|
||
or "b" in pre_token['class'] and not ("h" in token['class']) \
|
||
or re.search(r"к\.* ?\d", token['obj']):
|
||
return "b"
|
||
return ""
|
||
|
||
|
||
def find_house(token: pd.Series, pre_token: pd.Series) -> str:
|
||
if re.search(r"\d{1,4}", token['obj']):
|
||
if any_of_in(HOUSES_PREFIXES, token['obj'].lower()):
|
||
return "h"
|
||
if re.search(r"(д|д\.) ?\d{1,4} ?\/*\d* ?", token['obj']):
|
||
return "h"
|
||
if ("s" in pre_token['class'] or "h" in pre_token['class'] or "s" in token['class']) \
|
||
and not any_of_in(("-я", "-й", "-Я"), token['obj']) \
|
||
and not find_building(token, pre_token):
|
||
return "h"
|
||
return ""
|
||
|
||
|
||
def find_street(token: pd.Series, pre_token: pd.Series) -> str:
|
||
if any_of_in(STREET_PREFIXES, token['obj'].lower()) \
|
||
or re.search(r"[а-я]+ая", token['obj']):
|
||
return "s"
|
||
return ""
|
||
|
||
|
||
def find_territory(token: pd.Series, pre_token: pd.Series) -> str:
|
||
if any_of_in(TERRITORY_PREFIXES, token['obj'].lower()):
|
||
return "t"
|
||
return ""
|
||
|
||
|
||
def find_countryside(token: pd.Series, pre_token: pd.Series) -> str:
|
||
if any_of_in(COUNTRYSIDE_PREFIXES, token['obj'].lower()) \
|
||
and re.search(r"\b[гпдрпктc]{1,3}(\b|\. )", token['obj']) \
|
||
and not find_house(token, pre_token) \
|
||
and not find_street(token, pre_token):
|
||
return "c"
|
||
return ""
|
||
|
||
|
||
def find_district(token: pd.Series, pre_token: pd.Series) -> str:
|
||
if any_of_in(DISTRICTS_PREFIXES, token['obj'].lower()):
|
||
return "d"
|
||
return ""
|
||
|
||
def address_classification(token: pd.Series, pre_token: pd.Series) -> pd.Series:
|
||
brackets = re.search(r"\(.+\)", token["obj"])
|
||
if brackets:
|
||
token["obj"] = re.sub(r"\(.+\)", "()", token["obj"])
|
||
token["class"] += find_district(token, pre_token)
|
||
token["class"] += find_countryside(token, pre_token)
|
||
token["class"] += find_territory(token, pre_token)
|
||
token["class"] += find_street(token, pre_token)
|
||
token["class"] += find_house(token, pre_token)
|
||
token["class"] += find_building(token, pre_token)
|
||
token["class"] += find_litera(token, pre_token)
|
||
if token['class'] == "":
|
||
token['class'] = "w"
|
||
if brackets:
|
||
token["obj"] = re.sub(r"\(\)", brackets.group(), token["obj"])
|
||
return token
|
||
|
||
def cut_address(ad: pd.Series, cl: str) -> pd.Series:
|
||
while ad["class"] and CLASSES.index(ad["class"][-1]) > CLASSES.index(cl[0]):
|
||
if ad["class"][-1] == "h":
|
||
ad["address"] = re.sub(r"[мкдтпучасток]*\.? ?\d{1,4} ?\/*\d* ?", "",
|
||
ad["address"].lower())
|
||
elif ad["class"][-1] == "b":
|
||
num = re.findall("к{0,1}\.? ?\d", ad["address"])[-1]
|
||
ad["address"] = re.sub(num, "", ad["address"])
|
||
elif ad["class"][-1] == "l":
|
||
ad["address"] = re.sub(r"[литера]*\.? ?[А-Яа-я]{1}$", "", ad["address"])
|
||
elif ad["class"][-1] == "r":
|
||
ad["address"] = re.sub(r"пом\.? ?\d+", "", ad["address"])
|
||
ad["class"] = ad["class"][:-1]
|
||
return ad
|
||
|
||
|
||
# TODO: переработать систему из if в нормальный вид
|
||
def split_address(address: str) -> List[str]:
|
||
if ";" in address:
|
||
address = address.replace(";", ",")
|
||
if "," in address:
|
||
tokens = address.split(",")
|
||
|
||
t = list(map(str.strip, filter(lambda token: token != "", tokens)))
|
||
|
||
tokens = pd.DataFrame()
|
||
tokens['obj'] = t
|
||
tokens = tokens[tokens["obj"] != ""]
|
||
tokens.insert(len(tokens.columns), "class", "")
|
||
res = []
|
||
accumulator = pd.Series(data={"address": "", "class": ""})
|
||
|
||
for i in range(len(tokens)):
|
||
cur_tk = tokens.iloc[i]
|
||
|
||
if i == 0:
|
||
pre_token = pd.Series(data=["", ""], index=['obj', 'class'])
|
||
else:
|
||
pre_token = tokens.iloc[i - 1]
|
||
|
||
cur_tk = address_classification(cur_tk, pre_token)
|
||
tokens.iloc[i] = cur_tk
|
||
print(tokens.iloc[i])
|
||
|
||
if not accumulator["class"]:
|
||
accumulator["class"] = cur_tk['class']
|
||
accumulator["address"] = cur_tk["obj"]
|
||
continue
|
||
|
||
if CLASSES.index(accumulator["class"][-1]) < CLASSES.index(cur_tk["class"][0]) and accumulator["class"]!="w":
|
||
accumulator["class"] += cur_tk['class']
|
||
accumulator["address"] += " " + cur_tk["obj"]
|
||
else:
|
||
ad_no_ranges = unfold_house_ranges(accumulator["address"])
|
||
accumulator["address"] = ad_no_ranges[-1]
|
||
|
||
res.extend(ad_no_ranges)
|
||
|
||
accumulator = cut_address(accumulator, cur_tk["class"])
|
||
|
||
if not accumulator["class"] or CLASSES.index(cur_tk["class"][0]) <= CLASSES.index("s") or accumulator["class"]=="w":
|
||
accumulator["class"] = cur_tk["class"]
|
||
accumulator["address"] = cur_tk["obj"]
|
||
|
||
if cur_tk["class"][0] == "h":
|
||
num = re.findall("\d{1,4} ?\/?\d* ?", cur_tk['obj'])[0]
|
||
accumulator["address"] = re.sub(r"\d{1,4} ?\/*\d* ?", num, accumulator["address"])
|
||
cur_tk["class"] =cur_tk["class"][1:]
|
||
|
||
if cur_tk["class"] and cur_tk["class"][0] == "b":
|
||
num = re.findall("\d", cur_tk["obj"])[-1]
|
||
if num and not "b" in accumulator["class"]:
|
||
accumulator["class"] += "b"
|
||
accumulator["address"] += "к." + num
|
||
else:
|
||
accumulator["address"] = re.sub(r"\d$", num, accumulator["address"])
|
||
cur_tk["class"] = cur_tk["class"][1:]
|
||
|
||
if cur_tk["class"] and cur_tk["class"][0] == "l":
|
||
num = re.findall("[А-Яа-я]", cur_tk["obj"].strip())[-1]
|
||
accumulator["address"] = re.sub(r"[А-Яа-я]$", "", accumulator["address"].strip())
|
||
accumulator["address"] += num
|
||
if num and not "l" in accumulator["class"]:
|
||
accumulator["class"] += "l"
|
||
else:
|
||
if re.search(r"\d{1,3}([А-Я]|[а-я])( |$)", accumulator["address"]):
|
||
accumulator["address"] = re.sub(r"[А-Яа-я]$", "", accumulator["address"].strip())
|
||
res.extend(unfold_house_ranges(accumulator["address"]))
|
||
print(res)
|
||
return res
|
||
|
||
return [address]
|
||
|
||
def split_pesoch_res(address: str) -> List[str]:
|
||
t = re.sub(r",", " ", address)
|
||
t = re.split(r"(Санкт-Петербург|Ленинградская обл|Л\.О)", t)
|
||
t = list(map(str.strip, filter(lambda token: token != "", t)))
|
||
tokens = [t[i] + " " + t[i+1] for i in range(0, len(t)-1, 2)]
|
||
|
||
if tokens:
|
||
return list(set(tokens))
|
||
return [address]
|
||
|
||
def process_row(row: pd.Series[str]) -> pd.Series[str]:
|
||
row = row.copy()
|
||
|
||
if pd.isnull(row["Улица"]):
|
||
row["Улица"] = [None]
|
||
else:
|
||
if row["РЭС"] == "Песочинский РЭС":
|
||
addresses = split_pesoch_res(row["Улица"])
|
||
else:
|
||
addresses = split_address(row["Улица"])
|
||
row["Улица"] = addresses
|
||
|
||
return row
|
||
|
||
|
||
def split_addresses(df: pd.DataFrame) -> pd.DataFrame:
|
||
merged_df = df.apply(process_row, axis=1).reset_index()
|
||
|
||
return merged_df.explode("Улица", ignore_index=True) |