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Split address.py to module

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This commit is contained in:
Dmitriy Shishkov 2023-10-29 14:24:39 +03:00
parent 06f08d4933
commit cc2029802b
Signed by: dm1sh
GPG Key ID: 027994B0AA357688
5 changed files with 564 additions and 522 deletions
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522
parser/address.py
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@ -1,522 +0,0 @@
from __future__ import annotations
import re
from collections.abc import Sequence
from typing import Iterable, List, TypeVar
import pandas as pd
T = TypeVar("T")
CLASSES = ("w", "d", "c", "t", "s", "h", "b", "e", "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", "дома")
BUILDING_PREFIXES = ("к.", "к ", "корп", "корпус")
EDIFICE_PREFIXES = ("стр.", "строение")
LETTER = ("лит.", "литера", " л.")
PREFIXES = (
DISTRICTS_PREFIXES,
COUNTRYSIDE_PREFIXES,
TERRITORY_PREFIXES,
STREET_PREFIXES,
HOUSES_PREFIXES,
BUILDING_PREFIXES,
EDIFICE_PREFIXES,
LETTER,
)
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"\bпом\.?", token["obj"]):
return "r"
return ""
def find_litera(token: pd.Series, pre_token: pd.Series) -> str:
if find_room(token, pre_token):
return ""
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 (" ш" not in token["obj"])
and not find_countryside(token, pre_token)
):
return "l"
return ""
def find_edifice(token: pd.Series, pre_token: pd.Series) -> str:
if any_of_in(EDIFICE_PREFIXES, token["obj"].lower()):
return "e"
return ""
def find_building(token: pd.Series, pre_token: pd.Series) -> str:
if re.search(r"\d", token["obj"]) and not find_room(token, pre_token):
if (
any_of_in(BUILDING_PREFIXES, token["obj"].lower())
or "b" in pre_token["class"]
and ("h" not in token["class"])
and not find_edifice(token, pre_token)
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"]) and not find_room(token, pre_token):
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)
and not find_edifice(token, pre_token)
):
return "h"
if (
find_building(token, pre_token)
and not any_of_in(("-я", "-й", "-Я"), token["obj"])
and True
):
if len(re.findall(r"\d{1,4}", token["obj"])) > 1:
return "h"
if int(re.search(r"\d{1,4}", token["obj"]).group()) // 10 > 0:
return "h"
return ""
def find_street(token: pd.Series, pre_token: pd.Series) -> str:
if any_of_in(STREET_PREFIXES, token["obj"].lower()):
return "s"
if (
re.search(r"\b[А-Яа-я]{4,}\b", token["obj"])
and not any(
[el in token["obj"].lower() for pr in PREFIXES for el in pr if len(el) > 2]
)
and not (
"d" in token["class"] or "t" in token["class"] or "c" in token["class"]
)
):
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 any_of_in(STREET_PREFIXES, token["obj"].lower())
):
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_edifice(token, pre_token)
token["class"] += find_litera(token, pre_token)
token["class"] += find_room(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(r"к{0,1}\.? ?\d", ad["address"])[-1]
ad["address"] = re.sub(num, "", ad["address"])
elif ad["class"][-1] == "e":
ad["address"] = re.sub(r"cтр\.? ?\d", "", 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
def is_valid_token(string: str) -> bool:
return string not in ("", "уг.", "д.")
def create_token(obj: str = "", token_class: str = ""):
return pd.Series(
{
"obj": obj,
"class": token_class,
}
)
class AddressSplitter(Sequence):
def __init__(self, address: str):
self.input = address
self.addresses = self.split()
## Sequence abstract methods implementation
def __getitem__(self, key: int):
if key < len(self.addresses):
return self.addresses[key]
else:
raise IndexError()
def __len__(self):
return len(self.addresses)
## Address token class manipulations
def next_class(self) -> str:
return self.token["class"][0]
def correct_order(self) -> bool:
prev_class = self.accumulator["class"][-1]
return (
CLASSES.index(prev_class) < CLASSES.index(self.next_class())
and self.accumulator["class"] != "w"
)
def next_class_is(self, comparing_class: str) -> bool:
return len(self.token["class"]) > 0 and self.next_class() == comparing_class[0]
def has_no_class(self, comparing_class: str) -> bool:
return comparing_class[0] not in self.accumulator["class"]
def pop_token_class(self):
self.token["class"] = self.token["class"][1:]
## Accumulator constrains
def next_is_street_or_upper(self) -> bool:
is_unknown_class = self.accumulator["class"] in ("", "w")
return (
CLASSES.index(self.next_class()) <= CLASSES.index("s") or is_unknown_class
)
def has_numbered_street(self) -> bool:
return any_of_in(("-я", "-й", "-Я"), self.accumulator["address"])
## Accumulator manipulation
# House
def substitue_house(self) -> str:
house_regex = re.compile(r"\d{1,4} ?[\/\-]?\d* ?")
number = house_regex.findall(self.token["obj"])[0]
if self.has_numbered_street():
house_number_index = 1
else:
house_number_index = 0
number_in_accumulator = house_regex.findall(self.accumulator["address"])
if number_in_accumulator:
return re.sub(
number_in_accumulator[house_number_index],
number,
self.accumulator["address"],
)
else:
return self.accumulator["address"]
# Building
def append_building(self, number: int) -> pd.Series:
self.accumulator["class"] += "b"
self.accumulator["address"] += "к." + number
return self.accumulator
def substitue_building(self, number: int) -> str:
return re.sub(r"\d$", number, self.accumulator["address"])
def insert_building(self):
number = re.findall(r"\d", self.token["obj"])[-1]
if number and self.has_no_class("building"):
self.accumulator = self.append_building(number)
else:
self.accumulator["address"] = self.substitue_building(number)
# Edifice
def substitue_edifice(self, number: int) -> str:
return re.sub(r"cтр\. ?\d", number, self.accumulator["address"].strip())
def insert_edifice(self):
number = re.findall("стр\.? ?\d", self.token["obj"])[-1]
self.accumulator["address"] = self.substitue_edifice(number)
if number and self.has_no_class("edifice"):
self.accumulator["class"] += "e"
# Letter
def without_letter(self) -> str:
return re.sub(r"[А-Яа-я]$", "", self.accumulator["address"].strip())
def substitue_letter(self, letter: str) -> str:
address_without_letter = self.without_letter()
return address_without_letter + letter
def insert_letter(self):
letter = re.findall(r"[А-Яа-я]", self.token["obj"])[-1]
self.accumulator["address"] = self.substitue_letter(letter)
if letter and self.has_no_class("litera"):
self.accumulator["class"] += "l"
def has_letter_in(self) -> bool:
return re.search(r"\d{1,3}([А-Я]|[а-я])( |$)", self.accumulator["address"])
# Room
def substitue_room(self, number: int) -> str:
return re.sub(
r"пом\. ?\d\-?\d*\w?", number, self.accumulator["address"].strip()
)
def insert_room(self):
number = re.findall("пом\. ?\-?\d*\w?", self.token["obj"])[-1]
self.accumulator["address"] = self.substitue_room(number)
if number and self.has_no_class("room"):
self.accumulator["class"] += "r"
## Data preprocessing
def split_tokens(self) -> list[pd.Series]:
address = self.input.replace(";", ",")
parts = address.split(",")
parts = map(str.strip, parts)
parts = filter(is_valid_token, parts)
tokens = map(lambda part: create_token(part, ""), parts)
return list(tokens)
def split(self):
self.tokens = self.split_tokens()
result = []
self.accumulator = pd.Series({"address": "", "class": ""})
prev_token = create_token()
for cursor in self.tokens:
self.token = address_classification(cursor, prev_token)
prev_token = self.token.copy()
if self.accumulator["class"] == "":
self.accumulator = self.token.rename({"obj": "address"})
continue
if self.correct_order():
self.accumulator["address"] += " "
self.accumulator += self.token.rename({"obj": "address"})
else:
unfolded_address = unfold_house_ranges(self.accumulator["address"])
self.accumulator["address"] = unfolded_address[-1]
result.extend(unfolded_address)
self.accumulator = cut_address(self.accumulator, self.token["class"])
if self.next_is_street_or_upper():
self.accumulator = self.token.rename({"obj": "address"})
if self.next_class_is("house"):
self.accumulator["address"] = self.substitue_house()
self.pop_token_class()
if self.next_class_is("building"):
self.insert_building()
self.pop_token_class()
if self.next_class_is("edifice"):
self.insert_edifice()
self.pop_token_class()
if self.next_class_is("letter"):
self.insert_letter()
elif self.has_letter_in():
self.accumulator["address"] = self.without_letter()
if self.next_class_is("room"):
self.insert_room()
self.pop_token_class()
result.extend(unfold_house_ranges(self.accumulator["address"]))
return result
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 = AddressSplitter(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)

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parser/address/__init__.py Normal file
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from .classifier import CLASSES, address_classification
from .splitter import AddressSplitter, split_addresses, split_pesoch_res
from .utils import create_token
__all__ = (
"address_classification",
"AddressSplitter",
"CLASSES",
"create_token",
"split_addresses",
"split_pesoch_res",
)

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parser/address/classifier.py Normal file
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import re
import pandas as pd
from .utils import any_of_in
CLASSES = ("w", "d", "c", "t", "s", "h", "b", "e", "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", "дома")
BUILDING_PREFIXES = ("к.", "к ", "корп", "корпус")
EDIFICE_PREFIXES = ("стр.", "строение")
LETTER = ("лит.", "литера", " л.")
PREFIXES = (
DISTRICTS_PREFIXES,
COUNTRYSIDE_PREFIXES,
TERRITORY_PREFIXES,
STREET_PREFIXES,
HOUSES_PREFIXES,
BUILDING_PREFIXES,
EDIFICE_PREFIXES,
LETTER,
)
def find_room(token: pd.Series, pre_token: pd.Series) -> str:
if re.search(r"\bпом\.?", token["obj"]):
return "r"
return ""
def find_litera(token: pd.Series, pre_token: pd.Series) -> str:
if find_room(token, pre_token):
return ""
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 (" ш" not in token["obj"])
and not find_countryside(token, pre_token)
):
return "l"
return ""
def find_edifice(token: pd.Series, pre_token: pd.Series) -> str:
if any_of_in(EDIFICE_PREFIXES, token["obj"].lower()):
return "e"
return ""
def find_building(token: pd.Series, pre_token: pd.Series) -> str:
if re.search(r"\d", token["obj"]) and not find_room(token, pre_token):
if (
any_of_in(BUILDING_PREFIXES, token["obj"].lower())
or "b" in pre_token["class"]
and ("h" not in token["class"])
and not find_edifice(token, pre_token)
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"]) and not find_room(token, pre_token):
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)
and not find_edifice(token, pre_token)
):
return "h"
if (
find_building(token, pre_token)
and not any_of_in(("-я", "-й", "-Я"), token["obj"])
and True
):
if len(re.findall(r"\d{1,4}", token["obj"])) > 1:
return "h"
if int(re.search(r"\d{1,4}", token["obj"]).group()) // 10 > 0:
return "h"
return ""
def find_street(token: pd.Series, pre_token: pd.Series) -> str:
if any_of_in(STREET_PREFIXES, token["obj"].lower()):
return "s"
if (
re.search(r"\b[А-Яа-я]{4,}\b", token["obj"])
and not any(
[el in token["obj"].lower() for pr in PREFIXES for el in pr if len(el) > 2]
)
and not (
"d" in token["class"] or "t" in token["class"] or "c" in token["class"]
)
):
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 any_of_in(STREET_PREFIXES, token["obj"].lower())
):
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_edifice(token, pre_token)
token["class"] += find_litera(token, pre_token)
token["class"] += find_room(token, pre_token)
if token["class"] == "":
token["class"] = "w"
if brackets:
token["obj"] = re.sub(r"\(\)", brackets.group(), token["obj"])
return token

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parser/address/splitter.py Normal file
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from __future__ import annotations
import re
from collections.abc import Sequence
import pandas as pd
from .classifier import CLASSES, address_classification
from .utils import any_of_in, create_token, is_valid_token, unfold_house_ranges
class AddressSplitter(Sequence):
def __init__(self, address: str):
self.input = address
self.addresses = self.split()
# Sequence abstract methods implementation
def __getitem__(self, key: int):
if key < len(self.addresses):
return self.addresses[key]
else:
raise IndexError()
def __len__(self):
return len(self.addresses)
# Address token class manipulations
def next_class(self) -> str:
return self.token["class"][0]
def prev_class(self) -> str:
return self.accumulator["class"][-1]
def correct_order(self) -> bool:
return (
len(self.accumulator["class"]) > 0
and CLASSES.index(self.prev_class()) < CLASSES.index(self.next_class())
and self.accumulator["class"] != "w"
)
def next_class_is(self, comparing_class: str) -> bool:
return len(self.token["class"]) > 0 and self.next_class() == comparing_class[0]
def has_no_class(self, comparing_class: str) -> bool:
return comparing_class[0] not in self.accumulator["class"]
def pop_token_class(self):
self.token["class"] = self.token["class"][1:]
# Accumulator constrains
def next_is_street_or_upper(self) -> bool:
is_unknown_class = self.accumulator["class"] in ("", "w")
return (
CLASSES.index(self.next_class()) <= CLASSES.index("s") or is_unknown_class
)
def has_numbered_street(self) -> bool:
return any_of_in(("-я", "-й", "-Я"), self.accumulator["address"])
# Accumulator manipulation
## House
def substitue_house(self) -> str:
house_regex = re.compile(r"\d{1,4} ?[\/\-]?\d* ?")
number = house_regex.findall(self.token["obj"])[0]
if self.has_numbered_street():
house_number_index = 1
else:
house_number_index = 0
number_in_accumulator = house_regex.findall(self.accumulator["address"])
if number_in_accumulator:
return re.sub(
number_in_accumulator[house_number_index],
number,
self.accumulator["address"],
)
else:
return self.accumulator["address"]
## Building
def append_building(self, number: int) -> pd.Series:
self.accumulator["class"] += "b"
self.accumulator["address"] += "к." + number
return self.accumulator
def substitue_building(self, number: int) -> str:
return re.sub(r"\d$", number, self.accumulator["address"])
def insert_building(self):
number = re.findall(r"\d", self.token["obj"])[-1]
if number and self.has_no_class("building"):
self.accumulator = self.append_building(number)
else:
self.accumulator["address"] = self.substitue_building(number)
## Edifice
def substitue_edifice(self, number: int) -> str:
return re.sub(r"cтр\. ?\d", number, self.accumulator["address"].strip())
def insert_edifice(self):
number = re.findall("стр\.? ?\d", self.token["obj"])[-1]
self.accumulator["address"] = self.substitue_edifice(number)
if number and self.has_no_class("edifice"):
self.accumulator["class"] += "e"
## Letter
def without_letter(self) -> str:
return re.sub(r"[А-Яа-я]$", "", self.accumulator["address"].strip())
def substitue_letter(self, letter: str) -> str:
address_without_letter = self.without_letter()
return address_without_letter + letter
def insert_letter(self):
letter = re.findall(r"[А-Яа-я]", self.token["obj"])[-1]
self.accumulator["address"] = self.substitue_letter(letter)
if letter and self.has_no_class("litera"):
self.accumulator["class"] += "l"
def has_letter_in(self) -> bool:
return re.search(r"\d{1,3}([А-Я]|[а-я])( |$)", self.accumulator["address"])
## Room
def substitue_room(self, number: int) -> str:
return re.sub(
r"пом\. ?\d\-?\d*\w?", number, self.accumulator["address"].strip()
)
def insert_room(self):
number = re.findall("пом\. ?\-?\d*\w?", self.token["obj"])[-1]
self.accumulator["address"] = self.substitue_room(number)
if number and self.has_no_class("room"):
self.accumulator["class"] += "r"
# Data preprocessing
def split_tokens(self) -> list[pd.Series]:
address = self.input.replace(";", ",")
parts = address.split(",")
parts = map(str.strip, parts)
parts = filter(is_valid_token, parts)
tokens = map(lambda part: create_token(part, ""), parts)
return list(tokens)
def cut_address(self) -> pd.Series:
while len(self.accumulator["class"]) > 0 and CLASSES.index(
self.prev_class()
) > CLASSES.index(self.next_class()):
match self.accumulator["class"][-1]:
case "h":
self.accumulator["addresses"] = re.sub(
r"[мкдтпучасток]*\.? ?\d{1,4} ?\/*\d* ?",
"",
self.accumulator["address"].lower(),
)
case "b":
number = re.findall(r"к{0,1}\.? ?\d", self.accumulator["address"])[
-1
]
self.accumulator["address"] = re.sub(
number, "", self.accumulator["address"]
)
case "e":
self.accumulator["address"] = re.sub(
r"cтр\.? ?\d", "", self.accumulator["address"]
)
case "l":
self.accumulator["address"] = re.sub(
r"[литера]*\.? ?[А-Яа-я]{1}$", "", self.accumulator["address"]
)
case "r":
self.accumulator["address"] = re.sub(
r"пом\.? ?\d+", "", self.accumulator["address"]
)
self.accumulator["class"] = self.accumulator["class"][:-1]
return self.accumulator
# Splitting
def split(self):
self.tokens = self.split_tokens()
result = []
self.accumulator = pd.Series({"address": "", "class": ""})
prev_token = create_token()
for cursor in self.tokens:
self.token = address_classification(cursor, prev_token)
prev_token = self.token.copy()
if self.accumulator["class"] == "":
self.accumulator = self.token.rename({"obj": "address"})
continue
if self.correct_order():
self.accumulator["address"] += " "
self.accumulator += self.token.rename({"obj": "address"})
else:
unfolded_address = unfold_house_ranges(self.accumulator["address"])
self.accumulator["address"] = unfolded_address[-1]
result.extend(unfolded_address)
self.accumulator = self.cut_address()
if self.next_is_street_or_upper():
self.accumulator = self.token.rename({"obj": "address"})
if self.next_class_is("house"):
self.accumulator["address"] = self.substitue_house()
self.pop_token_class()
if self.next_class_is("building"):
self.insert_building()
self.pop_token_class()
if self.next_class_is("edifice"):
self.insert_edifice()
self.pop_token_class()
if self.next_class_is("letter"):
self.insert_letter()
elif self.has_letter_in():
self.accumulator["address"] = self.without_letter()
if self.next_class_is("room"):
self.insert_room()
self.pop_token_class()
result.extend(unfold_house_ranges(self.accumulator["address"]))
return result
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 = AddressSplitter(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)

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parser/address/utils.py Normal file
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import re
from collections.abc import Iterable
from typing import TypeVar
import pandas as pd
T = TypeVar("T")
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 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 is_valid_token(string: str) -> bool:
return string not in ("", "уг.", "д.")
def create_token(obj: str = "", token_class: str = ""):
return pd.Series(
{
"obj": obj,
"class": token_class,
}
)