mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
526 lines
14 KiB
Python
526 lines
14 KiB
Python
from epc_api.client import EpcClient
|
|
import os
|
|
from urllib.parse import urlencode
|
|
import pandas as pd
|
|
from difflib import SequenceMatcher
|
|
from tqdm import tqdm
|
|
from utils.logger import setup_logger
|
|
import re
|
|
from typing import Set
|
|
import json
|
|
|
|
logger = setup_logger()
|
|
|
|
|
|
EPC_AUTH_TOKEN = os.getenv(
|
|
"EPC_AUTH_TOKEN",
|
|
"a2Nvbm5rb3dsZXNzYXJAZ21haWwuY29tOjY5MGJiMWM0NmIyOGI5ZDUxYzAxMzQzYzNiZGNlZGJjZDNmODQwMzA=",
|
|
)
|
|
|
|
if EPC_AUTH_TOKEN is None:
|
|
raise RuntimeError("EPC_AUTH_TOKEN not defined in env")
|
|
|
|
|
|
def is_valid_postcode(postcode_clean: str) -> bool:
|
|
"""
|
|
Validate postcode using postcodes.io.
|
|
|
|
Expects a sanitised postcode (e.g. E84SQ).
|
|
Returns True if valid, False otherwise.
|
|
"""
|
|
POSTCODES_IO_VALIDATE_URL = "https://api.postcodes.io/postcodes/{postcode}/validate"
|
|
if not postcode_clean:
|
|
return False
|
|
|
|
try:
|
|
resp = requests.get(
|
|
POSTCODES_IO_VALIDATE_URL.format(postcode=postcode_clean),
|
|
timeout=5,
|
|
)
|
|
resp.raise_for_status()
|
|
return resp.json().get("result", False)
|
|
except requests.RequestException:
|
|
# Network issues, rate limits, etc.
|
|
return False
|
|
|
|
|
|
def levenshtein(a: str, b: str) -> float:
|
|
"""
|
|
Address similarity score in [0, 1].
|
|
|
|
Strategy:
|
|
- Normalise
|
|
- Strongly penalise mismatched house/flat numbers
|
|
- Combine token overlap + character similarity
|
|
"""
|
|
|
|
def extract_number_sequence(s: str) -> list[str]:
|
|
return re.findall(r"\d+[a-z]?", s)
|
|
|
|
def extract_numbers(s: str) -> Set[str]:
|
|
return set(extract_number_sequence(s))
|
|
|
|
def tokenise(s: str) -> Set[str]:
|
|
return set(s.split())
|
|
|
|
def extract_building_number(s: str) -> str | None:
|
|
"""
|
|
Extract the main building number (NOT flat/unit).
|
|
Assumes formats like:
|
|
- '42 moreton road'
|
|
- 'flat 3 42 moreton road'
|
|
"""
|
|
tokens = s.split()
|
|
|
|
# remove flat/unit context
|
|
cleaned = []
|
|
skip_next = False
|
|
for t in tokens:
|
|
if t in ("flat", "apt", "apartment", "unit"):
|
|
skip_next = True
|
|
continue
|
|
if skip_next:
|
|
skip_next = False
|
|
continue
|
|
cleaned.append(t)
|
|
|
|
# first remaining number is building number
|
|
for t in cleaned:
|
|
if re.fullmatch(r"\d+[a-z]?", t):
|
|
return t
|
|
|
|
return None
|
|
|
|
a_norm = normalise_address(a)
|
|
b_norm = normalise_address(b)
|
|
|
|
# --- hard signal: numbers ---
|
|
nums_a = extract_numbers(a_norm)
|
|
nums_b = extract_numbers(b_norm)
|
|
|
|
if nums_a and not nums_b:
|
|
return 0.0
|
|
|
|
# No shared numbers at all → impossible match
|
|
if nums_a and nums_b and nums_a.isdisjoint(nums_b):
|
|
return 0.0
|
|
|
|
# 🔒 HARD GUARD: building number must match
|
|
bld_a = extract_building_number(a_norm)
|
|
bld_b = extract_building_number(b_norm)
|
|
|
|
if bld_a and bld_b and bld_a != bld_b:
|
|
return 0.0
|
|
|
|
# --- order-sensitive flat/building guard ---
|
|
seq_a = extract_number_sequence(a_norm)
|
|
seq_b = extract_number_sequence(b_norm)
|
|
|
|
has_flat_token_user = any(
|
|
tok in a_norm for tok in ("flat", "apt", "apartment", "unit")
|
|
)
|
|
has_flat_token_epc = "flat" in b_norm
|
|
|
|
if (
|
|
len(seq_a) == 2
|
|
and len(seq_b) >= 2
|
|
and has_flat_token_epc
|
|
and not has_flat_token_user
|
|
and seq_a != seq_b[:2]
|
|
):
|
|
return 0.0
|
|
|
|
# --- token similarity (order-independent) ---
|
|
toks_a = tokenise(a_norm)
|
|
toks_b = tokenise(b_norm)
|
|
|
|
if not toks_a or not toks_b:
|
|
token_score = 0.0
|
|
else:
|
|
token_score = len(toks_a & toks_b) / len(toks_a | toks_b)
|
|
|
|
# --- character similarity (soft signal) ---
|
|
char_score = SequenceMatcher(None, a_norm, b_norm).ratio()
|
|
|
|
# --- weighted blend ---
|
|
return round(
|
|
0.65 * token_score + 0.35 * char_score,
|
|
4,
|
|
)
|
|
|
|
|
|
def normalise_address(s: str) -> str:
|
|
"""
|
|
Canonical UK-focused address normalisation.
|
|
|
|
- Lowercases
|
|
- Removes punctuation (keeps / for flats)
|
|
- Normalises whitespace
|
|
- Applies synonym compression at token level
|
|
"""
|
|
|
|
if not s:
|
|
return ""
|
|
|
|
ADDRESS_SYNONYMS = {
|
|
# street types
|
|
"rd": "road",
|
|
"rd.": "road",
|
|
"st": "street",
|
|
"st.": "street",
|
|
"ave": "avenue",
|
|
"ave.": "avenue",
|
|
"ln": "lane",
|
|
"ln.": "lane",
|
|
"cres": "crescent",
|
|
"ct": "court",
|
|
"dr": "drive",
|
|
# flats / units
|
|
"apt": "flat",
|
|
"apartment": "flat",
|
|
"unit": "flat",
|
|
"ste": "suite",
|
|
# numbering noise
|
|
"no": "",
|
|
"no.": "",
|
|
}
|
|
# 1. lowercase
|
|
s = s.lower()
|
|
|
|
# 1.5 split digit-letter suffixes
|
|
s = re.sub(r"(\d+)([a-z])\b", r"\1 \2", s)
|
|
|
|
# 2. remove punctuation except /
|
|
s = re.sub(r"[^\w\s/]", " ", s)
|
|
|
|
# 3. normalise whitespace
|
|
s = re.sub(r"\s+", " ", s).strip()
|
|
|
|
# 4. tokenise + synonym normalisation
|
|
tokens = []
|
|
for tok in s.split():
|
|
replacement = ADDRESS_SYNONYMS.get(tok, tok)
|
|
if replacement:
|
|
tokens.append(replacement)
|
|
|
|
return " ".join(tokens)
|
|
|
|
|
|
def score_addresses(
|
|
df: pd.DataFrame,
|
|
user_address: str,
|
|
column: str = "address",
|
|
) -> pd.Series:
|
|
if column not in df.columns:
|
|
raise ValueError(f"Missing column: {column}")
|
|
|
|
return df[column].apply(lambda x: levenshtein(user_address, x))
|
|
|
|
|
|
def get_epc_data_with_postcode(postcode, size=500, attempt=1, max_attempts=3):
|
|
"""
|
|
Recursively fetch EPC data by postcode.
|
|
If results hit the size limit, retry with double size up to max_attempts.
|
|
"""
|
|
client = EpcClient(auth_token=EPC_AUTH_TOKEN)
|
|
|
|
url = os.path.join(client.domestic.host, "search")
|
|
|
|
if size:
|
|
url += "?" + urlencode({"size": size})
|
|
|
|
search_resp = client.domestic.call(
|
|
url=url,
|
|
method="get",
|
|
params={"postcode": postcode},
|
|
)
|
|
if not search_resp or "rows" not in search_resp:
|
|
return pd.DataFrame()
|
|
|
|
results_df = pd.DataFrame(search_resp["rows"], columns=search_resp["column-names"])
|
|
|
|
row_count = len(results_df)
|
|
|
|
# If we hit the size limit, there *may* be more results
|
|
if row_count == size:
|
|
print(
|
|
f"⚠️ Warning: hit size limit ({size}) for postcode '{postcode}'. "
|
|
f"Attempt {attempt}/{max_attempts}."
|
|
)
|
|
|
|
if attempt < max_attempts:
|
|
print(f"🔁 Retrying with size={size * 2}")
|
|
return get_epc_data_with_postcode(
|
|
postcode=postcode,
|
|
size=size * 2,
|
|
attempt=attempt + 1,
|
|
max_attempts=max_attempts,
|
|
)
|
|
else:
|
|
print(
|
|
"🚨 Max attempts reached. Results may be truncated. "
|
|
"(Please do a manual review by the tech team.)"
|
|
)
|
|
|
|
return results_df
|
|
|
|
|
|
def df_has_single_uprn(df: pd.DataFrame, uprn: str, column: str = "uprn") -> bool:
|
|
"""
|
|
Returns True if all non-null UPRNs in df match the given uprn.
|
|
Returns False otherwise.
|
|
"""
|
|
|
|
if column not in df.columns:
|
|
return False
|
|
|
|
# Drop nulls and normalise to string
|
|
uprns = df[column].dropna().astype(str).str.strip().unique()
|
|
|
|
# No valid UPRNs to compare
|
|
if len(uprns) == 0:
|
|
return False
|
|
|
|
# Exactly one unique UPRN and it matches
|
|
return len(uprns) == 1 and uprns[0] == str(uprn)
|
|
|
|
|
|
def get_uprn_candidates(
|
|
df: pd.DataFrame,
|
|
user_address: str,
|
|
address_column: str = "address",
|
|
uprn_column: str = "uprn",
|
|
) -> pd.DataFrame:
|
|
"""
|
|
Annotate EPC results with lexicographical similarity scores and ranks.
|
|
|
|
Returns a DataFrame sorted by descending lexiscore.
|
|
DOES NOT choose or return a UPRN.
|
|
"""
|
|
|
|
if address_column not in df.columns:
|
|
raise ValueError(f"Missing column: {address_column}")
|
|
|
|
if uprn_column not in df.columns:
|
|
raise ValueError(f"Missing column: {uprn_column}")
|
|
|
|
out = df.copy()
|
|
|
|
user_norm = normalise_address(user_address)
|
|
|
|
out["lexiscore"] = out[address_column].apply(lambda x: levenshtein(user_norm, x))
|
|
|
|
# Normalise UPRN to string
|
|
out[uprn_column] = out[uprn_column].astype(str).str.replace(r"\.0$", "", regex=True)
|
|
|
|
# Rank: 1 = best match
|
|
out["lexirank"] = out["lexiscore"].rank(method="dense", ascending=False).astype(int)
|
|
|
|
return out.sort_values(
|
|
["lexirank", "lexiscore"],
|
|
ascending=[True, False],
|
|
)
|
|
|
|
|
|
def get_uprn(
|
|
user_inputed_address: str,
|
|
postcode: str,
|
|
return_address=False,
|
|
return_EPC=False,
|
|
return_score=True,
|
|
):
|
|
"""
|
|
Return uprn (str)
|
|
Return False if failed to find a sensible matching epc
|
|
Return Nons when epc found but no UPRN
|
|
"""
|
|
df = get_epc_data_with_postcode(postcode=postcode)
|
|
|
|
if df.empty:
|
|
return None
|
|
|
|
scored_df = get_uprn_candidates(
|
|
df,
|
|
user_address=user_inputed_address,
|
|
)
|
|
|
|
# Best score
|
|
best_score = scored_df.iloc[0]["lexiscore"]
|
|
|
|
if best_score <= 0:
|
|
return None
|
|
|
|
# All rank-1 rows (possible draw)
|
|
top_rank_df = scored_df[scored_df["lexirank"] == 1]
|
|
|
|
# If rank-1 rows do not agree on a single UPRN → ambiguous
|
|
if not df_has_single_uprn(top_rank_df, uprn=top_rank_df.iloc[0]["uprn"]):
|
|
return None
|
|
|
|
address = top_rank_df["address"].values[0]
|
|
lexiscore = float(top_rank_df["lexiscore"].values[0])
|
|
epc = top_rank_df["current-energy-efficiency"].values[0]
|
|
score = float(top_rank_df["lexiscore"].values[0])
|
|
|
|
# logger.info(f"Address found to be: {address}, with lexiscore {lexiscore}")
|
|
# Safe to return the agreed UPRN
|
|
found_uprn = top_rank_df.iloc[0]["uprn"]
|
|
|
|
if found_uprn == "":
|
|
return None
|
|
|
|
if return_address:
|
|
if return_EPC is False:
|
|
return found_uprn, address
|
|
else:
|
|
if return_score is False:
|
|
return found_uprn, address, epc
|
|
else:
|
|
return (
|
|
found_uprn,
|
|
address,
|
|
epc,
|
|
score,
|
|
)
|
|
return found_uprn
|
|
|
|
|
|
def resolve_uprns_for_postcode_group(
|
|
group_df: pd.DataFrame,
|
|
epc_df: pd.DataFrame,
|
|
address_col: str = "Address 1",
|
|
) -> pd.DataFrame:
|
|
"""
|
|
Given:
|
|
- group_df: rows sharing the same postcode
|
|
- epc_df: EPC search results for that postcode
|
|
|
|
Returns:
|
|
group_df + found_uprn + diagnostics
|
|
"""
|
|
|
|
results = []
|
|
|
|
for _, row in group_df.iterrows():
|
|
user_address = str(row[address_col]).strip()
|
|
|
|
scored_df = get_uprn_candidates(
|
|
epc_df,
|
|
user_address=user_address,
|
|
)
|
|
|
|
if scored_df.empty:
|
|
results.append(
|
|
{
|
|
"found_uprn": None,
|
|
"best_match_uprn": None,
|
|
"best_match_address": None,
|
|
"best_match_lexiscore": None,
|
|
"status": "no_epc_candidates",
|
|
}
|
|
)
|
|
continue
|
|
|
|
best_score = scored_df.iloc[0]["lexiscore"]
|
|
|
|
if best_score <= 0:
|
|
results.append(
|
|
{
|
|
"found_uprn": None,
|
|
"best_match_uprn": None,
|
|
"best_match_address": None,
|
|
"best_match_lexiscore": best_score,
|
|
"status": "zero_score",
|
|
}
|
|
)
|
|
continue
|
|
|
|
top_rank_df = scored_df[scored_df["lexirank"] == 1]
|
|
|
|
if not df_has_single_uprn(top_rank_df, top_rank_df.iloc[0]["uprn"]):
|
|
results.append(
|
|
{
|
|
"found_uprn": None,
|
|
"best_match_uprn": top_rank_df.iloc[0]["uprn"],
|
|
"best_match_address": top_rank_df.iloc[0]["address"],
|
|
"best_match_lexiscore": best_score,
|
|
"status": "ambiguous",
|
|
}
|
|
)
|
|
continue
|
|
|
|
results.append(
|
|
{
|
|
"found_uprn": str(top_rank_df.iloc[0]["uprn"]),
|
|
"best_match_uprn": str(top_rank_df.iloc[0]["uprn"]),
|
|
"best_match_address": top_rank_df.iloc[0]["address"],
|
|
"best_match_lexiscore": best_score,
|
|
"status": "matched",
|
|
}
|
|
)
|
|
|
|
return pd.concat(
|
|
[group_df.reset_index(drop=True), pd.DataFrame(results)],
|
|
axis=1,
|
|
)
|
|
|
|
|
|
def test(a, b):
|
|
assert a == b, f"erorr: {a}{type(a)} != {b}: {type(b)}"
|
|
|
|
|
|
def run_all_test():
|
|
# Basic usage with different post codes styles
|
|
test(get_epc_data_with_postcode("b93 8sy").shape[0], 63)
|
|
test(get_epc_data_with_postcode("B938sy").shape[0], 63)
|
|
test(get_epc_data_with_postcode("b93 8Sy").shape[0], 63)
|
|
test(get_epc_data_with_postcode("b93 8Sy").shape[0], 63)
|
|
|
|
test(get_uprn("68", "b93 8sy"), "100070989938")
|
|
test(get_uprn("68 Glendon Way", "b93 8sy"), "100070989938")
|
|
test(get_uprn("Flat A, 28, Nelgarde Road", "se6 4tf"), "100023278633")
|
|
test(get_uprn("28 A", "se6 4tf"), "100023278633")
|
|
test(get_uprn("28A", "se6 4tf"), "100023278633")
|
|
test(get_uprn("6 Aitken Close", "E8 4SQ"), False)
|
|
|
|
# unique case
|
|
test(get_uprn("Flat 5, 1, Semley Gate", "e9 5nh"), "10008238198")
|
|
test(get_uprn("5 , 1 Semley Gate", "e9 5nh"), "10008238198")
|
|
test(get_uprn("5 Semley Gate", "e9 5nh"), "10008238198")
|
|
test(get_uprn("1, 5 Semley Gate", "e9 5nh"), False)
|
|
test(
|
|
get_uprn("1 Semley Gate", "e9 5nh"), "10008238188"
|
|
) # this one return "flat 1, in 1 semley gate"
|
|
test(
|
|
get_uprn("48 Oswald Street", "E5 0BT"), False
|
|
) # this one return "flat 1, in 1 semley gate"
|
|
test(
|
|
get_uprn("42 Oswald Street", "E5 0BT"), False
|
|
) # this one return "flat 1, in 1 semley gate"
|
|
test(
|
|
get_uprn("46 Oswald Street", "E5 0BT"), False
|
|
) # this one return "flat 1, in 1 semley gate"
|
|
get_uprn_candidates(get_epc_data_with_postcode("e5 0bt"), "48 Oswald Street")
|
|
get_uprn_candidates(
|
|
get_epc_data_with_postcode("Cr2 7dl"),
|
|
"FLAT 3; 42 MORETON ROAD, SOUTH CROYDON, SURREY",
|
|
)
|
|
|
|
|
|
def handler(event, context):
|
|
print("=== Address2UPRN Lambda Handler ===")
|
|
print(f"Function: {context.function_name}")
|
|
print(f"Request ID: {context.aws_request_id}")
|
|
print(f"Event: {json.dumps(event, indent=2, default=str)}")
|
|
print(f"Context: {context}")
|
|
print("===================================")
|
|
return {"statusCode": 200, "body": "hello world"}
|
|
|
|
|
|
# TO do function dispatcher,
|
|
|
|
# get_uprn_candidates(get_epc_data_with_postcode("E9 5NH"),"Flat 1, 5 Semley Gate" and Flat 5, 1 Semley Gate)
|
|
# fix that
|
|
# Look again at flat 1
|
|
# pandas reader the seperate postcode_splitter
|
|
# dump into s3
|