Spaces:
Running
Running
File size: 66,651 Bytes
dd1cbb4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 |
import os
import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Type
import time
import re
import math
from datetime import datetime
import copy
import gradio as gr
PandasDataFrame = Type[pd.DataFrame]
PandasSeries = Type[pd.Series]
MatchedResults = Dict[str,Tuple[str,int]]
array = List[str]
today = datetime.now().strftime("%d%m%Y")
today_rev = datetime.now().strftime("%Y%m%d")
today_month_rev = datetime.now().strftime("%Y%m")
# Constants
run_fuzzy_match = True
run_nnet_match = True
run_standardise = True
from tools.preparation import prepare_search_address_string, prepare_search_address, prepare_ref_address, check_no_number_addresses, extract_street_name, remove_non_postal
from tools.standardise import standardise_wrapper_func
from tools.fuzzy_match import string_match_by_post_code_multiple, _create_fuzzy_match_results_output, join_to_orig_df
# Neural network functions
### Predict function for imported model
from tools.model_predict import full_predict_func, full_predict_torch, post_predict_clean
from tools.recordlinkage_funcs import score_based_match, check_matches_against_fuzzy
from tools.gradio import initial_data_load
# API functions
from tools.addressbase_api_funcs import places_api_query
# Maximum number of neural net predictions in a single batch
from tools.constants import max_predict_len, MatcherClass
# Load in data functions
def detect_file_type(filename):
"""Detect the file type based on its extension."""
if (filename.endswith('.csv')) | (filename.endswith('.csv.gz')) | (filename.endswith('.zip')):
return 'csv'
elif filename.endswith('.xlsx'):
return 'xlsx'
elif filename.endswith('.parquet'):
return 'parquet'
else:
raise ValueError("Unsupported file type.")
def read_file(filename):
"""Read the file based on its detected type."""
file_type = detect_file_type(filename)
if file_type == 'csv':
return pd.read_csv(filename, low_memory=False)
elif file_type == 'xlsx':
return pd.read_excel(filename)
elif file_type == 'parquet':
return pd.read_parquet(filename)
def get_file_name(in_name):
# Corrected regex pattern
match = re.search(r'\\(?!.*\\)(.*)', in_name)
if match:
matched_result = match.group(1)
else:
matched_result = None
return matched_result
def filter_not_matched(
matched_results: pd.DataFrame,
search_df: pd.DataFrame,
key_col: str
) -> pd.DataFrame:
"""Filters search_df to only rows with key_col not in matched_results"""
# Validate inputs
if not isinstance(matched_results, pd.DataFrame):
raise TypeError("not_matched_results must be a Pandas DataFrame")
if not isinstance(search_df, pd.DataFrame):
raise TypeError("search_df must be a Pandas DataFrame")
if not isinstance(key_col, str):
raise TypeError("key_col must be a string")
if key_col not in matched_results.columns:
raise ValueError(f"{key_col} not a column in matched_results")
matched_results_success = matched_results[matched_results["full_match"]==True]
# Filter search_df
#print(search_df.columns)
#print(key_col)
matched = search_df[key_col].astype(str).isin(matched_results_success[key_col].astype(str))#.drop(['level_0','index'], axis = 1, errors="ignore").reset_index() #
return search_df.iloc[np.where(~matched)[0]] # search_df[~matched]
def run_all_api_calls(in_api_key:str, Matcher:MatcherClass, query_type:str, progress=gr.Progress()):
if in_api_key == "":
print ("No API key provided, please provide one to continue")
return Matcher
else:
# Call the API
#Matcher.ref_df = pd.DataFrame()
# Check if the ref_df file already exists
def check_and_create_api_folder():
# Check if the environmental variable is available
file_path = os.environ.get('ADDRESSBASE_API_OUT') # Replace 'YOUR_ENV_VARIABLE_NAME' with the name of your environmental variable
if file_path is None:
# Environmental variable is not set
print("API output environmental variable not set.")
# Create the 'api/' folder if it doesn't already exist
api_folder_path = 'api/'
if not os.path.exists(api_folder_path):
os.makedirs(api_folder_path)
print(f"'{api_folder_path}' folder created.")
else:
# Environmental variable is set
api_folder_path = file_path
print(f"Environmental variable found: {api_folder_path}")
return api_folder_path
api_output_folder = check_and_create_api_folder()
# Check if the file exists
print("Matcher file name: ", Matcher.file_name)
search_file_name_without_extension = re.sub(r'\.[^.]+$', '', Matcher.file_name)
#print("Search file name without extension: ", search_file_name_without_extension)
api_ref_save_loc = api_output_folder + search_file_name_without_extension + "_api_" + today_month_rev + "_" + query_type + "_ckpt"
print("API reference save location: ", api_ref_save_loc)
# Allow for csv, parquet and gzipped csv files
if os.path.isfile(api_ref_save_loc + ".csv"):
print("API reference CSV file found")
Matcher.ref_df = pd.read_csv(api_ref_save_loc + ".csv")
elif os.path.isfile(api_ref_save_loc + ".parquet"):
print("API reference Parquet file found")
Matcher.ref_df = pd.read_parquet(api_ref_save_loc + ".parquet")
elif os.path.isfile(api_ref_save_loc + ".csv.gz"):
print("API reference gzipped CSV file found")
Matcher.ref_df = pd.read_csv(api_ref_save_loc + ".csv.gz", compression='gzip')
else:
print("API reference file not found, querying API for reference data.")
def conduct_api_loop(in_query, in_api_key, query_type, i, api_ref_save_loc, loop_list, api_search_index):
ref_addresses = places_api_query(in_query, in_api_key, query_type)
ref_addresses['Address_row_number'] = api_search_index[i]
loop_list.append(ref_addresses)
if (i + 1) % 500 == 0:
print("Saving api call checkpoint for query:", str(i + 1))
pd.concat(loop_list).to_parquet(api_ref_save_loc + ".parquet", index=False)
return loop_list
def check_postcode(postcode):
# Remove spaces on the ends or in the middle of the postcode, and any symbols
cleaned_postcode = re.sub(r'[^\w\s]|[\s]', '', postcode)
# Ensure that the postcode meets the specified format
postcode_pattern = r'\b(?:[A-Z][A-HJ-Y]?[0-9][0-9A-Z]?[0-9][A-Z]{2}|GIR0AA|GIR0A{2}|[A-Z][A-HJ-Y]?[0-9][0-9A-Z]?[0-9]{1}?)\b'
match = re.match(postcode_pattern, cleaned_postcode)
if match and len(cleaned_postcode) in (6, 7):
return cleaned_postcode # Return the matched postcode string
else:
return None # Return None if no match is found
if query_type == "Address":
save_file = True
# Do an API call for each unique address
if not Matcher.ref_df.empty:
api_search_df = Matcher.search_df.copy().drop(list(set(Matcher.ref_df["Address_row_number"])))
else:
print("Matcher ref_df data empty")
api_search_df = Matcher.search_df.copy()
i = 0
loop_df = Matcher.ref_df
loop_list = [Matcher.ref_df]
for address in progress.tqdm(api_search_df['full_address_postcode'], desc= "Making API calls", unit="addresses", total=len(api_search_df['full_address_postcode'])):
print("Query number: " + str(i+1), "with address: ", address)
api_search_index = api_search_df.index
loop_list = conduct_api_loop(address, in_api_key, query_type, i, api_ref_save_loc, loop_list, api_search_index)
i += 1
loop_df = pd.concat(loop_list)
Matcher.ref_df = loop_df.drop_duplicates(keep='first', ignore_index=True)
elif query_type == "Postcode":
save_file = True
# Do an API call for each unique postcode. Each API call can only return 100 results maximum :/
if not Matcher.ref_df.empty:
print("Excluding postcodes that already exist in API call data.")
# Retain original index values after filtering
Matcher.search_df["index_keep"] = Matcher.search_df.index
if 'invalid_request' in Matcher.ref_df.columns and 'Address_row_number' in Matcher.ref_df.columns:
print("Joining on invalid_request column")
Matcher.search_df = Matcher.search_df.merge(Matcher.ref_df[['Address_row_number', 'invalid_request']].drop_duplicates(subset="Address_row_number"), left_on = Matcher.search_df_key_field, right_on='Address_row_number', how='left')
elif not 'invalid_request' in Matcher.search_df.columns:
Matcher.search_df['invalid_request'] = False
postcode_col = Matcher.search_postcode_col[0]
# Check ref_df df against cleaned and non-cleaned postcodes
Matcher.search_df[postcode_col] = Matcher.search_df[postcode_col].astype(str)
search_df_cleaned_pcodes = Matcher.search_df[postcode_col].apply(check_postcode)
ref_df_cleaned_pcodes = Matcher.ref_df['POSTCODE_LOCATOR'].dropna().apply(check_postcode)
api_search_df = Matcher.search_df.copy().loc[
~Matcher.search_df[postcode_col].isin(Matcher.ref_df['POSTCODE_LOCATOR']) &
~(Matcher.search_df['invalid_request']==True) &
~(search_df_cleaned_pcodes.isin(ref_df_cleaned_pcodes)), :]
#api_search_index = api_search_df["index_keep"]
#api_search_df.index = api_search_index
print("Remaining invalid request count: ", Matcher.search_df['invalid_request'].value_counts())
else:
print("Matcher ref_df data empty")
api_search_df = Matcher.search_df.copy()
api_search_index = api_search_df.index
api_search_df['index_keep'] = api_search_index
postcode_col = Matcher.search_postcode_col[0]
unique_pcodes = api_search_df.loc[:, ["index_keep", postcode_col]].drop_duplicates(subset=[postcode_col], keep='first')
print("Unique postcodes: ", unique_pcodes[postcode_col])
# Apply the function to each postcode in the Series
unique_pcodes["cleaned_unique_postcodes"] = unique_pcodes[postcode_col].apply(check_postcode)
# Filter out the postcodes that comply with the specified format
valid_unique_postcodes = unique_pcodes.dropna(subset=["cleaned_unique_postcodes"])
valid_postcode_search_index = valid_unique_postcodes['index_keep']
valid_postcode_search_index_list = valid_postcode_search_index.tolist()
if not valid_unique_postcodes.empty:
print("Unique valid postcodes: ", valid_unique_postcodes)
print("Number of unique valid postcodes: ", len(valid_unique_postcodes))
tic = time.perf_counter()
i = 0
loop_df = Matcher.ref_df
loop_list = [Matcher.ref_df]
for pcode in progress.tqdm(valid_unique_postcodes["cleaned_unique_postcodes"], desc= "Making API calls", unit="unique postcodes", total=len(valid_unique_postcodes["cleaned_unique_postcodes"])):
#api_search_index = api_search_df.index
print("Query number: " + str(i+1), " with postcode: ", pcode, " and index: ", valid_postcode_search_index_list[i])
loop_list = conduct_api_loop(pcode, in_api_key, query_type, i, api_ref_save_loc, loop_list, valid_postcode_search_index_list)
i += 1
loop_df = pd.concat(loop_list)
Matcher.ref_df = loop_df.drop_duplicates(keep='first', ignore_index=True)
toc = time.perf_counter()
print("API call time in seconds: ", toc-tic)
else:
print("No valid postcodes found.")
elif query_type == "UPRN":
save_file = True
# Do an API call for each unique address
if not Matcher.ref_df.empty:
api_search_df = Matcher.search_df.copy().drop(list(set(Matcher.ref_df["Address_row_number"])))
else:
print("Matcher ref_df data empty")
api_search_df = Matcher.search_df.copy()
i = 0
loop_df = Matcher.ref_df
loop_list = [Matcher.ref_df]
uprn_check_col = 'ADR_UPRN'
for uprn in progress.tqdm(api_search_df[uprn_check_col], desc= "Making API calls", unit="UPRNs", total=len(api_search_df[uprn_check_col])):
print("Query number: " + str(i+1), "with address: ", uprn)
api_search_index = api_search_df.index
loop_list = conduct_api_loop(uprn, in_api_key, query_type, i, api_ref_save_loc, loop_list, api_search_index)
i += 1
loop_df = pd.concat(loop_list)
Matcher.ref_df = loop_df.drop_duplicates(keep='first', ignore_index=True)
else:
print("Reference file loaded from file, no API calls made.")
save_file = False
# Post API call processing
Matcher.ref_name = "API"
#Matcher.ref_df = Matcher.ref_df.reset_index(drop=True)
Matcher.ref_df['Reference file'] = Matcher.ref_name
if query_type == "Postcode":
#print(Matcher.ref_df.columns)
cols_of_interest = ["ADDRESS", "ORGANISATION", "SAO_TEXT", "SAO_START_NUMBER", "SAO_START_SUFFIX", "SAO_END_NUMBER", "SAO_END_SUFFIX", "PAO_TEXT", "PAO_START_NUMBER", "PAO_START_SUFFIX", "PAO_END_NUMBER", "PAO_END_SUFFIX", "STREET_DESCRIPTION", "TOWN_NAME" ,"ADMINISTRATIVE_AREA", "LOCALITY_NAME", "POSTCODE_LOCATOR", "UPRN", "PARENT_UPRN", "USRN", "LPI_KEY", "RPC", "LOGICAL_STATUS_CODE", "CLASSIFICATION_CODE", "LOCAL_CUSTODIAN_CODE", "COUNTRY_CODE", "POSTAL_ADDRESS_CODE", "BLPU_STATE_CODE", "LAST_UPDATE_DATE", "ENTRY_DATE", "STREET_STATE_CODE", "STREET_CLASSIFICATION_CODE", "LPI_LOGICAL_STATUS_CODE", "invalid_request", "Address_row_number", "Reference file"]
try:
# Attempt to select only the columns of interest
Matcher.ref_df = Matcher.ref_df[cols_of_interest]
except KeyError as e:
missing_columns = [col for col in e.args[0][1:-1].split(", ") if col not in cols_of_interest]
# Handle the missing columns gracefully
print(f"Some columns are missing: {missing_columns}")
#if "LOCAL_CUSTODIAN_CODE" in Matcher.ref_df.columns:
# These are items that are 'owned' by Ordnance Survey like telephone boxes, bus shelters
# Matcher.ref_df = Matcher.ref_df.loc[Matcher.ref_df["LOCAL_CUSTODIAN_CODE"] != 7655,:]
if save_file:
print("Saving reference file to: " + api_ref_save_loc[:-5] + ".parquet")
Matcher.ref_df.to_parquet(api_ref_save_loc + ".parquet", index=False) # Save checkpoint as well
Matcher.ref_df.to_parquet(api_ref_save_loc[:-5] + ".parquet", index=False)
if Matcher.ref_df.empty:
print ("No reference data found with API")
return Matcher
return Matcher
def check_ref_data_exists(Matcher:MatcherClass, ref_data_state:PandasDataFrame, in_ref:List[str], in_refcol:List[str], in_api:List[str], in_api_key:str, query_type:str, progress=gr.Progress()):
'''
Check for reference address data, do some preprocessing, and load in from the Addressbase API if required.
'''
# Check if reference data loaded, bring in if already there
if not ref_data_state.empty:
Matcher.ref_df = ref_data_state
Matcher.ref_name = get_file_name(in_ref[0].name)
Matcher.ref_df["Reference file"] = Matcher.ref_name
# Otherwise check for file name and load in. If nothing found, fail
else:
Matcher.ref_df = pd.DataFrame()
if not in_ref:
if in_api==False:
print ("No reference file provided, please provide one to continue")
return Matcher
# Check if api call required and api key is provided
else:
Matcher = run_all_api_calls(in_api_key, Matcher, query_type)
else:
Matcher.ref_name = get_file_name(in_ref[0].name)
# Concatenate all in reference files together
for ref_file in in_ref:
#print(ref_file.name)
temp_ref_file = read_file(ref_file.name)
file_name_out = get_file_name(ref_file.name)
temp_ref_file["Reference file"] = file_name_out
Matcher.ref_df = pd.concat([Matcher.ref_df, temp_ref_file])
# For the neural net model to work, the llpg columns have to be in the LPI format (e.g. with columns SaoText, SaoStartNumber etc. Here we check if we have that format.
if 'Address_LPI' in Matcher.ref_df.columns:
Matcher.ref_df = Matcher.ref_df.rename(columns={
"Name_LPI": "PaoText",
"Num_LPI": "PaoStartNumber",
"Num_Suffix_LPI":"PaoStartSuffix",
"Number End_LPI":"PaoEndNumber",
"Number_End_Suffix_LPI":"PaoEndSuffix",
"Secondary_Name_LPI":"SaoText",
"Secondary_Num_LPI":"SaoStartNumber",
"Secondary_Num_Suffix_LPI":"SaoStartSuffix",
"Secondary_Num_End_LPI":"SaoEndNumber",
"Secondary_Num_End_Suffix_LPI":"SaoEndSuffix",
"Postcode_LPI":"Postcode",
"Postal_Town_LPI":"PostTown",
"UPRN_BLPU": "UPRN"
})
#print("Matcher reference file: ", Matcher.ref_df['Reference file'])
# Check if the source is the Addressbase places API
if Matcher.ref_df.iloc[0]['Reference file'] == 'API' or '_api_' in Matcher.ref_df.iloc[0]['Reference file']:
Matcher.ref_df = Matcher.ref_df.rename(columns={
"ORGANISATION_NAME": "Organisation",
"ORGANISATION": "Organisation",
"PAO_TEXT": "PaoText",
"PAO_START_NUMBER": "PaoStartNumber",
"PAO_START_SUFFIX":"PaoStartSuffix",
"PAO_END_NUMBER":"PaoEndNumber",
"PAO_END_SUFFIX":"PaoEndSuffix",
"STREET_DESCRIPTION":"Street",
"SAO_TEXT":"SaoText",
"SAO_START_NUMBER":"SaoStartNumber",
"SAO_START_SUFFIX":"SaoStartSuffix",
"SAO_END_NUMBER":"SaoEndNumber",
"SAO_END_SUFFIX":"SaoEndSuffix",
"POSTCODE_LOCATOR":"Postcode",
"TOWN_NAME":"PostTown",
"LOCALITY_NAME":"LocalityName",
"ADMINISTRATIVE_AREA":"AdministrativeArea"
}, errors="ignore")
# Check ref_df file format
# If standard format, or it's an API call
if 'SaoText' in Matcher.ref_df.columns or in_api:
Matcher.standard_llpg_format = True
Matcher.ref_address_cols = ["Organisation", "SaoStartNumber", "SaoStartSuffix", "SaoEndNumber", "SaoEndSuffix", "SaoText", "PaoStartNumber", "PaoStartSuffix", "PaoEndNumber",
"PaoEndSuffix", "PaoText", "Street", "PostTown", "Postcode"]
# Add columns from the list if they don't exist
for col in Matcher.ref_address_cols:
if col not in Matcher.ref_df:
Matcher.ref_df[col] = np.nan
else:
Matcher.standard_llpg_format = False
Matcher.ref_address_cols = in_refcol
Matcher.ref_df = Matcher.ref_df.rename(columns={Matcher.ref_address_cols[-1]:"Postcode"})
Matcher.ref_address_cols[-1] = "Postcode"
# Reset index for ref_df as multiple files may have been combined with identical indices
Matcher.ref_df = Matcher.ref_df.reset_index() #.drop(["index","level_0"], axis = 1, errors="ignore").reset_index().drop(["index","level_0"], axis = 1, errors="ignore")
Matcher.ref_df.index.name = 'index'
return Matcher
def check_match_data_filter(Matcher, data_state, results_data_state, in_file, in_text, in_colnames, in_joincol, in_existing, in_api):
# Assign join field if not known
if not Matcher.search_df_key_field:
Matcher.search_df_key_field = "index"
# Set search address cols as entered column names
#print("In colnames in check match data: ", in_colnames)
Matcher.search_address_cols = in_colnames
# Check if data loaded already and bring it in
if not data_state.empty:
Matcher.search_df = data_state
Matcher.search_df['index'] = Matcher.search_df.index
else:
Matcher.search_df = pd.DataFrame()
# If someone has just entered open text, just load this instead
if in_text:
Matcher.search_df, Matcher.search_df_key_field, Matcher.search_address_cols, Matcher.search_postcode_col = prepare_search_address_string(in_text)
# If two matcher files are loaded in, the algorithm will combine them together
if Matcher.search_df.empty and in_file:
output_message, drop1, drop2, Matcher.search_df, results_data_state = initial_data_load(in_file)
file_list = [string.name for string in in_file]
data_file_names = [string for string in file_list if "results_on_orig" not in string.lower()]
#print("Data file names: ", data_file_names)
Matcher.file_name = get_file_name(data_file_names[0])
# search_df makes column to use as index
Matcher.search_df['index'] = Matcher.search_df.index
# Join previously created results file onto search_df if previous results file exists
if not results_data_state.empty:
print("Joining on previous results file")
Matcher.results_on_orig_df = results_data_state.copy()
Matcher.search_df = Matcher.search_df.merge(results_data_state, on = "index", how = "left")
# If no join on column suggested, assume the user wants the UPRN
# print("in_joincol: ", in_joincol)
if not in_joincol:
Matcher.new_join_col = ['UPRN']
#Matcher.new_join_col = Matcher.new_join_col#[0]
else:
Matcher.new_join_col = in_joincol
#Matcher.new_join_col = Matcher.new_join_col
# Extract the column names from the input data
print("In colnames: ", in_colnames)
if len(in_colnames) > 1:
Matcher.search_postcode_col = [in_colnames[-1]]
print("Postcode col: ", Matcher.search_postcode_col)
elif len(in_colnames) == 1:
Matcher.search_df['full_address_postcode'] = Matcher.search_df[in_colnames[0]]
Matcher.search_postcode_col = ['full_address_postcode']
Matcher.search_address_cols.append('full_address_postcode')
# Check for column that indicates there are existing matches. The code will then search this column for entries, and will remove them from the data to be searched
Matcher.existing_match_cols = in_existing
if in_existing:
if "Matched with reference address" in Matcher.search_df.columns:
Matcher.search_df.loc[~Matcher.search_df[in_existing].isna(), "Matched with reference address"] = True
else: Matcher.search_df["Matched with reference address"] = ~Matcher.search_df[in_existing].isna()
print("Shape of search_df before filtering is: ", Matcher.search_df.shape)
### Filter addresses to those with length > 0
zero_length_search_df = Matcher.search_df.copy()[Matcher.search_address_cols]
zero_length_search_df = zero_length_search_df.fillna('').infer_objects(copy=False)
Matcher.search_df["address_cols_joined"] = zero_length_search_df.astype(str).sum(axis=1).str.strip()
length_more_than_0 = Matcher.search_df["address_cols_joined"].str.len() > 0
### Filter addresses to match to postcode areas present in both search_df and ref_df_cleaned only (postcode without the last three characters). Only run if API call is false. When the API is called, relevant addresses and postcodes should be brought in by the API.
if not in_api:
if Matcher.filter_to_lambeth_pcodes == True:
Matcher.search_df["postcode_search_area"] = Matcher.search_df[Matcher.search_postcode_col[0]].str.strip().str.upper().str.replace(" ", "").str[:-2]
Matcher.ref_df["postcode_search_area"] = Matcher.ref_df["Postcode"].str.strip().str.upper().str.replace(" ", "").str[:-2]
unique_ref_pcode_area = (Matcher.ref_df["postcode_search_area"][Matcher.ref_df["postcode_search_area"].str.len() > 3]).unique()
postcode_found_in_search = Matcher.search_df["postcode_search_area"].isin(unique_ref_pcode_area)
Matcher.search_df["Excluded from search"] = "Included in search"
Matcher.search_df.loc[~(postcode_found_in_search), "Excluded from search"] = "Postcode area not found"
Matcher.search_df.loc[~(length_more_than_0), "Excluded from search"] = "Address length 0"
Matcher.pre_filter_search_df = Matcher.search_df.copy()#.drop(["index", "level_0"], axis = 1, errors = "ignore").reset_index()
Matcher.pre_filter_search_df = Matcher.pre_filter_search_df.drop("address_cols_joined", axis = 1)
Matcher.excluded_df = Matcher.search_df.copy()[~(postcode_found_in_search) | ~(length_more_than_0)]
Matcher.search_df = Matcher.search_df[(postcode_found_in_search) & (length_more_than_0)]
# Exclude records that have already been matched separately, i.e. if 'Matched with reference address' column exists, and has trues in it
if "Matched with reference address" in Matcher.search_df.columns:
previously_matched = Matcher.pre_filter_search_df["Matched with reference address"] == True
Matcher.pre_filter_search_df.loc[previously_matched, "Excluded from search"] = "Previously matched"
Matcher.excluded_df = Matcher.search_df.copy()[~(postcode_found_in_search) | ~(length_more_than_0) | (previously_matched)]
Matcher.search_df = Matcher.search_df[(postcode_found_in_search) & (length_more_than_0) & ~(previously_matched)]
else:
Matcher.excluded_df = Matcher.search_df.copy()[~(postcode_found_in_search) | ~(length_more_than_0)]
Matcher.search_df = Matcher.search_df[(postcode_found_in_search) & (length_more_than_0)]
print("Shape of ref_df before filtering is: ", Matcher.ref_df.shape)
unique_search_pcode_area = (Matcher.search_df["postcode_search_area"]).unique()
postcode_found_in_ref = Matcher.ref_df["postcode_search_area"].isin(unique_search_pcode_area)
Matcher.ref_df = Matcher.ref_df[postcode_found_in_ref]
Matcher.pre_filter_search_df = Matcher.pre_filter_search_df.drop("postcode_search_area", axis = 1)
Matcher.search_df = Matcher.search_df.drop("postcode_search_area", axis = 1)
Matcher.ref_df = Matcher.ref_df.drop("postcode_search_area", axis = 1)
Matcher.excluded_df = Matcher.excluded_df.drop("postcode_search_area", axis = 1)
else:
Matcher.pre_filter_search_df = Matcher.search_df.copy()
Matcher.search_df.loc[~(length_more_than_0), "Excluded from search"] = "Address length 0"
Matcher.excluded_df = Matcher.search_df[~(length_more_than_0)]
Matcher.search_df = Matcher.search_df[length_more_than_0]
Matcher.search_df = Matcher.search_df.drop("address_cols_joined", axis = 1, errors="ignore")
Matcher.excluded_df = Matcher.excluded_df.drop("address_cols_joined", axis = 1, errors="ignore")
Matcher.search_df_not_matched = Matcher.search_df
# If this is for an API call, we need to convert the search_df address columns to one column now. This is so the API call can be made and the reference dataframe created.
if in_api:
if in_file:
output_message, drop1, drop2, df, results_data_state = initial_data_load(in_file)
file_list = [string.name for string in in_file]
data_file_names = [string for string in file_list if "results_on_orig" not in string.lower()]
Matcher.file_name = get_file_name(data_file_names[0])
else:
if in_text:
Matcher.file_name = in_text
else:
Matcher.file_name = "API call"
# Exclude records that have already been matched separately, i.e. if 'Matched with reference address' column exists, and has trues in it
if in_existing:
print("Checking for previously matched records")
Matcher.pre_filter_search_df = Matcher.search_df.copy()
previously_matched = ~Matcher.pre_filter_search_df[in_existing].isnull()
Matcher.pre_filter_search_df.loc[previously_matched, "Excluded from search"] = "Previously matched"
Matcher.excluded_df = Matcher.search_df.copy()[~(length_more_than_0) | (previously_matched)]
Matcher.search_df = Matcher.search_df[(length_more_than_0) & ~(previously_matched)]
if type(Matcher.search_df) == str: search_df_cleaned, search_df_key_field, search_address_cols = prepare_search_address_string(Matcher.search_df)
else: search_df_cleaned = prepare_search_address(Matcher.search_df, Matcher.search_address_cols, Matcher.search_postcode_col, Matcher.search_df_key_field)
Matcher.search_df['full_address_postcode'] = search_df_cleaned["full_address"]
#Matcher.search_df = Matcher.search_df.reset_index(drop=True)
#Matcher.search_df.index.name = 'index'
return Matcher
def load_matcher_data(in_text, in_file, in_ref, data_state, results_data_state, ref_data_state, in_colnames, in_refcol, in_joincol, in_existing, Matcher, in_api, in_api_key):
'''
Load in user inputs from the Gradio interface. Convert all input types (single address, or csv input) into standardised data format that can be used downstream for the fuzzy matching.
'''
today_rev = datetime.now().strftime("%Y%m%d")
# Abort flag for if it's not even possible to attempt the first stage of the match for some reason
Matcher.abort_flag = False
### ref_df FILES ###
# If not an API call, run this first
if not in_api:
Matcher = check_ref_data_exists(Matcher, ref_data_state, in_ref, in_refcol, in_api, in_api_key, query_type=in_api)
### MATCH/SEARCH FILES ###
# If doing API calls, we need to know the search data before querying for specific addresses/postcodes
Matcher = check_match_data_filter(Matcher, data_state, results_data_state, in_file, in_text, in_colnames, in_joincol, in_existing, in_api)
# If an API call, ref_df data is loaded after
if in_api:
Matcher = check_ref_data_exists(Matcher, ref_data_state, in_ref, in_refcol, in_api, in_api_key, query_type=in_api)
#print("Resetting index.")
# API-called data will often have duplicate indexes in it - drop them to avoid conflicts down the line
#Matcher.ref_df = Matcher.ref_df.reset_index(drop = True)
print("Shape of ref_df after filtering is: ", Matcher.ref_df.shape)
print("Shape of search_df after filtering is: ", Matcher.search_df.shape)
Matcher.match_outputs_name = "diagnostics_initial_" + today_rev + ".csv"
Matcher.results_orig_df_name = "results_initial_" + today_rev + ".csv"
#Matcher.match_results_output.to_csv(Matcher.match_outputs_name, index = None)
#Matcher.results_on_orig_df.to_csv(Matcher.results_orig_df_name, index = None)
return Matcher
# DF preparation functions
# Run batch of matches
def run_match_batch(InitialMatch, batch_n, total_batches, progress=gr.Progress()):
if run_fuzzy_match == True:
overall_tic = time.perf_counter()
progress(0, desc= "Batch " + str(batch_n+1) + " of " + str(total_batches) + ". Fuzzy match - non-standardised dataset")
df_name = "Fuzzy not standardised"
''' FUZZY MATCHING '''
''' Run fuzzy match on non-standardised dataset '''
FuzzyNotStdMatch = orchestrate_match_run(Matcher = copy.copy(InitialMatch), standardise = False, nnet = False, file_stub= "not_std_", df_name = df_name)
if FuzzyNotStdMatch.abort_flag == True:
message = "Nothing to match! Aborting address check."
print(message)
return message, InitialMatch
FuzzyNotStdMatch = combine_two_matches(InitialMatch, FuzzyNotStdMatch, df_name)
if (len(FuzzyNotStdMatch.search_df_not_matched) == 0) | (sum(FuzzyNotStdMatch.match_results_output[FuzzyNotStdMatch.match_results_output['full_match']==False]['fuzzy_score'])==0):
overall_toc = time.perf_counter()
time_out = f"The fuzzy match script took {overall_toc - overall_tic:0.1f} seconds"
FuzzyNotStdMatch.output_summary = FuzzyNotStdMatch.output_summary + " Neural net match not attempted. "# + time_out
return FuzzyNotStdMatch.output_summary, FuzzyNotStdMatch
''' Run fuzzy match on standardised dataset '''
progress(.25, desc="Batch " + str(batch_n+1) + " of " + str(total_batches) + ". Fuzzy match - standardised dataset")
df_name = "Fuzzy standardised"
FuzzyStdMatch = orchestrate_match_run(Matcher = copy.copy(FuzzyNotStdMatch), standardise = True, nnet = False, file_stub= "std_", df_name = df_name)
FuzzyStdMatch = combine_two_matches(FuzzyNotStdMatch, FuzzyStdMatch, df_name)
''' Continue if reference file in correct format, and neural net model exists. Also if data not too long '''
if ((len(FuzzyStdMatch.search_df_not_matched) == 0) | (FuzzyStdMatch.standard_llpg_format == False) |\
(os.path.exists(FuzzyStdMatch.model_dir_name + '/saved_model.zip') == False) | (run_nnet_match == False)):
overall_toc = time.perf_counter()
time_out = f"The fuzzy match script took {overall_toc - overall_tic:0.1f} seconds"
FuzzyStdMatch.output_summary = FuzzyStdMatch.output_summary + " Neural net match not attempted. "# + time_out
return FuzzyStdMatch.output_summary, FuzzyStdMatch
if run_nnet_match == True:
''' NEURAL NET '''
if run_fuzzy_match == False:
FuzzyStdMatch = copy.copy(InitialMatch)
overall_tic = time.perf_counter()
''' First on non-standardised addresses '''
progress(.50, desc="Batch " + str(batch_n+1) + " of " + str(total_batches) + ". Neural net - non-standardised dataset")
df_name = "Neural net not standardised"
FuzzyNNetNotStdMatch = orchestrate_match_run(Matcher = copy.copy(FuzzyStdMatch), standardise = False, nnet = True, file_stub= "nnet_not_std_", df_name = df_name)
FuzzyNNetNotStdMatch = combine_two_matches(FuzzyStdMatch, FuzzyNNetNotStdMatch, df_name)
if (len(FuzzyNNetNotStdMatch.search_df_not_matched) == 0):
overall_toc = time.perf_counter()
time_out = f"The whole match script took {overall_toc - overall_tic:0.1f} seconds"
FuzzyNNetNotStdMatch.output_summary = FuzzyNNetNotStdMatch.output_summary# + time_out
return FuzzyNNetNotStdMatch.output_summary, FuzzyNNetNotStdMatch
''' Next on standardised addresses '''
progress(.75, desc="Batch " + str(batch_n+1) + " of " + str(total_batches) + ". Neural net - standardised dataset")
df_name = "Neural net standardised"
FuzzyNNetStdMatch = orchestrate_match_run(Matcher = copy.copy(FuzzyNNetNotStdMatch), standardise = True, nnet = True, file_stub= "nnet_std_", df_name = df_name)
FuzzyNNetStdMatch = combine_two_matches(FuzzyNNetNotStdMatch, FuzzyNNetStdMatch, df_name)
if run_fuzzy_match == False:
overall_toc = time.perf_counter()
time_out = f"The neural net match script took {overall_toc - overall_tic:0.1f} seconds"
FuzzyNNetStdMatch.output_summary = FuzzyNNetStdMatch.output_summary + " Only Neural net match attempted. "# + time_out
return FuzzyNNetStdMatch.output_summary, FuzzyNNetStdMatch
overall_toc = time.perf_counter()
time_out = f"The whole match script took {overall_toc - overall_tic:0.1f} seconds"
summary_of_summaries = FuzzyNotStdMatch.output_summary + "\n" + FuzzyStdMatch.output_summary + "\n" + FuzzyNNetStdMatch.output_summary + "\n" + time_out
return summary_of_summaries, FuzzyNNetStdMatch
# Overarching functions
def orchestrate_match_run(Matcher, standardise = False, nnet = False, file_stub= "not_std_", df_name = "Fuzzy not standardised"):
today_rev = datetime.now().strftime("%Y%m%d")
#print(Matcher.standardise)
Matcher.standardise = standardise
if Matcher.search_df_not_matched.empty:
print("Nothing to match! At start of preparing run.")
return Matcher
if nnet == False:
diag_shortlist,\
diag_best_match,\
match_results_output,\
results_on_orig_df,\
summary,\
search_address_cols =\
full_fuzzy_match(Matcher.search_df_not_matched.copy(),
Matcher.standardise,
Matcher.search_df_key_field,
Matcher.search_address_cols,
Matcher.search_df_cleaned,
Matcher.search_df_after_stand,
Matcher.search_df_after_full_stand,
Matcher.ref_df_cleaned,
Matcher.ref_df_after_stand,
Matcher.ref_df_after_full_stand,
Matcher.fuzzy_match_limit,
Matcher.fuzzy_scorer_used)
if match_results_output.empty:
print("Match results empty")
Matcher.abort_flag = True
return Matcher
else:
Matcher.diag_shortlist = diag_shortlist
Matcher.diag_best_match = diag_best_match
Matcher.match_results_output = match_results_output
else:
match_results_output,\
results_on_orig_df,\
summary,\
predict_df_nnet =\
full_nn_match(
Matcher.ref_address_cols,
Matcher.search_df_not_matched.copy(),
Matcher.search_address_cols,
Matcher.search_df_key_field,
Matcher.standardise,
Matcher.exported_model[0],
Matcher.matching_variables,
Matcher.text_columns,
Matcher.weights,
Matcher.fuzzy_method,
Matcher.score_cut_off,
Matcher.match_results_output.copy(),
Matcher.filter_to_lambeth_pcodes,
Matcher.model_type,
Matcher.word_to_index,
Matcher.cat_to_idx,
Matcher.device,
Matcher.vocab,
Matcher.labels_list,
Matcher.search_df_cleaned,
Matcher.ref_df_after_stand,
Matcher.search_df_after_stand,
Matcher.search_df_after_full_stand)
if match_results_output.empty:
print("Match results empty")
Matcher.abort_flag = True
return Matcher
else:
Matcher.match_results_output = match_results_output
Matcher.predict_df_nnet = predict_df_nnet
# Save to file
Matcher.results_on_orig_df = results_on_orig_df
Matcher.summary = summary
Matcher.output_summary = create_match_summary(Matcher.match_results_output, df_name = df_name)
Matcher.match_outputs_name = "diagnostics_" + file_stub + today_rev + ".csv"
Matcher.results_orig_df_name = "results_" + file_stub + today_rev + ".csv"
Matcher.match_results_output.to_csv(Matcher.match_outputs_name, index = None)
Matcher.results_on_orig_df.to_csv(Matcher.results_orig_df_name, index = None)
return Matcher
# Overarching fuzzy match function
def full_fuzzy_match(search_df:PandasDataFrame,
standardise:bool,
search_df_key_field:str,
search_address_cols:List[str],
search_df_cleaned:PandasDataFrame,
search_df_after_stand:PandasDataFrame,
search_df_after_full_stand:PandasDataFrame,
ref_df_cleaned:PandasDataFrame,
ref_df_after_stand:PandasDataFrame,
ref_df_after_full_stand:PandasDataFrame,
fuzzy_match_limit:float,
fuzzy_scorer_used:str,
new_join_col:List[str]=["UPRN"],
fuzzy_search_addr_limit:float = 100,
filter_to_lambeth_pcodes:bool=False):
'''
Compare addresses in a 'search address' dataframe with a 'reference address' dataframe by using fuzzy matching from the rapidfuzz package, blocked by postcode and then street.
'''
# Break if search item has length 0
if search_df.empty:
out_error = "Nothing to match! Just started fuzzy match."
print(out_error)
return pd.DataFrame(),pd.DataFrame(),pd.DataFrame(),pd.DataFrame(), out_error,search_address_cols
# If standardise is true, replace relevant variables with standardised versions
if standardise == True:
df_name = "standardised address"
search_df_after_stand = search_df_after_full_stand
ref_df_after_stand = ref_df_after_full_stand
else:
df_name = "non-standardised address"
# RUN WITH POSTCODE AS A BLOCKER #
# Fuzzy match against reference addresses
# Remove rows from ref search series where postcode is not found in the search_df
search_df_after_stand_series = search_df_after_stand.copy().set_index('postcode_search')['search_address_stand'].sort_index()
ref_df_after_stand_series = ref_df_after_stand.copy().set_index('postcode_search')['ref_address_stand'].sort_index()
#print(search_df_after_stand_series.index.tolist())
#print(ref_df_after_stand_series.index.tolist())
ref_df_after_stand_series_checked = ref_df_after_stand_series.copy()[ref_df_after_stand_series.index.isin(search_df_after_stand_series.index.tolist())]
# pd.DataFrame(ref_df_after_stand_series_checked.to_csv("ref_df_after_stand_series_checked.csv"))
if len(ref_df_after_stand_series_checked) == 0:
print("Nothing relevant in reference data to match!")
return pd.DataFrame(), pd.DataFrame(), pd.DataFrame(),pd.DataFrame(),"Nothing relevant in reference data to match!",search_address_cols
# 'matched' is the list for which every single row is searched for in the reference list (the ref_df).
print("Starting the fuzzy match")
tic = time.perf_counter()
results = string_match_by_post_code_multiple(match_address_series = search_df_after_stand_series.copy(),
reference_address_series = ref_df_after_stand_series_checked,
search_limit = fuzzy_search_addr_limit,
scorer_name = fuzzy_scorer_used)
toc = time.perf_counter()
print(f"Performed the fuzzy match in {toc - tic:0.1f} seconds")
# Create result dfs
match_results_output, diag_shortlist, diag_best_match = _create_fuzzy_match_results_output(results, search_df_after_stand, ref_df_cleaned, ref_df_after_stand, fuzzy_match_limit, search_df_cleaned, search_df_key_field, new_join_col, standardise, blocker_col = "Postcode")
match_results_output['match_method'] = "Fuzzy match - postcode"
search_df_not_matched = filter_not_matched(match_results_output, search_df_after_stand, search_df_key_field)
# If nothing left to match, break
if (sum(match_results_output['full_match']==False) == 0) | (sum(match_results_output[match_results_output['full_match']==False]['fuzzy_score'])==0):
print("Nothing left to match!")
summary = create_match_summary(match_results_output, df_name)
if type(search_df) != str:
results_on_orig_df = join_to_orig_df(match_results_output, search_df_cleaned, search_df_key_field, new_join_col)
else: results_on_orig_df = match_results_output
return diag_shortlist, diag_best_match, match_results_output, results_on_orig_df, summary, search_address_cols
# RUN WITH STREET AS A BLOCKER #
### Redo with street as blocker
search_df_after_stand_street = search_df_not_matched.copy()
search_df_after_stand_street['search_address_stand_w_pcode'] = search_df_after_stand_street['search_address_stand'] + " " + search_df_after_stand_street['postcode_search']
ref_df_after_stand['ref_address_stand_w_pcode'] = ref_df_after_stand['ref_address_stand'] + " " + ref_df_after_stand['postcode_search']
search_df_after_stand_street['street']= search_df_after_stand_street['full_address_search'].apply(extract_street_name)
# Exclude non-postal addresses from street-blocked search
search_df_after_stand_street.loc[search_df_after_stand_street['Excluded from search'] == "Excluded - non-postal address", 'street'] = ""
### Create lookup lists
search_df_match_series_street = search_df_after_stand_street.copy().set_index('street')['search_address_stand']
ref_df_after_stand_series_street = ref_df_after_stand.copy().set_index('Street')['ref_address_stand']
# Remove rows where street is not in ref_df df
#index_check = ref_df_after_stand_series_street.index.isin(search_df_match_series_street.index)
#ref_df_after_stand_series_street_checked = ref_df_after_stand_series_street.copy()[index_check == True]
ref_df_after_stand_series_street_checked = ref_df_after_stand_series_street.copy()[ref_df_after_stand_series_street.index.isin(search_df_match_series_street.index.tolist())]
# If nothing left to match, break
if (len(ref_df_after_stand_series_street_checked) == 0) | ((len(search_df_match_series_street) == 0)):
summary = create_match_summary(match_results_output, df_name)
if type(search_df) != str:
results_on_orig_df = join_to_orig_df(match_results_output, search_df_after_stand, search_df_key_field, new_join_col)
else: results_on_orig_df = match_results_output
return diag_shortlist, diag_best_match,\
match_results_output, results_on_orig_df, summary, search_address_cols
print("Starting the fuzzy match with street as blocker")
tic = time.perf_counter()
results_st = string_match_by_post_code_multiple(match_address_series = search_df_match_series_street.copy(),
reference_address_series = ref_df_after_stand_series_street_checked.copy(),
search_limit = fuzzy_search_addr_limit,
scorer_name = fuzzy_scorer_used)
toc = time.perf_counter()
print(f"Performed the fuzzy match in {toc - tic:0.1f} seconds")
match_results_output_st, diag_shortlist_st, diag_best_match_st = _create_fuzzy_match_results_output(results_st, search_df_after_stand_street, ref_df_cleaned, ref_df_after_stand,\
fuzzy_match_limit, search_df_cleaned, search_df_key_field, new_join_col, standardise, blocker_col = "Street")
match_results_output_st['match_method'] = "Fuzzy match - street"
match_results_output_st_out = combine_std_df_remove_dups(match_results_output, match_results_output_st, orig_addr_col = search_df_key_field)
match_results_output = match_results_output_st_out
summary = create_match_summary(match_results_output, df_name)
### Join URPN back onto orig df
if type(search_df) != str:
results_on_orig_df = join_to_orig_df(match_results_output, search_df_cleaned, search_df_key_field, new_join_col)
else: results_on_orig_df = match_results_output
return diag_shortlist, diag_best_match, match_results_output, results_on_orig_df, summary, search_address_cols
# Overarching NN function
def full_nn_match(ref_address_cols:List[str],
search_df:PandasDataFrame,
search_address_cols:List[str],
search_df_key_field:str,
standardise:bool,
exported_model:list,
matching_variables:List[str],
text_columns:List[str],
weights:dict,
fuzzy_method:str,
score_cut_off:float,
match_results:PandasDataFrame,
filter_to_lambeth_pcodes:bool,
model_type:str,
word_to_index:dict,
cat_to_idx:dict,
device:str,
vocab:List[str],
labels_list:List[str],
search_df_cleaned:PandasDataFrame,
ref_df_after_stand:PandasDataFrame,
search_df_after_stand:PandasDataFrame,
search_df_after_full_stand:PandasDataFrame,
new_join_col:List=["UPRN"]):
'''
Use a neural network model to partition 'search addresses' into consituent parts in the format of UK Ordnance Survey Land Property Identifier (LPI) addresses. These address components are compared individually against reference addresses in the same format to give an overall match score using the recordlinkage package.
'''
# Break if search item has length 0
if search_df.empty:
out_error = "Nothing to match!"
print(out_error)
return pd.DataFrame(),pd.DataFrame(),pd.DataFrame(),pd.DataFrame(),pd.DataFrame(), out_error, search_address_cols
# If it is the standardisation step, or you have come from the fuzzy match area
if (standardise == True): # | (run_fuzzy_match == True & standardise == False):
df_name = "standardised address"
search_df_after_stand = search_df_after_full_stand
else:
df_name = "non-standardised address"
print(search_df_after_stand.shape[0])
print(ref_df_after_stand.shape[0])
# Predict on search data to extract LPI address components
#predict_len = len(search_df_cleaned["full_address"])
all_columns = list(search_df_cleaned) # Creates list of all column headers
search_df_cleaned[all_columns] = search_df_cleaned[all_columns].astype(str)
predict_data = list(search_df_after_stand['search_address_stand'])
### Run predict function
print("Starting neural net prediction for " + str(len(predict_data)) + " addresses")
tic = time.perf_counter()
# Determine the number of chunks
num_chunks = math.ceil(len(predict_data) / max_predict_len)
list_out_all = []
predict_df_all = []
for i in range(num_chunks):
print("Starting to predict batch " + str(i+ 1) + " of " + str(num_chunks) + " batches.")
start_idx = i * max_predict_len
end_idx = start_idx + max_predict_len
# Extract the current chunk of data
chunk_data = predict_data[start_idx:end_idx]
# Replace blank strings with a single space
chunk_data = [" " if s in ("") else s for s in chunk_data]
if (model_type == "gru") | (model_type == "lstm"):
list_out, predict_df = full_predict_torch(model=exported_model, model_type=model_type,
input_text=chunk_data, word_to_index=word_to_index,
cat_to_idx=cat_to_idx, device=device)
else:
list_out, predict_df = full_predict_func(chunk_data, exported_model, vocab, labels_list)
# Append the results
list_out_all.extend(list_out)
predict_df_all.append(predict_df)
# Concatenate all the results dataframes
predict_df_all = pd.concat(predict_df_all, ignore_index=True)
toc = time.perf_counter()
print(f"Performed the NN prediction in {toc - tic:0.1f} seconds")
predict_df = post_predict_clean(predict_df=predict_df_all, orig_search_df=search_df_cleaned,
ref_address_cols=ref_address_cols, search_df_key_field=search_df_key_field)
# Score-based matching between neural net predictions and fuzzy match results
# Example of recordlinkage package in use: https://towardsdatascience.com/how-to-perform-fuzzy-dataframe-row-matching-with-recordlinkage-b53ca0cb944c
## Run with Postcode as blocker column
blocker_column = ["Postcode"]
scoresSBM_best_pc, matched_output_SBM_pc = score_based_match(predict_df_search = predict_df.copy(), ref_search = ref_df_after_stand.copy(),
orig_search_df = search_df_after_stand, matching_variables = matching_variables,
text_columns = text_columns, blocker_column = blocker_column, weights = weights, fuzzy_method = fuzzy_method, score_cut_off = score_cut_off, search_df_key_field=search_df_key_field, standardise=standardise, new_join_col=new_join_col)
if matched_output_SBM_pc.empty:
error_message = "Match results empty. Exiting neural net match."
print(error_message)
return pd.DataFrame(),pd.DataFrame(), error_message, predict_df
else:
matched_output_SBM_pc["match_method"] = "Neural net - Postcode"
match_results_output_final_pc = combine_std_df_remove_dups(match_results, matched_output_SBM_pc, orig_addr_col = search_df_key_field)
summary_pc = create_match_summary(match_results_output_final_pc, df_name = "NNet blocked by Postcode " + df_name)
print(summary_pc)
## Run with Street as blocker column
blocker_column = ["Street"]
scoresSBM_best_st, matched_output_SBM_st = score_based_match(predict_df_search = predict_df.copy(), ref_search = ref_df_after_stand.copy(),
orig_search_df = search_df_after_stand, matching_variables = matching_variables,
text_columns = text_columns, blocker_column = blocker_column, weights = weights, fuzzy_method = fuzzy_method, score_cut_off = score_cut_off, search_df_key_field=search_df_key_field, standardise=standardise, new_join_col=new_join_col)
# If no matching pairs are found in the function above then it returns 0 - below we replace these values with the postcode blocker values (which should almost always find at least one pair unless it's a very unusual situation)
if (type(matched_output_SBM_st) == int) | matched_output_SBM_st.empty:
print("Nothing to match for street block")
matched_output_SBM_st = matched_output_SBM_pc
matched_output_SBM_st["match_method"] = "Neural net - Postcode" #+ standard_label
else: matched_output_SBM_st["match_method"] = "Neural net - Street" #+ standard_label
### Join together old match df with new (model) match df
match_results_output_final_st = combine_std_df_remove_dups(match_results_output_final_pc,matched_output_SBM_st, orig_addr_col = search_df_key_field)
summary_street = create_match_summary(match_results_output_final_st, df_name = "NNet blocked by Street " + df_name)
print(summary_street)
# I decided in the end not to use PaoStartNumber as a blocker column. I get only a couple more matches in general for a big increase in processing time
matched_output_SBM_po = matched_output_SBM_st
matched_output_SBM_po["match_method"] = "Neural net - Street" #+ standard_label
match_results_output_final_po = match_results_output_final_st
match_results_output_final_three = match_results_output_final_po
summary_three = create_match_summary(match_results_output_final_three, df_name = "fuzzy and nn model street + postcode " + df_name)
### Join URPN back onto orig df
if type(search_df) != str:
results_on_orig_df = join_to_orig_df(match_results_output_final_three, search_df_after_stand, search_df_key_field, new_join_col)
else: results_on_orig_df = match_results_output_final_three
return match_results_output_final_three, results_on_orig_df, summary_three, predict_df
# Combiner/summary functions
def combine_std_df_remove_dups(df_not_std, df_std, orig_addr_col = "search_orig_address", match_address_series = "full_match", keep_only_duplicated = False):
if (df_not_std.empty) & (df_std.empty):
return df_not_std
combined_std_not_matches = pd.concat([df_not_std, df_std])#, ignore_index=True)
if combined_std_not_matches.empty: #| ~(match_address_series in combined_std_not_matches.columns) | ~(orig_addr_col in combined_std_not_matches.columns):
combined_std_not_matches[match_address_series] = False
if "full_address" in combined_std_not_matches.columns:
combined_std_not_matches[orig_addr_col] = combined_std_not_matches["full_address"]
combined_std_not_matches["fuzzy_score"] = 0
return combined_std_not_matches
combined_std_not_matches = combined_std_not_matches.sort_values([orig_addr_col, match_address_series], ascending=False)
if keep_only_duplicated == True:
combined_std_not_matches = combined_std_not_matches[combined_std_not_matches.duplicated(orig_addr_col)]
combined_std_not_matches_no_dups = combined_std_not_matches.drop_duplicates(orig_addr_col).sort_index()
return combined_std_not_matches_no_dups
def combine_two_matches(OrigMatchClass, NewMatchClass, df_name):
today_rev = datetime.now().strftime("%Y%m%d")
NewMatchClass.match_results_output = combine_std_df_remove_dups(OrigMatchClass.match_results_output, NewMatchClass.match_results_output, orig_addr_col = NewMatchClass.search_df_key_field)
NewMatchClass.results_on_orig_df = combine_std_df_remove_dups(OrigMatchClass.pre_filter_search_df, NewMatchClass.results_on_orig_df, orig_addr_col = NewMatchClass.search_df_key_field, match_address_series = 'Matched with reference address')
# Filter out search results where a match was found
NewMatchClass.pre_filter_search_df = NewMatchClass.results_on_orig_df
found_index = NewMatchClass.results_on_orig_df.loc[NewMatchClass.results_on_orig_df["Matched with reference address"] == True, NewMatchClass.search_df_key_field].astype(int)
#print(found_index)[NewMatchClass.search_df_key_field]
key_field_values = NewMatchClass.search_df_not_matched[NewMatchClass.search_df_key_field].astype(int) # Assuming list conversion is suitable
rows_to_drop = key_field_values[key_field_values.isin(found_index)].tolist()
NewMatchClass.search_df_not_matched = NewMatchClass.search_df_not_matched.loc[~NewMatchClass.search_df_not_matched[NewMatchClass.search_df_key_field].isin(rows_to_drop),:]#.drop(rows_to_drop, axis = 0)
# Filter out rows from NewMatchClass.search_df_cleaned
filtered_rows_to_keep = NewMatchClass.search_df_cleaned[NewMatchClass.search_df_key_field].astype(int).isin(NewMatchClass.search_df_not_matched[NewMatchClass.search_df_key_field].astype(int)).to_list()
NewMatchClass.search_df_cleaned = NewMatchClass.search_df_cleaned.loc[filtered_rows_to_keep,:]#.drop(rows_to_drop, axis = 0)
NewMatchClass.search_df_after_stand = NewMatchClass.search_df_after_stand.loc[filtered_rows_to_keep,:]#.drop(rows_to_drop)
NewMatchClass.search_df_after_full_stand = NewMatchClass.search_df_after_full_stand.loc[filtered_rows_to_keep,:]#.drop(rows_to_drop)
### Create lookup lists
NewMatchClass.search_df_after_stand_series = NewMatchClass.search_df_after_stand.copy().set_index('postcode_search')['search_address_stand'].str.lower().str.strip()
NewMatchClass.search_df_after_stand_series_full_stand = NewMatchClass.search_df_after_full_stand.copy().set_index('postcode_search')['search_address_stand'].str.lower().str.strip()
match_results_output_match_score_is_0 = NewMatchClass.match_results_output[NewMatchClass.match_results_output['fuzzy_score']==0.0][["index", "fuzzy_score"]].drop_duplicates(subset='index')
match_results_output_match_score_is_0["index"] = match_results_output_match_score_is_0["index"].astype(str)
#NewMatchClass.results_on_orig_df["index"] = NewMatchClass.results_on_orig_df["index"].astype(str)
NewMatchClass.results_on_orig_df = NewMatchClass.results_on_orig_df.merge(match_results_output_match_score_is_0, on = "index", how = "left")
NewMatchClass.results_on_orig_df.loc[NewMatchClass.results_on_orig_df["fuzzy_score"] == 0.0, "Excluded from search"] = "Match score is 0"
NewMatchClass.results_on_orig_df = NewMatchClass.results_on_orig_df.drop("fuzzy_score", axis = 1)
# Drop any duplicates, prioritise any matches
NewMatchClass.results_on_orig_df = NewMatchClass.results_on_orig_df.sort_values(by=["index", "Matched with reference address"], ascending=[True,False]).drop_duplicates(subset="index")
NewMatchClass.output_summary = create_match_summary(NewMatchClass.match_results_output, df_name = df_name)
print(NewMatchClass.output_summary)
NewMatchClass.search_df_not_matched = filter_not_matched(NewMatchClass.match_results_output, NewMatchClass.search_df, NewMatchClass.search_df_key_field)
### Rejoin the excluded matches onto the output file
#NewMatchClass.results_on_orig_df = pd.concat([NewMatchClass.results_on_orig_df, NewMatchClass.excluded_df])
NewMatchClass.match_outputs_name = "match_results_output_std_" + today_rev + ".csv" # + NewMatchClass.file_name + "_"
NewMatchClass.results_orig_df_name = "results_on_orig_df_std_" + today_rev + ".csv" # + NewMatchClass.file_name + "_"
# Only keep essential columns
essential_results_cols = [NewMatchClass.search_df_key_field, "Excluded from search", "Matched with reference address", "ref_index", "Reference matched address", "Reference file"]
essential_results_cols.extend(NewMatchClass.new_join_col)
NewMatchClass.match_results_output.to_csv(NewMatchClass.match_outputs_name, index = None)
NewMatchClass.results_on_orig_df[essential_results_cols].to_csv(NewMatchClass.results_orig_df_name, index = None)
return NewMatchClass
def create_match_summary(match_results_output:PandasDataFrame, df_name:str):
# Check if match_results_output is a dictionary-like object and has the key 'full_match'
if not isinstance(match_results_output, dict) or 'full_match' not in match_results_output or (len(match_results_output) == 0):
"Nothing in match_results_output"
full_match_count = 0
match_fail_count = 0
records_attempted = 0
dataset_length = 0
records_not_attempted = 0
match_rate = 0
match_fail_count_without_excluded = 0
match_fail_rate = 0
not_attempted_rate = 0
''' Create a summary paragraph '''
full_match_count = match_results_output['full_match'][match_results_output['full_match'] == True].count()
match_fail_count = match_results_output['full_match'][match_results_output['full_match'] == False].count()
records_attempted = int(sum((match_results_output['fuzzy_score']!=0.0) & ~(match_results_output['fuzzy_score'].isna())))
dataset_length = len(match_results_output["full_match"])
records_not_attempted = int(dataset_length - records_attempted)
match_rate = str(round((full_match_count / dataset_length) * 100,1))
match_fail_count_without_excluded = match_fail_count - records_not_attempted
match_fail_rate = str(round(((match_fail_count_without_excluded) / dataset_length) * 100,1))
not_attempted_rate = str(round((records_not_attempted / dataset_length) * 100,1))
summary = ("For the " + df_name + " dataset (" + str(dataset_length) + " records), the fuzzy matching algorithm successfully matched " + str(full_match_count) +
" records (" + match_rate + "%). The algorithm could not attempt to match " + str(records_not_attempted) +
" records (" + not_attempted_rate + "%). There are " + str(match_fail_count_without_excluded) + " records left to potentially match.")
return summary
|