diff --git a/Daten/Katalogabgleich/Kandidaten/no_BE_Kandidaten.xlsx b/Daten/Katalogabgleich/Kandidaten/no_BE_Kandidaten.xlsx
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diff --git a/Notebooks/Non_BE_matching.ipynb b/Notebooks/Non_BE_matching.ipynb
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@@ -0,0 +1,207 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "a910c4c5-3a61-462b-ac07-c9545fe7ae40",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import re\n",
+ "import numpy as np\n",
+ "from thefuzz import fuzz, process\n",
+ "from tqdm.notebook import tqdm\n",
+ "import matplotlib.pyplot as plt\n",
+ "import requests\n",
+ "import json\n",
+ "from IPython.display import display\n",
+ "\n",
+ "pd.set_option('display.max_colwidth', None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "83013484-2a55-4819-8b30-b2f8cbbe7981",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "entry_df = pd.read_excel('../Daten/Katalogabgleich/Einträge.xlsx', index_col=0)\n",
+ "\n",
+ "def prepare_string(string):\n",
+ " new = re.sub(r'[à áâãå]', 'a', string)\n",
+ " new = re.sub(r'[èéêë]', 'e', new)\n",
+ " new = re.sub(r'[ìÃîï]', 'i', new)\n",
+ " new = re.sub(r'[òóôõ]', 'o', new)\n",
+ " new = re.sub(r'[ùúû]', 'u', new)\n",
+ " new = re.sub(r'æ', 'ae', new)\n",
+ " new = re.sub('[.,:;()¬]|^[CLXVI]+? |^\\d+? |^\\d+?\\.+? |^\\.+ ?|= |# ', '', new)\n",
+ " return new\n",
+ "\n",
+ "entry_df['cleaned entry'] = entry_df['entry'].apply(lambda x: prepare_string(x))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "1279d6ea-48a2-4f65-9cfa-b1f92eac16f1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "non_BE_df = pd.read_excel('../Daten/Vorhersagen/WIP_final_BE_3.xlsx', index_col=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "ddf5d11c-5f72-4bc8-ab8f-0a1e0f01e60d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# fix 22871 without metadata\n",
+ "i = 22871\n",
+ "non_BE_df.at[i, 'Titel'] = 'De La coronica general de toda Espana y especialmente del Reyno de Valencia. etc'\n",
+ "non_BE_df.at[i, 'Autor'] = 'Beuter, Pero-Anton'\n",
+ "non_BE_df.at[i, 'Mitwirkender'] = ''\n",
+ "non_BE_df.at[i, 'Anfang Veröffentlichungsdatum'] = '1546'\n",
+ "non_BE_df.at[i, 'Ende Veröffentlichungsdatum'] = '1551'\n",
+ "non_BE_df.at[i, 'Veröffentlichungsdatum'] = '1546-1551'\n",
+ "non_BE_df.at[i, 'Veröffentlichungsort'] = 'Valencia'\n",
+ "non_BE_df.at[i, 'Veröffentlichungsort (normiert)'] = 'Valencia'\n",
+ "non_BE_df.at[i, 'Sprache'] = 'Spanish'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "e9c00ca9-c051-4e3a-93cf-133031ca9e7f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "no_BE = non_BE_df[~(non_BE_df['Signatur'].str.contains('BE') | non_BE_df['Signatur'].str.contains('Ink'))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "ca242a1c-baf8-4183-a565-a3797d6f4747",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a7b5d48f5fcd4cbbbf56291a871746c5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/804 [00:00<?, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "better_matches = []\n",
+ "scorer = fuzz.token_set_ratio\n",
+ "\n",
+ "for index, row in tqdm(no_BE.iterrows(), total=len(no_BE)):\n",
+ " keys = ['Autor', 'Mitwirkender', 'Titel', 'Veröffentlichungsort', 'Anfang Veröffentlichungsdatum']\n",
+ " comb_string = ''\n",
+ " for key in keys:\n",
+ " val = row[key]\n",
+ " if not pd.isna(val):\n",
+ " if key == 'Autor' or key == 'Mitwirkender':\n",
+ " if ',' in val: # falls name, vorname\n",
+ " val = val.split(',')[0]\n",
+ " val = val.split(' ')[0]\n",
+ " elif key == 'Titel':\n",
+ " val = prepare_string(val)\n",
+ " elif key == 'Anfang Veröffentlichungsdatum':\n",
+ " val = str(int(val))\n",
+ " else: # key == 'Veröffentlichungsort'\n",
+ " pass\n",
+ " comb_string += val + ' '\n",
+ " \n",
+ " matches_lis = process.extract(comb_string, entry_df['cleaned entry'], scorer=scorer, limit=5)\n",
+ " flat_matches = []\n",
+ " for match in matches_lis:\n",
+ " flat_matches.append(match[0])\n",
+ " flat_matches.append(match[1])\n",
+ " flat_matches.append(match[2])\n",
+ " better_matches.append([comb_string] + flat_matches)\n",
+ "\n",
+ "matches_df = pd.DataFrame(better_matches, columns=['input', 'match_1', 'score_1', 'id_1', 'match_2', 'score_2', 'id_2', 'match_3', 'score_3', 'id_3', 'match_4', 'score_4', 'id_4', 'match_5', 'score_5', 'id_5'])\n",
+ "matches_df['control'] = ''"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "e9dd6e64-a45d-4f25-9ad1-624cfc5268fb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "matches_df_no_score = matches_df.drop(['score_1', 'score_2', 'score_3', 'score_4', 'score_5'], axis=1)\n",
+ "matches_df_no_score.insert(1, 'input_id', no_BE.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "b1f7e1a3-b886-496c-a9ca-ec484d196c24",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "matches_df_no_score.to_excel('../Daten/Katalogabgleich/Kandidaten/no_BE_Kandidaten.xlsx', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a4639759-8344-452e-96ce-cfca485165a4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}