From cf56642a69e875c0509edf72c3251b299d957775 Mon Sep 17 00:00:00 2001 From: serta Date: Wed, 14 Jan 2026 10:26:04 +0000 Subject: [PATCH 1/6] German prompt added --- codauto/prompt_text.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/codauto/prompt_text.py b/codauto/prompt_text.py index cb4c64b..037d737 100644 --- a/codauto/prompt_text.py +++ b/codauto/prompt_text.py @@ -47,4 +47,11 @@ Texto: """ +promt_data_de = """ +Stell dir vor, du bist ein Gewerbetreibender und deine Tätigkeit fällt in diese Klasse rein . +Schreibe etwa 50 fiktive Tätigkeiten. Und zeige lediglich die Tätigkeiten sonst nichts kurz und knapp. +In der genannten Klasse sind folgende Tätigkeiten eingeschlossen: <includes>, die berücksichtigt werden können. +Diese Tätigkeiten sind ausgeschlossen: <excludes>. +""" + From 81d8efe4cd22c638265bd61a90986d230763a2b0 Mon Sep 17 00:00:00 2001 From: serta <aleyna.sert@destatis.de> Date: Mon, 26 Jan 2026 08:00:52 +0000 Subject: [PATCH 2/6] Adaptation of spanish prompt to german requirements --- codauto/prompt_text.py | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/codauto/prompt_text.py b/codauto/prompt_text.py index 037d737..35cc374 100644 --- a/codauto/prompt_text.py +++ b/codauto/prompt_text.py @@ -48,10 +48,28 @@ """ promt_data_de = """ -Stell dir vor, du bist ein Gewerbetreibender und deine Tätigkeit fällt in diese Klasse rein <title>. -Schreibe etwa 50 fiktive Tätigkeiten. Und zeige lediglich die Tätigkeiten sonst nichts kurz und knapp. -In der genannten Klasse sind folgende Tätigkeiten eingeschlossen: <includes>, die berücksichtigt werden können. -Diese Tätigkeiten sind ausgeschlossen: <excludes>. +Eres un experto en generación de datos de entrenamiento para modelos de clasificación. +Tu tarea es tomar una frase raíz de una actividad económica general y generar frases de entrenamiento a partir de ejemplos concretos incluidos en un texto. +Cuando corresponda, procura introducir la palabra 'otras' u 'otros' y también el uso o destino del producto. +Siempre que sea fabricación de un objeto, incluye el material con el que se hace. +Evita generar frases completamente distintas o que cambien el contexto y devuelve ÚNICAMENTE las frases sin presentaciones, comentarios, explicaciones ni frases adicionales. +Evita introducir tu output con frases como 'aquí te dejo las frases generadas' o similares. +Quiero utilizar directamente tu output así que SOLO quiero las frases. +Aquí tienes un ejemplo: +Raiz: Elaboración de productos de panadería y pastelería. +Texto: Esta clase incluye la fabricación de cualquier producto de panadería como: barras de pan, pasteles, otras harinas y pan para pienso. +Salida: +Elaboración de barras de pan. +Elaboración de pan para pienso. +roducción de productos de pasteles. +Elaboración de otras harinas. +Ahora hazlo tú con la siguiente raíz y texto: +Raiz: <title> +Texto: <includes>. + +Erstelle je Klasse insgesamt genau 50 deutsche Daten. Ein drittel der erzeugten Daten pro Klasse sollen detaillierte Beschreibung aus Sicht des Gewerbetreibenden beinhalten. +Ein Beispiel: Wir sind ein Betrieb und arbeiten im Garten- und Landschaftsbau. Wir pflanzen Schnittblumen zu jeder Jahreszeit für den Verkauf an Floristen an. Beachte, was in der Klasse ein- und auschlossen ist. +Ändere nicht die Tätigkeit der vorgegebenen Klasse. """ From 11f134984583430d206bfd84d20ff2a3e77bd061 Mon Sep 17 00:00:00 2001 From: suweg <onyxia@jupyter-python-338962-0.jupyter-python-338962.projet-aiml4os-wp10.svc.cluster.local> Date: Fri, 6 Feb 2026 11:35:27 +0000 Subject: [PATCH 3/6] Created dummy demo notebook and started with WZ --- jupyter_notebooks/DummyCodifierDemo.ipynb | 455 ++++++++++++++++++++++ jupyter_notebooks/German_NACE.ipynb | 417 ++++++++++++++++++++ jupyter_notebooks/test/test | Bin 0 -> 489 bytes 3 files changed, 872 insertions(+) create mode 100644 jupyter_notebooks/DummyCodifierDemo.ipynb create mode 100644 jupyter_notebooks/German_NACE.ipynb create mode 100644 jupyter_notebooks/test/test diff --git a/jupyter_notebooks/DummyCodifierDemo.ipynb b/jupyter_notebooks/DummyCodifierDemo.ipynb new file mode 100644 index 0000000..357d878 --- /dev/null +++ b/jupyter_notebooks/DummyCodifierDemo.ipynb @@ -0,0 +1,455 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "id": "3d4c2db3-c578-45be-b59d-f5e77a3a358e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import sys\n", + "import re\n", + "import pickle\n", + "from sklearn.dummy import DummyClassifier\n", + "sys.path.append(\"/home/onyxia/work/WP10_Cluster1_StatCodGen/codauto\")\n", + "from structured import Structured\n", + "from codifier import Codifier" + ] + }, + { + "cell_type": "markdown", + "id": "08cbff7e-66e8-48b0-9e9e-38d4509df7d9", + "metadata": {}, + "source": [ + "# Dummy Codifier Demo" + ] + }, + { + "cell_type": "markdown", + "id": "3f5c9b6e-9fe8-4f46-ba99-8b39badddaaf", + "metadata": {}, + "source": [ + "## Mock-up Structure" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0371f787-7e5e-4284-85fe-31e1b8712ff4", + "metadata": {}, + "outputs": [], + "source": [ + "class StructuredMockup(Structured): \n", + " \"\"\"\n", + " Structured hierarchy for demo purposes\n", + "\n", + " Levels are determined strictly by the length of the classification code.\n", + " \n", + " \"\"\"\n", + " def get_level(self, code): # Structured child class specific to WZ\n", + " \"\"\"\n", + " Structured hierarchy for German WZ\n", + " \n", + " Parameters\n", + " ----------\n", + " code : str\n", + " WZ classification code.\n", + " \n", + " Returns\n", + " -------\n", + " int\n", + " Hierarchical level based on code length\n", + " \"\"\"\n", + " code = str(code).replace('.', '') \n", + " return len(code)-1" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "3ffd38b2-0a81-427b-82ea-4df93293ff34", + "metadata": {}, + "outputs": [], + "source": [ + "# The dummy classification with one level and three classes\n", + "df = pd.DataFrame(data=\n", + " {\"code\": ['1', '2', '3', '4', '5'], \n", + " \"text\": ['Wohnen', 'Aktivitaet', 'Nahrungsmittel', 'Fahrzeug', 'Sonstiges']}\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "18a08e7e-70c1-44cb-921d-698f7de07c6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'Class'}" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Generate the instance of StructuredMockup\n", + "mock_structure = StructuredMockup(\n", + " structure_df=df,\n", + " names_l=['Class']\n", + ")\n", + "mock_structure.level_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "d63b1301-6e1c-4a32-8e8c-e0411f389662", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>code</th>\n", + " <th>text</th>\n", + " <th>level</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1</td>\n", + " <td>Wohnen</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>2</td>\n", + " <td>Aktivitaet</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>3</td>\n", + " <td>Nahrungsmittel</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>4</td>\n", + " <td>Fahrzeug</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>5</td>\n", + " <td>Sonstiges</td>\n", + " <td>0</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " code text level\n", + "0 1 Wohnen 0\n", + "1 2 Aktivitaet 0\n", + "2 3 Nahrungsmittel 0\n", + "3 4 Fahrzeug 0\n", + "4 5 Sonstiges 0" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load to check the structure\n", + "mus = mock_structure.load_structue(df)\n", + "mus" + ] + }, + { + "cell_type": "markdown", + "id": "3fe5e180-eb48-4dac-a116-f655ed5a2275", + "metadata": {}, + "source": [ + "## Dummy Codifier" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "6a350cc6-3c87-42df-bed1-78b76a2ea1eb", + "metadata": {}, + "outputs": [], + "source": [ + "# Now we create a Codifier's son with a method that always predicts the majority class and assigns 0/1 confidence scores.\n", + "class CodifierDummy(Codifier):\n", + " def save(self, name):\n", + " with open(os.path.join(self.root_path, name),'wb') as f:\n", + " pickle.dump(self.model,f)\n", + " \n", + " def load(self, name):\n", + " with open(os.path.join(self.root_path, name),'rb') as f:\n", + " self.model = pickle.load(f)\n", + " \n", + " def train(self, **kwargs):\n", + " train_set = kwargs.get('train_set', self.train_df)\n", + " train_data = self.get_train_dataset(train_set, name='test')\n", + " \n", + " dummy_clf = DummyClassifier(strategy=\"most_frequent\")\n", + " self.model = dummy_clf.fit(train_data['text'], train_data['label'])\n", + " \n", + " def get_pred_for_batch(self, samples, idxs, clean_samples=False):\n", + " \"\"\"\n", + " Generate hierarchical predictions for a batch of samples with random probabilities,\n", + " ensuring that upper levels and their children are consistently sorted.\n", + " \n", + " Parameters\n", + " ----------\n", + " samples : list of str\n", + " Text samples to predict.\n", + " idxs : list of int\n", + " Indices corresponding to each sample.\n", + " clean_samples : bool, optional (default=False)\n", + " Whether to preprocess the samples before prediction.\n", + " \n", + " Returns\n", + " -------\n", + " dict\n", + " Dictionary mapping each index to a hierarchical prediction with sorted labels and confidences.\n", + " \"\"\"\n", + " # if clean_samples:\n", + " # samples = [preprocess_text(s) for s in samples]\n", + "\n", + " last_level_labels = list(self.structure.reversed_hierarchy.keys())\n", + "\n", + " results = dict()\n", + "\n", + " preds_raw = self.model.predict(samples)\n", + " probs = self.model.predict_proba(samples)\n", + "\n", + " for idx in idxs:\n", + " results[idx] = {}\n", + " results[idx][f'label_{self.structure.level_dict[0]}'] = last_level_labels\n", + " results[idx][f'conf_{self.structure.level_dict[0]}'] = probs\n", + "\n", + " return results " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "0c4e37a2-13ad-4419-ad63-3a781a92af0a", + "metadata": {}, + "outputs": [], + "source": [ + "# Train and test data for the mockup structure\n", + "train_df = pd.DataFrame({'label': ['1', '2', '3', '4', '2', '5'], \n", + " 'text': ['Huette', 'schwimmen', 'Pizza', 'Bus', 'schreiben', 'blabla']}\n", + " ) \n", + "test_df = pd.DataFrame({'label': ['1', '2', '3', '4'], \n", + " 'text': ['Villa', 'rennen', 'Nudeln', 'Auto'], 'source': ['s1', 's1', 's1', 's1']}\n", + " ) " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "36896ceb-74d3-4348-8a1f-d85b9d44bd46", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-06 11:20:50,611 - INFO - Loading train_df..\n", + "2026-02-06 11:20:50,612 - INFO - train_df raw data count: 6\n", + "2026-02-06 11:20:50,615 - INFO - train_df bad codes count: 0\n", + "2026-02-06 11:20:50,616 - INFO - train_df bad descriptions count: 0\n", + "2026-02-06 11:20:50,618 - INFO - train_df pruned data count: 6\n", + "2026-02-06 11:20:50,620 - INFO - Loading test_df..\n", + "2026-02-06 11:20:50,621 - INFO - test_df raw data count: 4\n", + "2026-02-06 11:20:50,625 - INFO - test_df bad codes count: 0\n", + "2026-02-06 11:20:50,625 - INFO - test_df bad descriptions count: 0\n", + "2026-02-06 11:20:50,627 - INFO - test_df pruned data count: 4\n" + ] + } + ], + "source": [ + "cd = CodifierDummy(\n", + " structure_instance=mock_structure, \n", + " train_df=train_df, \n", + " test_df=test_df, \n", + " root_path=\"./test\", \n", + " min_lenght_texts=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "1a7a11ee-3a9b-46c8-b6f2-669a377d1066", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-06 11:20:52,912 - INFO - Loading test..\n", + "2026-02-06 11:20:52,913 - INFO - test raw data count: 6\n", + "2026-02-06 11:20:52,917 - INFO - test bad codes count: 0\n", + "2026-02-06 11:20:52,919 - INFO - test bad descriptions count: 0\n", + "2026-02-06 11:20:52,923 - INFO - test pruned data count: 6\n" + ] + } + ], + "source": [ + "# Train and save a model or load a pretrained one\n", + "\n", + "cd.train()\n", + "cd.save(name='test')\n", + "#cd.load(name='test')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "ac43a42a-b1a0-4352-983b-dd544e0cad12", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: {'label_Class': ['1', '2', '3', '4', '5'],\n", + " 'conf_Class': array([[0., 1., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0.]])},\n", + " 1: {'label_Class': ['1', '2', '3', '4', '5'],\n", + " 'conf_Class': array([[0., 1., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0.]])}}" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Predict some samples in batch mode\n", + "\n", + "samples = [\"tanzen\", \"Flugzeug\"]\n", + "idxs = [0, 1]\n", + "\n", + "cd.get_pred_for_batch(samples=samples, idxs=idxs)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "b4459196-7d68-48a4-93ce-9623c0b17e7f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-06 11:09:44,096 - INFO - Test set size 4 for source all\n", + "2026-02-06 11:09:44,099 - INFO - test_set_all has 1 codes unrepresented\n", + "Predicting test_set for 1 classes: 100%|██████████| 4/4 [00:00<00:00, 2250.77it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Class': {'accuracy': 0.25,\n", + " 'f1_score': {'micro': 0.25, 'macro': 0.1, 'weighted': 0.1}}}" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cd.evaluate(version='example', get_curve=False) # Some errors when producing the curve" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "99e42d01-1e59-475f-9717-c747c7aee414", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Per-column arrays must each be 1-dimensional", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[65]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdesc_l\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtest\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mblah\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mblubb\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcodification\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mhierarchical_level\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0\u001b[39;49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/work/WP10_Cluster1_StatCodGen/codauto/codifier.py:967\u001b[39m, in \u001b[36mCodifier.predict\u001b[39m\u001b[34m(self, desc_l, mode, hierarchical_level, threshold, original_code_l, identifier_l)\u001b[39m\n\u001b[32m 957\u001b[39m raw_predictions = \u001b[38;5;28mself\u001b[39m.get_pred_for_batch(\n\u001b[32m 958\u001b[39m desc_l_not_direct_recoding,\n\u001b[32m 959\u001b[39m idxs_not_direct_recoding,\n\u001b[32m 960\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 961\u001b[39m )\n\u001b[32m 963\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m idx, original_code \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[32m 964\u001b[39m idxs_not_direct_recoding,\n\u001b[32m 965\u001b[39m original_code_l_not_direct_recoding\n\u001b[32m 966\u001b[39m ):\n\u001b[32m--> \u001b[39m\u001b[32m967\u001b[39m pred_df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 968\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mlabels\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mraw_predictions\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\n\u001b[32m 969\u001b[39m \u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mlabel_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mhierarchical_level_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\n\u001b[32m 970\u001b[39m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 971\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mraw_confs\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mraw_predictions\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\n\u001b[32m 972\u001b[39m \u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mconf_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mhierarchical_level_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\n\u001b[32m 973\u001b[39m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 974\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mconfs\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 975\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 976\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m original_code \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 977\u001b[39m pred_df_filtered = pred_df.copy()\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/pandas/core/frame.py:782\u001b[39m, in \u001b[36mDataFrame.__init__\u001b[39m\u001b[34m(self, data, index, columns, dtype, copy)\u001b[39m\n\u001b[32m 776\u001b[39m mgr = \u001b[38;5;28mself\u001b[39m._init_mgr(\n\u001b[32m 777\u001b[39m data, axes={\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m: index, \u001b[33m\"\u001b[39m\u001b[33mcolumns\u001b[39m\u001b[33m\"\u001b[39m: columns}, dtype=dtype, copy=copy\n\u001b[32m 778\u001b[39m )\n\u001b[32m 780\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[32m 781\u001b[39m \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m782\u001b[39m mgr = \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma.MaskedArray):\n\u001b[32m 784\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mma\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m mrecords\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/pandas/core/internals/construction.py:503\u001b[39m, in \u001b[36mdict_to_mgr\u001b[39m\u001b[34m(data, index, columns, dtype, typ, copy)\u001b[39m\n\u001b[32m 499\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 500\u001b[39m \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[32m 501\u001b[39m arrays = [x.copy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[33m\"\u001b[39m\u001b[33mdtype\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[32m--> \u001b[39m\u001b[32m503\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/pandas/core/internals/construction.py:114\u001b[39m, in \u001b[36marrays_to_mgr\u001b[39m\u001b[34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[39m\n\u001b[32m 111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m verify_integrity:\n\u001b[32m 112\u001b[39m \u001b[38;5;66;03m# figure out the index, if necessary\u001b[39;00m\n\u001b[32m 113\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m114\u001b[39m index = \u001b[43m_extract_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 115\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 116\u001b[39m index = ensure_index(index)\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/pandas/core/internals/construction.py:664\u001b[39m, in \u001b[36m_extract_index\u001b[39m\u001b[34m(data)\u001b[39m\n\u001b[32m 662\u001b[39m raw_lengths.append(\u001b[38;5;28mlen\u001b[39m(val))\n\u001b[32m 663\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(val, np.ndarray) \u001b[38;5;129;01mand\u001b[39;00m val.ndim > \u001b[32m1\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m664\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mPer-column arrays must each be 1-dimensional\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 666\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m indexes \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m raw_lengths:\n\u001b[32m 667\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mIf using all scalar values, you must pass an index\u001b[39m\u001b[33m\"\u001b[39m)\n", + "\u001b[31mValueError\u001b[39m: Per-column arrays must each be 1-dimensional" + ] + } + ], + "source": [ + "cd.predict(desc_l=['test', 'blah', 'blubb'],\n", + " mode='codification',\n", + " hierarchical_level=1,\n", + " threshold=0\n", + " )" + ] + } + ], + "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": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter_notebooks/German_NACE.ipynb b/jupyter_notebooks/German_NACE.ipynb new file mode 100644 index 0000000..c6f1050 --- /dev/null +++ b/jupyter_notebooks/German_NACE.ipynb @@ -0,0 +1,417 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 62, + "id": "e713189e-3555-4fb9-9af2-337c511cce40", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import sys\n", + "import re\n", + "import pickle\n", + "sys.path.append(\"/home/onyxia/work/WP10_Cluster1_StatCodGen/codauto\")\n", + "from structured import Structured\n", + "from codifier import Codifier" + ] + }, + { + "cell_type": "markdown", + "id": "8a8e3891-bb95-48b9-9bea-44dc1f6ca0e5", + "metadata": {}, + "source": [ + "# Using Structured for German NACE" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "bf5a1bf4-204b-49f7-9645-ef09ebd9a66b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/python/lib/python3.13/site-packages/openpyxl/styles/stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default\n", + " warn(\"Workbook contains no default style, apply openpyxl's default\")\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Schlüssel WZ 2025</th>\n", + " <th>Titel</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A</td>\n", + " <td>Land- und Forstwirtschaft, Fischerei</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>01</td>\n", + " <td>Landwirtschaft, Jagd und damit verbundene Täti...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>01.1</td>\n", + " <td>Anbau ein- und zweijähriger Pflanzen</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>01.11</td>\n", + " <td>Anbau von anderem Getreide als Reis sowie von ...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>01.11.0</td>\n", + " <td>Anbau von anderem Getreide als Reis sowie von ...</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Schlüssel WZ 2025 Titel\n", + "0 A Land- und Forstwirtschaft, Fischerei\n", + "1 01 Landwirtschaft, Jagd und damit verbundene Täti...\n", + "2 01.1 Anbau ein- und zweijähriger Pflanzen\n", + "3 01.11 Anbau von anderem Getreide als Reis sowie von ...\n", + "4 01.11.0 Anbau von anderem Getreide als Reis sowie von ..." + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the structure downloaded from the link provided earlier\n", + "path_struc_wz = r'./../resources'#/WP10_Cluster1_StatCodGen/de/wz_classification/' # Change the path to the location of the downloaded file\n", + "name_stuc_wz = 'WZ_2025-DE-2026-01-27-Gliederung.xlsx'\n", + "structure_wz_df = pd.read_excel(\n", + " os.path.join(path_struc_wz, name_stuc_wz),\n", + " sheet_name=1,\n", + " dtype=str,\n", + " skiprows=0,\n", + " header=0,\n", + " usecols=[0,2]\n", + ")\n", + "# For any classification, a DataFrame with two columns must be used: the first with the classification codes and the second with the titles.\n", + "# The codes must be provided in hierarchical order, meaning that parent classes must appear before their children.\n", + "structure_wz_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "91b28796-d13b-4e88-a47d-04f786656540", + "metadata": {}, + "outputs": [], + "source": [ + "# class StructuredWZ(Structured): \n", + "# \"\"\"\n", + "# Structured hierarchy for German NACE version called WZ (Wirtschaftszweigklassifikation)\n", + "\n", + "# Levels are determined strictly by the length of the classification code.\n", + " \n", + "# \"\"\"\n", + "# def get_level(self, code): # Structured child class specific to WZ\n", + "# \"\"\"\n", + "# Structured hierarchy for German WZ\n", + " \n", + "# Parameters\n", + "# ----------\n", + "# code : str\n", + "# WZ classification code.\n", + " \n", + "# Returns\n", + "# -------\n", + "# int\n", + "# Hierarchical level based on code length\n", + "# \"\"\"\n", + "# code = str(code).replace('.', '') \n", + "# return len(code)-1" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "f77b53df-56c1-4ec9-9347-d2ab6aae7e95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'Section', 1: 'Division', 2: 'Group', 3: 'Class', 4: 'Subclass'}" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Generate the instance of StructuredWZ\n", + "wz_structure = StructuredWZ(\n", + " structure_df=structure_wz_df,\n", + " names_l=[\n", + " 'Section',\n", + " 'Division',\n", + " 'Group',\n", + " 'Class',\n", + " 'Subclass'\n", + " ] # Names of the levels ordered from highest to lowest hierarchy\n", + "# As a best practice, it is recommended to use underscores instead of spaces and to avoid accents\n", + ")\n", + "wz_structure.level_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "3b753c70-fbd7-4a04-9f87-fa308af12edb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wz_structure.get_level('01.1')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "da669261-ae19-40c5-8276-13a8526eb088", + "metadata": {}, + "outputs": [], + "source": [ + "wzs = wz_structure.load_structue(structure_wz_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "f799e353-7204-4bf2-a280-9e7453b3d8d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Schlüssel WZ 2025</th>\n", + " <th>Titel</th>\n", + " <th>level</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>01</td>\n", + " <td>Landwirtschaft, Jagd und damit verbundene Täti...</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>78</th>\n", + " <td>02</td>\n", + " <td>Forstwirtschaft und Holzeinschlag</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>91</th>\n", + " <td>03</td>\n", + " <td>Fischerei und Aquakultur</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>106</th>\n", + " <td>05</td>\n", + " <td>Kohlenbergbau</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>113</th>\n", + " <td>06</td>\n", + " <td>Gewinnung von Erdöl und Erdgas</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1957</th>\n", + " <td>95</td>\n", + " <td>Reparatur und Instandhaltung von Datenverarbei...</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1986</th>\n", + " <td>96</td>\n", + " <td>Erbringung von überwiegend persönlichen Dienst...</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2014</th>\n", + " <td>97</td>\n", + " <td>Private Haushalte mit Hauspersonal</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2018</th>\n", + " <td>98</td>\n", + " <td>Herstellung von Waren und Erbringung von Diens...</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2026</th>\n", + " <td>99</td>\n", + " <td>Exterritoriale Organisationen und Körperschaften</td>\n", + " <td>1</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>87 rows × 3 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Schlüssel WZ 2025 Titel \\\n", + "1 01 Landwirtschaft, Jagd und damit verbundene Täti... \n", + "78 02 Forstwirtschaft und Holzeinschlag \n", + "91 03 Fischerei und Aquakultur \n", + "106 05 Kohlenbergbau \n", + "113 06 Gewinnung von Erdöl und Erdgas \n", + "... ... ... \n", + "1957 95 Reparatur und Instandhaltung von Datenverarbei... \n", + "1986 96 Erbringung von überwiegend persönlichen Dienst... \n", + "2014 97 Private Haushalte mit Hauspersonal \n", + "2018 98 Herstellung von Waren und Erbringung von Diens... \n", + "2026 99 Exterritoriale Organisationen und Körperschaften \n", + "\n", + " level \n", + "1 1 \n", + "78 1 \n", + "91 1 \n", + "106 1 \n", + "113 1 \n", + "... ... \n", + "1957 1 \n", + "1986 1 \n", + "2014 1 \n", + "2018 1 \n", + "2026 1 \n", + "\n", + "[87 rows x 3 columns]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wzs[wzs['level'] == 1]" + ] + }, + { + "cell_type": "markdown", + "id": "6e31df63-e3f4-4602-9dd2-da9cda926090", + "metadata": {}, + "source": [ + "# Using Codifier for German NACE" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "bae39345-83e8-4720-9041-bebf1220df10", + "metadata": {}, + "outputs": [], + "source": [ + "# WZ test data\n", + "train_df = pd.DataFrame({'label': ['35122', '78202', '23200', '71126'], 'text': ['text1', 'text2', 'word1', 'word2']}) \n", + "test_df = pd.DataFrame({'label': ['35122', '78202'], 'text': ['tekst3', 'tekstt2'], 'source': ['s1', 's1']}) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81e19685-7c4b-426f-94f0-c9285e863b5f", + "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": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/jupyter_notebooks/test/test b/jupyter_notebooks/test/test new file mode 100644 index 0000000000000000000000000000000000000000..9281bd734908127d23fb810d8ce7c7f77ebddaf3 GIT binary patch literal 489 zcmZ8eO-sW-5N)c_Dy=Odg8!gig7pJ>5PGO50}%v|vZT${1e@KsA3{Mv4@C=e>-dxW z310mxI!WW_!0g-EdBc0V`>_1-?o^6^hQ1#2Pzf)xX*xk@p5)jchgv72ge#om?jzn~ z8*199(C{lFHBzY!8!3KcxiAQI6^clvOdBF#0JWhMw5L`8Yjj%9#VfF(vruWyq&1l} z3}>(|%0e^r9U9H0ITf%iY?@6xHk68csU4dnR4OF-I#aw*P<Cj@Ah-n+VO2y6${hX5 z6#LEqpYR;pzT>-7j9cJFW|HxIq<YrH+4G{i1kN1>L2&v(Q7nGX;9z!1w{|)R&OYS< z<ssz}a_Z%_zn%In)Qf^;Dv^p|+>AGK1M${x`5mZ^{)is`6RqT;{q{u>ue}AnmH1Wv aAm%<=to%TB%au-~px5YlU2hNV!21L<BgFpz literal 0 HcmV?d00001 From 8c947ec20780944b3255a6b06f430f9735a7df0b Mon Sep 17 00:00:00 2001 From: suweg <susanne.wegner@destatis.de> Date: Tue, 10 Feb 2026 10:34:58 +0000 Subject: [PATCH 4/6] Added language attribute to codifier and German preprocessing --- codauto/codifier.py | 7 +- codauto/utils.py | 24 +- jupyter_notebooks/DummyCodifierDemo.ipynb | 106 +++---- jupyter_notebooks/German_NACE.ipynb | 330 +++++++++------------- 4 files changed, 206 insertions(+), 261 deletions(-) diff --git a/codauto/codifier.py b/codauto/codifier.py index 904ff9f..3016981 100644 --- a/codauto/codifier.py +++ b/codauto/codifier.py @@ -39,6 +39,7 @@ class Codifier(ABC): correspondences: Dictionary mapping old codes to new codes. train_df (pd.DataFrame): Cleaned training dataset. test_df (pd.DataFrame): Cleaned testing dataset with hierarchical labels. + language (str): The language of your textual data. Methods: get_correspondences(corres_df): @@ -99,9 +100,11 @@ def __init__(self, test_df, root_path, corres_df=None, - min_lenght_texts=3 + min_lenght_texts=3, + language='es' ): self.root_path = root_path + self.language = language os.makedirs(self.root_path, exist_ok=True) self.logger = logging.getLogger(f'CNAECodifier.{id(self)}') self.logger.setLevel(logging.INFO) @@ -229,7 +232,7 @@ def clean_data(self, data_df): data_df[col_n_0] = data_df[col_n_0].apply( lambda n: self.get_code(str(n).replace('.', '')) ) - data_df[col_n_1] = data_df[col_n_1].apply(preprocess_text) + data_df[col_n_1] = data_df[col_n_1].apply(preprocess_text, args=self.language) data_df[col_n_1] = data_df[col_n_1].apply( lambda desc: np.nan if len(desc) <= self.min_lenght_texts else desc ) diff --git a/codauto/utils.py b/codauto/utils.py index 79d3021..2a7a407 100644 --- a/codauto/utils.py +++ b/codauto/utils.py @@ -12,7 +12,7 @@ import matplotlib.colors as colors -def preprocess_text(input_str): +def preprocess_text(input_str, language): """ Cleans and normalizes the input text by: - Converting to lowercase @@ -23,15 +23,27 @@ def preprocess_text(input_str): Args: input_str (str): Input text to preprocess. + language (str) ['es', 'de']: Preprocess according to an available locale Returns: str: Cleaned, normalized UTF-8 string. """ output_str = str(input_str).lower() - output_str = output_str.replace('ñ', '__enie__') - output_str = unicodedata.normalize('NFD', output_str) - output_str = output_str.encode('ascii', 'ignore').decode('utf-8') - output_str = output_str.replace('__enie__', 'ñ') - output_str = re.sub(r'[^a-z ñ]', '', output_str) + + if language == 'es': + output_str = output_str.replace('ñ', '__enie__') + output_str = unicodedata.normalize('NFD', output_str) + output_str = output_str.encode('ascii', 'ignore').decode('utf-8') + output_str = output_str.replace('__enie__', 'ñ') + output_str = re.sub(r'[^a-z ñ]', '', output_str) + + if language == 'de': + output_str = output_str.replace('ä', 'ae') + output_str = output_str.replace('ö', 'oe') + output_str = output_str.replace('ü', 'ue') + output_str = output_str.replace('ß', 'ss') + output_str = unicodedata.normalize('NFD', output_str) + output_str = output_str.encode('ascii', 'ignore').decode('utf-8') + return ' '.join(output_str.split()) diff --git a/jupyter_notebooks/DummyCodifierDemo.ipynb b/jupyter_notebooks/DummyCodifierDemo.ipynb index 357d878..450edaf 100644 --- a/jupyter_notebooks/DummyCodifierDemo.ipynb +++ b/jupyter_notebooks/DummyCodifierDemo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "3d4c2db3-c578-45be-b59d-f5e77a3a358e", "metadata": {}, "outputs": [], @@ -42,33 +42,33 @@ "outputs": [], "source": [ "class StructuredMockup(Structured): \n", - " \"\"\"\n", - " Structured hierarchy for demo purposes\n", + " \"\"\"\n", + " Structured hierarchy for demo purposes\n", "\n", - " Levels are determined strictly by the length of the classification code.\n", + " Levels are determined strictly by the length of the classification code.\n", " \n", + " \"\"\"\n", + " def get_level(self, code): # Structured child class specific to WZ\n", " \"\"\"\n", - " def get_level(self, code): # Structured child class specific to WZ\n", - " \"\"\"\n", - " Structured hierarchy for German WZ\n", + " Structured hierarchy for German WZ\n", " \n", - " Parameters\n", - " ----------\n", - " code : str\n", - " WZ classification code.\n", + " Parameters\n", + " ---------\n", + " code : str\n", + " WZ classification code.\n", " \n", - " Returns\n", - " -------\n", - " int\n", - " Hierarchical level based on code length\n", - " \"\"\"\n", - " code = str(code).replace('.', '') \n", - " return len(code)-1" + " Returns\n", + " -------\n", + " int\n", + " Hierarchical level based on code length\n", + " \"\"\"\n", + " code = str(code).replace('.', '') \n", + " return len(code)-1" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 5, "id": "3ffd38b2-0a81-427b-82ea-4df93293ff34", "metadata": {}, "outputs": [], @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 6, "id": "18a08e7e-70c1-44cb-921d-698f7de07c6c", "metadata": {}, "outputs": [ @@ -92,7 +92,7 @@ "{0: 'Class'}" ] }, - "execution_count": 39, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 7, "id": "d63b1301-6e1c-4a32-8e8c-e0411f389662", "metadata": {}, "outputs": [ @@ -182,7 +182,7 @@ "4 5 Sonstiges 0" ] }, - "execution_count": 40, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -203,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 8, "id": "6a350cc6-3c87-42df-bed1-78b76a2ea1eb", "metadata": {}, "outputs": [], @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 9, "id": "0c4e37a2-13ad-4419-ad63-3a781a92af0a", "metadata": {}, "outputs": [], @@ -280,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 10, "id": "36896ceb-74d3-4348-8a1f-d85b9d44bd46", "metadata": {}, "outputs": [ @@ -288,16 +288,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-02-06 11:20:50,611 - INFO - Loading train_df..\n", - "2026-02-06 11:20:50,612 - INFO - train_df raw data count: 6\n", - "2026-02-06 11:20:50,615 - INFO - train_df bad codes count: 0\n", - "2026-02-06 11:20:50,616 - INFO - train_df bad descriptions count: 0\n", - "2026-02-06 11:20:50,618 - INFO - train_df pruned data count: 6\n", - "2026-02-06 11:20:50,620 - INFO - Loading test_df..\n", - "2026-02-06 11:20:50,621 - INFO - test_df raw data count: 4\n", - "2026-02-06 11:20:50,625 - INFO - test_df bad codes count: 0\n", - "2026-02-06 11:20:50,625 - INFO - test_df bad descriptions count: 0\n", - "2026-02-06 11:20:50,627 - INFO - test_df pruned data count: 4\n" + "2026-02-09 08:50:45,412 - INFO - Loading train_df..\n", + "2026-02-09 08:50:45,414 - INFO - train_df raw data count: 6\n", + "2026-02-09 08:50:45,418 - INFO - train_df bad codes count: 0\n", + "2026-02-09 08:50:45,419 - INFO - train_df bad descriptions count: 0\n", + "2026-02-09 08:50:45,421 - INFO - train_df pruned data count: 6\n", + "2026-02-09 08:50:45,422 - INFO - Loading test_df..\n", + "2026-02-09 08:50:45,423 - INFO - test_df raw data count: 4\n", + "2026-02-09 08:50:45,426 - INFO - test_df bad codes count: 0\n", + "2026-02-09 08:50:45,427 - INFO - test_df bad descriptions count: 0\n", + "2026-02-09 08:50:45,429 - INFO - test_df pruned data count: 4\n" ] } ], @@ -313,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 11, "id": "1a7a11ee-3a9b-46c8-b6f2-669a377d1066", "metadata": {}, "outputs": [ @@ -321,11 +321,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-02-06 11:20:52,912 - INFO - Loading test..\n", - "2026-02-06 11:20:52,913 - INFO - test raw data count: 6\n", - "2026-02-06 11:20:52,917 - INFO - test bad codes count: 0\n", - "2026-02-06 11:20:52,919 - INFO - test bad descriptions count: 0\n", - "2026-02-06 11:20:52,923 - INFO - test pruned data count: 6\n" + "2026-02-09 08:50:46,826 - INFO - Loading test..\n", + "2026-02-09 08:50:46,828 - INFO - test raw data count: 6\n", + "2026-02-09 08:50:46,831 - INFO - test bad codes count: 0\n", + "2026-02-09 08:50:46,832 - INFO - test bad descriptions count: 0\n", + "2026-02-09 08:50:46,833 - INFO - test pruned data count: 6\n" ] } ], @@ -339,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 12, "id": "ac43a42a-b1a0-4352-983b-dd544e0cad12", "metadata": {}, "outputs": [ @@ -354,7 +354,7 @@ " [0., 1., 0., 0., 0.]])}}" ] }, - "execution_count": 50, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -370,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 13, "id": "b4459196-7d68-48a4-93ce-9623c0b17e7f", "metadata": {}, "outputs": [ @@ -378,9 +378,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-02-06 11:09:44,096 - INFO - Test set size 4 for source all\n", - "2026-02-06 11:09:44,099 - INFO - test_set_all has 1 codes unrepresented\n", - "Predicting test_set for 1 classes: 100%|██████████| 4/4 [00:00<00:00, 2250.77it/s]\n" + "2026-02-09 08:50:52,685 - INFO - Test set size 4 for source all\n", + "2026-02-09 08:50:52,687 - INFO - test_set_all has 1 codes unrepresented\n", + "Predicting test_set for 1 classes: 100%|██████████| 4/4 [00:00<00:00, 2533.94it/s]\n" ] }, { @@ -390,7 +390,7 @@ " 'f1_score': {'micro': 0.25, 'macro': 0.1, 'weighted': 0.1}}}" ] }, - "execution_count": 52, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -401,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 17, "id": "99e42d01-1e59-475f-9717-c747c7aee414", "metadata": {}, "outputs": [ @@ -412,7 +412,7 @@ "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[65]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdesc_l\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtest\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mblah\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mblubb\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mcodification\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mhierarchical_level\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0\u001b[39;49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdesc_l\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtest\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mblah\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mblubb\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43massistance\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;66;43;03m#'codification',\u001b[39;49;00m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mhierarchical_level\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0.01\u001b[39;49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/work/WP10_Cluster1_StatCodGen/codauto/codifier.py:967\u001b[39m, in \u001b[36mCodifier.predict\u001b[39m\u001b[34m(self, desc_l, mode, hierarchical_level, threshold, original_code_l, identifier_l)\u001b[39m\n\u001b[32m 957\u001b[39m raw_predictions = \u001b[38;5;28mself\u001b[39m.get_pred_for_batch(\n\u001b[32m 958\u001b[39m desc_l_not_direct_recoding,\n\u001b[32m 959\u001b[39m idxs_not_direct_recoding,\n\u001b[32m 960\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 961\u001b[39m )\n\u001b[32m 963\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m idx, original_code \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\n\u001b[32m 964\u001b[39m idxs_not_direct_recoding,\n\u001b[32m 965\u001b[39m original_code_l_not_direct_recoding\n\u001b[32m 966\u001b[39m ):\n\u001b[32m--> \u001b[39m\u001b[32m967\u001b[39m pred_df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[32m 968\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mlabels\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mraw_predictions\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\n\u001b[32m 969\u001b[39m \u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mlabel_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mhierarchical_level_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\n\u001b[32m 970\u001b[39m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 971\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mraw_confs\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mraw_predictions\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\n\u001b[32m 972\u001b[39m \u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mconf_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mhierarchical_level_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\n\u001b[32m 973\u001b[39m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 974\u001b[39m \u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mconfs\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 975\u001b[39m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 976\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m original_code \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 977\u001b[39m pred_df_filtered = pred_df.copy()\n", "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/pandas/core/frame.py:782\u001b[39m, in \u001b[36mDataFrame.__init__\u001b[39m\u001b[34m(self, data, index, columns, dtype, copy)\u001b[39m\n\u001b[32m 776\u001b[39m mgr = \u001b[38;5;28mself\u001b[39m._init_mgr(\n\u001b[32m 777\u001b[39m data, axes={\u001b[33m\"\u001b[39m\u001b[33mindex\u001b[39m\u001b[33m\"\u001b[39m: index, \u001b[33m\"\u001b[39m\u001b[33mcolumns\u001b[39m\u001b[33m\"\u001b[39m: columns}, dtype=dtype, copy=copy\n\u001b[32m 778\u001b[39m )\n\u001b[32m 780\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, \u001b[38;5;28mdict\u001b[39m):\n\u001b[32m 781\u001b[39m \u001b[38;5;66;03m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m782\u001b[39m mgr = \u001b[43mdict_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 783\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ma.MaskedArray):\n\u001b[32m 784\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mma\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m mrecords\n", "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/pandas/core/internals/construction.py:503\u001b[39m, in \u001b[36mdict_to_mgr\u001b[39m\u001b[34m(data, index, columns, dtype, typ, copy)\u001b[39m\n\u001b[32m 499\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 500\u001b[39m \u001b[38;5;66;03m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[32m 501\u001b[39m arrays = [x.copy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[33m\"\u001b[39m\u001b[33mdtype\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[32m--> \u001b[39m\u001b[32m503\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marrays_to_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsolidate\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n", @@ -424,9 +424,9 @@ ], "source": [ "cd.predict(desc_l=['test', 'blah', 'blubb'],\n", - " mode='codification',\n", + " mode='assistance',#'codification',\n", " hierarchical_level=1,\n", - " threshold=0\n", + " threshold=0.01\n", " )" ] } diff --git a/jupyter_notebooks/German_NACE.ipynb b/jupyter_notebooks/German_NACE.ipynb index c6f1050..51cca6e 100644 --- a/jupyter_notebooks/German_NACE.ipynb +++ b/jupyter_notebooks/German_NACE.ipynb @@ -2,7 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 62, + "execution_count": null, + "id": "ada02a93-0ce7-4438-a8b2-39426d3b53b7", + "metadata": {}, + "outputs": [], + "source": [ + "# python setup.py install\n", + "# pip install fasttext openpyxl pyreadstat utils xlrd codauto" + ] + }, + { + "cell_type": "code", + "execution_count": 1, "id": "e713189e-3555-4fb9-9af2-337c511cce40", "metadata": {}, "outputs": [], @@ -12,9 +23,32 @@ "import sys\n", "import re\n", "import pickle\n", - "sys.path.append(\"/home/onyxia/work/WP10_Cluster1_StatCodGen/codauto\")\n", - "from structured import Structured\n", - "from codifier import Codifier" + "from codauto import Structured, StructuredWZ\n", + "from codauto import Codifier\n", + "from codauto import CodifierFastText\n", + "#sys.path.append(\"/home/onyxia/work/WP10_Cluster1_StatCodGen/codauto\")\n", + "#from structured import Structured, StructuredWZ\n", + "#from codifier import Codifier\n", + "#from codifier_fasttext import CodifierFastText" + ] + }, + { + "cell_type": "markdown", + "id": "64c93688-c938-42db-8a00-9173f5d4cb2b", + "metadata": {}, + "source": [ + "# German WZ Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "216b184e-9c4b-4450-a581-9110d1eea30e", + "metadata": {}, + "outputs": [], + "source": [ + "root_path = './test'\n", + "language = 'de'" ] }, { @@ -22,12 +56,12 @@ "id": "8a8e3891-bb95-48b9-9bea-44dc1f6ca0e5", "metadata": {}, "source": [ - "# Using Structured for German NACE" + "## Using Structured for German NACE" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 4, "id": "bf5a1bf4-204b-49f7-9645-ef09ebd9a66b", "metadata": {}, "outputs": [ @@ -103,7 +137,7 @@ "4 01.11.0 Anbau von anderem Getreide als Reis sowie von ..." ] }, - "execution_count": 63, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -127,39 +161,7 @@ }, { "cell_type": "code", - "execution_count": 65, - "id": "91b28796-d13b-4e88-a47d-04f786656540", - "metadata": {}, - "outputs": [], - "source": [ - "# class StructuredWZ(Structured): \n", - "# \"\"\"\n", - "# Structured hierarchy for German NACE version called WZ (Wirtschaftszweigklassifikation)\n", - "\n", - "# Levels are determined strictly by the length of the classification code.\n", - " \n", - "# \"\"\"\n", - "# def get_level(self, code): # Structured child class specific to WZ\n", - "# \"\"\"\n", - "# Structured hierarchy for German WZ\n", - " \n", - "# Parameters\n", - "# ----------\n", - "# code : str\n", - "# WZ classification code.\n", - " \n", - "# Returns\n", - "# -------\n", - "# int\n", - "# Hierarchical level based on code length\n", - "# \"\"\"\n", - "# code = str(code).replace('.', '') \n", - "# return len(code)-1" - ] - }, - { - "cell_type": "code", - "execution_count": 67, + "execution_count": 5, "id": "f77b53df-56c1-4ec9-9347-d2ab6aae7e95", "metadata": {}, "outputs": [ @@ -169,7 +171,7 @@ "{0: 'Section', 1: 'Division', 2: 'Group', 3: 'Class', 4: 'Subclass'}" ] }, - "execution_count": 67, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -192,205 +194,133 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "id": "3b753c70-fbd7-4a04-9f87-fa308af12edb", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "wz_structure.get_level('01.1')" + "# Inspect the structure\n", + "#wz_structure.get_level('01.1')" ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "da669261-ae19-40c5-8276-13a8526eb088", "metadata": {}, "outputs": [], "source": [ - "wzs = wz_structure.load_structue(structure_wz_df)" + "#wzs = wz_structure.load_structue(structure_wz_df)\n", + "#wzs[wzs['level'] == 1]" + ] + }, + { + "cell_type": "markdown", + "id": "6e31df63-e3f4-4602-9dd2-da9cda926090", + "metadata": {}, + "source": [ + "## Using Codifier for German NACE" ] }, { "cell_type": "code", - "execution_count": 70, - "id": "f799e353-7204-4bf2-a280-9e7453b3d8d3", + "execution_count": 6, + "id": "bae39345-83e8-4720-9041-bebf1220df10", + "metadata": {}, + "outputs": [], + "source": [ + "# WZ test data\n", + "train_df = pd.DataFrame({'label': ['35122', '78202', '23200', '71126']*20,\n", + " 'text': ['text1', 'text2', 'word1', 'word2']*20}) \n", + "test_df = pd.DataFrame({'label': ['35122', '78202']*20, \n", + " 'text': ['tekst3', 'tekstt2']*20, \n", + " 'source': ['s1', 's1']*20}) " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "81e19685-7c4b-426f-94f0-c9285e863b5f", "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>Schlüssel WZ 2025</th>\n", - " <th>Titel</th>\n", - " <th>level</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>01</td>\n", - " <td>Landwirtschaft, Jagd und damit verbundene Täti...</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>78</th>\n", - " <td>02</td>\n", - " <td>Forstwirtschaft und Holzeinschlag</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>91</th>\n", - " <td>03</td>\n", - " <td>Fischerei und Aquakultur</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>106</th>\n", - " <td>05</td>\n", - " <td>Kohlenbergbau</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>113</th>\n", - " <td>06</td>\n", - " <td>Gewinnung von Erdöl und Erdgas</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>...</th>\n", - " <td>...</td>\n", - " <td>...</td>\n", - " <td>...</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1957</th>\n", - " <td>95</td>\n", - " <td>Reparatur und Instandhaltung von Datenverarbei...</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1986</th>\n", - " <td>96</td>\n", - " <td>Erbringung von überwiegend persönlichen Dienst...</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2014</th>\n", - " <td>97</td>\n", - " <td>Private Haushalte mit Hauspersonal</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2018</th>\n", - " <td>98</td>\n", - " <td>Herstellung von Waren und Erbringung von Diens...</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2026</th>\n", - " <td>99</td>\n", - " <td>Exterritoriale Organisationen und Körperschaften</td>\n", - " <td>1</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "<p>87 rows × 3 columns</p>\n", - "</div>" - ], - "text/plain": [ - " Schlüssel WZ 2025 Titel \\\n", - "1 01 Landwirtschaft, Jagd und damit verbundene Täti... \n", - "78 02 Forstwirtschaft und Holzeinschlag \n", - "91 03 Fischerei und Aquakultur \n", - "106 05 Kohlenbergbau \n", - "113 06 Gewinnung von Erdöl und Erdgas \n", - "... ... ... \n", - "1957 95 Reparatur und Instandhaltung von Datenverarbei... \n", - "1986 96 Erbringung von überwiegend persönlichen Dienst... \n", - "2014 97 Private Haushalte mit Hauspersonal \n", - "2018 98 Herstellung von Waren und Erbringung von Diens... \n", - "2026 99 Exterritoriale Organisationen und Körperschaften \n", - "\n", - " level \n", - "1 1 \n", - "78 1 \n", - "91 1 \n", - "106 1 \n", - "113 1 \n", - "... ... \n", - "1957 1 \n", - "1986 1 \n", - "2014 1 \n", - "2018 1 \n", - "2026 1 \n", - "\n", - "[87 rows x 3 columns]" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-10 10:06:26,319 - INFO - Loading train_df..\n", + "2026-02-10 10:06:26,320 - INFO - train_df raw data count: 80\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'CodifierFastText' object has no attribute 'language'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m wz_codifier = \u001b[43mCodifierFastText\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mroot_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mroot_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mstructure_instance\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwz_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mtest_df\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtest_df\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43mlanguage\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlanguage\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/codauto/codifier.py:125\u001b[39m, in \u001b[36mCodifier.__init__\u001b[39m\u001b[34m(self, structure_instance, train_df, test_df, root_path, corres_df, min_lenght_texts, language)\u001b[39m\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 124\u001b[39m \u001b[38;5;28mself\u001b[39m.correspondences = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m125\u001b[39m \u001b[38;5;28mself\u001b[39m.train_df = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mget_train_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtrain_df\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 126\u001b[39m \u001b[38;5;28mself\u001b[39m.test_df = \u001b[38;5;28mself\u001b[39m.get_test_dataset(test_df, \u001b[33m'\u001b[39m\u001b[33mtest_df\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 127\u001b[39m \u001b[38;5;28mself\u001b[39m.language = language\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/codauto/codifier.py:360\u001b[39m, in \u001b[36mCodifier.get_train_dataset\u001b[39m\u001b[34m(self, train_dataset, name)\u001b[39m\n\u001b[32m 356\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m train_dataset.shape[\u001b[32m1\u001b[39m] > \u001b[32m2\u001b[39m:\n\u001b[32m 357\u001b[39m warnings.warn(\n\u001b[32m 358\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mtrain_dataset has more than 2 columns. Only columns 0 (expected: code) and 1 (expected: description) will be used.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 359\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m360\u001b[39m out_df = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mload_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 361\u001b[39m len_unre_codes = \u001b[38;5;28mself\u001b[39m.check_codes(out_df, name)\n\u001b[32m 362\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m len_unre_codes != \u001b[32m0\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/codauto/codifier.py:277\u001b[39m, in \u001b[36mCodifier.load_data\u001b[39m\u001b[34m(self, data_df, data_name)\u001b[39m\n\u001b[32m 275\u001b[39m input_df = data_df.copy()\n\u001b[32m 276\u001b[39m \u001b[38;5;28mself\u001b[39m.logger.info(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdata_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m raw data count: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_data\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m277\u001b[39m out_df = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclean_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_df\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 278\u001b[39m bad_codes = out_df.iloc[:, \u001b[32m0\u001b[39m].isna().sum()\n\u001b[32m 279\u001b[39m bad_desc = out_df.iloc[:, \u001b[32m1\u001b[39m].isna().sum()\n", + "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/codauto/codifier.py:235\u001b[39m, in \u001b[36mCodifier.clean_data\u001b[39m\u001b[34m(self, data_df)\u001b[39m\n\u001b[32m 231\u001b[39m col_n_1 = col_l[\u001b[32m1\u001b[39m]\n\u001b[32m 232\u001b[39m data_df[col_n_0] = data_df[col_n_0].apply(\n\u001b[32m 233\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m n: \u001b[38;5;28mself\u001b[39m.get_code(\u001b[38;5;28mstr\u001b[39m(n).replace(\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m))\n\u001b[32m 234\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m235\u001b[39m data_df[col_n_1] = data_df[col_n_1].apply(preprocess_text, args=\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlanguage\u001b[49m)\n\u001b[32m 236\u001b[39m data_df[col_n_1] = data_df[col_n_1].apply(\n\u001b[32m 237\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m desc: np.nan \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(desc) <= \u001b[38;5;28mself\u001b[39m.min_lenght_texts \u001b[38;5;28;01melse\u001b[39;00m desc\n\u001b[32m 238\u001b[39m )\n\u001b[32m 239\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m data_df\n", + "\u001b[31mAttributeError\u001b[39m: 'CodifierFastText' object has no attribute 'language'" + ] } ], "source": [ - "wzs[wzs['level'] == 1]" + "wz_codifier = CodifierFastText(\n", + " root_path=root_path,\n", + " structure_instance=wz_structure,\n", + " train_df=train_df,\n", + " test_df=test_df\n", + " ,language=language\n", + ")" ] }, { - "cell_type": "markdown", - "id": "6e31df63-e3f4-4602-9dd2-da9cda926090", + "cell_type": "code", + "execution_count": null, + "id": "4804534d-7189-4830-bf2b-92d6af73293c", "metadata": {}, + "outputs": [], "source": [ - "# Using Codifier for German NACE" + "wz_codifier.train()" ] }, { "cell_type": "code", - "execution_count": 71, - "id": "bae39345-83e8-4720-9041-bebf1220df10", + "execution_count": null, + "id": "c6f0c8f4-1fa1-4834-8d00-374623d5b8eb", "metadata": {}, "outputs": [], "source": [ - "# WZ test data\n", - "train_df = pd.DataFrame({'label': ['35122', '78202', '23200', '71126'], 'text': ['text1', 'text2', 'word1', 'word2']}) \n", - "test_df = pd.DataFrame({'label': ['35122', '78202'], 'text': ['tekst3', 'tekstt2'], 'source': ['s1', 's1']}) " + "# Example prediction\n", + "desc_l=[\n", + " 'Frisör',\n", + " 'Haarschneider'\n", + " ]\n", + "\n", + "mode = 'assistance'\n", + "hierarchical_level = 5\n", + "threshold = 0.03\n", + "\n", + "wz_codifier.predict(\n", + " desc_l=desc_l,\n", + " mode=mode,\n", + " hierarchical_level=hierarchical_level,\n", + " threshold=threshold\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "81e19685-7c4b-426f-94f0-c9285e863b5f", + "id": "b474b04d-3ab0-427c-a8c8-c3e32aced712", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "wz_codifier.evaluate()" + ] } ], "metadata": { From ee1c51b890ec1080b2e3078cca9386ec6fb2c7a0 Mon Sep 17 00:00:00 2001 From: suweg <susanne.wegner@destatis.de> Date: Tue, 10 Feb 2026 11:26:07 +0000 Subject: [PATCH 5/6] Fixed a bug and adjusted codifier_fasttext for different language support --- codauto/codifier.py | 2 +- codauto/codifier_fasttext.py | 2 +- codauto/structured.py | 24 +++ jupyter_notebooks/German_NACE.ipynb | 257 ++++++++++++++++++++++++---- 4 files changed, 251 insertions(+), 34 deletions(-) diff --git a/codauto/codifier.py b/codauto/codifier.py index 3016981..6388e43 100644 --- a/codauto/codifier.py +++ b/codauto/codifier.py @@ -232,7 +232,7 @@ def clean_data(self, data_df): data_df[col_n_0] = data_df[col_n_0].apply( lambda n: self.get_code(str(n).replace('.', '')) ) - data_df[col_n_1] = data_df[col_n_1].apply(preprocess_text, args=self.language) + data_df[col_n_1] = data_df[col_n_1].apply(preprocess_text, args=(self.language,)) data_df[col_n_1] = data_df[col_n_1].apply( lambda desc: np.nan if len(desc) <= self.min_lenght_texts else desc ) diff --git a/codauto/codifier_fasttext.py b/codauto/codifier_fasttext.py index 878f3cd..37f551a 100644 --- a/codauto/codifier_fasttext.py +++ b/codauto/codifier_fasttext.py @@ -279,7 +279,7 @@ def get_pred_for_batch(self, samples, idxs, clean_samples=False): """ if clean_samples: samples = [ - preprocess_text(sample) for sample in samples + preprocess_text(sample, self.language) for sample in samples ] preds_raw = self.model.predict( diff --git a/codauto/structured.py b/codauto/structured.py index f23a8be..88ef107 100644 --- a/codauto/structured.py +++ b/codauto/structured.py @@ -289,3 +289,27 @@ def get_level(self, code): """ code = code.replace('.', '') return len(code)-1 + +class StructuredWZ(Structured): + """ + Structured hierarchy for Wirtschaftszweigklassifikation/WZ (German NACE). + + Levels are determined strictly by the length of the classification code. + """ + + def get_level(self, code): + """ + Compute the hierarchical level for a WZ code. + + Parameters + ---------- + code : str + CPA classification code. + + Returns + ------- + int + Hierarchical level based on code length minus one. + """ + code = code.replace('.', '') + return len(code)-1 \ No newline at end of file diff --git a/jupyter_notebooks/German_NACE.ipynb b/jupyter_notebooks/German_NACE.ipynb index 51cca6e..53a6cb5 100644 --- a/jupyter_notebooks/German_NACE.ipynb +++ b/jupyter_notebooks/German_NACE.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "216b184e-9c4b-4450-a581-9110d1eea30e", "metadata": {}, "outputs": [], @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "bf5a1bf4-204b-49f7-9645-ef09ebd9a66b", "metadata": {}, "outputs": [ @@ -137,7 +137,7 @@ "4 01.11.0 Anbau von anderem Getreide als Reis sowie von ..." ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -161,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "f77b53df-56c1-4ec9-9347-d2ab6aae7e95", "metadata": {}, "outputs": [ @@ -171,7 +171,7 @@ "{0: 'Section', 1: 'Division', 2: 'Group', 3: 'Class', 4: 'Subclass'}" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -224,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "bae39345-83e8-4720-9041-bebf1220df10", "metadata": {}, "outputs": [], @@ -239,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "81e19685-7c4b-426f-94f0-c9285e863b5f", "metadata": {}, "outputs": [ @@ -247,23 +247,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-02-10 10:06:26,319 - INFO - Loading train_df..\n", - "2026-02-10 10:06:26,320 - INFO - train_df raw data count: 80\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'CodifierFastText' object has no attribute 'language'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m wz_codifier = \u001b[43mCodifierFastText\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43mroot_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mroot_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mstructure_instance\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwz_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mtest_df\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtest_df\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43mlanguage\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlanguage\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/codauto/codifier.py:125\u001b[39m, in \u001b[36mCodifier.__init__\u001b[39m\u001b[34m(self, structure_instance, train_df, test_df, root_path, corres_df, min_lenght_texts, language)\u001b[39m\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 124\u001b[39m \u001b[38;5;28mself\u001b[39m.correspondences = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m125\u001b[39m \u001b[38;5;28mself\u001b[39m.train_df = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mget_train_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mtrain_df\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 126\u001b[39m \u001b[38;5;28mself\u001b[39m.test_df = \u001b[38;5;28mself\u001b[39m.get_test_dataset(test_df, \u001b[33m'\u001b[39m\u001b[33mtest_df\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 127\u001b[39m \u001b[38;5;28mself\u001b[39m.language = language\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/codauto/codifier.py:360\u001b[39m, in \u001b[36mCodifier.get_train_dataset\u001b[39m\u001b[34m(self, train_dataset, name)\u001b[39m\n\u001b[32m 356\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m train_dataset.shape[\u001b[32m1\u001b[39m] > \u001b[32m2\u001b[39m:\n\u001b[32m 357\u001b[39m warnings.warn(\n\u001b[32m 358\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mtrain_dataset has more than 2 columns. Only columns 0 (expected: code) and 1 (expected: description) will be used.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 359\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m360\u001b[39m out_df = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mload_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 361\u001b[39m len_unre_codes = \u001b[38;5;28mself\u001b[39m.check_codes(out_df, name)\n\u001b[32m 362\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m len_unre_codes != \u001b[32m0\u001b[39m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/codauto/codifier.py:277\u001b[39m, in \u001b[36mCodifier.load_data\u001b[39m\u001b[34m(self, data_df, data_name)\u001b[39m\n\u001b[32m 275\u001b[39m input_df = data_df.copy()\n\u001b[32m 276\u001b[39m \u001b[38;5;28mself\u001b[39m.logger.info(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdata_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m raw data count: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_data\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m277\u001b[39m out_df = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclean_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_df\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 278\u001b[39m bad_codes = out_df.iloc[:, \u001b[32m0\u001b[39m].isna().sum()\n\u001b[32m 279\u001b[39m bad_desc = out_df.iloc[:, \u001b[32m1\u001b[39m].isna().sum()\n", - "\u001b[36mFile \u001b[39m\u001b[32m/opt/python/lib/python3.13/site-packages/codauto/codifier.py:235\u001b[39m, in \u001b[36mCodifier.clean_data\u001b[39m\u001b[34m(self, data_df)\u001b[39m\n\u001b[32m 231\u001b[39m col_n_1 = col_l[\u001b[32m1\u001b[39m]\n\u001b[32m 232\u001b[39m data_df[col_n_0] = data_df[col_n_0].apply(\n\u001b[32m 233\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m n: \u001b[38;5;28mself\u001b[39m.get_code(\u001b[38;5;28mstr\u001b[39m(n).replace(\u001b[33m'\u001b[39m\u001b[33m.\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m))\n\u001b[32m 234\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m235\u001b[39m data_df[col_n_1] = data_df[col_n_1].apply(preprocess_text, args=\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlanguage\u001b[49m)\n\u001b[32m 236\u001b[39m data_df[col_n_1] = data_df[col_n_1].apply(\n\u001b[32m 237\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m desc: np.nan \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(desc) <= \u001b[38;5;28mself\u001b[39m.min_lenght_texts \u001b[38;5;28;01melse\u001b[39;00m desc\n\u001b[32m 238\u001b[39m )\n\u001b[32m 239\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m data_df\n", - "\u001b[31mAttributeError\u001b[39m: 'CodifierFastText' object has no attribute 'language'" + "2026-02-10 11:22:16,685 - INFO - Loading train_df..\n", + "2026-02-10 11:22:16,686 - INFO - train_df raw data count: 80\n", + "2026-02-10 11:22:16,691 - INFO - train_df bad codes count: 0\n", + "2026-02-10 11:22:16,692 - INFO - train_df bad descriptions count: 0\n", + "2026-02-10 11:22:16,694 - INFO - train_df pruned data count: 80\n", + "2026-02-10 11:22:16,695 - INFO - train_df has 979 codes unrepresented: {'77399', '91301', '56400', '33150', '85402', '84110', '03300', '25120', '47632', '08930', '86103', '10131', '43321', '82920', '01259', '31009', '23131', '90120', '47301', '46491', '68111', '20599', '28150', '86941', '43210', '46450', '25220', '73300', '23140', '10920', '23650', '46133', '95313', '52320', '88104', '92004', '59203', '91220', '93291', '46822', '23430', '47781', '72101', '92003', '01301', '96992', '38330', '25510', '38322', '47210', '64192', '97000', '26111', '85311', '30920', '74130', '92001', '46154', '46220', '25634', '01191', '84220', '24100', '10840', '52100', '22230', '35150', '28970', '28112', '28422', '77120', '16280', '23510', '46492', '62202', '46901', '46461', '43230', '46181', '46837', '28130', '55102', '68312', '53202', '46642', '47911', '90202', '08110', '77210', '55201', '47551', '43910', '90319', '14103', '53100', '23150', '64992', '35230', '46494', '47789', '86220', '24430', '59140', '30310', '71111', '85599', '64923', '81302', '87999', '90201', '47530', '14220', '95290', '74302', '20510', '25300', '49110', '61102', '55301', '66300', '84122', '46835', '90203', '64196', '32400', '74201', '07210', '26120', '90205', '26513', '86990', '17120', '87101', '88102', '82100', '90391', '46821', '64999', '01632', '47640', '47230', '11030', '11010', '52310', '90312', '86911', '29102', '25632', '56114', '41001', '01700', '47122', '84250', '90112', '46330', '62102', '22262', '42120', '82300', '43120', '46410', '01132', '94120', '24460', '16241', '46210', '93299', '15120', '46163', '35302', '46493', '87301', '14230', '24330', '46172', '26400', '46141', '47921', '32503', '46838', '23139', '52239', '53300', '31005', '22269', '43220', '47559', '46144', '27200', '86942', '95250', '52221', '10132', '77111', '66191', '08920', '95210', '47721', '69103', '84124', '25405', '29320', '27900', '28423', '64991', '20593', '35304', '49320', '80014', '14105', '35130', '87103', '95320', '86952', '29310', '46630', '94920', '71125', '74140', '28240', '52213', '85332', '28429', '84130', '46502', '01481', '30110', '52240', '38212', '31003', '39003', '46903', '49501', '90393', '01280', '01199', '10730', '14101', '47750', '47710', '55203', '95240', '10860', '31004', '28991', '53201', '22261', '32502', '46462', '65300', '10420', '85610', '28120', '26300', '28999', '46152', '46159', '69203', '32130', '25991', '53209', '56302', '77220', '77330', '27112', '90392', '93210', '30202', '47730', '60399', '42220', '24540', '77400', '47742', '95230', '59120', '33171', '22129', '23640', '85592', '46188', '03220', '62101', '35400', '43500', '33183', '68121', '93130', '46610', '56303', '46730', '81100', '46833', '52229', '01251', '20410', '58210', '96302', '96910', '56121', '86930', '46860', '88993', '08990', '87200', '05200', '26200', '46171', '01160', '53203', '23440', '47792', '23420', '56113', '43342', '96301', '23660', '49420', '70104', '98100', '56111', '23320', '30201', '47220', '14219', '50100', '25920', '22240', '61909', '90204', '32501', '21100', '43130', '47250', '47260', '46321', '46322', '74911', '19100', '01302', '10620', '60100', '13940', '96993', '42110', '46350', '64110', '86920', '25212', '28940', '58110', '47799', '38211', '77310', '65121', '27330', '85532', '22110', '92002', '49200', '06100', '88991', '13950', '09900', '46641', '66221', '81210', '82991', '80012', '27320', '13990', '49310', '35211', '73200', '20300', '55400', '01450', '17110', '74111', '30130', '43110', '14240', '86230', '94110', '01110', '77391', '80015', '85200', '16242', '47610', '42991', '08122', '23612', '47762', '64310', '64320', '84230', '26600', '43429', '46186', '15110', '64922', '01440', '64910', '46470', '58290', '20592', '85510', '88999', '20160', '64210', '91302', '90130', '96210', '10200', '43999', '26702', '16230', '20140', '47631', '43350', '13920', '32200', '94999', '01460', '87102', '10112', '28300', '71121', '52250', '10910', '84121', '85591', '23620', '46132', '19200', '93292', '28921', '65122', '47540', '10320', '46185', '10510', '24511', '26512', '35160', '86102', '42999', '16110', '73120', '90114', '81231', '25620', '46120', '16220', '01631', '46890', '47621', '58120', '61901', '33160', '01489', '18110', '46230', '91110', '46162', '01610', '64921', '91410', '28140', '39001', '27310', '46182', '46184', '62201', '95102', '30990', '88919', '70103', '86912', '52211', '96999', '95220', '65110', '64193', '35121', '35112', '96400', '90313', '47121', '25999', '77340', '28960', '86960', '01192', '56309', '90206', '46161', '10120', '26520', '64195', '01472', '28930', '50400', '46187', '46643', '46831', '96230', '79901', '80013', '46340', '74991', '68201', '85103', '68123', '16210', '26511', '52219', '20200', '24320', '46143', '60310', '79120', '46110', '56220', '85690', '66110', '90113', '59130', '55302', '46381', '61902', '22210', '71114', '03110', '01220', '08121', '33120', '60392', '46501', '47401', '55104', '33110', '10111', '70102', '79110', '47791', '63102', '20120', '95101', '81222', '59111', '46645', '85333', '01260', '84300', '20420', '46849', '46156', '23110', '75009', '30120', '43412', '31001', '15200', '58190', '24340', '36003', '46644', '56122', '62104', '10710', '01131', '10310', '17220', '01290', '70200', '85531', '50300', '68311', '13960', '78201', '95312', '81232', '10890', '01150', '64191', '85520', '59202', '23611', '63101', '07290', '43341', '47622', '63910', '94991', '20130', '28230', '43600', '18140', '81221', '42130', '66290', '60200', '25631', '25530', '68112', '23410', '28290', '25610', '01479', '87910', '68202', '16260', '47552', '64220', '46432', '46421', '14102', '01120', '23613', '46711', '47111', '55101', '82401', '96101', '49390', '90311', '71200', '72102', '02400', '03210', '47692', '63109', '90111', '58130', '23700', '47510', '88992', '13300', '46441', '46480', '28950', '10520', '47830', '28910', '08123', '87991', '10390', '27510', '47691', '28219', '35212', '24310', '52214', '33140', '29200', '62103', '09100', '01410', '66192', '71122', '46902', '47302', '11070', '01240', '33181', '56210', '87302', '43322', '77350', '56112', '32300', '05100', '37001', '46496', '46497', '01500', '81239', '28211', '11020', '49330', '46495', '47722', '35301', '82999', '46153', '32990', '46712', '32120', '13100', '16120', '37002', '43310', '25110', '46149', '14104', '28220', '51220', '36002', '24520', '63920', '38230', '86951', '24202', '24420', '25401', '96991', '46183', '11040', '06200', '46422', '84125', '10830', '38110', '95311', '10720', '10610', '14211', '86970', '02200', '46842', '35240', '18200', '84123', '46841', '13930', '56115', '80011', '46190', '52261', '52269', '28250', '46433', '69202', '66210', '62900', '24203', '47402', '94910', '96220', '24519', '35303', '35140', '84240', '46151', '49410', '47912', '25930', '80090', '82912', '84129', '28410', '59112', '85312', '01140', '95400', '22250', '24530', '66199', '23910', '33200', '69101', '74120', '30400', '79909', '94200', '49120', '75001', '47240', '50200', '82409', '68321', '98200', '28111', '46442', '17210', '03120', '91120', '43991', '01471', '85320', '23310', '31002', '24201', '25639', '27400', '47112', '08910', '38321', '43421', '55900', '77510', '73110', '46142', '47270', '66120', '52212', '46189', '43240', '69102', '39002', '46810', '25403', '47770', '95314', '23630', '10139', '51210', '27520', '85533', '68322', '17250', '66222', '25402', '85409', '25940', '12000', '71112', '24450', '74202', '46389', '38310', '46834', '47811', '35111', '01620', '20110', '90399', '22220', '93110', '32910', '64194', '88101', '99000', '46431', '46646', '47820', '10119', '69201', '26701', '85401', '46720', '47403', '17230', '35220', '02100', '31006', '33130', '94993', '77320', '46836', '47741', '68122', '18130', '43330', '46390', '51100', '70101', '86101', '81301', '84210', '94994', '71113', '47922', '46240', '10810', '46503', '52231', '74301', '46870', '88103', '46131', '74912', '25520', '22121', '23990', '72200', '55202', '23120', '41002', '46850', '25404', '94992', '07100', '65200', '46620', '10410', '33190', '78100', '01210', '60391', '02300', '20600', '11060', '38220', '82200', '24440', '25910', '29101', '56301', '53204', '55103', '18120', '71123', '27120', '32110', '10820', '61200', '82911', '25633', '25211', '24410', '85331', '47812', '42910', '42210', '85102', '49340', '46310', '46155', '74999', '93120', '46499', '28421', '88911', '11050', '93190', '46360', '13200', '21200', '14290', '86210', '85403', '46832', '01430', '61101', '30910', '23520', '28212', '77112', '47699', '20150', '77520', '36001', '33179', '71124', '47761', '23450', '27111', '59201', '16250', '14212', '13910', '01230', '62109', '20170', '91210', '91420', '38120', '47520', '16270', '10850', '30320', '85101', '49502', '56304', '96102', '26119', '43411', '74112', '20591', '01270', '17240', '33182', '46370', '60393', '28922', '01420'}\n", + "/opt/python/lib/python3.13/site-packages/codauto/codifier.py:363: UserWarning: train_df has 979 codes unrepresented, look log doc\n", + " warnings.warn(\n", + "2026-02-10 11:22:16,698 - INFO - Loading test_df..\n", + "2026-02-10 11:22:16,699 - INFO - test_df raw data count: 40\n", + "2026-02-10 11:22:16,701 - INFO - test_df bad codes count: 0\n", + "2026-02-10 11:22:16,702 - INFO - test_df bad descriptions count: 0\n", + "2026-02-10 11:22:16,704 - INFO - test_df pruned data count: 40\n" ] } ], @@ -272,27 +268,69 @@ " root_path=root_path,\n", " structure_instance=wz_structure,\n", " train_df=train_df,\n", - " test_df=test_df\n", - " ,language=language\n", + " test_df=test_df,\n", + " language=language\n", ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4804534d-7189-4830-bf2b-92d6af73293c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Read 0M words\n", + "Number of words: 5\n", + "Number of labels: 4\n", + "2026-02-10 11:22:20,645 - INFO - Train time: 0h 0m 0s\n", + "Progress: 100.0% words/sec/thread: 8008 lr: 0.000000 avg.loss: 0.999700 ETA: 0h 0m 0s\n" + ] + } + ], "source": [ "wz_codifier.train()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "c6f0c8f4-1fa1-4834-8d00-374623d5b8eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[{'description': 'Frisör',\n", + " 'original_code': None,\n", + " 'label': ('71126', '78202', '35122', '23200'),\n", + " 'confidence': (26, 25, 25, 24),\n", + " 'hierarchical_level': 5,\n", + " 'identifier': 0,\n", + " 'title': ('Sonstige Ingenieurbüros',\n", + " 'Sonstige Überlassung von Arbeitskräften',\n", + " 'Elektrizitätserzeugung aus erneuerbaren Energieträgern zur Verteilung',\n", + " 'Herstellung von feuerfesten keramischen Werkstoffen und Waren')},\n", + " {'description': 'Haarschneider',\n", + " 'original_code': None,\n", + " 'label': ('71126', '78202', '35122', '23200'),\n", + " 'confidence': (26, 26, 24, 24),\n", + " 'hierarchical_level': 5,\n", + " 'identifier': 1,\n", + " 'title': ('Sonstige Ingenieurbüros',\n", + " 'Sonstige Überlassung von Arbeitskräften',\n", + " 'Elektrizitätserzeugung aus erneuerbaren Energieträgern zur Verteilung',\n", + " 'Herstellung von feuerfesten keramischen Werkstoffen und Waren')}]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Example prediction\n", "desc_l=[\n", @@ -314,10 +352,165 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "b474b04d-3ab0-427c-a8c8-c3e32aced712", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-10 11:23:07,903 - INFO - Test set size 40 for source all\n", + "2026-02-10 11:23:07,905 - INFO - test_set_all has 981 codes unrepresented\n", + "Predicting test_set for 1 classes: 100%|██████████| 40/40 [00:00<00:00, 104.72it/s]\n", + "Predicting test_set for 15 classes: 100%|██████████| 40/40 [00:00<00:00, 126.48it/s]\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "Predicting test_set for 1 classes: 100%|██████████| 40/40 [00:00<00:00, 125.86it/s]\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n", + "2026-02-10 11:23:16,153 - INFO - Threshold 0.8 not found for level Section in precision_vs_recall_multi_conf_15.\n", + "2026-02-10 11:23:16,162 - INFO - Threshold 0.8 not found for level Section in precision_vs_recall_multi_conf_1.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1500x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-10 11:23:16,429 - INFO - Threshold 0.8 not found for level Division in precision_vs_recall_multi_conf_15.\n", + "2026-02-10 11:23:16,437 - INFO - Threshold 0.8 not found for level Division in precision_vs_recall_multi_conf_1.\n" + ] + }, + { + "data": { + "image/png": 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N+yxdulT9+/cvcH///v312WefudV2iQdtpk2bplq1aik4OFht2rTRDz/8UGj9o0ePavDgwYqJiZHdbtfFF1+sJUuWXKDeAgAAAAAA/CMpKUk1atQocH/16tW1c+dOt9ou0YmIFyxYoPj4eM2YMUNt2rTR1KlT1a1bN23ZskVVqlTJUz8jI0NdunRRlSpVtHDhQlWrVk27du1SZGTkhe88AAAAAADIw1Pzz3jrnDYhISHauXNngYGbnTt3KiQkxK22SzTTZvLkybrvvvs0cOBANW7cWDNmzFBoaKhmz56db/3Zs2fr8OHDWrx4sdq1a6datWqpffv2at68+QXuOQAAAAAAgNSmTRu9++67Be5/55131Lp1a7faLrFMm4yMDK1bt04jRoxwlvn5+alz585as2ZNvsd8+umniouL0+DBg/XJJ5+ocuXKuv322/X444/L398/32PS09OVnp7ufJ07m3NmZqYyMzMtPCPr5fbP2/sJz+Ne8G2Mv29hvMsextS7MT6lB2NlnbJyLUvTeZSGPlrJGIeMsX7+GU+0aYVHH31UXbp0Ufny5TV8+HBFRUVJklJSUvTcc89pzpw5Wr58uVttl1jQ5uDBg8rOznaeTK6oqCht3rw532N27Nihr776Sv369dOSJUu0bds2PfTQQ8rMzNTo0aPzPWbixIkaO3ZsnvLly5crNDT0/E/kAlixYkVJdwFegnvBtzH+voXxLnsYU+/G+JQejJV1ysq1LA3nkZaWVtJduKB8LWjTsWNHTZs2TUOHDtWUKVMUEREhm82mY8eOKTAwUK+88oo6derkVtslOqdNcTkcDlWpUkVvvPGG/P391apVK+3bt0/PP/98gUGbESNGKD4+3vk6NTVVsbGx6tq1a77LcXmTzMxMrVixQl26dFFgYGBJdwcliHvBtzH+voXxLnsYU+/G+JQejJV1ysq1LE3nkfvEB8quBx54QNddd50++OADbdu2TcYYXXzxxbrllltUvXp1t9stsaBNpUqV5O/vr5SUFJfylJQURUdH53tMTEyMAgMDXR6FatSokZKTk5WRkaGgoKA8x9jtdtnt9jzlgYGBXv+Dnas09RWexb3g2xh/38J4lz2MqXdjfEoPxso6ZeValobz8Pb+Wc2YbBmT7ZF2vVm1atU0bNgwS9sssYmIg4KC1KpVKyUkJDjLHA6HEhISFBcXl+8x7dq107Zt2+Rw/JMS9ccffygmJibfgA0AAAAAAEBpVaKrR8XHx+vNN9/U22+/rU2bNunBBx/UyZMnNXDgQElS//79XSYqfvDBB3X48GENHTpUf/zxhz7//HM988wzGjx4cEmdAgAAAAAAOEPunDae2HxNic5p06dPH/31118aNWqUkpOT1aJFCy1dutQ5OfHu3bvl5/dPXCk2NlbLli3TsGHD1KxZM1WrVk1Dhw7V448/XlKnAAAAAAAA4BElPhHxkCFDNGTIkHz3JSYm5imLi4vT999/7+FeAQAAAAAAdxgjGWM80q6vKdHHowAAAAAAAMqCOnXq6NChQ3nKjx49qjp16rjVZoln2gAAAAAAgLLDU/PPePucNjt37lR2dt4VrtLT07Vv3z632iRoAwAAAAAALGOM8dDjUd75fNSnn37q/PuyZctUvnx55+vs7GwlJCSoVq1abrVN0AYAAAAAAMBNvXr1kiTZbDYNGDDAZV9gYKBq1aqlF1980a22CdoAAAAAAAALOf7ePNGu93E4cvpVu3Zt/fjjj6pUqZJlbRO0AQAAAAAAOE9JSUmWt0nQBgAAAAAAWMbX5rTJNW7cuEL3jxo1qthtErQBAAAAAAA4T4sWLXJ5nZmZqaSkJAUEBKhu3boEbQAAAAAAQMny1SW/N2zYkKcsNTVVd911l2688Ua32vQ7304BAAAAAAAgr4iICI0dO1YjR45063i3Mm2ys7M1Z84cJSQk6MCBA86ZknN99dVXbnUGAAAAAACUbr46p01Bjh07pmPHjrl1rFtBm6FDh2rOnDm69tpr1aRJE9lsNrfeHAAAAAAAlC2++njUyy+/7PLaGKP9+/fr3XffVffu3d1q062gzfz58/XBBx+oR48ebr0pAAAAAABAWTJlyhSX135+fqpcubIGDBigESNGuNWmW0GboKAg1atXz603BAAAAAAAZZnj780T7XqvpKQky9t0ayLiRx55RC+99FKpfZ4MAAAAAADAU/bs2aM9e/acdztuZdqsWrVKK1eu1BdffKFLLrlEgYGBLvs//vjj8+4YAAAAAAAofXx1IuKsrCyNHTtWL7/8sk6cOCFJCgsL07///W+NHj06T+ykKNwK2kRGRrq9xjgAAAAAAEBZ8+9//1sff/yxnnvuOcXFxUmS1qxZozFjxujQoUOaPn16sdt0K2jz1ltvuXMYAAAAAAAo43x19ai5c+dq/vz5LitFNWvWTLGxserbt++FC9rk+uuvv7RlyxZJUoMGDVS5cuXzaQ4AAAAAAKBUstvtqlWrVp7y2rVrKygoyK023ZqI+OTJk7r77rsVExOjq666SldddZWqVq2qe+65R2lpaW51BAAAAAAAlAXGOa+NlZvk3XPaDBkyROPHj1d6erqzLD09XU8//bSGDBniVptuZdrEx8fr66+/1n//+1+1a9dOUs7kxP/3f/+nRx55xK2UHwAAAAAAUBYYeWZ5bu8O2mzYsEEJCQmqXr26mjdvLkn66aeflJGRoauvvlo33XSTs25RF3ByK2jz0UcfaeHCherQoYOzrEePHgoJCdGtt95K0AYAAAAAAPiUyMhI3XzzzS5lsbGx59WmW0GbtLQ0RUVF5SmvUqUKj0cBAAAAAODDfHXJb08s2uTWnDZxcXEaPXq0Tp8+7Sw7deqUxo4d61zWCgAAAAAAwFd06tRJR48ezVOempqqTp06udWmW5k2L730krp165bnOa3g4GAtW7bMrY4AAAAAAIDSz1eX/E5MTFRGRkae8tOnT+vbb791q023gjZNmjTR1q1b9f7772vz5s2SpL59+6pfv34KCQlxqyMAAAAAAAClzc8//+z8+++//67k5GTn6+zsbC1dulTVqlVzq223gjaSFBoaqvvuu8/dwwEAAAAAQBnka3PatGjRQjabTTabLd/HoEJCQvTKK6+41XaRgzaffvqpunfvrsDAQH366aeF1u3Zs6dbnQEAAAAAAChNkpKSZIxRnTp19MMPP6hy5crOfUFBQapSpYr8/f3darvIQZtevXopOTlZVapUUa9evQqsZ7PZlJ2d7VZnAAAAAABA6eZrc9rUrFlTkuRwWN+/IgdtznxzT3QEAAAAAABPysyUhg2T3n9fstmkfv2kKVOkgLO+GaenS0OGSF9+KR08KFWrJj32mHT33f/U6dBBWrNGCgz8p+yPP6SqVS/IqcALvfPOO4Xu79+/f7HbdHtOm7MdPXpUkZGRVjUHAAAAAIClJkyQVq2Sfv8953X37tIzz0ijRrnWy8qSYmJygjZ16kj/+19O3erVpa5d/6n37LPSww9fsO6XIo6/N0+0672GDh3q8jozM1NpaWkKCgpSaGioW0EbP3c68uyzz2rBggXO171791bFihVVrVo1/fTTT+40CQAAAACAR82eLT31VE5AJiZGevJJadasvPXKlZPGjZPq1s3JyPnXv6SOHXMCPji33ImIPbF5syNHjrhsJ06c0JYtW3TFFVdo3rx5brXpVtBmxowZio2NlSStWLFCX375pZYuXaru3btr+PDhbnUEAAAAAABPOXJE2rtXatHin7IWLaTdu6Vjxwo/9vRp6YcfpGbNXMsnTJAqVpRatpTO8WQMfFT9+vU1adKkPFk4ReVW0CY5OdkZtPnss8906623qmvXrnrsscf0448/utURAAAAAACK4uRJqXz5nL+npRXtmBMncv48c1aP3L8fP17wccZI994r1a8v3XTTP+UTJ0rbt0spKdKkSdK//y0tWlTUMyjbcici9sRWVBMnTtTll1+u8PBw54JKW7ZsKfSYOXPmOJfuzt2Cg4PP93IoICBAf/75p3vHunNQhQoVtGfPHsXGxmrp0qWaMGGCpJwUKFaOAgAAAAB4m7CwnD+PHZMqVfrn75IUHp7/McZIDz0kbdmSM7+N3xlpD3Fx//y9WzfpgQekBQukG2+0vu8ovq+//lqDBw/W5ZdfrqysLD3xxBPq2rWrfv/9d5UrV67A4yIiIlyCOzabrcjv+emnn7q8NsZo//79evXVV9WuXbvin4TcDNrcdNNNuv3221W/fn0dOnRI3bt3lyRt2LBB9erVc6sjAAAAAAAUxZ9/nlLu19mlS/frmmtiVL58YKHHVKiQM5Hwxo05c9VIOX+Pjf0na+dMxkiDB+dMQpyQkH+dM/m59RxL2eSp+WeK0+bSpUtdXs+ZM0dVqlTRunXrdNVVVxV4nM1mU3R0tFv969WrV562KleurE6dOunFF190q023gjZTpkxRrVq1tGfPHj333HMK+ztkuX//fj300ENudQQAAAAAgHNZt+6InnjiQ0kDJUlvvfWRPvooSlOn3qiqVUMKPXbgQOnpp6XcpIdnnsl59Ck/Q4ZI330nffVVTsDnTEePSqtX5yz7bbdLiYnSjBnSm2+ez5mhqFJTU11e2+122e32Qo859ndaVcWKFQutd+LECdWsWVMOh0OXXnqpnnnmGV1yySVF6pfDYf3qVm4FbQIDA/Xoo4/mKR82bNh5dwgAAAAAgDOdPJnzZ0aGQ2PG/FenT6c792VnB+rgwWRNmrRGEyd2kpSz+lN+Ro6UDh2SGjXKeX3HHdITT+T8fdCgnD9nzJB27ZJeey0nIFOz5j/H33FHzv7MTGnsWOm223LKa9WSJk+Weve26IRLPc8u+Z07x26u0aNHa8yYMQUf5XDo4YcfVrt27dSkSZMC6zVo0ECzZ89Ws2bNdOzYMb3wwgtq27atfvvtN1WvXr3IvTx48KAkqVLuc3jnochBm08//VTdu3dXYGBgnue0ztazZ8/z7hgAAAAAANI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", + "text/plain": [ + "<Figure size 1500x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-10 11:23:16,645 - INFO - Threshold 0.8 not found for level Group in precision_vs_recall_multi_conf_15.\n", + "2026-02-10 11:23:16,653 - INFO - Threshold 0.8 not found for level Group in precision_vs_recall_multi_conf_1.\n" + ] + }, + { + "data": { + "image/png": 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pKalIbTzyyCOqWbOm8xdK7nmFtZmUlKTq1au7vB8QEKAqVaoU+XNhLXeehV27dmnp0qXKzs7WihUrNGbMGD377LOaPHlykdtMSkpSUFCQIiIiivy5sJ4743/rrbdq4sSJuvTSSxUYGKgGDRqoS5cuevTRR4vcJuNfetwZ8549e2r69Onavn27HA6HVq9erffff18HDhwocpuMuWd4YjwlqUOHDlqwYIFWrlyp2bNnKzExUZdddpmOHz8uifEsKk+Nz5mWL1+uY8eO6Y477nApv/XWW/Xmm29qzZo1iouL08KFC3XbbbdZcl3lkSfGqlKlSoqJidGkSZP0559/Kjs7W2+++abWr1/vrFOUfz+XNaV1L//t9ddfV6VKlXTjjTdae4GFSEpKyve6U1NTderUKf57iXKLoI0XmjZtmhYvXqxly5bliXij/HM4HKpevbpeeeUVtW3bVv369dNjjz2mOXPmlHbXcA4kJCRoypQpeumll7Rp0ya9//77+vjjjzVp0qTS7ho85LnnnlOjRo3UpEkTBQUFacSIERo8eLD8/PhPdFlUlPHs1auX+vbtq5YtW6pnz55asWKFjh07pnfeeacUe+4bivvzNm/ePPXq1Us1a9Z0Kb/77rvVs2dPtWjRQgMGDNAbb7yhZcuWaefOnefiMnxCUcZq4cKFMsaoVq1astvtev7559W/f39+f/6LJ+7l/PnzNWDAAL6rAOcAv9E8oGrVqvL398+zCnlycrKioqIKPfeZZ57RtGnTtGrVKrVs2dJZnnteYW1GRUXp4MGDLu9nZWXpyJEjZ/1ceIY7z0KNGjV0wQUXyN/f31nWtGlTJSUlKSMjo0htRkVFKSMjQ8eOHSvy58J67oz/mDFjdPvtt+uuu+5SixYtdMMNN2jKlCmaOnWqHA4H4+/l3BnzatWqafny5Tp58qT27NmjrVu3KjQ0VPXr1y9ym4y5Z3hiPPMTERGhCy64QDt27JDEeBaVp8dnz549+uyzz3TXXXedtS8dOnSQJOcYwpWnxqpBgwb64osvdOLECe3bt0/fffedMjMznXWK8u/nsqa07uWZvvrqK23btq1IPxtWioqKyve6w8LCVKFCBf57iXKLoI0HBAUFqW3btoqPj3eWORwOxcfHKyYmpsDznnrqKU2aNEkrV650WZdGkurVq6eoqCiXNlNTU/Xtt98624yJidGxY8e0ceNGZ53PP/9cDofD+Y8JnFvuPAudOnXSjh075HA4nGW///67atSooaCgoCK12bZtWwUGBrrU2bZtm/bu3VvoMwhruTP+aWlpef6vVm4AzxjD+Hs5d3//S1JwcLBq1aqlrKwsvffee7r++uuL3CZj7hmeGM/8nDhxQjt37lSNGjUkMZ5F5enxee2111S9enVdffXVZ+3Lli1bJMk5hnDl6bGqWLGiatSooaNHj+rTTz911inKv5/LmtK6l2eaN2+e2rZtq1atWpX8goohJibG5bolafXq1c7r5r+XKLdKeSHkcmvx4sXGbrebBQsWmF9//dXcfffdJiIiwiQlJRljjLn99tvNf//7X2f9adOmmaCgILN06VKXrfSOHz/uUiciIsJ88MEH5scffzTXX399vlt+X3TRRebbb781a9euNY0aNWLL71JW3Gdh7969plKlSmbEiBFm27Zt5qOPPjLVq1c3kydPLnKbxuRsZ3j++eebzz//3Hz//fcmJibGxMTEnLsLhzGm+OM/btw4U6lSJfP222+bXbt2mVWrVpkGDRqYm2++uchtGsP4l6bijvk333xj3nvvPbNz507z5Zdfmq5du5p69eqZo0ePFrlNYxhzT/HEeD744IMmISHBJCYmmq+//tp069bNVK1a1Rw8eNBZh/EsGk+MjzE5O86cf/755pFHHsnzmTt27DATJ04033//vUlMTDQffPCBqV+/vrn88ss9eq1lnSfGauXKleaTTz5x/veyVatWpkOHDi47cBbl389lTWndS2OMSUlJMSEhIWb27Nklvo7jx4+bzZs3m82bNxtJZvr06Wbz5s1mz549xhhj/vvf/5rbb7/dWT93y+/Ro0eb3377zcyaNSvfLb/57yXKG4I2HvTCCy+Y888/3wQFBZn27dubb775xvle586dzaBBg5yv69SpYyTlOcaNG+es43A4zJgxY0xkZKSx2+3myiuvNNu2bXP5zMOHD5v+/fub0NBQExYWZgYPHuwS+EHpKM6zYIwx69atMx06dDB2u93Ur1/fPPHEEyYrK6vIbRpjzKlTp8x9991nKleubEJCQswNN9xgDhw44LFrRMGKM/6ZmZlm/PjxpkGDBiY4ONhER0eb++67L88XCsbfuxVnzBMSEkzTpk2N3W435513nrn99tvN/v37i9WmMYy5J1k9nv369TM1atQwQUFBplatWqZfv35mx44dLnUYz6LzxM/bp59+aiTl+XeWMTn/c+Xyyy83VapUMXa73TRs2NCMHj3apKSkeOT6yhOrx2rJkiWmfv36JigoyERFRZnhw4ebY8eOudQpyr+fy6LSuJfGGPPyyy+bChUq5Pteca1Zsybf7z+5fR80aJDp3LlznnNat25tgoKCTP369c1rr72Wp13+e4nyxmaMMaWT4wMAAAAAAICCsKYNAAAAAACAFyJoAwAAAAAA4IUI2gAAAAAAAHghgjYAAAAAAABeiKANAAAAAACAFyJoAwAAAAAA4IUI2gAAAAAAAHghgjYAAAAAAABeiKANAADwOJvNpuXLl0uSdu/eLZvNpi1btpRqnwAAALwdQRsAAMq5O+64QzabTTabTYGBgapXr54efvhhnT59urS7BgAAgEIElHYHAACA51111VV67bXXlJmZqY0bN2rQoEGy2Wx68sknS7trAAAAKACZNgAA+AC73a6oqChFR0erT58+6tatm1avXi1Jcjgcmjp1qurVq6cKFSqoVatWWrp0qcv5v/zyi6655hqFhYWpUqVKuuyyy7Rz505J0oYNG9S9e3dVrVpV4eHh6ty5szZt2nTOrxEAAKC8IWgDAICP+fnnn7Vu3ToFBQVJkqZOnao33nhDc+bM0S+//KJRo0bptttu0xdffCFJ2r9/vy6//HLZ7XZ9/vnn2rhxo+68805lZWVJko4fP65BgwZp7dq1+uabb9SoUSP17t1bx48fL7VrBAAAKA+YHgUAgA/46KOPFBoaqqysLKWnp8vPz08vvvii0tPTNWXKFH322WeKiYmRJNWvX19r167Vyy+/rM6dO2vWrFkKDw/X4sWLFRgYKEm64IILnG137drV5bNeeeUVRURE6IsvvtA111xz7i4SAACgnCFoAwCAD7jiiis0e/ZsnTx5UjNmzFBAQIBuuukm/fLLL0pLS1P37t1d6mdkZOiiiy6SJG3ZskWXXXaZM2Dzb8nJyXr88ceVkJCggwcPKjs7W2lpadq7d6/HrwsAAKA8I2gDAIAPqFixoho2bChJmj9/vlq1aqV58+apefPmkqSPP/5YtWrVcjnHbrdLkipUqFBo24MGDdLhw4f13HPPqU6dOrLb7YqJiVFGRoYHrgQAAMB3ELQBAMDH+Pn56dFHH1VsbKx+//132e127d27V507d863fsuWLfX6668rMzMz32ybr7/+Wi+99JJ69+4tSdq3b58OHTrk0WsAAADwBSxEDACAD+rbt6/8/f318ssv66GHHtKoUaP0+uuva+fOndq0aZNeeOEFvf7665KkESNGKDU1Vbfccou+//57bd++XQsXLtS2bdskSY0aNdLChQv122+/6dtvv9WAAQPOmp0DAACAsyPTBgAAHxQQEKARI0boqaeeUmJioqpVq6apU6dq165dioiIUJs2bfToo49Kks477zx9/vnnGj16tDp37ix/f3+1bt1anTp1kiTNmzdPd999t9q0aaPo6GhNmTJFDz30UGleHgAAQLlgM8aY0u4EAAAAAAAAXDE9Cvj/duxYAAAAAGCQv/U0dhRGAAAAMCRtAAAAAIakDQAAAMCQtAEAAAAYkjYAAAAAQ9IGAAAAYEjaAAAAAAxJGwAAAIAhaQMAAAAwJG0AAAAAhqQNAAAAwFCJtVbLqNGWAgAAAABJRU5ErkJggg==", + "text/plain": [ + "<Figure size 1500x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-10 11:23:16,855 - INFO - Threshold 0.8 not found for level Class in precision_vs_recall_multi_conf_15.\n", + "2026-02-10 11:23:16,862 - INFO - Threshold 0.8 not found for level Class in precision_vs_recall_multi_conf_1.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1500x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-10 11:23:17,065 - INFO - Threshold 0.8 not found for level Subclass in precision_vs_recall_multi_conf_15.\n", + "2026-02-10 11:23:17,073 - INFO - Threshold 0.8 not found for level Subclass in precision_vs_recall_multi_conf_1.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1500x600 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "{'Section': {'accuracy': 0.0,\n", + " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}},\n", + " 'Division': {'accuracy': 0.0,\n", + " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}},\n", + " 'Group': {'accuracy': 0.0,\n", + " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}},\n", + " 'Class': {'accuracy': 0.0,\n", + " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}},\n", + " 'Subclass': {'accuracy': 0.0,\n", + " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}}}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "wz_codifier.evaluate()" ] From c088f8983fd236714dc02d582ab278ebd963ed75 Mon Sep 17 00:00:00 2001 From: SuWeg <susanne.wegner@destatis.de> Date: Mon, 23 Feb 2026 14:12:44 +0000 Subject: [PATCH 6/6] First draft of German NACE Notebook --- jupyter_notebooks/German_NACE.ipynb | 764 +++++++++++++++++++++++----- 1 file changed, 643 insertions(+), 121 deletions(-) diff --git a/jupyter_notebooks/German_NACE.ipynb b/jupyter_notebooks/German_NACE.ipynb index 53a6cb5..11eee38 100644 --- a/jupyter_notebooks/German_NACE.ipynb +++ b/jupyter_notebooks/German_NACE.ipynb @@ -7,13 +7,14 @@ "metadata": {}, "outputs": [], "source": [ - "# python setup.py install\n", + "# cd WP10WP10_Cluster1_StatCodGen$\n", + "# pip install .\n", "# pip install fasttext openpyxl pyreadstat utils xlrd codauto" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 73, "id": "e713189e-3555-4fb9-9af2-337c511cce40", "metadata": {}, "outputs": [], @@ -23,9 +24,20 @@ "import sys\n", "import re\n", "import pickle\n", + "\n", + "import requests\n", + "from io import BytesIO\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", "from codauto import Structured, StructuredWZ\n", "from codauto import Codifier\n", "from codauto import CodifierFastText\n", + "\n", + "\n", + "\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", "#sys.path.append(\"/home/onyxia/work/WP10_Cluster1_StatCodGen/codauto\")\n", "#from structured import Structured, StructuredWZ\n", "#from codifier import Codifier\n", @@ -161,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 74, "id": "f77b53df-56c1-4ec9-9347-d2ab6aae7e95", "metadata": {}, "outputs": [ @@ -171,7 +183,7 @@ "{0: 'Section', 1: 'Division', 2: 'Group', 3: 'Class', 4: 'Subclass'}" ] }, - "execution_count": 4, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -222,6 +234,117 @@ "## Using Codifier for German NACE" ] }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7952196f-954b-4e53-970b-98db1ebe5aa7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Titel</th>\n", + " <th>wz_code</th>\n", + " <th>Generierte Daten</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>Herstellung von vorgefertigten Bauelementen un...</td>\n", + " <td>23.61.3</td>\n", + " <td>Herstellung von vorgefertigten Betonwänden.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>Herstellung von vorgefertigten Bauelementen un...</td>\n", + " <td>23.61.3</td>\n", + " <td>Herstellung von vorgefertigten Zementfundament...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Herstellung von vorgefertigten Bauelementen un...</td>\n", + " <td>23.61.3</td>\n", + " <td>Herstellung von vorgefertigten Kalksandsteintr...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Herstellung von vorgefertigten Bauelementen un...</td>\n", + " <td>23.61.3</td>\n", + " <td>Herstellung von vorgefertigten Betonstützen.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>Herstellung von vorgefertigten Bauelementen un...</td>\n", + " <td>23.61.3</td>\n", + " <td>Herstellung von vorgefertigten Zementdecks.</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Titel wz_code \\\n", + "0 Herstellung von vorgefertigten Bauelementen un... 23.61.3 \n", + "1 Herstellung von vorgefertigten Bauelementen un... 23.61.3 \n", + "2 Herstellung von vorgefertigten Bauelementen un... 23.61.3 \n", + "3 Herstellung von vorgefertigten Bauelementen un... 23.61.3 \n", + "4 Herstellung von vorgefertigten Bauelementen un... 23.61.3 \n", + "\n", + " Generierte Daten \n", + "0 Herstellung von vorgefertigten Betonwänden. \n", + "1 Herstellung von vorgefertigten Zementfundament... \n", + "2 Herstellung von vorgefertigten Kalksandsteintr... \n", + "3 Herstellung von vorgefertigten Betonstützen. \n", + "4 Herstellung von vorgefertigten Zementdecks. " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the synthetic training data\n", + "\n", + "url = \"https://minio.lab.sspcloud.fr/projet-aiml4os-wp10/Cluster1/synthetic_data_DE.parquet\"\n", + "\n", + "response = requests.get(url)\n", + "file_bytes = BytesIO(response.content)\n", + "df = pd.read_parquet(file_bytes)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8aa5a316-1f55-44ce-9266-098dca34d79b", + "metadata": {}, + "outputs": [], + "source": [ + "# df_all = pd.DataFrame({\n", + "# 'label': df['wz_code'],\n", + "# 'text': df['Generierte Daten']})" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -229,17 +352,122 @@ "metadata": {}, "outputs": [], "source": [ - "# WZ test data\n", - "train_df = pd.DataFrame({'label': ['35122', '78202', '23200', '71126']*20,\n", - " 'text': ['text1', 'text2', 'word1', 'word2']*20}) \n", - "test_df = pd.DataFrame({'label': ['35122', '78202']*20, \n", - " 'text': ['tekst3', 'tekstt2']*20, \n", - " 'source': ['s1', 's1']*20}) " + "# # WZ test data\n", + "# train_df = pd.DataFrame({'label': ['35122', '78202', '23200', '71126']*20,\n", + "# 'text': ['text1', 'text2', 'word1', 'word2']*20}) \n", + "# test_df = pd.DataFrame({'label': ['35122', '78202']*20, \n", + "# 'text': ['tekst3', 'tekstt2']*20, \n", + "# 'source': ['s1', 's1']*20}) " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 63, + "id": "11104fd1-d03b-48a8-b5ed-465f4538816d", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(df['Generierte Daten'], df['wz_code'], test_size=0.05, random_state=42)\n", + "\n", + "train_df = pd.DataFrame({\n", + " 'label': y_train,\n", + " 'text': X_train})\n", + "\n", + "test_df = pd.DataFrame({\n", + " 'label': y_test,\n", + " 'text': X_test,\n", + " 'source': ['synthetic']*len(y_test)})" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "be4df2b9-0cc5-45f5-b067-78b3869117f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>label</th>\n", + " <th>text</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>119879</th>\n", + " <td>95.29.0</td>\n", + " <td>Reparatur von Türschlössern.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>103694</th>\n", + " <td>94.99.4</td>\n", + " <td>Organisation von Jugendgottesdiensten.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>860</th>\n", + " <td>23.99.0</td>\n", + " <td>Wir produzieren keramische Bauteile für die Elektrotechnik aus hochreinem Aluminiumoxid.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>15795</th>\n", + " <td>33.13.0</td>\n", + " <td>Unser Service umfasst die Wartung von Scannern und die Kalibrierung der Bildauflösung.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>121958</th>\n", + " <td>99.00.0</td>\n", + " <td>Durchführung von Projekten für exterritoriale Organisationen.</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " label \\\n", + "119879 95.29.0 \n", + "103694 94.99.4 \n", + "860 23.99.0 \n", + "15795 33.13.0 \n", + "121958 99.00.0 \n", + "\n", + " text \n", + "119879 Reparatur von Türschlössern. \n", + "103694 Organisation von Jugendgottesdiensten. \n", + "860 Wir produzieren keramische Bauteile für die Elektrotechnik aus hochreinem Aluminiumoxid. \n", + "15795 Unser Service umfasst die Wartung von Scannern und die Kalibrierung der Bildauflösung. \n", + "121958 Durchführung von Projekten für exterritoriale Organisationen. " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, "id": "81e19685-7c4b-426f-94f0-c9285e863b5f", "metadata": {}, "outputs": [ @@ -247,19 +475,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-02-10 11:22:16,685 - INFO - Loading train_df..\n", - "2026-02-10 11:22:16,686 - INFO - train_df raw data count: 80\n", - "2026-02-10 11:22:16,691 - INFO - train_df bad codes count: 0\n", - "2026-02-10 11:22:16,692 - INFO - train_df bad descriptions count: 0\n", - "2026-02-10 11:22:16,694 - INFO - train_df pruned data count: 80\n", - "2026-02-10 11:22:16,695 - INFO - train_df has 979 codes unrepresented: {'77399', '91301', '56400', '33150', '85402', '84110', '03300', '25120', '47632', '08930', '86103', '10131', '43321', '82920', '01259', '31009', '23131', '90120', '47301', '46491', '68111', '20599', '28150', '86941', '43210', '46450', '25220', 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'27310', '46182', '46184', '62201', '95102', '30990', '88919', '70103', '86912', '52211', '96999', '95220', '65110', '64193', '35121', '35112', '96400', '90313', '47121', '25999', '77340', '28960', '86960', '01192', '56309', '90206', '46161', '10120', '26520', '64195', '01472', '28930', '50400', '46187', '46643', '46831', '96230', '79901', '80013', '46340', '74991', '68201', '85103', '68123', '16210', '26511', '52219', '20200', '24320', '46143', '60310', '79120', '46110', '56220', '85690', '66110', '90113', '59130', '55302', '46381', '61902', '22210', '71114', '03110', '01220', '08121', '33120', '60392', '46501', '47401', '55104', '33110', '10111', '70102', '79110', '47791', '63102', '20120', '95101', '81222', '59111', '46645', '85333', '01260', '84300', '20420', '46849', '46156', '23110', '75009', '30120', '43412', '31001', '15200', '58190', '24340', '36003', '46644', '56122', '62104', '10710', '01131', '10310', '17220', '01290', '70200', '85531', '50300', '68311', '13960', '78201', 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'42910', '42210', '85102', '49340', '46310', '46155', '74999', '93120', '46499', '28421', '88911', '11050', '93190', '46360', '13200', '21200', '14290', '86210', '85403', '46832', '01430', '61101', '30910', '23520', '28212', '77112', '47699', '20150', '77520', '36001', '33179', '71124', '47761', '23450', '27111', '59201', '16250', '14212', '13910', '01230', '62109', '20170', '91210', '91420', '38120', '47520', '16270', '10850', '30320', '85101', '49502', '56304', '96102', '26119', '43411', '74112', '20591', '01270', '17240', '33182', '46370', '60393', '28922', '01420'}\n", - "/opt/python/lib/python3.13/site-packages/codauto/codifier.py:363: UserWarning: train_df has 979 codes unrepresented, look log doc\n", + "2026-02-23 10:49:35,778 - INFO - Loading train_df..\n", + "2026-02-23 10:49:35,785 - INFO - train_df raw data count: 119014\n", + "2026-02-23 10:49:36,389 - INFO - train_df bad codes count: 0\n", + "2026-02-23 10:49:36,390 - INFO - train_df bad descriptions count: 34\n", + "2026-02-23 10:49:36,433 - INFO - train_df pruned data count: 118980\n", + "2026-02-23 10:49:36,446 - INFO - train_df has 222 codes unrepresented: {'01192', '90114', '01460', '17240', '11040', '10111', '08123', '01230', '18130', '01481', '90113', '20510', '23430', '01450', '20600', '01440', '14219', '11020', '01472', '01620', '01610', '10710', '01280', '10132', '23110', '23150', '14103', '22230', '17250', '18200', '08930', '01260', '03220', '08110', '14211', '90313', '11010', '01251', '10820', '01131', '01191', '22240', '23120', '20150', '08121', '16210', '17110', '23612', '16250', '17230', '16242', '88999', '90205', '10510', '21200', '23510', '14105', '23611', '01290', '90399', '22121', '23131', '10131', '03210', '10840', '01120', '16270', '01471', '16230', '23420', '10420', '90111', '22110', '10730', '14104', '90204', '23450', '20593', '01150', '03300', '90201', '90203', '23320', '01410', '14212', '01160', '22220', '13990', '16120', '16260', '13910', '22129', '03110', '10119', '20591', '20592', '13200', '11070', '13950', '05100', '90130', '14220', '23140', '10610', '14101', '01132', '10200', '09100', '13300', '23520', '22210', '01700', '46890', '01302', '10910', '15200', '10410', '14290', '10890', '20420', '10520', '13940', '09900', '20120', '10310', '01259', '20170', '22250', '08910', '05200', '20200', '23310', '08122', '01110', '10850', '18140', '11030', '21100', '17210', '01210', '01140', '13100', '01500', '90392', '15110', '01489', '07290', '10112', '18120', '90319', '17220', '13960', '90312', '01631', '10620', '20410', '20140', '10810', '90120', '01430', '22261', '20300', '06200', '08990', '23139', '19200', '11050', '10920', '01632', '23410', '19100', '17120', '90206', '18110', '08920', '20599', '02400', '20160', '11060', '02200', '14230', '10390', '16280', '22262', '01240', '01199', '12000', '10120', '10830', '20130', '90311', '16241', '07100', '02300', '23200', '13930', '10720', '14240', '03120', '01301', '90393', '22269', '01220', '16110', '10320', '16220', '13920', '02100', '14102', '01270', '90202', '01420', '90391', '20110', '10139', '01479', '10860', '90112', '15120', '23440', '06100', '07210'}\n", + "/opt/python/lib/python3.13/site-packages/codauto/codifier.py:363: UserWarning: train_df has 222 codes unrepresented, look log doc\n", " warnings.warn(\n", - "2026-02-10 11:22:16,698 - INFO - Loading test_df..\n", - "2026-02-10 11:22:16,699 - INFO - test_df raw data count: 40\n", - "2026-02-10 11:22:16,701 - INFO - test_df bad codes count: 0\n", - "2026-02-10 11:22:16,702 - INFO - test_df bad descriptions count: 0\n", - "2026-02-10 11:22:16,704 - INFO - test_df pruned data count: 40\n" + "2026-02-23 10:49:36,451 - INFO - Loading test_df..\n", + "2026-02-23 10:49:36,452 - INFO - test_df raw data count: 6264\n", + "2026-02-23 10:49:36,497 - INFO - test_df bad codes count: 0\n", + "2026-02-23 10:49:36,502 - INFO - test_df bad descriptions count: 2\n", + "2026-02-23 10:49:36,510 - INFO - test_df pruned data count: 6262\n" ] } ], @@ -275,7 +503,193 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 56, + "id": "6fc3b784-5111-4793-b5b5-dca796fc7cca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Schlüssel WZ 2025</th>\n", + " <th>Titel</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>1993</th>\n", + " <td>96.21.0</td>\n", + " <td>Frisör- und Barbiersalons</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Schlüssel WZ 2025 Titel\n", + "1993 96.21.0 Frisör- und Barbiersalons" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "structure_wz_df[structure_wz_df['Schlüssel WZ 2025'] == '96.21.0']" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "936fc3d7-a60e-49a6-be92-e489110a7d2e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>label</th>\n", + " <th>text</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>99256</th>\n", + " <td>86.94.2</td>\n", + " <td>Schwangerschaftsangst-Bewältigung.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>99215</th>\n", + " <td>86.94.2</td>\n", + " <td>Wir führen Aufklärung über Geburtsmethoden wie natürliche Geburt, Wassergeburt und medikamentöse Schmerztherapie.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>99267</th>\n", + " <td>86.94.2</td>\n", + " <td>Geburtsheim.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>99197</th>\n", + " <td>86.94.2</td>\n", + " <td>Wir bieten Nachsorgeuntersuchungen im Wochenbett, inklusive Wundversorgung und Rückbildungstraining.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>114791</th>\n", + " <td>86.94.2</td>\n", + " <td>Medikation bei Geburt.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>114726</th>\n", + " <td>86.94.2</td>\n", + " <td>Wir führen Hebammenvisiten in Pflegeeinrichtungen für Schwangere und neue Mütter durch.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>114793</th>\n", + " <td>86.94.2</td>\n", + " <td>Hebammenpraxis.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>99259</th>\n", + " <td>86.94.2</td>\n", + " <td>Atemtechniken.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>114813</th>\n", + " <td>86.94.2</td>\n", + " <td>Klinik-Entbindung.</td>\n", + " </tr>\n", + " <tr>\n", + " <th>114752</th>\n", + " <td>86.94.2</td>\n", + " <td>Klinikgeburt.</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>163 rows × 2 columns</p>\n", + "</div>" + ], + "text/plain": [ + " label \\\n", + "99256 86.94.2 \n", + "99215 86.94.2 \n", + "99267 86.94.2 \n", + "99197 86.94.2 \n", + "114791 86.94.2 \n", + "... ... \n", + "114726 86.94.2 \n", + "114793 86.94.2 \n", + "99259 86.94.2 \n", + "114813 86.94.2 \n", + "114752 86.94.2 \n", + "\n", + " text \n", + "99256 Schwangerschaftsangst-Bewältigung. \n", + "99215 Wir führen Aufklärung über Geburtsmethoden wie natürliche Geburt, Wassergeburt und medikamentöse Schmerztherapie. \n", + "99267 Geburtsheim. \n", + "99197 Wir bieten Nachsorgeuntersuchungen im Wochenbett, inklusive Wundversorgung und Rückbildungstraining. \n", + "114791 Medikation bei Geburt. \n", + "... ... \n", + "114726 Wir führen Hebammenvisiten in Pflegeeinrichtungen für Schwangere und neue Mütter durch. \n", + "114793 Hebammenpraxis. \n", + "99259 Atemtechniken. \n", + "114813 Klinik-Entbindung. \n", + "114752 Klinikgeburt. \n", + "\n", + "[163 rows x 2 columns]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_df[train_df['label'].str.contains('86.94.2')]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, "id": "4804534d-7189-4830-bf2b-92d6af73293c", "metadata": {}, "outputs": [ @@ -283,11 +697,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "Read 0M words\n", - "Number of words: 5\n", - "Number of labels: 4\n", - "2026-02-10 11:22:20,645 - INFO - Train time: 0h 0m 0s\n", - "Progress: 100.0% words/sec/thread: 8008 lr: 0.000000 avg.loss: 0.999700 ETA: 0h 0m 0s\n" + "Read 1M words\n", + "Number of words: 72045\n", + "Number of labels: 761\n", + "Progress: 100.0% words/sec/thread: 3757 lr: 0.000000 avg.loss: 3.698983 ETA: 0h 0m 0s\n", + "2026-02-23 10:50:26,568 - INFO - Train time: 0h 0m 36s\n" ] } ], @@ -297,36 +711,178 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 72, "id": "c6f0c8f4-1fa1-4834-8d00-374623d5b8eb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'description': 'Frisör',\n", + "[{'description': 'Abfallentsorgung',\n", " 'original_code': None,\n", - " 'label': ('71126', '78202', '35122', '23200'),\n", - " 'confidence': (26, 25, 25, 24),\n", + " 'label': ('86942',\n", + " '96210',\n", + " '70103',\n", + " '96993',\n", + " '86941',\n", + " '94993',\n", + " '49320',\n", + " '85531'),\n", + " 'confidence': (87, 9, 1, 1, 1, 1, 0, 0),\n", " 'hierarchical_level': 5,\n", " 'identifier': 0,\n", - " 'title': ('Sonstige Ingenieurbüros',\n", - " 'Sonstige Überlassung von Arbeitskräften',\n", - " 'Elektrizitätserzeugung aus erneuerbaren Energieträgern zur Verteilung',\n", - " 'Herstellung von feuerfesten keramischen Werkstoffen und Waren')},\n", - " {'description': 'Haarschneider',\n", + " 'title': ('Geburtshilfe und Hebammen',\n", + " 'Frisör- und Barbiersalons',\n", + " 'Managementtätigkeiten von sonstigen Holdinggesellschaften',\n", + " 'Betreuungsdienste für Heimtiere',\n", + " 'Ambulante Pflegedienste',\n", + " 'Verbraucherorganisationen',\n", + " 'Personenbeförderung im Gelegenheitsverkehr auf der Straße',\n", + " 'Fahrschulen')},\n", + " {'description': 'Schule',\n", " 'original_code': None,\n", - " 'label': ('71126', '78202', '35122', '23200'),\n", - " 'confidence': (26, 26, 24, 24),\n", + " 'label': ('86942', '86941', '85531', '96210', '94993'),\n", + " 'confidence': (67, 23, 8, 1, 1),\n", " 'hierarchical_level': 5,\n", " 'identifier': 1,\n", - " 'title': ('Sonstige Ingenieurbüros',\n", - " 'Sonstige Überlassung von Arbeitskräften',\n", - " 'Elektrizitätserzeugung aus erneuerbaren Energieträgern zur Verteilung',\n", - " 'Herstellung von feuerfesten keramischen Werkstoffen und Waren')}]" + " 'title': ('Geburtshilfe und Hebammen',\n", + " 'Ambulante Pflegedienste',\n", + " 'Fahrschulen',\n", + " 'Frisör- und Barbiersalons',\n", + " 'Verbraucherorganisationen')},\n", + " {'description': 'Auto',\n", + " 'original_code': None,\n", + " 'label': ('86942', '96210', '86941', '94993', '70103', '96993', '49320'),\n", + " 'confidence': (85, 12, 1, 1, 0, 0, 0),\n", + " 'hierarchical_level': 5,\n", + " 'identifier': 2,\n", + " 'title': ('Geburtshilfe und Hebammen',\n", + " 'Frisör- und Barbiersalons',\n", + " 'Ambulante Pflegedienste',\n", + " 'Verbraucherorganisationen',\n", + " 'Managementtätigkeiten von sonstigen Holdinggesellschaften',\n", + " 'Betreuungsdienste für Heimtiere',\n", + " 'Personenbeförderung im Gelegenheitsverkehr auf der Straße')},\n", + " {'description': 'Chemie',\n", + " 'original_code': None,\n", + " 'label': ('86942', '96210', '86941', '96993', '94993', '70103', '49320'),\n", + " 'confidence': (89, 8, 1, 0, 0, 0, 0),\n", + " 'hierarchical_level': 5,\n", + " 'identifier': 3,\n", + " 'title': ('Geburtshilfe und Hebammen',\n", + " 'Frisör- und Barbiersalons',\n", + " 'Ambulante Pflegedienste',\n", + " 'Betreuungsdienste für Heimtiere',\n", + " 'Verbraucherorganisationen',\n", + " 'Managementtätigkeiten von sonstigen Holdinggesellschaften',\n", + " 'Personenbeförderung im Gelegenheitsverkehr auf der Straße')},\n", + " {'description': 'Influencer',\n", + " 'original_code': None,\n", + " 'label': ('86942',\n", + " '96210',\n", + " '86941',\n", + " '70103',\n", + " '94993',\n", + " '96993',\n", + " '49320',\n", + " '85531'),\n", + " 'confidence': (86, 10, 1, 1, 1, 0, 0, 0),\n", + " 'hierarchical_level': 5,\n", + " 'identifier': 4,\n", + " 'title': ('Geburtshilfe und Hebammen',\n", + " 'Frisör- und Barbiersalons',\n", + " 'Ambulante Pflegedienste',\n", + " 'Managementtätigkeiten von sonstigen Holdinggesellschaften',\n", + " 'Verbraucherorganisationen',\n", + " 'Betreuungsdienste für Heimtiere',\n", + " 'Personenbeförderung im Gelegenheitsverkehr auf der Straße',\n", + " 'Fahrschulen')},\n", + " {'description': 'Promotion',\n", + " 'original_code': None,\n", + " 'label': ('86942', '96210', '86941', '94993', '70103', '96993', '49320'),\n", + " 'confidence': (85, 11, 1, 1, 1, 0, 0),\n", + " 'hierarchical_level': 5,\n", + " 'identifier': 5,\n", + " 'title': ('Geburtshilfe und Hebammen',\n", + " 'Frisör- und Barbiersalons',\n", + " 'Ambulante Pflegedienste',\n", + " 'Verbraucherorganisationen',\n", + " 'Managementtätigkeiten von sonstigen Holdinggesellschaften',\n", + " 'Betreuungsdienste für Heimtiere',\n", + " 'Personenbeförderung im Gelegenheitsverkehr auf der Straße')},\n", + " {'description': 'Reparatur von Türschlössern',\n", + " 'original_code': None,\n", + " 'label': ('33110',\n", + " '33183',\n", + " '95210',\n", + " '95290',\n", + " '33171',\n", + " '33140',\n", + " '95101',\n", + " '33120',\n", + " '95240',\n", + " '33150',\n", + " '33130',\n", + " '95102',\n", + " '95314',\n", + " '95250',\n", + " '33160'),\n", + " 'confidence': (9, 8, 8, 7, 6, 6, 6, 6, 6, 5, 4, 4, 4, 4, 4),\n", + " 'hierarchical_level': 5,\n", + " 'identifier': 6,\n", + " 'title': ('Reparatur und Instandhaltung von Metallerzeugnissen',\n", + " 'Reparatur und Instandhaltung von militärischen Luft- und Raumfahrzeugen',\n", + " 'Reparatur und Instandhaltung von Geräten der Unterhaltungselektronik',\n", + " 'Reparatur und Instandhaltung von Gebrauchsgütern a. n. g.',\n", + " 'Reparatur und Instandhaltung von Schienenfahrzeugen',\n", + " 'Reparatur und Instandhaltung von elektrischen Ausrüstungen',\n", + " 'Reparatur und Instandhaltung von Datenverarbeitungsgeräten und peripheren Geräten',\n", + " 'Reparatur und Instandhaltung von Maschinen',\n", + " 'Reparatur und Instandhaltung von Möbeln und Einrichtungsgegenständen',\n", + " 'Reparatur und Instandhaltung von zivilen Schiffen, Booten und Yachten',\n", + " 'Reparatur und Instandhaltung von elektronischen und optischen Geräten',\n", + " 'Reparatur und Instandhaltung von Telekommunikationsgeräten',\n", + " 'Sonstige Instandhaltung und Reparatur von Kraftwagen mit einem zulässigen Gesamtgewicht von mehr als 3,5 t',\n", + " 'Reparatur und Instandhaltung von Uhren und Schmuck',\n", + " 'Reparatur und Instandhaltung von zivilen Luft- und Raumfahrzeugen')},\n", + " {'description': 'Organisation von Jugendgottesdiensten.',\n", + " 'original_code': None,\n", + " 'label': ('94994',\n", + " '94992',\n", + " '82300',\n", + " '85200',\n", + " '84123',\n", + " '79120',\n", + " '88991',\n", + " '85409',\n", + " '85401',\n", + " '91110',\n", + " '84210',\n", + " '94920',\n", + " '94991',\n", + " '84129',\n", + " '70104'),\n", + " 'confidence': (30, 29, 5, 3, 3, 3, 2, 2, 2, 2, 2, 1, 1, 1, 1),\n", + " 'hierarchical_level': 5,\n", + " 'identifier': 7,\n", + " 'title': ('Jugendorganisationen',\n", + " 'Organisationen der Kultur',\n", + " 'Messe-, Kongress- und Business-Event-Veranstalter',\n", + " 'Grundschulen',\n", + " 'Öffentliche Verwaltung der Kultur und des Sports',\n", + " 'Reiseveranstalter',\n", + " 'Jugendarbeit (auch Jugendsozialarbeit)',\n", + " 'Einrichtungen des tertiären Bildungsbereichs a. n. g.',\n", + " 'Universitäten',\n", + " 'Bibliotheken',\n", + " 'Auswärtige Angelegenheiten',\n", + " 'Politische Parteien und Vereinigungen',\n", + " 'Organisationen der Bildung, Wissenschaft und Forschung',\n", + " 'Öffentliche Verwaltung a. n. g.',\n", + " 'Sonstige Verwaltung und Führung von Unternehmen und Betrieben')}]" ] }, - "execution_count": 8, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -334,13 +890,19 @@ "source": [ "# Example prediction\n", "desc_l=[\n", - " 'Frisör',\n", - " 'Haarschneider'\n", + " 'Abfallentsorgung',\n", + " 'Schule',\n", + " 'Auto',\n", + " 'Chemie',\n", + " 'Influencer',\n", + " 'Promotion',\n", + " 'Reparatur von Türschlössern',\n", + " 'Organisation von Jugendgottesdiensten.',\n", " ]\n", "\n", "mode = 'assistance'\n", "hierarchical_level = 5\n", - "threshold = 0.03\n", + "threshold = 0.003\n", "\n", "wz_codifier.predict(\n", " desc_l=desc_l,\n", @@ -352,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 67, "id": "b474b04d-3ab0-427c-a8c8-c3e32aced712", "metadata": {}, "outputs": [ @@ -360,18 +922,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-02-10 11:23:07,903 - INFO - Test set size 40 for source all\n", - "2026-02-10 11:23:07,905 - INFO - test_set_all has 981 codes unrepresented\n", - "Predicting test_set for 1 classes: 100%|██████████| 40/40 [00:00<00:00, 104.72it/s]\n", - "Predicting test_set for 15 classes: 100%|██████████| 40/40 [00:00<00:00, 126.48it/s]\n", - "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n", + "2026-02-23 10:50:38,450 - INFO - Test set size 6262 for source all\n", + "2026-02-23 10:50:38,454 - INFO - test_set_all has 226 codes unrepresented\n", + "Predicting test_set for 1 classes: 100%|██████████| 6262/6262 [49:19<00:00, 2.12it/s] \n", + "Predicting test_set for 15 classes: 100%|██████████| 6262/6262 [48:22<00:00, 2.16it/s] \n", "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", " return _methods._mean(a, axis=axis, dtype=dtype,\n", "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", @@ -384,7 +938,7 @@ " return _methods._mean(a, axis=axis, dtype=dtype,\n", "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", " ret = ret.dtype.type(ret / rcount)\n", - "Predicting test_set for 1 classes: 100%|██████████| 40/40 [00:00<00:00, 125.86it/s]\n", + "Predicting test_set for 1 classes: 100%|██████████| 6262/6262 [36:43<00:00, 2.84it/s]\n", "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", " return _methods._mean(a, axis=axis, dtype=dtype,\n", "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", @@ -396,22 +950,12 @@ "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", " return _methods._mean(a, axis=axis, dtype=dtype,\n", "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/opt/python/lib/python3.13/site-packages/numpy/_core/fromnumeric.py:3824: RuntimeWarning: Mean of empty slice\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/python/lib/python3.13/site-packages/numpy/_core/_methods.py:142: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n", - "2026-02-10 11:23:16,153 - INFO - Threshold 0.8 not found for level Section in precision_vs_recall_multi_conf_15.\n", - "2026-02-10 11:23:16,162 - INFO - Threshold 0.8 not found for level Section in precision_vs_recall_multi_conf_1.\n" + " ret = ret.dtype.type(ret / rcount)\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1500x600 with 2 Axes>" ] @@ -419,17 +963,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 11:23:16,429 - INFO - Threshold 0.8 not found for level Division in precision_vs_recall_multi_conf_15.\n", - "2026-02-10 11:23:16,437 - INFO - Threshold 0.8 not found for level Division in precision_vs_recall_multi_conf_1.\n" - ] - }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1500x600 with 2 Axes>" ] @@ -437,17 +973,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 11:23:16,645 - INFO - Threshold 0.8 not found for level Group in precision_vs_recall_multi_conf_15.\n", - "2026-02-10 11:23:16,653 - INFO - Threshold 0.8 not found for level Group in precision_vs_recall_multi_conf_1.\n" - ] - }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1500x600 with 2 Axes>" ] @@ -455,17 +983,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 11:23:16,855 - INFO - Threshold 0.8 not found for level Class in precision_vs_recall_multi_conf_15.\n", - "2026-02-10 11:23:16,862 - INFO - Threshold 0.8 not found for level Class in precision_vs_recall_multi_conf_1.\n" - ] - }, { "data": { - "image/png": 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6ePBgvttkzD3DE+MpSa1bt9bs2bO1ZMkSTZs2TfHx8brqqqt08uRJSYxnfnlqfM61aNEiHT9+XPfcc49L+Z133qn3339fK1as0PDhwzVnzhzdddddllxXSeSJsQoPD1dsbKzGjRunv/76SxkZGXr//fe1Zs0aZ538/Pu5uCmqe3m+d999V+Hh4brlllusvcA8JCQk5HjdSUlJOnPmDP+9RIlF0MYLTZw4UfPmzdPChQuzRbxR8jkcDlWoUEFvvfWWWrZsqdtvv10jRozQ9OnTi7pruAhWrlyp5557Tm+88YY2bNigTz/9VF9++aXGjRtX1F2Dh7zyyiuqU6eO6tWrp6CgIA0cOFD9+vWTnx//iS6O8jOeXbt2Vc+ePdWkSRN16dJFixcv1vHjx/XRRx8VYc99Q0F/3mbOnKmuXbuqUqVKLuUPPvigunTposaNG6t379567733tHDhQu3cufNiXIZPyM9YzZkzR8YYVa5cWXa7Xa+++qp69erF78/zeOJezpo1S7179+a7CnAR8BvNA8qVKyd/f/9sq5AnJiYqOjo6z3NfeuklTZw4UcuWLVOTJk2c5Vnn5dVmdHS0Dh065PJ+enq6jh49esHPhWe48yxUrFhRl156qfz9/Z1l9evXV0JCglJTU/PVZnR0tFJTU3X8+PF8fy6s5874P/PMM7r77rt1//33q3Hjxrr55pv13HPPacKECXI4HIy/l3NnzMuXL69Fixbp9OnT2rNnj7Zu3aqwsDDVrFkz320y5p7hifHMSWRkpC699FLt2LFDEuOZX54enz179ujrr7/W/ffff8G+tG7dWpKcYwhXnhqrWrVq6dtvv9WpU6e0b98+/fzzz0pLS3PWyc+/n4uborqX5/r++++1bdu2fP1sWCk6OjrH646IiFBISAj/vUSJRdDGA4KCgtSyZUvFxcU5yxwOh+Li4hQbG5vreS+88ILGjRunJUuWuKxLI0k1atRQdHS0S5tJSUn66aefnG3Gxsbq+PHjWr9+vbPON998I4fD4fzHBC4ud56Ftm3baseOHXI4HM6yP//8UxUrVlRQUFC+2mzZsqUCAwNd6mzbtk179+7N8xmEtdwZ/+Tk5Gz/VysrgGeMYfy9nLu//yUpODhYlStXVnp6uj755BPddNNN+W6TMfcMT4xnTk6dOqWdO3eqYsWKkhjP/PL0+LzzzjuqUKGCrr/++gv2ZdOmTZLkHEO48vRYlSpVShUrVtSxY8e0dOlSZ538/Pu5uCmqe3mumTNnqmXLlmratGnhL6gAYmNjXa5bkpYvX+68bv57iRKriBdCLrHmzZtn7Ha7mT17tvnjjz/Mgw8+aCIjI01CQoIxxpi7777b/N///Z+z/sSJE01QUJD5+OOPXbbSO3nypEudyMhI89lnn5lff/3V3HTTTTlu+d28eXPz008/mVWrVpk6deqw5XcRK+izsHfvXhMeHm4GDhxotm3bZr744gtToUIFM378+Hy3aUzmdoZVq1Y133zzjVm3bp2JjY01sbGxF+/CYYwp+PiPGjXKhIeHmw8//NDs2rXLLFu2zNSqVcvcdttt+W7TGMa/KBV0zH/88UfzySefmJ07d5rvvvvOdOjQwdSoUcMcO3Ys320aw5h7iifG8/HHHzcrV6408fHx5ocffjAdO3Y05cqVM4cOHXLWYTzzxxPjY0zmjjNVq1Y1Tz75ZLbP3LFjhxk7dqxZt26diY+PN5999pmpWbOmufrqqz16rcWdJ8ZqyZIl5quvvnL+97Jp06amdevWLjtw5uffz8VNUd1LY4w5ceKECQ0NNdOmTSv0dZw8edJs3LjRbNy40UgykyZNMhs3bjR79uwxxhjzf//3f+buu+921s/a8nvo0KFmy5YtZurUqTlu+c1/L1HSELTxoNdee81UrVrVBAUFmVatWpkff/zR+V67du1M3759na+rVatmJGU7Ro0a5azjcDjMM888Y6KioozdbjfXXnut2bZtm8tnHjlyxPTq1cuEhYWZiIgI069fP5fAD4pGQZ4FY4xZvXq1ad26tbHb7aZmzZrm2WefNenp6flu0xhjzpw5Yx555BFTpkwZExoaam6++WZz8OBBj10jcleQ8U9LSzOjR482tWrVMsHBwSYmJsY88sgj2b5QMP7erSBjvnLlSlO/fn1jt9vNJZdcYu6++25z4MCBArVpDGPuSVaP5+23324qVqxogoKCTOXKlc3tt99uduzY4VKH8cw/T/y8LV261EjK9u8sYzL/58rVV19typYta+x2u6ldu7YZOnSoOXHihEeurySxeqzmz59vatasaYKCgkx0dLQZMGCAOX78uEud/Pz7uTgqintpjDFvvvmmCQkJyfG9glqxYkWO33+y+t63b1/Trl27bOc0a9bMBAUFmZo1a5p33nknW7v89xIljc0YY4omxwcAAAAAAAC5YU0bAAAAAAAAL0TQBgAAAAAAwAsRtAEAAAAAAPBCBG0AAAAAAAC8EEEbAAAAAAAAL0TQBgAAAAAAwAsRtAEAAAAAAPBCBG0AAAAAAAC8EEEbAADgcTabTYsWLZIk7d69WzabTZs2bSrSPgEAAHg7gjYAAJRw99xzj2w2m2w2mwIDA1WjRg0NGzZMZ8+eLequAQAAIA8BRd0BAADgedddd53eeecdpaWlaf369erbt69sNpuef/75ou4aAAAAckGmDQAAPsButys6OloxMTHq0aOHOnbsqOXLl0uSHA6HJkyYoBo1aigkJERNmzbVxx9/7HL+77//rhtuuEEREREKDw/XVVddpZ07d0qS1q5dq06dOqlcuXIqXbq02rVrpw0bNlz0awQAAChpCNoAAOBjNm/erNWrVysoKEiSNGHCBL333nuaPn26fv/9dw0ePFh33XWXvv32W0nSgQMHdPXVV8tut+ubb77R+vXrde+99yo9PV2SdPLkSfXt21erVq3Sjz/+qDp16qhbt246efJkkV0jAABAScD0KAAAfMAXX3yhsLAwpaenKyUlRX5+fnr99deVkpKi5557Tl9//bViY2MlSTVr1tSqVav05ptvql27dpo6dapKly6tefPmKTAwUJJ06aWXOtvu0KGDy2e99dZbioyM1Lfffqsbbrjh4l0kAABACUPQBgAAH3DNNddo2rRpOn36tCZPnqyAgADdeuut+v3335WcnKxOnTq51E9NTVXz5s0lSZs2bdJVV13lDNicLzExUU8//bRWrlypQ4cOKSMjQ8nJydq7d6/HrwsAAKAkI2gDAIAPKFWqlGrXri1JmjVrlpo2baqZM2eqUaNGkqQvv/xSlStXdjnHbrdLkkJCQvJsu2/fvjpy5IheeeUVVatWTXa7XbGxsUpNTfXAlQAAAPgOgjYAAPgYPz8/PfXUUxoyZIj+/PNP2e127d27V+3atcuxfpMmTfTuu+8qLS0tx2ybH374QW+88Ya6desmSdq3b58OHz7s0WsAAADwBSxEDACAD+rZs6f8/f315ptv6oknntDgwYP17rvvaufOndqwYYNee+01vfvuu5KkgQMHKikpSXfccYfWrVun7du3a86cOdq2bZskqU6dOpozZ462bNmin376Sb17975gdg4AAAAujEwbAAB8UEBAgAYOHKgXXnhB8fHxKl++vCZMmKBdu3YpMjJSLVq00FNPPSVJuuSSS/TNN99o6NChateunfz9/dWsWTO1bdtWkjRz5kw9+OCDatGihWJiYvTcc8/piSeeKMrLAwAAKBFsxhhT1J0AAAAAAACAK6ZHAQAAAAAAeCGCNgAAAAAAAF6IoA0AAAAAAIAXImgDAAAAAADw/+3YsQAAAADAIH/raewojIakDQAAAMCQtAEAAAAYkjYAAAAAQ9IGAAAAYEjaAAAAAAxJGwAAAIAhaQMAAAAwFO9fyjMClgC2AAAAAElFTkSuQmCC", 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", "text/plain": [ "<Figure size 1500x600 with 2 Axes>" ] @@ -473,17 +993,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-10 11:23:17,065 - INFO - Threshold 0.8 not found for level Subclass in precision_vs_recall_multi_conf_15.\n", - "2026-02-10 11:23:17,073 - INFO - Threshold 0.8 not found for level Subclass in precision_vs_recall_multi_conf_1.\n" - ] - }, { "data": { - "image/png": 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", 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"text/plain": [ "<Figure size 1500x600 with 2 Axes>" ] @@ -494,19 +1006,29 @@ { "data": { "text/plain": [ - "{'Section': {'accuracy': 0.0,\n", - " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}},\n", - " 'Division': {'accuracy': 0.0,\n", - " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}},\n", - " 'Group': {'accuracy': 0.0,\n", - " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}},\n", - " 'Class': {'accuracy': 0.0,\n", - " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}},\n", - " 'Subclass': {'accuracy': 0.0,\n", - " 'f1_score': {'micro': 0.0, 'macro': 0.0, 'weighted': 0.0}}}" + "{'Section': {'accuracy': 0.8040562120728202,\n", + " 'f1_score': {'micro': 0.8040562120728202,\n", + " 'macro': 0.7317102737711878,\n", + " 'weighted': 0.7971678399374031}},\n", + " 'Division': {'accuracy': 0.7080804854679016,\n", + " 'f1_score': {'micro': 0.7080804854679016,\n", + " 'macro': 0.6601359714309609,\n", + " 'weighted': 0.6984902770594784}},\n", + " 'Group': {'accuracy': 0.6304694985627595,\n", + " 'f1_score': {'micro': 0.6304694985627595,\n", + " 'macro': 0.5515670262798558,\n", + " 'weighted': 0.6174423413292373}},\n", + " 'Class': {'accuracy': 0.5803257745129352,\n", + " 'f1_score': {'micro': 0.5803257745129352,\n", + " 'macro': 0.4820367238994799,\n", + " 'weighted': 0.5647718922099709}},\n", + " 'Subclass': {'accuracy': 0.5582880868732034,\n", + " 'f1_score': {'micro': 0.5582880868732034,\n", + " 'macro': 0.44428761509153464,\n", + " 'weighted': 0.5373548107634541}}}" ] }, - "execution_count": 9, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" }