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'B-Gene\\n']\n", "['is', 'O\\n']\n", "['expressed', 'O\\n']\n", "['in', 'O\\n']\n", "['the', 'O\\n']\n", "['mature', 'O\\n']\n", "['mouse', 'O\\n']\n", "['retina', 'O\\n']\n", "['.', 'O\\n']\n", "[' ', ' \\n']\n", "['Our', 'O\\n']\n", "['findings', 'O\\n']\n", "['reveal', 'O\\n']\n", "['another', 'O\\n']\n", "['human', 'O\\n']\n", "['visual', 'O\\n']\n", "['-', 'O\\n']\n", "['disease', 'O\\n']\n", "['gene', 'O\\n']\n", "['that', 'O\\n']\n", "['causes', 'O\\n']\n", "['LCA', 'O\\n']\n", "['and', 'O\\n']\n", "['juvenile', 'O\\n']\n", "['RP', 'O\\n']\n", "['.', 'O\\n']\n", "[' ', ' \\n']\n" ] } ], "source": [ "import re\n", "pattern = r\"#\\d+\"\n", "with open(\"/mnt/c/Users/MAIERS3/OneDrive - ZF Friedrichshafen AG/Documents/DHBW/DHBW Master/Advanced Machine Learning und XAI/NER_Abgabe/seth/SETH-train.iob\",\"r\") as fin:\n", " lines = fin.readlines()\n", "document = {\"id\":None,\n", " \"tokens\":[],\n", " \"labels\":[]\n", " }\n", "documents = []\n", "tokenSet = {}\n", "labelSet = {}\n", "\n", "def addToTokenSet(token:str)->None:\n", " if token in tokenSet.keys():\n", " tokenSet[token]+=1\n", " else:\n", " tokenSet[token] = 1\n", "\n", "def addToLabelSet(label:str)->None:\n", " if label in labelSet.keys():\n", " labelSet[label]+=1\n", " else:\n", " labelSet[label] = 1\n", "\n", "for idx,line in enumerate(lines):\n", " match = re.match(pattern, line)\n", " #Überspringe erste Zeile weil das ein Header ist\n", " if idx == 0:\n", " continue\n", " # Here ist die Dokumented ID\n", " if match:\n", " print(\"Document Tag detected\")\n", " print(line)\n", " if document[\"id\"] != None:\n", " # Speichere altes Dokument bevor ein neues angelegt wird\n", " documents.append(document)\n", " else:\n", " #Initialisiere neues DOkument\n", " document = {\"id\":int(line[1:]), # Speichere nur die Nummer ohne die Raute\n", " \"tokens\":[],\n", " \"labels\":[]\n", " }\n", " # Hier detectieren wir ein Komma weil sonst das parsen mit einem CSV Konverter wie in Pandas fehl schlägt\n", " elif len(line.split(\",\")) >2:\n", " print(\"Komma Detected\")\n", " print(line)\n", " document[\"tokens\"].append(line[0])\n", " document[\"labels\"].append(line[2].split(\"\\n\")[0])\n", " addToTokenSet(\",\")\n", " addToLabelSet(line[2].split(\"\\n\")[0])\n", " # Sonst gehen wir einfach vom standard aus Word sowie Tag\n", " else:\n", " print(line.split(\",\"))\n", " word_tag = line.split(\",\")\n", " document[\"tokens\"].append(word_tag[0])\n", " document[\"labels\"].append(word_tag[1].split(\"\\n\")[0])\n", " addToTokenSet(word_tag[0])\n", " addToLabelSet(word_tag[1].split(\"\\n\")[0])\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'O': 101406, 'B-Gene': 1930, ' ': 4007, 'B-SNP': 734, 'I-SNP': 1209, ',': 32, 'I-Gene': 1247, 'I': 460, '6': 6, '0': 16, '1': 10, '3': 6, '9': 6, '5': 6, 'o': 1, '2': 11, '7': 3, '4': 4, 'g': 1, 'M': 1, 'B-RS': 9}\n" ] } ], "source": [ "print(labelSet)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "myenv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 2 }