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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kütüphaneler eklenmesi"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"import pandas as pd \n",
"from pymongo import MongoClient\n",
"from transformers import BertTokenizer, BertForMaskedLM, DPRContextEncoderTokenizer,DPRContextEncoder;\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parquet dosyalarının dataframe olarak yüklenmesi(okuma yapabilmek için)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Parquet dosyalarını DataFrame olarak yükleyin\n",
"train_df1 = pd.read_parquet('C:\\\\gitProjects\\\\yeni\\\\wikipedia-tr\\\\data\\\\train-00000-of-00002-ed6b025df7a1f653.parquet')\n",
"train_df2 = pd.read_parquet('C:\\\\gitProjects\\\\yeni\\\\wikipedia-tr\\\\data\\\\train-00001-of-00002-0aa63953f8b51c17.parquet')\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# İki DataFrame'i birleştirin\n",
"merged_train = pd.concat([train_df1, train_df2], ignore_index=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Örneğin %80 train, %20 test olarak ayırın\n",
"train_data = merged_train.sample(frac=0.8, random_state=42)\n",
"test_data = merged_train.drop(train_data.index)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Dosya yolları\n",
"train_dir = 'C:\\\\gitProjects\\\\yeni\\\\datasets\\\\train_Egitim'\n",
"test_dir = 'C:\\\\gitProjects\\\\yeni\\\\datasets\\\\test_Egitim'\n",
"train_file_path = os.path.join(train_dir, 'merged_train.parquet')\n",
"test_file_path = os.path.join(test_dir, 'merged_test.parquet')\n",
"\n",
"# Dizinlerin var olup olmadığını kontrol etme, gerekirse oluşturma\n",
"os.makedirs(train_dir, exist_ok=True)\n",
"os.makedirs(test_dir, exist_ok=True)\n",
"\n",
"# Veriyi .parquet formatında kaydetme\n",
"train_data.to_parquet(train_file_path)\n",
"test_data.to_parquet(test_file_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dataframe deki bilgileri görme "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id url \\\n",
"515773 3525037 https://tr.wikipedia.org/wiki/P%C5%9F%C4%B1qo%... \n",
"517811 3532700 https://tr.wikipedia.org/wiki/Craterolophinae \n",
"436350 3203545 https://tr.wikipedia.org/wiki/Notocrabro \n",
"223281 1765445 https://tr.wikipedia.org/wiki/Ibrahim%20Sissoko \n",
"100272 575462 https://tr.wikipedia.org/wiki/Salah%20Cedid \n",
"\n",
" title text \n",
"515773 Pşıqo Ahecaqo Pşıqo Ahecaqo (), Çerkes siyasetçi, askeri kom... \n",
"517811 Craterolophinae Craterolophinae, Depastridae familyasına bağlı... \n",
"436350 Notocrabro Notocrabro Crabronina oymağına bağlı bir cinst... \n",
"223281 Ibrahim Sissoko İbrahim Sissoko (d. 30 Kasım 1991), Fildişi Sa... \n",
"100272 Salah Cedid Salah Cedid (1926-1993) (Arapça: صلاح جديد) Su... \n",
" id url title \\\n",
"5 35 https://tr.wikipedia.org/wiki/Karl%20Marx Karl Marx \n",
"13 48 https://tr.wikipedia.org/wiki/Ruhi%20Su Ruhi Su \n",
"15 53 https://tr.wikipedia.org/wiki/Bilgisayar Bilgisayar \n",
"18 59 https://tr.wikipedia.org/wiki/Edebiyat Edebiyat \n",
"19 64 https://tr.wikipedia.org/wiki/M%C3%BChendislik Mühendislik \n",
"\n",
" text \n",
"5 Karl Marx (; 5 Mayıs 1818, Trier – 14 Mart 188... \n",
"13 Mehmet Ruhi Su (1 Ocak 1912, Van - 20 Eylül 19... \n",
"15 Bilgisayar, aritmetik veya mantıksal işlem diz... \n",
"18 Edebiyat, yazın veya literatür; olay, düşünce,... \n",
"19 Mühendis, insanların her türlü ihtiyacını karş... \n"
]
}
],
"source": [
"print(train_data.head())\n",
"print(test_data.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MongoDb'ye bağlama ve bilgi çekme "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Veriler başarıyla Collection(Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'EgitimDatabase'), 'train') MongoDb koleksiyonuna indirildi.\n",
" Veriler başarıyla Collection(Database(MongoClient(host=['localhost:27017'], document_class=dict, tz_aware=False, connect=True), 'EgitimDatabase'), 'test') MongoDb koleksiyonuna indirildi.\n"
]
}
],
"source": [
"import pandas as pd\n",
"from pymongo import MongoClient\n",
"\n",
"def get_mongodb(database_name='EgitimDatabase', train_collection_name='train', test_collection_name='test', host='localhost', port=27017):\n",
" \"\"\"\n",
" MongoDB connection and collection selection for train and test collections.\n",
" \"\"\"\n",
" client = MongoClient(f'mongodb://{host}:{port}/')\n",
" \n",
" # Veritabanını seçin\n",
" db = client[database_name]\n",
" \n",
" # Train ve test koleksiyonlarını seçin\n",
" train_collection = db[train_collection_name]\n",
" test_collection = db[test_collection_name]\n",
" \n",
" return train_collection, test_collection\n",
"\n",
"# Function to load dataset into MongoDB\n",
"def dataset_read(train_file_path,test_file_path):\n",
" data_train = pd.read_parquet(train_file_path, columns=['id', 'url', 'title', 'text'])\n",
" data_test = pd.read_parquet(test_file_path, columns=['id', 'url', 'title', 'text'])\n",
" data_dict_train = data_train.to_dict(\"records\")\n",
" data_dict_test = data_test.to_dict(\"records\")\n",
"\n",
"\n",
"\n",
" # Get the MongoDB collections\n",
" train_collection, test_collection = get_mongodb(database_name='EgitimDatabase')\n",
"\n",
" \n",
"\n",
" # Insert data into MongoDB\n",
" train_collection.insert_many(data_dict_train)\n",
" test_collection.insert_many(data_dict_test)\n",
"\n",
"\n",
" print(f\" Veriler başarıyla {train_collection} MongoDb koleksiyonuna indirildi.\")\n",
" print(f\" Veriler başarıyla {test_collection} MongoDb koleksiyonuna indirildi.\")\n",
" return train_collection,test_collection\n",
"\n",
"# Train ve test datasetlerini MongoDB'ye yüklemek için fonksiyonu çağır\n",
"train_file_path = 'C:\\\\gitProjects\\\\yeni\\\\datasets\\\\train_Egitim\\\\merged_train.parquet'\n",
"test_file_path = 'C:\\\\gitProjects\\\\yeni\\\\datasets\\\\test_Egitim\\\\merged_test.parquet'\n",
"\n",
"train_collection, test_collection = dataset_read(train_file_path, test_file_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pymongo import MongoClient,errors\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sentence_transformers import SentenceTransformer\n",
"\n",
"# MongoDB bağlantı ve koleksiyon seçimi için fonksiyon\n",
"def get_mongodb(database_name='EgitimDatabase', train_collection_name='train', test_collection_name='test', host='localhost', port=27017):\n",
" client = MongoClient(f'mongodb://{host}:{port}/')\n",
" db = client[database_name]\n",
" train_collection = db[train_collection_name]\n",
" test_collection = db[test_collection_name]\n",
" return train_collection, test_collection\n",
"\n",
"# Dataset'i MongoDB'ye yükleme fonksiyonu\n",
"def dataset_read(train_file_path, test_file_path):\n",
" try:\n",
" # MongoDB koleksiyonlarını al\n",
" train_collection, test_collection = get_mongodb()\n",
"\n",
" # Eğer koleksiyonlar zaten doluysa, veri yüklemesi yapma\n",
" if train_collection.estimated_document_count() > 0 or test_collection.estimated_document_count() > 0:\n",
" print(\"Veriler zaten yüklendi, işlem yapılmadı.\")\n",
" return train_collection, test_collection\n",
"\n",
" # Datasetleri oku\n",
" data_train = pd.read_parquet(train_file_path, columns=['id', 'url', 'title', 'text'])\n",
" data_test = pd.read_parquet(test_file_path, columns=['id', 'url', 'title', 'text'])\n",
"\n",
" # Verileri MongoDB'ye yükle\n",
" train_collection.insert_many(data_train.to_dict(\"records\"))\n",
" test_collection.insert_many(data_test.to_dict(\"records\"))\n",
"\n",
" print(f\"Veriler başarıyla {train_collection.name} koleksiyonuna yüklendi.\")\n",
" print(f\"Veriler başarıyla {test_collection.name} koleksiyonuna yüklendi.\")\n",
" \n",
" except errors.PyMongoError as e:\n",
" print(f\"Veri yükleme sırasında hata oluştu: {e}\")\n",
"\n",
" return train_collection, test_collection\n",
"\n",
"\n",
"\n",
"# Database sınıfı: Veritabanı bağlantıları ve verileri çekme işlevleri\n",
"class Database:\n",
" @staticmethod\n",
" def get_mongodb():\n",
" return get_mongodb()\n",
"\n",
" @staticmethod\n",
" def get_titles_and_texts():\n",
" # MongoDB bağlantısı ve koleksiyonları al\n",
" train_collection, _ = Database.get_mongodb()\n",
"\n",
" # Sorgu: Hem \"title\" hem de \"text\" alanı mevcut olan belgeler\n",
" query = {\"title\": {\"$exists\": True}, \"text\": {\"$exists\": True}}\n",
"\n",
" # Belirtilen alanları seçiyoruz: \"title\", \"text\"\n",
" cursor = train_collection.find(query, {\"title\": 1, \"text\": 1, \"_id\": 0})\n",
"\n",
" # Başlık ve metinleri doğru bir şekilde birleştiriyoruz\n",
" documents = [{\"title\": doc['title'], \"text\": doc['text']} for doc in cursor]\n",
" document_count = len(documents)\n",
" return documents, document_count\n",
"\n",
"# Train ve test datasetlerini MongoDB'ye yüklemek için fonksiyonu çağır\n",
"train_file_path = 'C:\\\\gitProjects\\\\yeni\\\\datasets\\\\train_Egitim\\\\merged_train.parquet'\n",
"test_file_path = 'C:\\\\gitProjects\\\\yeni\\\\datasets\\\\test_Egitim\\\\merged_test.parquet'\n",
"\n",
"train_collection, test_collection = dataset_read(train_file_path, test_file_path)\n",
"\n",
"# Veritabanından başlıklar ve metinler alınır\n",
"documents, document_count = Database.get_titles_and_texts()\n",
"\n",
"# Sonuçların belirlenmesi\n",
"print(f\"Başlık ve metin çiftleri: {documents}\")\n",
"print(f\"Toplam çift sayısı: {document_count}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MongoDb üzerinden title ve text verilerinin çekilmesi "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"@staticmethod\n",
" def get_input_titles():\n",
" collection = Database.get_mongodb(collection_name='train')\n",
" query = {\"title\": {\"$exists\": True}}\n",
" cursor = collection.find(query, {\"title\": 1, \"_id\": 0})\n",
" title_from_db = [doc['title'] for doc in cursor]\n",
"\n",
" return title_from_db\"\"\"\n",
"\n",
"\"\"\"@staticmethod\n",
" def get_input_texts():\n",
" collection = Database.get_mongodb(collection_name='train')\n",
" query = {\"texts\": {\"$exists\": True}}\n",
" cursor = collection.find(query, {\"texts\": 1, \"_id\": 0})\n",
" texts_from_db = [doc['texts'] for doc in cursor]\n",
" return texts_from_db\"\"\"\n",
" \n",
" #bin tane veri çekerek csv dosyası olarak kaydetme \n",
" \n",
" \n",
"\"\"\"@staticmethod\n",
" def get_titles_and_texts(batch_size=1000):\n",
"\n",
" \n",
" titles = Database.get_input_titles(batch_size=batch_size)\n",
" texts = Database.get_input_texts(batch_size=batch_size )\n",
" \n",
"\n",
"\n",
" def test_queries():\n",
"\n",
" collection = Database.get_mongodb(collection_name='train')\n",
" # Başlık sorgusu\n",
" titles_cursor = collection.find({\"title\": {\"$exists\": True}}, {\"title\": 1, \"_id\": 0})\n",
" titles = [doc['title'] for doc in titles_cursor]\n",
" \n",
"\n",
" # Metin sorgusu\n",
" texts_cursor = collection.find({\"text\": {\"$exists\": True}}, {\"text\": 1, \"_id\": 0})\n",
" texts = [doc['text'] for doc in texts_cursor]\n",
" \n",
" # Başlık ve metinlerin eşleşmesini sağlamak için zip kullanarak birleştiriyoruz\n",
" documents = [{\"title\": title, \"text\": text} for title, text in zip(titles, texts)]\n",
" document_count = len(documents)\n",
" return documents, document_count\n",
"\n",
"Database.test_queries()\n",
"\n",
"# Veritabanından başlıklar ve metinler alınır\n",
"documents, document_count = Database.get_titles_and_texts(batch_size=1000)\n",
"\n",
"# Sonuçların belirlenmesi\n",
"print(f\"Başlık ve metin çiftleri: {documents}\")\n",
"print(f\"Toplam çift sayısı: {document_count}\")\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Output'u vereceğimiz title ve textin kodu"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 **Pşıqo Ahecaqo** Pşıqo Ahecaqo (), Çerkes siy...\n",
"1 **Craterolophinae** Craterolophinae, Depastrid...\n",
"2 **Notocrabro** Notocrabro Crabronina oymağına ...\n",
"3 **Ibrahim Sissoko** İbrahim Sissoko (d. 30 Kas...\n",
"4 **Salah Cedid** Salah Cedid (1926-1993) (Arapç...\n",
"Name: combined, dtype: object\n",
"Veriler combined_output.csv dosyasına başarıyla kaydedildi.\n"
]
}
],
"source": [
"from pymongo import MongoClient\n",
"import pandas as pd\n",
"from tqdm.auto import tqdm, trange\n",
"\n",
"# Database bağlantıları ve verileri çekme işlevleri\n",
"class Database:\n",
" @staticmethod\n",
" def get_mongodb(database_name='EgitimDatabase', train_collection_name='train', test_collection_name='test', host='localhost', port=27017):\n",
" client = MongoClient(f'mongodb://{host}:{port}/')\n",
" db = client[database_name]\n",
" train_collection = db[train_collection_name]\n",
" test_collection = db[test_collection_name]\n",
" return train_collection, test_collection\n",
"\n",
" def export_to_csv(batch_size=1000, output_file='combined_output.csv'):\n",
" train_collection, _ = Database.get_mongodb()\n",
" cursor = train_collection.find({}, {\"title\": 1, \"text\": 1, \"_id\": 0})\n",
" cursor = cursor.batch_size(batch_size) # Fix: Call batch_size on the cursor object\n",
"\n",
" # Verileri DataFrame'e dönüştürme\n",
" df= pd.DataFrame(list(cursor))\n",
" \n",
" # title ve text sütunlarını birleştirme\n",
" df['combined'] = df.apply(lambda row: f'**{row[\"title\"]}** {row[\"text\"]}', axis=1)\n",
" \n",
" #title,text and combined sütunlarını ayrı ayrı tutma\n",
" #df2['title_only'] = df2['title']\n",
" #df2['text_only'] = df2['text']\n",
" #df['combined']= output_file\n",
"\n",
" # Sonuçları kontrol etme\n",
" combined_text= df['combined'] \n",
" # Print the combined column directly\n",
" \n",
" print(combined_text.head())\n",
"\n",
" # Birleşmiş verileri CSV'ye kaydetme\n",
" \n",
" df.to_csv(output_file, index=False)\n",
" \n",
" print(f\"Veriler combined_output.csv dosyasına başarıyla kaydedildi.\")\n",
" \n",
"\n",
"# CSV dosyasını okuma ve birleştirme işlemi\n",
"Database.export_to_csv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TF-IDF HESAPLAMA"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package wordnet to\n",
"[nltk_data] C:\\Users\\info\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n",
"[nltk_data] Downloading package omw-1.4 to\n",
"[nltk_data] C:\\Users\\info\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package omw-1.4 is already up-to-date!\n",
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] C:\\Users\\info\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"ename": "ValueError",
"evalue": "empty vocabulary; perhaps the documents only contain stop words",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[20], line 100\u001b[0m\n\u001b[0;32m 97\u001b[0m documents, document_count \u001b[38;5;241m=\u001b[39m Database\u001b[38;5;241m.\u001b[39mget_input_documents()\n\u001b[0;32m 99\u001b[0m \u001b[38;5;66;03m# Calculate TF-IDF and get feature names\u001b[39;00m\n\u001b[1;32m--> 100\u001b[0m tfidf_matrix, feature_names \u001b[38;5;241m=\u001b[39m \u001b[43mDatabase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate_tfidf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mturkish_stop_words\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 102\u001b[0m \u001b[38;5;66;03m# Extract keywords\u001b[39;00m\n\u001b[0;32m 103\u001b[0m keywords \u001b[38;5;241m=\u001b[39m Database\u001b[38;5;241m.\u001b[39mextract_keywords(tfidf_matrix, feature_names, stop_words\u001b[38;5;241m=\u001b[39mturkish_stop_words)\n",
"Cell \u001b[1;32mIn[20], line 43\u001b[0m, in \u001b[0;36mDatabase.calculate_tfidf\u001b[1;34m(documents, stop_words)\u001b[0m\n\u001b[0;32m 40\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[0;32m 41\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalculate_tfidf\u001b[39m(documents, stop_words):\n\u001b[0;32m 42\u001b[0m vectorizer \u001b[38;5;241m=\u001b[39m TfidfVectorizer(stop_words\u001b[38;5;241m=\u001b[39mstop_words, max_features\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10000\u001b[39m,min_df\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m---> 43\u001b[0m tfidf_matrix \u001b[38;5;241m=\u001b[39m \u001b[43mvectorizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 44\u001b[0m feature_names \u001b[38;5;241m=\u001b[39m vectorizer\u001b[38;5;241m.\u001b[39mget_feature_names_out()\n\u001b[0;32m 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tfidf_matrix, feature_names\n",
"File \u001b[1;32mc:\\gitProjects\\yeni\\.venv\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:2091\u001b[0m, in \u001b[0;36mTfidfVectorizer.fit_transform\u001b[1;34m(self, raw_documents, y)\u001b[0m\n\u001b[0;32m 2084\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_params()\n\u001b[0;32m 2085\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf \u001b[38;5;241m=\u001b[39m TfidfTransformer(\n\u001b[0;32m 2086\u001b[0m norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm,\n\u001b[0;32m 2087\u001b[0m use_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_idf,\n\u001b[0;32m 2088\u001b[0m smooth_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msmooth_idf,\n\u001b[0;32m 2089\u001b[0m sublinear_tf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msublinear_tf,\n\u001b[0;32m 2090\u001b[0m )\n\u001b[1;32m-> 2091\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2092\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[0;32m 2093\u001b[0m \u001b[38;5;66;03m# X is already a transformed view of raw_documents so\u001b[39;00m\n\u001b[0;32m 2094\u001b[0m \u001b[38;5;66;03m# we set copy to False\u001b[39;00m\n",
"File \u001b[1;32mc:\\gitProjects\\yeni\\.venv\\lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1466\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1471\u001b[0m )\n\u001b[0;32m 1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[1;32mc:\\gitProjects\\yeni\\.venv\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:1372\u001b[0m, in \u001b[0;36mCountVectorizer.fit_transform\u001b[1;34m(self, raw_documents, y)\u001b[0m\n\u001b[0;32m 1364\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 1365\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUpper case characters found in\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1366\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m vocabulary while \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlowercase\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1367\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m is True. These entries will not\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1368\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m be matched with any documents\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1369\u001b[0m )\n\u001b[0;32m 1370\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m-> 1372\u001b[0m vocabulary, X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_count_vocab\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfixed_vocabulary_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1374\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbinary:\n\u001b[0;32m 1375\u001b[0m X\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mfill(\u001b[38;5;241m1\u001b[39m)\n",
"File \u001b[1;32mc:\\gitProjects\\yeni\\.venv\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:1278\u001b[0m, in \u001b[0;36mCountVectorizer._count_vocab\u001b[1;34m(self, raw_documents, fixed_vocab)\u001b[0m\n\u001b[0;32m 1276\u001b[0m vocabulary \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(vocabulary)\n\u001b[0;32m 1277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vocabulary:\n\u001b[1;32m-> 1278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 1279\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mempty vocabulary; perhaps the documents only contain stop words\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1280\u001b[0m )\n\u001b[0;32m 1282\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m indptr[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m>\u001b[39m np\u001b[38;5;241m.\u001b[39miinfo(np\u001b[38;5;241m.\u001b[39mint32)\u001b[38;5;241m.\u001b[39mmax: \u001b[38;5;66;03m# = 2**31 - 1\u001b[39;00m\n\u001b[0;32m 1283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _IS_32BIT:\n",
"\u001b[1;31mValueError\u001b[0m: empty vocabulary; perhaps the documents only contain stop words"
]
}
],
"source": [
"#---------------------------güncel en yeni \n",
"from pymongo import MongoClient\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from textblob import TextBlob as tb\n",
"import numpy as np\n",
"import math\n",
"from tqdm.auto import tqdm, trange\n",
"import tensorflow as tf\n",
"import nltk\n",
"from nltk.stem import WordNetLemmatizer\n",
"from nltk.corpus import stopwords\n",
"\n",
"turkish_stop_words = stopwords.words('turkish')\n",
"\n",
"nltk.download('wordnet')\n",
"nltk.download('omw-1.4')\n",
"nltk.download('stopwords')\n",
"\n",
"\n",
"import matplotlib.pyplot as plt \n",
"\n",
"class Database:\n",
" @staticmethod\n",
" def get_mongodb():\n",
" return 'mongodb://localhost:27017/', 'combined', 'combined_output'\n",
"\n",
" # Get input documents from MongoDB\n",
" @staticmethod\n",
" def get_input_documents(limit=1000):\n",
" mongo_url, db_name, collection_name = Database.get_mongodb()\n",
" client = MongoClient(mongo_url)\n",
" db = client[db_name]\n",
" collection = db[collection_name]\n",
" cursor = collection.find().limit(limit)\n",
" combined_text = [doc['text'] for doc in cursor]\n",
" document_count = len(combined_text)\n",
" return combined_text, document_count\n",
" \n",
" # Calculate TF-IDF and get feature names\n",
" @staticmethod\n",
" def calculate_tfidf(documents, stop_words):\n",
" vectorizer = TfidfVectorizer(stop_words=stop_words, max_features=10000,min_df=2)\n",
" tfidf_matrix = vectorizer.fit_transform(documents)\n",
" feature_names = vectorizer.get_feature_names_out()\n",
" return tfidf_matrix, feature_names\n",
"\n",
" # Extract keywords using TF-IDF\n",
" def extract_keywords(tfidf_matrix, feature_names, top_n=10, stop_words=[]):\n",
" keywords = {}\n",
" for doc_idx, row in enumerate(tfidf_matrix):\n",
" filtered_feature_names = [name for name in feature_names if name.lower() not in stop_words]\n",
" scores = np.asarray(row.T.todense()).flatten()\n",
" sorted_indices = np.argsort(scores)[::-1]\n",
" top_features = sorted_indices[:top_n]\n",
" doc_keywords = [(filtered_feature_names[idx], scores[idx]) for idx in top_features]\n",
" keywords[f'document_{doc_idx+1}'] = doc_keywords\n",
" return keywords\n",
" \n",
" #zip keywords and combined text \n",
" \n",
" # Identify low TF-IDF words\n",
" @staticmethod\n",
" def identify_low_tfidf_words(tfidf_matrix, feature_names, threshold=0.001):\n",
" avg_scores = np.mean(tfidf_matrix, axis=0).A1\n",
" low_tfidf_words = [feature_names[i] for i, score in enumerate(avg_scores) if score < threshold]\n",
" return low_tfidf_words\n",
" \n",
" # Update stop words with low TF-IDF words\n",
" @staticmethod\n",
" def update_stop_words(existing_stop_words, low_tfidf_words):\n",
" updated_stop_words = set(existing_stop_words) | set(low_tfidf_words)\n",
" return list(updated_stop_words)\n",
"\n",
"\n",
"#tf-ıdf ile döküman içerisinden kelime seçme \n",
"#Term Frequency (TF): Bir kelimenin belli bir dökümanda tekrar etme değeri\n",
"#Inverse Document Frequency (IDF):bir kelimenin tüm dökümanlar arasındaki yaygınlığı Nadir bulunan kelimeler, daha yüksek IDF değerine sahip olur.\n",
"#tf-ıdf skoru ise bu ikisinin çarpımıdır.\n",
"\n",
" #buraya eşik değer belirlenmeli\n",
"\n",
"\n",
"turkish_stop_words = [\n",
" 'ah', 'ama', 'an', 'ancak', 'araba', 'aralar', 'aslında', \n",
" 'b', 'başlayan', 'bağlı', 'bazı', 'belirli', 'ben', 'bence', \n",
" 'birkaç', 'birlikte', 'bunu', 'burada', 'biten', 'biz', \n",
" 'bu', 'buna', 'çünkü', 'da', 'de', 'demek', 'den', 'derken', \n",
" 'değil', 'daha', 'dolayı', 'edilir', 'eğer', 'en', 'fakat', \n",
" 'genellikle', 'gibi', 'hem', 'her', 'herhangi', 'hiç', 'ise', \n",
" 'işte', 'itibaren', 'iyi', 'kadar', 'karşı', 'ki', 'kime', \n",
" 'kısaca', 'mu', 'mü', 'nasıl', 'ne', 'neden', 'niye', 'o', \n",
" 'olasılıkla', 'olabilir', 'oluşur', 'önce', 'şu', 'sadece', \n",
" 'se', 'şey', 'şimdi', 'tabi', 'tüm', 've', 'ya', 'ya da', \n",
" 'yanı', 'yani', 'yılında', 'yetenekli', 'yine'\n",
"]\n",
"# Get input documents\n",
"documents, document_count = Database.get_input_documents()\n",
"\n",
"# Calculate TF-IDF and get feature names\n",
"tfidf_matrix, feature_names = Database.calculate_tfidf(documents, turkish_stop_words)\n",
"\n",
"# Extract keywords\n",
"keywords = Database.extract_keywords(tfidf_matrix, feature_names, stop_words=turkish_stop_words)\n",
"print(keywords)\n",
"\n",
"# Identify low TF-IDF words\n",
"low_tfidf_words = Database.identify_low_tfidf_words(tfidf_matrix, feature_names)\n",
"print(low_tfidf_words)\n",
"\n",
"# Update stop words\n",
"updated_stop_words = Database.update_stop_words(turkish_stop_words, low_tfidf_words)\n",
"print(updated_stop_words) "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "unhashable type: 'set'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[15], line 162\u001b[0m\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tfidf_matrix, feature_names,keywords\n\u001b[0;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 162\u001b[0m tfidf_matrix, feature_names,keywords\u001b[38;5;241m=\u001b[39m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 164\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAnahtar Kelimler:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 165\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m doc, words \u001b[38;5;129;01min\u001b[39;00m keywords\u001b[38;5;241m.\u001b[39mitems():\n",
"Cell \u001b[1;32mIn[15], line 148\u001b[0m, in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m 146\u001b[0m initial_stop_words \u001b[38;5;241m=\u001b[39m turkish_stop_words\n\u001b[0;32m 147\u001b[0m \u001b[38;5;66;03m# Stop-words listesini iteratif olarak güncelleyin\u001b[39;00m\n\u001b[1;32m--> 148\u001b[0m final_stop_words \u001b[38;5;241m=\u001b[39m \u001b[43miterative_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocuments_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_stop_words\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 149\u001b[0m \u001b[38;5;66;03m#tf-ıdf hesaplama\u001b[39;00m\n\u001b[0;32m 150\u001b[0m tfidf_matrix, feature_names\u001b[38;5;241m=\u001b[39mcalculate_tfidf(documents_list,final_stop_words)\n",
"Cell \u001b[1;32mIn[15], line 127\u001b[0m, in \u001b[0;36miterative_update\u001b[1;34m(documents, initial_stop_words, iterations)\u001b[0m\n\u001b[0;32m 126\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21miterative_update\u001b[39m(documents, initial_stop_words, iterations\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m):\n\u001b[1;32m--> 127\u001b[0m stop_words \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minitial_stop_words\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 128\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(iterations):\n\u001b[0;32m 129\u001b[0m tfidf_matrix, feature_names \u001b[38;5;241m=\u001b[39m calculate_tfidf(documents, stop_words)\n",
"\u001b[1;31mTypeError\u001b[0m: unhashable type: 'set'"
]
}
],
"source": [
"\n",
"\n",
"\"\"\"class Tf:\n",
" @staticmethod\n",
" def tf(word, blob):\n",
" return blob.words.count(word) / len(blob.words)\n",
"\n",
" @staticmethod\n",
" def n_containing(word, bloblist):\n",
" return sum(1 for blob in bloblist if word in blob.words)\n",
"\n",
" @staticmethod\n",
" def idf(word, bloblist):\n",
" return math.log(len(bloblist) / (1 + Tf.n_containing(word, bloblist)))\n",
"\n",
" @staticmethod\n",
" def tfidf(word, blob, bloblist):\n",
" return Tf.tf(word, blob) * Tf.idf(word, bloblist)\n",
"\n",
" @staticmethod\n",
" def get_input_documents(limit=1000):\n",
" return Database.get_input_documents(limit)\"\"\"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \"\"\"\n",
" Her döküman için anahtar kelimeleri seç.\n",
" :param tfidf_matrix: TF-IDF matris\n",
" :param feature_names: TF-IDF özellik isimleri\n",
" :param top_n: Her döküman için seçilecek anahtar kelime sayısı\n",
" :return: Anahtar kelimeler ve skorlari\n",
" \"\"\"\n",
" \n",
"\n",
"#--------------------------------------------------------------- burada aldığımız dökümanları listeliyoruz\n",
"# Dokümanları işleyerek TF-IDF hesaplama\n",
"#bloblist dökümanların bir listesi\n",
"\"\"\"bloblist = []\n",
"for i, blob in enumerate(bloblist):\n",
" print(\"Top words in document {}\".format(i + 1))\n",
" scores = {word: Tf.tfidf(word, blob, bloblist) for word in blob.words} #dökümanların içerisinde bulunan kelimeleri alır.\n",
" sorted_words = sorted(scores.items(), key=lambda x: x[1], reverse=True)\n",
" for word, score in sorted_words[:3]:\n",
" print(\"\\tWord: {}, TF-IDF: {}\".format(word, round(score, 5)))\"\"\"\n",
"\n",
"\n",
"# Dökümanları isimlendir\n",
"#named_documents = {f'döküman {i+1}': doc for i, doc in enumerate(combined_text)}\n",
"\n",
"#features olarak top_keywordsleri belirleyerek metnin bu kelimelerin etrafında olması sağlanmalı \n",
"def calculate_tfidf(documents, stop_words):\n",
" vectorizer = TfidfVectorizer(stop_words=stop_words, max_features=10000)\n",
" tfidf_matrix = vectorizer.fit_transform(documents)\n",
" feature_names = vectorizer.get_feature_names_out()\n",
" return tfidf_matrix, feature_names\n",
"\n",
"#---------------------------------------------------------------------------------\n",
"#kelimelerin ortalama skorlarını hesaplama \n",
"def identify_low_tfidf_words(tfidf_matrix, feature_names, threshold=0.001):\n",
" # TF-IDF skorlarını toplayarak her kelimenin ortalama skorunu hesaplayın\n",
" avg_scores = np.mean(tfidf_matrix, axis=0).A1\n",
" low_tfidf_words = [feature_names[i] for i, score in enumerate(avg_scores) if score < threshold]\n",
" return low_tfidf_words\n",
"\n",
"#kelimelerin yeni geliştirilen eşik değere göre güncellenmesi \n",
"def update_stop_words(existing_stop_words, low_tfidf_words):\n",
" updated_stop_words = set(existing_stop_words) | set(low_tfidf_words)\n",
" return list(updated_stop_words)\n",
"\n",
"\n",
"#bu kısım detaylandırılmalı \n",
"def iterative_update(documents, initial_stop_words, iterations=5):\n",
" stop_words = set(initial_stop_words)\n",
" for _ in range(iterations):\n",
" tfidf_matrix, feature_names = calculate_tfidf(documents, stop_words)\n",
" low_tfidf_words = identify_low_tfidf_words(tfidf_matrix, feature_names)\n",
" stop_words = update_stop_words(stop_words, low_tfidf_words)\n",
" return list(stop_words)\n",
"\n",
"\n",
"\n",
"def main ():\n",
"\n",
" \n",
"#anlam ilişkisini de kontrol edecek bir yapı oluşpturulacak title ile benzerlik kontrol ederek yüksek benzerlik içeren kelimler sıralnacak .\n",
"\n",
"# Dökümanları liste olarak al\n",
" named_documents, _ = Tf.get_input_documents(limit=1000)\n",
" documents_list = [doc.get('text', '') if isinstance(doc, dict) else doc for doc in list(named_documents.values())]\n",
"\n",
" #başlangıç stop değerleriyle yeni olanları arasında değişim yapma \n",
" initial_stop_words = turkish_stop_words\n",
" # Stop-words listesini iteratif olarak güncelleyin\n",
" final_stop_words = iterative_update(documents_list, initial_stop_words)\n",
" #tf-ıdf hesaplama\n",
" tfidf_matrix, feature_names=calculate_tfidf(documents_list,final_stop_words)\n",
" keywords=extract_keywords(tfidf_matrix,feature_names,top_n=10)\n",
"\n",
" \n",
"\n",
" print(\"Güncellenmiş Stop-Words Listesi:\", final_stop_words)\n",
" print(\"TF-IDF Matrix Shape:\", tfidf_matrix.shape)\n",
" print(\"Feature Names Sample:\", feature_names[:10]) # İlk 10 feature adını gösterir\n",
"\n",
" return tfidf_matrix, feature_names,keywords\n",
"\n",
"if __name__==\"__main__\":\n",
" tfidf_matrix, feature_names,keywords= main()\n",
"\n",
" print(\"Anahtar Kelimler:\")\n",
" for doc, words in keywords.items():\n",
" print(f\"{doc}: {words}\")\n",
" \n",
"\n",
"#---------------------------------------------------------\n",
" \"\"\"blobs = [tb(doc) for doc in documents_list] # veya 'title' kullanarak başlıkları işleyebilirsiniz\n",
" all_words = set(word for blob in blobs for word in blob.words)\n",
"\n",
" tfidf_scores = {}\n",
" for word in all_words:\n",
" tfidf_scores[word] = [Tf.tfidf(word, blob, blobs) for blob in blobs]\n",
"\n",
" print(\"TF-IDF Skorları:\")\n",
" for word, scores in tfidf_scores.items():\n",
" print(f\"Kelime: {word}, Skorlar: {scores}\")\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "InvalidParameterError",
"evalue": "The 'stop_words' parameter of TfidfVectorizer must be a str among {'english'}, an instance of 'list' or None. Got {'o', 'den', 'an', 'şey', 'burada', 've', 'ah', 'ise', 'hiç', 'yine', 'biz', 'bu', 'da', 'genellikle', 'yılında', 'belirli', 'se', 'ne', 'kadar', 'neden', 'hem', 'aralar', 'yani', 'daha', 'araba', 'derken', 'dolayı', 'kısaca', 'karşı', 'niye', 'ki', 'bunu', 'buna', 'de', 'herhangi', 'önce', 'tabi', 'kime', 'biten', 'ben', 'ya', 'ya da', 'çünkü', 'mu', 'b', 'demek', 'fakat', 'şimdi', 'birlikte', 'her', 'bağlı', 'nasıl', 'şu', 'sadece', 'tüm', 'aslında', 'edilir', 'ama', 'bence', 'en', 'işte', 'gibi', 'ancak', 'birkaç', 'itibaren', 'mü', 'olabilir', 'bazı', 'oluşur', 'başlayan', 'yanı', 'olasılıkla', 'iyi', 'değil', 'eğer', 'yetenekli'} instead.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mInvalidParameterError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[2], line 155\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tfidf_matrix, feature_names,documents_list \n\u001b[0;32m 154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 155\u001b[0m tfidf_matrix, feature_names,documents_list\u001b[38;5;241m=\u001b[39m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;66;03m# Sonuçları yazdır\u001b[39;00m\n\u001b[0;32m 159\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mİsimlendirilmiş Dökümanlar:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"Cell \u001b[1;32mIn[2], line 142\u001b[0m, in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m 140\u001b[0m initial_stop_words \u001b[38;5;241m=\u001b[39m turkish_stop_words\n\u001b[0;32m 141\u001b[0m \u001b[38;5;66;03m# Stop-words listesini iteratif olarak güncelleyin\u001b[39;00m\n\u001b[1;32m--> 142\u001b[0m final_stop_words \u001b[38;5;241m=\u001b[39m \u001b[43miterative_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocuments_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial_stop_words\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 143\u001b[0m \u001b[38;5;66;03m#tf-ıdf hesaplama\u001b[39;00m\n\u001b[0;32m 144\u001b[0m tfidf_matrix, feature_names\u001b[38;5;241m=\u001b[39mcalculate_tfidf(documents_list,final_stop_words)\n",
"Cell \u001b[1;32mIn[2], line 124\u001b[0m, in \u001b[0;36miterative_update\u001b[1;34m(documents, initial_stop_words, iterations)\u001b[0m\n\u001b[0;32m 122\u001b[0m stop_words \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(initial_stop_words)\n\u001b[0;32m 123\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(iterations):\n\u001b[1;32m--> 124\u001b[0m tfidf_matrix, feature_names \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_tfidf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop_words\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 125\u001b[0m low_tfidf_words \u001b[38;5;241m=\u001b[39m identify_low_tfidf_words(tfidf_matrix, feature_names)\n\u001b[0;32m 126\u001b[0m stop_words \u001b[38;5;241m=\u001b[39m update_stop_words(stop_words, low_tfidf_words)\n",
"Cell \u001b[1;32mIn[2], line 103\u001b[0m, in \u001b[0;36mcalculate_tfidf\u001b[1;34m(documents, stop_words)\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcalculate_tfidf\u001b[39m(documents, stop_words):\n\u001b[0;32m 102\u001b[0m vectorizer \u001b[38;5;241m=\u001b[39m TfidfVectorizer(stop_words\u001b[38;5;241m=\u001b[39mstop_words, max_features\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10000\u001b[39m)\n\u001b[1;32m--> 103\u001b[0m tfidf_matrix \u001b[38;5;241m=\u001b[39m \u001b[43mvectorizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 104\u001b[0m feature_names \u001b[38;5;241m=\u001b[39m vectorizer\u001b[38;5;241m.\u001b[39mget_feature_names_out()\n\u001b[0;32m 105\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tfidf_matrix, feature_names\n",
"File \u001b[1;32mc:\\gitProjects\\yeni\\.venv\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:2091\u001b[0m, in \u001b[0;36mTfidfVectorizer.fit_transform\u001b[1;34m(self, raw_documents, y)\u001b[0m\n\u001b[0;32m 2084\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_params()\n\u001b[0;32m 2085\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf \u001b[38;5;241m=\u001b[39m TfidfTransformer(\n\u001b[0;32m 2086\u001b[0m norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm,\n\u001b[0;32m 2087\u001b[0m use_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_idf,\n\u001b[0;32m 2088\u001b[0m smooth_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msmooth_idf,\n\u001b[0;32m 2089\u001b[0m sublinear_tf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msublinear_tf,\n\u001b[0;32m 2090\u001b[0m )\n\u001b[1;32m-> 2091\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2092\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[0;32m 2093\u001b[0m \u001b[38;5;66;03m# X is already a transformed view of raw_documents so\u001b[39;00m\n\u001b[0;32m 2094\u001b[0m \u001b[38;5;66;03m# we set copy to False\u001b[39;00m\n",
"File \u001b[1;32mc:\\gitProjects\\yeni\\.venv\\lib\\site-packages\\sklearn\\base.py:1466\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1461\u001b[0m partial_fit_and_fitted \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 1462\u001b[0m fit_method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpartial_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m _is_fitted(estimator)\n\u001b[0;32m 1463\u001b[0m )\n\u001b[0;32m 1465\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m global_skip_validation \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m partial_fit_and_fitted:\n\u001b[1;32m-> 1466\u001b[0m \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1471\u001b[0m )\n\u001b[0;32m 1472\u001b[0m ):\n\u001b[0;32m 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[1;32mc:\\gitProjects\\yeni\\.venv\\lib\\site-packages\\sklearn\\base.py:666\u001b[0m, in \u001b[0;36mBaseEstimator._validate_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 659\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Validate types and values of constructor parameters\u001b[39;00m\n\u001b[0;32m 660\u001b[0m \n\u001b[0;32m 661\u001b[0m \u001b[38;5;124;03m The expected type and values must be defined in the `_parameter_constraints`\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 664\u001b[0m \u001b[38;5;124;03m accepted constraints.\u001b[39;00m\n\u001b[0;32m 665\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 666\u001b[0m \u001b[43mvalidate_parameter_constraints\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 667\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parameter_constraints\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 668\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 669\u001b[0m \u001b[43m \u001b[49m\u001b[43mcaller_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 670\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\gitProjects\\yeni\\.venv\\lib\\site-packages\\sklearn\\utils\\_param_validation.py:95\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[1;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[0;32m 89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 90\u001b[0m constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 91\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 92\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 93\u001b[0m )\n\u001b[1;32m---> 95\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[0;32m 96\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 97\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 98\u001b[0m )\n",
"\u001b[1;31mInvalidParameterError\u001b[0m: The 'stop_words' parameter of TfidfVectorizer must be a str among {'english'}, an instance of 'list' or None. Got {'o', 'den', 'an', 'şey', 'burada', 've', 'ah', 'ise', 'hiç', 'yine', 'biz', 'bu', 'da', 'genellikle', 'yılında', 'belirli', 'se', 'ne', 'kadar', 'neden', 'hem', 'aralar', 'yani', 'daha', 'araba', 'derken', 'dolayı', 'kısaca', 'karşı', 'niye', 'ki', 'bunu', 'buna', 'de', 'herhangi', 'önce', 'tabi', 'kime', 'biten', 'ben', 'ya', 'ya da', 'çünkü', 'mu', 'b', 'demek', 'fakat', 'şimdi', 'birlikte', 'her', 'bağlı', 'nasıl', 'şu', 'sadece', 'tüm', 'aslında', 'edilir', 'ama', 'bence', 'en', 'işte', 'gibi', 'ancak', 'birkaç', 'itibaren', 'mü', 'olabilir', 'bazı', 'oluşur', 'başlayan', 'yanı', 'olasılıkla', 'iyi', 'değil', 'eğer', 'yetenekli'} instead."
]
}
],
"source": [
"from pymongo import MongoClient\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from textblob import TextBlob as tb\n",
"import numpy as np\n",
"import math\n",
"import nltk \n",
"import matplotlib.pyplot as plt \n",
"\n",
"class Database:\n",
" @staticmethod\n",
" def get_mongodb():\n",
" return 'mongodb://localhost:27017/', 'EgitimDatabase', 'train'\n",
"\n",
"#--------------------------------------------------------------------------\n",
"#combined_text eklenmeli \n",
" @staticmethod\n",
" def get_input_documents(limit=3):\n",
" mongo_url, db_name, collection_name = Database.get_mongodb()\n",
" client = MongoClient(mongo_url)\n",
" db = client[db_name]\n",
" collection = db[collection_name]\n",
" cursor = collection.find().limit(limit)\n",
" combined_text = [doc for doc in cursor]\n",
" document_count = len(combined_text)\n",
" \n",
" # Dökümanları isimlendir\n",
" named_documents = {f'döküman {i+1}': doc for i, doc in enumerate(combined_text)}\n",
" \n",
" return named_documents, document_count\n",
"\n",
"\n",
"class Tf:\n",
" @staticmethod\n",
" def tf(word, blob):\n",
" return blob.words.count(word) / len(blob.words)\n",
"\n",
" @staticmethod\n",
" def n_containing(word, bloblist):\n",
" return sum(1 for blob in bloblist if word in blob.words)\n",
"\n",
" @staticmethod\n",
" def idf(word, bloblist):\n",
" return math.log(len(bloblist) / (1 + Tf.n_containing(word, bloblist)))\n",
"\n",
" @staticmethod\n",
" def tfidf(word, blob, bloblist):\n",
" return Tf.tf(word, blob) * Tf.idf(word, bloblist)\n",
"\n",
" @staticmethod\n",
" def get_input_documents(limit=3):\n",
" return Database.get_input_documents(limit)\n",
"\n",
"# Kullanım örneği\n",
"named_documents, document_count = Tf.get_input_documents(limit=1000)\n",
"\n",
"#tf-ıdf ile döküman içerisinden kelime seçme \n",
"\n",
"def extract_keywords(tfidf_matrix, feature_names, top_n=10):\n",
" \"\"\"\n",
" Her döküman için anahtar kelimeleri seç.\n",
" :param tfidf_matrix: TF-IDF matris\n",
" :param feature_names: TF-IDF özellik isimleri\n",
" :param top_n: Her döküman için seçilecek anahtar kelime sayısı\n",
" :return: Anahtar kelimeler ve skorlari\n",
" \"\"\"\n",
" keywords = {}\n",
" for doc_idx, row in enumerate(tfidf_matrix):\n",
" # TF-IDF değerlerini ve özellik isimlerini al\n",
" scores = np.asarray(row.T.todense()).flatten()\n",
" sorted_indices = np.argsort(scores)[::-1] # Skorları azalan sırada\n",
" top_features = sorted_indices[:top_n]\n",
" \n",
" doc_keywords = [(feature_names[idx], scores[idx]) for idx in top_features]\n",
" keywords[f'document_{doc_idx+1}'] = doc_keywords\n",
" \n",
" return keywords\n",
"\n",
"# Dokümanları işleyerek TF-IDF hesaplama\n",
"#bloblist dökümanların bir listesi\n",
"bloblist = []\n",
"for i, blob in enumerate(bloblist):\n",
" print(\"Top words in document {}\".format(i + 1))\n",
" scores = {word: Tf.tfidf(word, blob, bloblist) for word in blob.words} #dökümanların içerisinde bulunan kelimeleri alır.\n",
" sorted_words = sorted(scores.items(), key=lambda x: x[1], reverse=True)\n",
" for word, score in sorted_words[:3]:\n",
" print(\"\\tWord: {}, TF-IDF: {}\".format(word, round(score, 5)))\n",
"\n",
"\n",
"#buraya eşik değer belirlenmeli\n",
"turkish_stop_words = [\n",
" 'ah', 'ama', 'an', 'ancak', 'araba', 'aralar', 'aslında', \n",
" 'b', 'başlayan', 'bağlı', 'bazı', 'belirli', 'ben', 'bence', \n",
" 'birkaç', 'birlikte', 'bunu', 'burada', 'biten', 'biz', \n",
" 'bu', 'buna', 'çünkü', 'da', 'de', 'demek', 'den', 'derken', \n",
" 'değil', 'daha', 'dolayı', 'edilir', 'eğer', 'en', 'fakat', \n",
" 'genellikle', 'gibi', 'hem', 'her', 'herhangi', 'hiç', 'ise', \n",
" 'işte', 'itibaren', 'iyi', 'kadar', 'karşı', 'ki', 'kime', \n",
" 'kısaca', 'mu', 'mü', 'nasıl', 'ne', 'neden', 'niye', 'o', \n",
" 'olasılıkla', 'olabilir', 'oluşur', 'önce', 'şu', 'sadece', \n",
" 'se', 'şey', 'şimdi', 'tabi', 'tüm', 've', 'ya', 'ya da', \n",
" 'yanı', 'yani', 'yılında', 'yetenekli', 'yine'\n",
"]\n",
"\n",
"#features olarak top_keywordsleri belirleyerek metnin bu kelimelerin etrafında olması sağlanmalı \n",
"def calculate_tfidf(combined_text, stop_words):\n",
" vectorizer = TfidfVectorizer(stop_words=stop_words, max_features=10000)\n",
" tfidf_matrix = vectorizer.fit_transform(combined_text)\n",
" feature_names = vectorizer.get_feature_names_out()\n",
" return tfidf_matrix, feature_names\n",
"\n",
"#---------------------------------------------------------------------------------\n",
"#kelimelerin ortalama skorlarını hesaplama \n",
"def identify_low_tfidf_words(tfidf_matrix, feature_names, threshold=0.001):\n",
" # TF-IDF skorlarını toplayarak her kelimenin ortalama skorunu hesaplayın\n",
" avg_scores = np.mean(tfidf_matrix, axis=0).A1\n",
" low_tfidf_words = [feature_names[i] for i, score in enumerate(avg_scores) if score < threshold]\n",
" return low_tfidf_words\n",
"\n",
"#kelimelerin yeni geliştirilen eşik değere göre güncellenmesi \n",
"def update_stop_words(existing_stop_words, low_tfidf_words):\n",
" updated_stop_words = set(existing_stop_words) | set(low_tfidf_words)\n",
" return list(updated_stop_words)\n",
"\n",
"\n",
"#bu kısım detaylandırılmalı \n",
"def iterative_update(combined_text, initial_stop_words, iterations=5):\n",
" stop_words = set(initial_stop_words)\n",
" for _ in range(iterations):\n",
" tfidf_matrix, feature_names = calculate_tfidf(combined_text, stop_words)\n",
" low_tfidf_words = identify_low_tfidf_words(tfidf_matrix, feature_names)\n",
" stop_words = update_stop_words(stop_words, low_tfidf_words)\n",
" return list(stop_words)\n",
"\n",
"\n",
"\n",
"def main ():\n",
"\n",
" \n",
"#anlam ilişkisini de kontrol edecek bir yapı oluşpturulacak title ile benzerlik kontrol ederek yüksek benzerlik içeren kelimler sıralnacak .\n",
"\n",
"# Dökümanları liste olarak al\n",
" documents_list = [doc.get('text', '') if isinstance(doc, dict) else doc for doc in list(named_documents.values())]\n",
"\n",
" #başlangıç stop değerleriyle yeni olanları arasında değişim yapma \n",
" initial_stop_words = turkish_stop_words\n",
" # Stop-words listesini iteratif olarak güncelleyin\n",
" final_stop_words = iterative_update(documents_list, initial_stop_words)\n",
" #tf-ıdf hesaplama\n",
" tfidf_matrix, feature_names=calculate_tfidf(documents_list,final_stop_words)\n",
"\n",
" \n",
"\n",
" print(\"Güncellenmiş Stop-Words Listesi:\", final_stop_words)\n",
" print(\"TF-IDF Matrix Shape:\", tfidf_matrix.shape)\n",
" print(\"Feature Names Sample:\", feature_names[:10]) # İlk 10 feature adını gösterir\n",
"\n",
" return tfidf_matrix, feature_names,documents_list \n",
"\n",
"if __name__==\"__main__\":\n",
" tfidf_matrix, feature_names,documents_list= main()\n",
"\n",
"\n",
"# Sonuçları yazdır\n",
"print(\"İsimlendirilmiş Dökümanlar:\")\n",
"for name, doc in named_documents.items():\n",
" print(f\"{name}: {doc}\")\n",
"\n",
" print(\"\\nDökümanlar Listesi:\")\n",
" print(documents_list)\n",
"\n",
"#---------------------------------------------------------\n",
" blobs = [tb(doc) for doc in documents_list] # veya 'title' kullanarak başlıkları işleyebilirsiniz\n",
" all_words = set(word for blob in blobs for word in blob.words)\n",
"\n",
" tfidf_scores = {}\n",
" for word in all_words:\n",
" tfidf_scores[word] = [Tf.tfidf(word, blob, blobs) for blob in blobs]\n",
"\n",
" print(\"TF-IDF Skorları:\")\n",
" for word, scores in tfidf_scores.items():\n",
" print(f\"Kelime: {word}, Skorlar: {scores}\")\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"#----------------------------------------------\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"#alternatif keywordleri belirleme \n",
"#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"\"\"\"turkish_stop_words = set([\n",
" 'ad', 'adım', 'ah', 'ama', 'an', 'ancak', 'araba', 'aralar', 'aslında', \n",
" 'b', 'bazı', 'belirli', 'ben', 'bence', 'bunu', 'burada', 'biz', 'bu', 'buna', 'çünkü', \n",
" 'da', 'de', 'demek', 'den', 'derken', 'değil', 'daha', 'dolayı', 'edilir', 'eğer', 'en', 'fakat', \n",
" 'genellikle', 'gibi', 'hem', 'her', 'herhangi', 'hiç', 'ise', 'işte', 'itibaren', 'iyi', 'kadar', \n",
" 'karşı', 'ki', 'kime', 'kısaca', 'mu', 'mü', 'nasıl', 'ne', 'neden', 'niye', 'o', 'olabilir', 'oluşur', \n",
" 'önce', 'şu', 'sadece', 'se', 'şey', 'şimdi', 'tabi', 'tüm', 've', 'ya', 'ya da', 'yani', 'yine'\n",
"])\n",
"def calculate_tfidf(documents):\n",
" vectorizer = TfidfVectorizer(stop_words=turkish_stop_words, max_features=10000) # max_features ile özellik sayısını sınırlıyoruz\n",
" tfidf_matrix = vectorizer.fit_transform(documents)\n",
" feature_names = vectorizer.get_feature_names_out()\n",
" return tfidf_matrix, feature_names\n",
"\n",
"#feature_names lerin belirlenmesi grekir \n",
"tfidf_matrix, feature_names=calculate_tfidf(documents)\n",
"\n",
"\n",
"\n",
"# En yüksek TF-IDF skorlarına sahip anahtar kelimeleri çıkarın\n",
"#sıkışık format kullanmarak tf-ıdf matrisini işleme \n",
"def get_top_n_keywords_sparse(n=10):\n",
"\n",
" # TF-IDF hesaplayıcı oluşturun\n",
" vectorizer = TfidfVectorizer()\n",
"\n",
" # Başlıklar ve metinler ile TF-IDF matrisini oluşturun\n",
" texts = Database.get_input_texts()\n",
" titles = Database.get_input_titles()\n",
" \n",
"\n",
" #title ve text değerlerini alarak vektörleştirdik.\n",
" tfidf_matrix = vectorizer.fit_transform(documents)\n",
"\n",
" # Özellik adlarını (kelimeleri) alın\n",
"\n",
" feature_names = vectorizer.get_feature_names_out()\n",
"\n",
" # TF-IDF sonuçlarını DataFrame'e dönüştürün\n",
" df = pd.DataFrame(tfidf_matrix.toarray(), columns=feature_names)\n",
" print(df)\n",
" keywords = {}\n",
" for i in range(tfidf_matrix.shape[0]):\n",
" row = tfidf_matrix[i].toarray().flatten() #list yapısından çıkarma \n",
" sorted_indices = row.argsort()[::-1] # Büyükten küçüğe sıralama\n",
" top_indices = sorted_indices[:n]\n",
" top_keywords = [feature_names[idx] for idx in top_indices]\n",
" keywords[i] = top_keywords\n",
" return keywords\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'TfidfVectorizer' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[1], line 41\u001b[0m\n\u001b[0;32m 31\u001b[0m turkish_stop_words \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m([\n\u001b[0;32m 32\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mabide\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mabi\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mabla\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mad\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124madım\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mah\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mama\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124man\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mancak\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maraba\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maralar\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maslında\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[0;32m 33\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maşşağı\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124maz\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbazı\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbelirli\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mben\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbence\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbunu\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mburada\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbiz\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbu\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuna\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mçünkü\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 37\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mönce\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mşu\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msadece\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msana\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mse\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mşey\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mşimdi\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtabi\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtüm\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mve\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mya\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mya da\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myani\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myine\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 38\u001b[0m ])\n\u001b[0;32m 40\u001b[0m \u001b[38;5;66;03m# TF-IDF hesaplayıcı oluşturun ve Türkçe durak kelimelerini dahil edin\u001b[39;00m\n\u001b[1;32m---> 41\u001b[0m vectorizer \u001b[38;5;241m=\u001b[39m \u001b[43mTfidfVectorizer\u001b[49m(stop_words\u001b[38;5;241m=\u001b[39mturkish_stop_words)\n\u001b[0;32m 44\u001b[0m \u001b[38;5;124;03m\"\"\"IDF, derlemedeki belge sayısının,\u001b[39;00m\n\u001b[0;32m 45\u001b[0m \u001b[38;5;124;03mincelenen anahtar kelimeyi içeren topluluktaki belge sayısına \u001b[39;00m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;124;03mbölünmesiyle elde edilen algoritmadır. \u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[38;5;124;03mkülliyat yani incelenen tüm belgelerin adedi 10 ise ve test edilen anahtar kelime,\u001b[39;00m\n\u001b[0;32m 50\u001b[0m \u001b[38;5;124;03mkülliyattaki üç belgede görünüyorsa, bu durumda IDF değeri 0.52’dir (log (10/3)).\"\"\"\u001b[39;00m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;66;03m#TF-IDF puanı; Naive Bayes ve Destek Vektör Makineleri gibi algoritmalara aktarılabilir. Böylece kelime sayısı gibi daha temel yöntemlerin sonuçları büyük ölçüde iyileştirilebilir.\u001b[39;00m\n\u001b[0;32m 52\u001b[0m \u001b[38;5;66;03m#IDF = log ( Dokuman Sayısı / Terimin Geçtiği Dokuman Sayısı )\u001b[39;00m\n\u001b[0;32m 53\u001b[0m \u001b[38;5;66;03m#dokuman sayısılarını almakla başlayacağız.\u001b[39;00m\n\u001b[0;32m 54\u001b[0m \u001b[38;5;66;03m# : titlelerın sayısı / terimler ise \u001b[39;00m\n",
"\u001b[1;31mNameError\u001b[0m: name 'TfidfVectorizer' is not defined"
]
}
],
"source": [
"\n",
"#---------------------------------------------------------------------------------------------------------------------------------\n",
"#transformers kütüphanesine ait generation fonksiyonu özellikleri ,PyTorch generate() is implemented in GenerationMixin. \n",
"\n",
"\n",
"\"\"\"from transformers import GenerationConfig\n",
"\n",
"# Download configuration from huggingface.co and cache.\n",
"generation_config = GenerationConfig.from_pretrained(\"openai-community/gpt2\")\n",
"\n",
"# E.g. config was saved using *save_pretrained('./test/saved_model/')*\n",
"generation_config.save_pretrained(\"./test/saved_model/\")\n",
"generation_config = GenerationConfig.from_pretrained(\"./test/saved_model/\")\n",
"\n",
"# You can also specify configuration names to your generation configuration file\n",
"generation_config.save_pretrained(\"./test/saved_model/\", config_file_name=\"my_configuration.json\")\n",
"generation_config = GenerationConfig.from_pretrained(\"./test/saved_model/\", \"my_configuration.json\")\n",
"\n",
"# If you'd like to try a minor variation to an existing configuration, you can also pass generation\n",
"# arguments to `.from_pretrained()`. Be mindful that typos and unused arguments will be ignored\n",
"generation_config, unused_kwargs = GenerationConfig.from_pretrained(\n",
" \"openai-community/gpt2\", top_k=1, foo=False, do_sample=True, return_unused_kwargs=True\n",
")\n",
"generation_config.top_k\n",
"\n",
"unused_kwargs\n",
"\"\"\"\n",
"\n",
"\n",
"#tf-ıdf hesaplama (anahtar kelimeler için) #Bir kelimenin TF IDF puanı ne kadar yüksekse, kelime bulunduğu belgeyle o kadar alakalıdır.\n",
"\n",
"turkish_stop_words = set([\n",
" 'a', 'abide', 'abi', 'abla', 'ad', 'adım', 'ah', 'ama', 'an', 'ancak', 'araba', 'aralar', 'aslında', \n",
" 'aşşağı', 'az', 'b', 'bazı', 'belirli', 'ben', 'bence', 'bunu', 'burada', 'biz', 'bu', 'buna', 'çünkü', \n",
" 'da', 'de', 'demek', 'den', 'derken', 'değil', 'daha', 'dolayı', 'e', 'edilir', 'eğer', 'en', 'fakat', \n",
" 'genellikle', 'gibi', 'hem', 'her', 'herhangi', 'hiç', 'i', 'ise', 'işte', 'itibaren', 'iyi', 'kadar', \n",
" 'karşı', 'ki', 'kime', 'kısaca', 'mu', 'mü', 'nasıl', 'ne', 'neden', 'niye', 'o', 'olabilir', 'oluşur', \n",
" 'önce', 'şu', 'sadece', 'sana', 'se', 'şey', 'şimdi', 'tabi', 'tüm', 've', 'ya', 'ya da', 'yani', 'yine'\n",
"])\n",
"\n",
"# TF-IDF hesaplayıcı oluşturun ve Türkçe durak kelimelerini dahil edin\n",
"vectorizer = TfidfVectorizer(stop_words=turkish_stop_words)\n",
"\n",
"\n",
"\"\"\"IDF, derlemedeki belge sayısının,\n",
"incelenen anahtar kelimeyi içeren topluluktaki belge sayısına \n",
"bölünmesiyle elde edilen algoritmadır. \n",
"Yani ters belge sıklığı bir terimin önemini ölçer,\n",
"toplam belge sayısının, terimi içeren belge sayısına bölünmesiyle elde edilir.\n",
"külliyat yani incelenen tüm belgelerin adedi 10 ise ve test edilen anahtar kelime,\n",
"külliyattaki üç belgede görünüyorsa, bu durumda IDF değeri 0.52’dir (log (10/3)).\"\"\"\n",
"#TF-IDF puanı; Naive Bayes ve Destek Vektör Makineleri gibi algoritmalara aktarılabilir. Böylece kelime sayısı gibi daha temel yöntemlerin sonuçları büyük ölçüde iyileştirilebilir.\n",
"#IDF = log ( Dokuman Sayısı / Terimin Geçtiği Dokuman Sayısı )\n",
"#dokuman sayısılarını almakla başlayacağız.\n",
"# : titlelerın sayısı / terimler ise \n",
"\n",
"document_number=416434\n",
"\"\"\"Sonuç olarak TF IDF’nin, SEO’da pratik ve önemli bir kullanım alanına sahip olduğunu söylenebilir,\n",
" özellikle yüksek kaliteli içeriğin optimize edilmesinde ve oluşturulmasında yararlıdır. \n",
" Ancak TF IDF, içerik optimizasyonu için tek başına kullanıldığında ciddi sınırlamalarla karşı karşıya kalır:\"\"\"\n",
"\n",
"# TF-IDF hesaplayıcı oluşturun\n",
"vectorizer = TfidfVectorizer()\n",
"\n",
"# Başlıklar ve metinler ile TF-IDF matrisini oluşturun\n",
"texts = Database.get_input_texts()\n",
"titles,title_count = Database.get_input_titles()\n",
"documents = titles + texts # Başlıklar ve metinleri birleştir\n",
"\n",
"#title ve text değerlerini alarak vektörleştirdik.\n",
"tfidf_matrix = vectorizer.fit_transform(documents)\n",
"\n",
"# Özellik adlarını (kelimeleri) alın\n",
"\n",
"feature_names = vectorizer.get_feature_names_out()\n",
"\n",
"# TF-IDF sonuçlarını DataFrame'e dönüştürün\n",
"df = pd.DataFrame(tfidf_matrix.toarray(), columns=feature_names)\n",
"\n",
"\n",
"\"\"\"def get_top_n_keywords(df, n=10):\n",
" keywords = {}\n",
" for i, row in df.iterrows():\n",
" sorted_row = row.sort_values(ascending=False)\n",
" top_keywords = sorted_row.head(n).index\n",
" keywords[i] = top_keywords.tolist()\n",
" return keywords\"\"\"\n",
"\n",
"# En yüksek TF-IDF skorlarına sahip anahtar kelimeleri çıkarın\n",
"#sıkışık format kullanmarak tf-ıdf matrisini işleme \n",
"def get_top_n_keywords_sparse(n=10):\n",
"\n",
" # TF-IDF hesaplayıcı oluşturun\n",
" vectorizer = TfidfVectorizer()\n",
"\n",
" # Başlıklar ve metinler ile TF-IDF matrisini oluşturun\n",
" texts = Database.get_input_texts()\n",
" titles = Database.get_input_titles()\n",
" \n",
"\n",
" #title ve text değerlerini alarak vektörleştirdik.\n",
" tfidf_matrix = vectorizer.fit_transform(documents)\n",
"\n",
" # Özellik adlarını (kelimeleri) alın\n",
"\n",
" feature_names = vectorizer.get_feature_names_out()\n",
"\n",
" # TF-IDF sonuçlarını DataFrame'e dönüştürün\n",
" df = pd.DataFrame(tfidf_matrix.toarray(), columns=feature_names)\n",
" print(df)\n",
" keywords = {}\n",
" for i in range(tfidf_matrix.shape[0]):\n",
" row = tfidf_matrix[i].toarray().flatten() #list yapısından çıkarma \n",
" sorted_indices = row.argsort()[::-1] # Büyükten küçüğe sıralama\n",
" top_indices = sorted_indices[:n]\n",
" top_keywords = [feature_names[idx] for idx in top_indices]\n",
" keywords[i] = top_keywords\n",
" return keywords\n",
"\n",
"\n",
"top_keywords = get_top_n_keywords_sparse(tfidf_matrix, feature_names)\n",
"print(top_keywords)\n",
"print(f\"Başlıklar: {titles}\")\n",
"print(f\"Başlık sayısı: {title_count}\")\n",
"print(f\"Metinler: {texts}\")\n",
"print(f\"Metin sayısı: {len(texts)}\")\n",
"print(f\"Birleştirilmiş Belgeler: {documents[:5]}\") # İlk birkaç belgeyi kontrol etme\n",
"\n",
"def calculate_tfidf(docs):\n",
" vectorizer = TfidfVectorizer(stop_words=turkish_stop_words)\n",
" tfidf_matrix = vectorizer.fit_transform(docs)\n",
" feature_names = vectorizer.get_feature_names_out()\n",
" return tfidf_matrix, feature_names\n",
"\n",
"# İşlem için dökümanları parçalayarak kullanın\n",
"def process_documents_in_batches(docs, batch_size=1000, top_n=5):\n",
" all_keywords = {}\n",
" for start in range(0, len(docs), batch_size):\n",
" end = min(start + batch_size, len(docs))\n",
" batch_docs = docs[start:end]\n",
" tfidf_matrix, feature_names = calculate_tfidf(batch_docs)\n",
" batch_keywords = get_top_n_keywords_sparse(tfidf_matrix, feature_names, n=top_n)\n",
" all_keywords.update(batch_keywords)\n",
" return all_keywords\n",
"\n",
"#buraya mango db üzerindeki tüm dökümanlar gelmewli \n",
"keywords= process_documents_in_batches(documents,batch_size=1000,top_n=5)\n",
"\n",
"documents = titles + texts # Başlıklar ve metinleri birleştir\n",
"print(f\"en yüksek tf-ıdf skoruna sahip anahtar kelimeler:{keywords}\")\n",
"\n",
"\n",
"# Belgeleri TF-IDF matrisine dönüştürün\n",
"\"\"\"tfidf_matrix = vectorizer.fit_transform(documents)\n",
"\n",
"# Özellik adlarını (kelimeleri) alın\n",
"feature_names = vectorizer.get_feature_names_out()\n",
"\n",
"# TF-IDF sonuçlarını DataFrame'e dönüştürün\n",
"df = pd.DataFrame(tfidf_matrix.toarray(), columns=feature_names)\n",
"\n",
"print(df)\"\"\"\n",
"\n",
"#text ve title a göre keywords belirlenmesi\n",
"\n",
"#------------------------------------------------------------------------------\n",
"\n",
"\n",
"#sbert ile alt başlıkların oluşturulması\n",
"\n",
"#kümelenme ile alt başlıkların belirlenmesi \n",
"\n",
"#-------------------------------------------------------------------------------\n",
"\n",
"#anahatar kelime ve alt başlıkların veri tabnaına eklnemesi "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#benzerlik hesaplaması için kullanılacak \n",
"from sentence_transformers import SentenceTransformer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarity Sentences "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#prompt oluştururak generate etmek için hazırlık"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bert Modeliyle tokenizer atama"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tokenizer= BertTokenizer.from_pretrained('bert-base-uncased')\n",
"model=BertForMaskedLM.from_pretrained('bert-base-uncased')\n",
"\n",
"\"\"\"BERT MODELİNİ AYARLAMA\n",
"\n",
"input_file: Modelin işlem yapacağı giriş dosyasının yolunu belirtir. Bu dosya, metin verilerini içermelidir.\n",
"-----------------------------------------------------------------------------------------------------------------\n",
"output_file: Modelin çıktılarının kaydedileceği dosyanın yolunu belirtir.\n",
"------------------------------------------------------------------------------------------------------------------\n",
"layers: Hangi BERT katmanlarının kullanılacağını belirler. Örneğin, \"-1,-2,-3,-4\" son dört katmanı ifade eder.\n",
"----------------------------------------------------------------------------------------------------------------------\n",
"bert_config_file: Önceden eğitilmiş BERT modelinin yapılandırma dosyasının yolu. Bu dosya modelin mimarisini belirler.\n",
"--------------------------------------------------------------------------------------------------------------------------\n",
"max_seq_length: Giriş sekanslarının maksimum uzunluğu. Sekanslar bu uzunluktan uzunsa kesilir, kısa ise sıfır ile doldurulur.\n",
"--------------------------------------------------------------------------------------------------------------------------------\n",
"init_checkpoint: Başlangıç ağırlıkları. Genellikle önceden eğitilmiş bir BERT modelinin ağırlıkları buradan yüklenir.\n",
"----------------------------------------------------------------------------------------------------------------------------\n",
"vocab_file: BERT modelinin eğitildiği kelime dağarcığının (vocabulary) dosya yolu. Modelin kelime parçacıklarını tanıması için gereklidir.\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------\n",
"do_lower_case: Giriş metinlerinin küçük harfe mi dönüştürüleceğini belirler. Küçük harfli model için True, büyük harfli model için False olmalıdır.\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"batch_size: Tahminler sırasında kullanılacak veri kümesi boyutu.\n",
"--------------------------------------------------------------------------------------------------------------------------------------\n",
"use_tpu: TPU (Tensor Processing Unit) kullanılıp kullanılmayacağını belirler. True ise TPU, False ise GPU/CPU kullanılır.\n",
"--------------------------------------------------------------------------------------------------------------------------------\n",
"master: TPU kullanılıyorsa, TPU'nun ana makinesinin adresi.\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
"num_tpu_cores: TPU kullanılacaksa, toplam TPU çekirdek sayısını belirtir.\n",
"-----------------------------------------------------------------------------------------------------------------------------------------\n",
"use_one_hot_embeddings: TPUs'da genellikle True olarak ayarlanır çünkü bu, tf.one_hot fonksiyonunu kullanarak embedding lookup işlemlerini hızlandırır. GPU/CPU kullanılıyorsa False tercih edilir.\"\"\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"t5 Modeli"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"from dotenv import load_dotenv\n",
"import os \n",
"# Load model directly\n",
"from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
"\n",
"\n",
"#tokenizer ve modelin yüklenmesi\n",
"tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-small\")\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-small\")\n",
"prompt = \"Write an article about Machine Learning in Healthcare focusing on Introduction to ML and Applications in Healthcare.\"\n",
"#api anahtarını çevresel değişken al\n",
"api_key= os.getenv('HUGGINGFACE_API_KEY')\n",
"#env dosyasını yükleme\n",
"load_dotenv()\n",
"\n",
"#---------------------------------------------------------------------------------\n",
"if api_key is None:\n",
" raise ValueError(\"Apı anahtarı .env dosyasında bulunamadı\")\n",
"\n",
"# Başlıkları oluştur\n",
"headers = {\"Authorization\": f\"Bearer {api_key}\"}\n",
"\n",
"inputs=tokenizer(prompt, return_tensors=\"pt\")\n",
"input_sequence = \"[CLS] Machine Learning in Healthcare [SEP] Introduction to ML [SEP] Applications in Healthcare [SEP] machine learning, healthcare, AI [SEP]\"\n",
"#deneme data parçası\n",
"data = {\n",
" \"title\": \"Machine Learning in Healthcare\",\n",
" \"sub_headings\": [\"Introduction to ML\", \"Applications in Healthcare\"],\n",
" \"keywords\": [\"machine learning\", \"healthcare\", \"AI\"]\n",
"}\n",
"\n",
"# Girdiyi oluşturma\n",
"prompt = (\n",
" f\"Title: {data['title']}\\n\"\n",
" f\"Sub-headings: {', '.join(data['sub_headings'])}\\n\"\n",
" f\"Keywords: {', '.join(data['keywords'])}\\n\"\n",
" f\"Content: {input_sequence}\\n\"\n",
" \"Please generate a detailed article based on the above information.\"\n",
")\n",
"\n",
"#metin üretimi \n",
"output_sequences = model.generate(\n",
" inputs['input_ids'],\n",
" max_length=300, # Üretilecek metnin maksimum uzunluğu\n",
" min_length=150, # Üretilecek metnin minimum uzunluğu\n",
" num_return_sequences=1, # Döndürülecek metin sayısı\n",
" do_sample=True, # Örneklemeye izin ver\n",
" top_k=50, # Top-k sampling kullan\n",
" top_p=0.95, # Top-p sampling kullan\n",
" repetition_penalty=1.2, # Anlamsız tekrarları önlemek için ceza\n",
" eos_token_id=tokenizer.eos_token_id # Tam cümlelerin oluşturulmasını sağla\n",
")\n",
"\n",
"\n",
"# Üretilen metni token'lardan çözüp string'e çevir\n",
"generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)\n",
"\n",
"print(generated_text)\n"
]
}
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