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topicquestionexcerpt
0electronicsWhat is the effective differencial effective o...I'm trying to work out, in general terms, the ...
1electronicsHeat sensor with fan coolingCan I know which component senses heat or acts...
2electronicsOutlet Installation--more wires than my new ou...I am replacing a wall outlet with a Cooper Wir...
3electronicsBuck Converter Operation Questioni have been reading about the buck converter, ...
4electronicsUrgent help in area of ASIC design, verificati...I need help with deciding on a Master's Projec...
............
20214wordpressHow to set a Custom Post Type as the parent of...I have a Custom Post Type called Recipe with p...
20215wordpressTracking last login and last visitI'm using the code below to track when a user ...
20216wordpressHow to exclude the particular category from th...add_action( 'pre_get_posts', 'custom_pre_get_p...
20217wordpressdisplay sub categories assoccited with each po...i have wordpress blog with many posts. each po...
20218wordpressLost of query parameter when using permalinkI have many issues with the use of rewriting, ...
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20219 rows × 3 columns

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" ], "text/plain": [ " topic question \\\n", "0 electronics What is the effective differencial effective o... \n", "1 electronics Heat sensor with fan cooling \n", "2 electronics Outlet Installation--more wires than my new ou... \n", "3 electronics Buck Converter Operation Question \n", "4 electronics Urgent help in area of ASIC design, verificati... \n", "... ... ... \n", "20214 wordpress How to set a Custom Post Type as the parent of... \n", "20215 wordpress Tracking last login and last visit \n", "20216 wordpress How to exclude the particular category from th... \n", "20217 wordpress display sub categories assoccited with each po... \n", "20218 wordpress Lost of query parameter when using permalink \n", "\n", " excerpt \n", "0 I'm trying to work out, in general terms, the ... \n", "1 Can I know which component senses heat or acts... \n", "2 I am replacing a wall outlet with a Cooper Wir... \n", "3 i have been reading about the buck converter, ... \n", "4 I need help with deciding on a Master's Projec... \n", "... ... \n", "20214 I have a Custom Post Type called Recipe with p... \n", "20215 I'm using the code below to track when a user ... \n", "20216 add_action( 'pre_get_posts', 'custom_pre_get_p... \n", "20217 i have wordpress blog with many posts. each po... \n", "20218 I have many issues with the use of rewriting, ... \n", "\n", "[20219 rows x 3 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import json\n", "\n", "def load_data(path):\n", " with open(path, 'r') as file:\n", " data = file.read()\n", "\n", " data = data.strip().split('\\n')\n", "\n", " dataset = []\n", " for i in data:\n", " try:\n", " dataset.append(json.loads(i))\n", " except:\n", " print(i)\n", "\n", " df = pd.DataFrame(dataset)\n", "\n", " return df\n", "\n", "TRAIN = load_data('data/train/train.json')\n", "TRAIN\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['topic', 'question', 'excerpt'], dtype='object')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TRAIN.columns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import re\n", "import nltk\n", "from nltk.corpus import stopwords\n", "from nltk.tokenize import word_tokenize\n", "from nltk.stem import WordNetLemmatizer\n", "\n", "# Download necessary NLTK resources\n", "# nltk.download('stopwords')\n", "# nltk.download('punkt')\n", "# nltk.download('wordnet')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "lemmatizer = WordNetLemmatizer()\n", "stop_words = set(stopwords.words('english'))\n", "\n", "def preprocess_text(text):\n", " \"\"\"Function to clean text and perform lemitisation\"\"\"\n", " text = text.lower()\n", " text = re.sub(r'[^\\w\\s]', '', text)\n", " words = word_tokenize(text)\n", " words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]\n", " return \" \".join(words)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df = TRAIN.copy()\n", "df['text'] = df['question'] + \" \" + df['excerpt']\n", "df['text'] = df['text'].apply(preprocess_text)\n", "df = df[['text', 'topic']]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'effective differencial effective circuit im trying work general term effective capacitance circuit see diagram httpistackimgurcombs85bpng effective capacitance circuit'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['text'][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TF-IDF Vectorizer Methord" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "def vectorirse_text(text):\n", " \"\"\" Recieves text as input and returns TF-IDF vectors\"\"\"\n", " tfidf = TfidfVectorizer(max_features=500000)\n", " X = tfidf.fit_transform(text)\n", " return X\n", "\n", "def label_encoding(input):\n", " label_encoder = LabelEncoder()\n", " return label_encoder.fit_transform(input)\n", "\n", "\n", "X = vectorirse_text(df['text'])\n", "y = label_encoding(df['topic'])\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Multi-Layer Perceptron (MLP - Neural Network):\n", "Accuracy: 0.7925321463897131\n", " precision recall f1-score support\n", "\n", " 0 0.80 0.77 0.79 458\n", " 1 0.61 0.72 0.66 396\n", " 2 0.91 0.80 0.85 363\n", " 3 0.75 0.92 0.83 497\n", " 4 0.73 0.74 0.73 294\n", " 5 0.83 0.84 0.84 368\n", " 6 0.99 0.82 0.89 498\n", " 7 0.74 0.78 0.76 388\n", " 8 0.73 0.66 0.69 396\n", " 9 0.90 0.83 0.86 386\n", "\n", " accuracy 0.79 4044\n", " macro avg 0.80 0.79 0.79 4044\n", "weighted avg 0.80 0.79 0.79 4044\n", "\n" ] } ], "source": [ "from models import *\n", "print(\"Logistic Regression:\")\n", "report, acc = logistic_regression(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nDecision Tree Classifier:\")\n", "report, acc = decision_tree(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nRandom Forest Classifier:\")\n", "report, acc = random_forest(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nSupport Vector Machine (SVM):\")\n", "report, acc = support_vector_machine(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nk-Nearest Neighbors (k-NN):\")\n", "report, acc = knn(X, y, k=5)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nNaïve Bayes Classifier:\")\n", "report, acc = naive_bayes(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nGradient Boosting (XGBoost):\")\n", "report, acc = xgboost_classifier(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nMulti-Layer Perceptron (MLP - Neural Network):\")\n", "report, acc = mlp_classifier(X, y)\n", "\n", "print(\"Accuracy:\", acc)\n", "print(report)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Word2Vector Methord" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "import gensim\n", "import numpy as np\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "df['tokens'] = df['text'].apply(lambda x: x.split())\n", "\n", "w2v_model = gensim.models.Word2Vec(sentences=df['tokens'], vector_size=10000, window=5, min_count=2, workers=10)\n", "\n", "def get_sentence_embedding(tokens):\n", " vectors = [w2v_model.wv[word] for word in tokens if word in w2v_model.wv]\n", " return np.mean(vectors, axis=0) if vectors else np.zeros(100)\n", "\n", "X_w2v = np.array(df['tokens'].apply(get_sentence_embedding).tolist())\n", "y_encoded = label_encoding(df['topic'])\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(X_w2v)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "X = X_w2v\n", "y = y_encoded" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression:\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6434223541048467\n", " precision recall f1-score support\n", "\n", " 0 0.78 0.74 0.76 458\n", " 1 0.52 0.48 0.50 396\n", " 2 0.56 0.68 0.61 363\n", " 3 0.68 0.69 0.69 497\n", " 4 0.56 0.53 0.55 294\n", " 5 0.73 0.75 0.74 368\n", " 6 0.70 0.79 0.74 498\n", " 7 0.57 0.49 0.53 388\n", " 8 0.50 0.48 0.49 396\n", " 9 0.76 0.70 0.73 386\n", "\n", " accuracy 0.64 4044\n", " macro avg 0.64 0.63 0.63 4044\n", "weighted avg 0.64 0.64 0.64 4044\n", "\n", "\n", "Decision Tree Classifier:\n", "Accuracy: 0.5054401582591493\n", " precision recall f1-score support\n", "\n", " 0 0.68 0.62 0.65 458\n", " 1 0.37 0.39 0.38 396\n", " 2 0.44 0.49 0.46 363\n", " 3 0.54 0.51 0.52 497\n", " 4 0.34 0.33 0.34 294\n", " 5 0.60 0.68 0.64 368\n", " 6 0.73 0.65 0.69 498\n", " 7 0.37 0.34 0.36 388\n", " 8 0.31 0.33 0.32 396\n", " 9 0.58 0.62 0.60 386\n", "\n", " accuracy 0.51 4044\n", " macro avg 0.50 0.50 0.50 4044\n", "weighted avg 0.51 0.51 0.51 4044\n", "\n", "\n", "Random Forest Classifier:\n", "Accuracy: 0.6271018793273986\n", " precision recall f1-score support\n", "\n", " 0 0.76 0.74 0.75 458\n", " 1 0.48 0.49 0.49 396\n", " 2 0.52 0.62 0.56 363\n", " 3 0.62 0.66 0.64 497\n", " 4 0.53 0.51 0.52 294\n", " 5 0.77 0.77 0.77 368\n", " 6 0.79 0.78 0.78 498\n", " 7 0.57 0.45 0.50 388\n", " 8 0.47 0.50 0.49 396\n", " 9 0.70 0.67 0.69 386\n", "\n", " accuracy 0.63 4044\n", " macro avg 0.62 0.62 0.62 4044\n", "weighted avg 0.63 0.63 0.63 4044\n", "\n", "\n", "Support Vector Machine (SVM):\n", "[LibSVM]*.*\n", "optimization finished, #iter = 1036\n", "obj = -1517.315510, rho = -1.694230\n", "nSV = 1625, nBSV = 1606\n", "Total nSV = 1625\n", "*\n", "optimization finished, #iter = 982\n", "obj = -1479.052357, rho = -1.032787\n", "nSV = 1587, nBSV = 1574\n", "Total nSV = 1587\n", "*.*\n", "optimization finished, #iter = 1019\n", "obj = -1511.285642, rho = -1.209375\n", "nSV = 1618, nBSV = 1605\n", "Total nSV = 1618\n", "*.*\n", "optimization finished, #iter = 1038\n", "obj = -1524.714607, rho = -1.314464\n", "nSV = 1633, nBSV = 1616\n", "Total nSV = 1633\n", "*.*\n", "optimization finished, #iter = 1095\n", "obj = -1494.181777, rho = -0.849238\n", "nSV = 1600, nBSV = 1581\n", "Total nSV = 1600\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1290\n", "obj = -1858.225228, rho = 1.212579\n", "nSV = 1983, nBSV = 1964\n", "*\n", "optimization finished, #iter = 382\n", "obj = -490.424949, rho = -0.180521\n", "nSV = 572, nBSV = 561\n", "Total nSV = 572\n", "*\n", "optimization finished, #iter = 399\n", "obj = -491.822784, rho = -0.089091\n", "nSV = 576, nBSV = 564\n", "Total nSV = 576\n", "*\n", "optimization finished, #iter = 393\n", "obj = -474.182300, rho = 0.230688\n", "nSV = 557, nBSV = 546\n", "Total nSV = 557\n", "*\n", "optimization finished, #iter = 416\n", "obj = -505.775569, rho = 0.153069\n", "nSV = 588, nBSV = 575\n", "Total nSV = 588\n", "*\n", "optimization finished, #iter = 458\n", "obj = -498.473668, rho = 0.204678\n", "nSV = 581, nBSV = 567\n", "Total nSV = 581\n", "Line search fails in two-class probability estimates\n", "*\n", "optimization finished, #iter = 522\n", "obj = -596.023598, rho = -0.052335\n", "nSV = 691, nBSV = 676\n", "*\n", "optimization finished, #iter = 244\n", "obj = -319.875873, rho = -0.182741\n", "nSV = 367, nBSV = 360\n", "Total nSV = 367\n", "*\n", "optimization finished, #iter = 252\n", "obj = -323.460100, rho = -1.047336\n", "nSV = 371, nBSV = 364\n", "Total nSV = 371\n", "*\n", "optimization finished, #iter = 274\n", "obj = -326.774597, rho = -0.588755\n", "nSV = 377, nBSV = 366\n", "Total nSV = 377\n", "*\n", "optimization finished, #iter = 260\n", "obj = -330.207475, rho = -0.283656\n", "nSV = 379, nBSV = 370\n", "Total nSV = 379\n", "*\n", "optimization finished, #iter = 269\n", "obj = -307.486998, rho = -0.843473\n", "nSV = 359, nBSV = 349\n", "Total nSV = 359\n", "*\n", "optimization finished, #iter = 319\n", "obj = -391.805767, rho = 0.626391\n", "nSV = 449, nBSV = 436\n", "*\n", "optimization finished, #iter = 232\n", "obj = -269.133768, rho = -0.874751\n", "nSV = 319, nBSV = 310\n", "Total nSV = 319\n", "*\n", "optimization finished, #iter = 225\n", "obj = -269.519125, rho = -0.953848\n", "nSV = 320, nBSV = 312\n", "Total nSV = 320\n", "*\n", "optimization finished, #iter = 226\n", "obj = -267.347997, rho = -0.869605\n", "nSV = 317, nBSV = 309\n", "Total nSV = 317\n", "*\n", "optimization finished, #iter = 237\n", "obj = -267.065306, rho = -1.017752\n", "nSV = 317, nBSV = 307\n", "Total nSV = 317\n", "*\n", "optimization finished, #iter = 241\n", "obj = -268.581593, rho = -1.077961\n", "nSV = 319, nBSV = 308\n", "Total nSV = 319\n", "*\n", "optimization finished, #iter = 315\n", "obj = -324.261367, rho = 1.030535\n", "nSV = 382, nBSV = 371\n", "*\n", "optimization finished, #iter = 438\n", "obj = -574.864069, rho = 0.429757\n", "nSV = 659, nBSV = 648\n", "Total nSV = 659\n", "*\n", "optimization finished, #iter = 435\n", "obj = -567.882714, rho = 0.040911\n", "nSV = 655, nBSV = 644\n", "Total nSV = 655\n", "*\n", "optimization finished, #iter = 478\n", "obj = -580.675366, rho = 0.246804\n", "nSV = 668, nBSV = 656\n", "Total nSV = 668\n", "*\n", "optimization finished, #iter = 467\n", "obj = -574.076239, rho = 0.347992\n", "nSV = 666, nBSV = 652\n", "Total nSV = 666\n", "*\n", "optimization finished, #iter = 465\n", "obj = -580.355347, rho = 0.377928\n", "nSV = 666, nBSV = 656\n", "Total nSV = 666\n", "*\n", "optimization finished, #iter = 566\n", "obj = -699.220027, rho = -0.391056\n", "nSV = 798, nBSV = 785\n", "*\n", "optimization finished, #iter = 259\n", "obj = -316.467187, rho = 1.022385\n", "nSV = 388, nBSV = 380\n", "Total nSV = 388\n", "*\n", "optimization finished, #iter = 272\n", "obj = -328.430107, rho = 0.396892\n", "nSV = 401, nBSV = 389\n", "Total nSV = 401\n", "*\n", "optimization finished, #iter = 270\n", "obj = -314.593285, rho = 0.881492\n", "nSV = 389, nBSV = 380\n", "Total nSV = 389\n", "*\n", "optimization finished, #iter = 270\n", "obj = -327.279770, rho = 1.090755\n", "nSV = 400, nBSV = 393\n", "Total nSV = 400\n", "*\n", "optimization finished, #iter = 244\n", "obj = -328.086763, rho = 0.776954\n", "nSV = 401, nBSV = 392\n", "Total nSV = 401\n", "*\n", "optimization finished, #iter = 346\n", "obj = -386.703173, rho = -0.894051\n", "nSV = 469, nBSV = 457\n", "*\n", "optimization finished, #iter = 509\n", "obj = -640.814864, rho = 0.478834\n", "nSV = 730, nBSV = 717\n", "Total nSV = 730\n", "*\n", "optimization finished, #iter = 524\n", "obj = -630.628282, rho = -0.003386\n", "nSV = 716, nBSV = 699\n", "Total nSV = 716\n", "*\n", "optimization finished, #iter = 489\n", "obj = -630.190682, rho = 0.161799\n", "nSV = 714, nBSV = 703\n", "Total nSV = 714\n", "*\n", "optimization finished, #iter = 516\n", "obj = -652.129120, rho = 0.198138\n", "nSV = 741, nBSV = 727\n", "Total nSV = 741\n", "*\n", "optimization finished, #iter = 480\n", "obj = -627.104121, rho = 0.043070\n", "nSV = 712, nBSV = 702\n", "Total nSV = 712\n", "*\n", "optimization finished, #iter = 605\n", "obj = -774.908387, rho = -0.218843\n", "nSV = 876, nBSV = 863\n", "*\n", "optimization finished, #iter = 491\n", "obj = -662.461178, rho = 0.064231\n", "nSV = 745, nBSV = 731\n", "Total nSV = 745\n", "*\n", "optimization finished, #iter = 504\n", "obj = -661.785826, rho = 0.193410\n", "nSV = 744, nBSV = 729\n", "Total nSV = 744\n", "*\n", "optimization finished, #iter = 535\n", "obj = -667.479321, rho = 0.127282\n", "nSV = 748, nBSV = 736\n", "Total nSV = 748\n", "*\n", "optimization finished, #iter = 471\n", "obj = -665.825970, rho = 0.424384\n", "nSV = 748, nBSV = 736\n", "Total nSV = 748\n", "*\n", "optimization finished, #iter = 475\n", "obj = -677.242928, rho = 0.038673\n", "nSV = 759, nBSV = 749\n", "Total nSV = 759\n", "*\n", "optimization finished, #iter = 609\n", "obj = -814.707363, rho = -0.210829\n", "nSV = 909, nBSV = 896\n", "*\n", "optimization finished, #iter = 209\n", "obj = -279.481643, rho = 0.142011\n", "nSV = 326, nBSV = 318\n", "Total nSV = 326\n", "*\n", "optimization finished, #iter = 226\n", "obj = -300.217214, rho = -0.062227\n", "nSV = 344, nBSV = 335\n", "Total nSV = 344\n", "*\n", "optimization finished, #iter = 218\n", "obj = -278.322420, rho = 0.035744\n", "nSV = 322, nBSV = 313\n", "Total nSV = 322\n", "*\n", "optimization finished, #iter = 217\n", "obj = -279.938647, rho = -0.068915\n", "nSV = 322, nBSV = 314\n", "Total nSV = 322\n", "*\n", "optimization finished, #iter = 220\n", "obj = -280.775590, rho = 0.143540\n", "nSV = 325, nBSV = 317\n", "Total nSV = 325\n", "*\n", "optimization finished, #iter = 263\n", "obj = -345.444907, rho = -0.014041\n", "nSV = 393, nBSV = 385\n", "*\n", "optimization finished, #iter = 596\n", "obj = -808.372683, rho = 0.529659\n", "nSV = 913, nBSV = 902\n", "Total nSV = 913\n", "*\n", "optimization finished, #iter = 664\n", "obj = -796.047721, rho = 0.797036\n", "nSV = 903, nBSV = 890\n", "Total nSV = 903\n", "*\n", "optimization finished, #iter = 596\n", "obj = -799.071063, rho = 0.838157\n", "nSV = 903, nBSV = 892\n", "Total nSV = 903\n", "*\n", "optimization finished, #iter = 611\n", "obj = -807.920794, rho = 0.909712\n", "nSV = 913, nBSV = 899\n", "Total nSV = 913\n", "*\n", "optimization finished, #iter = 631\n", "obj = -794.963544, rho = 0.805850\n", "nSV = 902, nBSV = 890\n", "Total nSV = 902\n", "*\n", "optimization finished, #iter = 716\n", "obj = -976.588912, rho = -0.639328\n", "nSV = 1097, nBSV = 1084\n", "*\n", "optimization finished, #iter = 525\n", "obj = -694.998443, rho = -0.365176\n", "nSV = 749, nBSV = 736\n", "Total nSV = 749\n", "*\n", "optimization finished, #iter = 502\n", "obj = -691.055002, rho = -0.347344\n", "nSV = 745, nBSV = 733\n", "Total nSV = 745\n", "*\n", "optimization finished, #iter = 505\n", "obj = -668.098947, rho = -0.094008\n", "nSV = 726, nBSV = 715\n", "Total nSV = 726\n", "*\n", "optimization finished, #iter = 483\n", "obj = -670.936635, rho = -0.313520\n", "nSV = 729, nBSV = 716\n", "Total nSV = 729\n", "*\n", "optimization finished, #iter = 482\n", "obj = -655.443853, rho = 0.184217\n", "nSV = 712, nBSV = 702\n", "Total nSV = 712\n", "*\n", "optimization finished, #iter = 593\n", "obj = -833.337867, rho = 0.326926\n", "nSV = 896, nBSV = 887\n", "*\n", "optimization finished, #iter = 401\n", "obj = -496.319171, rho = -1.017513\n", "nSV = 556, nBSV = 544\n", "Total nSV = 556\n", "*\n", "optimization finished, #iter = 363\n", "obj = -493.450681, rho = -1.389993\n", "nSV = 550, nBSV = 543\n", "Total nSV = 550\n", "*\n", "optimization finished, #iter = 361\n", "obj = -481.327593, rho = -0.992433\n", "nSV = 543, nBSV = 532\n", "Total nSV = 543\n", "*\n", "optimization finished, #iter = 417\n", "obj = -492.483055, rho = -1.202376\n", "nSV = 555, nBSV = 543\n", "Total nSV = 555\n", "*\n", "optimization finished, #iter = 400\n", "obj = -483.317361, rho = -0.836515\n", "nSV = 542, nBSV = 528\n", "Total nSV = 542\n", "*\n", "optimization finished, #iter = 435\n", "obj = -598.755597, rho = 1.241718\n", "nSV = 665, nBSV = 655\n", "*\n", "optimization finished, #iter = 610\n", "obj = -734.986633, rho = 0.730311\n", "nSV = 825, nBSV = 810\n", "Total nSV = 825\n", "*\n", "optimization finished, #iter = 537\n", "obj = -707.652687, rho = 1.140989\n", "nSV = 796, nBSV = 781\n", "Total nSV = 796\n", "*\n", "optimization finished, #iter = 591\n", "obj = -717.045186, rho = 0.819133\n", "nSV = 807, nBSV = 792\n", "Total nSV = 807\n", "*\n", "optimization finished, #iter = 546\n", "obj = -750.494142, rho = 0.964832\n", "nSV = 838, nBSV = 825\n", "Total nSV = 838\n", "*\n", "optimization finished, #iter = 564\n", "obj = -725.779809, rho = 1.141367\n", "nSV = 816, nBSV = 803\n", "Total nSV = 816\n", "*\n", "optimization finished, #iter = 621\n", "obj = -888.387417, rho = -1.021146\n", "nSV = 986, nBSV = 976\n", "*\n", "optimization finished, #iter = 341\n", "obj = -428.589089, rho = 1.675353\n", "nSV = 530, nBSV = 522\n", "Total nSV = 530\n", "*\n", "optimization finished, #iter = 379\n", "obj = -451.101273, rho = 1.934258\n", "nSV = 552, nBSV = 540\n", "Total nSV = 552\n", "*\n", "optimization finished, #iter = 349\n", "obj = -455.400963, rho = 1.919493\n", "nSV = 554, nBSV = 543\n", "Total nSV = 554\n", "*\n", "optimization finished, #iter = 355\n", "obj = -446.570890, rho = 1.966051\n", "nSV = 546, nBSV = 538\n", "Total nSV = 546\n", "*\n", "optimization finished, #iter = 350\n", "obj = -449.551825, rho = 1.778115\n", "nSV = 547, nBSV = 538\n", "Total nSV = 547\n", "*\n", "optimization finished, #iter = 478\n", "obj = -533.684301, rho = -2.007665\n", "nSV = 646, nBSV = 634\n", "*\n", "optimization finished, #iter = 859\n", "obj = -1195.774601, rho = 0.652084\n", "nSV = 1329, nBSV = 1313\n", "Total nSV = 1329\n", "*\n", "optimization finished, #iter = 846\n", "obj = -1220.815988, rho = 0.351598\n", "nSV = 1350, nBSV = 1337\n", "Total nSV = 1350\n", "*\n", "optimization finished, #iter = 864\n", "obj = -1185.235880, rho = 0.629402\n", "nSV = 1318, nBSV = 1304\n", "Total nSV = 1318\n", "*\n", "optimization finished, #iter = 853\n", "obj = -1188.919608, rho = 0.547903\n", "nSV = 1319, nBSV = 1307\n", "Total nSV = 1319\n", "*\n", "optimization finished, #iter = 853\n", "obj = -1187.489772, rho = 0.653883\n", "nSV = 1306, nBSV = 1294\n", "Total nSV = 1306\n", "Line search fails in two-class probability estimates\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1059\n", "obj = -1463.243268, rho = -0.428823\n", "nSV = 1617, nBSV = 1604\n", "*\n", "optimization finished, #iter = 908\n", "obj = -1408.287449, rho = 0.573238\n", "nSV = 1498, nBSV = 1484\n", "Total nSV = 1498\n", "*\n", "optimization finished, #iter = 953\n", "obj = -1373.429295, rho = 0.418926\n", "nSV = 1462, nBSV = 1446\n", "Total nSV = 1462\n", "*\n", "optimization finished, #iter = 949\n", "obj = -1363.937797, rho = 0.675250\n", "nSV = 1452, nBSV = 1438\n", "Total nSV = 1452\n", "*\n", "optimization finished, #iter = 941\n", "obj = -1397.982221, rho = 0.354201\n", "nSV = 1483, nBSV = 1470\n", "Total nSV = 1483\n", "*\n", "optimization finished, #iter = 964\n", "obj = -1416.322625, rho = 0.568611\n", "nSV = 1501, nBSV = 1486\n", "Total nSV = 1501\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1195\n", "obj = -1720.145250, rho = -0.609608\n", "nSV = 1831, nBSV = 1813\n", "*\n", "optimization finished, #iter = 397\n", "obj = -526.693145, rho = 0.585664\n", "nSV = 588, nBSV = 575\n", "Total nSV = 588\n", "*\n", "optimization finished, #iter = 413\n", "obj = -525.819465, rho = 0.332271\n", "nSV = 585, nBSV = 573\n", "Total nSV = 585\n", "*\n", "optimization finished, #iter = 367\n", "obj = -509.270546, rho = 0.109283\n", "nSV = 571, nBSV = 561\n", "Total nSV = 571\n", "*\n", "optimization finished, #iter = 378\n", "obj = -519.012797, rho = 0.697541\n", "nSV = 576, nBSV = 565\n", "Total nSV = 576\n", "*\n", "optimization finished, #iter = 370\n", "obj = -516.448303, rho = -0.094913\n", "nSV = 574, nBSV = 563\n", "Total nSV = 574\n", "*\n", "optimization finished, #iter = 460\n", "obj = -636.960555, rho = -0.281530\n", "nSV = 706, nBSV = 696\n", "*\n", "optimization finished, #iter = 529\n", "obj = -703.383037, rho = -0.265754\n", "nSV = 777, nBSV = 764\n", "Total nSV = 777\n", "*\n", "optimization finished, #iter = 518\n", "obj = -694.464431, rho = -0.400270\n", "nSV = 768, nBSV = 756\n", "Total nSV = 768\n", "*\n", "optimization finished, #iter = 529\n", "obj = -660.258155, rho = -0.402594\n", "nSV = 738, nBSV = 726\n", "Total nSV = 738\n", "*\n", "optimization finished, #iter = 496\n", "obj = -676.745445, rho = -0.219431\n", "nSV = 752, nBSV = 741\n", "Total nSV = 752\n", "*\n", "optimization finished, #iter = 462\n", "obj = -675.118510, rho = -0.088180\n", "nSV = 747, nBSV = 739\n", "Total nSV = 747\n", "*\n", "optimization finished, #iter = 580\n", "obj = -835.407522, rho = 0.221647\n", "nSV = 918, nBSV = 908\n", "*\n", "optimization finished, #iter = 562\n", "obj = -800.697176, rho = -0.952110\n", "nSV = 869, nBSV = 859\n", "Total nSV = 869\n", "*\n", "optimization finished, #iter = 590\n", "obj = -807.997219, rho = -1.294996\n", "nSV = 880, nBSV = 868\n", "Total nSV = 880\n", "*\n", "optimization finished, #iter = 597\n", "obj = -838.974356, rho = -1.345748\n", "nSV = 908, nBSV = 900\n", "Total nSV = 908\n", "*\n", "optimization finished, #iter = 627\n", "obj = -819.594385, rho = -1.474150\n", "nSV = 892, nBSV = 880\n", "Total nSV = 892\n", "*\n", "optimization finished, #iter = 603\n", "obj = -814.577269, rho = -1.471206\n", "nSV = 888, nBSV = 879\n", "Total nSV = 888\n", "*\n", "optimization finished, #iter = 727\n", "obj = -1004.134171, rho = 1.448066\n", "nSV = 1089, nBSV = 1073\n", "*\n", "optimization finished, #iter = 664\n", "obj = -944.875252, rho = -0.086917\n", "nSV = 1058, nBSV = 1046\n", "Total nSV = 1058\n", "*\n", "optimization finished, #iter = 704\n", "obj = -946.609174, rho = 0.143785\n", "nSV = 1069, nBSV = 1051\n", "Total nSV = 1069\n", "*\n", "optimization finished, #iter = 687\n", "obj = -945.484375, rho = 0.137203\n", "nSV = 1064, nBSV = 1053\n", "Total nSV = 1064\n", "*\n", "optimization finished, #iter = 700\n", "obj = -932.750378, rho = 0.368735\n", "nSV = 1048, nBSV = 1034\n", "Total nSV = 1048\n", "*\n", "optimization finished, #iter = 714\n", "obj = -958.030726, rho = 0.003513\n", "nSV = 1073, nBSV = 1058\n", "Total nSV = 1073\n", "*\n", "optimization finished, #iter = 825\n", "obj = -1153.839393, rho = -0.094534\n", "nSV = 1287, nBSV = 1275\n", "*\n", "optimization finished, #iter = 847\n", "obj = -1225.736117, rho = 1.636879\n", "nSV = 1407, nBSV = 1398\n", "Total nSV = 1407\n", "*\n", "optimization finished, #iter = 892\n", "obj = -1237.373881, rho = 1.660310\n", "nSV = 1417, nBSV = 1404\n", "Total nSV = 1417\n", "*\n", "optimization finished, #iter = 854\n", "obj = -1232.563784, rho = 1.544912\n", "nSV = 1404, nBSV = 1392\n", "Total nSV = 1404\n", "*\n", "optimization finished, #iter = 871\n", "obj = -1226.475845, rho = 1.399756\n", "nSV = 1404, nBSV = 1390\n", "Total nSV = 1404\n", "*\n", "optimization finished, #iter = 868\n", "obj = -1240.657955, rho = 1.752230\n", "nSV = 1416, nBSV = 1405\n", "Total nSV = 1416\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1091\n", "obj = -1495.229407, rho = -1.795388\n", "nSV = 1703, nBSV = 1694\n", "*\n", "optimization finished, #iter = 886\n", "obj = -1230.739742, rho = 0.275501\n", "nSV = 1352, nBSV = 1343\n", "Total nSV = 1352\n", "*\n", "optimization finished, #iter = 900\n", "obj = -1248.387624, rho = 0.118039\n", "nSV = 1373, nBSV = 1361\n", "Total nSV = 1373\n", "*\n", "optimization finished, #iter = 863\n", "obj = -1239.071656, rho = 0.431101\n", "nSV = 1357, nBSV = 1346\n", "Total nSV = 1357\n", "*\n", "optimization finished, #iter = 902\n", "obj = -1222.639363, rho = 0.487116\n", "nSV = 1347, nBSV = 1332\n", "Total nSV = 1347\n", "*\n", "optimization finished, #iter = 869\n", "obj = -1203.177539, rho = 0.446269\n", "nSV = 1325, nBSV = 1313\n", "Total nSV = 1325\n", "*.*\n", "optimization finished, #iter = 1096\n", "obj = -1505.679512, rho = -0.345098\n", "nSV = 1656, nBSV = 1640\n", "*\n", "optimization finished, #iter = 675\n", "obj = -913.142216, rho = -0.987289\n", "nSV = 1007, nBSV = 993\n", "Total nSV = 1007\n", "*\n", "optimization finished, #iter = 675\n", "obj = -941.880661, rho = -0.651493\n", "nSV = 1037, nBSV = 1026\n", "Total nSV = 1037\n", "*\n", "optimization finished, #iter = 689\n", "obj = -941.809968, rho = -0.501660\n", "nSV = 1039, nBSV = 1026\n", "Total nSV = 1039\n", "*\n", "optimization finished, #iter = 621\n", "obj = -922.453889, rho = -0.730588\n", "nSV = 1017, nBSV = 1010\n", "Total nSV = 1017\n", "*\n", "optimization finished, #iter = 674\n", "obj = -940.605762, rho = -0.862924\n", "nSV = 1038, nBSV = 1026\n", "Total nSV = 1038\n", "*\n", "optimization finished, #iter = 784\n", "obj = -1142.683927, rho = 0.672064\n", "nSV = 1247, nBSV = 1237\n", "*\n", "optimization finished, #iter = 456\n", "obj = -544.071756, rho = -0.176196\n", "nSV = 640, nBSV = 628\n", "Total nSV = 640\n", "*\n", "optimization finished, #iter = 465\n", "obj = -552.033115, rho = -0.101496\n", "nSV = 648, nBSV = 636\n", "Total nSV = 648\n", "*\n", "optimization finished, #iter = 453\n", "obj = -560.511273, rho = -0.609682\n", "nSV = 657, nBSV = 644\n", "Total nSV = 657\n", "*\n", "optimization finished, #iter = 442\n", "obj = -535.413141, rho = -0.008853\n", "nSV = 631, nBSV = 620\n", "Total nSV = 631\n", "*\n", "optimization finished, #iter = 425\n", "obj = -533.578899, rho = -0.522805\n", "nSV = 629, nBSV = 621\n", "Total nSV = 629\n", "*\n", "optimization finished, #iter = 592\n", "obj = -658.611060, rho = 0.247137\n", "nSV = 770, nBSV = 756\n", "*\n", "optimization finished, #iter = 886\n", "obj = -1276.039758, rho = -2.360935\n", "nSV = 1384, nBSV = 1370\n", "Total nSV = 1384\n", "*\n", "optimization finished, #iter = 861\n", "obj = -1291.322698, rho = -2.130760\n", "nSV = 1390, nBSV = 1380\n", "Total nSV = 1390\n", "*\n", "optimization finished, #iter = 893\n", "obj = -1261.681619, rho = -2.002617\n", "nSV = 1365, nBSV = 1354\n", "Total nSV = 1365\n", "*\n", "optimization finished, #iter = 909\n", "obj = -1280.517679, rho = -1.999982\n", "nSV = 1387, nBSV = 1376\n", "Total nSV = 1387\n", "*\n", "optimization finished, #iter = 875\n", "obj = -1248.562862, rho = -2.103486\n", "nSV = 1347, nBSV = 1336\n", "Total nSV = 1347\n", "*.*\n", "optimization finished, #iter = 1053\n", "obj = -1565.402216, rho = 2.283029\n", "nSV = 1693, nBSV = 1677\n", "*\n", "optimization finished, #iter = 274\n", "obj = -353.682388, rho = 0.422252\n", "nSV = 409, nBSV = 397\n", "Total nSV = 409\n", "*\n", "optimization finished, #iter = 263\n", "obj = -341.054851, rho = 0.958824\n", "nSV = 395, nBSV = 387\n", "Total nSV = 395\n", "*\n", "optimization finished, #iter = 282\n", "obj = -340.479523, rho = 0.777040\n", "nSV = 396, nBSV = 383\n", "Total nSV = 396\n", "*\n", "optimization finished, #iter = 259\n", "obj = -346.889051, rho = 0.257842\n", "nSV = 401, nBSV = 391\n", "Total nSV = 401\n", "*\n", "optimization finished, #iter = 252\n", "obj = -351.227647, rho = 0.666157\n", "nSV = 404, nBSV = 399\n", "Total nSV = 404\n", "*\n", "optimization finished, #iter = 308\n", "obj = -421.560279, rho = -0.639157\n", "nSV = 481, nBSV = 471\n", "*\n", "optimization finished, #iter = 201\n", "obj = -247.761172, rho = 0.460835\n", "nSV = 307, nBSV = 298\n", "Total nSV = 307\n", "*\n", "optimization finished, #iter = 207\n", "obj = -260.626058, rho = 0.542610\n", "nSV = 321, nBSV = 314\n", "Total nSV = 321\n", "*\n", "optimization finished, #iter = 207\n", "obj = -249.170040, rho = 0.563039\n", "nSV = 309, nBSV = 302\n", "Total nSV = 309\n", "*\n", "optimization finished, #iter = 226\n", "obj = -255.683717, rho = 0.435524\n", "nSV = 317, nBSV = 306\n", "Total nSV = 317\n", "*\n", "optimization finished, #iter = 183\n", "obj = -248.204389, rho = 0.536352\n", "nSV = 304, nBSV = 299\n", "Total nSV = 304\n", "*\n", "optimization finished, #iter = 234\n", "obj = -301.564996, rho = -0.578948\n", "nSV = 366, nBSV = 359\n", "*\n", "optimization finished, #iter = 744\n", "obj = -1017.279382, rho = 0.884369\n", "nSV = 1146, nBSV = 1134\n", "Total nSV = 1146\n", "*\n", "optimization finished, #iter = 719\n", "obj = -1038.444337, rho = 0.678322\n", "nSV = 1174, nBSV = 1163\n", "Total nSV = 1174\n", "*\n", "optimization finished, #iter = 722\n", "obj = -1014.957663, rho = 0.908041\n", "nSV = 1148, nBSV = 1134\n", "Total nSV = 1148\n", "*\n", "optimization finished, #iter = 764\n", "obj = -1009.940919, rho = 1.069797\n", "nSV = 1142, nBSV = 1132\n", "Total nSV = 1142\n", "*\n", "optimization finished, #iter = 737\n", "obj = -1021.153562, rho = 0.735972\n", "nSV = 1148, nBSV = 1137\n", "Total nSV = 1148\n", "*\n", "optimization finished, #iter = 951\n", "obj = -1242.612795, rho = -0.717574\n", "nSV = 1401, nBSV = 1385\n", "*\n", "optimization finished, #iter = 919\n", "obj = -1307.187797, rho = -0.414818\n", "nSV = 1429, nBSV = 1415\n", "Total nSV = 1429\n", "*.*\n", "optimization finished, #iter = 1025\n", "obj = -1288.040096, rho = -0.211321\n", "nSV = 1406, nBSV = 1387\n", "Total nSV = 1406\n", "*\n", "optimization finished, #iter = 983\n", "obj = -1312.391878, rho = -0.266344\n", "nSV = 1435, nBSV = 1417\n", "Total nSV = 1435\n", "*\n", "optimization finished, #iter = 912\n", "obj = -1271.498820, rho = -0.433876\n", "nSV = 1392, nBSV = 1378\n", "Total nSV = 1392\n", "*\n", "optimization finished, #iter = 904\n", "obj = -1300.462160, rho = -0.281881\n", "nSV = 1413, nBSV = 1400\n", "Total nSV = 1413\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1110\n", "obj = -1590.135247, rho = 0.282618\n", "nSV = 1741, nBSV = 1722\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1110\n", "obj = -1394.311777, rho = 0.023940\n", "nSV = 1551, nBSV = 1531\n", "Total nSV = 1551\n", "*.*\n", "optimization finished, #iter = 1057\n", "obj = -1385.793274, rho = -0.363508\n", "nSV = 1546, nBSV = 1531\n", "Total nSV = 1546\n", "*.*\n", "optimization finished, #iter = 1052\n", "obj = -1417.223302, rho = 0.001485\n", "nSV = 1579, nBSV = 1564\n", "Total nSV = 1579\n", "*.*\n", "optimization finished, #iter = 1025\n", "obj = -1397.191776, rho = -0.117289\n", "nSV = 1555, nBSV = 1539\n", "Total nSV = 1555\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1049\n", "obj = -1428.644400, rho = -0.286629\n", "nSV = 1593, nBSV = 1578\n", "Total nSV = 1593\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1319\n", "obj = -1715.849162, rho = 0.258820\n", "nSV = 1918, nBSV = 1899\n", "*\n", "optimization finished, #iter = 310\n", "obj = -434.848837, rho = 1.545303\n", "nSV = 482, nBSV = 473\n", "Total nSV = 482\n", "*\n", "optimization finished, #iter = 319\n", "obj = -443.099938, rho = 2.015500\n", "nSV = 493, nBSV = 485\n", "Total nSV = 493\n", "*\n", "optimization finished, #iter = 358\n", "obj = -442.635029, rho = 1.983684\n", "nSV = 495, nBSV = 485\n", "Total nSV = 495\n", "*\n", "optimization finished, #iter = 323\n", "obj = -433.594495, rho = 1.783756\n", "nSV = 486, nBSV = 475\n", "Total nSV = 486\n", "*\n", "optimization finished, #iter = 324\n", "obj = -444.891161, rho = 2.055807\n", "nSV = 495, nBSV = 486\n", "Total nSV = 495\n", "*\n", "optimization finished, #iter = 388\n", "obj = -538.508415, rho = -2.116720\n", "nSV = 594, nBSV = 586\n", "*\n", "optimization finished, #iter = 256\n", "obj = -376.343307, rho = 1.758690\n", "nSV = 435, nBSV = 428\n", "Total nSV = 435\n", "*\n", "optimization finished, #iter = 269\n", "obj = -357.704375, rho = 0.976174\n", "nSV = 417, nBSV = 407\n", "Total nSV = 417\n", "*\n", "optimization finished, #iter = 273\n", "obj = -368.319636, rho = 1.769638\n", "nSV = 428, nBSV = 419\n", "Total nSV = 428\n", "*\n", "optimization finished, #iter = 263\n", "obj = -375.107030, rho = 1.556016\n", "nSV = 432, nBSV = 426\n", "Total nSV = 432\n", "*\n", "optimization finished, #iter = 319\n", "obj = -364.504755, rho = 1.780705\n", "nSV = 430, nBSV = 416\n", "Total nSV = 430\n", "*\n", "optimization finished, #iter = 403\n", "obj = -446.849679, rho = -1.818424\n", "nSV = 515, nBSV = 502\n", "*\n", "optimization finished, #iter = 647\n", "obj = -838.990476, rho = 1.977418\n", "nSV = 964, nBSV = 947\n", "Total nSV = 964\n", "*\n", "optimization finished, #iter = 613\n", "obj = -829.472847, rho = 1.857684\n", "nSV = 951, nBSV = 938\n", "Total nSV = 951\n", "*\n", "optimization finished, #iter = 623\n", "obj = -829.222756, rho = 1.921228\n", "nSV = 948, nBSV = 933\n", "Total nSV = 948\n", "*\n", "optimization finished, #iter = 608\n", "obj = -842.397275, rho = 1.815620\n", "nSV = 962, nBSV = 949\n", "Total nSV = 962\n", "*\n", "optimization finished, #iter = 590\n", "obj = -831.003028, rho = 1.839229\n", "nSV = 946, nBSV = 934\n", "Total nSV = 946\n", "*\n", "optimization finished, #iter = 763\n", "obj = -1012.993604, rho = -1.857632\n", "nSV = 1158, nBSV = 1144\n", "*\n", "optimization finished, #iter = 614\n", "obj = -895.403937, rho = 0.673833\n", "nSV = 971, nBSV = 958\n", "Total nSV = 971\n", "*\n", "optimization finished, #iter = 660\n", "obj = -897.138172, rho = 0.857856\n", "nSV = 974, nBSV = 958\n", "Total nSV = 974\n", "*\n", "optimization finished, #iter = 588\n", "obj = -884.415041, rho = 1.549122\n", "nSV = 961, nBSV = 950\n", "Total nSV = 961\n", "*\n", "optimization finished, #iter = 629\n", "obj = -880.833569, rho = 1.057431\n", "nSV = 958, nBSV = 945\n", "Total nSV = 958\n", "*\n", "optimization finished, #iter = 646\n", "obj = -885.761665, rho = 1.065871\n", "nSV = 963, nBSV = 948\n", "Total nSV = 963\n", "*\n", "optimization finished, #iter = 786\n", "obj = -1093.816606, rho = -1.158807\n", "nSV = 1181, nBSV = 1165\n", "*\n", "optimization finished, #iter = 766\n", "obj = -989.314372, rho = 0.786801\n", "nSV = 1177, nBSV = 1163\n", "Total nSV = 1177\n", "*\n", "optimization finished, #iter = 741\n", "obj = -970.955024, rho = 0.995479\n", "nSV = 1157, nBSV = 1143\n", "Total nSV = 1157\n", "*\n", "optimization finished, #iter = 735\n", "obj = -972.825916, rho = 1.170404\n", "nSV = 1150, nBSV = 1140\n", "Total nSV = 1150\n", "*\n", "optimization finished, #iter = 791\n", "obj = -1006.727797, rho = 0.680510\n", "nSV = 1190, nBSV = 1174\n", "Total nSV = 1190\n", "*\n", "optimization finished, #iter = 724\n", "obj = -986.582294, rho = 1.188049\n", "nSV = 1171, nBSV = 1154\n", "Total nSV = 1171\n", "*\n", "optimization finished, #iter = 917\n", "obj = -1184.963798, rho = -0.840322\n", "nSV = 1408, nBSV = 1390\n", "*\n", "optimization finished, #iter = 784\n", "obj = -1143.887640, rho = 1.665747\n", "nSV = 1264, nBSV = 1256\n", "Total nSV = 1264\n", "*\n", "optimization finished, #iter = 787\n", "obj = -1155.912659, rho = 1.523238\n", "nSV = 1271, nBSV = 1263\n", "Total nSV = 1271\n", "*\n", "optimization finished, #iter = 790\n", "obj = -1165.427925, rho = 1.741795\n", "nSV = 1285, nBSV = 1275\n", "Total nSV = 1285\n", "*\n", "optimization finished, #iter = 762\n", "obj = -1150.265839, rho = 1.182400\n", "nSV = 1265, nBSV = 1256\n", "Total nSV = 1265\n", "*\n", "optimization finished, #iter = 821\n", "obj = -1168.541574, rho = 1.428443\n", "nSV = 1289, nBSV = 1277\n", "Total nSV = 1289\n", "*\n", "optimization finished, #iter = 938\n", "obj = -1416.706690, rho = -1.541312\n", "nSV = 1555, nBSV = 1547\n", "*\n", "optimization finished, #iter = 507\n", "obj = -649.824058, rho = -0.464617\n", "nSV = 750, nBSV = 738\n", "Total nSV = 750\n", "*\n", "optimization finished, #iter = 460\n", "obj = -648.597307, rho = -0.820532\n", "nSV = 748, nBSV = 741\n", "Total nSV = 748\n", "*\n", "optimization finished, #iter = 482\n", "obj = -644.191057, rho = -0.514781\n", "nSV = 743, nBSV = 733\n", "Total nSV = 743\n", "*\n", "optimization finished, #iter = 482\n", "obj = -663.505024, rho = -0.810700\n", "nSV = 761, nBSV = 752\n", "Total nSV = 761\n", "*\n", "optimization finished, #iter = 522\n", "obj = -660.957425, rho = -0.489073\n", "nSV = 764, nBSV = 751\n", "Total nSV = 764\n", "*\n", "optimization finished, #iter = 672\n", "obj = -792.313602, rho = 0.845277\n", "nSV = 909, nBSV = 893\n", "*\n", "optimization finished, #iter = 351\n", "obj = -472.163158, rho = -1.177538\n", "nSV = 545, nBSV = 536\n", "Total nSV = 545\n", "*\n", "optimization finished, #iter = 400\n", "obj = -502.260690, rho = -0.823866\n", "nSV = 575, nBSV = 564\n", "Total nSV = 575\n", "*\n", "optimization finished, #iter = 403\n", "obj = -501.743197, rho = -0.879794\n", "nSV = 576, nBSV = 564\n", "Total nSV = 576\n", "*\n", "optimization finished, #iter = 393\n", "obj = -503.123532, rho = -0.596362\n", "nSV = 578, nBSV = 566\n", "Total nSV = 578\n", "*\n", "optimization finished, #iter = 386\n", "obj = -500.427078, rho = -0.955284\n", "nSV = 575, nBSV = 565\n", "Total nSV = 575\n", "*\n", "optimization finished, #iter = 463\n", "obj = -603.367971, rho = 0.830234\n", "nSV = 682, nBSV = 674\n", "*\n", "optimization finished, #iter = 235\n", "obj = -336.573554, rho = -0.595926\n", "nSV = 386, nBSV = 379\n", "Total nSV = 386\n", "*\n", "optimization finished, #iter = 217\n", "obj = -321.476909, rho = -1.081118\n", "nSV = 373, nBSV = 367\n", "Total nSV = 373\n", "*\n", "optimization finished, #iter = 232\n", "obj = -344.548569, rho = -0.406270\n", "nSV = 393, nBSV = 386\n", "Total nSV = 393\n", "*\n", "optimization finished, #iter = 292\n", "obj = -346.950734, rho = -0.816791\n", "nSV = 400, nBSV = 389\n", "Total nSV = 400\n", "*\n", "optimization finished, #iter = 266\n", "obj = -327.443612, rho = -0.921138\n", "nSV = 378, nBSV = 368\n", "Total nSV = 378\n", "*\n", "optimization finished, #iter = 318\n", "obj = -408.019102, rho = 0.856814\n", "nSV = 465, nBSV = 454\n", "*\n", "optimization finished, #iter = 474\n", "obj = -589.997211, rho = -0.645898\n", "nSV = 693, nBSV = 684\n", "Total nSV = 693\n", "*\n", "optimization finished, #iter = 443\n", "obj = -566.739876, rho = -0.641433\n", "nSV = 670, nBSV = 662\n", "Total nSV = 670\n", "*\n", "optimization finished, #iter = 445\n", "obj = -576.368449, rho = -0.172344\n", "nSV = 677, nBSV = 670\n", "Total nSV = 677\n", "*\n", "optimization finished, #iter = 478\n", "obj = -574.112357, rho = -0.836500\n", "nSV = 675, nBSV = 665\n", "Total nSV = 675\n", "*\n", "optimization finished, #iter = 480\n", "obj = -564.290313, rho = -0.833488\n", "nSV = 661, nBSV = 651\n", "Total nSV = 661\n", "*\n", "optimization finished, #iter = 518\n", "obj = -692.964352, rho = 0.758181\n", "nSV = 810, nBSV = 799\n", "*\n", "optimization finished, #iter = 326\n", "obj = -383.423843, rho = -1.504687\n", "nSV = 465, nBSV = 456\n", "Total nSV = 465\n", "*\n", "optimization finished, #iter = 337\n", "obj = -392.879538, rho = -1.723836\n", "nSV = 474, nBSV = 465\n", "Total nSV = 474\n", "*\n", "optimization finished, #iter = 333\n", "obj = -370.971140, rho = -1.511079\n", "nSV = 458, nBSV = 448\n", "Total nSV = 458\n", "*\n", "optimization finished, #iter = 314\n", "obj = -389.800933, rho = -1.460215\n", "nSV = 472, nBSV = 468\n", "Total nSV = 472\n", "*\n", "optimization finished, #iter = 341\n", "obj = -407.183602, rho = -1.624302\n", "nSV = 489, nBSV = 480\n", "Total nSV = 489\n", "*\n", "optimization finished, #iter = 365\n", "obj = -466.228589, rho = 1.864773\n", "nSV = 559, nBSV = 550\n", "*\n", "optimization finished, #iter = 180\n", "obj = -217.622724, rho = -0.825188\n", "nSV = 266, nBSV = 261\n", "Total nSV = 266\n", "*\n", "optimization finished, #iter = 173\n", "obj = -217.640218, rho = -0.584114\n", "nSV = 265, nBSV = 260\n", "Total nSV = 265\n", "*\n", "optimization finished, #iter = 195\n", "obj = -216.116730, rho = -0.724322\n", "nSV = 268, nBSV = 258\n", "Total nSV = 268\n", "*\n", "optimization finished, #iter = 228\n", "obj = -222.413738, rho = -1.020844\n", "nSV = 275, nBSV = 265\n", "Total nSV = 275\n", "*\n", "optimization finished, #iter = 230\n", "obj = -214.256660, rho = -0.914044\n", "nSV = 266, nBSV = 256\n", "Total nSV = 266\n", "*\n", "optimization finished, #iter = 200\n", "obj = -260.350240, rho = 0.993815\n", "nSV = 317, nBSV = 309\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1130\n", "obj = -1565.076935, rho = -0.807701\n", "nSV = 1786, nBSV = 1767\n", "Total nSV = 1786\n", ".\n", "Warning: using -h 0 may be faster\n", "*.\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1136\n", "obj = -1596.205821, rho = -0.408915\n", "nSV = 1809, nBSV = 1794\n", "Total nSV = 1809\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1086\n", "obj = -1604.002624, rho = -0.082496\n", "nSV = 1824, nBSV = 1810\n", "Total nSV = 1824\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1108\n", "obj = -1587.339179, rho = -0.563140\n", "nSV = 1796, nBSV = 1782\n", "Total nSV = 1796\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1143\n", "obj = -1612.323278, rho = -0.213667\n", "nSV = 1825, nBSV = 1812\n", "Total nSV = 1825\n", ".\n", "Warning: using -h 0 may be faster\n", "*\n", "optimization finished, #iter = 1373\n", "obj = -1938.204300, rho = 0.305299\n", "nSV = 2189, nBSV = 2175\n", "*\n", "optimization finished, #iter = 603\n", "obj = -766.280254, rho = -0.620234\n", "nSV = 864, nBSV = 853\n", "Total nSV = 864\n", "*\n", "optimization finished, #iter = 606\n", "obj = -772.893351, rho = -0.366332\n", "nSV = 870, nBSV = 857\n", "Total nSV = 870\n", "*\n", "optimization finished, #iter = 597\n", "obj = -762.972355, rho = -0.586103\n", "nSV = 859, nBSV = 846\n", "Total nSV = 859\n", "*\n", "optimization finished, #iter = 604\n", "obj = -755.512908, rho = -0.649734\n", "nSV = 850, nBSV = 838\n", "Total nSV = 850\n", "*\n", "optimization finished, #iter = 618\n", "obj = -762.919179, rho = -0.670479\n", "nSV = 866, nBSV = 853\n", "Total nSV = 866\n", "*\n", "optimization finished, #iter = 642\n", "obj = -931.562797, rho = 0.604323\n", "nSV = 1045, nBSV = 1035\n", "*\n", "optimization finished, #iter = 668\n", "obj = -804.671815, rho = 0.290643\n", "nSV = 935, nBSV = 918\n", "Total nSV = 935\n", "*\n", "optimization finished, #iter = 627\n", "obj = -802.330257, rho = 0.497385\n", "nSV = 934, nBSV = 917\n", "Total nSV = 934\n", "*\n", "optimization finished, #iter = 590\n", "obj = -764.731250, rho = 0.469255\n", "nSV = 883, nBSV = 873\n", "Total nSV = 883\n", "*\n", "optimization finished, #iter = 639\n", "obj = -792.026027, rho = 0.369281\n", "nSV = 910, nBSV = 897\n", "Total nSV = 910\n", "*\n", "optimization finished, #iter = 689\n", "obj = -810.824770, rho = 0.499082\n", "nSV = 940, nBSV = 924\n", "Total nSV = 940\n", "*\n", "optimization finished, #iter = 741\n", "obj = -963.036012, rho = -0.389368\n", "nSV = 1114, nBSV = 1100\n", "Total nSV = 12845\n", "Accuracy: 0.6107814045499506\n", " precision recall f1-score support\n", "\n", " 0 0.77 0.73 0.75 458\n", " 1 0.49 0.47 0.48 396\n", " 2 0.49 0.62 0.55 363\n", " 3 0.56 0.68 0.62 497\n", " 4 0.53 0.48 0.50 294\n", " 5 0.73 0.71 0.72 368\n", " 6 0.72 0.80 0.76 498\n", " 7 0.55 0.45 0.49 388\n", " 8 0.49 0.45 0.47 396\n", " 9 0.75 0.61 0.67 386\n", "\n", " accuracy 0.61 4044\n", " macro avg 0.61 0.60 0.60 4044\n", "weighted avg 0.61 0.61 0.61 4044\n", "\n", "\n", "k-Nearest Neighbors (k-NN):\n", "Accuracy: 0.5452522255192879\n", " precision recall f1-score support\n", "\n", " 0 0.63 0.73 0.68 458\n", " 1 0.41 0.42 0.41 396\n", " 2 0.45 0.59 0.51 363\n", " 3 0.47 0.61 0.53 497\n", " 4 0.48 0.37 0.42 294\n", " 5 0.75 0.69 0.72 368\n", " 6 0.73 0.78 0.75 498\n", " 7 0.42 0.32 0.36 388\n", " 8 0.36 0.28 0.31 396\n", " 9 0.67 0.54 0.60 386\n", "\n", " accuracy 0.55 4044\n", " macro avg 0.54 0.53 0.53 4044\n", "weighted avg 0.54 0.55 0.54 4044\n", "\n", "\n", "Naïve Bayes Classifier:\n" ] }, { "ename": "ValueError", "evalue": "Negative values in data passed to MultinomialNB (input X).", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[34], line 27\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28mprint\u001b[39m(report)\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mNaïve Bayes Classifier:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 27\u001b[0m report, acc \u001b[38;5;241m=\u001b[39m \u001b[43mnaive_bayes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccuracy:\u001b[39m\u001b[38;5;124m\"\u001b[39m, acc)\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28mprint\u001b[39m(report)\n", "File \u001b[0;32m~/#/Major02/models.py:83\u001b[0m, in \u001b[0;36mnaive_bayes\u001b[0;34m(X, y)\u001b[0m\n\u001b[1;32m 79\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m train_test_split(X, y, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m 81\u001b[0m model \u001b[38;5;241m=\u001b[39m MultinomialNB()\n\u001b[0;32m---> 83\u001b[0m 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\u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1387\u001b[0m )\n\u001b[1;32m 1388\u001b[0m ):\n\u001b[0;32m-> 1389\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/naive_bayes.py:762\u001b[0m, in \u001b[0;36m_BaseDiscreteNB.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 760\u001b[0m n_classes \u001b[38;5;241m=\u001b[39m Y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 761\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_counters(n_classes, n_features)\n\u001b[0;32m--> 762\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_count\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 763\u001b[0m alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_alpha()\n\u001b[1;32m 764\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_feature_log_prob(alpha)\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/naive_bayes.py:889\u001b[0m, in \u001b[0;36mMultinomialNB._count\u001b[0;34m(self, X, Y)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_count\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, Y):\n\u001b[1;32m 888\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Count and smooth feature occurrences.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 889\u001b[0m \u001b[43mcheck_non_negative\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMultinomialNB (input X)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 890\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_count_ \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m safe_sparse_dot(Y\u001b[38;5;241m.\u001b[39mT, X)\n\u001b[1;32m 891\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclass_count_ \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m Y\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/utils/validation.py:1827\u001b[0m, in \u001b[0;36mcheck_non_negative\u001b[0;34m(X, whom)\u001b[0m\n\u001b[1;32m 1824\u001b[0m X_min \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mmin(X)\n\u001b[1;32m 1826\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m X_min \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 1827\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNegative values in data passed to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwhom\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[0;31mValueError\u001b[0m: Negative values in data passed to MultinomialNB (input X)." ] } ], "source": [ "print(\"Logistic Regression:\")\n", "report, acc = logistic_regression(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nDecision Tree Classifier:\")\n", "report, acc = decision_tree(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nRandom Forest Classifier:\")\n", "report, acc = random_forest(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nSupport Vector Machine (SVM):\")\n", "report, acc = support_vector_machine(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nk-Nearest Neighbors (k-NN):\")\n", "report, acc = knn(X, y, k=5)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nNaïve Bayes Classifier:\")\n", "report, acc = naive_bayes(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nGradient Boosting (XGBoost):\")\n", "report, acc = xgboost_classifier(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nMulti-Layer Perceptron (MLP - Neural Network):\")\n", "report, acc = mlp_classifier(X, y)\n", "\n", "print(\"Accuracy:\", acc)\n", "print(report)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Using device: cuda\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Generating BERT Embeddings on GPU: 100%|██████████| 20219/20219 [02:22<00:00, 141.59it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "BERT + Logistic Regression Accuracy: 0.8029\n" ] } ], "source": [ "import torch\n", "from transformers import BertTokenizer, BertModel\n", "import numpy as np\n", "from tqdm import tqdm\n", "\n", "# Check if GPU is available\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(f\"Using device: {device}\")\n", "\n", "# Load pre-trained BERT tokenizer & model on GPU\n", "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", "bert_model = BertModel.from_pretrained('bert-base-uncased').to(device) # Move model to GPU\n", "\n", "# Function to get BERT embeddings using GPU\n", "def get_bert_embedding(text):\n", " inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=\"max_length\", max_length=128).to(device)\n", " with torch.no_grad():\n", " outputs = bert_model(**inputs)\n", " return outputs.last_hidden_state[:, 0, :].squeeze().cpu().numpy() # Move result back to CPU for NumPy\n", "\n", "# Convert text to BERT embeddings with progress bar\n", "X_bert = np.array([get_bert_embedding(text) for text in tqdm(df['text'], desc=\"Generating BERT Embeddings on GPU\")])\n", "\n", "\n", "# Train-test split\n", "X_train_bert, X_test_bert, y_train_bert, y_test_bert = train_test_split(X_bert, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded)\n", "\n", "# Train Logistic Regression\n", "lr_model = LogisticRegression(max_iter=1000)\n", "lr_model.fit(X_train_bert, y_train_bert)\n", "\n", "# Predict & Evaluate\n", "y_pred_bert = lr_model.predict(X_test_bert)\n", "bert_acc = accuracy_score(y_test_bert, y_pred_bert)\n", "print(f\"BERT + Logistic Regression Accuracy: {bert_acc:.4f}\")\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['topic', 'question', 'excerpt'], dtype='object')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TRAIN.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df['x_bert']=X_bert.tolist()\n", "df['x_w2v']=X_w2v.tolist()\n", "df['x_tfidf']=X.toarray().tolist()\n", "df['question']=TRAIN['question']\n", "df['excerpt']=TRAIN['excerpt']\n", "df.to_csv('df.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using device: cuda\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", 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"\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[19], line 22\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state[:, \u001b[38;5;241m0\u001b[39m, :]\u001b[38;5;241m.\u001b[39msqueeze()\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy() \u001b[38;5;66;03m# Move result back to CPU for NumPy\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# Convert text to BERT embeddings with progress bar\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m X_bert \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([get_bert_embedding(text) \u001b[38;5;28;01mfor\u001b[39;00m text \u001b[38;5;129;01min\u001b[39;00m tqdm(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m], desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGenerating BERT Embeddings on GPU\u001b[39m\u001b[38;5;124m\"\u001b[39m)])\n", "Cell \u001b[0;32mIn[19], line 22\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state[:, \u001b[38;5;241m0\u001b[39m, :]\u001b[38;5;241m.\u001b[39msqueeze()\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy() \u001b[38;5;66;03m# Move result back to CPU for NumPy\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# Convert text to BERT embeddings with progress bar\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m X_bert \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[43mget_bert_embedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m text \u001b[38;5;129;01min\u001b[39;00m tqdm(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m], desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGenerating BERT Embeddings on GPU\u001b[39m\u001b[38;5;124m\"\u001b[39m)])\n", "Cell \u001b[0;32mIn[19], line 18\u001b[0m, in \u001b[0;36mget_bert_embedding\u001b[0;34m(text)\u001b[0m\n\u001b[1;32m 16\u001b[0m inputs \u001b[38;5;241m=\u001b[39m tokenizer(text, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m, truncation\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_length\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m---> 18\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mbert_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state[:, \u001b[38;5;241m0\u001b[39m, :]\u001b[38;5;241m.\u001b[39msqueeze()\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1739\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1748\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1750\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py:1142\u001b[0m, in \u001b[0;36mBertModel.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1135\u001b[0m \u001b[38;5;66;03m# Prepare head mask if needed\u001b[39;00m\n\u001b[1;32m 1136\u001b[0m \u001b[38;5;66;03m# 1.0 in head_mask indicate we keep the head\u001b[39;00m\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;66;03m# attention_probs has shape bsz x n_heads x N x N\u001b[39;00m\n\u001b[1;32m 1138\u001b[0m \u001b[38;5;66;03m# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\u001b[39;00m\n\u001b[1;32m 1139\u001b[0m \u001b[38;5;66;03m# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\u001b[39;00m\n\u001b[1;32m 1140\u001b[0m head_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_head_mask(head_mask, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mnum_hidden_layers)\n\u001b[0;32m-> 1142\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1143\u001b[0m \u001b[43m \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1144\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1145\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1146\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1147\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_extended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1148\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1149\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1150\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1151\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1152\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1153\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1154\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1155\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(sequence_output) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1739\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1748\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m 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attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 684\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m 685\u001b[0m layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m 686\u001b[0m hidden_states,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 692\u001b[0m output_attentions,\n\u001b[1;32m 693\u001b[0m )\n\u001b[1;32m 694\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 695\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 696\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 697\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 698\u001b[0m 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we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1748\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1750\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py:627\u001b[0m, in \u001b[0;36mBertLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 624\u001b[0m cross_attn_present_key_value \u001b[38;5;241m=\u001b[39m cross_attention_outputs[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 625\u001b[0m present_key_value \u001b[38;5;241m=\u001b[39m present_key_value \u001b[38;5;241m+\u001b[39m cross_attn_present_key_value\n\u001b[0;32m--> 627\u001b[0m layer_output \u001b[38;5;241m=\u001b[39m \u001b[43mapply_chunking_to_forward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 628\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeed_forward_chunk\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[43mchunk_size_feed_forward\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[43mseq_len_dim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_output\u001b[49m\n\u001b[1;32m 629\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 630\u001b[0m outputs 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\u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py:639\u001b[0m, in \u001b[0;36mBertLayer.feed_forward_chunk\u001b[0;34m(self, attention_output)\u001b[0m\n\u001b[1;32m 638\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfeed_forward_chunk\u001b[39m(\u001b[38;5;28mself\u001b[39m, attention_output):\n\u001b[0;32m--> 639\u001b[0m intermediate_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintermediate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattention_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 640\u001b[0m layer_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(intermediate_output, attention_output)\n\u001b[1;32m 641\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m layer_output\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1739\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1748\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1750\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py:539\u001b[0m, in \u001b[0;36mBertIntermediate.forward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[0;32m--> 539\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdense\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 540\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintermediate_act_fn(hidden_states)\n\u001b[1;32m 541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m hidden_states\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/module.py:1739\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1738\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1739\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/module.py:1750\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1748\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1749\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1750\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1752\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1753\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/torch/nn/modules/linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 125\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;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[43mweight\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[43mbias\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "\n", "\u001b[A\u001b[A\u001b[A" ] } ], "source": [ "# import torch\n", "# from transformers import BertTokenizer, BertModel\n", "# import numpy as np\n", "# from tqdm import tqdm\n", "\n", "# # Check if GPU is available\n", "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "# print(f\"Using device: {device}\")\n", "\n", "# # Load pre-trained BERT tokenizer & model on GPU\n", "# tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", "# bert_model = BertModel.from_pretrained('bert-base-uncased').to(device) # Move model to GPU\n", "\n", "# # Function to get BERT embeddings using GPU\n", "# def get_bert_embedding(text):\n", "# inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=\"max_length\", max_length=128).to(device)\n", "# with torch.no_grad():\n", "# outputs = bert_model(**inputs)\n", "# return outputs.last_hidden_state[:, 0, :].squeeze().cpu().numpy() # Move result back to CPU for NumPy\n", "\n", "# # Convert text to BERT embeddings with progress bar\n", "# X_bert = np.array([get_bert_embedding(text) for text in tqdm(df['text'], desc=\"Generating BERT Embeddings on GPU\")])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GloVe Embeddings Method\n", "\n", "In this section, we will use GloVe (Global Vectors for Word Representation) embeddings to convert text data into numerical vectors. GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.\n", "\n", "We will follow these steps:\n", "1. Load pre-trained GloVe embeddings.\n", "2. Create a function to convert text into GloVe embeddings.\n", "3. Apply the function to our dataset to obtain GloVe embeddings for each text.\n", "4. Train and evaluate machine learning models using these embeddings." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import gensim.downloader as api\n", "\n", "# Load Pretrained GloVe Model\n", "glove_model = api.load(\"glove-wiki-gigaword-300\") \n", "\n", "# Convert a sentence to a vector by averaging word embeddings\n", "def get_sentence_embedding(sentence, model, dim=300):\n", " words = sentence.split()\n", " word_vectors = [model[word] for word in words if word in model]\n", " return np.mean(word_vectors, axis=0) if word_vectors else np.zeros(dim)\n", "\n", "text_samples = [\"GloVe embeddings capture word semantics\"]\n", "embeddings = np.array([get_sentence_embedding(text, glove_model) for text in text_samples])\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generating GloVe Embeddings: 100%|██████████| 20219/20219 [00:00<00:00, 26234.90it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(20219, 300)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from tqdm import tqdm\n", "\n", "# Generate GloVe embeddings for the entire dataset with a progress bar\n", "df['glove_embeddings'] = df['text'].apply(lambda x: get_sentence_embedding(x, glove_model))\n", "\n", "# Convert the list of embeddings to a numpy array\n", "X_glove = np.array([get_sentence_embedding(text, glove_model) for text in tqdm(df['text'], desc=\"Generating GloVe Embeddings\")])\n", "\n", "# Check the shape of the generated embeddings\n", "print(X_glove.shape)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 [-0.115741186, -0.20021904, 0.12441649, -0.324...\n", "1 [-0.0010313676, -0.06625991, 0.31336138, -0.32...\n", "2 [-0.08451371, -0.041777406, -0.17886283, -0.18...\n", "3 [-0.1282833, 0.19806932, 0.16693015, -0.017683...\n", "4 [-0.073781654, 0.05777748, -0.10044446, -0.301...\n", " ... \n", "20214 [-0.36480942, 0.14811745, 0.0026721125, -0.169...\n", "20215 [-0.12177454, 0.06218006, -0.05326008, -0.0159...\n", "20216 [-0.02713412, 0.04559438, 0.006360337, -0.1446...\n", "20217 [-0.29473746, 0.2867133, -0.12950301, -0.03180...\n", "20218 [-0.3100589, 0.15244281, 0.0920101, -0.0856748...\n", "Name: glove_embeddings, Length: 20219, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['glove_embeddings']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "X = df['glove_embeddings']\n", "y = label_encoding(df['topic']) \n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "loop of ufunc does not support argument 0 of type numpy.ndarray which has no callable exp method", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'exp'", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[14], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msigmoid\u001b[39m(x):\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39mexp(\u001b[38;5;241m-\u001b[39mx))\n\u001b[0;32m----> 6\u001b[0m X_sigmoid \u001b[38;5;241m=\u001b[39m \u001b[43msigmoid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n", "Cell \u001b[0;32mIn[14], line 4\u001b[0m, in \u001b[0;36msigmoid\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msigmoid\u001b[39m(x):\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/pandas/core/generic.py:2171\u001b[0m, in \u001b[0;36mNDFrame.__array_ufunc__\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 2167\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 2168\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__array_ufunc__\u001b[39m(\n\u001b[1;32m 2169\u001b[0m \u001b[38;5;28mself\u001b[39m, ufunc: np\u001b[38;5;241m.\u001b[39mufunc, method: \u001b[38;5;28mstr\u001b[39m, \u001b[38;5;241m*\u001b[39minputs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any\n\u001b[1;32m 2170\u001b[0m ):\n\u001b[0;32m-> 2171\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43marraylike\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_ufunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/pandas/core/arraylike.py:399\u001b[0m, in \u001b[0;36marray_ufunc\u001b[0;34m(self, ufunc, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 397\u001b[0m \u001b[38;5;66;03m# ufunc(series, ...)\u001b[39;00m\n\u001b[1;32m 398\u001b[0m inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(extract_array(x, extract_numpy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m inputs)\n\u001b[0;32m--> 399\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mufunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 401\u001b[0m \u001b[38;5;66;03m# ufunc(dataframe)\u001b[39;00m\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__call__\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwargs:\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# for np.(..) calls\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;66;03m# kwargs cannot necessarily be handled block-by-block, so only\u001b[39;00m\n\u001b[1;32m 405\u001b[0m \u001b[38;5;66;03m# take this path if there are no kwargs\u001b[39;00m\n", "\u001b[0;31mTypeError\u001b[0m: loop of ufunc does not support argument 0 of type numpy.ndarray which has no callable exp method" ] } ], "source": [ "import numpy as np\n", "\n", "def sigmoid(x):\n", " return 1 / (1 + np.exp(-x))\n", "\n", "X_sigmoid = sigmoid(X)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "def sigmoid(x):\n", " return 1 / (1 + np.exp(-x))\n", "\n", "# X_sigmoid = sigmoid(X)\n", "\n", "X_s = X.apply(lambda emb: sigmoid(np.array(emb)))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1 2 3 4 5 6 \\\n", "0 0.471097 0.450112 0.531064 0.419600 0.529166 0.501688 0.539391 \n", "1 0.499742 0.483441 0.577706 0.419251 0.488925 0.483124 0.525759 \n", "2 0.478884 0.489557 0.455403 0.454474 0.504673 0.511277 0.525448 \n", "3 0.467973 0.549356 0.541636 0.495579 0.533500 0.510909 0.493776 \n", "4 0.481563 0.514440 0.474910 0.425199 0.494169 0.486540 0.507572 \n", "\n", " 7 8 9 ... 290 291 292 293 \\\n", "0 0.504431 0.471951 0.210885 ... 0.484047 0.508446 0.468081 0.511043 \n", "1 0.498380 0.512296 0.295908 ... 0.508435 0.516001 0.475152 0.510036 \n", "2 0.496180 0.487645 0.263449 ... 0.493034 0.459211 0.474147 0.547312 \n", "3 0.516288 0.510662 0.222204 ... 0.497405 0.476240 0.471470 0.508767 \n", "4 0.513841 0.517520 0.228781 ... 0.491363 0.455957 0.501189 0.502150 \n", "\n", " 294 295 296 297 298 299 \n", "0 0.517152 0.517413 0.513905 0.434282 0.511256 0.497362 \n", "1 0.555465 0.471034 0.474005 0.468187 0.533218 0.529335 \n", "2 0.483106 0.559323 0.500045 0.493877 0.498814 0.523413 \n", "3 0.494089 0.537543 0.479127 0.483230 0.505795 0.493756 \n", "4 0.512769 0.518822 0.506260 0.456117 0.517466 0.527945 \n", "\n", "[5 rows x 300 columns]\n" ] } ], "source": [ "import numpy as np\n", "\n", "def sigmoid(x):\n", " return 1 / (1 + np.exp(-x))\n", "\n", "# Apply sigmoid function element-wise to each embedding\n", "X_sigmoid = X.apply(lambda emb: np.array([sigmoid(val) for val in emb]))\n", "\n", "# If you need it as a DataFrame:\n", "X_sigmoid_df = pd.DataFrame(X_sigmoid.tolist())\n", "\n", "print(X_sigmoid_df.head()) # Display first few rows\n", "\n", "df['glove_sigmoid']=X_sigmoid\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "X = df['glove_sigmoid']\n", "y = label_encoding(df['topic'])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from models import *" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Multi-Layer Perceptron (MLP - Neural Network):\n" ] }, { "ename": "ValueError", "evalue": "setting an array element with a sequence.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mTypeError\u001b[0m: only length-1 arrays can be converted to Python scalars", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[22], line 37\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# print(\"Logistic Regression:\")\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# report, acc = logistic_regression(X, y)\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# print(\"Accuracy:\", acc)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# print(\"Accuracy:\", acc)\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;66;03m# print(report)\u001b[39;00m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mMulti-Layer Perceptron (MLP - Neural Network):\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 37\u001b[0m report, acc \u001b[38;5;241m=\u001b[39m \u001b[43mmlp_classifier\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccuracy:\u001b[39m\u001b[38;5;124m\"\u001b[39m, acc)\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28mprint\u001b[39m(report)\n", "File \u001b[0;32m~/#/Major02/models.py:118\u001b[0m, in \u001b[0;36mmlp_classifier\u001b[0;34m(X, y, epochs, lr)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mmlp_classifier\u001b[39m(X, y, epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m500\u001b[39m, lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.01\u001b[39m):\n\u001b[1;32m 116\u001b[0m \u001b[38;5;66;03m# Normalize data without centering (for sparse matrices)\u001b[39;00m\n\u001b[1;32m 117\u001b[0m scaler \u001b[38;5;241m=\u001b[39m StandardScaler(with_mean\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;66;03m# Fix applied here\u001b[39;00m\n\u001b[0;32m--> 118\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mscaler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;66;03m# Convert to PyTorch tensors\u001b[39;00m\n\u001b[1;32m 121\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m train_test_split(X, y, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/utils/_set_output.py:319\u001b[0m, in \u001b[0;36m_wrap_method_output..wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 319\u001b[0m data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 321\u001b[0m \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m 322\u001b[0m return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 323\u001b[0m _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m 324\u001b[0m \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m 325\u001b[0m )\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/base.py:918\u001b[0m, in \u001b[0;36mTransformerMixin.fit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 903\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 904\u001b[0m (\n\u001b[1;32m 905\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis object (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) has a `transform`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[1;32m 914\u001b[0m )\n\u001b[1;32m 916\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 917\u001b[0m \u001b[38;5;66;03m# fit method of arity 1 (unsupervised transformation)\u001b[39;00m\n\u001b[0;32m--> 918\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtransform(X)\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 920\u001b[0m \u001b[38;5;66;03m# fit method of arity 2 (supervised transformation)\u001b[39;00m\n\u001b[1;32m 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\u001b[38;5;241m.\u001b[39mtransform(X)\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:894\u001b[0m, in \u001b[0;36mStandardScaler.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 892\u001b[0m \u001b[38;5;66;03m# Reset internal state before fitting\u001b[39;00m\n\u001b[1;32m 893\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()\n\u001b[0;32m--> 894\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpartial_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/base.py:1389\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1382\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 1384\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 1385\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 1386\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1387\u001b[0m )\n\u001b[1;32m 1388\u001b[0m ):\n\u001b[0;32m-> 1389\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:930\u001b[0m, in \u001b[0;36mStandardScaler.partial_fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 898\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Online computation of mean and std on X for later scaling.\u001b[39;00m\n\u001b[1;32m 899\u001b[0m \n\u001b[1;32m 900\u001b[0m \u001b[38;5;124;03mAll of X is processed as a single batch. This is intended for cases\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 927\u001b[0m \u001b[38;5;124;03m Fitted scaler.\u001b[39;00m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 929\u001b[0m first_call \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_samples_seen_\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 930\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mvalidate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 931\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 932\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 933\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 934\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mFLOAT_DTYPES\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 935\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 936\u001b[0m \u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfirst_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 937\u001b[0m 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\u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2945\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n\u001b[1;32m 2946\u001b[0m out \u001b[38;5;241m=\u001b[39m _check_y(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/utils/validation.py:1055\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_all_finite, ensure_non_negative, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 1053\u001b[0m array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 1054\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1055\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[43m_asarray_with_order\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1056\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[1;32m 1057\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1058\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m 1059\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/utils/_array_api.py:839\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp, device)\u001b[0m\n\u001b[1;32m 837\u001b[0m array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39marray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 838\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 839\u001b[0m array \u001b[38;5;241m=\u001b[39m \u001b[43mnumpy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# At this point array is a NumPy ndarray. We convert it to an array\u001b[39;00m\n\u001b[1;32m 842\u001b[0m \u001b[38;5;66;03m# container that is consistent with the input's namespace.\u001b[39;00m\n\u001b[1;32m 843\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array)\n", "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/pandas/core/series.py:1031\u001b[0m, in \u001b[0;36mSeries.__array__\u001b[0;34m(self, dtype, copy)\u001b[0m\n\u001b[1;32m 981\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 982\u001b[0m \u001b[38;5;124;03mReturn the values as a NumPy array.\u001b[39;00m\n\u001b[1;32m 983\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1028\u001b[0m \u001b[38;5;124;03m dtype='datetime64[ns]')\u001b[39;00m\n\u001b[1;32m 1029\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1030\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values\n\u001b[0;32m-> 1031\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1032\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m using_copy_on_write() \u001b[38;5;129;01mand\u001b[39;00m astype_is_view(values\u001b[38;5;241m.\u001b[39mdtype, arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[1;32m 1033\u001b[0m arr \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mview()\n", "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence." ] } ], "source": [ "# print(\"Logistic Regression:\")\n", "# report, acc = logistic_regression(X, y)\n", "# print(\"Accuracy:\", acc)\n", "# print(report)\n", "\n", "# print(\"\\nDecision Tree Classifier:\")\n", "# report, acc = decision_tree(X, y)\n", "# print(\"Accuracy:\", acc)\n", "# print(report)\n", "\n", "# print(\"\\nRandom Forest Classifier:\")\n", "# report, acc = random_forest(X, y)\n", "# print(\"Accuracy:\", acc)\n", "# print(report)\n", "\n", "# print(\"\\nSupport Vector Machine (SVM):\")\n", "# report, acc = support_vector_machine(X, y)\n", "# print(\"Accuracy:\", acc)\n", "# print(report)\n", "\n", "# print(\"\\nk-Nearest Neighbors (k-NN):\")\n", "# report, acc = knn(X, y, k=5)\n", "# print(\"Accuracy:\", acc)\n", "# print(report)\n", "\n", "# print(\"\\nNaïve Bayes Classifier:\")\n", "# report, acc = naive_bayes(X, y)\n", "# print(\"Accuracy:\", acc)\n", "# print(report)\n", "\n", "# print(\"\\nGradient Boosting (XGBoost):\")\n", "# report, acc = xgboost_classifier(X, y)\n", "# print(\"Accuracy:\", acc)\n", "# print(report)\n", "\n", "print(\"\\nMulti-Layer Perceptron (MLP - Neural Network):\")\n", "report, acc = mlp_classifier(X, y)\n", "\n", "print(\"Accuracy:\", acc)\n", "print(report)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting tensorflow\n", " Downloading tensorflow-2.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (644.8 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m644.8/644.8 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:02\u001b[0m\n", "\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.6 in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from tensorflow) (4.12.2)\n", "Requirement already satisfied: requests<3,>=2.21.0 in /home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages (from tensorflow) (2.32.3)\n", "Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1\n", " Using cached gast-0.6.0-py3-none-any.whl (21 kB)\n", "Collecting grpcio<2.0,>=1.24.3\n", " 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flatbuffers-25.2.10 gast-0.6.0 google-pasta-0.2.0 grpcio-1.71.0 h5py-3.13.0 keras-3.9.0 libclang-18.1.1 markdown-3.7 markdown-it-py-3.0.0 mdurl-0.1.2 ml-dtypes-0.5.1 namex-0.0.8 opt-einsum-3.4.0 optree-0.14.1 protobuf-5.29.3 rich-13.9.4 tensorboard-2.19.0 tensorboard-data-server-0.7.2 tensorflow-2.19.0 tensorflow-io-gcs-filesystem-0.37.1 termcolor-2.5.0 werkzeug-3.1.3 wheel-0.45.1\n", "\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install tensorflow" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20219, 300)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.shape" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(16175, 300) (4044, 300) (16175, 10) (4044, 10)\n", "Epoch [1/100], Loss: 2.2988\n", "Epoch [2/100], Loss: 2.2972\n", "Epoch [3/100], Loss: 2.3108\n", "Epoch [4/100], Loss: 2.3176\n", "Epoch [5/100], Loss: 2.2910\n", "Epoch [6/100], Loss: 2.2063\n", "Epoch [7/100], Loss: 2.1915\n", "Epoch [8/100], Loss: 2.1855\n", "Epoch [9/100], Loss: 2.1844\n", "Epoch [10/100], Loss: 2.1815\n", "Epoch [11/100], Loss: 2.1742\n", "Epoch [12/100], Loss: 2.1727\n", "Epoch [13/100], Loss: 2.1802\n", "Epoch [14/100], Loss: 2.1745\n", "Epoch [15/100], Loss: 2.1711\n", "Epoch [16/100], Loss: 2.1736\n", "Epoch [17/100], Loss: 2.1669\n", "Epoch [18/100], Loss: 2.1763\n", "Epoch [19/100], Loss: 2.1600\n", "Epoch [20/100], Loss: 2.1624\n", "Epoch [21/100], Loss: 2.1802\n", "Epoch [22/100], Loss: 2.1658\n", "Epoch [23/100], Loss: 2.1810\n", "Epoch [24/100], Loss: 2.1584\n", "Epoch [25/100], Loss: 2.1622\n", "Epoch [26/100], Loss: 2.1523\n", "Epoch [27/100], Loss: 2.1576\n", "Epoch [28/100], Loss: 2.1665\n", "Epoch [29/100], Loss: 2.1527\n", "Epoch [30/100], Loss: 2.1565\n", "Epoch [31/100], Loss: 2.1611\n", "Epoch [32/100], Loss: 2.1486\n", "Epoch [33/100], Loss: 2.1582\n", "Epoch [34/100], Loss: 2.1855\n", "Epoch [35/100], Loss: 2.1679\n", "Epoch [36/100], Loss: 2.1514\n", "Epoch [37/100], Loss: 2.1492\n", "Epoch [38/100], Loss: 2.1557\n", "Epoch [39/100], Loss: 2.1563\n", "Epoch [40/100], Loss: 2.1463\n", "Epoch [41/100], Loss: 2.1550\n", "Epoch [42/100], Loss: 2.1489\n", "Epoch [43/100], Loss: 2.1419\n", "Epoch [44/100], Loss: 2.1455\n", "Epoch [45/100], Loss: 2.1416\n", "Epoch [46/100], Loss: 2.1431\n", "Epoch [47/100], Loss: 2.1468\n", "Epoch [48/100], Loss: 2.1395\n", "Epoch [49/100], Loss: 2.1423\n", "Epoch [50/100], Loss: 2.1460\n", "Epoch [51/100], Loss: 2.1425\n", "Epoch [52/100], Loss: 2.1399\n", "Epoch [53/100], Loss: 2.1387\n", "Epoch [54/100], Loss: 2.1471\n", "Epoch [55/100], Loss: 2.1539\n", "Epoch [56/100], Loss: 2.1409\n", "Epoch [57/100], Loss: 2.1416\n", "Epoch [58/100], Loss: 2.1374\n", "Epoch [59/100], Loss: 2.1391\n", "Epoch [60/100], Loss: 2.1367\n", "Epoch [61/100], Loss: 2.1439\n", "Epoch [62/100], Loss: 2.1326\n", "Epoch [63/100], Loss: 2.1446\n", "Epoch [64/100], Loss: 2.1549\n", "Epoch [65/100], Loss: 2.1417\n", "Epoch [66/100], Loss: 2.1525\n", "Epoch [67/100], Loss: 2.1434\n", "Epoch [68/100], Loss: 2.1409\n", "Epoch [69/100], Loss: 2.1429\n", "Epoch [70/100], Loss: 2.1439\n", "Epoch [71/100], Loss: 2.1426\n", "Epoch [72/100], Loss: 2.1729\n", "Epoch [73/100], Loss: 2.1379\n", "Epoch [74/100], Loss: 2.1332\n", "Epoch [75/100], Loss: 2.1319\n", "Epoch [76/100], Loss: 2.1334\n", "Epoch [77/100], Loss: 2.1354\n", "Epoch [78/100], Loss: 2.1512\n", "Epoch [79/100], Loss: 2.1431\n", "Epoch [80/100], Loss: 2.1457\n", "Epoch [81/100], Loss: 2.1301\n", "Epoch [82/100], Loss: 2.1337\n", "Epoch [83/100], Loss: 2.1342\n", "Epoch [84/100], Loss: 2.1313\n", "Epoch [85/100], Loss: 2.1345\n", "Epoch [86/100], Loss: 2.1347\n", "Epoch [87/100], Loss: 2.1343\n", "Epoch [88/100], Loss: 2.1290\n", "Epoch [89/100], Loss: 2.1390\n", "Epoch [90/100], Loss: 2.1294\n", "Epoch [91/100], Loss: 2.1265\n", "Epoch [92/100], Loss: 2.1326\n", "Epoch [93/100], Loss: 2.1374\n", "Epoch [94/100], Loss: 2.1328\n", "Epoch [95/100], Loss: 2.1300\n", "Epoch [96/100], Loss: 2.1366\n", "Epoch [97/100], Loss: 2.1238\n", "Epoch [98/100], Loss: 2.1348\n", "Epoch [99/100], Loss: 2.1298\n", "Epoch [100/100], Loss: 2.1264\n", "\n", "Test Accuracy: 0.3205\n", "\n", "Predicted Labels: [0, 3, 5, 9, 6]\n" ] } ], "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from torch.utils.data import DataLoader, TensorDataset\n", "\n", "# Check if GPU is available\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "# Convert data to NumPy arrays (if not already)\n", "X = np.array(X) # Ensure X is a NumPy array\n", "y = np.array(y) # Convert labels to NumPy array\n", "\n", "# Encode labels to integers (0-9)\n", "unique_labels = np.unique(y)\n", "label_map = {label: idx for idx, label in enumerate(unique_labels)}\n", "y_encoded = np.array([label_map[label] for label in y])\n", "\n", "# One-hot encode labels\n", "y_one_hot = np.eye(10)[y_encoded]\n", "\n", "# Train-test split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y_one_hot, test_size=0.2, random_state=42)\n", "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n", "\n", "# Convert to PyTorch tensors\n", "X_train_tensor = torch.tensor(X_train, dtype=torch.float32).to(device)\n", "X_test_tensor = torch.tensor(X_test, dtype=torch.float32).to(device)\n", "y_train_tensor = torch.tensor(y_train, dtype=torch.float32).to(device)\n", "y_test_tensor = torch.tensor(y_test, dtype=torch.float32).to(device)\n", "\n", "# Create DataLoader for efficient training\n", "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n", "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n", "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n", "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n", "\n", "# Define the Neural Network Model\n", "class NeuralNet(nn.Module):\n", " def __init__(self, input_size, num_classes):\n", " super(NeuralNet, self).__init__()\n", " self.fc1 = nn.Linear(input_size, 256)\n", " self.fc2 = nn.Linear(256, 128)\n", " self.fc3 = nn.Linear(128, 64)\n", " self.fc4 = nn.Linear(64, 32)\n", " self.fc5 = nn.Linear(32, num_classes)\n", " self.relu = nn.ReLU()\n", " self.softmax = nn.Softmax(dim=1)\n", " \n", " def forward(self, x):\n", " x = self.relu(self.fc1(x))\n", " x = self.relu(self.fc2(x))\n", " x = self.relu(self.fc3(x))\n", " x = self.relu(self.fc4(x))\n", " x = self.softmax(self.fc5(x))\n", " return x\n", "\n", "# Initialize model, loss function, and optimizer\n", "model = NeuralNet(input_size=X.shape[1], num_classes=10).to(device)\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", "\n", "# Training loop\n", "epochs = 100\n", "for epoch in range(epochs):\n", " model.train()\n", " total_loss = 0\n", " for batch_X, batch_y in train_loader:\n", " optimizer.zero_grad()\n", " outputs = model(batch_X)\n", " loss = criterion(outputs, batch_y)\n", " loss.backward()\n", " optimizer.step()\n", " total_loss += loss.item()\n", " print(f\"Epoch [{epoch+1}/{epochs}], Loss: {total_loss/len(train_loader):.4f}\")\n", "\n", "# Evaluate the model\n", "model.eval()\n", "correct, total = 0, 0\n", "with torch.no_grad():\n", " for batch_X, batch_y in test_loader:\n", " outputs = model(batch_X)\n", " _, predicted = torch.max(outputs, 1)\n", " _, labels = torch.max(batch_y, 1)\n", " correct += (predicted == labels).sum().item()\n", " total += labels.size(0)\n", "\n", "accuracy = correct / total\n", "print(f\"\\nTest Accuracy: {accuracy:.4f}\")\n", "\n", "# Predict on new data\n", "sample_X = X_test_tensor[:5]\n", "model.eval()\n", "predictions = model(sample_X)\n", "predicted_classes = torch.argmax(predictions, dim=1).cpu().numpy()\n", "predicted_labels = [unique_labels[idx] for idx in predicted_classes]\n", "print(\"\\nPredicted Labels:\", predicted_labels)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Albert Embeddings" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", "2025-03-18 22:45:59.369565: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "2025-03-18 22:45:59.377294: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1742318159.387742 95942 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1742318159.392140 95942 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "W0000 00:00:1742318159.400755 95942 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1742318159.400773 95942 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1742318159.400774 95942 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "W0000 00:00:1742318159.400775 95942 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", "2025-03-18 22:45:59.403564: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "Processing: 100%|██████████| 1264/1264 [00:40<00:00, 31.03it/s]\n" ] } ], "source": [ "import torch\n", "import pandas as pd\n", "import numpy as np\n", "from tqdm import tqdm\n", "from transformers import AlbertTokenizer, AlbertModel\n", "\n", "# Load ALBERT tokenizer and model\n", "tokenizer = AlbertTokenizer.from_pretrained(\"albert-base-v2\")\n", "model = AlbertModel.from_pretrained(\"albert-base-v2\")\n", "\n", "# Set device (GPU if available, otherwise CPU)\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model.to(device)\n", "\n", "# Load your dataset (Ensure df['text'] contains the text data)\n", "# df = pd.read_csv(\"your_dataset.csv\") # Replace with your dataset file\n", "texts = df[\"text\"].tolist()\n", "\n", "# Function to generate embeddings\n", "def get_albert_embeddings(texts, batch_size=16):\n", " model.eval() # Set model to evaluation mode\n", " embeddings_list = []\n", "\n", " for i in tqdm(range(0, len(texts), batch_size), desc=\"Processing\"):\n", " batch_texts = texts[i : i + batch_size]\n", " \n", " # Tokenize the batch\n", " inputs = tokenizer(batch_texts, return_tensors=\"pt\", padding=True, truncation=True, max_length=512)\n", " inputs = {key: val.to(device) for key, val in inputs.items()} # Move to GPU/CPU\n", " \n", " # Get embeddings\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", " \n", " # Extract [CLS] token representation (sentence embedding)\n", " cls_embeddings = outputs.last_hidden_state[:, 0, :].cpu().numpy()\n", " embeddings_list.append(cls_embeddings)\n", "\n", " return np.vstack(embeddings_list) # Stack all embeddings\n", "\n", "# Generate embeddings for all texts\n", "embeddings = get_albert_embeddings(texts)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "X = embeddings\n", "y = label_encoding(df['topic'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression:\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.755192878338279\n", " precision recall f1-score support\n", "\n", " 0 0.75 0.74 0.75 458\n", " 1 0.63 0.59 0.61 396\n", " 2 0.76 0.77 0.76 363\n", " 3 0.78 0.76 0.77 497\n", " 4 0.71 0.69 0.70 294\n", " 5 0.84 0.88 0.86 368\n", " 6 0.94 0.95 0.95 498\n", " 7 0.71 0.71 0.71 388\n", " 8 0.55 0.57 0.56 396\n", " 9 0.82 0.84 0.83 386\n", "\n", " accuracy 0.76 4044\n", " macro avg 0.75 0.75 0.75 4044\n", "weighted avg 0.75 0.76 0.75 4044\n", "\n", "\n", "Decision Tree Classifier:\n", "Accuracy: 0.3508902077151335\n", " precision recall f1-score support\n", "\n", " 0 0.31 0.31 0.31 458\n", " 1 0.23 0.24 0.23 396\n", " 2 0.28 0.31 0.30 363\n", " 3 0.33 0.34 0.33 497\n", " 4 0.35 0.29 0.31 294\n", " 5 0.44 0.45 0.44 368\n", " 6 0.75 0.71 0.73 498\n", " 7 0.21 0.22 0.21 388\n", " 8 0.22 0.22 0.22 396\n", " 9 0.35 0.33 0.34 386\n", "\n", " accuracy 0.35 4044\n", " macro avg 0.35 0.34 0.34 4044\n", "weighted avg 0.36 0.35 0.35 4044\n", "\n", "\n", "Random Forest Classifier:\n", "Accuracy: 0.5558852621167161\n", " precision recall f1-score support\n", "\n", " 0 0.51 0.61 0.56 458\n", " 1 0.39 0.28 0.32 396\n", " 2 0.51 0.59 0.55 363\n", " 3 0.47 0.58 0.52 497\n", " 4 0.69 0.40 0.51 294\n", " 5 0.62 0.71 0.66 368\n", " 6 0.81 0.90 0.85 498\n", " 7 0.47 0.38 0.42 388\n", " 8 0.47 0.32 0.38 396\n", " 9 0.55 0.66 0.60 386\n", "\n", " accuracy 0.56 4044\n", " macro avg 0.55 0.54 0.54 4044\n", "weighted avg 0.55 0.56 0.55 4044\n", "\n", "\n", "Support Vector Machine (SVM):\n", "Accuracy: 0.7101879327398615\n", " precision recall f1-score support\n", "\n", " 0 0.71 0.69 0.70 458\n", " 1 0.59 0.51 0.55 396\n", " 2 0.70 0.73 0.72 363\n", " 3 0.68 0.71 0.70 497\n", " 4 0.71 0.65 0.68 294\n", " 5 0.82 0.83 0.83 368\n", " 6 0.95 0.93 0.94 498\n", " 7 0.64 0.64 0.64 388\n", " 8 0.52 0.57 0.55 396\n", " 9 0.73 0.78 0.75 386\n", "\n", " accuracy 0.71 4044\n", " macro avg 0.71 0.70 0.70 4044\n", "weighted avg 0.71 0.71 0.71 4044\n", "\n", "\n", "k-Nearest Neighbors (k-NN):\n", "Accuracy: 0.4394164193867458\n", " precision recall f1-score support\n", "\n", " 0 0.33 0.47 0.39 458\n", " 1 0.27 0.26 0.26 396\n", " 2 0.37 0.55 0.44 363\n", " 3 0.36 0.47 0.41 497\n", " 4 0.51 0.39 0.44 294\n", " 5 0.61 0.43 0.51 368\n", " 6 0.91 0.78 0.84 498\n", " 7 0.35 0.23 0.28 388\n", " 8 0.27 0.25 0.26 396\n", " 9 0.59 0.45 0.51 386\n", "\n", " accuracy 0.44 4044\n", " macro avg 0.46 0.43 0.43 4044\n", "weighted avg 0.46 0.44 0.44 4044\n", "\n", "\n", "Gradient Boosting (XGBoost):\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/core.py:158: UserWarning: [23:00:38] WARNING: /workspace/src/learner.cc:740: \n", "Parameters: { \"use_label_encoder\" } are not used.\n", "\n", " warnings.warn(smsg, UserWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.6923837784371909\n", " precision recall f1-score support\n", "\n", " 0 0.67 0.68 0.67 458\n", " 1 0.58 0.49 0.53 396\n", " 2 0.69 0.69 0.69 363\n", " 3 0.66 0.71 0.68 497\n", " 4 0.69 0.61 0.65 294\n", " 5 0.78 0.84 0.81 368\n", " 6 0.94 0.92 0.93 498\n", " 7 0.62 0.61 0.62 388\n", " 8 0.51 0.54 0.53 396\n", " 9 0.74 0.75 0.74 386\n", "\n", " accuracy 0.69 4044\n", " macro avg 0.69 0.69 0.69 4044\n", "weighted avg 0.69 0.69 0.69 4044\n", "\n", "Accuracy: 0.6923837784371909\n", " precision recall f1-score support\n", "\n", " 0 0.67 0.68 0.67 458\n", " 1 0.58 0.49 0.53 396\n", " 2 0.69 0.69 0.69 363\n", " 3 0.66 0.71 0.68 497\n", " 4 0.69 0.61 0.65 294\n", " 5 0.78 0.84 0.81 368\n", " 6 0.94 0.92 0.93 498\n", " 7 0.62 0.61 0.62 388\n", " 8 0.51 0.54 0.53 396\n", " 9 0.74 0.75 0.74 386\n", "\n", " accuracy 0.69 4044\n", " macro avg 0.69 0.69 0.69 4044\n", "weighted avg 0.69 0.69 0.69 4044\n", "\n" ] } ], "source": [ "from models import *\n", "\n", "print(\"Logistic Regression:\")\n", "report, acc = logistic_regression(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nDecision Tree Classifier:\")\n", "report, acc = decision_tree(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nRandom Forest Classifier:\")\n", "report, acc = random_forest(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nSupport Vector Machine (SVM):\")\n", "report, acc = support_vector_machine(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "print(\"\\nk-Nearest Neighbors (k-NN):\")\n", "report, acc = knn(X, y, k=5)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "# print(\"\\nNaïve Bayes Classifier:\")\n", "# report, acc = naive_bayes(X, y)\n", "# print(\"Accuracy:\", acc)\n", "# print(report)\n", "\n", "print(\"\\nGradient Boosting (XGBoost):\")\n", "report, acc = xgboost_classifier(X, y)\n", "print(\"Accuracy:\", acc)\n", "print(report)\n", "\n", "# print(\"\\nMulti-Layer Perceptron (MLP - Neural Network):\")\n", "# report, acc = mlp_classifier(X, y)\n", "\n", "print(\"Accuracy:\", acc)\n", "print(report)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "major02", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }