diff --git "a/multi-label-processing.ipynb" "b/multi-label-processing.ipynb" new file mode 100644--- /dev/null +++ "b/multi-label-processing.ipynb" @@ -0,0 +1,10308 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d80b6bf4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Input Tags\n", + "0 Title: What is the effective differencial effe... ['electronics']\n", + "1 Title: Heat sensor with fan cooling Body: Can ... ['electronics']\n", + "2 Title: Outlet Installation--more wires than my... ['electronics']\n", + "3 Title: Buck Converter Operation Question Body:... ['electronics']\n", + "4 Title: Urgent help in area of ASIC design, ver... ['electronics']\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Read the CSV file\n", + "file_path = '/home/darth/#/SEQuestionClassifier/data/all_combined_data.csv'\n", + "TRAIN = pd.read_csv(file_path)\n", + "\n", + "# Display the first few rows of the dataframe\n", + "print(TRAIN.head())\n", + "import ast\n", + "\n", + "TRAIN[\"Tags\"] = TRAIN[\"Tags\"].apply(ast.literal_eval)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b4a4bcec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1000, 2)\n" + ] + } + ], + "source": [ + "TRAIN = TRAIN.sample(n=1000, random_state=42).reset_index(drop=True)\n", + "print(TRAIN.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9a25e401", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InputTags
990Title: Isolating traffic between two physical...[ubuntu, network-access-control]
991Title: Why storing a payroll in XML type fiel...[asp.net, sql, sql-server, xml, tsql]
992Title: Foders and files created from external...[windows, file, batch-file, cmd, directory]
993Title: Measured VS Ltspice Difference in P-Cha...[electronics]
994Title: When pushing a DNS resolver to an Open...[vpn, dns, openvpn]
995Title: PCI DSS -- eWallet Not storing just di...[pci-dss, pci-scope]
996Title: Rooted tree - Representation & Perform...[c, data-structures, trees]
997Title: How to tie emitted events events into ...[javascript, ecmascript-6, redux, redux-saga]
998Title: Laravel storage with configBody: I am ...[php, laravel, config]
999Title: I can not extract the final statevecto...[qiskit, programming, hadamard]
\n", + "
" + ], + "text/plain": [ + " Input \\\n", + "990 Title: Isolating traffic between two physical... \n", + "991 Title: Why storing a payroll in XML type fiel... \n", + "992 Title: Foders and files created from external... \n", + "993 Title: Measured VS Ltspice Difference in P-Cha... \n", + "994 Title: When pushing a DNS resolver to an Open... \n", + "995 Title: PCI DSS -- eWallet Not storing just di... \n", + "996 Title: Rooted tree - Representation & Perform... \n", + "997 Title: How to tie emitted events events into ... \n", + "998 Title: Laravel storage with configBody: I am ... \n", + "999 Title: I can not extract the final statevecto... \n", + "\n", + " Tags \n", + "990 [ubuntu, network-access-control] \n", + "991 [asp.net, sql, sql-server, xml, tsql] \n", + "992 [windows, file, batch-file, cmd, directory] \n", + "993 [electronics] \n", + "994 [vpn, dns, openvpn] \n", + "995 [pci-dss, pci-scope] \n", + "996 [c, data-structures, trees] \n", + "997 [javascript, ecmascript-6, redux, redux-saga] \n", + "998 [php, laravel, config] \n", + "999 [qiskit, programming, hadamard] " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TRAIN.tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "33f486f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Input', 'Tags'], dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TRAIN.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e2c54b8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package stopwords to /home/darth/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package punkt to /home/darth/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package wordnet to /home/darth/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n", + "[nltk_data] Downloading package punkt_tab to /home/darth/nltk_data...\n", + "[nltk_data] Package punkt_tab is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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')\n", + "# nltk.download('punkt_tab')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a6a7c6ff", + "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": 7, + "id": "ee97c932", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Input', 'Tags'], dtype='object')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = TRAIN.copy()\n", + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4262907c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' Title: sql - trying to select a column with datetime with a condition of date onlyBody:

so i have this table: http://prntscr.com/it53pm (in this link).

\\n\\n

what im trying to do is select schedule where schedule date = current date.

\\n'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Input'][0]" + ] + }, + { + "cell_type": "markdown", + "id": "ad77885f", + "metadata": {}, + "source": [ + "## TF-IDF Vectorizer Methord" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3bb1241f", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.preprocessing import MultiLabelBinarizer\n", + "\n", + "def vectorirse_text(text):\n", + " \"\"\" Recieves text as input and returns TF-IDF vectors\"\"\"\n", + " text = text.apply(preprocess_text)\n", + " tfidf = TfidfVectorizer(max_features=500000)\n", + " X = tfidf.fit_transform(text)\n", + " return X\n", + "\n", + "def label_encoding(input):\n", + " mlb = MultiLabelBinarizer()\n", + " return mlb.fit_transform(input)\n", + "\n", + "\n", + "X = vectorirse_text(df['Input'])\n", + "y = label_encoding(df['Tags'])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0088e7c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(987,)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b191f862", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Multi-label Random Forest Classifier:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.0\n", + " precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 2\n", + " 1 0.00 0.00 0.00 0\n", + " 2 0.00 0.00 0.00 0\n", + " 3 0.00 0.00 0.00 0\n", + " 4 0.00 0.00 0.00 1\n", + " 5 0.00 0.00 0.00 1\n", + " 6 0.00 0.00 0.00 1\n", + " 7 0.00 0.00 0.00 0\n", + " 8 0.00 0.00 0.00 0\n", + " 9 0.00 0.00 0.00 0\n", + " 10 0.00 0.00 0.00 0\n", + " 11 0.00 0.00 0.00 0\n", + " 12 0.00 0.00 0.00 0\n", + " 13 0.00 0.00 0.00 0\n", + " 14 0.00 0.00 0.00 0\n", + " 15 0.00 0.00 0.00 0\n", + " 16 0.00 0.00 0.00 0\n", + " 17 0.00 0.00 0.00 1\n", + " 18 0.00 0.00 0.00 0\n", + " 19 0.00 0.00 0.00 0\n", + " 20 0.00 0.00 0.00 0\n", + " 21 0.00 0.00 0.00 0\n", + " 22 0.00 0.00 0.00 0\n", + " 23 0.00 0.00 0.00 0\n", + " 24 0.00 0.00 0.00 0\n", + " 25 0.00 0.00 0.00 9\n", 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1\n", + " 925 0.00 0.00 0.00 1\n", + " 926 0.00 0.00 0.00 0\n", + " 927 0.00 0.00 0.00 0\n", + " 928 0.00 0.00 0.00 1\n", + " 929 0.00 0.00 0.00 1\n", + " 930 0.00 0.00 0.00 0\n", + " 931 0.00 0.00 0.00 1\n", + " 932 0.00 0.00 0.00 0\n", + " 933 0.00 0.00 0.00 0\n", + " 934 0.00 0.00 0.00 0\n", + " 935 0.00 0.00 0.00 0\n", + " 936 0.00 0.00 0.00 1\n", + " 937 0.00 0.00 0.00 0\n", + " 938 0.00 0.00 0.00 0\n", + " 939 0.00 0.00 0.00 0\n", + " 940 0.00 0.00 0.00 0\n", + " 941 0.00 0.00 0.00 0\n", + " 942 0.00 0.00 0.00 0\n", + " 943 0.00 0.00 0.00 0\n", + " 944 0.00 0.00 0.00 1\n", + " 945 0.00 0.00 0.00 1\n", + " 946 0.00 0.00 0.00 0\n", + " 947 0.00 0.00 0.00 1\n", + " 948 0.00 0.00 0.00 1\n", + " 949 0.00 0.00 0.00 0\n", + " 950 0.00 0.00 0.00 0\n", + " 951 0.00 0.00 0.00 0\n", + " 952 0.00 0.00 0.00 0\n", + " 953 0.00 0.00 0.00 0\n", + " 954 0.00 0.00 0.00 0\n", + " 955 0.00 0.00 0.00 0\n", + " 956 0.00 0.00 0.00 1\n", + " 957 0.00 0.00 0.00 1\n", + " 958 0.00 0.00 0.00 0\n", + " 959 0.00 0.00 0.00 1\n", + " 960 0.00 0.00 0.00 0\n", + " 961 0.00 0.00 0.00 1\n", + " 962 0.00 0.00 0.00 0\n", + " 963 0.00 0.00 0.00 1\n", + " 964 0.00 0.00 0.00 0\n", + " 965 0.00 0.00 0.00 1\n", + " 966 0.00 0.00 0.00 0\n", + " 967 0.00 0.00 0.00 0\n", + " 968 0.00 0.00 0.00 0\n", + " 969 0.00 0.00 0.00 0\n", + " 970 0.00 0.00 0.00 0\n", + " 971 0.00 0.00 0.00 7\n", + " 972 0.00 0.00 0.00 0\n", + " 973 0.00 0.00 0.00 0\n", + " 974 0.00 0.00 0.00 0\n", + " 975 0.00 0.00 0.00 0\n", + " 976 0.00 0.00 0.00 1\n", + " 977 0.00 0.00 0.00 0\n", + " 978 0.00 0.00 0.00 0\n", + " 979 0.00 0.00 0.00 0\n", + " 980 0.00 0.00 0.00 0\n", + " 981 0.00 0.00 0.00 0\n", + " 982 0.00 0.00 0.00 0\n", + " 983 0.00 0.00 0.00 0\n", + " 984 0.00 0.00 0.00 0\n", + " 985 0.00 0.00 0.00 0\n", + " 986 0.00 0.00 0.00 0\n", + "\n", + " micro avg 0.00 0.00 0.00 475\n", + " macro avg 0.00 0.00 0.00 475\n", + "weighted avg 0.00 0.00 0.00 475\n", + " samples avg 0.00 0.00 0.00 475\n", + "\n", + "\n", + "Multi-label Support Vector Machine (SVM):\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 4 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 29 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 32 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 38 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 48 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 52 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 59 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 65 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 68 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 70 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 71 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 72 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 79 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 87 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 98 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 99 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 101 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 110 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 113 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 114 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 116 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 127 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 129 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 133 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 136 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 140 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 141 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 152 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 157 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 163 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 173 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 179 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 190 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 196 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 197 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 206 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 210 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 216 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 232 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 252 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 257 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 264 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 273 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 278 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 280 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 286 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 288 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 303 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 308 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 319 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 325 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 329 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 330 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 331 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 334 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 335 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 344 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 347 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 351 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 358 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 369 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 385 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 391 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 405 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 410 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 412 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 414 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 422 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 425 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 432 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 433 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 462 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 463 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 478 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 498 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 499 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 501 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 504 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 509 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 514 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 537 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 538 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 545 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 550 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 551 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 564 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 568 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 574 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 576 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 583 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 594 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 597 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 617 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 652 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 653 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 663 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 693 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 697 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 708 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 712 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 730 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 734 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 762 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 764 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 771 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 777 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 792 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 797 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 798 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 811 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 812 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 823 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 836 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 842 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 847 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 849 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 850 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 851 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 870 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 873 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 876 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 882 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 912 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 923 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 925 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 931 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 956 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 959 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 961 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 976 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 4 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 29 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 32 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 38 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 48 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 52 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 59 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 65 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 68 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 70 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 71 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 72 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 79 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 87 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 98 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 99 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 101 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 110 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 113 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 114 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 116 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 127 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 129 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 133 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 136 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 140 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 141 is present in all training examples.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.0\n", + " precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 2\n", + " 1 0.00 0.00 0.00 0\n", + " 2 0.00 0.00 0.00 0\n", + " 3 0.00 0.00 0.00 0\n", + " 4 0.00 0.00 0.00 1\n", + " 5 0.00 0.00 0.00 1\n", + " 6 0.00 0.00 0.00 1\n", + " 7 0.00 0.00 0.00 0\n", + " 8 0.00 0.00 0.00 0\n", + " 9 0.00 0.00 0.00 0\n", + " 10 0.00 0.00 0.00 0\n", + " 11 0.00 0.00 0.00 0\n", + " 12 0.00 0.00 0.00 0\n", + " 13 0.00 0.00 0.00 0\n", + " 14 0.00 0.00 0.00 0\n", + " 15 0.00 0.00 0.00 0\n", + " 16 0.00 0.00 0.00 0\n", + " 17 0.00 0.00 0.00 1\n", + " 18 0.00 0.00 0.00 0\n", + " 19 0.00 0.00 0.00 0\n", + " 20 0.00 0.00 0.00 0\n", + " 21 0.00 0.00 0.00 0\n", + " 22 0.00 0.00 0.00 0\n", + " 23 0.00 0.00 0.00 0\n", + " 24 0.00 0.00 0.00 0\n", + " 25 0.00 0.00 0.00 9\n", + " 26 0.00 0.00 0.00 0\n", + " 27 0.00 0.00 0.00 0\n", + " 28 0.00 0.00 0.00 0\n", + " 29 0.00 0.00 0.00 1\n", + " 30 0.00 0.00 0.00 0\n", + " 31 0.00 0.00 0.00 0\n", + " 32 0.00 0.00 0.00 1\n", + " 33 0.00 0.00 0.00 0\n", + " 34 0.00 0.00 0.00 0\n", + " 35 0.00 0.00 0.00 3\n", + " 36 0.00 0.00 0.00 0\n", + " 37 0.00 0.00 0.00 0\n", + " 38 0.00 0.00 0.00 1\n", + " 39 0.00 0.00 0.00 0\n", + " 40 0.00 0.00 0.00 0\n", + " 41 0.00 0.00 0.00 0\n", + " 42 0.00 0.00 0.00 0\n", + " 43 0.00 0.00 0.00 0\n", + " 44 0.00 0.00 0.00 0\n", + " 45 0.00 0.00 0.00 2\n", + " 46 0.00 0.00 0.00 0\n", + " 47 0.00 0.00 0.00 0\n", + " 48 0.00 0.00 0.00 1\n", + " 49 0.00 0.00 0.00 0\n", + " 50 0.00 0.00 0.00 0\n", + " 51 0.00 0.00 0.00 0\n", + " 52 0.00 0.00 0.00 1\n", + " 53 0.00 0.00 0.00 2\n", + " 54 0.00 0.00 0.00 1\n", + " 55 0.00 0.00 0.00 1\n", + " 56 0.00 0.00 0.00 0\n", + " 57 0.00 0.00 0.00 1\n", + " 58 0.00 0.00 0.00 3\n", + " 59 0.00 0.00 0.00 1\n", + " 60 0.00 0.00 0.00 0\n", + " 61 0.00 0.00 0.00 0\n", + " 62 0.00 0.00 0.00 3\n", + " 63 0.00 0.00 0.00 0\n", + " 64 0.00 0.00 0.00 0\n", + " 65 0.00 0.00 0.00 1\n", + " 66 0.00 0.00 0.00 0\n", + " 67 0.00 0.00 0.00 1\n", + " 68 0.00 0.00 0.00 1\n", + " 69 0.00 0.00 0.00 1\n", + " 70 0.00 0.00 0.00 1\n", + " 71 0.00 0.00 0.00 1\n", + " 72 0.00 0.00 0.00 1\n", + " 73 0.00 0.00 0.00 0\n", + " 74 0.00 0.00 0.00 0\n", + " 75 0.00 0.00 0.00 1\n", + " 76 0.00 0.00 0.00 0\n", + " 77 0.00 0.00 0.00 0\n", + " 78 0.00 0.00 0.00 1\n", + " 79 0.00 0.00 0.00 1\n", + " 80 0.00 0.00 0.00 0\n", + " 81 0.00 0.00 0.00 0\n", + " 82 0.00 0.00 0.00 0\n", + " 83 0.00 0.00 0.00 0\n", + " 84 0.00 0.00 0.00 1\n", + " 85 0.00 0.00 0.00 0\n", + " 86 0.00 0.00 0.00 0\n", + " 87 0.00 0.00 0.00 1\n", + " 88 0.00 0.00 0.00 0\n", + " 89 0.00 0.00 0.00 0\n", + " 90 0.00 0.00 0.00 0\n", + " 91 0.00 0.00 0.00 0\n", + " 92 0.00 0.00 0.00 0\n", + " 93 0.00 0.00 0.00 0\n", + " 94 0.00 0.00 0.00 1\n", + " 95 0.00 0.00 0.00 0\n", + " 96 0.00 0.00 0.00 0\n", + " 97 0.00 0.00 0.00 0\n", + " 98 0.00 0.00 0.00 1\n", + " 99 0.00 0.00 0.00 1\n", + " 100 0.00 0.00 0.00 1\n", + " 101 0.00 0.00 0.00 1\n", + " 102 0.00 0.00 0.00 0\n", + " 103 0.00 0.00 0.00 0\n", + " 104 0.00 0.00 0.00 0\n", + " 105 0.00 0.00 0.00 0\n", + " 106 0.00 0.00 0.00 0\n", + " 107 0.00 0.00 0.00 0\n", + " 108 0.00 0.00 0.00 0\n", + " 109 0.00 0.00 0.00 0\n", + " 110 0.00 0.00 0.00 1\n", + " 111 0.00 0.00 0.00 0\n", + " 112 0.00 0.00 0.00 0\n", + " 113 0.00 0.00 0.00 1\n", + " 114 0.00 0.00 0.00 1\n", + " 115 0.00 0.00 0.00 0\n", + " 116 0.00 0.00 0.00 1\n", + " 117 0.00 0.00 0.00 0\n", + " 118 0.00 0.00 0.00 0\n", + " 119 0.00 0.00 0.00 0\n", + " 120 0.00 0.00 0.00 0\n", + " 121 0.00 0.00 0.00 0\n", + " 122 0.00 0.00 0.00 1\n", + " 123 0.00 0.00 0.00 3\n", + " 124 0.00 0.00 0.00 10\n", + " 125 0.00 0.00 0.00 4\n", + " 126 0.00 0.00 0.00 0\n", + " 127 0.00 0.00 0.00 1\n", + " 128 0.00 0.00 0.00 0\n", + " 129 0.00 0.00 0.00 1\n", + " 130 0.00 0.00 0.00 0\n", + " 131 0.00 0.00 0.00 0\n", + " 132 0.00 0.00 0.00 0\n", + " 133 0.00 0.00 0.00 1\n", + " 134 0.00 0.00 0.00 0\n", + " 135 0.00 0.00 0.00 2\n", + " 136 0.00 0.00 0.00 1\n", + " 137 0.00 0.00 0.00 0\n", + " 138 0.00 0.00 0.00 2\n", + " 139 0.00 0.00 0.00 0\n", + " 140 0.00 0.00 0.00 1\n", + " 141 0.00 0.00 0.00 1\n", + " 142 0.00 0.00 0.00 0\n", + " 143 0.00 0.00 0.00 0\n", + " 144 0.00 0.00 0.00 0\n", + " 145 0.00 0.00 0.00 1\n", + " 146 0.00 0.00 0.00 0\n", + " 147 0.00 0.00 0.00 0\n", + " 148 0.00 0.00 0.00 0\n", + " 149 0.00 0.00 0.00 0\n", + " 150 0.00 0.00 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0.00 0.00 0.00 1\n", + " 772 0.00 0.00 0.00 0\n", + " 773 0.00 0.00 0.00 2\n", + " 774 0.00 0.00 0.00 0\n", + " 775 0.00 0.00 0.00 0\n", + " 776 0.00 0.00 0.00 0\n", + " 777 0.00 0.00 0.00 1\n", + " 778 0.00 0.00 0.00 0\n", + " 779 0.00 0.00 0.00 0\n", + " 780 0.00 0.00 0.00 0\n", + " 781 0.00 0.00 0.00 0\n", + " 782 0.00 0.00 0.00 0\n", + " 783 0.00 0.00 0.00 0\n", + " 784 0.00 0.00 0.00 0\n", + " 785 0.00 0.00 0.00 1\n", + " 786 0.00 0.00 0.00 0\n", + " 787 0.00 0.00 0.00 0\n", + " 788 0.00 0.00 0.00 0\n", + " 789 0.00 0.00 0.00 0\n", + " 790 0.00 0.00 0.00 0\n", + " 791 0.00 0.00 0.00 1\n", + " 792 0.00 0.00 0.00 1\n", + " 793 0.00 0.00 0.00 0\n", + " 794 0.00 0.00 0.00 0\n", + " 795 0.00 0.00 0.00 0\n", + " 796 0.00 0.00 0.00 0\n", + " 797 0.00 0.00 0.00 2\n", + " 798 0.00 0.00 0.00 1\n", + " 799 0.00 0.00 0.00 0\n", + " 800 0.00 0.00 0.00 0\n", + " 801 0.00 0.00 0.00 0\n", + " 802 0.00 0.00 0.00 0\n", + " 803 0.00 0.00 0.00 1\n", + " 804 0.00 0.00 0.00 0\n", + " 805 0.00 0.00 0.00 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0\n", + " 875 0.00 0.00 0.00 0\n", + " 876 0.00 0.00 0.00 1\n", + " 877 0.00 0.00 0.00 0\n", + " 878 0.00 0.00 0.00 0\n", + " 879 0.00 0.00 0.00 0\n", + " 880 0.00 0.00 0.00 0\n", + " 881 0.00 0.00 0.00 0\n", + " 882 0.00 0.00 0.00 1\n", + " 883 0.00 0.00 0.00 1\n", + " 884 0.00 0.00 0.00 0\n", + " 885 0.00 0.00 0.00 0\n", + " 886 0.00 0.00 0.00 0\n", + " 887 0.00 0.00 0.00 0\n", + " 888 0.00 0.00 0.00 1\n", + " 889 0.00 0.00 0.00 3\n", + " 890 0.00 0.00 0.00 0\n", + " 891 0.00 0.00 0.00 0\n", + " 892 0.00 0.00 0.00 0\n", + " 893 0.00 0.00 0.00 0\n", + " 894 0.00 0.00 0.00 0\n", + " 895 0.00 0.00 0.00 0\n", + " 896 0.00 0.00 0.00 0\n", + " 897 0.00 0.00 0.00 1\n", + " 898 0.00 0.00 0.00 0\n", + " 899 0.00 0.00 0.00 0\n", + " 900 0.00 0.00 0.00 0\n", + " 901 0.00 0.00 0.00 4\n", + " 902 0.00 0.00 0.00 0\n", + " 903 0.00 0.00 0.00 0\n", + " 904 0.00 0.00 0.00 0\n", + " 905 0.00 0.00 0.00 0\n", + " 906 0.00 0.00 0.00 0\n", + " 907 0.00 0.00 0.00 0\n", + " 908 0.00 0.00 0.00 0\n", + " 909 0.00 0.00 0.00 0\n", + " 910 0.00 0.00 0.00 0\n", + " 911 0.00 0.00 0.00 0\n", + " 912 0.00 0.00 0.00 1\n", + " 913 0.00 0.00 0.00 0\n", + " 914 0.00 0.00 0.00 0\n", + " 915 0.00 0.00 0.00 0\n", + " 916 0.00 0.00 0.00 0\n", + " 917 0.00 0.00 0.00 0\n", + " 918 0.00 0.00 0.00 0\n", + " 919 0.00 0.00 0.00 0\n", + " 920 0.00 0.00 0.00 0\n", + " 921 0.00 0.00 0.00 0\n", + " 922 0.00 0.00 0.00 0\n", + " 923 0.00 0.00 0.00 1\n", + " 924 0.00 0.00 0.00 1\n", + " 925 0.00 0.00 0.00 1\n", + " 926 0.00 0.00 0.00 0\n", + " 927 0.00 0.00 0.00 0\n", + " 928 0.00 0.00 0.00 1\n", + " 929 0.00 0.00 0.00 1\n", + " 930 0.00 0.00 0.00 0\n", + " 931 0.00 0.00 0.00 1\n", + " 932 0.00 0.00 0.00 0\n", + " 933 0.00 0.00 0.00 0\n", + " 934 0.00 0.00 0.00 0\n", + " 935 0.00 0.00 0.00 0\n", + " 936 0.00 0.00 0.00 1\n", + " 937 0.00 0.00 0.00 0\n", + " 938 0.00 0.00 0.00 0\n", + " 939 0.00 0.00 0.00 0\n", + " 940 0.00 0.00 0.00 0\n", + " 941 0.00 0.00 0.00 0\n", + " 942 0.00 0.00 0.00 0\n", + " 943 0.00 0.00 0.00 0\n", + " 944 0.00 0.00 0.00 1\n", + " 945 0.00 0.00 0.00 1\n", + " 946 0.00 0.00 0.00 0\n", + " 947 0.00 0.00 0.00 1\n", + " 948 0.00 0.00 0.00 1\n", + " 949 0.00 0.00 0.00 0\n", + " 950 0.00 0.00 0.00 0\n", + " 951 0.00 0.00 0.00 0\n", + " 952 0.00 0.00 0.00 0\n", + " 953 0.00 0.00 0.00 0\n", + " 954 0.00 0.00 0.00 0\n", + " 955 0.00 0.00 0.00 0\n", + " 956 0.00 0.00 0.00 1\n", + " 957 0.00 0.00 0.00 1\n", + " 958 0.00 0.00 0.00 0\n", + " 959 0.00 0.00 0.00 1\n", + " 960 0.00 0.00 0.00 0\n", + " 961 0.00 0.00 0.00 1\n", + " 962 0.00 0.00 0.00 0\n", + " 963 0.00 0.00 0.00 1\n", + " 964 0.00 0.00 0.00 0\n", + " 965 0.00 0.00 0.00 1\n", + " 966 0.00 0.00 0.00 0\n", + " 967 0.00 0.00 0.00 0\n", + " 968 0.00 0.00 0.00 0\n", + " 969 0.00 0.00 0.00 0\n", + " 970 0.00 0.00 0.00 0\n", + " 971 0.00 0.00 0.00 7\n", + " 972 0.00 0.00 0.00 0\n", + " 973 0.00 0.00 0.00 0\n", + " 974 0.00 0.00 0.00 0\n", + " 975 0.00 0.00 0.00 0\n", + " 976 0.00 0.00 0.00 1\n", + " 977 0.00 0.00 0.00 0\n", + " 978 0.00 0.00 0.00 0\n", + " 979 0.00 0.00 0.00 0\n", + " 980 0.00 0.00 0.00 0\n", + " 981 0.00 0.00 0.00 0\n", + " 982 0.00 0.00 0.00 0\n", + " 983 0.00 0.00 0.00 0\n", + " 984 0.00 0.00 0.00 0\n", + " 985 0.00 0.00 0.00 0\n", + " 986 0.00 0.00 0.00 0\n", + "\n", + " micro avg 0.00 0.00 0.00 475\n", + " macro avg 0.00 0.00 0.00 475\n", + "weighted avg 0.00 0.00 0.00 475\n", + " samples avg 0.00 0.00 0.00 475\n", + "\n", + "\n", + "Multi-label k-Nearest Neighbors (k-NN):\n", + "Accuracy: 0.04\n", + " precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 2\n", + " 1 0.00 0.00 0.00 0\n", + " 2 0.00 0.00 0.00 0\n", + " 3 0.00 0.00 0.00 0\n", + " 4 0.00 0.00 0.00 1\n", + " 5 0.00 0.00 0.00 1\n", + " 6 0.00 0.00 0.00 1\n", + " 7 0.00 0.00 0.00 0\n", + " 8 0.00 0.00 0.00 0\n", + " 9 0.00 0.00 0.00 0\n", + " 10 0.00 0.00 0.00 0\n", + " 11 0.00 0.00 0.00 0\n", + " 12 0.00 0.00 0.00 0\n", + " 13 0.00 0.00 0.00 0\n", + " 14 0.00 0.00 0.00 0\n", + " 15 0.00 0.00 0.00 0\n", + " 16 0.00 0.00 0.00 0\n", + " 17 0.00 0.00 0.00 1\n", + " 18 0.00 0.00 0.00 0\n", + " 19 0.00 0.00 0.00 0\n", + " 20 0.00 0.00 0.00 0\n", + " 21 0.00 0.00 0.00 0\n", + " 22 0.00 0.00 0.00 0\n", + " 23 0.00 0.00 0.00 0\n", + " 24 0.00 0.00 0.00 0\n", + " 25 0.67 0.22 0.33 9\n", + " 26 0.00 0.00 0.00 0\n", + " 27 0.00 0.00 0.00 0\n", + " 28 0.00 0.00 0.00 0\n", + " 29 0.00 0.00 0.00 1\n", + " 30 0.00 0.00 0.00 0\n", + " 31 0.00 0.00 0.00 0\n", + " 32 0.00 0.00 0.00 1\n", + " 33 0.00 0.00 0.00 0\n", + " 34 0.00 0.00 0.00 0\n", + " 35 0.00 0.00 0.00 3\n", + " 36 0.00 0.00 0.00 0\n", + " 37 0.00 0.00 0.00 0\n", + " 38 0.00 0.00 0.00 1\n", + " 39 0.00 0.00 0.00 0\n", + " 40 0.00 0.00 0.00 0\n", + " 41 0.00 0.00 0.00 0\n", + " 42 0.00 0.00 0.00 0\n", + " 43 0.00 0.00 0.00 0\n", + " 44 0.00 0.00 0.00 0\n", + " 45 0.00 0.00 0.00 2\n", + " 46 0.00 0.00 0.00 0\n", + " 47 0.00 0.00 0.00 0\n", + " 48 0.00 0.00 0.00 1\n", + " 49 0.00 0.00 0.00 0\n", + " 50 0.00 0.00 0.00 0\n", + " 51 0.00 0.00 0.00 0\n", + " 52 0.00 0.00 0.00 1\n", + " 53 0.00 0.00 0.00 2\n", + " 54 0.00 0.00 0.00 1\n", + " 55 0.00 0.00 0.00 1\n", + " 56 0.00 0.00 0.00 0\n", + " 57 0.00 0.00 0.00 1\n", + " 58 0.00 0.00 0.00 3\n", + " 59 0.00 0.00 0.00 1\n", + " 60 0.00 0.00 0.00 0\n", + " 61 0.00 0.00 0.00 0\n", + " 62 0.00 0.00 0.00 3\n", + " 63 0.00 0.00 0.00 0\n", + " 64 0.00 0.00 0.00 0\n", + " 65 0.00 0.00 0.00 1\n", + " 66 0.00 0.00 0.00 0\n", + " 67 0.00 0.00 0.00 1\n", + " 68 0.00 0.00 0.00 1\n", + " 69 0.00 0.00 0.00 1\n", + " 70 0.00 0.00 0.00 1\n", + " 71 0.00 0.00 0.00 1\n", + " 72 0.00 0.00 0.00 1\n", + " 73 0.00 0.00 0.00 0\n", + " 74 0.00 0.00 0.00 0\n", + " 75 0.00 0.00 0.00 1\n", + " 76 0.00 0.00 0.00 0\n", + " 77 0.00 0.00 0.00 0\n", + " 78 0.00 0.00 0.00 1\n", + " 79 0.00 0.00 0.00 1\n", + " 80 0.00 0.00 0.00 0\n", + " 81 0.00 0.00 0.00 0\n", + " 82 0.00 0.00 0.00 0\n", + " 83 0.00 0.00 0.00 0\n", + " 84 0.00 0.00 0.00 1\n", + " 85 0.00 0.00 0.00 0\n", + " 86 0.00 0.00 0.00 0\n", + " 87 0.00 0.00 0.00 1\n", + " 88 0.00 0.00 0.00 0\n", + " 89 0.00 0.00 0.00 0\n", + " 90 0.00 0.00 0.00 0\n", + " 91 0.00 0.00 0.00 0\n", + " 92 0.00 0.00 0.00 0\n", + " 93 0.00 0.00 0.00 0\n", + " 94 0.00 0.00 0.00 1\n", + " 95 0.00 0.00 0.00 0\n", + " 96 0.00 0.00 0.00 0\n", + " 97 0.00 0.00 0.00 0\n", + " 98 0.00 0.00 0.00 1\n", + " 99 0.00 0.00 0.00 1\n", + " 100 0.00 0.00 0.00 1\n", + " 101 0.00 0.00 0.00 1\n", + " 102 0.00 0.00 0.00 0\n", + " 103 0.00 0.00 0.00 0\n", + " 104 0.00 0.00 0.00 0\n", + " 105 0.00 0.00 0.00 0\n", + " 106 0.00 0.00 0.00 0\n", + " 107 0.00 0.00 0.00 0\n", + " 108 0.00 0.00 0.00 0\n", + " 109 0.00 0.00 0.00 0\n", + " 110 0.00 0.00 0.00 1\n", + " 111 0.00 0.00 0.00 0\n", + " 112 0.00 0.00 0.00 0\n", + " 113 0.00 0.00 0.00 1\n", + " 114 0.00 0.00 0.00 1\n", + " 115 0.00 0.00 0.00 0\n", + " 116 0.00 0.00 0.00 1\n", + " 117 0.00 0.00 0.00 0\n", + " 118 0.00 0.00 0.00 0\n", + " 119 0.00 0.00 0.00 0\n", + " 120 0.00 0.00 0.00 0\n", + " 121 0.00 0.00 0.00 0\n", + " 122 0.00 0.00 0.00 1\n", + " 123 0.00 0.00 0.00 3\n", + " 124 0.67 0.20 0.31 10\n", + " 125 0.00 0.00 0.00 4\n", + " 126 0.00 0.00 0.00 0\n", + " 127 0.00 0.00 0.00 1\n", + " 128 0.00 0.00 0.00 0\n", + " 129 0.00 0.00 0.00 1\n", + " 130 0.00 0.00 0.00 0\n", + " 131 0.00 0.00 0.00 0\n", + " 132 0.00 0.00 0.00 0\n", + " 133 0.00 0.00 0.00 1\n", + " 134 0.00 0.00 0.00 0\n", + " 135 0.00 0.00 0.00 2\n", + " 136 0.00 0.00 0.00 1\n", + " 137 0.00 0.00 0.00 0\n", + " 138 0.00 0.00 0.00 2\n", + " 139 0.00 0.00 0.00 0\n", + " 140 0.00 0.00 0.00 1\n", + " 141 0.00 0.00 0.00 1\n", + " 142 0.00 0.00 0.00 0\n", + " 143 0.00 0.00 0.00 0\n", + " 144 0.00 0.00 0.00 0\n", + " 145 0.00 0.00 0.00 1\n", + " 146 0.00 0.00 0.00 0\n", + " 147 0.00 0.00 0.00 0\n", + " 148 0.00 0.00 0.00 0\n", + " 149 0.00 0.00 0.00 0\n", + " 150 0.00 0.00 0.00 0\n", + " 151 0.00 0.00 0.00 0\n", + " 152 0.00 0.00 0.00 1\n", + " 153 0.00 0.00 0.00 0\n", + " 154 0.00 0.00 0.00 1\n", + " 155 0.00 0.00 0.00 0\n", + " 156 0.00 0.00 0.00 0\n", + " 157 0.00 0.00 0.00 1\n", + " 158 0.00 0.00 0.00 0\n", + " 159 0.00 0.00 0.00 0\n", + " 160 0.00 0.00 0.00 0\n", + " 161 0.00 0.00 0.00 0\n", + " 162 0.00 0.00 0.00 0\n", + " 163 0.00 0.00 0.00 1\n", + " 164 0.00 0.00 0.00 0\n", + " 165 0.00 0.00 0.00 0\n", + " 166 0.00 0.00 0.00 0\n", + " 167 0.00 0.00 0.00 0\n", + " 168 0.00 0.00 0.00 1\n", + " 169 0.00 0.00 0.00 0\n", + " 170 0.00 0.00 0.00 1\n", + " 171 0.00 0.00 0.00 0\n", + " 172 0.00 0.00 0.00 0\n", + " 173 0.00 0.00 0.00 1\n", + " 174 0.00 0.00 0.00 0\n", + " 175 0.00 0.00 0.00 0\n", + " 176 0.00 0.00 0.00 0\n", + " 177 0.00 0.00 0.00 0\n", + " 178 0.00 0.00 0.00 0\n", + " 179 0.00 0.00 0.00 1\n", + " 180 0.00 0.00 0.00 0\n", + " 181 0.00 0.00 0.00 0\n", + " 182 0.00 0.00 0.00 0\n", + " 183 0.00 0.00 0.00 0\n", + " 184 0.00 0.00 0.00 1\n", + " 185 0.00 0.00 0.00 0\n", + " 186 0.00 0.00 0.00 1\n", + " 187 0.00 0.00 0.00 0\n", + " 188 0.00 0.00 0.00 0\n", + " 189 0.00 0.00 0.00 0\n", + " 190 0.00 0.00 0.00 1\n", + " 191 0.00 0.00 0.00 0\n", + " 192 0.00 0.00 0.00 0\n", + " 193 0.00 0.00 0.00 0\n", + " 194 0.00 0.00 0.00 0\n", + " 195 0.00 0.00 0.00 0\n", + " 196 0.00 0.00 0.00 1\n", + " 197 0.00 0.00 0.00 2\n", + " 198 0.00 0.00 0.00 0\n", + " 199 0.00 0.00 0.00 0\n", + " 200 0.00 0.00 0.00 1\n", + " 201 0.00 0.00 0.00 3\n", + " 202 0.00 0.00 0.00 0\n", + " 203 0.00 0.00 0.00 0\n", + " 204 0.00 0.00 0.00 0\n", + " 205 0.00 0.00 0.00 0\n", + " 206 0.00 0.00 0.00 1\n", + " 207 0.00 0.00 0.00 0\n", + " 208 0.00 0.00 0.00 0\n", + " 209 0.00 0.00 0.00 0\n", + " 210 0.00 0.00 0.00 1\n", + " 211 0.00 0.00 0.00 0\n", + " 212 0.00 0.00 0.00 0\n", + " 213 0.00 0.00 0.00 2\n", + " 214 0.00 0.00 0.00 1\n", + " 215 0.00 0.00 0.00 0\n", + " 216 0.00 0.00 0.00 1\n", + " 217 0.00 0.00 0.00 0\n", + " 218 0.00 0.00 0.00 2\n", + " 219 0.00 0.00 0.00 0\n", + " 220 0.00 0.00 0.00 1\n", + " 221 0.00 0.00 0.00 0\n", + " 222 0.00 0.00 0.00 0\n", + " 223 0.00 0.00 0.00 0\n", + " 224 0.00 0.00 0.00 0\n", + " 225 0.00 0.00 0.00 0\n", + " 226 0.00 0.00 0.00 0\n", + " 227 0.00 0.00 0.00 0\n", + " 228 0.00 0.00 0.00 0\n", + " 229 0.00 0.00 0.00 2\n", + " 230 0.00 0.00 0.00 0\n", + " 231 0.00 0.00 0.00 0\n", + " 232 0.00 0.00 0.00 1\n", + " 233 0.00 0.00 0.00 0\n", + " 234 0.00 0.00 0.00 0\n", + " 235 0.00 0.00 0.00 0\n", + " 236 0.00 0.00 0.00 0\n", + " 237 0.00 0.00 0.00 0\n", + " 238 0.00 0.00 0.00 0\n", + " 239 0.00 0.00 0.00 2\n", + " 240 0.00 0.00 0.00 0\n", + " 241 0.00 0.00 0.00 0\n", + " 242 0.00 0.00 0.00 0\n", + " 243 0.00 0.00 0.00 3\n", + " 244 0.00 0.00 0.00 5\n", + " 245 0.00 0.00 0.00 0\n", + " 246 0.00 0.00 0.00 0\n", + " 247 0.00 0.00 0.00 0\n", + " 248 0.00 0.00 0.00 0\n", + " 249 0.00 0.00 0.00 0\n", + " 250 0.00 0.00 0.00 0\n", + " 251 0.00 0.00 0.00 0\n", + " 252 0.00 0.00 0.00 1\n", + " 253 0.00 0.00 0.00 1\n", + " 254 0.00 0.00 0.00 0\n", + " 255 0.00 0.00 0.00 0\n", + " 256 0.00 0.00 0.00 0\n", + " 257 0.00 0.00 0.00 1\n", + " 258 0.00 0.00 0.00 0\n", + " 259 0.00 0.00 0.00 0\n", + " 260 0.00 0.00 0.00 1\n", + " 261 0.00 0.00 0.00 0\n", + " 262 0.00 0.00 0.00 0\n", + " 263 0.00 0.00 0.00 0\n", + " 264 0.00 0.00 0.00 1\n", + " 265 0.00 0.00 0.00 1\n", + " 266 0.00 0.00 0.00 0\n", + " 267 0.00 0.00 0.00 1\n", + " 268 0.00 0.00 0.00 0\n", + " 269 0.00 0.00 0.00 0\n", + " 270 0.00 0.00 0.00 0\n", + " 271 0.00 0.00 0.00 2\n", + " 272 0.00 0.00 0.00 0\n", + " 273 0.00 0.00 0.00 1\n", + " 274 0.00 0.00 0.00 0\n", + " 275 0.00 0.00 0.00 0\n", + " 276 0.00 0.00 0.00 0\n", + " 277 0.00 0.00 0.00 0\n", + " 278 0.00 0.00 0.00 1\n", + " 279 0.00 0.00 0.00 0\n", + " 280 0.00 0.00 0.00 1\n", + " 281 0.00 0.00 0.00 2\n", + " 282 0.00 0.00 0.00 0\n", + " 283 1.00 0.12 0.22 8\n", + " 284 0.00 0.00 0.00 0\n", + " 285 0.00 0.00 0.00 0\n", + " 286 0.00 0.00 0.00 1\n", + " 287 0.00 0.00 0.00 1\n", + " 288 0.00 0.00 0.00 1\n", + " 289 0.00 0.00 0.00 0\n", + " 290 0.00 0.00 0.00 0\n", + " 291 0.00 0.00 0.00 0\n", + " 292 0.00 0.00 0.00 4\n", + " 293 0.00 0.00 0.00 0\n", + " 294 0.00 0.00 0.00 0\n", + " 295 0.00 0.00 0.00 0\n", + " 296 0.00 0.00 0.00 1\n", + " 297 0.00 0.00 0.00 0\n", + " 298 0.00 0.00 0.00 0\n", + " 299 0.00 0.00 0.00 1\n", + " 300 0.00 0.00 0.00 0\n", + " 301 0.00 0.00 0.00 0\n", + " 302 0.00 0.00 0.00 0\n", + " 303 0.00 0.00 0.00 1\n", + " 304 0.00 0.00 0.00 0\n", + " 305 0.00 0.00 0.00 0\n", + " 306 0.00 0.00 0.00 0\n", + " 307 0.00 0.00 0.00 0\n", + " 308 0.00 0.00 0.00 1\n", + " 309 0.00 0.00 0.00 0\n", + " 310 0.00 0.00 0.00 0\n", + " 311 0.00 0.00 0.00 0\n", + " 312 0.00 0.00 0.00 0\n", + " 313 0.00 0.00 0.00 0\n", + " 314 0.00 0.00 0.00 0\n", + " 315 0.00 0.00 0.00 0\n", + " 316 0.00 0.00 0.00 1\n", + " 317 0.00 0.00 0.00 0\n", + " 318 0.00 0.00 0.00 0\n", + " 319 0.00 0.00 0.00 1\n", + " 320 0.00 0.00 0.00 0\n", + " 321 0.00 0.00 0.00 0\n", + " 322 0.00 0.00 0.00 0\n", + " 323 0.00 0.00 0.00 0\n", + " 324 0.00 0.00 0.00 0\n", + " 325 0.00 0.00 0.00 1\n", + " 326 0.00 0.00 0.00 0\n", + " 327 0.00 0.00 0.00 0\n", + " 328 0.00 0.00 0.00 0\n", + " 329 0.00 0.00 0.00 2\n", + " 330 0.00 0.00 0.00 1\n", + " 331 0.00 0.00 0.00 1\n", + " 332 0.00 0.00 0.00 0\n", + " 333 0.00 0.00 0.00 1\n", + " 334 0.00 0.00 0.00 1\n", + " 335 0.00 0.00 0.00 1\n", + " 336 0.00 0.00 0.00 0\n", + " 337 0.00 0.00 0.00 0\n", + " 338 0.00 0.00 0.00 0\n", + " 339 0.00 0.00 0.00 0\n", + " 340 0.00 0.00 0.00 0\n", + " 341 0.00 0.00 0.00 0\n", + " 342 0.00 0.00 0.00 0\n", + " 343 0.00 0.00 0.00 0\n", + " 344 0.00 0.00 0.00 1\n", + " 345 0.00 0.00 0.00 2\n", + " 346 0.00 0.00 0.00 0\n", + " 347 0.00 0.00 0.00 2\n", + " 348 0.00 0.00 0.00 0\n", + " 349 0.00 0.00 0.00 0\n", + " 350 0.00 0.00 0.00 0\n", + " 351 0.00 0.00 0.00 1\n", + " 352 0.00 0.00 0.00 0\n", + " 353 0.00 0.00 0.00 0\n", + " 354 0.00 0.00 0.00 0\n", + " 355 0.00 0.00 0.00 0\n", + " 356 0.00 0.00 0.00 0\n", + " 357 0.00 0.00 0.00 1\n", + " 358 0.00 0.00 0.00 1\n", + " 359 0.00 0.00 0.00 1\n", + " 360 1.00 0.50 0.67 2\n", + " 361 0.00 0.00 0.00 0\n", + " 362 0.00 0.00 0.00 0\n", + " 363 0.00 0.00 0.00 0\n", + " 364 0.00 0.00 0.00 0\n", + " 365 0.00 0.00 0.00 2\n", + " 366 0.00 0.00 0.00 0\n", + " 367 0.00 0.00 0.00 0\n", + " 368 0.00 0.00 0.00 0\n", + " 369 0.00 0.00 0.00 1\n", + " 370 0.00 0.00 0.00 0\n", + " 371 0.00 0.00 0.00 0\n", + " 372 0.00 0.00 0.00 0\n", + " 373 0.00 0.00 0.00 1\n", + " 374 0.00 0.00 0.00 0\n", + " 375 0.00 0.00 0.00 0\n", + " 376 0.00 0.00 0.00 0\n", + " 377 0.00 0.00 0.00 0\n", + " 378 0.00 0.00 0.00 0\n", + " 379 0.00 0.00 0.00 0\n", + " 380 0.00 0.00 0.00 1\n", + " 381 0.00 0.00 0.00 0\n", + " 382 0.00 0.00 0.00 0\n", + " 383 0.00 0.00 0.00 0\n", + " 384 0.00 0.00 0.00 0\n", + " 385 0.00 0.00 0.00 1\n", + " 386 0.00 0.00 0.00 0\n", + " 387 0.00 0.00 0.00 0\n", + " 388 0.00 0.00 0.00 0\n", + " 389 0.00 0.00 0.00 0\n", + " 390 0.00 0.00 0.00 0\n", + " 391 0.00 0.00 0.00 1\n", + " 392 0.00 0.00 0.00 0\n", + " 393 0.00 0.00 0.00 0\n", + " 394 0.00 0.00 0.00 0\n", + " 395 0.00 0.00 0.00 0\n", + " 396 0.00 0.00 0.00 0\n", + " 397 0.00 0.00 0.00 0\n", + " 398 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881 0.00 0.00 0.00 0\n", + " 882 0.00 0.00 0.00 1\n", + " 883 0.00 0.00 0.00 1\n", + " 884 0.00 0.00 0.00 0\n", + " 885 0.00 0.00 0.00 0\n", + " 886 0.00 0.00 0.00 0\n", + " 887 0.00 0.00 0.00 0\n", + " 888 0.00 0.00 0.00 1\n", + " 889 0.00 0.00 0.00 3\n", + " 890 0.00 0.00 0.00 0\n", + " 891 0.00 0.00 0.00 0\n", + " 892 0.00 0.00 0.00 0\n", + " 893 0.00 0.00 0.00 0\n", + " 894 0.00 0.00 0.00 0\n", + " 895 0.00 0.00 0.00 0\n", + " 896 0.00 0.00 0.00 0\n", + " 897 1.00 1.00 1.00 1\n", + " 898 0.00 0.00 0.00 0\n", + " 899 0.00 0.00 0.00 0\n", + " 900 0.00 0.00 0.00 0\n", + " 901 0.00 0.00 0.00 4\n", + " 902 0.00 0.00 0.00 0\n", + " 903 0.00 0.00 0.00 0\n", + " 904 0.00 0.00 0.00 0\n", + " 905 0.00 0.00 0.00 0\n", + " 906 0.00 0.00 0.00 0\n", + " 907 0.00 0.00 0.00 0\n", + " 908 0.00 0.00 0.00 0\n", + " 909 0.00 0.00 0.00 0\n", + " 910 0.00 0.00 0.00 0\n", + " 911 0.00 0.00 0.00 0\n", + " 912 0.00 0.00 0.00 1\n", + " 913 0.00 0.00 0.00 0\n", + " 914 0.00 0.00 0.00 0\n", + " 915 0.00 0.00 0.00 0\n", + " 916 0.00 0.00 0.00 0\n", + " 917 0.00 0.00 0.00 0\n", + " 918 0.00 0.00 0.00 0\n", + " 919 0.00 0.00 0.00 0\n", + " 920 0.00 0.00 0.00 0\n", + " 921 0.00 0.00 0.00 0\n", + " 922 0.00 0.00 0.00 0\n", + " 923 0.00 0.00 0.00 1\n", + " 924 0.00 0.00 0.00 1\n", + " 925 0.00 0.00 0.00 1\n", + " 926 0.00 0.00 0.00 0\n", + " 927 0.00 0.00 0.00 0\n", + " 928 0.00 0.00 0.00 1\n", + " 929 0.00 0.00 0.00 1\n", + " 930 0.00 0.00 0.00 0\n", + " 931 0.00 0.00 0.00 1\n", + " 932 0.00 0.00 0.00 0\n", + " 933 0.00 0.00 0.00 0\n", + " 934 0.00 0.00 0.00 0\n", + " 935 0.00 0.00 0.00 0\n", + " 936 0.00 0.00 0.00 1\n", + " 937 0.00 0.00 0.00 0\n", + " 938 0.00 0.00 0.00 0\n", + " 939 0.00 0.00 0.00 0\n", + " 940 0.00 0.00 0.00 0\n", + " 941 0.00 0.00 0.00 0\n", + " 942 0.00 0.00 0.00 0\n", + " 943 0.00 0.00 0.00 0\n", + " 944 0.00 0.00 0.00 1\n", + " 945 0.00 0.00 0.00 1\n", + " 946 0.00 0.00 0.00 0\n", + " 947 0.00 0.00 0.00 1\n", + " 948 0.00 0.00 0.00 1\n", + " 949 0.00 0.00 0.00 0\n", + " 950 0.00 0.00 0.00 0\n", + " 951 0.00 0.00 0.00 0\n", + " 952 0.00 0.00 0.00 0\n", + " 953 0.00 0.00 0.00 0\n", + " 954 0.00 0.00 0.00 0\n", + " 955 0.00 0.00 0.00 0\n", + " 956 0.00 0.00 0.00 1\n", + " 957 0.00 0.00 0.00 1\n", + " 958 0.00 0.00 0.00 0\n", + " 959 0.00 0.00 0.00 1\n", + " 960 0.00 0.00 0.00 0\n", + " 961 0.00 0.00 0.00 1\n", + " 962 0.00 0.00 0.00 0\n", + " 963 0.00 0.00 0.00 1\n", + " 964 0.00 0.00 0.00 0\n", + " 965 0.00 0.00 0.00 1\n", + " 966 0.00 0.00 0.00 0\n", + " 967 0.00 0.00 0.00 0\n", + " 968 0.00 0.00 0.00 0\n", + " 969 0.00 0.00 0.00 0\n", + " 970 0.00 0.00 0.00 0\n", + " 971 0.80 0.57 0.67 7\n", + " 972 0.00 0.00 0.00 0\n", + " 973 0.00 0.00 0.00 0\n", + " 974 0.00 0.00 0.00 0\n", + " 975 0.00 0.00 0.00 0\n", + " 976 0.00 0.00 0.00 1\n", + " 977 0.00 0.00 0.00 0\n", + " 978 0.00 0.00 0.00 0\n", + " 979 0.00 0.00 0.00 0\n", + " 980 0.00 0.00 0.00 0\n", + " 981 0.00 0.00 0.00 0\n", + " 982 0.00 0.00 0.00 0\n", + " 983 0.00 0.00 0.00 0\n", + " 984 0.00 0.00 0.00 0\n", + " 985 0.00 0.00 0.00 0\n", + " 986 0.00 0.00 0.00 0\n", + "\n", + " micro avg 0.57 0.04 0.08 475\n", + " macro avg 0.01 0.00 0.01 475\n", + "weighted avg 0.12 0.04 0.06 475\n", + " samples avg 0.10 0.07 0.08 475\n", + "\n", + "\n", + "Multi-label Naïve Bayes Classifier:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 152 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 157 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 163 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 173 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 179 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 190 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 196 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 197 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 206 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 210 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 216 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 232 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 252 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 257 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 264 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 273 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 278 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 280 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 286 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 288 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 303 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 308 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 319 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 325 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 329 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 330 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 331 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 334 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 335 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 344 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 347 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 351 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 358 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 369 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 385 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 391 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 405 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 410 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 412 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 414 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 422 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 425 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 432 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 433 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 462 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 463 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 478 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 498 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 499 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 501 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 504 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 509 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 514 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 537 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 538 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 545 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 550 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 551 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 564 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 568 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 574 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 576 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 583 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 594 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 597 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 617 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 652 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 653 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 663 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 693 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 697 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 708 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 712 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 730 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 734 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 762 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 764 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 771 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 777 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 792 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 797 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 798 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 811 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 812 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 823 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 836 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 842 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 847 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 849 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 850 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 851 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 870 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 873 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 876 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 882 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 912 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 923 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 925 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 931 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 956 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 959 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 961 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 976 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.0\n", + " precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 2\n", + " 1 0.00 0.00 0.00 0\n", + " 2 0.00 0.00 0.00 0\n", + " 3 0.00 0.00 0.00 0\n", + " 4 0.00 0.00 0.00 1\n", + " 5 0.00 0.00 0.00 1\n", + " 6 0.00 0.00 0.00 1\n", + " 7 0.00 0.00 0.00 0\n", + " 8 0.00 0.00 0.00 0\n", + " 9 0.00 0.00 0.00 0\n", + " 10 0.00 0.00 0.00 0\n", + " 11 0.00 0.00 0.00 0\n", + " 12 0.00 0.00 0.00 0\n", + " 13 0.00 0.00 0.00 0\n", + " 14 0.00 0.00 0.00 0\n", + " 15 0.00 0.00 0.00 0\n", + " 16 0.00 0.00 0.00 0\n", + " 17 0.00 0.00 0.00 1\n", + " 18 0.00 0.00 0.00 0\n", + " 19 0.00 0.00 0.00 0\n", + " 20 0.00 0.00 0.00 0\n", + " 21 0.00 0.00 0.00 0\n", + " 22 0.00 0.00 0.00 0\n", + " 23 0.00 0.00 0.00 0\n", + " 24 0.00 0.00 0.00 0\n", + " 25 0.00 0.00 0.00 9\n", + " 26 0.00 0.00 0.00 0\n", + " 27 0.00 0.00 0.00 0\n", + " 28 0.00 0.00 0.00 0\n", + " 29 0.00 0.00 0.00 1\n", + " 30 0.00 0.00 0.00 0\n", + " 31 0.00 0.00 0.00 0\n", + " 32 0.00 0.00 0.00 1\n", + " 33 0.00 0.00 0.00 0\n", + " 34 0.00 0.00 0.00 0\n", + " 35 0.00 0.00 0.00 3\n", + " 36 0.00 0.00 0.00 0\n", + " 37 0.00 0.00 0.00 0\n", + " 38 0.00 0.00 0.00 1\n", + " 39 0.00 0.00 0.00 0\n", + " 40 0.00 0.00 0.00 0\n", + " 41 0.00 0.00 0.00 0\n", + " 42 0.00 0.00 0.00 0\n", + " 43 0.00 0.00 0.00 0\n", + " 44 0.00 0.00 0.00 0\n", + " 45 0.00 0.00 0.00 2\n", + " 46 0.00 0.00 0.00 0\n", + " 47 0.00 0.00 0.00 0\n", + " 48 0.00 0.00 0.00 1\n", + " 49 0.00 0.00 0.00 0\n", + " 50 0.00 0.00 0.00 0\n", + " 51 0.00 0.00 0.00 0\n", + " 52 0.00 0.00 0.00 1\n", + " 53 0.00 0.00 0.00 2\n", + " 54 0.00 0.00 0.00 1\n", + " 55 0.00 0.00 0.00 1\n", + " 56 0.00 0.00 0.00 0\n", + " 57 0.00 0.00 0.00 1\n", + " 58 0.00 0.00 0.00 3\n", + " 59 0.00 0.00 0.00 1\n", + " 60 0.00 0.00 0.00 0\n", + " 61 0.00 0.00 0.00 0\n", + " 62 0.00 0.00 0.00 3\n", + " 63 0.00 0.00 0.00 0\n", + " 64 0.00 0.00 0.00 0\n", + " 65 0.00 0.00 0.00 1\n", + " 66 0.00 0.00 0.00 0\n", + " 67 0.00 0.00 0.00 1\n", + " 68 0.00 0.00 0.00 1\n", + " 69 0.00 0.00 0.00 1\n", + " 70 0.00 0.00 0.00 1\n", + " 71 0.00 0.00 0.00 1\n", + " 72 0.00 0.00 0.00 1\n", + " 73 0.00 0.00 0.00 0\n", + " 74 0.00 0.00 0.00 0\n", + " 75 0.00 0.00 0.00 1\n", + " 76 0.00 0.00 0.00 0\n", + " 77 0.00 0.00 0.00 0\n", + " 78 0.00 0.00 0.00 1\n", + " 79 0.00 0.00 0.00 1\n", + " 80 0.00 0.00 0.00 0\n", + " 81 0.00 0.00 0.00 0\n", + " 82 0.00 0.00 0.00 0\n", + " 83 0.00 0.00 0.00 0\n", + " 84 0.00 0.00 0.00 1\n", + " 85 0.00 0.00 0.00 0\n", + " 86 0.00 0.00 0.00 0\n", + " 87 0.00 0.00 0.00 1\n", + " 88 0.00 0.00 0.00 0\n", + " 89 0.00 0.00 0.00 0\n", + " 90 0.00 0.00 0.00 0\n", + " 91 0.00 0.00 0.00 0\n", + " 92 0.00 0.00 0.00 0\n", + " 93 0.00 0.00 0.00 0\n", + " 94 0.00 0.00 0.00 1\n", + " 95 0.00 0.00 0.00 0\n", + " 96 0.00 0.00 0.00 0\n", + " 97 0.00 0.00 0.00 0\n", + " 98 0.00 0.00 0.00 1\n", + " 99 0.00 0.00 0.00 1\n", + " 100 0.00 0.00 0.00 1\n", + " 101 0.00 0.00 0.00 1\n", + " 102 0.00 0.00 0.00 0\n", + " 103 0.00 0.00 0.00 0\n", + " 104 0.00 0.00 0.00 0\n", + " 105 0.00 0.00 0.00 0\n", + " 106 0.00 0.00 0.00 0\n", + " 107 0.00 0.00 0.00 0\n", + " 108 0.00 0.00 0.00 0\n", + " 109 0.00 0.00 0.00 0\n", + " 110 0.00 0.00 0.00 1\n", + " 111 0.00 0.00 0.00 0\n", + " 112 0.00 0.00 0.00 0\n", + " 113 0.00 0.00 0.00 1\n", + " 114 0.00 0.00 0.00 1\n", + " 115 0.00 0.00 0.00 0\n", + " 116 0.00 0.00 0.00 1\n", + " 117 0.00 0.00 0.00 0\n", + " 118 0.00 0.00 0.00 0\n", + " 119 0.00 0.00 0.00 0\n", + " 120 0.00 0.00 0.00 0\n", + " 121 0.00 0.00 0.00 0\n", + " 122 0.00 0.00 0.00 1\n", + " 123 0.00 0.00 0.00 3\n", + " 124 0.00 0.00 0.00 10\n", + " 125 0.00 0.00 0.00 4\n", + " 126 0.00 0.00 0.00 0\n", + " 127 0.00 0.00 0.00 1\n", + " 128 0.00 0.00 0.00 0\n", + " 129 0.00 0.00 0.00 1\n", + " 130 0.00 0.00 0.00 0\n", + " 131 0.00 0.00 0.00 0\n", + " 132 0.00 0.00 0.00 0\n", + " 133 0.00 0.00 0.00 1\n", + " 134 0.00 0.00 0.00 0\n", + " 135 0.00 0.00 0.00 2\n", + " 136 0.00 0.00 0.00 1\n", + " 137 0.00 0.00 0.00 0\n", + " 138 0.00 0.00 0.00 2\n", + " 139 0.00 0.00 0.00 0\n", + " 140 0.00 0.00 0.00 1\n", + " 141 0.00 0.00 0.00 1\n", + " 142 0.00 0.00 0.00 0\n", + " 143 0.00 0.00 0.00 0\n", + " 144 0.00 0.00 0.00 0\n", + " 145 0.00 0.00 0.00 1\n", + " 146 0.00 0.00 0.00 0\n", + " 147 0.00 0.00 0.00 0\n", + " 148 0.00 0.00 0.00 0\n", + " 149 0.00 0.00 0.00 0\n", + " 150 0.00 0.00 0.00 0\n", + " 151 0.00 0.00 0.00 0\n", + " 152 0.00 0.00 0.00 1\n", + " 153 0.00 0.00 0.00 0\n", + " 154 0.00 0.00 0.00 1\n", + " 155 0.00 0.00 0.00 0\n", + " 156 0.00 0.00 0.00 0\n", + " 157 0.00 0.00 0.00 1\n", + " 158 0.00 0.00 0.00 0\n", + " 159 0.00 0.00 0.00 0\n", + " 160 0.00 0.00 0.00 0\n", + " 161 0.00 0.00 0.00 0\n", + " 162 0.00 0.00 0.00 0\n", + " 163 0.00 0.00 0.00 1\n", + " 164 0.00 0.00 0.00 0\n", + " 165 0.00 0.00 0.00 0\n", + " 166 0.00 0.00 0.00 0\n", + " 167 0.00 0.00 0.00 0\n", + " 168 0.00 0.00 0.00 1\n", + " 169 0.00 0.00 0.00 0\n", + " 170 0.00 0.00 0.00 1\n", + " 171 0.00 0.00 0.00 0\n", + " 172 0.00 0.00 0.00 0\n", + " 173 0.00 0.00 0.00 1\n", + " 174 0.00 0.00 0.00 0\n", + " 175 0.00 0.00 0.00 0\n", + " 176 0.00 0.00 0.00 0\n", + " 177 0.00 0.00 0.00 0\n", + " 178 0.00 0.00 0.00 0\n", + " 179 0.00 0.00 0.00 1\n", + " 180 0.00 0.00 0.00 0\n", + " 181 0.00 0.00 0.00 0\n", + " 182 0.00 0.00 0.00 0\n", + " 183 0.00 0.00 0.00 0\n", + " 184 0.00 0.00 0.00 1\n", + " 185 0.00 0.00 0.00 0\n", + " 186 0.00 0.00 0.00 1\n", + " 187 0.00 0.00 0.00 0\n", + " 188 0.00 0.00 0.00 0\n", + " 189 0.00 0.00 0.00 0\n", + " 190 0.00 0.00 0.00 1\n", + " 191 0.00 0.00 0.00 0\n", + " 192 0.00 0.00 0.00 0\n", + " 193 0.00 0.00 0.00 0\n", + " 194 0.00 0.00 0.00 0\n", + " 195 0.00 0.00 0.00 0\n", + " 196 0.00 0.00 0.00 1\n", + " 197 0.00 0.00 0.00 2\n", + " 198 0.00 0.00 0.00 0\n", + " 199 0.00 0.00 0.00 0\n", + " 200 0.00 0.00 0.00 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0.00 0.00 0.00 0\n", + " 891 0.00 0.00 0.00 0\n", + " 892 0.00 0.00 0.00 0\n", + " 893 0.00 0.00 0.00 0\n", + " 894 0.00 0.00 0.00 0\n", + " 895 0.00 0.00 0.00 0\n", + " 896 0.00 0.00 0.00 0\n", + " 897 0.00 0.00 0.00 1\n", + " 898 0.00 0.00 0.00 0\n", + " 899 0.00 0.00 0.00 0\n", + " 900 0.00 0.00 0.00 0\n", + " 901 0.00 0.00 0.00 4\n", + " 902 0.00 0.00 0.00 0\n", + " 903 0.00 0.00 0.00 0\n", + " 904 0.00 0.00 0.00 0\n", + " 905 0.00 0.00 0.00 0\n", + " 906 0.00 0.00 0.00 0\n", + " 907 0.00 0.00 0.00 0\n", + " 908 0.00 0.00 0.00 0\n", + " 909 0.00 0.00 0.00 0\n", + " 910 0.00 0.00 0.00 0\n", + " 911 0.00 0.00 0.00 0\n", + " 912 0.00 0.00 0.00 1\n", + " 913 0.00 0.00 0.00 0\n", + " 914 0.00 0.00 0.00 0\n", + " 915 0.00 0.00 0.00 0\n", + " 916 0.00 0.00 0.00 0\n", + " 917 0.00 0.00 0.00 0\n", + " 918 0.00 0.00 0.00 0\n", + " 919 0.00 0.00 0.00 0\n", + " 920 0.00 0.00 0.00 0\n", + " 921 0.00 0.00 0.00 0\n", + " 922 0.00 0.00 0.00 0\n", + " 923 0.00 0.00 0.00 1\n", + " 924 0.00 0.00 0.00 1\n", + " 925 0.00 0.00 0.00 1\n", + " 926 0.00 0.00 0.00 0\n", + " 927 0.00 0.00 0.00 0\n", + " 928 0.00 0.00 0.00 1\n", + " 929 0.00 0.00 0.00 1\n", + " 930 0.00 0.00 0.00 0\n", + " 931 0.00 0.00 0.00 1\n", + " 932 0.00 0.00 0.00 0\n", + " 933 0.00 0.00 0.00 0\n", + " 934 0.00 0.00 0.00 0\n", + " 935 0.00 0.00 0.00 0\n", + " 936 0.00 0.00 0.00 1\n", + " 937 0.00 0.00 0.00 0\n", + " 938 0.00 0.00 0.00 0\n", + " 939 0.00 0.00 0.00 0\n", + " 940 0.00 0.00 0.00 0\n", + " 941 0.00 0.00 0.00 0\n", + " 942 0.00 0.00 0.00 0\n", + " 943 0.00 0.00 0.00 0\n", + " 944 0.00 0.00 0.00 1\n", + " 945 0.00 0.00 0.00 1\n", + " 946 0.00 0.00 0.00 0\n", + " 947 0.00 0.00 0.00 1\n", + " 948 0.00 0.00 0.00 1\n", + " 949 0.00 0.00 0.00 0\n", + " 950 0.00 0.00 0.00 0\n", + " 951 0.00 0.00 0.00 0\n", + " 952 0.00 0.00 0.00 0\n", + " 953 0.00 0.00 0.00 0\n", + " 954 0.00 0.00 0.00 0\n", + " 955 0.00 0.00 0.00 0\n", + " 956 0.00 0.00 0.00 1\n", + " 957 0.00 0.00 0.00 1\n", + " 958 0.00 0.00 0.00 0\n", + " 959 0.00 0.00 0.00 1\n", + " 960 0.00 0.00 0.00 0\n", + " 961 0.00 0.00 0.00 1\n", + " 962 0.00 0.00 0.00 0\n", + " 963 0.00 0.00 0.00 1\n", + " 964 0.00 0.00 0.00 0\n", + " 965 0.00 0.00 0.00 1\n", + " 966 0.00 0.00 0.00 0\n", + " 967 0.00 0.00 0.00 0\n", + " 968 0.00 0.00 0.00 0\n", + " 969 0.00 0.00 0.00 0\n", + " 970 0.00 0.00 0.00 0\n", + " 971 0.00 0.00 0.00 7\n", + " 972 0.00 0.00 0.00 0\n", + " 973 0.00 0.00 0.00 0\n", + " 974 0.00 0.00 0.00 0\n", + " 975 0.00 0.00 0.00 0\n", + " 976 0.00 0.00 0.00 1\n", + " 977 0.00 0.00 0.00 0\n", + " 978 0.00 0.00 0.00 0\n", + " 979 0.00 0.00 0.00 0\n", + " 980 0.00 0.00 0.00 0\n", + " 981 0.00 0.00 0.00 0\n", + " 982 0.00 0.00 0.00 0\n", + " 983 0.00 0.00 0.00 0\n", + " 984 0.00 0.00 0.00 0\n", + " 985 0.00 0.00 0.00 0\n", + " 986 0.00 0.00 0.00 0\n", + "\n", + " micro avg 0.00 0.00 0.00 475\n", + " macro avg 0.00 0.00 0.00 475\n", + "weighted avg 0.00 0.00 0.00 475\n", + " samples avg 0.00 0.00 0.00 475\n", + "\n", + "\n", + "Multi-label 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/training.py:183: UserWarning: [16:08:12] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:13] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 4 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:13] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:14] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 29 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:14] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 32 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:14] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 38 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:14] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 48 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:14] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 52 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:14] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:15] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 59 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:15] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 65 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:15] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 68 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:15] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 70 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 71 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 72 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:15] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 79 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:15] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 87 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:15] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:16] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 98 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 99 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:16] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 101 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:16] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 110 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 113 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 114 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 116 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 127 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 129 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 133 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 136 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:17] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 140 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 141 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:18] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 152 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:18] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 157 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:18] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 163 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:18] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:19] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 173 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:19] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 179 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:19] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 190 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:19] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 196 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 197 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:19] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 206 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:19] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:20] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 210 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:20] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 216 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:20] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:21] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 232 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:21] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 252 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:21] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 257 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:21] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:22] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 264 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:22] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 273 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:22] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:23] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 278 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:23] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 280 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:23] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 286 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:23] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 288 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:23] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:24] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 303 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:24] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 308 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:24] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 319 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:24] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 325 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:25] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 329 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 330 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 331 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:25] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 334 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 335 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:25] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 344 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:25] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 347 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:25] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 351 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:25] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:26] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 358 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:26] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 369 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:26] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:27] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 385 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:27] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 391 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:27] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 405 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:27] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 410 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:27] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 412 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:28] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 414 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:28] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 422 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:28] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 425 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:28] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 432 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 433 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:28] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:29] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 462 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 463 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:29] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:30] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 478 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:30] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:31] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 498 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 499 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:31] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 501 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:31] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 504 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:31] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 509 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:31] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 514 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:31] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:32] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 537 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 538 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:32] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 545 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:32] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 550 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 551 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:32] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:33] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 564 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:33] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 568 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:33] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 574 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:33] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 576 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:33] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 583 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:34] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 594 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:34] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 597 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:34] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:35] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 617 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:35] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:36] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 652 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 653 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:36] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 663 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:36] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:37] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:38] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 693 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:38] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 697 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 708 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 712 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:39] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:40] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 730 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:40] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 734 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:40] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:41] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 762 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:41] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 764 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:41] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:42] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 771 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:42] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 777 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:42] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:43] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 792 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:43] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 797 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 798 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:43] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 811 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 812 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:43] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:44] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:45] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 823 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:45] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 836 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:45] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 842 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:45] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:46] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 847 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:46] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 849 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 850 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 851 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:46] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 870 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:46] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 873 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:46] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 876 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:47] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 882 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:47] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:48] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 912 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:48] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:49] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 923 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:49] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 925 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:49] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 931 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:49] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:50] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 956 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:50] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 959 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:50] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 961 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:50] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:51] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 976 is present in all training examples.\n", + " warnings.warn(\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [16:08:51] WARNING: /workspace/src/learner.cc:738: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " bst.update(dtrain, iteration=i, fobj=obj)\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.05\n", + " precision recall f1-score support\n", + "\n", + " 0 0.00 0.00 0.00 2\n", + " 1 0.00 0.00 0.00 0\n", + " 2 0.00 0.00 0.00 0\n", + " 3 0.00 0.00 0.00 0\n", + " 4 0.00 0.00 0.00 1\n", + " 5 0.00 0.00 0.00 1\n", + " 6 0.00 0.00 0.00 1\n", + " 7 0.00 0.00 0.00 0\n", + " 8 0.00 0.00 0.00 0\n", + " 9 0.00 0.00 0.00 0\n", + " 10 0.00 0.00 0.00 0\n", + " 11 0.00 0.00 0.00 0\n", + " 12 0.00 0.00 0.00 0\n", + " 13 0.00 0.00 0.00 0\n", + " 14 0.00 0.00 0.00 0\n", + " 15 0.00 0.00 0.00 0\n", + " 16 0.00 0.00 0.00 0\n", + " 17 0.00 0.00 0.00 1\n", + " 18 0.00 0.00 0.00 0\n", + " 19 0.00 0.00 0.00 0\n", + " 20 0.00 0.00 0.00 0\n", + " 21 0.00 0.00 0.00 0\n", + " 22 0.00 0.00 0.00 0\n", + " 23 0.00 0.00 0.00 0\n", + " 24 0.00 0.00 0.00 0\n", + " 25 0.88 0.78 0.82 9\n", + " 26 0.00 0.00 0.00 0\n", + " 27 0.00 0.00 0.00 0\n", + " 28 0.00 0.00 0.00 0\n", + " 29 0.00 0.00 0.00 1\n", + " 30 0.00 0.00 0.00 0\n", + " 31 0.00 0.00 0.00 0\n", + " 32 0.00 0.00 0.00 1\n", + " 33 0.00 0.00 0.00 0\n", + " 34 0.00 0.00 0.00 0\n", + " 35 0.00 0.00 0.00 3\n", + " 36 0.00 0.00 0.00 0\n", + " 37 0.00 0.00 0.00 0\n", + " 38 0.00 0.00 0.00 1\n", + " 39 0.00 0.00 0.00 0\n", + " 40 0.00 0.00 0.00 0\n", + " 41 0.00 0.00 0.00 0\n", + " 42 0.00 0.00 0.00 0\n", + " 43 0.00 0.00 0.00 0\n", + " 44 0.00 0.00 0.00 0\n", + " 45 0.00 0.00 0.00 2\n", + " 46 0.00 0.00 0.00 0\n", + " 47 0.00 0.00 0.00 0\n", + " 48 0.00 0.00 0.00 1\n", + " 49 0.00 0.00 0.00 0\n", + " 50 0.00 0.00 0.00 0\n", + " 51 0.00 0.00 0.00 0\n", + " 52 0.00 0.00 0.00 1\n", + " 53 0.50 0.50 0.50 2\n", + " 54 0.00 0.00 0.00 1\n", + " 55 0.00 0.00 0.00 1\n", + " 56 0.00 0.00 0.00 0\n", + " 57 0.00 0.00 0.00 1\n", + " 58 0.00 0.00 0.00 3\n", + " 59 0.00 0.00 0.00 1\n", + " 60 0.00 0.00 0.00 0\n", + " 61 0.00 0.00 0.00 0\n", + " 62 0.00 0.00 0.00 3\n", + " 63 0.00 0.00 0.00 0\n", + " 64 0.00 0.00 0.00 0\n", + " 65 0.00 0.00 0.00 1\n", + " 66 0.00 0.00 0.00 0\n", + " 67 0.00 0.00 0.00 1\n", + " 68 0.00 0.00 0.00 1\n", + " 69 0.00 0.00 0.00 1\n", + " 70 0.00 0.00 0.00 1\n", + " 71 0.00 0.00 0.00 1\n", + " 72 0.00 0.00 0.00 1\n", + " 73 0.00 0.00 0.00 0\n", + " 74 0.00 0.00 0.00 0\n", + " 75 0.00 0.00 0.00 1\n", + " 76 0.00 0.00 0.00 0\n", + " 77 0.00 0.00 0.00 0\n", + " 78 0.00 0.00 0.00 1\n", + " 79 0.00 0.00 0.00 1\n", + " 80 0.00 0.00 0.00 0\n", + " 81 0.00 0.00 0.00 0\n", + " 82 0.00 0.00 0.00 0\n", + " 83 0.00 0.00 0.00 0\n", + " 84 0.00 0.00 0.00 1\n", + " 85 0.00 0.00 0.00 0\n", + " 86 0.00 0.00 0.00 0\n", + " 87 0.00 0.00 0.00 1\n", + " 88 0.00 0.00 0.00 0\n", + " 89 0.00 0.00 0.00 0\n", + " 90 0.00 0.00 0.00 0\n", + " 91 0.00 0.00 0.00 0\n", + " 92 0.00 0.00 0.00 0\n", + " 93 0.00 0.00 0.00 0\n", + " 94 0.00 0.00 0.00 1\n", + " 95 0.00 0.00 0.00 0\n", + " 96 0.00 0.00 0.00 0\n", + " 97 0.00 0.00 0.00 0\n", + " 98 0.00 0.00 0.00 1\n", + " 99 0.00 0.00 0.00 1\n", + " 100 0.00 0.00 0.00 1\n", + " 101 0.00 0.00 0.00 1\n", + " 102 0.00 0.00 0.00 0\n", + " 103 0.00 0.00 0.00 0\n", + " 104 0.00 0.00 0.00 0\n", + " 105 0.00 0.00 0.00 0\n", + " 106 0.00 0.00 0.00 0\n", + " 107 0.00 0.00 0.00 0\n", + " 108 0.00 0.00 0.00 0\n", + " 109 0.00 0.00 0.00 0\n", + " 110 0.00 0.00 0.00 1\n", + " 111 0.00 0.00 0.00 0\n", + " 112 0.00 0.00 0.00 0\n", + " 113 0.00 0.00 0.00 1\n", + " 114 0.00 0.00 0.00 1\n", + " 115 0.00 0.00 0.00 0\n", + " 116 0.00 0.00 0.00 1\n", + " 117 0.00 0.00 0.00 0\n", + " 118 0.00 0.00 0.00 0\n", + " 119 0.00 0.00 0.00 0\n", + " 120 0.00 0.00 0.00 0\n", + " 121 0.00 0.00 0.00 0\n", + " 122 0.00 0.00 0.00 1\n", + " 123 0.00 0.00 0.00 3\n", + " 124 0.00 0.00 0.00 10\n", + " 125 0.00 0.00 0.00 4\n", + " 126 0.00 0.00 0.00 0\n", + " 127 0.00 0.00 0.00 1\n", + " 128 0.00 0.00 0.00 0\n", + " 129 0.00 0.00 0.00 1\n", + " 130 0.00 0.00 0.00 0\n", + " 131 0.00 0.00 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1\n", + " 925 0.00 0.00 0.00 1\n", + " 926 0.00 0.00 0.00 0\n", + " 927 0.00 0.00 0.00 0\n", + " 928 0.00 0.00 0.00 1\n", + " 929 0.00 0.00 0.00 1\n", + " 930 0.00 0.00 0.00 0\n", + " 931 0.00 0.00 0.00 1\n", + " 932 0.00 0.00 0.00 0\n", + " 933 0.00 0.00 0.00 0\n", + " 934 0.00 0.00 0.00 0\n", + " 935 0.00 0.00 0.00 0\n", + " 936 0.00 0.00 0.00 1\n", + " 937 0.00 0.00 0.00 0\n", + " 938 0.00 0.00 0.00 0\n", + " 939 0.00 0.00 0.00 0\n", + " 940 0.00 0.00 0.00 0\n", + " 941 0.00 0.00 0.00 0\n", + " 942 0.00 0.00 0.00 0\n", + " 943 0.00 0.00 0.00 0\n", + " 944 0.00 0.00 0.00 1\n", + " 945 0.00 0.00 0.00 1\n", + " 946 0.00 0.00 0.00 0\n", + " 947 0.00 0.00 0.00 1\n", + " 948 0.00 0.00 0.00 1\n", + " 949 0.00 0.00 0.00 0\n", + " 950 0.00 0.00 0.00 0\n", + " 951 0.00 0.00 0.00 0\n", + " 952 0.00 0.00 0.00 0\n", + " 953 0.00 0.00 0.00 0\n", + " 954 0.00 0.00 0.00 0\n", + " 955 0.00 0.00 0.00 0\n", + " 956 0.00 0.00 0.00 1\n", + " 957 0.00 0.00 0.00 1\n", + " 958 0.00 0.00 0.00 0\n", + " 959 0.00 0.00 0.00 1\n", + " 960 0.00 0.00 0.00 0\n", + " 961 0.00 0.00 0.00 1\n", + " 962 0.00 0.00 0.00 0\n", + " 963 0.00 0.00 0.00 1\n", + " 964 0.00 0.00 0.00 0\n", + " 965 0.00 0.00 0.00 1\n", + " 966 0.00 0.00 0.00 0\n", + " 967 0.00 0.00 0.00 0\n", + " 968 0.00 0.00 0.00 0\n", + " 969 0.00 0.00 0.00 0\n", + " 970 0.00 0.00 0.00 0\n", + " 971 1.00 0.71 0.83 7\n", + " 972 0.00 0.00 0.00 0\n", + " 973 0.00 0.00 0.00 0\n", + " 974 0.00 0.00 0.00 0\n", + " 975 0.00 0.00 0.00 0\n", + " 976 0.00 0.00 0.00 1\n", + " 977 0.00 0.00 0.00 0\n", + " 978 0.00 0.00 0.00 0\n", + " 979 0.00 0.00 0.00 0\n", + " 980 0.00 0.00 0.00 0\n", + " 981 0.00 0.00 0.00 0\n", + " 982 0.00 0.00 0.00 0\n", + " 983 0.00 0.00 0.00 0\n", + " 984 0.00 0.00 0.00 0\n", + " 985 0.00 0.00 0.00 0\n", + " 986 0.00 0.00 0.00 0\n", + "\n", + " micro avg 0.52 0.05 0.10 475\n", + " macro avg 0.01 0.00 0.00 475\n", + "weighted avg 0.11 0.05 0.07 475\n", + " samples avg 0.12 0.08 0.09 475\n", + "\n", + "\n", + "Multi-label Multi-Layer Perceptron (MLP - Neural Network):\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/darth/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:698: UserWarning: Training interrupted by user.\n", + " warnings.warn(\"Training interrupted by user.\")\n" + ] + } + ], + "source": [ + "from multi_label_models import *\n", + "\n", + "# print(\"Multi-label Logistic Regression:\")\n", + "# report, acc = multilabel_logistic_regression(X, y)\n", + "# print(\"Accuracy:\", acc)\n", + "# print(report)\n", + "\n", + "# print(\"\\nMulti-label Decision Tree Classifier:\")\n", + "# report, acc = multilabel_decision_tree(X, y)\n", + "# print(\"Accuracy:\", acc)\n", + "# print(report)\n", + "\n", + "print(\"\\nMulti-label Random Forest Classifier:\")\n", + "report, acc = multilabel_random_forest(X, y)\n", + "print(\"Accuracy:\", acc)\n", + "print(report)\n", + "\n", + "print(\"\\nMulti-label Support Vector Machine (SVM):\")\n", + "report, acc = multilabel_svm(X, y)\n", + "print(\"Accuracy:\", acc)\n", + "print(report)\n", + "\n", + "print(\"\\nMulti-label k-Nearest Neighbors (k-NN):\")\n", + "report, acc = multilabel_knn(X, y, k=5)\n", + "print(\"Accuracy:\", acc)\n", + "print(report)\n", + "\n", + "print(\"\\nMulti-label Naïve Bayes Classifier:\")\n", + "report, acc = multilabel_naive_bayes(X, y)\n", + "print(\"Accuracy:\", acc)\n", + "print(report)\n", + "\n", + "print(\"\\nMulti-label Gradient Boosting (XGBoost):\")\n", + "report, acc = multilabel_xgboost(X, y)\n", + "print(\"Accuracy:\", acc)\n", + "print(report)\n", + "\n", + "print(\"\\nMulti-label Multi-Layer Perceptron (MLP - Neural Network):\")\n", + "report, acc = multilabel_mlp(X, y)\n", + "print(\"Accuracy:\", acc)\n", + "print(report)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a3c528df", + "metadata": {}, + "source": [ + "## Word2Vector Methord" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a14a751e", + "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": null, + "id": "c30741d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "type(X_w2v)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9dd80f7", + "metadata": {}, + "outputs": [], + "source": [ + "X = X_w2v\n", + "y = y_encoded" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62cd91f3", + "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\n", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)\n", + "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", + "\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 \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[1;32m 85\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(X_test)\n", + "\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m classification_report(y_test, y_pred), accuracy_score(y_test, y_pred)\n", + "\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", + "\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", + "\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", + "\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", + "\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": null, + "id": "b2673a6a", + "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": null, + "id": "ddd64978", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['topic', 'question', 'excerpt'], dtype='object')" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TRAIN.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61f1bc47", + "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, + "id": "9a8d3ab8", + "metadata": 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"\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\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)\n", + "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", + "\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", + "\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", + "\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", + "\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", + "\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", + "\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", + "\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", + "\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py:695\u001b[0m, in \u001b[0;36mBertEncoder.forward\u001b[0;34m(self, hidden_states, 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 \u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n", + "\u001b[1;32m 699\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n", + "\u001b[1;32m 700\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n", + "\u001b[1;32m 701\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n", + "\u001b[1;32m 702\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n", + "\u001b[1;32m 703\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[1;32m 705\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n", + "\u001b[1;32m 706\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n", + "\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", + "\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", + "\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 \u001b[38;5;241m=\u001b[39m (layer_output,) \u001b[38;5;241m+\u001b[39m outputs\n", + "\u001b[1;32m 632\u001b[0m \u001b[38;5;66;03m# if decoder, return the attn key/values as the last output\u001b[39;00m\n", + "\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/transformers/pytorch_utils.py:261\u001b[0m, in \u001b[0;36mapply_chunking_to_forward\u001b[0;34m(forward_fn, chunk_size, chunk_dim, *input_tensors)\u001b[0m\n", + "\u001b[1;32m 258\u001b[0m \u001b[38;5;66;03m# concatenate output at same dimension\u001b[39;00m\n", + "\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mcat(output_chunks, dim\u001b[38;5;241m=\u001b[39mchunk_dim)\n", + "\u001b[0;32m--> 261\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minput_tensors\u001b[49m\u001b[43m)\u001b[49m\n", + "\n", + "File \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", + "\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", + "\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", + "\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", + "\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", + "\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", + "\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", + "\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", + "id": "7eaad60e", + "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": null, + "id": "ab51caf3", + "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": null, + "id": "68c3d4d8", + "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": null, + "id": "3a48cd39", + "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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df['glove_embeddings']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e13b8017", + "metadata": {}, + "outputs": [], + "source": [ + "X = df['glove_embeddings']\n", + "y = label_encoding(df['topic']) \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3777339", + "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\n", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'exp'\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)\n", + "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", + "\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", + "\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", + "\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", + "\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": null, + "id": "8e28e166", + "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, + "id": "f5ff968b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25ee5594", + "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": null, + "id": "31792b25", + "metadata": {}, + "outputs": [], + "source": [ + "X = df['glove_sigmoid']\n", + "y = label_encoding(df['topic'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f6de067", + "metadata": {}, + "outputs": [], + "source": [ + "from models import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ac172a1", + "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\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)\n", + "\u001b[0;31mTypeError\u001b[0m: only length-1 arrays can be converted to Python scalars\n", + "\n", + "The above exception was the direct cause of the following exception:\n", + "\n", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)\n", + "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", + "\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", + "\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", + "\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", + "\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", + "\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", + "\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 \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[1;32m 938\u001b[0m n_features \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n", + "\u001b[1;32m 940\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\n", + "File \u001b[0;32m~/.pyenv/versions/3.10.12/envs/major02/lib/python3.10/site-packages/sklearn/utils/validation.py:2944\u001b[0m, in \u001b[0;36mvalidate_data\u001b[0;34m(_estimator, X, y, reset, validate_separately, skip_check_array, **check_params)\u001b[0m\n", + "\u001b[1;32m 2942\u001b[0m out \u001b[38;5;241m=\u001b[39m X, y\n", + "\u001b[1;32m 2943\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n", + "\u001b[0;32m-> 2944\u001b[0m out \u001b[38;5;241m=\u001b[39m \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", + "\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", + "\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", + "\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", + "\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": null, + "id": "3f65fd58", + "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) 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packages: namex, libclang, flatbuffers, wheel, werkzeug, termcolor, tensorflow-io-gcs-filesystem, tensorboard-data-server, protobuf, optree, opt-einsum, ml-dtypes, mdurl, markdown, h5py, grpcio, google-pasta, gast, absl-py, tensorboard, markdown-it-py, astunparse, rich, keras, tensorflow\n", + "Successfully installed absl-py-2.1.0 astunparse-1.6.3 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": null, + "id": "ad5d6ef7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20219, 300)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28931f84", + "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", + "id": "2a1b1110", + "metadata": {}, + "source": [ + "# Albert Embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44ca9364", + "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": null, + "id": "7cad495a", + "metadata": {}, + "outputs": [], + "source": [ + "X = embeddings\n", + "y = label_encoding(df['topic'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ca547e4", + "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, + "id": "1001ac9b", + "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": 5 +}