cuda_sintetic_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8dddb3fe-fc2a-4b84-8c77-0339c4f542a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# importando as bibliotecas necessárias\n",
    "import pandas as pd # manipulação de datasets\n",
    "import numpy as np  # operações matemáticas \n",
    "\n",
    "# sklearn é uma biblioteca de data science e machine learning\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn import svm\n",
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "# biblioteca para visualização\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22de5cf1-04a1-475d-a062-f2ff3706943b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_lista = []\n",
    "tests = [100, 1000, 10000, 100000, 1000000]\n",
    "\n",
    "for i, test in enumerate(tests):\n",
    "    X, y = make_classification(n_samples = 1000,\n",
    "                               n_features = test,\n",
    "                               n_informative = 20,\n",
    "                               random_state=42,\n",
    "                               shuffle=False\n",
    "                               )\n",
    "    X = pd.DataFrame(X, columns=range(len(X[0])))\n",
    "    X = X[shuffle(list(X.columns), random_state=42)]\n",
    "    X_lista.append(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aaf8c251-cde4-4d80-bcfa-a6c4fb1a0d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 499, number of negative: 501\n",
      "[LightGBM] [Info] This is the GPU trainer!!\n",
      "[LightGBM] [Info] Total Bins 6300\n",
      "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 100\n",
      "[LightGBM] [Info] Using GPU Device: TITAN Xp, Vendor: NVIDIA Corporation\n",
      "[LightGBM] [Info] Compiling OpenCL Kernel with 64 bins...\n",
      "[LightGBM] [Info] GPU programs have been built\n",
      "[LightGBM] [Info] Size of histogram bin entry: 8\n",
      "[LightGBM] [Info] 100 dense feature groups (0.10 MB) transferred to GPU in 0.034332 secs. 0 sparse feature groups\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499000 -> initscore=-0.004000\n",
      "[LightGBM] [Info] Start training from score -0.004000\n",
      "Tempo de execução: 0.9456939697265625 segundos\n",
      "[LightGBM] [Info] Number of positive: 499, number of negative: 501\n",
      "[LightGBM] [Info] This is the GPU trainer!!\n",
      "[LightGBM] [Info] Total Bins 63000\n",
      "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 1000\n",
      "[LightGBM] [Info] Using GPU Device: TITAN Xp, Vendor: NVIDIA Corporation\n",
      "[LightGBM] [Info] Compiling OpenCL Kernel with 64 bins...\n",
      "[LightGBM] [Info] GPU programs have been built\n",
      "[LightGBM] [Info] Size of histogram bin entry: 8\n",
      "[LightGBM] [Info] 1000 dense feature groups (0.95 MB) transferred to GPU in 0.010045 secs. 0 sparse feature groups\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499000 -> initscore=-0.004000\n",
      "[LightGBM] [Info] Start training from score -0.004000\n",
      "Tempo de execução: 1.72792387008667 segundos\n",
      "[LightGBM] [Info] Number of positive: 499, number of negative: 501\n",
      "[LightGBM] [Info] This is the GPU trainer!!\n",
      "[LightGBM] [Info] Total Bins 630000\n",
      "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 10000\n",
      "[LightGBM] [Info] Using GPU Device: TITAN Xp, Vendor: NVIDIA Corporation\n",
      "[LightGBM] [Info] Compiling OpenCL Kernel with 64 bins...\n",
      "[LightGBM] [Info] GPU programs have been built\n",
      "[LightGBM] [Info] Size of histogram bin entry: 8\n",
      "[LightGBM] [Info] 10000 dense feature groups (9.54 MB) transferred to GPU in 0.059974 secs. 0 sparse feature groups\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499000 -> initscore=-0.004000\n",
      "[LightGBM] [Info] Start training from score -0.004000\n",
      "[LightGBM] [Info] Increasing preallocd_max_num_wg_ to 2500 for launching more workgroups\n",
      "Tempo de execução: 11.672232151031494 segundos\n",
      "[LightGBM] [Info] Number of positive: 499, number of negative: 501\n",
      "[LightGBM] [Info] This is the GPU trainer!!\n",
      "[LightGBM] [Info] Total Bins 6300000\n",
      "[LightGBM] [Info] Number of data points in the train set: 1000, number of used features: 100000\n",
      "[LightGBM] [Info] Using GPU Device: TITAN Xp, Vendor: NVIDIA Corporation\n",
      "[LightGBM] [Info] Compiling OpenCL Kernel with 64 bins...\n",
      "[LightGBM] [Info] GPU programs have been built\n",
      "[LightGBM] [Info] Size of histogram bin entry: 8\n",
      "[LightGBM] [Info] 100000 dense feature groups (95.37 MB) transferred to GPU in 0.391363 secs. 0 sparse feature groups\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499000 -> initscore=-0.004000\n",
      "[LightGBM] [Info] Start training from score -0.004000\n",
      "[LightGBM] [Info] Increasing preallocd_max_num_wg_ to 25000 for launching more workgroups\n"
     ]
    },
    {
     "ename": "LightGBMError",
     "evalue": "Invalid Kernel Arguments",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLightGBMError\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [24], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m      4\u001b[0m clf_lgbm \u001b[38;5;241m=\u001b[39m lgb\u001b[38;5;241m.\u001b[39mLGBMClassifier(device_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgpu\u001b[39m\u001b[38;5;124m'\u001b[39m, max_bin \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m63\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mclf_lgbm\u001b[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[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m classifiers\u001b[38;5;241m.\u001b[39mappend(clf_lgbm)\n\u001b[1;32m      7\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/lightgbm/sklearn.py:1142\u001b[0m, in \u001b[0;36mLGBMClassifier.fit\u001b[0;34m(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, feature_name, categorical_feature, callbacks, init_model)\u001b[0m\n\u001b[1;32m   1139\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1140\u001b[0m             valid_sets\u001b[38;5;241m.\u001b[39mappend((valid_x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_le\u001b[38;5;241m.\u001b[39mtransform(valid_y)))\n\u001b[0;32m-> 1142\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1143\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1144\u001b[0m \u001b[43m    \u001b[49m\u001b[43m_y\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1145\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1146\u001b[0m \u001b[43m    \u001b[49m\u001b[43minit_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minit_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1147\u001b[0m \u001b[43m    \u001b[49m\u001b[43meval_set\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalid_sets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1148\u001b[0m \u001b[43m    \u001b[49m\u001b[43meval_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1149\u001b[0m \u001b[43m    \u001b[49m\u001b[43meval_sample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_sample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1150\u001b[0m \u001b[43m    \u001b[49m\u001b[43meval_class_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_class_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1151\u001b[0m \u001b[43m    \u001b[49m\u001b[43meval_init_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_init_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1152\u001b[0m \u001b[43m    \u001b[49m\u001b[43meval_metric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_metric\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1153\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeature_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1154\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcategorical_feature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcategorical_feature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1156\u001b[0m \u001b[43m    \u001b[49m\u001b[43minit_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minit_model\u001b[49m\n\u001b[1;32m   1157\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/lightgbm/sklearn.py:842\u001b[0m, in \u001b[0;36mLGBMModel.fit\u001b[0;34m(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, feature_name, categorical_feature, callbacks, init_model)\u001b[0m\n\u001b[1;32m    839\u001b[0m evals_result: _EvalResultDict \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m    840\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mappend(record_evaluation(evals_result))\n\u001b[0;32m--> 842\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_Booster \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    843\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    844\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_set\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_set\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    845\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_boost_round\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_estimators\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    846\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalid_sets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalid_sets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    847\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalid_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    848\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_metrics_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    849\u001b[0m \u001b[43m    \u001b[49m\u001b[43minit_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minit_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    850\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeature_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfeature_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    851\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\n\u001b[1;32m    852\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    854\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_evals_result \u001b[38;5;241m=\u001b[39m evals_result\n\u001b[1;32m    855\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_best_iteration \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_Booster\u001b[38;5;241m.\u001b[39mbest_iteration\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/lightgbm/engine.py:266\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, feature_name, categorical_feature, keep_training_booster, callbacks)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cb \u001b[38;5;129;01min\u001b[39;00m callbacks_before_iter:\n\u001b[1;32m    259\u001b[0m     cb(callback\u001b[38;5;241m.\u001b[39mCallbackEnv(model\u001b[38;5;241m=\u001b[39mbooster,\n\u001b[1;32m    260\u001b[0m                             params\u001b[38;5;241m=\u001b[39mparams,\n\u001b[1;32m    261\u001b[0m                             iteration\u001b[38;5;241m=\u001b[39mi,\n\u001b[1;32m    262\u001b[0m                             begin_iteration\u001b[38;5;241m=\u001b[39minit_iteration,\n\u001b[1;32m    263\u001b[0m                             end_iteration\u001b[38;5;241m=\u001b[39minit_iteration \u001b[38;5;241m+\u001b[39m num_boost_round,\n\u001b[1;32m    264\u001b[0m                             evaluation_result_list\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m--> 266\u001b[0m \u001b[43mbooster\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    268\u001b[0m evaluation_result_list: List[_LGBM_BoosterEvalMethodResultType] \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    269\u001b[0m \u001b[38;5;66;03m# check evaluation result.\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/lightgbm/basic.py:3557\u001b[0m, in \u001b[0;36mBooster.update\u001b[0;34m(self, train_set, fobj)\u001b[0m\n\u001b[1;32m   3555\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__set_objective_to_none:\n\u001b[1;32m   3556\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LightGBMError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCannot update due to null objective function.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 3557\u001b[0m \u001b[43m_safe_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_LIB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLGBM_BoosterUpdateOneIter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3558\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3559\u001b[0m \u001b[43m    \u001b[49m\u001b[43mctypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbyref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mis_finished\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3560\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__is_predicted_cur_iter \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__num_dataset)]\n\u001b[1;32m   3561\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m is_finished\u001b[38;5;241m.\u001b[39mvalue \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/lightgbm/basic.py:237\u001b[0m, in \u001b[0;36m_safe_call\u001b[0;34m(ret)\u001b[0m\n\u001b[1;32m    229\u001b[0m \u001b[38;5;124;03m\"\"\"Check the return value from C API call.\u001b[39;00m\n\u001b[1;32m    230\u001b[0m \n\u001b[1;32m    231\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;124;03m    The return value from C API calls.\u001b[39;00m\n\u001b[1;32m    235\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 237\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LightGBMError(_LIB\u001b[38;5;241m.\u001b[39mLGBM_GetLastError()\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
      "\u001b[0;31mLightGBMError\u001b[0m: Invalid Kernel Arguments"
     ]
    }
   ],
   "source": [
    "classifiers = []\n",
    "for X in X_lista:\n",
    "    start = time.time()\n",
    "    clf_lgbm = lgb.LGBMClassifier(device_type='gpu', max_bin = 63)\n",
    "    clf_lgbm.fit(X,y)\n",
    "    classifiers.append(clf_lgbm)\n",
    "    end = time.time()\n",
    "    print(\"Tempo de execução: \" + str(end-start) + \" segundos\")"
   ]
  }
 ],
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