pid/notebooks/old_notebooks/.ipynb_checkpoints/test_clean-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7c5d059b-ed8a-4e2e-9420-25890f648895",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-07 15:41:54,116] A new study created in memory with name: no-name-15856b4f-0922-494b-bffe-6aa5a8b1a949\n",
      "[I 2024-02-07 15:41:54,414] Trial 0 finished with value: 0.8250111659909584 and parameters: {'learning_rate': 0.10931848666921812, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 1.0874822508422055, 'subsample': 0.22853436663285595, 'colsample_bytree': 0.8800679642371595, 'alpha': 7.857683069294414, 'lambda': 0.9945601089987677}. Best is trial 0 with value: 0.8250111659909584.\n",
      "[I 2024-02-07 15:41:54,454] Trial 1 finished with value: 0.8479754500993045 and parameters: {'learning_rate': 0.12773722311402283, 'max_depth': 11, 'min_child_weight': 8, 'gamma': 5.8300312437557755, 'subsample': 0.9714643342834144, 'colsample_bytree': 0.4492729210955732, 'alpha': 9.472507025810854, 'lambda': 0.5521132045319688}. Best is trial 1 with value: 0.8479754500993045.\n",
      "[I 2024-02-07 15:41:54,523] Trial 2 finished with value: 0.8195601516622714 and parameters: {'learning_rate': 0.1452348237916476, 'max_depth': 15, 'min_child_weight': 8, 'gamma': 7.52123455498376, 'subsample': 0.3782889399284397, 'colsample_bytree': 0.4958388827499578, 'alpha': 8.06060978486498, 'lambda': 5.104988592949954}. Best is trial 1 with value: 0.8479754500993045.\n",
      "[I 2024-02-07 15:41:54,569] Trial 3 finished with value: 0.8250111659909584 and parameters: {'learning_rate': 0.13235329333426052, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 9.20511310522702, 'subsample': 0.5977091599719062, 'colsample_bytree': 0.9126286580883513, 'alpha': 4.880822146557326, 'lambda': 1.3097213551979836}. Best is trial 1 with value: 0.8479754500993045.\n",
      "[I 2024-02-07 15:41:54,637] Trial 4 finished with value: 0.8250111659909584 and parameters: {'learning_rate': 0.03664710178746885, 'max_depth': 11, 'min_child_weight': 6, 'gamma': 2.8631643549193053, 'subsample': 0.1492478764474232, 'colsample_bytree': 0.9415055728816055, 'alpha': 4.826022456836898, 'lambda': 6.423191826601227}. Best is trial 1 with value: 0.8479754500993045.\n",
      "[I 2024-02-07 15:41:54,726] Trial 5 finished with value: 0.8250111659909584 and parameters: {'learning_rate': 0.04879914932030434, 'max_depth': 10, 'min_child_weight': 5, 'gamma': 6.4205120528729225, 'subsample': 0.5565818947709297, 'colsample_bytree': 0.6534740157234821, 'alpha': 3.059416836349013, 'lambda': 5.56888958121185}. Best is trial 1 with value: 0.8479754500993045.\n",
      "[I 2024-02-07 15:41:54,815] Trial 6 finished with value: 0.8403074413182223 and parameters: {'learning_rate': 0.12613186327880382, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 5.837919855385595, 'subsample': 0.7630436569057912, 'colsample_bytree': 0.09964588156452996, 'alpha': 6.95028403318458, 'lambda': 6.338304825233139}. Best is trial 1 with value: 0.8479754500993045.\n",
      "[I 2024-02-07 15:41:54,880] Trial 7 finished with value: 0.8872382135913467 and parameters: {'learning_rate': 0.15264665680789755, 'max_depth': 9, 'min_child_weight': 7, 'gamma': 0.23987730799109097, 'subsample': 0.878300073432315, 'colsample_bytree': 0.1531268925513012, 'alpha': 3.594849729428682, 'lambda': 7.569881741503836}. Best is trial 7 with value: 0.8872382135913467.\n",
      "[I 2024-02-07 15:41:54,963] Trial 8 finished with value: 0.8837073749524114 and parameters: {'learning_rate': 0.03452118070835249, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 0.482833641777648, 'subsample': 0.6227266999377797, 'colsample_bytree': 0.345284612318643, 'alpha': 3.9761335607397195, 'lambda': 7.010529796311969}. Best is trial 7 with value: 0.8872382135913467.\n",
      "[I 2024-02-07 15:41:55,024] Trial 9 finished with value: 0.8250111659909584 and parameters: {'learning_rate': 0.08951754327216684, 'max_depth': 15, 'min_child_weight': 7, 'gamma': 4.399389962180205, 'subsample': 0.26680591840642465, 'colsample_bytree': 0.9308412581024057, 'alpha': 5.647650163098489, 'lambda': 2.6458404406561433}. Best is trial 7 with value: 0.8872382135913467.\n",
      "[I 2024-02-07 15:41:55,234] Trial 10 finished with value: 0.8403074413182223 and parameters: {'learning_rate': 0.1994577158239775, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 2.8216823771224457, 'subsample': 0.9998736161716308, 'colsample_bytree': 0.013669607764711794, 'alpha': 0.968423334718608, 'lambda': 9.639422057962069}. Best is trial 7 with value: 0.8872382135913467.\n",
      "[I 2024-02-07 15:41:55,487] Trial 11 finished with value: 0.8790725936012187 and parameters: {'learning_rate': 0.17566162458960233, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 0.10538788052368281, 'subsample': 0.7722546111754938, 'colsample_bytree': 0.2719528641575852, 'alpha': 2.5536835789749075, 'lambda': 8.650647165823552}. Best is trial 7 with value: 0.8872382135913467.\n",
      "[I 2024-02-07 15:41:55,822] Trial 12 finished with value: 0.8837073749524114 and parameters: {'learning_rate': 0.07277674475433626, 'max_depth': 13, 'min_child_weight': 3, 'gamma': 1.7412555578016482, 'subsample': 0.7709641976971812, 'colsample_bytree': 0.28099972781802873, 'alpha': 3.204131591260179, 'lambda': 7.839791172699534}. Best is trial 7 with value: 0.8872382135913467.\n",
      "[I 2024-02-07 15:41:56,130] Trial 13 finished with value: 0.9068580161590464 and parameters: {'learning_rate': 0.023247361776747598, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0.45600920677991513, 'subsample': 0.6713831105393014, 'colsample_bytree': 0.266849945140222, 'alpha': 0.7678999274009279, 'lambda': 3.5903703678967576}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:56,266] Trial 14 finished with value: 0.0 and parameters: {'learning_rate': 0.010190234549835869, 'max_depth': 8, 'min_child_weight': 5, 'gamma': 2.8620059981203214, 'subsample': 0.011740063519041721, 'colsample_bytree': 0.14138664548281876, 'alpha': 0.24653587041766922, 'lambda': 3.6150370904140154}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:56,500] Trial 15 finished with value: 0.8996983865364487 and parameters: {'learning_rate': 0.17734603601714133, 'max_depth': 5, 'min_child_weight': 4, 'gamma': 1.6454721235284642, 'subsample': 0.8633779969607012, 'colsample_bytree': 0.6625681554758988, 'alpha': 1.7211034682151594, 'lambda': 3.8715671515474197}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:56,577] Trial 16 finished with value: 0.8548231158680646 and parameters: {'learning_rate': 0.17245890007673156, 'max_depth': 5, 'min_child_weight': 3, 'gamma': 3.9841825463424887, 'subsample': 0.6618178521295225, 'colsample_bytree': 0.684011969650861, 'alpha': 1.7264821323717316, 'lambda': 3.3813068825815464}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:56,664] Trial 17 finished with value: 0.8872382135913467 and parameters: {'learning_rate': 0.07757672364138546, 'max_depth': 6, 'min_child_weight': 4, 'gamma': 1.851154579461829, 'subsample': 0.42231201807506147, 'colsample_bytree': 0.6514312247087053, 'alpha': 1.6787956398750663, 'lambda': 3.9552311340835438}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:56,881] Trial 18 finished with value: 0.8934362587872203 and parameters: {'learning_rate': 0.05963132189221232, 'max_depth': 7, 'min_child_weight': 2, 'gamma': 1.9619581936969845, 'subsample': 0.8336603308786524, 'colsample_bytree': 0.718457725717861, 'alpha': 0.009284217272603756, 'lambda': 1.92850514484592}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:57,102] Trial 19 finished with value: 0.8567676005664036 and parameters: {'learning_rate': 0.014665948251630243, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 3.605326926934277, 'subsample': 0.6804469690919507, 'colsample_bytree': 0.4015869314536434, 'alpha': 1.4391761154718359, 'lambda': 4.307614779940577}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:57,204] Trial 20 finished with value: 0.8719048981077858 and parameters: {'learning_rate': 0.10546552833119541, 'max_depth': 8, 'min_child_weight': 4, 'gamma': 1.1463016376900423, 'subsample': 0.4796579113500009, 'colsample_bytree': 0.5667922082720253, 'alpha': 2.431603521109148, 'lambda': 2.86698307325882}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:57,354] Trial 21 finished with value: 0.8934362587872203 and parameters: {'learning_rate': 0.05703589081469176, 'max_depth': 6, 'min_child_weight': 2, 'gamma': 1.7274327171081643, 'subsample': 0.87538102845061, 'colsample_bytree': 0.7614701554956426, 'alpha': 0.05278676379453584, 'lambda': 2.000982300810066}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:57,701] Trial 22 finished with value: 0.9068580161590464 and parameters: {'learning_rate': 0.03200946227990842, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 2.339077878206829, 'subsample': 0.8647198644011549, 'colsample_bytree': 0.7914459998561119, 'alpha': 0.8073616286920541, 'lambda': 2.174576617715597}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:57,796] Trial 23 finished with value: 0.9006335335964547 and parameters: {'learning_rate': 0.030526711631747493, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 2.5925721338604366, 'subsample': 0.9375863797705061, 'colsample_bytree': 0.5700846051431561, 'alpha': 0.8924713879905333, 'lambda': 0.0829972918010311}. Best is trial 13 with value: 0.9068580161590464.\n",
      "[I 2024-02-07 15:41:57,883] Trial 24 finished with value: 0.9131472773212349 and parameters: {'learning_rate': 0.03076259699219965, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 2.7867736684265396, 'subsample': 0.9390488615801248, 'colsample_bytree': 0.8125233155062028, 'alpha': 0.8830250545354885, 'lambda': 0.3081643099185871}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:57,984] Trial 25 finished with value: 0.8996983865364487 and parameters: {'learning_rate': 0.024354292738841238, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 3.581365537366999, 'subsample': 0.7051735872292021, 'colsample_bytree': 0.8113277737388322, 'alpha': 0.7760201798204263, 'lambda': 1.6490016057290637}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,085] Trial 26 finished with value: 0.9131472773212349 and parameters: {'learning_rate': 0.046330491105780136, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0.808130846614936, 'subsample': 0.9165544710080992, 'colsample_bytree': 0.992109945897121, 'alpha': 2.1919704249801053, 'lambda': 2.5760076375109313}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,176] Trial 27 finished with value: 0.8996983865364487 and parameters: {'learning_rate': 0.049868981958297115, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0.790947064747731, 'subsample': 0.9334978094721238, 'colsample_bytree': 0.991223703200783, 'alpha': 2.368459044517509, 'lambda': 4.586102549600192}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,255] Trial 28 finished with value: 0.8403074413182223 and parameters: {'learning_rate': 0.0745783086217513, 'max_depth': 11, 'min_child_weight': 3, 'gamma': 4.9974634623153165, 'subsample': 0.7351690040596934, 'colsample_bytree': 0.8357736379120739, 'alpha': 4.069643849199305, 'lambda': 3.119497245841073}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,360] Trial 29 finished with value: 0.9060260569439971 and parameters: {'learning_rate': 0.044362172177743284, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 1.0036266112484404, 'subsample': 0.8163121898660696, 'colsample_bytree': 0.8638065924773058, 'alpha': 2.5284633779975425, 'lambda': 0.7510951435628954}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,458] Trial 30 finished with value: 0.8862494957516626 and parameters: {'learning_rate': 0.09341347813591605, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 1.0558436709139802, 'subsample': 0.5443886910036675, 'colsample_bytree': 0.9854151528353731, 'alpha': 1.9286424975667935, 'lambda': 9.93416157406557e-05}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,684] Trial 31 finished with value: 0.9060260569439971 and parameters: {'learning_rate': 0.02115264027259898, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0.0124945833043269, 'subsample': 0.915891021369379, 'colsample_bytree': 0.7678047235942072, 'alpha': 0.7499482695851305, 'lambda': 2.5484511816035362}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,745] Trial 32 finished with value: 0.8996983865364487 and parameters: {'learning_rate': 0.06050857031696061, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 2.3663953510895483, 'subsample': 0.984681074307907, 'colsample_bytree': 0.8820293957112848, 'alpha': 1.0732623008764843, 'lambda': 1.0405981284743675}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,842] Trial 33 finished with value: 0.8403074413182223 and parameters: {'learning_rate': 0.04023166962792717, 'max_depth': 7, 'min_child_weight': 1, 'gamma': 1.1918525626596266, 'subsample': 0.7970547222375419, 'colsample_bytree': 0.5720436859733836, 'alpha': 9.524222782821491, 'lambda': 2.2907631109795243}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:58,938] Trial 34 finished with value: 0.9131472773212349 and parameters: {'learning_rate': 0.022776531947001882, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 2.2752376597867214, 'subsample': 0.9064351195886393, 'colsample_bytree': 0.8056421522405302, 'alpha': 0.49480386683478095, 'lambda': 1.4446938437672692}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:59,021] Trial 35 finished with value: 0.8403074413182223 and parameters: {'learning_rate': 0.02194349323392438, 'max_depth': 11, 'min_child_weight': 2, 'gamma': 3.3632737775753476, 'subsample': 0.9254177962915437, 'colsample_bytree': 0.48940188295027187, 'alpha': 6.127518890602813, 'lambda': 1.4003124935774358}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:59,107] Trial 36 finished with value: 0.8250111659909584 and parameters: {'learning_rate': 0.011371483724863221, 'max_depth': 12, 'min_child_weight': 1, 'gamma': 9.621457280313958, 'subsample': 0.31855642010128593, 'colsample_bytree': 0.9050585876276493, 'alpha': 0.38090632154781146, 'lambda': 0.5627386546714983}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:59,175] Trial 37 finished with value: 0.8403074413182223 and parameters: {'learning_rate': 0.02531629343016742, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 4.7496187255615085, 'subsample': 0.9569122109269644, 'colsample_bytree': 0.22678628233295178, 'alpha': 8.521634013890703, 'lambda': 5.090809131598643}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:59,259] Trial 38 finished with value: 0.8996983865364487 and parameters: {'learning_rate': 0.051877997680410466, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.6147424931912705, 'subsample': 0.6544393198758687, 'colsample_bytree': 0.3980219847240709, 'alpha': 3.043178015090963, 'lambda': 1.3538232311142206}. Best is trial 24 with value: 0.9131472773212349.\n",
      "[I 2024-02-07 15:41:59,336] Trial 39 finished with value: 0.8479754500993045 and parameters: {'learning_rate': 0.039962448267853834, 'max_depth': 11, 'min_child_weight': 1, 'gamma': 6.91497576494532, 'subsample': 0.8216095474977796, 'colsample_bytree': 0.9580855139494451, 'alpha': 1.3219354104493588, 'lambda': 5.731383740968771}. Best is trial 24 with value: 0.9131472773212349.\n"
     ]
    }
   ],
   "source": [
    "import optuna\n",
    "import xgboost as xgb\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import matthews_corrcoef, accuracy_score,confusion_matrix\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "X,y = make_classification(n_samples=2000, n_classes=2,flip_y=0.1) \n",
    "##remove 500 samples of class 0 if you maximize accuracy you will se that mcc decreases! \n",
    "idx = np.where(y==0)[0][0:500]\n",
    "X = np.delete(X,idx,axis=0)\n",
    "y = np.delete(y,idx)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split( X,y, test_size=0.33, random_state=0,stratify=y)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train,y_train, test_size=0.33, random_state=0,stratify=y_train)\n",
    "\n",
    "def objective(trial):\n",
    "\n",
    "    ##here the non default parameters to test: tria.suggest... means that optuna can change these parameters according to the ranges\n",
    "    params = dict(\n",
    "                learning_rate = trial.suggest_float(\"learning_rate\", 0.01, 0.2),\n",
    "                max_depth= trial.suggest_int(\"max_depth\",5, 15),\n",
    "                min_child_weight = trial.suggest_int(\"min_child_weight\", 1, 8),\n",
    "                gamma = trial.suggest_float(\"gamma\", 0, 10),\n",
    "                subsample = trial.suggest_float(\"subsample\", 0.01,1),\n",
    "                colsample_bytree = trial.suggest_float(\"colsample_bytree\", 0.01,1),\n",
    "                alpha = trial.suggest_float(\"alpha\", 0, 10),\n",
    "                objective= 'binary:logistic', \n",
    "                nthread=4, \n",
    "                seed=27)\n",
    "    params['lambda'] = trial.suggest_float(\"lambda\", 0, 10) ## care, lambda can not be placed in the dictionary becasue it is a keyword\n",
    "\n",
    "    \n",
    "    dtrain = xgb.DMatrix(X_train,y_train) ## THIS ARE THE GLOBAL NAMES CARE\n",
    "    dvalid = xgb.DMatrix(X_valid,y_valid)\n",
    "\n",
    "\n",
    "    bst = xgb.train(params, dtrain,verbose_eval=False, num_boost_round=400,\n",
    "                    evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                    early_stopping_rounds=100)\n",
    "\n",
    "    preds = bst.predict(dvalid)\n",
    "    ##MCC is more solid\n",
    "    mcc = matthews_corrcoef(y_valid,np.round(preds) )\n",
    "    \n",
    "    return mcc\n",
    "\n",
    "\n",
    "\n",
    "study = optuna.create_study(direction=\"maximize\")\n",
    "study.optimize(objective, n_trials=40, timeout=600)\n",
    "##retrain the model with the best parameters\n",
    "params_final = dict(\n",
    "            objective= 'binary:logistic', \n",
    "            nthread=4, \n",
    "            seed=27)\n",
    "params_final.update(study.best_params)\n",
    "dtrain = xgb.DMatrix(X_train,y_train)\n",
    "dvalid = xgb.DMatrix(X_valid,y_valid)   \n",
    "bst = xgb.train(params_final, dtrain,verbose_eval=False, num_boost_round=400,\n",
    "                evals = [(dtrain, \"train\"), (dvalid, \"valid\")],\n",
    "                early_stopping_rounds=100,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b794f14-4b51-40ad-9d18-8ad72138922f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2eafaf84-04d1-429a-b630-a785fa70e7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature Importance Score')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.Series(bst.get_fscore()).sort_values(ascending=False)\n",
    "feat_imp.plot(kind='bar', title='Feature Importances')\n",
    "plt.ylabel('Feature Importance Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "06b5f27d-2f28-48f0-b584-5a1729631cdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[106   4]\n",
      " [  9 213]]\n",
      "0.9131472773212349\n",
      "0.9608433734939759\n",
      "########################################\n",
      "[[205  17]\n",
      " [ 30 421]]\n",
      "0.8451352846787803\n",
      "0.9301634472511144\n"
     ]
    }
   ],
   "source": [
    "preds_class_valid = np.round(bst.predict(dvalid))\n",
    "preds_class_train= np.round(bst.predict(dtrain))\n",
    "print(confusion_matrix(y_valid,preds_class_valid))\n",
    "print(matthews_corrcoef(y_valid,preds_class_valid))\n",
    "print(accuracy_score(y_valid,preds_class_valid))\n",
    "print('########################################')\n",
    "print(confusion_matrix(y_train,preds_class_train))\n",
    "print(matthews_corrcoef(y_train,preds_class_train))\n",
    "print(accuracy_score(y_train,preds_class_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d7c1f2f6-e484-4b57-adc8-7307bbb2022f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "cliponaxis": false,
         "hovertemplate": [
          "max_depth (IntDistribution): 6.431914383047734e-07<extra></extra>",
          "min_child_weight (IntDistribution): 8.846522408815936e-07<extra></extra>",
          "lambda (FloatDistribution): 8.168063872509273e-06<extra></extra>",
          "learning_rate (FloatDistribution): 1.5936395480688326e-05<extra></extra>",
          "colsample_bytree (FloatDistribution): 1.706320036404351e-05<extra></extra>",
          "alpha (FloatDistribution): 4.8035062211044554e-05<extra></extra>",
          "gamma (FloatDistribution): 6.245358103202788e-05<extra></extra>",
          "subsample (FloatDistribution): 0.9998468158533603<extra></extra>"
         ],
         "name": "Objective Value",
         "orientation": "h",
         "text": [
          "<0.01",
          "<0.01",
          "<0.01",
          "<0.01",
          "<0.01",
          "<0.01",
          "<0.01",
          "1.00"
         ],
         "textposition": "outside",
         "type": "bar",
         "x": [
          6.431914383047734e-07,
          8.846522408815936e-07,
          8.168063872509273e-06,
          1.5936395480688326e-05,
          1.706320036404351e-05,
          4.8035062211044554e-05,
          6.245358103202788e-05,
          0.9998468158533603
         ],
         "y": [
          "max_depth",
          "min_child_weight",
          "lambda",
          "learning_rate",
          "colsample_bytree",
          "alpha",
          "gamma",
          "subsample"
         ]
        }
       ],
       "layout": {
        "autosize": true,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from optuna.visualization import plot_param_importances,plot_edf,plot_optimization_history\n",
    "#plot_edf(study)\n",
    "plot_optimization_history(study)\n",
    "\n",
    "plot_param_importances(study)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "194208d3-f37f-4b8a-8e1c-3371833dd35c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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