{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2651c9df-94c4-4b15-8e02-e156647f06af",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install xgboost --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ac8fd8f-8435-4ba0-9da3-7bf6e8bacbb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "#max_depth = [1, 2, 3, 4, 5, 6]\n",
    "max_depth = [1, 2, 3, 4, 5, 6]\n",
    "#learning_rate_done=[ 0.00002, 0.00001, 0.00003, 0.002, 0.0002, 0.0001]\n",
    "#learning_rate=[0.1, 0.01, 0.001, 0.2, 0.02, 0.002, 0.3, 0.03, 0.003]\n",
    "learning_rate=[0.002, 0.003, 0.0001, 0.0002, 0.0003]\n",
    "#n_estimators=[500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 5000, 6000]\n",
    "n_estimators=[2500, 3000, 3500, 4000]\n",
    "#test_size_done = [ 0.1, 0.2, 0.3]\n",
    "test_size = [0.09]\n",
    "\n",
    "model_variables = [max_depth, learning_rate, test_size, n_estimators ]\n",
    "\n",
    "\n",
    "#calculate the metamodel extension\n",
    "meta_ext = len(max_depth)*len(learning_rate)* len(n_estimators) * len(test_size)\n",
    "print(meta_ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb280b46-17be-46a5-9993-2d3940200754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 0.002, 0.09, 2500),\n",
       " (1, 0.002, 0.09, 3000),\n",
       " (1, 0.002, 0.09, 3500),\n",
       " (1, 0.002, 0.09, 4000),\n",
       " (1, 0.003, 0.09, 2500),\n",
       " (1, 0.003, 0.09, 3000),\n",
       " (1, 0.003, 0.09, 3500),\n",
       " (1, 0.003, 0.09, 4000),\n",
       " (1, 0.0001, 0.09, 2500),\n",
       " (1, 0.0001, 0.09, 3000),\n",
       " (1, 0.0001, 0.09, 3500),\n",
       " (1, 0.0001, 0.09, 4000),\n",
       " (1, 0.0002, 0.09, 2500),\n",
       " (1, 0.0002, 0.09, 3000),\n",
       " (1, 0.0002, 0.09, 3500),\n",
       " (1, 0.0002, 0.09, 4000),\n",
       " (1, 0.0003, 0.09, 2500),\n",
       " (1, 0.0003, 0.09, 3000),\n",
       " (1, 0.0003, 0.09, 3500),\n",
       " (1, 0.0003, 0.09, 4000),\n",
       " (2, 0.002, 0.09, 2500),\n",
       " (2, 0.002, 0.09, 3000),\n",
       " (2, 0.002, 0.09, 3500),\n",
       " (2, 0.002, 0.09, 4000),\n",
       " (2, 0.003, 0.09, 2500),\n",
       " (2, 0.003, 0.09, 3000),\n",
       " (2, 0.003, 0.09, 3500),\n",
       " (2, 0.003, 0.09, 4000),\n",
       " (2, 0.0001, 0.09, 2500),\n",
       " (2, 0.0001, 0.09, 3000),\n",
       " (2, 0.0001, 0.09, 3500),\n",
       " (2, 0.0001, 0.09, 4000),\n",
       " (2, 0.0002, 0.09, 2500),\n",
       " (2, 0.0002, 0.09, 3000),\n",
       " (2, 0.0002, 0.09, 3500),\n",
       " (2, 0.0002, 0.09, 4000),\n",
       " (2, 0.0003, 0.09, 2500),\n",
       " (2, 0.0003, 0.09, 3000),\n",
       " (2, 0.0003, 0.09, 3500),\n",
       " (2, 0.0003, 0.09, 4000),\n",
       " (3, 0.002, 0.09, 2500),\n",
       " (3, 0.002, 0.09, 3000),\n",
       " (3, 0.002, 0.09, 3500),\n",
       " (3, 0.002, 0.09, 4000),\n",
       " (3, 0.003, 0.09, 2500),\n",
       " (3, 0.003, 0.09, 3000),\n",
       " (3, 0.003, 0.09, 3500),\n",
       " (3, 0.003, 0.09, 4000),\n",
       " (3, 0.0001, 0.09, 2500),\n",
       " (3, 0.0001, 0.09, 3000),\n",
       " (3, 0.0001, 0.09, 3500),\n",
       " (3, 0.0001, 0.09, 4000),\n",
       " (3, 0.0002, 0.09, 2500),\n",
       " (3, 0.0002, 0.09, 3000),\n",
       " (3, 0.0002, 0.09, 3500),\n",
       " (3, 0.0002, 0.09, 4000),\n",
       " (3, 0.0003, 0.09, 2500),\n",
       " (3, 0.0003, 0.09, 3000),\n",
       " (3, 0.0003, 0.09, 3500),\n",
       " (3, 0.0003, 0.09, 4000),\n",
       " (4, 0.002, 0.09, 2500),\n",
       " (4, 0.002, 0.09, 3000),\n",
       " (4, 0.002, 0.09, 3500),\n",
       " (4, 0.002, 0.09, 4000),\n",
       " (4, 0.003, 0.09, 2500),\n",
       " (4, 0.003, 0.09, 3000),\n",
       " (4, 0.003, 0.09, 3500),\n",
       " (4, 0.003, 0.09, 4000),\n",
       " (4, 0.0001, 0.09, 2500),\n",
       " (4, 0.0001, 0.09, 3000),\n",
       " (4, 0.0001, 0.09, 3500),\n",
       " (4, 0.0001, 0.09, 4000),\n",
       " (4, 0.0002, 0.09, 2500),\n",
       " (4, 0.0002, 0.09, 3000),\n",
       " (4, 0.0002, 0.09, 3500),\n",
       " (4, 0.0002, 0.09, 4000),\n",
       " (4, 0.0003, 0.09, 2500),\n",
       " (4, 0.0003, 0.09, 3000),\n",
       " (4, 0.0003, 0.09, 3500),\n",
       " (4, 0.0003, 0.09, 4000),\n",
       " (5, 0.002, 0.09, 2500),\n",
       " (5, 0.002, 0.09, 3000),\n",
       " (5, 0.002, 0.09, 3500),\n",
       " (5, 0.002, 0.09, 4000),\n",
       " (5, 0.003, 0.09, 2500),\n",
       " (5, 0.003, 0.09, 3000),\n",
       " (5, 0.003, 0.09, 3500),\n",
       " (5, 0.003, 0.09, 4000),\n",
       " (5, 0.0001, 0.09, 2500),\n",
       " (5, 0.0001, 0.09, 3000),\n",
       " (5, 0.0001, 0.09, 3500),\n",
       " (5, 0.0001, 0.09, 4000),\n",
       " (5, 0.0002, 0.09, 2500),\n",
       " (5, 0.0002, 0.09, 3000),\n",
       " (5, 0.0002, 0.09, 3500),\n",
       " (5, 0.0002, 0.09, 4000),\n",
       " (5, 0.0003, 0.09, 2500),\n",
       " (5, 0.0003, 0.09, 3000),\n",
       " (5, 0.0003, 0.09, 3500),\n",
       " (5, 0.0003, 0.09, 4000),\n",
       " (6, 0.002, 0.09, 2500),\n",
       " (6, 0.002, 0.09, 3000),\n",
       " (6, 0.002, 0.09, 3500),\n",
       " (6, 0.002, 0.09, 4000),\n",
       " (6, 0.003, 0.09, 2500),\n",
       " (6, 0.003, 0.09, 3000),\n",
       " (6, 0.003, 0.09, 3500),\n",
       " (6, 0.003, 0.09, 4000),\n",
       " (6, 0.0001, 0.09, 2500),\n",
       " (6, 0.0001, 0.09, 3000),\n",
       " (6, 0.0001, 0.09, 3500),\n",
       " (6, 0.0001, 0.09, 4000),\n",
       " (6, 0.0002, 0.09, 2500),\n",
       " (6, 0.0002, 0.09, 3000),\n",
       " (6, 0.0002, 0.09, 3500),\n",
       " (6, 0.0002, 0.09, 4000),\n",
       " (6, 0.0003, 0.09, 2500),\n",
       " (6, 0.0003, 0.09, 3000),\n",
       " (6, 0.0003, 0.09, 3500),\n",
       " (6, 0.0003, 0.09, 4000)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the model cards\n",
    "\n",
    "cards = list(itertools.product(*model_variables))\n",
    "\n",
    "cards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e210e55e-3377-4b6a-a7a7-bf43fdef1bce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "# Lib & Dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "import requests\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f9a7c4ba-6418-4d6a-bd74-b6e6177d08ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Download (may take a few minutes depending on your network)\n",
    "train_datalink_X = 'https://tournament.datacrunch.com/data/X_train.csv'  \n",
    "train_datalink_y = 'https://tournament.datacrunch.com/data/y_train.csv' \n",
    "hackathon_data_link = 'https://tournament.datacrunch.com/data/X_test.csv' \n",
    "\n",
    "# Data for training\n",
    "train_data = pd.read_csv(train_datalink_X)\n",
    "# Data for which you will submit your prediction\n",
    "test_data = pd.read_csv(hackathon_data_link)\n",
    "# Targets to be predicted\n",
    "train_targets = pd.read_csv(train_datalink_y)\n",
    "\n",
    "#If you don't want to work with time serie\n",
    "train_data = train_data.drop(columns=['Moons', 'id'])\n",
    "test_data = test_data.drop(columns=['Moons', 'id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06945d55-c2ac-4208-a8df-f1b06ebc9a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "mx_d=cards[0][0]\n",
    "lr_r= cards[0][1]\n",
    "tst_s = cards[0][2]\n",
    "n_est = cards[0][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e5dc918-7454-4c83-a20a-2f9d90213a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 0.002, 0.09, 2500)\n",
      " mx_d: 1  lr_r: 0.002  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.22911386115408916\n",
      "[0.22911386115408916]\n",
      "(1, 0.002, 0.09, 3000)\n",
      " mx_d: 1  lr_r: 0.002  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23156057270154565\n",
      "[0.23156057270154565]\n",
      "(1, 0.002, 0.09, 3500)\n",
      " mx_d: 1  lr_r: 0.002  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23330887960737373\n",
      "[0.23330887960737373]\n",
      "(1, 0.002, 0.09, 4000)\n",
      " mx_d: 1  lr_r: 0.002  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23505294167816537\n",
      "[0.23505294167816537]\n",
      "(1, 0.003, 0.09, 2500)\n",
      " mx_d: 1  lr_r: 0.003  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23401484313178583\n",
      "[0.23401484313178583]\n",
      "(1, 0.003, 0.09, 3000)\n",
      " mx_d: 1  lr_r: 0.003  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23669684336308008\n",
      "[0.23669684336308008]\n",
      "(1, 0.003, 0.09, 3500)\n",
      " mx_d: 1  lr_r: 0.003  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2387594555869373\n",
      "[0.2387594555869373]\n",
      "(1, 0.003, 0.09, 4000)\n",
      " mx_d: 1  lr_r: 0.003  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2405866560752258\n",
      "[0.2405866560752258]\n",
      "(1, 0.0001, 0.09, 2500)\n",
      " mx_d: 1  lr_r: 0.0001  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.15915757115011436\n",
      "[0.15915757115011436]\n",
      "(1, 0.0001, 0.09, 3000)\n",
      " mx_d: 1  lr_r: 0.0001  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.1591999146943814\n",
      "[0.1591999146943814]\n",
      "(1, 0.0001, 0.09, 3500)\n",
      " mx_d: 1  lr_r: 0.0001  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.16000168356749095\n",
      "[0.16000168356749095]\n",
      "(1, 0.0001, 0.09, 4000)\n",
      " mx_d: 1  lr_r: 0.0001  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.17112839798735488\n",
      "[0.17112839798735488]\n",
      "(1, 0.0002, 0.09, 2500)\n",
      " mx_d: 1  lr_r: 0.0002  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.18322285652810932\n",
      "[0.18322285652810932]\n",
      "(1, 0.0002, 0.09, 3000)\n",
      " mx_d: 1  lr_r: 0.0002  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.18899561172260412\n",
      "[0.18899561172260412]\n",
      "(1, 0.0002, 0.09, 3500)\n",
      " mx_d: 1  lr_r: 0.0002  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.19102570599834742\n",
      "[0.19102570599834742]\n",
      "(1, 0.0002, 0.09, 4000)\n",
      " mx_d: 1  lr_r: 0.0002  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.19276762600473452\n",
      "[0.19276762600473452]\n",
      "(1, 0.0003, 0.09, 2500)\n",
      " mx_d: 1  lr_r: 0.0003  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.19219939632679564\n",
      "[0.19219939632679564]\n",
      "(1, 0.0003, 0.09, 3000)\n",
      " mx_d: 1  lr_r: 0.0003  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.19575635475918893\n",
      "[0.19575635475918893]\n",
      "(1, 0.0003, 0.09, 3500)\n",
      " mx_d: 1  lr_r: 0.0003  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.19747383884117395\n",
      "[0.19747383884117395]\n",
      "(1, 0.0003, 0.09, 4000)\n",
      " mx_d: 1  lr_r: 0.0003  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.20164542563604515\n",
      "[0.20164542563604515]\n",
      "(2, 0.002, 0.09, 2500)\n",
      " mx_d: 2  lr_r: 0.002  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2466767686948563\n",
      "[0.2466767686948563]\n",
      "(2, 0.002, 0.09, 3000)\n",
      " mx_d: 2  lr_r: 0.002  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.24985835058016534\n",
      "[0.24985835058016534]\n",
      "(2, 0.002, 0.09, 3500)\n",
      " mx_d: 2  lr_r: 0.002  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2527047948530534\n",
      "[0.2527047948530534]\n",
      "(2, 0.002, 0.09, 4000)\n",
      " mx_d: 2  lr_r: 0.002  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.25457142406046734\n",
      "[0.25457142406046734]\n",
      "(2, 0.003, 0.09, 2500)\n",
      " mx_d: 2  lr_r: 0.003  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2537943497613943\n",
      "[0.2537943497613943]\n",
      "(2, 0.003, 0.09, 3000)\n",
      " mx_d: 2  lr_r: 0.003  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2561424254918985\n",
      "[0.2561424254918985]\n",
      "(2, 0.003, 0.09, 3500)\n",
      " mx_d: 2  lr_r: 0.003  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.25837278649177225\n",
      "[0.25837278649177225]\n",
      "(2, 0.003, 0.09, 4000)\n",
      " mx_d: 2  lr_r: 0.003  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2603101057756482\n",
      "[0.2603101057756482]\n",
      "(2, 0.0001, 0.09, 2500)\n",
      " mx_d: 2  lr_r: 0.0001  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2111354137766888\n",
      "[0.2111354137766888]\n",
      "(2, 0.0001, 0.09, 3000)\n",
      " mx_d: 2  lr_r: 0.0001  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.21101546419543624\n",
      "[0.21101546419543624]\n",
      "(2, 0.0001, 0.09, 3500)\n",
      " mx_d: 2  lr_r: 0.0001  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.21110613540098544\n",
      "[0.21110613540098544]\n",
      "(2, 0.0001, 0.09, 4000)\n",
      " mx_d: 2  lr_r: 0.0001  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.21206187502678825\n",
      "[0.21206187502678825]\n",
      "(2, 0.0002, 0.09, 2500)\n",
      " mx_d: 2  lr_r: 0.0002  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.21355969653098394\n",
      "[0.21355969653098394]\n",
      "(2, 0.0002, 0.09, 3000)\n",
      " mx_d: 2  lr_r: 0.0002  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2149140967132513\n",
      "[0.2149140967132513]\n",
      "(2, 0.0002, 0.09, 3500)\n",
      " mx_d: 2  lr_r: 0.0002  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2156212555558716\n",
      "[0.2156212555558716]\n",
      "(2, 0.0002, 0.09, 4000)\n",
      " mx_d: 2  lr_r: 0.0002  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.21608224755986385\n",
      "[0.21608224755986385]\n",
      "(2, 0.0003, 0.09, 2500)\n",
      " mx_d: 2  lr_r: 0.0003  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.21592034557313589\n",
      "[0.21592034557313589]\n",
      "(2, 0.0003, 0.09, 3000)\n",
      " mx_d: 2  lr_r: 0.0003  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.21728256102679827\n",
      "[0.21728256102679827]\n",
      "(2, 0.0003, 0.09, 3500)\n",
      " mx_d: 2  lr_r: 0.0003  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.22021462877376116\n",
      "[0.22021462877376116]\n",
      "(2, 0.0003, 0.09, 4000)\n",
      " mx_d: 2  lr_r: 0.0003  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.22341875832127758\n",
      "[0.22341875832127758]\n",
      "(3, 0.002, 0.09, 2500)\n",
      " mx_d: 3  lr_r: 0.002  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.25543674276384637\n",
      "[0.25543674276384637]\n",
      "(3, 0.002, 0.09, 3000)\n",
      " mx_d: 3  lr_r: 0.002  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2582347644139174\n",
      "[0.2582347644139174]\n",
      "(3, 0.002, 0.09, 3500)\n",
      " mx_d: 3  lr_r: 0.002  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.26062021405689784\n",
      "[0.26062021405689784]\n",
      "(3, 0.002, 0.09, 4000)\n",
      " mx_d: 3  lr_r: 0.002  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.26272956946209935\n",
      "[0.26272956946209935]\n",
      "(3, 0.003, 0.09, 2500)\n",
      " mx_d: 3  lr_r: 0.003  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.26152087771049054\n",
      "[0.26152087771049054]\n",
      "(3, 0.003, 0.09, 3000)\n",
      " mx_d: 3  lr_r: 0.003  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.26442733885252534\n",
      "[0.26442733885252534]\n",
      "(3, 0.003, 0.09, 3500)\n",
      " mx_d: 3  lr_r: 0.003  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.26665067184759267\n",
      "[0.26665067184759267]\n",
      "(3, 0.003, 0.09, 4000)\n",
      " mx_d: 3  lr_r: 0.003  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.26851736246662156\n",
      "[0.26851736246662156]\n",
      "(3, 0.0001, 0.09, 2500)\n",
      " mx_d: 3  lr_r: 0.0001  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.22940837410993217\n",
      "[0.22940837410993217]\n",
      "(3, 0.0001, 0.09, 3000)\n",
      " mx_d: 3  lr_r: 0.0001  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.22978862008530446\n",
      "[0.22978862008530446]\n",
      "(3, 0.0001, 0.09, 3500)\n",
      " mx_d: 3  lr_r: 0.0001  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23026835256535647\n",
      "[0.23026835256535647]\n",
      "(3, 0.0001, 0.09, 4000)\n",
      " mx_d: 3  lr_r: 0.0001  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23067967455822783\n",
      "[0.23067967455822783]\n",
      "(3, 0.0002, 0.09, 2500)\n",
      " mx_d: 3  lr_r: 0.0002  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23140964732053684\n",
      "[0.23140964732053684]\n",
      "(3, 0.0002, 0.09, 3000)\n",
      " mx_d: 3  lr_r: 0.0002  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23211470192725647\n",
      "[0.23211470192725647]\n",
      "(3, 0.0002, 0.09, 3500)\n",
      " mx_d: 3  lr_r: 0.0002  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23275276862867197\n",
      "[0.23275276862867197]\n",
      "(3, 0.0002, 0.09, 4000)\n",
      " mx_d: 3  lr_r: 0.0002  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23347585261903875\n",
      "[0.23347585261903875]\n",
      "(3, 0.0003, 0.09, 2500)\n",
      " mx_d: 3  lr_r: 0.0003  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23329880366215658\n",
      "[0.23329880366215658]\n",
      "(3, 0.0003, 0.09, 3000)\n",
      " mx_d: 3  lr_r: 0.0003  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2342898365743632\n",
      "[0.2342898365743632]\n",
      "(3, 0.0003, 0.09, 3500)\n",
      " mx_d: 3  lr_r: 0.0003  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2351367496011578\n",
      "[0.2351367496011578]\n",
      "(3, 0.0003, 0.09, 4000)\n",
      " mx_d: 3  lr_r: 0.0003  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.23600032430108348\n",
      "[0.23600032430108348]\n",
      "(4, 0.002, 0.09, 2500)\n",
      " mx_d: 4  lr_r: 0.002  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.262582385504368\n",
      "[0.262582385504368]\n",
      "(4, 0.002, 0.09, 3000)\n",
      " mx_d: 4  lr_r: 0.002  tst_s: 0.09  n_est: 3000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2654371072091133\n",
      "[0.2654371072091133]\n",
      "(4, 0.002, 0.09, 3500)\n",
      " mx_d: 4  lr_r: 0.002  tst_s: 0.09  n_est: 3500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2678373905087767\n",
      "[0.2678373905087767]\n",
      "(4, 0.002, 0.09, 4000)\n",
      " mx_d: 4  lr_r: 0.002  tst_s: 0.09  n_est: 4000\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2697877743854762\n",
      "[0.2697877743854762]\n",
      "(4, 0.003, 0.09, 2500)\n",
      " mx_d: 4  lr_r: 0.003  tst_s: 0.09  n_est: 2500\n",
      "Score as calculated for the leader board (っಠ‿ಠ)っ 0.2688820767734505\n",
      "[0.2688820767734505]\n",
      "[[0.22911386115408916], [0.23156057270154565], [0.23330887960737373], [0.23505294167816537], [0.23401484313178583], [0.23669684336308008], [0.2387594555869373], [0.2405866560752258], [0.15915757115011436], [0.1591999146943814], [0.16000168356749095], [0.17112839798735488], [0.18322285652810932], [0.18899561172260412], [0.19102570599834742], [0.19276762600473452], [0.19219939632679564], [0.19575635475918893], [0.19747383884117395], [0.20164542563604515], [0.2466767686948563], [0.24985835058016534], [0.2527047948530534], [0.25457142406046734], [0.2537943497613943], [0.2561424254918985], [0.25837278649177225], [0.2603101057756482], [0.2111354137766888], [0.21101546419543624], [0.21110613540098544], [0.21206187502678825], [0.21355969653098394], [0.2149140967132513], [0.2156212555558716], [0.21608224755986385], [0.21592034557313589], [0.21728256102679827], [0.22021462877376116], [0.22341875832127758], [0.25543674276384637], [0.2582347644139174], [0.26062021405689784], [0.26272956946209935], [0.26152087771049054], [0.26442733885252534], [0.26665067184759267], [0.26851736246662156], [0.22940837410993217], [0.22978862008530446], [0.23026835256535647], [0.23067967455822783], [0.23140964732053684], [0.23211470192725647], [0.23275276862867197], [0.23347585261903875], [0.23329880366215658], [0.2342898365743632], [0.2351367496011578], [0.23600032430108348], [0.262582385504368], [0.2654371072091133], [0.2678373905087767], [0.2697877743854762], [0.2688820767734505]]\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "len(cards)\n",
    "\n",
    "meta_rank =[]\n",
    "\n",
    "for card in cards[:65]:\n",
    "  model_rank = []\n",
    "\n",
    "\n",
    "  mx_d=card[0]\n",
    "  lr_r= card[1]\n",
    "  tst_s = card[2]\n",
    "  n_est = card[3]\n",
    "  print(card)\n",
    "  i += 1\n",
    "\n",
    "  print(' mx_d:',mx_d,' lr_r:', lr_r,' tst_s:', tst_s,' n_est:', n_est)\n",
    "  \n",
    "\n",
    "  def scorer(y_test, y_pred):\n",
    "    score = (stats.spearmanr(y_test, y_pred)*100)[0]\n",
    "    model_rank.append(score)\n",
    "    print('Score as calculated for the leader board (っಠ‿ಠ)っ {}'.format(score))\n",
    "\n",
    "  def xg_boost_hackathon(data, target):\n",
    "    X, y = data, target\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=tst_s, shuffle=False)\n",
    "    model = xgb.XGBRegressor(objective='reg:squarederror', max_depth=mx_d, learning_rate=lr_r, n_estimators=n_est, n_jobs=-1, colsample_bytree=0.5)\n",
    "    model.fit(X_train, y_train)\n",
    "    pred = model.predict(X_test)\n",
    "    scorer(y_test, pred)\n",
    "    \n",
    "    return model\n",
    "\n",
    "  # Making prediction for target r\n",
    "  model_target_r = xg_boost_hackathon(train_data, train_targets.target_r)\n",
    "  # Making prediction for target g\n",
    "  #model_target_g = xg_boost_hackathon(train_data, train_targets.target_g)\n",
    "  # Making prediction for target b\n",
    "  #model_target_b = xg_boost_hackathon(train_data, train_targets.target_b)\n",
    "\n",
    "  meta_rank.append(model_rank)\n",
    "  print(model_rank)\n",
    "\n",
    "print(meta_rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2689d09-3c2e-4eeb-b154-0fbfa2f896a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "771d9d12-9f93-4407-800a-7306479398b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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