BLOOD_MODEL_TRAINING.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mindf\\anaconda3\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load csv\n",
    "BLOOD_DATASET_D1 = pd.read_csv(r'DATASETS\\BLOOD\\BLOOD_WBCELLS_HEMO_(D1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      gender  hematocrit       RBC        MCH         MCV        RDW  \\\n",
       "0         1   26.272727  2.960000  28.250000   89.900000  16.220000   \n",
       "1         2   27.700000  2.620000  34.320000  109.800000  23.820000   \n",
       "2         2   36.637500  4.277500  26.062500   85.625000  17.037500   \n",
       "3         1   27.333333  3.235000  26.566667   84.500000  16.516667   \n",
       "4         1   28.937500  3.720000  24.337500   77.875000  17.400000   \n",
       "..      ...         ...       ...        ...         ...        ...   \n",
       "913       1   33.628571  3.685714  27.842857   91.142857  15.557143   \n",
       "914       1   28.715789  3.270000  29.772727   89.545455  18.672727   \n",
       "915       2   27.685714  3.065714  26.900000   90.285714  17.285714   \n",
       "916       2   33.537500  3.446250  31.150000   97.375000  16.000000   \n",
       "917       1   29.937500  2.797143  36.771429  106.571429  16.157143   \n",
       "\n",
       "     Leucocyte   Platelets  Neutrophils  Basophils  Lymphocyte  Creatinine  \\\n",
       "0     7.650000  305.100000        74.65       0.40       13.30    1.958333   \n",
       "1     5.480000  204.200000        68.10       0.55       24.50    1.871429   \n",
       "2     8.225000  216.375000        81.80       0.15       14.50    0.585714   \n",
       "3     9.516667  273.000000        85.40       0.30        9.30    1.612500   \n",
       "4     7.975000  140.250000        80.70       0.20       13.45    2.960000   \n",
       "..         ...         ...          ...        ...         ...         ...   \n",
       "913   7.742857  251.857143        81.55       0.70       10.65    8.385714   \n",
       "914  22.309091  283.000000        84.58       0.80        7.70    1.900000   \n",
       "915   9.228571  409.285714        65.90       0.40       24.40    2.728571   \n",
       "916   6.062500  307.375000        79.85       0.30       13.20    1.736364   \n",
       "917   7.100000  275.285714        77.00       0.10       13.90    8.257143   \n",
       "\n",
       "     Blood potassium  Blood sodium    Chloride  SCORE  \n",
       "0           4.816667    138.750000  109.166667      0  \n",
       "1           5.825000    140.714286  105.857143      0  \n",
       "2           4.386667    138.500000   92.071429      0  \n",
       "3           4.075000    136.250000   96.750000      0  \n",
       "4           4.606667    144.133333  106.666667      0  \n",
       "..               ...           ...         ...    ...  \n",
       "913         5.557143    136.714286   94.428571      0  \n",
       "914         4.520000    135.680000  101.720000      0  \n",
       "915         4.100000    136.000000   97.285714      0  \n",
       "916         4.091667    140.000000  104.000000      0  \n",
       "917         5.228571    135.285714   93.857143      0  \n",
       "\n",
       "[918 rows x 16 columns]>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "BLOOD_DATASET_D1.head"
   ]
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      "END SCORE:  10.834864753000002\n",
      " =================================== \n",
      "END SCORE:  20.819125524937498\n",
      " =================================== \n",
      "END SCORE:  17.542267511875\n",
      " =================================== \n",
      "END SCORE:  9.816770837500002\n",
      " =================================== \n",
      "END SCORE:  11.7283895021875\n",
      " =================================== \n",
      "END SCORE:  13.379738631875\n",
      " =================================== \n",
      "END SCORE:  18.7677650136875\n",
      " =================================== \n",
      "END SCORE:  14.706000090875\n",
      " =================================== \n",
      "END SCORE:  14.362550082875\n",
      " =================================== \n",
      "END SCORE:  10.1576136394375\n",
      " =================================== \n",
      "END SCORE:  16.192526040062504\n",
      " =================================== \n",
      "END SCORE:  12.233370535687502\n",
      " =================================== \n",
      "END SCORE:  11.492532046875\n",
      " =================================== \n",
      "END SCORE:  14.837916664562501\n",
      " =================================== \n",
      "END SCORE:  19.8801762825\n",
      " =================================== \n",
      "END SCORE:  14.91041666875\n",
      " =================================== \n",
      "END SCORE:  12.668611111250001\n",
      " =================================== \n",
      "END SCORE:  9.769918832500004\n",
      " =================================== \n",
      "END SCORE:  26.675535710812504\n",
      " =================================== \n",
      "END SCORE:  11.208664379937499\n",
      " =================================== \n",
      "END SCORE:  11.214325284687499\n",
      " =================================== \n",
      "END SCORE:  23.1438924974375\n",
      " =================================== \n",
      "END SCORE:  16.262979314375002\n",
      " =================================== \n",
      "END SCORE:  8.828732642375002\n",
      " =================================== \n",
      "END SCORE:  6.8395833331250016\n",
      " =================================== \n",
      "END SCORE:  11.049397323062497\n",
      " =================================== \n",
      "END SCORE:  17.564722227125\n",
      " =================================== \n",
      "END SCORE:  21.293378335562505\n",
      " =================================== \n",
      "END SCORE:  11.8068551585625\n",
      " =================================== \n",
      "END SCORE:  8.724409720624998\n",
      " =================================== \n",
      "END SCORE:  10.849176584625003\n",
      " =================================== \n",
      "END SCORE:  10.794203868499999\n",
      " =================================== \n",
      "END SCORE:  20.700763890187503\n",
      " =================================== \n",
      "END SCORE:  12.506142683312504\n",
      " =================================== \n",
      "END SCORE:  9.6791096374375\n",
      " =================================== \n",
      "END SCORE:  8.629553567062498\n",
      " =================================== \n",
      "END SCORE:  15.224874999999999\n",
      " =================================== \n",
      "END SCORE:  6.2817187500000005\n",
      " =================================== \n",
      "END SCORE:  9.576614583312502\n",
      " =================================== \n",
      "END SCORE:  7.256517853937499\n",
      " =================================== \n",
      "END SCORE:  13.4271678315\n",
      " =================================== \n",
      "END SCORE:  11.214647814\n",
      " =================================== \n",
      "END SCORE:  11.454687500000002\n",
      " =================================== \n",
      "END SCORE:  8.097500002500002\n",
      " =================================== \n",
      "END SCORE:  12.208958335624999\n",
      " =================================== \n",
      "END SCORE:  12.126514423187501\n",
      " =================================== \n",
      "END SCORE:  12.810590278750002\n",
      " =================================== \n",
      "END SCORE:  12.149920629562502\n",
      " =================================== \n",
      "END SCORE:  16.762117141562502\n",
      " =================================== \n",
      "END SCORE:  14.945775789625003\n",
      " =================================== \n",
      "END SCORE:  9.3575297568125\n",
      " =================================== \n",
      "END SCORE:  10.8454513890625\n",
      " =================================== \n",
      "END SCORE:  18.2433617395\n",
      " =================================== \n",
      "END SCORE:  10.363924276062502\n",
      " =================================== \n",
      "END SCORE:  10.569251895312501\n",
      " =================================== \n",
      "END SCORE:  10.843787876874998\n",
      " =================================== \n",
      "END SCORE:  17.9883744473125\n",
      " =================================== \n",
      "END SCORE:  11.432186148\n",
      " =================================== \n",
      "END SCORE:  17.2144444436875\n",
      " =================================== \n",
      "END SCORE:  11.786324404937499\n",
      " =================================== \n",
      "END SCORE:  14.650882936437501\n",
      " =================================== \n",
      "END SCORE:  15.370744948625003\n",
      " =================================== \n",
      "END SCORE:  7.969734509562501\n",
      " =================================== \n",
      "END SCORE:  18.2580037859375\n",
      " =================================== \n",
      "END SCORE:  13.208888890187499\n",
      " =================================== \n",
      "END SCORE:  16.501866259\n",
      " =================================== \n",
      "END SCORE:  19.180486113375004\n",
      " =================================== \n",
      "END SCORE:  10.168437499999998\n",
      " =================================== \n",
      "END SCORE:  8.737152774749998\n",
      " =================================== \n",
      "END SCORE:  10.684791664625001\n",
      " =================================== \n",
      "END SCORE:  13.275694445875\n",
      " =================================== \n",
      "END SCORE:  18.975849929375\n",
      " =================================== \n",
      "END SCORE:  17.460797618125\n",
      " =================================== \n",
      "END SCORE:  14.460183570625002\n",
      " =================================== \n",
      "END SCORE:  17.288883928999997\n",
      " =================================== \n",
      "END SCORE:  9.4468229166875\n",
      " =================================== \n",
      "END SCORE:  15.175572914562501\n",
      " =================================== \n",
      "END SCORE:  18.5599164059375\n",
      " =================================== \n",
      "END SCORE:  12.195274439062501\n",
      " =================================== \n",
      "END SCORE:  9.9898674220625\n",
      " =================================== \n",
      "END SCORE:  17.2079600696875\n",
      " =================================== \n",
      "END SCORE:  10.153888886312497\n",
      " =================================== \n",
      "END SCORE:  13.474410174749998\n",
      " =================================== \n",
      "END SCORE:  14.206900252937498\n",
      " =================================== \n",
      "END SCORE:  18.692863637499997\n",
      " =================================== \n",
      "END SCORE:  11.391875\n",
      " =================================== \n",
      "END SCORE:  25.412923469437498\n",
      " =================================== \n",
      "END SCORE:  6.486041666687498\n",
      " =================================== \n",
      "END SCORE:  19.6506547653125\n",
      " =================================== \n",
      "END SCORE:  16.547118488812497\n",
      " =================================== \n",
      "END SCORE:  8.992272729125002\n",
      " =================================== \n",
      "END SCORE:  7.877651100687501\n",
      " =================================== \n",
      "END SCORE:  7.585994317125\n",
      " =================================== \n",
      "END SCORE:  11.9817552083125\n",
      " =================================== \n",
      "END SCORE:  8.863038197187501\n",
      " =================================== \n",
      "END SCORE:  10.070213289312502\n",
      " =================================== \n",
      "END SCORE:  11.2788541670625\n",
      " =================================== \n",
      "END SCORE:  14.005796133062502\n",
      " =================================== \n",
      "END SCORE:  10.250546874812501\n",
      " =================================== \n",
      "END SCORE:  8.351328125\n",
      " =================================== \n",
      "END SCORE:  16.603995538375003\n",
      " =================================== \n",
      "END SCORE:  6.8479966519375\n",
      " =================================== \n",
      "END SCORE:  11.3474053081875\n",
      " =================================== \n",
      "END SCORE:  6.767291671250001\n",
      " =================================== \n",
      "END SCORE:  12.0438958333125\n",
      " =================================== \n",
      "END SCORE:  8.447121211625\n",
      " =================================== \n",
      "END SCORE:  15.523571426812502\n",
      " =================================== \n",
      "END SCORE:  13.612155935187497\n",
      " =================================== \n",
      "END SCORE:  13.986562500000002\n",
      " =================================== \n",
      "END SCORE:  7.5657583649375\n",
      " =================================== \n",
      "END SCORE:  9.374200599874998\n",
      " =================================== \n",
      "END SCORE:  12.389444444375002\n",
      " =================================== \n",
      "END SCORE:  7.2627083335\n",
      " =================================== \n",
      "END SCORE:  9.130786113249997\n",
      " =================================== \n",
      "END SCORE:  14.94738095775\n",
      " =================================== \n",
      "END SCORE:  11.12347537375\n",
      " =================================== \n",
      "END SCORE:  11.6695833375\n",
      " =================================== \n",
      "END SCORE:  10.758229164812498\n",
      " =================================== \n",
      "END SCORE:  8.996562499562499\n",
      " =================================== \n",
      "END SCORE:  13.427144208312502\n",
      " =================================== \n",
      "END SCORE:  14.4762202368125\n",
      " =================================== \n",
      "END SCORE:  6.700726188500001\n",
      " =================================== \n",
      "END SCORE:  16.559531250000003\n",
      " =================================== \n",
      "END SCORE:  6.496901041875\n",
      " =================================== \n",
      "END SCORE:  9.550502140125\n",
      " =================================== \n",
      "END SCORE:  11.427013886625\n",
      " =================================== \n",
      "END SCORE:  18.219246526687506\n",
      " =================================== \n",
      "END SCORE:  16.3334794585\n",
      " =================================== \n",
      "END SCORE:  12.951041662624998\n",
      " =================================== \n",
      "END SCORE:  8.436726189937502\n",
      " =================================== \n",
      "END SCORE:  10.160630952375\n",
      " =================================== \n",
      "END SCORE:  12.457564103812501\n",
      " =================================== \n",
      "END SCORE:  10.026614583312499\n",
      " =================================== \n",
      "END SCORE:  11.7038100978125\n",
      " =================================== \n",
      "END SCORE:  17.7625\n",
      " =================================== \n",
      "END SCORE:  23.840157583937497\n",
      " =================================== \n",
      "END SCORE:  9.227083331249998\n",
      " =================================== \n",
      "END SCORE:  20.658541667312505\n",
      " =================================== \n",
      "END SCORE:  12.1927232115\n",
      " =================================== \n",
      "END SCORE:  14.912812495187499\n",
      " =================================== \n",
      "END SCORE:  14.9446858976875\n",
      " =================================== \n",
      "END SCORE:  17.871375\n",
      " =================================== \n",
      "END SCORE:  16.3816713710625\n",
      " =================================== \n",
      "END SCORE:  9.674453125000001\n",
      " =================================== \n",
      "END SCORE:  9.08579166625\n",
      " =================================== \n",
      "END SCORE:  9.418303575\n",
      " =================================== \n",
      "END SCORE:  12.191513158125002\n",
      " =================================== \n",
      "END SCORE:  17.8641964295\n",
      " =================================== \n",
      "END SCORE:  11.8999029355625\n",
      " =================================== \n",
      "END SCORE:  12.202745537437497\n"
     ]
    }
   ],
   "source": [
    "#Create dictionary of ideal values for each score; calculate L2 norm to generate score)\n",
    "IDEAL_VALS = {'hematocrit': {'male': 45.5, 'female': 42}, 'RBC': {'male': 5.4, 'female': 4.8}, 'MCH': {'male': 29, 'female': 29}, \"MCV\": {'male': 30.5, 'female': 29.5}, \n",
    "              'RDW': {'male': 13.5, 'female': 13.5}, 'Leucocyte': {'male': 7.2, 'female': 7.2}, \"Platelets\": {'male': 240, 'female': 240}, \n",
    "              'Neutrophils': {'male': 50, 'female': 50}, 'Basophils': {'male': 0.75, 'female': 0.75}, 'Lymphocyte': {'male': 20, 'female': 20}, \n",
    "              'Creatinine': {'male': 1, 'female': 1.1}, 'Blood potassium': {'male': 4.4, 'female': 4.4}, \n",
    "              'Blood sodium': {'male': 140, 'female': 140}, 'Chloride': {'male': 102.5, 'female': 102.5}}\n",
    "columns = BLOOD_DATASET_D1.columns.values.tolist()\n",
    "#Generate scores for each lever - compare iteratively and generate average score on its basis\n",
    "def generate_score(row):\n",
    "    #Iterate over each column except for gender\n",
    "    print(\" =================================== \")\n",
    "    total_score = 0\n",
    "    for column in columns:\n",
    "        if column == \"gender\" or column == \"SCORE\": continue\n",
    "        #Check gender\n",
    "        if row['gender'] == 1:\n",
    "            #Compute absolute value between the current value of the column and the recommended value\n",
    "            column_score = abs(row[column] - IDEAL_VALS[column]['male'])\n",
    "            # print('COLUMN: ', column)\n",
    "            # print('COLUMN SCORE: ', column_score)\n",
    "            # print('COLUMN VALUE: ', row[column])\n",
    "            # print('IDEAL VALUE: ', IDEAL_VALS[column]['male'])\n",
    "        else:\n",
    "            column_score = abs(row[column] - IDEAL_VALS[column]['female'])\n",
    "            # print('COLUMN: ', column)\n",
    "            # print('COLUMN SCORE: ', column_score)\n",
    "            # print('COLUMN VALUE: ', row[column])\n",
    "            # print('IDEAL VALUE: ', IDEAL_VALS[column]['male'])\n",
    "        total_score += column_score\n",
    "        \n",
    "    #Average out the score\n",
    "    row[\"SCORE\"] = total_score / len(columns)\n",
    "    print('END SCORE: ', row[\"SCORE\"])\n",
    "    return row\n",
    "\n",
    "BLOOD_DATASET_D1 = BLOOD_DATASET_D1.apply(generate_score, axis = \"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "BLOOD_DATASET_D1.to_pickle('PREPROCESSED_BLOOD_D1_DATASET.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape tensors for XGBOOST\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_features = BLOOD_DATASET_D1.iloc[:, 1:15]\n",
    "y_labels = BLOOD_DATASET_D1[\"SCORE\"]\n",
    "train_x, valid_x, train_y, valid_y = train_test_split(x_features, y_labels, random_state = 2, shuffle = True)\n",
    "\n",
    "train_x = tf.convert_to_tensor(train_x)\n",
    "train_x = tf.reshape(train_x, [len(train_x), 14])\n",
    "\n",
    "valid_x = tf.convert_to_tensor(valid_x)\n",
    "valid_x = tf.reshape(valid_x, [len(valid_x), 14])\n",
    "\n",
    "train_y = tf.convert_to_tensor(train_y)\n",
    "train_y = tf.reshape(train_y, [len(train_y), 1])\n",
    "\n",
    "valid_y = tf.convert_to_tensor(valid_y)\n",
    "valid_y = tf.reshape(valid_y, [len(valid_y), 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[07:19:39] WARNING: C:\\Windows\\Temp\\abs_557yfx631l\\croots\\recipe\\xgboost-split_1659548953302\\work\\src\\learner.cc:576: \n",
      "Parameters: { \"n_estimators\" } might not be used.\n",
      "\n",
      "  This could be a false alarm, with some parameters getting used by language bindings but\n",
      "  then being mistakenly passed down to XGBoost core, or some parameter actually being used\n",
      "  but getting flagged wrongly here. Please open an issue if you find any such cases.\n",
      "\n",
      "\n",
      "[0]\tTRAIN_DATA-rmse:12.81229\tVALID_DATA-rmse:12.12894\n",
      "[1]\tTRAIN_DATA-rmse:11.75022\tVALID_DATA-rmse:11.04232\n",
      "[2]\tTRAIN_DATA-rmse:10.73744\tVALID_DATA-rmse:10.05028\n",
      "[3]\tTRAIN_DATA-rmse:9.79794\tVALID_DATA-rmse:9.12993\n",
      "[4]\tTRAIN_DATA-rmse:8.97348\tVALID_DATA-rmse:8.32236\n",
      "[5]\tTRAIN_DATA-rmse:8.19311\tVALID_DATA-rmse:7.57078\n",
      "[6]\tTRAIN_DATA-rmse:7.58328\tVALID_DATA-rmse:6.96329\n",
      "[7]\tTRAIN_DATA-rmse:7.02221\tVALID_DATA-rmse:6.39212\n",
      "[8]\tTRAIN_DATA-rmse:6.56486\tVALID_DATA-rmse:5.92131\n",
      "[9]\tTRAIN_DATA-rmse:6.03059\tVALID_DATA-rmse:5.40248\n",
      "[10]\tTRAIN_DATA-rmse:5.66479\tVALID_DATA-rmse:5.03224\n",
      "[11]\tTRAIN_DATA-rmse:5.26215\tVALID_DATA-rmse:4.62843\n",
      "[12]\tTRAIN_DATA-rmse:4.97521\tVALID_DATA-rmse:4.33145\n",
      "[13]\tTRAIN_DATA-rmse:4.64250\tVALID_DATA-rmse:3.99777\n",
      "[14]\tTRAIN_DATA-rmse:4.34729\tVALID_DATA-rmse:3.71984\n",
      "[15]\tTRAIN_DATA-rmse:4.04539\tVALID_DATA-rmse:3.47055\n",
      "[16]\tTRAIN_DATA-rmse:3.84344\tVALID_DATA-rmse:3.28598\n",
      "[17]\tTRAIN_DATA-rmse:3.68792\tVALID_DATA-rmse:3.10781\n",
      "[18]\tTRAIN_DATA-rmse:3.44973\tVALID_DATA-rmse:2.89897\n",
      "[19]\tTRAIN_DATA-rmse:3.28206\tVALID_DATA-rmse:2.75446\n",
      "[20]\tTRAIN_DATA-rmse:3.08420\tVALID_DATA-rmse:2.59186\n",
      "[21]\tTRAIN_DATA-rmse:2.99005\tVALID_DATA-rmse:2.47411\n",
      "[22]\tTRAIN_DATA-rmse:2.83071\tVALID_DATA-rmse:2.33176\n",
      "[23]\tTRAIN_DATA-rmse:2.65038\tVALID_DATA-rmse:2.21208\n",
      "[24]\tTRAIN_DATA-rmse:2.56520\tVALID_DATA-rmse:2.15165\n",
      "[25]\tTRAIN_DATA-rmse:2.47088\tVALID_DATA-rmse:2.06942\n",
      "[26]\tTRAIN_DATA-rmse:2.41882\tVALID_DATA-rmse:2.02048\n",
      "[27]\tTRAIN_DATA-rmse:2.34776\tVALID_DATA-rmse:1.97032\n",
      "[28]\tTRAIN_DATA-rmse:2.30905\tVALID_DATA-rmse:1.92475\n",
      "[29]\tTRAIN_DATA-rmse:2.24834\tVALID_DATA-rmse:1.89688\n",
      "[30]\tTRAIN_DATA-rmse:2.21707\tVALID_DATA-rmse:1.86541\n",
      "[31]\tTRAIN_DATA-rmse:2.16896\tVALID_DATA-rmse:1.85569\n",
      "[32]\tTRAIN_DATA-rmse:2.05733\tVALID_DATA-rmse:1.77300\n",
      "[33]\tTRAIN_DATA-rmse:2.03894\tVALID_DATA-rmse:1.75032\n",
      "[34]\tTRAIN_DATA-rmse:1.96702\tVALID_DATA-rmse:1.68230\n",
      "[35]\tTRAIN_DATA-rmse:1.94573\tVALID_DATA-rmse:1.67253\n",
      "[36]\tTRAIN_DATA-rmse:1.85999\tVALID_DATA-rmse:1.61732\n",
      "[37]\tTRAIN_DATA-rmse:1.79391\tVALID_DATA-rmse:1.57783\n",
      "[38]\tTRAIN_DATA-rmse:1.72027\tVALID_DATA-rmse:1.52783\n",
      "[39]\tTRAIN_DATA-rmse:1.69068\tVALID_DATA-rmse:1.51769\n",
      "[40]\tTRAIN_DATA-rmse:1.64064\tVALID_DATA-rmse:1.47513\n",
      "[41]\tTRAIN_DATA-rmse:1.61938\tVALID_DATA-rmse:1.46402\n",
      "[42]\tTRAIN_DATA-rmse:1.55055\tVALID_DATA-rmse:1.42370\n",
      "[43]\tTRAIN_DATA-rmse:1.54086\tVALID_DATA-rmse:1.41442\n",
      "[44]\tTRAIN_DATA-rmse:1.51202\tVALID_DATA-rmse:1.39542\n",
      "[45]\tTRAIN_DATA-rmse:1.46294\tVALID_DATA-rmse:1.37098\n",
      "[46]\tTRAIN_DATA-rmse:1.44390\tVALID_DATA-rmse:1.36547\n",
      "[47]\tTRAIN_DATA-rmse:1.42245\tVALID_DATA-rmse:1.36334\n",
      "[48]\tTRAIN_DATA-rmse:1.40950\tVALID_DATA-rmse:1.35649\n",
      "[49]\tTRAIN_DATA-rmse:1.37106\tVALID_DATA-rmse:1.32988\n",
      "[50]\tTRAIN_DATA-rmse:1.35145\tVALID_DATA-rmse:1.33007\n",
      "[51]\tTRAIN_DATA-rmse:1.31789\tVALID_DATA-rmse:1.30896\n",
      "[52]\tTRAIN_DATA-rmse:1.30628\tVALID_DATA-rmse:1.29601\n",
      "[53]\tTRAIN_DATA-rmse:1.29130\tVALID_DATA-rmse:1.28398\n",
      "[54]\tTRAIN_DATA-rmse:1.27371\tVALID_DATA-rmse:1.27197\n",
      "[55]\tTRAIN_DATA-rmse:1.25886\tVALID_DATA-rmse:1.26274\n",
      "[56]\tTRAIN_DATA-rmse:1.23065\tVALID_DATA-rmse:1.25419\n",
      "[57]\tTRAIN_DATA-rmse:1.20781\tVALID_DATA-rmse:1.24902\n",
      "[58]\tTRAIN_DATA-rmse:1.19788\tVALID_DATA-rmse:1.24769\n",
      "[59]\tTRAIN_DATA-rmse:1.17083\tVALID_DATA-rmse:1.23318\n",
      "[60]\tTRAIN_DATA-rmse:1.16361\tVALID_DATA-rmse:1.22088\n",
      "[61]\tTRAIN_DATA-rmse:1.15058\tVALID_DATA-rmse:1.21614\n",
      "[62]\tTRAIN_DATA-rmse:1.14703\tVALID_DATA-rmse:1.21287\n",
      "[63]\tTRAIN_DATA-rmse:1.13524\tVALID_DATA-rmse:1.20171\n",
      "[64]\tTRAIN_DATA-rmse:1.13451\tVALID_DATA-rmse:1.20084\n",
      "[65]\tTRAIN_DATA-rmse:1.11832\tVALID_DATA-rmse:1.19770\n",
      "[66]\tTRAIN_DATA-rmse:1.10475\tVALID_DATA-rmse:1.19464\n",
      "[67]\tTRAIN_DATA-rmse:1.09682\tVALID_DATA-rmse:1.18398\n",
      "[68]\tTRAIN_DATA-rmse:1.08026\tVALID_DATA-rmse:1.17760\n",
      "[69]\tTRAIN_DATA-rmse:1.07849\tVALID_DATA-rmse:1.17568\n",
      "[70]\tTRAIN_DATA-rmse:1.07673\tVALID_DATA-rmse:1.17366\n",
      "[71]\tTRAIN_DATA-rmse:1.04965\tVALID_DATA-rmse:1.16583\n",
      "[72]\tTRAIN_DATA-rmse:1.04753\tVALID_DATA-rmse:1.16396\n",
      "[73]\tTRAIN_DATA-rmse:1.03467\tVALID_DATA-rmse:1.16198\n",
      "[74]\tTRAIN_DATA-rmse:1.01117\tVALID_DATA-rmse:1.15553\n",
      "[75]\tTRAIN_DATA-rmse:1.00627\tVALID_DATA-rmse:1.15321\n",
      "[76]\tTRAIN_DATA-rmse:1.00277\tVALID_DATA-rmse:1.15191\n",
      "[77]\tTRAIN_DATA-rmse:0.99668\tVALID_DATA-rmse:1.15317\n",
      "[78]\tTRAIN_DATA-rmse:0.99220\tVALID_DATA-rmse:1.14898\n",
      "[79]\tTRAIN_DATA-rmse:0.99132\tVALID_DATA-rmse:1.14790\n",
      "[80]\tTRAIN_DATA-rmse:0.98443\tVALID_DATA-rmse:1.14714\n",
      "[81]\tTRAIN_DATA-rmse:0.97549\tVALID_DATA-rmse:1.14227\n",
      "[82]\tTRAIN_DATA-rmse:0.97286\tVALID_DATA-rmse:1.14111\n",
      "[83]\tTRAIN_DATA-rmse:0.97213\tVALID_DATA-rmse:1.14024\n",
      "[84]\tTRAIN_DATA-rmse:0.96981\tVALID_DATA-rmse:1.14080\n",
      "[85]\tTRAIN_DATA-rmse:0.96643\tVALID_DATA-rmse:1.13956\n",
      "[86]\tTRAIN_DATA-rmse:0.96600\tVALID_DATA-rmse:1.13882\n",
      "[87]\tTRAIN_DATA-rmse:0.96094\tVALID_DATA-rmse:1.13698\n",
      "[88]\tTRAIN_DATA-rmse:0.94355\tVALID_DATA-rmse:1.13605\n",
      "[89]\tTRAIN_DATA-rmse:0.94161\tVALID_DATA-rmse:1.13428\n",
      "[90]\tTRAIN_DATA-rmse:0.94015\tVALID_DATA-rmse:1.13307\n",
      "[91]\tTRAIN_DATA-rmse:0.93946\tVALID_DATA-rmse:1.13195\n",
      "[92]\tTRAIN_DATA-rmse:0.93368\tVALID_DATA-rmse:1.13002\n",
      "[93]\tTRAIN_DATA-rmse:0.92820\tVALID_DATA-rmse:1.12853\n",
      "[94]\tTRAIN_DATA-rmse:0.92624\tVALID_DATA-rmse:1.12840\n",
      "[95]\tTRAIN_DATA-rmse:0.92132\tVALID_DATA-rmse:1.12847\n",
      "[96]\tTRAIN_DATA-rmse:0.91576\tVALID_DATA-rmse:1.12561\n",
      "[97]\tTRAIN_DATA-rmse:0.91529\tVALID_DATA-rmse:1.12480\n",
      "[98]\tTRAIN_DATA-rmse:0.90011\tVALID_DATA-rmse:1.12166\n",
      "[99]\tTRAIN_DATA-rmse:0.89894\tVALID_DATA-rmse:1.12186\n",
      "[100]\tTRAIN_DATA-rmse:0.89633\tVALID_DATA-rmse:1.11941\n",
      "[101]\tTRAIN_DATA-rmse:0.89582\tVALID_DATA-rmse:1.11898\n",
      "[102]\tTRAIN_DATA-rmse:0.89380\tVALID_DATA-rmse:1.11921\n",
      "[103]\tTRAIN_DATA-rmse:0.89341\tVALID_DATA-rmse:1.11902\n",
      "[104]\tTRAIN_DATA-rmse:0.89220\tVALID_DATA-rmse:1.11779\n",
      "[105]\tTRAIN_DATA-rmse:0.88038\tVALID_DATA-rmse:1.11779\n",
      "[106]\tTRAIN_DATA-rmse:0.87512\tVALID_DATA-rmse:1.11211\n",
      "[107]\tTRAIN_DATA-rmse:0.87330\tVALID_DATA-rmse:1.10939\n",
      "[108]\tTRAIN_DATA-rmse:0.87243\tVALID_DATA-rmse:1.10810\n",
      "[109]\tTRAIN_DATA-rmse:0.86028\tVALID_DATA-rmse:1.10602\n",
      "[110]\tTRAIN_DATA-rmse:0.85939\tVALID_DATA-rmse:1.10541\n",
      "[111]\tTRAIN_DATA-rmse:0.85903\tVALID_DATA-rmse:1.10473\n",
      "[112]\tTRAIN_DATA-rmse:0.84763\tVALID_DATA-rmse:1.10346\n",
      "[113]\tTRAIN_DATA-rmse:0.84479\tVALID_DATA-rmse:1.10254\n",
      "[114]\tTRAIN_DATA-rmse:0.83960\tVALID_DATA-rmse:1.10275\n",
      "[115]\tTRAIN_DATA-rmse:0.83837\tVALID_DATA-rmse:1.10176\n",
      "[116]\tTRAIN_DATA-rmse:0.83700\tVALID_DATA-rmse:1.10007\n",
      "[117]\tTRAIN_DATA-rmse:0.83626\tVALID_DATA-rmse:1.10011\n",
      "[118]\tTRAIN_DATA-rmse:0.83414\tVALID_DATA-rmse:1.10034\n",
      "[119]\tTRAIN_DATA-rmse:0.83283\tVALID_DATA-rmse:1.09862\n",
      "[120]\tTRAIN_DATA-rmse:0.82373\tVALID_DATA-rmse:1.09806\n",
      "[121]\tTRAIN_DATA-rmse:0.82142\tVALID_DATA-rmse:1.09641\n",
      "[122]\tTRAIN_DATA-rmse:0.82132\tVALID_DATA-rmse:1.09621\n",
      "[123]\tTRAIN_DATA-rmse:0.81884\tVALID_DATA-rmse:1.09501\n",
      "[124]\tTRAIN_DATA-rmse:0.81642\tVALID_DATA-rmse:1.09224\n",
      "[125]\tTRAIN_DATA-rmse:0.81437\tVALID_DATA-rmse:1.09182\n",
      "[126]\tTRAIN_DATA-rmse:0.81280\tVALID_DATA-rmse:1.09040\n",
      "[127]\tTRAIN_DATA-rmse:0.81153\tVALID_DATA-rmse:1.09016\n",
      "[128]\tTRAIN_DATA-rmse:0.80429\tVALID_DATA-rmse:1.09016\n",
      "[129]\tTRAIN_DATA-rmse:0.80312\tVALID_DATA-rmse:1.08913\n",
      "[130]\tTRAIN_DATA-rmse:0.80043\tVALID_DATA-rmse:1.08895\n",
      "[131]\tTRAIN_DATA-rmse:0.79004\tVALID_DATA-rmse:1.09005\n",
      "[132]\tTRAIN_DATA-rmse:0.78786\tVALID_DATA-rmse:1.09096\n",
      "[133]\tTRAIN_DATA-rmse:0.78751\tVALID_DATA-rmse:1.09033\n",
      "[134]\tTRAIN_DATA-rmse:0.78705\tVALID_DATA-rmse:1.08933\n",
      "[135]\tTRAIN_DATA-rmse:0.78444\tVALID_DATA-rmse:1.08807\n",
      "[136]\tTRAIN_DATA-rmse:0.78444\tVALID_DATA-rmse:1.08807\n",
      "[137]\tTRAIN_DATA-rmse:0.78229\tVALID_DATA-rmse:1.08688\n",
      "[138]\tTRAIN_DATA-rmse:0.78216\tVALID_DATA-rmse:1.08662\n",
      "[139]\tTRAIN_DATA-rmse:0.78165\tVALID_DATA-rmse:1.08602\n",
      "[140]\tTRAIN_DATA-rmse:0.77042\tVALID_DATA-rmse:1.08632\n",
      "[141]\tTRAIN_DATA-rmse:0.77037\tVALID_DATA-rmse:1.08622\n",
      "[142]\tTRAIN_DATA-rmse:0.76332\tVALID_DATA-rmse:1.08970\n",
      "[143]\tTRAIN_DATA-rmse:0.76332\tVALID_DATA-rmse:1.08970\n",
      "[144]\tTRAIN_DATA-rmse:0.76315\tVALID_DATA-rmse:1.08957\n",
      "[145]\tTRAIN_DATA-rmse:0.76177\tVALID_DATA-rmse:1.08776\n",
      "[146]\tTRAIN_DATA-rmse:0.75795\tVALID_DATA-rmse:1.08693\n",
      "[147]\tTRAIN_DATA-rmse:0.75691\tVALID_DATA-rmse:1.08594\n",
      "[148]\tTRAIN_DATA-rmse:0.75580\tVALID_DATA-rmse:1.08443\n",
      "[149]\tTRAIN_DATA-rmse:0.75570\tVALID_DATA-rmse:1.08423\n",
      "[150]\tTRAIN_DATA-rmse:0.74711\tVALID_DATA-rmse:1.08659\n",
      "[151]\tTRAIN_DATA-rmse:0.74605\tVALID_DATA-rmse:1.08586\n",
      "[152]\tTRAIN_DATA-rmse:0.74301\tVALID_DATA-rmse:1.08684\n",
      "[153]\tTRAIN_DATA-rmse:0.74273\tVALID_DATA-rmse:1.08642\n",
      "[154]\tTRAIN_DATA-rmse:0.74227\tVALID_DATA-rmse:1.08579\n",
      "[155]\tTRAIN_DATA-rmse:0.74204\tVALID_DATA-rmse:1.08532\n",
      "[156]\tTRAIN_DATA-rmse:0.74111\tVALID_DATA-rmse:1.08448\n",
      "[157]\tTRAIN_DATA-rmse:0.74111\tVALID_DATA-rmse:1.08448\n",
      "[158]\tTRAIN_DATA-rmse:0.74062\tVALID_DATA-rmse:1.08399\n",
      "[159]\tTRAIN_DATA-rmse:0.73861\tVALID_DATA-rmse:1.08160\n",
      "[160]\tTRAIN_DATA-rmse:0.73831\tVALID_DATA-rmse:1.08119\n",
      "[161]\tTRAIN_DATA-rmse:0.73804\tVALID_DATA-rmse:1.08105\n",
      "[162]\tTRAIN_DATA-rmse:0.73769\tVALID_DATA-rmse:1.08075\n",
      "[163]\tTRAIN_DATA-rmse:0.73754\tVALID_DATA-rmse:1.08079\n",
      "[164]\tTRAIN_DATA-rmse:0.73737\tVALID_DATA-rmse:1.08055\n",
      "[165]\tTRAIN_DATA-rmse:0.73540\tVALID_DATA-rmse:1.07865\n",
      "[166]\tTRAIN_DATA-rmse:0.73540\tVALID_DATA-rmse:1.07865\n",
      "[167]\tTRAIN_DATA-rmse:0.73486\tVALID_DATA-rmse:1.07827\n",
      "[168]\tTRAIN_DATA-rmse:0.73344\tVALID_DATA-rmse:1.07679\n",
      "[169]\tTRAIN_DATA-rmse:0.72925\tVALID_DATA-rmse:1.07588\n",
      "[170]\tTRAIN_DATA-rmse:0.72844\tVALID_DATA-rmse:1.07487\n",
      "[171]\tTRAIN_DATA-rmse:0.72793\tVALID_DATA-rmse:1.07453\n",
      "[172]\tTRAIN_DATA-rmse:0.72793\tVALID_DATA-rmse:1.07453\n",
      "[173]\tTRAIN_DATA-rmse:0.72777\tVALID_DATA-rmse:1.07467\n",
      "[174]\tTRAIN_DATA-rmse:0.72777\tVALID_DATA-rmse:1.07467\n",
      "[175]\tTRAIN_DATA-rmse:0.72459\tVALID_DATA-rmse:1.07467\n",
      "[176]\tTRAIN_DATA-rmse:0.72396\tVALID_DATA-rmse:1.07381\n",
      "[177]\tTRAIN_DATA-rmse:0.72385\tVALID_DATA-rmse:1.07377\n",
      "[178]\tTRAIN_DATA-rmse:0.72385\tVALID_DATA-rmse:1.07377\n",
      "[179]\tTRAIN_DATA-rmse:0.72385\tVALID_DATA-rmse:1.07377\n",
      "[180]\tTRAIN_DATA-rmse:0.71623\tVALID_DATA-rmse:1.07522\n",
      "[181]\tTRAIN_DATA-rmse:0.71455\tVALID_DATA-rmse:1.07453\n",
      "[182]\tTRAIN_DATA-rmse:0.71455\tVALID_DATA-rmse:1.07453\n",
      "[183]\tTRAIN_DATA-rmse:0.71455\tVALID_DATA-rmse:1.07453\n",
      "[184]\tTRAIN_DATA-rmse:0.71438\tVALID_DATA-rmse:1.07437\n",
      "[185]\tTRAIN_DATA-rmse:0.71438\tVALID_DATA-rmse:1.07437\n",
      "[186]\tTRAIN_DATA-rmse:0.71438\tVALID_DATA-rmse:1.07437\n",
      "[187]\tTRAIN_DATA-rmse:0.71438\tVALID_DATA-rmse:1.07437\n",
      "[188]\tTRAIN_DATA-rmse:0.71427\tVALID_DATA-rmse:1.07431\n",
      "[189]\tTRAIN_DATA-rmse:0.71179\tVALID_DATA-rmse:1.07422\n",
      "[190]\tTRAIN_DATA-rmse:0.70954\tVALID_DATA-rmse:1.07422\n",
      "[191]\tTRAIN_DATA-rmse:0.70939\tVALID_DATA-rmse:1.07397\n",
      "[192]\tTRAIN_DATA-rmse:0.70719\tVALID_DATA-rmse:1.07379\n",
      "[193]\tTRAIN_DATA-rmse:0.70470\tVALID_DATA-rmse:1.07347\n",
      "[194]\tTRAIN_DATA-rmse:0.70424\tVALID_DATA-rmse:1.07358\n",
      "[195]\tTRAIN_DATA-rmse:0.70421\tVALID_DATA-rmse:1.07359\n",
      "[196]\tTRAIN_DATA-rmse:0.70163\tVALID_DATA-rmse:1.07235\n",
      "[197]\tTRAIN_DATA-rmse:0.70143\tVALID_DATA-rmse:1.07216\n",
      "[198]\tTRAIN_DATA-rmse:0.69960\tVALID_DATA-rmse:1.07216\n",
      "[199]\tTRAIN_DATA-rmse:0.69626\tVALID_DATA-rmse:1.07273\n",
      "ERROR:  1.1507578015175497\n"
     ]
    }
   ],
   "source": [
    "#TRAIN GRADIENT BOOSTED TREE\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "#Convert to Dmatrix\n",
    "TRAIN_DATA = xgb.DMatrix(train_x, train_y, feature_names = columns[1:15])\n",
    "VALID_DATA = xgb.DMatrix(valid_x, valid_y, feature_names = columns[1:15])\n",
    "#Parameters for boosted tree\n",
    "XGBOOST_PARAMS = {\"objective\": \"reg:squarederror\", \"subsample\": 0.6,\n",
    "                  \"colsample_bytree\" : 0.6, \"learning_rate\" : 0.1, \"max_depth\" : 100,\n",
    "                  \"alpha\": 20, \"n_estimators\": 12}\n",
    "#Train the xgboost model\n",
    "# XGB_BLOOD_MODEL = xgb.XGBRegressor()\n",
    "# XGB_BLOOD_MODEL.fit(train_x, train_y)\n",
    "XGB_BLOOD_MODEL = xgb.train(XGBOOST_PARAMS, TRAIN_DATA, evals = [(TRAIN_DATA, \"TRAIN_DATA\"), (VALID_DATA, \"VALID_DATA\")],\n",
    "                            num_boost_round = 200, early_stopping_rounds = 40)\n",
    "predictions = XGB_BLOOD_MODEL.predict(VALID_DATA)\n",
    "error = mean_squared_error(valid_y, predictions)\n",
    "print('ERROR: ', error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save Model\n",
    "XGB_BLOOD_MODEL.save_model('REPORTS_BLOOD_GRADBOOSTED_MODELS[1.5].model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #Print desicion tree -> transparency into exactly what the model is doing\n",
    "xgb.plot_tree(XGB_BLOOD_MODEL, num_trees = 0)\n",
    "plt.rcParams['figure.figsize'] = [1920, 1080]\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "bb33c15c13a14f140d690812bf0a39a2e5e1e289641f45cea7a1bbeb79eff1a7"
  },
  "kernelspec": {
   "display_name": "Python 3.8.13 ('base')",
   "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": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}