{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read CSV with TrackId and Genre Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "tracks_info_df = pd.read_csv('combined_dataset.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read processed song data from pkl file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 100390 processed tracks\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import os\n",
    "\n",
    "\n",
    "results_file = 'audio_features_backup.pkl'\n",
    "\n",
    "if os.path.exists(results_file):\n",
    "    with open(results_file, 'rb') as file:\n",
    "        saved_data = pickle.load(file)\n",
    "    X = saved_data.get('X', [])\n",
    "    y_labels = saved_data.get('y_labels', [])\n",
    "    \n",
    "    processed_files = set(saved_data.get('processed_files', []))\n",
    "    print(f\"Loaded {len(processed_files)} processed tracks\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Turn X into an np array for easier processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of feature matrix X:  (100390, 55)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = np.array(X)\n",
    "print(\"shape of feature matrix X: \", X.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove all entries where the genre is defined as None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = X[np.array(y_labels) != None]\n",
    "y_labels = np.array(y_labels)[np.array(y_labels) != None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remap genres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace sub-genres with their parent genre based on a predefined mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_mapping = {\n",
    "    'lo-fi beats': 'lo-fi',\n",
    "    'chillwave': 'lo-fi',\n",
    "\n",
    "    'nu metal': 'metal',\n",
    "    'folk metal': 'metal',\n",
    "    'heavy metal': 'metal',\n",
    "    'metalcore': 'metal',\n",
    "    'power metal': 'metal',\n",
    "    'industrial metal': 'metal',\n",
    "    'glam metal': 'metal',\n",
    "    'melodic death metal': 'metal',\n",
    "    'progressive metal': 'metal',\n",
    "    'thrash metal': 'metal',\n",
    "    'gothic metal': 'metal',\n",
    "    'groove metal': 'metal',\n",
    "    'death metal': 'metal',\n",
    "    'alternative metal': 'metal',\n",
    "    'black metal': 'metal',\n",
    "    'speed metal': 'metal',\n",
    "\n",
    "    'acid house': 'acid',\n",
    "    'acid techno': 'acid',\n",
    "\n",
    "    'afrobeat': 'afro',\n",
    "    'afropop': 'afro',\n",
    "\n",
    "    'art rock': 'rock',\n",
    "\n",
    "    'britpop': 'alternative rock',\n",
    "    \n",
    "    'art pop': 'pop',\n",
    "    'acoustic pop': 'pop',\n",
    "    'bedroom pop': 'pop',\n",
    "    'soft pop': 'pop', \n",
    "    'folk pop': 'pop',\n",
    "    'norwegian pop': 'pop',\n",
    "    'acoustic pop': 'pop',\n",
    "    'dream pop': 'pop',\n",
    "    'chamber pop': 'pop',\n",
    "    'german pop': 'pop',\n",
    "    'bedroom pop': 'pop',\n",
    "    'city pop': 'pop',\n",
    "    'dance pop': 'pop',\n",
    "    'art pop': 'pop',\n",
    "    'indie pop': 'pop',\n",
    "\n",
    "    'alt country': 'country',\n",
    "    'traditional country': 'country',\n",
    "\n",
    "    'blues rock': 'blues',\n",
    "\n",
    "    'brooklyn drill': 'drill',\n",
    "\n",
    "    'gangster rap': 'rap',\n",
    "    'memphis rap': 'rap',\n",
    "    'rock rap': 'rap',\n",
    "    'meme rap': 'rap',\n",
    "    'punk rap': 'rap',\n",
    "    'emo rap': 'rap',\n",
    "    'jazz rap': 'rap',\n",
    "    'melodic rap': 'rap',\n",
    "    'cloud rap': 'rap',\n",
    "    'k-rap': 'rap',\n",
    "    'rage rap': 'rap',\n",
    "\n",
    "    'edm trap': 'trap',\n",
    "    'italian trap': 'trap',\n",
    "    'dark trap': 'trap',\n",
    "\n",
    "    'hip-hop': 'hip hop',\n",
    "    'southern hip hop': 'hip hop',\n",
    "    'west coast hip hop': 'hip hop',\n",
    "    'east coast hip hop': 'hip hop',\n",
    "    'finnish hip hop': 'hip hop',\n",
    "    'german hip hop': 'hip hop',\n",
    "    'underground hip hop': 'hip hop',\n",
    "    'norwegian hip hop': 'hip hop',\n",
    "    'experimental hip hop': 'hip hop',\n",
    "    'alternative hip hop': 'hip hop',\n",
    "    'latin hip hop': 'hip hop',\n",
    "\n",
    "    'jazz blues': 'jazz',\n",
    "    'vocal jazz': 'jazz',\n",
    "    'french jazz': 'jazz',\n",
    "    'free jazz': 'jazz',\n",
    "    'soul jazz': 'jazz',\n",
    "    'jazz fusion': 'jazz',\n",
    "    'nu jazz': 'jazz',\n",
    "    'jazz funk': 'jazz',\n",
    "    'cool jazz': 'jazz',\n",
    "    'jazz beats': 'jazz',\n",
    "    'jazz house': 'jazz',\n",
    "    'indie jazz': 'jazz',\n",
    "    'smooth jazz': 'jazz',\n",
    "\n",
    "    'melodic techno': 'techno',\n",
    "    'minimal techno': 'techno',\n",
    "    'hypertechno': 'techno',\n",
    "    'hard techno': 'techno',\n",
    "    'hardcore techno': 'techno',\n",
    "    'dub techno': 'techno',\n",
    "\n",
    "    'future house': 'house',\n",
    "    'tech house': 'house',\n",
    "    'bass house': 'house',\n",
    "    'electro house': 'house',\n",
    "    'tropical house': 'house',\n",
    "    'french house': 'house',\n",
    "    'melodic house': 'house',\n",
    "    'organic house': 'house',\n",
    "    'progressive house': 'house',\n",
    "    'latin house': 'house',\n",
    "    'deep house': 'house',\n",
    "    'slap house': 'house',\n",
    "    'chicago house': 'house'\n",
    "    \n",
    "} \n",
    "\n",
    "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map hip-hop to rap -> Models without this are too confused due to the genres being so alike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_mapping = {\n",
    "    'hip hop': 'rap'\n",
    "} \n",
    "\n",
    "y_labels_normalized = [genre_mapping.get(label, label) for label in y_labels_normalized]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Or remove hip hop\n",
    "\n",
    "X = X[np.array(y_labels_normalized) != \"hip hop\"]\n",
    "y_labels_normalized = np.array(y_labels_normalized)[np.array(y_labels_normalized) != \"hip hop\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run this code in case the last two cells were not executed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_labels_normalized = y_labels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra: Keep only a popular type of genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered dataset size: 13447 entries (from 100383 original)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "original_labels_list = y_labels_normalized\n",
    "y_labels_normalized = np.array(y_labels_normalized)\n",
    "\n",
    "\n",
    "popular_genres = {\n",
    "    'country', 'idm', 'black-metal', 'hardstyle', 'heavy-metal',\n",
    "    'blues', 'rap', 'hip hop', 'classical', 'folk', 'rock-n-roll',\n",
    "    'electronic', 'jazz', 'lo-fi', 'punk-rock', 'soul', 'punk',\n",
    "    'rock'\n",
    "}\n",
    "\n",
    "\n",
    "#popular_genres = {\n",
    "#    'pop', 'rock', 'rap', 'hip hop', 'metal', 'country', 'jazz',\n",
    "#    'classical', 'reggae', 'punk', 'funk',\n",
    "#    'r&b', 'soul', 'trap', 'techno', 'indie'\n",
    "#}\n",
    "# blues folk\n",
    "\n",
    "y_lower = np.array([genre.lower() for genre in y_labels_normalized])\n",
    "\n",
    "mask = np.array([genre in popular_genres for genre in y_lower])\n",
    "\n",
    "X = X[mask]\n",
    "y_labels_normalized = y_labels_normalized[mask]\n",
    "\n",
    "# Optional: print some info\n",
    "print(f\"Filtered dataset size: {len(y_labels_normalized)} entries (from {len(original_labels_list)} original)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chart the leftover genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "y = y_labels_normalized\n",
    "\n",
    "freq_original = Counter(y)\n",
    "\n",
    "orig_genres = list(freq_original.keys())\n",
    "orig_counts = list(freq_original.values())\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "sns.barplot(x=orig_genres, y=orig_counts, ax=axs)\n",
    "axs.set_title(\"Original Genre Frequencies\")\n",
    "axs.set_xlabel(\"Genre\")\n",
    "axs.set_ylabel(\"Count\")\n",
    "axs.tick_params(axis='x', rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove all songs with genres which do not come up more than n times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class counts: Counter({'country': 1012, 'idm': 989, 'black-metal': 989, 'hardstyle': 987, 'heavy-metal': 985, 'blues': 931, 'rap': 925, 'classical': 849, 'folk': 819, 'rock-n-roll': 813, 'electronic': 807, 'jazz': 738, 'lo-fi': 727, 'punk-rock': 642, 'soul': 457, 'punk': 360, 'rock': 351, 'hip hop': 66})\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_49934/2892467199.py:49: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axs.set_xticklabels(le.classes_)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# --- Encoding/Standardizing X ---\n",
    "# Remove compex object variables\n",
    "if np.iscomplexobj(X):\n",
    "    X = np.abs(X)\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# --- Counting Labels ---\n",
    "# Check class counts.\n",
    "counter = Counter(y)\n",
    "print(\"Class counts:\", counter)\n",
    "\n",
    "# Keep only classes with at least n samples. At least 2  or any other % 2 == 0 number\n",
    "n = 100\n",
    "classes_to_keep = {cls for cls, count in counter.items() if count >= n}\n",
    "indices_to_keep = [i for i, label in enumerate(y) if label in classes_to_keep]\n",
    "\n",
    "y = np.array(y)\n",
    "\n",
    "# Filter the data.\n",
    "X_filtered = X_scaled[indices_to_keep]\n",
    "y_filtered = y[indices_to_keep]\n",
    "\n",
    "# Encode the Genres into numerical values\n",
    "le = LabelEncoder()\n",
    "y_encoded = le.fit_transform(y_filtered)\n",
    "\n",
    "freq_leftover = Counter(y_encoded)\n",
    "leftover_genres = list(freq_leftover.keys())\n",
    "leftover_counts = list(freq_leftover.values())\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "# Plot normalized label frequencies.\n",
    "sns.barplot(x=leftover_genres, y=leftover_counts, ax=axs)\n",
    "axs.set_title(\"Normalized Genre Frequencies\")\n",
    "axs.set_xlabel(\"Genre\")\n",
    "axs.set_ylabel(\"Count\")\n",
    "axs.tick_params(axis='x', rotation=45)\n",
    "axs.set_xticklabels(le.classes_)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove highly collerating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping columns: ['feature_5', 'feature_8', 'feature_9', 'feature_10', 'feature_12', 'feature_14', 'feature_16', 'feature_20', 'feature_21', 'feature_23', 'feature_25', 'feature_26', 'feature_30', 'feature_31', 'feature_32', 'feature_34', 'feature_37', 'feature_38', 'feature_41', 'feature_43', 'feature_44', 'feature_48', 'feature_50', 'feature_51', 'feature_53']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "CORRELATING_VALUE = 0.9\n",
    "\n",
    "X = X_filtered\n",
    "\n",
    "def remove_highly_correlated_features(X, threshold):\n",
    "    # If X is a NumPy array, convert it to a DataFrame.\n",
    "    if isinstance(X, np.ndarray):\n",
    "        # Create column names if not provided\n",
    "        X_df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])\n",
    "    else:\n",
    "        X_df = X.copy()\n",
    "    \n",
    "    # Compute the absolute correlation matrix\n",
    "    corr_matrix = X_df.corr().abs()\n",
    "    \n",
    "    # Create an upper triangle matrix of correlations\n",
    "    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
    "    \n",
    "    # Identify columns to drop: any feature with correlation greater than the threshold\n",
    "    to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
    "    print(\"Dropping columns:\", to_drop)\n",
    "    \n",
    "    # Drop these columns from the dataframe\n",
    "    X_df_reduced = X_df.drop(columns=to_drop)\n",
    "    \n",
    "    # If original X was a NumPy array, return as a NumPy array.\n",
    "    if isinstance(X, np.ndarray):\n",
    "        return X_df_reduced.values\n",
    "    else:\n",
    "        return X_df_reduced\n",
    "\n",
    "\n",
    "X_reduced = remove_highly_correlated_features(X, threshold=CORRELATING_VALUE)\n",
    "\n",
    "# -- Create a feature correlation matrix --\n",
    "df_features = pd.DataFrame(X_reduced, columns=[f'feature_{i}' for i in range(X_reduced.shape[1])])\n",
    "\n",
    "# Compute the correlation matrix.\n",
    "corr = df_features.corr()\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(corr, cmap='coolwarm', annot=False)\n",
    "plt.title('Feature Correlation Matrix')\n",
    "plt.show()\n",
    "\n",
    "X_filtered = X_reduced\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Test-Train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape: (10704, 30) (10704,)\n",
      "Test set shape: (2677, 30) (2677,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_filtered, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded\n",
    ")\n",
    "\n",
    "print(\"Training set shape:\", X_train.shape, y_train.shape)\n",
    "print(\"Test set shape:\", X_test.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:51:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:34] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:52:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:44] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:44] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:45] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:53:47] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:35] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:42] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:47] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:47] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:49] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:53] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:53] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:54] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:54:58] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:55:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:55:50] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:55:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:55:53] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:00] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:01] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:07] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:56:58] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:16] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:16] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:18] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:19] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:19] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:57:21] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:05] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:28] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:58:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:13] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:23] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:26] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:28] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:43] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [23:59:44] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:28] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:34] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:34] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:43] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:44] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:47] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:00:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:27] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:44] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:45] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:54] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:56] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:01:59] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:02:07] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:02:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:02:51] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:02:58] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:00] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:00] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:01] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:16] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:19] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:20] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:27] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:03:48] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:02] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:10] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:12] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:16] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:34] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:36] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:04:45] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:07] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:20] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:28] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:47] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:47] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:05:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:01] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:05] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:06] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:42] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:45] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:53] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:54] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:58] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:06:59] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:07:01] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:07:08] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:07:34] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:07:47] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:07:51] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:07:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:00] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:02] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:05] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:05] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:06] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:07] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:09] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:42] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:08:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:06] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:08] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:14] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:19] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:19] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:21] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:22] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:24] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:09:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:09] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:21] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:23] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:24] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:10:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:36] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:43] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:45] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:48] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:49] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:11:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:12:07] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:12:16] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:12:35] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:12:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:00] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:06] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:07] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:08] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:14] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:26] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:47] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:13:58] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:01] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:20] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:25] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:26] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:27] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:35] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:36] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:43] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:48] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:51] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:56] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:58] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:14:59] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:09] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:12] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:16] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:24] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:26] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:34] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:34] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:45] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:50] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:15:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:02] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:10] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:12] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:13] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:18] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:19] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:24] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:42] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:49] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:51] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:51] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:16:58] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:18] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:21] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:28] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:44] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:17:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:12] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:14] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:22] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:22] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:24] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:26] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:27] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:34] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:42] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:45] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:48] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:54] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:18:59] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:01] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:18] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:21] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:22] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:23] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:25] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:36] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:38] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:48] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:49] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:19:55] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:00] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:02] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:04] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:10] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:13] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:14] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:16] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:42] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:43] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:43] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:44] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:53] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:54] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:20:57] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:10] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:18] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:23] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:24] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:24] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:28] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:36] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:53] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:21:53] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:02] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:08] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:08] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:09] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:14] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:19] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:22] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:23] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:23] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:24] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:27] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:33] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:39] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:40] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:48] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:50] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:54] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:56] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:56] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:22:59] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:01] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:05] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:14] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:16] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:23] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:25] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:28] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:35] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:41] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:51] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:51] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:23:52] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:02] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:03] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:07] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:09] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:10] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:15] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:17] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:20] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:31] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:32] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:42] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:43] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:46] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:48] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:48] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:49] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:54] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:56] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:24:59] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:10] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:11] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:12] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:22] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:23] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:27] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:28] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:29] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:30] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:35] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:37] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:25:42] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n",
      "/home/agres/projects/predictify/.venv/lib/python3.10/site-packages/xgboost/training.py:183: UserWarning: [00:26:14] WARNING: /workspace/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  bst.update(dtrain, iteration=i, fobj=obj)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best XGBoost parameters: {'learning_rate': 0.1, 'max_depth': 11, 'n_estimators': 600, 'subsample': 0.8}\n",
      "Best XGBoost accuracy: 0.6641\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Parameter grid\n",
    "param_grid_xgb = {\n",
    "    'n_estimators': [300, 400, 500, 500, 600],\n",
    "    'max_depth': [9, 11, 13],\n",
    "    'learning_rate': [0.1, 0.3],\n",
    "    'subsample': [0.4, 0.8, 1],\n",
    "}\n",
    "\n",
    "# Grid search\n",
    "xgb = XGBClassifier(objective='multi:softmax', num_class=len(set(y)), use_label_encoder=False, eval_metric='mlogloss', random_state=42)\n",
    "grid_search_xgb = GridSearchCV(xgb, param_grid_xgb, cv=5, n_jobs=-1, scoring='accuracy')\n",
    "grid_search_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Best model\n",
    "final_model = grid_search_xgb.best_estimator_\n",
    "print(\"Best XGBoost parameters:\", grid_search_xgb.best_params_)\n",
    "print(f\"Best XGBoost accuracy: {grid_search_xgb.best_score_:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation Charts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix - Shows what was the predicted value compared to the true value when evaluating the Model\n",
    "\n",
    "The more values in the diagonal line the better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = final_model.predict(X_test)\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n",
    "disp.plot(cmap='Blues')\n",
    "plt.title(\"XGBoost Confusion Matrix\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}