GRANDE_minimal_example_with_comparison_MULT.ipynb

{
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   "outputs": [],
   "source": [
    "#!pip install catboost\n",
    "#!pip install xgboost\n",
    "#!pip install openml\n",
    "#!pip install numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf584ce8-8849-4a53-8ce9-2de8f9dd752d",
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [],
   "source": [
    "#specify GPU to use\n",
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '3'\n",
    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52b07238-f972-45df-86ce-a11cc11d72e3",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 400\n",
      "Validation set size: 100\n",
      "Test set size: 125\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>left-weight</th>\n",
       "      <th>left-distance</th>\n",
       "      <th>right-weight</th>\n",
       "      <th>right-distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2.0</td>\n",
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       "      <th>232</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     left-weight  left-distance  right-weight  right-distance\n",
       "538          5.0            2.0           3.0             4.0\n",
       "161          2.0            2.0           3.0             2.0\n",
       "541          5.0            2.0           4.0             2.0\n",
       "233          2.0            5.0           2.0             4.0\n",
       "232          2.0            5.0           2.0             3.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import openml\n",
    "import category_encoders as ce\n",
    "import numpy as np\n",
    "import sklearn\n",
    "\n",
    "# Load balance-scale dataset\n",
    "dataset = openml.datasets.get_dataset(11, download_data=True, download_qualities=True, download_features_meta_data=True)\n",
    "X, y, categorical_indicator, attribute_names = dataset.get_data(target=dataset.default_target_attribute)\n",
    "categorical_feature_indices = [idx for idx, idx_bool in enumerate(categorical_indicator) if idx_bool]\n",
    "\n",
    "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_temp, y_temp, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Training set size:\", len(X_train))\n",
    "print(\"Validation set size:\", len(X_valid))\n",
    "print(\"Test set size:\", len(X_test))\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf0a93c2-e9a4-4fa5-a7f6-44689dd07d37",
   "metadata": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-15 13:02:43.587816: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-03-15 13:02:44.911511: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "2024-03-15 13:02:47.736208: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
      "2024-03-15 13:02:47.736616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1928] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 34057 MB memory:  -> device: 0, name: NVIDIA RTX A6000, pci bus id: 0000:61:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1710504171.163998 1532411 service.cc:145] XLA service 0x7f7700004180 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1710504171.164035 1532411 service.cc:153]   StreamExecutor device (0): NVIDIA RTX A6000, Compute Capability 8.6\n",
      "2024-03-15 13:02:51.222540: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-03-15 13:02:51.461178: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8902\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/7\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m18s\u001b[0m 3s/step - loss: 1.2052"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1710504172.478097 1532411 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 336ms/step - loss: 1.1278 - val_loss: 1.0527\n",
      "Epoch 2/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 1.0825 - val_loss: 1.0108\n",
      "Epoch 3/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1.0406 - val_loss: 0.9691\n",
      "Epoch 4/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.9993 - val_loss: 0.9278\n",
      "Epoch 5/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.9588 - val_loss: 0.8876\n",
      "Epoch 6/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.9199 - val_loss: 0.8495\n",
      "Epoch 7/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.8831 - val_loss: 0.8125\n",
      "Epoch 8/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.8477 - val_loss: 0.7776\n",
      "Epoch 9/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.8137 - val_loss: 0.7451\n",
      "Epoch 10/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.7808 - val_loss: 0.7128\n",
      "Epoch 11/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.7470 - val_loss: 0.6839\n",
      "Epoch 12/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.7156 - val_loss: 0.6542\n",
      "Epoch 13/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6862 - val_loss: 0.6268\n",
      "Epoch 14/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.6576 - val_loss: 0.6018\n",
      "Epoch 15/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6324 - val_loss: 0.5765\n",
      "Epoch 16/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.6091 - val_loss: 0.5541\n",
      "Epoch 17/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5864 - val_loss: 0.5345\n",
      "Epoch 18/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5665 - val_loss: 0.5179\n",
      "Epoch 19/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.5502 - val_loss: 0.4998\n",
      "Epoch 20/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5340 - val_loss: 0.4873\n",
      "Epoch 21/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.5200 - val_loss: 0.4750\n",
      "Epoch 22/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.5059 - val_loss: 0.4628\n",
      "Epoch 23/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.4940 - val_loss: 0.4525\n",
      "Epoch 24/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.4827 - val_loss: 0.4424\n",
      "Epoch 25/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.4711 - val_loss: 0.4336\n",
      "Epoch 26/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.4627 - val_loss: 0.4251\n",
      "Epoch 27/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.4535 - val_loss: 0.4177\n",
      "Epoch 28/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.4427 - val_loss: 0.4087\n",
      "Epoch 29/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.4362 - val_loss: 0.4021\n",
      "Epoch 30/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.4279 - val_loss: 0.3952\n",
      "Epoch 31/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.4210 - val_loss: 0.3896\n",
      "Epoch 32/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.4140 - val_loss: 0.3830\n",
      "Epoch 33/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.4070 - val_loss: 0.3811\n",
      "Epoch 34/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.4015 - val_loss: 0.3754\n",
      "Epoch 35/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3961 - val_loss: 0.3711\n",
      "Epoch 36/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3903 - val_loss: 0.3649\n",
      "Epoch 37/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3835 - val_loss: 0.3616\n",
      "Epoch 38/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3803 - val_loss: 0.3564\n",
      "Epoch 39/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3736 - val_loss: 0.3528\n",
      "Epoch 40/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3691 - val_loss: 0.3495\n",
      "Epoch 41/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3633 - val_loss: 0.3471\n",
      "Epoch 42/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.3587 - val_loss: 0.3417\n",
      "Epoch 43/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3563 - val_loss: 0.3417\n",
      "Epoch 44/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3502 - val_loss: 0.3383\n",
      "Epoch 45/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3482 - val_loss: 0.3342\n",
      "Epoch 46/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3436 - val_loss: 0.3300\n",
      "Epoch 47/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3395 - val_loss: 0.3289\n",
      "Epoch 48/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3356 - val_loss: 0.3243\n",
      "Epoch 49/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3318 - val_loss: 0.3237\n",
      "Epoch 50/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.3295 - val_loss: 0.3205\n",
      "Epoch 51/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3257 - val_loss: 0.3180\n",
      "Epoch 52/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3218 - val_loss: 0.3150\n",
      "Epoch 53/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3185 - val_loss: 0.3138\n",
      "Epoch 54/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3163 - val_loss: 0.3109\n",
      "Epoch 55/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3129 - val_loss: 0.3069\n",
      "Epoch 56/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.3091 - val_loss: 0.3065\n",
      "Epoch 57/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3068 - val_loss: 0.3032\n",
      "Epoch 58/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.3024 - val_loss: 0.3023\n",
      "Epoch 59/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.3012 - val_loss: 0.3012\n",
      "Epoch 60/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2978 - val_loss: 0.2980\n",
      "Epoch 61/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2936 - val_loss: 0.2945\n",
      "Epoch 62/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2939 - val_loss: 0.2951\n",
      "Epoch 63/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2904 - val_loss: 0.2922\n",
      "Epoch 64/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2871 - val_loss: 0.2892\n",
      "Epoch 65/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2844 - val_loss: 0.2891\n",
      "Epoch 66/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2832 - val_loss: 0.2884\n",
      "Epoch 67/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2787 - val_loss: 0.2860\n",
      "Epoch 68/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2779 - val_loss: 0.2836\n",
      "Epoch 69/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2739 - val_loss: 0.2846\n",
      "Epoch 70/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2739 - val_loss: 0.2797\n",
      "Epoch 71/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2708 - val_loss: 0.2795\n",
      "Epoch 72/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2678 - val_loss: 0.2776\n",
      "Epoch 73/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2660 - val_loss: 0.2790\n",
      "Epoch 74/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2655 - val_loss: 0.2799\n",
      "Epoch 75/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2620 - val_loss: 0.2728\n",
      "Epoch 76/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2598 - val_loss: 0.2749\n",
      "Epoch 77/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2567 - val_loss: 0.2717\n",
      "Epoch 78/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2545 - val_loss: 0.2708\n",
      "Epoch 79/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2544 - val_loss: 0.2695\n",
      "Epoch 80/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2515 - val_loss: 0.2705\n",
      "Epoch 81/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2499 - val_loss: 0.2654\n",
      "Epoch 82/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2470 - val_loss: 0.2654\n",
      "Epoch 83/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2469 - val_loss: 0.2651\n",
      "Epoch 84/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2432 - val_loss: 0.2622\n",
      "Epoch 85/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2399 - val_loss: 0.2643\n",
      "Epoch 86/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2419 - val_loss: 0.2573\n",
      "Epoch 87/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2387 - val_loss: 0.2616\n",
      "Epoch 88/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2351 - val_loss: 0.2585\n",
      "Epoch 89/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2332 - val_loss: 0.2560\n",
      "Epoch 90/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2332 - val_loss: 0.2554\n",
      "Epoch 91/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2313 - val_loss: 0.2539\n",
      "Epoch 92/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2280 - val_loss: 0.2565\n",
      "Epoch 93/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2276 - val_loss: 0.2494\n",
      "Epoch 94/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2254 - val_loss: 0.2526\n",
      "Epoch 95/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2253 - val_loss: 0.2505\n",
      "Epoch 96/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.2233 - val_loss: 0.2510\n",
      "Epoch 97/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2201 - val_loss: 0.2464\n",
      "Epoch 98/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.2195 - val_loss: 0.2459\n",
      "Epoch 99/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2169 - val_loss: 0.2437\n",
      "Epoch 100/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2149 - val_loss: 0.2489\n",
      "Epoch 101/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2143 - val_loss: 0.2436\n",
      "Epoch 102/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2114 - val_loss: 0.2457\n",
      "Epoch 103/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2084 - val_loss: 0.2394\n",
      "Epoch 104/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2085 - val_loss: 0.2446\n",
      "Epoch 105/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2091 - val_loss: 0.2413\n",
      "Epoch 106/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2056 - val_loss: 0.2418\n",
      "Epoch 107/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2053 - val_loss: 0.2378\n",
      "Epoch 108/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2016 - val_loss: 0.2375\n",
      "Epoch 109/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.2035 - val_loss: 0.2400\n",
      "Epoch 110/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2007 - val_loss: 0.2338\n",
      "Epoch 111/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1987 - val_loss: 0.2397\n",
      "Epoch 112/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.2002 - val_loss: 0.2364\n",
      "Epoch 113/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1936 - val_loss: 0.2355\n",
      "Epoch 114/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1924 - val_loss: 0.2332\n",
      "Epoch 115/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1921 - val_loss: 0.2378\n",
      "Epoch 116/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1944 - val_loss: 0.2304\n",
      "Epoch 117/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1902 - val_loss: 0.2333\n",
      "Epoch 118/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1892 - val_loss: 0.2335\n",
      "Epoch 119/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1844 - val_loss: 0.2303\n",
      "Epoch 120/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1866 - val_loss: 0.2304\n",
      "Epoch 121/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1844 - val_loss: 0.2290\n",
      "Epoch 122/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1858 - val_loss: 0.2265\n",
      "Epoch 123/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1842 - val_loss: 0.2368\n",
      "Epoch 124/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1811 - val_loss: 0.2307\n",
      "Epoch 125/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1788 - val_loss: 0.2269\n",
      "Epoch 126/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1790 - val_loss: 0.2216\n",
      "Epoch 127/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1774 - val_loss: 0.2204\n",
      "Epoch 128/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1751 - val_loss: 0.2286\n",
      "Epoch 129/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1738 - val_loss: 0.2253\n",
      "Epoch 130/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1733 - val_loss: 0.2210\n",
      "Epoch 131/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1735 - val_loss: 0.2277\n",
      "Epoch 132/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1741 - val_loss: 0.2262\n",
      "Epoch 133/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1695 - val_loss: 0.2209\n",
      "Epoch 134/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.1668 - val_loss: 0.2265\n",
      "Epoch 135/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1668 - val_loss: 0.2237\n",
      "Epoch 136/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1652 - val_loss: 0.2239\n",
      "Epoch 137/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.1655 - val_loss: 0.2229\n",
      "Epoch 138/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1647 - val_loss: 0.2164\n",
      "Epoch 139/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1632 - val_loss: 0.2246\n",
      "Epoch 140/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1628 - val_loss: 0.2210\n",
      "Epoch 141/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1608 - val_loss: 0.2186\n",
      "Epoch 142/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1604 - val_loss: 0.2184\n",
      "Epoch 143/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1582 - val_loss: 0.2173\n",
      "Epoch 144/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1557 - val_loss: 0.2211\n",
      "Epoch 145/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.1531 - val_loss: 0.2180\n",
      "Epoch 146/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1568 - val_loss: 0.2309\n",
      "Epoch 147/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1581 - val_loss: 0.2217\n",
      "Epoch 148/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1524 - val_loss: 0.2128\n",
      "Epoch 149/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1500 - val_loss: 0.2209\n",
      "Epoch 150/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1512 - val_loss: 0.2243\n",
      "Epoch 151/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1521 - val_loss: 0.2188\n",
      "Epoch 152/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1482 - val_loss: 0.2167\n",
      "Epoch 153/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1467 - val_loss: 0.2152\n",
      "Epoch 154/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1445 - val_loss: 0.2145\n",
      "Epoch 155/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1426 - val_loss: 0.2116\n",
      "Epoch 156/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1467 - val_loss: 0.2131\n",
      "Epoch 157/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1396 - val_loss: 0.2202\n",
      "Epoch 158/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1464 - val_loss: 0.2150\n",
      "Epoch 159/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1387 - val_loss: 0.2133\n",
      "Epoch 160/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1439 - val_loss: 0.2181\n",
      "Epoch 161/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1389 - val_loss: 0.2098\n",
      "Epoch 162/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1402 - val_loss: 0.2229\n",
      "Epoch 163/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1411 - val_loss: 0.2091\n",
      "Epoch 164/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1331 - val_loss: 0.2144\n",
      "Epoch 165/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1377 - val_loss: 0.2131\n",
      "Epoch 166/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1359 - val_loss: 0.2138\n",
      "Epoch 167/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1328 - val_loss: 0.2083\n",
      "Epoch 168/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1324 - val_loss: 0.2121\n",
      "Epoch 169/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1290 - val_loss: 0.2141\n",
      "Epoch 170/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1288 - val_loss: 0.2078\n",
      "Epoch 171/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1265 - val_loss: 0.2156\n",
      "Epoch 172/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.1296 - val_loss: 0.2105\n",
      "Epoch 173/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1258 - val_loss: 0.2190\n",
      "Epoch 174/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1268 - val_loss: 0.2205\n",
      "Epoch 175/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1271 - val_loss: 0.2276\n",
      "Epoch 176/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1246 - val_loss: 0.2185\n",
      "Epoch 177/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1238 - val_loss: 0.2134\n",
      "Epoch 178/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1254 - val_loss: 0.2168\n",
      "Epoch 179/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1222 - val_loss: 0.2106\n",
      "Epoch 180/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1217 - val_loss: 0.2127\n",
      "Epoch 181/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1272 - val_loss: 0.2204\n",
      "Epoch 182/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1221 - val_loss: 0.2097\n",
      "Epoch 183/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1209 - val_loss: 0.2323\n",
      "Epoch 184/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1172 - val_loss: 0.2044\n",
      "Epoch 185/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1167 - val_loss: 0.2202\n",
      "Epoch 186/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1202 - val_loss: 0.2198\n",
      "Epoch 187/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1104 - val_loss: 0.2201\n",
      "Epoch 188/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1137 - val_loss: 0.2083\n",
      "Epoch 189/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1145 - val_loss: 0.2086\n",
      "Epoch 190/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1123 - val_loss: 0.2104\n",
      "Epoch 191/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1119 - val_loss: 0.2232\n",
      "Epoch 192/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1097 - val_loss: 0.2176\n",
      "Epoch 193/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1091 - val_loss: 0.2059\n",
      "Epoch 194/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1102 - val_loss: 0.2159\n",
      "Epoch 195/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1071 - val_loss: 0.2116\n",
      "Epoch 196/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1058 - val_loss: 0.2247\n",
      "Epoch 197/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1032 - val_loss: 0.2104\n",
      "Epoch 198/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.1029 - val_loss: 0.2222\n",
      "Epoch 199/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1054 - val_loss: 0.2218\n",
      "Epoch 200/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1071 - val_loss: 0.2115\n",
      "Epoch 201/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1055 - val_loss: 0.2112\n",
      "Epoch 202/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1013 - val_loss: 0.2237\n",
      "Epoch 203/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1011 - val_loss: 0.2195\n",
      "Epoch 204/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1037 - val_loss: 0.2153\n",
      "Epoch 205/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1029 - val_loss: 0.2226\n",
      "Epoch 206/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0969 - val_loss: 0.2112\n",
      "Epoch 207/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0943 - val_loss: 0.2245\n",
      "Epoch 208/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0995 - val_loss: 0.2244\n",
      "Epoch 209/1000\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1015 - val_loss: 0.2211\n"
     ]
    }
   ],
   "source": [
    "from GRANDE import GRANDE\n",
    "\n",
    "params = {\n",
    "        'depth': 5, # tree depth\n",
    "        'n_estimators': 2048, # number of estimators / trees\n",
    "\n",
    "        'learning_rate_weights': 0.005, # learning rate for leaf weights\n",
    "        'learning_rate_index': 0.01, # learning rate for split indices\n",
    "        'learning_rate_values': 0.01, # learning rate for split values\n",
    "        'learning_rate_leaf': 0.01, # learning rate for leafs (logits)\n",
    "\n",
    "        'optimizer': 'adam', # optimizer\n",
    "        'cosine_decay_steps': 0, # decay steps for lr schedule (CosineDecayRestarts)\n",
    "\n",
    "        'loss': 'crossentropy', # loss function (default 'crossentropy' for binary & multi-class classification and 'mse' for regression)\n",
    "        'focal_loss': False, # use focal loss {True, False}\n",
    "        'temperature': 0.0, # temperature for stochastic re-weighted GD (0.0, 1.0)\n",
    "\n",
    "        'from_logits': True, # use logits for weighting {True, False}\n",
    "        'use_class_weights': True, # use class weights for training {True, False}\n",
    "\n",
    "        'dropout': 0.0, # dropout rate (here, dropout randomly disables individual estimators of the ensemble during training)\n",
    "\n",
    "        'selected_variables': 0.8, # feature subset percentage (0.0, 1.0)\n",
    "        'data_subset_fraction': 1.0, # data subset percentage (0.0, 1.0)\n",
    "}\n",
    "\n",
    "args = {\n",
    "    'epochs': 1_000, # number of epochs for training\n",
    "    'early_stopping_epochs': 25, # patience for early stopping (best weights are restored)\n",
    "    'batch_size': 64,  # batch size for training\n",
    "\n",
    "    'cat_idx': categorical_feature_indices, # put list of categorical indices\n",
    "    'objective': 'classification', # objective / task {'binary', 'classification', 'regression'}\n",
    "    \n",
    "    'random_seed': 42,\n",
    "    'verbose': 1,       \n",
    "}\n",
    "\n",
    "model_grande = GRANDE(params=params, args=args)\n",
    "\n",
    "model_grande.fit(X_train=X_train,\n",
    "          y_train=y_train,\n",
    "          X_val=X_valid,\n",
    "          y_val=y_valid)\n",
    "\n",
    "preds_grande = model_grande.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b251df9d-67f0-4dd1-a0cc-379621e33fad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:12.113268Z",
     "iopub.status.busy": "2024-03-15T12:03:12.112609Z",
     "iopub.status.idle": "2024-03-15T12:03:12.117059Z",
     "shell.execute_reply": "2024-03-15T12:03:12.116533Z",
     "shell.execute_reply.started": "2024-03-15T12:03:12.113242Z"
    }
   },
   "outputs": [],
   "source": [
    "def calculate_sample_weights(y_data):\n",
    "    class_weights = sklearn.utils.class_weight.compute_class_weight(class_weight = 'balanced', classes = np.unique(y_data), y = y_data)\n",
    "    sample_weights = sklearn.utils.class_weight.compute_sample_weight(class_weight = 'balanced', y =y_data)\n",
    "    return sample_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f7e55785-bf56-4aad-9825-7c5737be5ab7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:12.118225Z",
     "iopub.status.busy": "2024-03-15T12:03:12.117822Z",
     "iopub.status.idle": "2024-03-15T12:03:12.124409Z",
     "shell.execute_reply": "2024-03-15T12:03:12.123909Z",
     "shell.execute_reply.started": "2024-03-15T12:03:12.118208Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    y_train = y_train.values.codes.astype(np.float64)\n",
    "    y_valid = y_valid.values.codes.astype(np.float64)\n",
    "    y_test = y_test.values.codes.astype(np.float64)\n",
    "except:\n",
    "    y_train = y_train.values.astype(np.float64)\n",
    "    y_valid = y_valid.values.astype(np.float64)\n",
    "    y_test = y_test.values.astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9d9d053-fbc3-4a01-988f-b0a93ede49e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:12.125558Z",
     "iopub.status.busy": "2024-03-15T12:03:12.125132Z",
     "iopub.status.idle": "2024-03-15T12:03:12.132757Z",
     "shell.execute_reply": "2024-03-15T12:03:12.132079Z",
     "shell.execute_reply.started": "2024-03-15T12:03:12.125541Z"
    }
   },
   "outputs": [],
   "source": [
    "binary_indices = []\n",
    "low_cardinality_indices = []\n",
    "high_cardinality_indices = []\n",
    "num_columns = []\n",
    "for column_index, column in enumerate(X_train.columns):\n",
    "    if column_index in categorical_feature_indices:\n",
    "        if len(X_train.iloc[:,column_index].unique()) <= 2:\n",
    "            binary_indices.append(column)\n",
    "        if len(X_train.iloc[:,column_index].unique()) < 5:\n",
    "            low_cardinality_indices.append(column)\n",
    "        else:\n",
    "            high_cardinality_indices.append(column)\n",
    "    else:\n",
    "        num_columns.append(column)     \n",
    "cat_columns = [col for col in X_train.columns if col not in num_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d11cf7a3-9247-49a7-a75d-9766013c8744",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:12.134107Z",
     "iopub.status.busy": "2024-03-15T12:03:12.133870Z",
     "iopub.status.idle": "2024-03-15T12:03:12.147255Z",
     "shell.execute_reply": "2024-03-15T12:03:12.146750Z",
     "shell.execute_reply.started": "2024-03-15T12:03:12.134086Z"
    }
   },
   "outputs": [],
   "source": [
    "if len(num_columns) > 0:\n",
    "    mean_train_num = X_train[num_columns].mean(axis=0).iloc[0]\n",
    "    X_train[num_columns] = X_train[num_columns].fillna(mean_train_num)\n",
    "    X_valid[num_columns] = X_valid[num_columns].fillna(mean_train_num)\n",
    "    X_test[num_columns] = X_test[num_columns].fillna(mean_train_num)\n",
    "if len(cat_columns) > 0:\n",
    "    mode_train_cat = X_train[cat_columns].mode(axis=0).iloc[0]\n",
    "    X_train[cat_columns] = X_train[cat_columns].fillna(mode_train_cat)\n",
    "    X_valid[cat_columns] = X_valid[cat_columns].fillna(mode_train_cat)\n",
    "    X_test[cat_columns] = X_test[cat_columns].fillna(mode_train_cat)\n",
    "\n",
    "X_train_raw = X_train.copy()\n",
    "X_valid_raw = X_valid.copy()\n",
    "X_test_raw = X_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5d5f00f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:12.148259Z",
     "iopub.status.busy": "2024-03-15T12:03:12.148125Z",
     "iopub.status.idle": "2024-03-15T12:03:12.155176Z",
     "shell.execute_reply": "2024-03-15T12:03:12.154675Z",
     "shell.execute_reply.started": "2024-03-15T12:03:12.148247Z"
    }
   },
   "outputs": [],
   "source": [
    "encoder_ordinal = ce.OrdinalEncoder(cols=binary_indices)\n",
    "encoder_ordinal.fit(X_train)\n",
    "X_train = encoder_ordinal.transform(X_train)\n",
    "X_valid = encoder_ordinal.transform(X_valid)  \n",
    "X_test = encoder_ordinal.transform(X_test)       \n",
    "\n",
    "encoder = ce.LeaveOneOutEncoder(cols=high_cardinality_indices)\n",
    "encoder.fit(X_train, y_train)\n",
    "X_train = encoder.transform(X_train)\n",
    "X_valid = encoder.transform(X_valid)\n",
    "X_test = encoder.transform(X_test)\n",
    "\n",
    "encoder = ce.OneHotEncoder(cols=low_cardinality_indices)\n",
    "encoder.fit(X_train)\n",
    "X_train = encoder.transform(X_train)\n",
    "X_valid = encoder.transform(X_valid)\n",
    "X_test = encoder.transform(X_test)\n",
    "\n",
    "X_train = X_train.astype(np.float32)\n",
    "X_valid = X_valid.astype(np.float32)\n",
    "X_test = X_test.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "361f7d9f-7e58-4bc1-8855-153b9e972742",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:12.156183Z",
     "iopub.status.busy": "2024-03-15T12:03:12.155838Z",
     "iopub.status.idle": "2024-03-15T12:03:12.667984Z",
     "shell.execute_reply": "2024-03-15T12:03:12.667146Z",
     "shell.execute_reply.started": "2024-03-15T12:03:12.156168Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-mlogloss:0.92906\n",
      "[1]\tvalidation_0-mlogloss:0.84543\n",
      "[2]\tvalidation_0-mlogloss:0.77895\n",
      "[3]\tvalidation_0-mlogloss:0.74425\n",
      "[4]\tvalidation_0-mlogloss:0.73254\n",
      "[5]\tvalidation_0-mlogloss:0.71118\n",
      "[6]\tvalidation_0-mlogloss:0.70206\n",
      "[7]\tvalidation_0-mlogloss:0.69809\n",
      "[8]\tvalidation_0-mlogloss:0.70716\n",
      "[9]\tvalidation_0-mlogloss:0.71560\n",
      "[10]\tvalidation_0-mlogloss:0.72703\n",
      "[11]\tvalidation_0-mlogloss:0.72930\n",
      "[12]\tvalidation_0-mlogloss:0.73781\n",
      "[13]\tvalidation_0-mlogloss:0.74436\n",
      "[14]\tvalidation_0-mlogloss:0.75612\n",
      "[15]\tvalidation_0-mlogloss:0.76647\n",
      "[16]\tvalidation_0-mlogloss:0.77294\n",
      "[17]\tvalidation_0-mlogloss:0.76826\n",
      "[18]\tvalidation_0-mlogloss:0.77106\n",
      "[19]\tvalidation_0-mlogloss:0.78535\n",
      "[20]\tvalidation_0-mlogloss:0.78614\n",
      "[21]\tvalidation_0-mlogloss:0.79861\n",
      "[22]\tvalidation_0-mlogloss:0.80108\n",
      "[23]\tvalidation_0-mlogloss:0.81176\n",
      "[24]\tvalidation_0-mlogloss:0.81198\n",
      "[25]\tvalidation_0-mlogloss:0.81683\n",
      "[26]\tvalidation_0-mlogloss:0.82233\n",
      "[27]\tvalidation_0-mlogloss:0.83074\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "model_xgb = XGBClassifier(n_estimators=1000, early_stopping_rounds=20)\n",
    "model_xgb.fit(X_train, \n",
    "                y_train, \n",
    "                sample_weight=calculate_sample_weights(y_train), \n",
    "                eval_set=[(X_valid, y_valid)], \n",
    "                sample_weight_eval_set=[calculate_sample_weights(y_valid)]\n",
    "             )\n",
    "if args['objective'] == 'regression':\n",
    "    preds_xgb = model_xgb.predict(X_test)\n",
    "else:\n",
    "    preds_xgb = model_xgb.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b23c67e-cdde-40c2-842a-61383e1a62d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:12.670444Z",
     "iopub.status.busy": "2024-03-15T12:03:12.670127Z",
     "iopub.status.idle": "2024-03-15T12:03:13.316178Z",
     "shell.execute_reply": "2024-03-15T12:03:13.315288Z",
     "shell.execute_reply.started": "2024-03-15T12:03:12.670422Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.106015\n",
      "0:\tlearn: 1.0490346\ttest: 1.0632694\tbest: 1.0632694 (0)\ttotal: 47.3ms\tremaining: 47.2s\n",
      "1:\tlearn: 0.9981136\ttest: 1.0086553\tbest: 1.0086553 (1)\ttotal: 48.9ms\tremaining: 24.4s\n",
      "2:\tlearn: 0.9493243\ttest: 0.9615261\tbest: 0.9615261 (2)\ttotal: 50.1ms\tremaining: 16.6s\n",
      "3:\tlearn: 0.9089973\ttest: 0.9258129\tbest: 0.9258129 (3)\ttotal: 51.5ms\tremaining: 12.8s\n",
      "4:\tlearn: 0.8773156\ttest: 0.9026201\tbest: 0.9026201 (4)\ttotal: 67.2ms\tremaining: 13.4s\n",
      "5:\tlearn: 0.8428892\ttest: 0.8817201\tbest: 0.8817201 (5)\ttotal: 71.1ms\tremaining: 11.8s\n",
      "6:\tlearn: 0.8179774\ttest: 0.8639109\tbest: 0.8639109 (6)\ttotal: 76.2ms\tremaining: 10.8s\n",
      "7:\tlearn: 0.7867788\ttest: 0.8385414\tbest: 0.8385414 (7)\ttotal: 77.1ms\tremaining: 9.56s\n",
      "8:\tlearn: 0.7617402\ttest: 0.8156207\tbest: 0.8156207 (8)\ttotal: 77.8ms\tremaining: 8.57s\n",
      "9:\tlearn: 0.7363277\ttest: 0.7937842\tbest: 0.7937842 (9)\ttotal: 78.6ms\tremaining: 7.78s\n",
      "10:\tlearn: 0.7159307\ttest: 0.7786301\tbest: 0.7786301 (10)\ttotal: 79.6ms\tremaining: 7.16s\n",
      "11:\tlearn: 0.7000605\ttest: 0.7715548\tbest: 0.7715548 (11)\ttotal: 80.6ms\tremaining: 6.64s\n",
      "12:\tlearn: 0.6883598\ttest: 0.7589939\tbest: 0.7589939 (12)\ttotal: 81.2ms\tremaining: 6.16s\n",
      "13:\tlearn: 0.6778845\ttest: 0.7504221\tbest: 0.7504221 (13)\ttotal: 81.9ms\tremaining: 5.76s\n",
      "14:\tlearn: 0.6714652\ttest: 0.7455446\tbest: 0.7455446 (14)\ttotal: 82.3ms\tremaining: 5.4s\n",
      "15:\tlearn: 0.6538533\ttest: 0.7355449\tbest: 0.7355449 (15)\ttotal: 83ms\tremaining: 5.1s\n",
      "16:\tlearn: 0.6381894\ttest: 0.7245504\tbest: 0.7245504 (16)\ttotal: 83.7ms\tremaining: 4.84s\n",
      "17:\tlearn: 0.6324706\ttest: 0.7194577\tbest: 0.7194577 (17)\ttotal: 84.1ms\tremaining: 4.58s\n",
      "18:\tlearn: 0.6245506\ttest: 0.7126864\tbest: 0.7126864 (18)\ttotal: 84.6ms\tremaining: 4.37s\n",
      "19:\tlearn: 0.6094649\ttest: 0.7040337\tbest: 0.7040337 (19)\ttotal: 85.3ms\tremaining: 4.18s\n",
      "20:\tlearn: 0.5971472\ttest: 0.6978941\tbest: 0.6978941 (20)\ttotal: 86ms\tremaining: 4.01s\n",
      "21:\tlearn: 0.5850698\ttest: 0.6872102\tbest: 0.6872102 (21)\ttotal: 86.6ms\tremaining: 3.85s\n",
      "22:\tlearn: 0.5772163\ttest: 0.6835691\tbest: 0.6835691 (22)\ttotal: 87ms\tremaining: 3.7s\n",
      "23:\tlearn: 0.5623486\ttest: 0.6821599\tbest: 0.6821599 (23)\ttotal: 87.7ms\tremaining: 3.56s\n",
      "24:\tlearn: 0.5501289\ttest: 0.6734774\tbest: 0.6734774 (24)\ttotal: 88.2ms\tremaining: 3.44s\n",
      "25:\tlearn: 0.5391216\ttest: 0.6653700\tbest: 0.6653700 (25)\ttotal: 88.8ms\tremaining: 3.32s\n",
      "26:\tlearn: 0.5299837\ttest: 0.6603153\tbest: 0.6603153 (26)\ttotal: 89.7ms\tremaining: 3.23s\n",
      "27:\tlearn: 0.5171550\ttest: 0.6558189\tbest: 0.6558189 (27)\ttotal: 90.4ms\tremaining: 3.14s\n",
      "28:\tlearn: 0.5100945\ttest: 0.6532612\tbest: 0.6532612 (28)\ttotal: 90.9ms\tremaining: 3.04s\n",
      "29:\tlearn: 0.5000430\ttest: 0.6544895\tbest: 0.6532612 (28)\ttotal: 91.5ms\tremaining: 2.96s\n",
      "30:\tlearn: 0.4907201\ttest: 0.6529126\tbest: 0.6529126 (30)\ttotal: 92ms\tremaining: 2.88s\n",
      "31:\tlearn: 0.4827371\ttest: 0.6521201\tbest: 0.6521201 (31)\ttotal: 92.6ms\tremaining: 2.8s\n",
      "32:\tlearn: 0.4768118\ttest: 0.6488893\tbest: 0.6488893 (32)\ttotal: 93.1ms\tremaining: 2.73s\n",
      "33:\tlearn: 0.4693847\ttest: 0.6466417\tbest: 0.6466417 (33)\ttotal: 93.7ms\tremaining: 2.66s\n",
      "34:\tlearn: 0.4630932\ttest: 0.6435843\tbest: 0.6435843 (34)\ttotal: 94.2ms\tremaining: 2.6s\n",
      "35:\tlearn: 0.4552199\ttest: 0.6395041\tbest: 0.6395041 (35)\ttotal: 94.7ms\tremaining: 2.54s\n",
      "36:\tlearn: 0.4443714\ttest: 0.6367745\tbest: 0.6367745 (36)\ttotal: 95.2ms\tremaining: 2.48s\n",
      "37:\tlearn: 0.4377722\ttest: 0.6370233\tbest: 0.6367745 (36)\ttotal: 95.8ms\tremaining: 2.42s\n",
      "38:\tlearn: 0.4316221\ttest: 0.6349342\tbest: 0.6349342 (38)\ttotal: 96.3ms\tremaining: 2.37s\n",
      "39:\tlearn: 0.4245884\ttest: 0.6296966\tbest: 0.6296966 (39)\ttotal: 96.8ms\tremaining: 2.32s\n",
      "40:\tlearn: 0.4212981\ttest: 0.6291476\tbest: 0.6291476 (40)\ttotal: 97.3ms\tremaining: 2.28s\n",
      "41:\tlearn: 0.4151772\ttest: 0.6275823\tbest: 0.6275823 (41)\ttotal: 97.9ms\tremaining: 2.23s\n",
      "42:\tlearn: 0.4085568\ttest: 0.6247146\tbest: 0.6247146 (42)\ttotal: 98.4ms\tremaining: 2.19s\n",
      "43:\tlearn: 0.4012723\ttest: 0.6230498\tbest: 0.6230498 (43)\ttotal: 99ms\tremaining: 2.15s\n",
      "44:\tlearn: 0.3965581\ttest: 0.6185522\tbest: 0.6185522 (44)\ttotal: 99.5ms\tremaining: 2.11s\n",
      "45:\tlearn: 0.3906870\ttest: 0.6203548\tbest: 0.6185522 (44)\ttotal: 100ms\tremaining: 2.08s\n",
      "46:\tlearn: 0.3862025\ttest: 0.6185170\tbest: 0.6185170 (46)\ttotal: 101ms\tremaining: 2.04s\n",
      "47:\tlearn: 0.3814050\ttest: 0.6191841\tbest: 0.6185170 (46)\ttotal: 101ms\tremaining: 2.01s\n",
      "48:\tlearn: 0.3782025\ttest: 0.6186386\tbest: 0.6185170 (46)\ttotal: 102ms\tremaining: 1.97s\n",
      "49:\tlearn: 0.3730194\ttest: 0.6182683\tbest: 0.6182683 (49)\ttotal: 102ms\tremaining: 1.94s\n",
      "50:\tlearn: 0.3697137\ttest: 0.6196174\tbest: 0.6182683 (49)\ttotal: 103ms\tremaining: 1.91s\n",
      "51:\tlearn: 0.3637058\ttest: 0.6189350\tbest: 0.6182683 (49)\ttotal: 103ms\tremaining: 1.88s\n",
      "52:\tlearn: 0.3584447\ttest: 0.6212308\tbest: 0.6182683 (49)\ttotal: 104ms\tremaining: 1.85s\n",
      "53:\tlearn: 0.3497174\ttest: 0.6180271\tbest: 0.6180271 (53)\ttotal: 104ms\tremaining: 1.83s\n",
      "54:\tlearn: 0.3457180\ttest: 0.6183244\tbest: 0.6180271 (53)\ttotal: 105ms\tremaining: 1.8s\n",
      "55:\tlearn: 0.3425354\ttest: 0.6207255\tbest: 0.6180271 (53)\ttotal: 106ms\tremaining: 1.78s\n",
      "56:\tlearn: 0.3368542\ttest: 0.6162374\tbest: 0.6162374 (56)\ttotal: 106ms\tremaining: 1.76s\n",
      "57:\tlearn: 0.3316262\ttest: 0.6188520\tbest: 0.6162374 (56)\ttotal: 107ms\tremaining: 1.73s\n",
      "58:\tlearn: 0.3255566\ttest: 0.6120271\tbest: 0.6120271 (58)\ttotal: 107ms\tremaining: 1.71s\n",
      "59:\tlearn: 0.3222694\ttest: 0.6107498\tbest: 0.6107498 (59)\ttotal: 108ms\tremaining: 1.69s\n",
      "60:\tlearn: 0.3184668\ttest: 0.6072412\tbest: 0.6072412 (60)\ttotal: 108ms\tremaining: 1.67s\n",
      "61:\tlearn: 0.3146727\ttest: 0.6060594\tbest: 0.6060594 (61)\ttotal: 109ms\tremaining: 1.65s\n",
      "62:\tlearn: 0.3096085\ttest: 0.6033444\tbest: 0.6033444 (62)\ttotal: 110ms\tremaining: 1.63s\n",
      "63:\tlearn: 0.3064155\ttest: 0.6006286\tbest: 0.6006286 (63)\ttotal: 110ms\tremaining: 1.61s\n",
      "64:\tlearn: 0.3033682\ttest: 0.5976912\tbest: 0.5976912 (64)\ttotal: 111ms\tremaining: 1.59s\n",
      "65:\tlearn: 0.2994302\ttest: 0.5977032\tbest: 0.5976912 (64)\ttotal: 111ms\tremaining: 1.57s\n",
      "66:\tlearn: 0.2940040\ttest: 0.5957439\tbest: 0.5957439 (66)\ttotal: 112ms\tremaining: 1.56s\n",
      "67:\tlearn: 0.2912391\ttest: 0.5936835\tbest: 0.5936835 (67)\ttotal: 112ms\tremaining: 1.54s\n",
      "68:\tlearn: 0.2877755\ttest: 0.5926172\tbest: 0.5926172 (68)\ttotal: 113ms\tremaining: 1.52s\n",
      "69:\tlearn: 0.2837043\ttest: 0.5894699\tbest: 0.5894699 (69)\ttotal: 113ms\tremaining: 1.51s\n",
      "70:\tlearn: 0.2798531\ttest: 0.5871315\tbest: 0.5871315 (70)\ttotal: 114ms\tremaining: 1.49s\n",
      "71:\tlearn: 0.2766974\ttest: 0.5834539\tbest: 0.5834539 (71)\ttotal: 115ms\tremaining: 1.48s\n",
      "72:\tlearn: 0.2733347\ttest: 0.5817169\tbest: 0.5817169 (72)\ttotal: 115ms\tremaining: 1.46s\n",
      "73:\tlearn: 0.2707058\ttest: 0.5837019\tbest: 0.5817169 (72)\ttotal: 116ms\tremaining: 1.45s\n",
      "74:\tlearn: 0.2676788\ttest: 0.5823989\tbest: 0.5817169 (72)\ttotal: 116ms\tremaining: 1.43s\n",
      "75:\tlearn: 0.2650410\ttest: 0.5823255\tbest: 0.5817169 (72)\ttotal: 117ms\tremaining: 1.42s\n",
      "76:\tlearn: 0.2623592\ttest: 0.5835963\tbest: 0.5817169 (72)\ttotal: 117ms\tremaining: 1.41s\n",
      "77:\tlearn: 0.2597867\ttest: 0.5838944\tbest: 0.5817169 (72)\ttotal: 118ms\tremaining: 1.39s\n",
      "78:\tlearn: 0.2570547\ttest: 0.5872337\tbest: 0.5817169 (72)\ttotal: 119ms\tremaining: 1.38s\n",
      "79:\tlearn: 0.2543289\ttest: 0.5844890\tbest: 0.5817169 (72)\ttotal: 119ms\tremaining: 1.37s\n",
      "80:\tlearn: 0.2517924\ttest: 0.5887527\tbest: 0.5817169 (72)\ttotal: 120ms\tremaining: 1.36s\n",
      "81:\tlearn: 0.2496392\ttest: 0.5901883\tbest: 0.5817169 (72)\ttotal: 120ms\tremaining: 1.35s\n",
      "82:\tlearn: 0.2470284\ttest: 0.5908447\tbest: 0.5817169 (72)\ttotal: 121ms\tremaining: 1.34s\n",
      "83:\tlearn: 0.2446767\ttest: 0.5895447\tbest: 0.5817169 (72)\ttotal: 122ms\tremaining: 1.33s\n",
      "84:\tlearn: 0.2420297\ttest: 0.5907498\tbest: 0.5817169 (72)\ttotal: 122ms\tremaining: 1.32s\n",
      "85:\tlearn: 0.2396913\ttest: 0.5912199\tbest: 0.5817169 (72)\ttotal: 123ms\tremaining: 1.31s\n",
      "86:\tlearn: 0.2373851\ttest: 0.5938497\tbest: 0.5817169 (72)\ttotal: 124ms\tremaining: 1.3s\n",
      "87:\tlearn: 0.2343313\ttest: 0.5902858\tbest: 0.5817169 (72)\ttotal: 124ms\tremaining: 1.29s\n",
      "88:\tlearn: 0.2313769\ttest: 0.5895402\tbest: 0.5817169 (72)\ttotal: 125ms\tremaining: 1.28s\n",
      "89:\tlearn: 0.2285002\ttest: 0.5896463\tbest: 0.5817169 (72)\ttotal: 125ms\tremaining: 1.27s\n",
      "90:\tlearn: 0.2260068\ttest: 0.5880630\tbest: 0.5817169 (72)\ttotal: 126ms\tremaining: 1.26s\n",
      "91:\tlearn: 0.2239972\ttest: 0.5873528\tbest: 0.5817169 (72)\ttotal: 127ms\tremaining: 1.25s\n",
      "92:\tlearn: 0.2211751\ttest: 0.5859427\tbest: 0.5817169 (72)\ttotal: 127ms\tremaining: 1.24s\n",
      "Stopped by overfitting detector  (20 iterations wait)\n",
      "\n",
      "bestTest = 0.5817169143\n",
      "bestIteration = 72\n",
      "\n",
      "Shrink model to first 73 iterations.\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier, Pool\n",
    "\n",
    "model_catboost = CatBoostClassifier(n_estimators=1000, \n",
    "                                    early_stopping_rounds=20)\n",
    "train_data = Pool(\n",
    "        data=X_train_raw,\n",
    "        label=y_train,\n",
    "        cat_features=categorical_feature_indices,\n",
    "        weight=calculate_sample_weights(y_train)\n",
    "    )\n",
    "\n",
    "eval_data = Pool(\n",
    "        data=X_valid_raw,\n",
    "        label=y_valid,\n",
    "        cat_features=categorical_feature_indices,\n",
    "        weight=calculate_sample_weights(y_valid),\n",
    "    )\n",
    "\n",
    "model_catboost.fit(X=train_data, \n",
    "                   eval_set=eval_data)\n",
    "\n",
    "if args['objective'] == 'regression':\n",
    "    preds_catboost = model_catboost.predict(X_test_raw)\n",
    "else:\n",
    "    preds_catboost = model_catboost.predict_proba(X_test_raw)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "21b9be01-ba30-4300-b3e0-f4fd43b55c87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:13.317299Z",
     "iopub.status.busy": "2024-03-15T12:03:13.316964Z",
     "iopub.status.idle": "2024-03-15T12:03:13.338342Z",
     "shell.execute_reply": "2024-03-15T12:03:13.337742Z",
     "shell.execute_reply.started": "2024-03-15T12:03:13.317284Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy GRANDE: 0.896\n",
      "F1 Score GRANDE: 0.7093081509667817\n",
      "ROC AUC GRANDE: 0.928491385348088\n",
      "\n",
      "\n",
      "Accuracy XGB: 0.768\n",
      "F1 Score XGB: 0.6161896745230079\n",
      "ROC AUC XGB: 0.7848764999766541\n",
      "\n",
      "\n",
      "Accuracy CatBoost: 0.792\n",
      "F1 Score CatBoost: 0.6638439468628149\n",
      "ROC AUC CatBoost: 0.866746042863146\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if args['objective'] == 'binary':\n",
    "    accuracy = sklearn.metrics.accuracy_score(y_test, np.round(preds_grande[:,1]))\n",
    "    f1_score = sklearn.metrics.f1_score(y_test, np.round(preds_grande[:,1]), average='macro')\n",
    "    roc_auc = sklearn.metrics.roc_auc_score(y_test, preds_grande[:,1], average='macro', multi_class='ovo')\n",
    "\n",
    "    print('Accuracy GRANDE:', accuracy)\n",
    "    print('F1 Score GRANDE:', f1_score)\n",
    "    print('ROC AUC GRANDE:', roc_auc)\n",
    "    print('\\n')\n",
    "\n",
    "    accuracy = sklearn.metrics.accuracy_score(y_test, np.round(preds_xgb[:,1]))\n",
    "    f1_score = sklearn.metrics.f1_score(y_test, np.round(preds_xgb[:,1]), average='macro')\n",
    "    roc_auc = sklearn.metrics.roc_auc_score(y_test, preds_xgb[:,1], average='macro', multi_class='ovo')\n",
    "\n",
    "    print('Accuracy XGB:', accuracy)\n",
    "    print('F1 Score XGB:', f1_score)\n",
    "    print('ROC AUC XGB:', roc_auc)\n",
    "    print('\\n')\n",
    "\n",
    "    accuracy = sklearn.metrics.accuracy_score(y_test, np.round(preds_catboost[:,1]))\n",
    "    f1_score = sklearn.metrics.f1_score(y_test, np.round(preds_catboost[:,1]), average='macro')\n",
    "    roc_auc = sklearn.metrics.roc_auc_score(y_test, preds_catboost[:,1], average='macro', multi_class='ovo')\n",
    "\n",
    "    print('Accuracy CatBoost:', accuracy)\n",
    "    print('F1 Score CatBoost:', f1_score)\n",
    "    print('ROC AUC CatBoost:', roc_auc)\n",
    "    print('\\n')\n",
    "elif args['objective'] == 'classification':\n",
    "    accuracy = sklearn.metrics.accuracy_score(y_test, np.argmax(preds_grande, axis=1))\n",
    "    f1_score = sklearn.metrics.f1_score(y_test, np.argmax(preds_grande, axis=1), average='macro')\n",
    "    roc_auc = sklearn.metrics.roc_auc_score(y_test, preds_grande, average='macro', multi_class='ovo', labels=[i for i in range(preds_grande.shape[1])])\n",
    "\n",
    "    print('Accuracy GRANDE:', accuracy)\n",
    "    print('F1 Score GRANDE:', f1_score)\n",
    "    print('ROC AUC GRANDE:', roc_auc)\n",
    "    print('\\n')\n",
    "\n",
    "    accuracy = sklearn.metrics.accuracy_score(y_test, np.argmax(preds_xgb, axis=1))\n",
    "    f1_score = sklearn.metrics.f1_score(y_test, np.argmax(preds_xgb, axis=1), average='macro')\n",
    "    roc_auc = sklearn.metrics.roc_auc_score(y_test, preds_xgb, average='macro', multi_class='ovo', labels=[i for i in range(preds_grande.shape[1])])\n",
    "\n",
    "    print('Accuracy XGB:', accuracy)\n",
    "    print('F1 Score XGB:', f1_score)\n",
    "    print('ROC AUC XGB:', roc_auc)\n",
    "    print('\\n')\n",
    "\n",
    "    accuracy = sklearn.metrics.accuracy_score(y_test, np.argmax(preds_catboost, axis=1))\n",
    "    f1_score = sklearn.metrics.f1_score(y_test, np.argmax(preds_catboost, axis=1), average='macro')\n",
    "    roc_auc = sklearn.metrics.roc_auc_score(y_test, preds_catboost, average='macro', multi_class='ovo', labels=[i for i in range(preds_grande.shape[1])])\n",
    "\n",
    "    print('Accuracy CatBoost:', accuracy)\n",
    "    print('F1 Score CatBoost:', f1_score)\n",
    "    print('ROC AUC CatBoost:', roc_auc)\n",
    "    print('\\n')\n",
    "else:\n",
    "    mean_absolute_error = sklearn.metrics.mean_absolute_error(y_test, np.round(preds_grande))\n",
    "    r2_score = sklearn.metrics.r2_score(y_test, np.round(preds_grande))\n",
    "\n",
    "    print('MAE GRANDE:', mean_absolute_error)\n",
    "    print('R2 Score GRANDE:', r2_score)\n",
    "    print('\\n')\n",
    "\n",
    "    mean_absolute_error = sklearn.metrics.mean_absolute_error(y_test, np.round(preds_xgb))\n",
    "    r2_score = sklearn.metrics.r2_score(y_test, np.round(preds_xgb))\n",
    "\n",
    "    print('MAE XGB:', mean_absolute_error)\n",
    "    print('R2 Score XGB:', r2_score)\n",
    "    print('\\n')\n",
    "\n",
    "    mean_absolute_error = sklearn.metrics.mean_absolute_error(y_test, np.round(preds_catboost))\n",
    "    r2_score = sklearn.metrics.r2_score(y_test, np.round(preds_catboost))\n",
    "\n",
    "    print('MAE CatBoost:', mean_absolute_error)\n",
    "    print('R2 Score CatBoost:', r2_score)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ef0e2bc0-9b43-4e5c-9f55-d1dae0de8930",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-15T12:03:13.339201Z",
     "iopub.status.busy": "2024-03-15T12:03:13.338974Z",
     "iopub.status.idle": "2024-03-15T12:03:14.181945Z",
     "shell.execute_reply": "2024-03-15T12:03:14.181260Z",
     "shell.execute_reply.started": "2024-03-15T12:03:13.339187Z"
    }
   },
   "outputs": [],
   "source": [
    "from numba import cuda \n",
    "device = cuda.get_current_device()\n",
    "device.reset()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "name": "python",
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