tabular-playground-eda.ipynb

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      "Cloning into 'kuma_utils'...\r\n",
      "remote: Enumerating objects: 894, done.\u001b[K\r\n",
      "remote: Counting objects: 100% (100/100), done.\u001b[K\r\n",
      "remote: Compressing objects: 100% (10/10), done.\u001b[K\r\n",
      "remote: Total 894 (delta 93), reused 90 (delta 90), pack-reused 794\u001b[K\r\n",
      "Receiving objects: 100% (894/894), 668.74 KiB | 11.33 MiB/s, done.\r\n",
      "Resolving deltas: 100% (581/581), done.\r\n",
      "/kaggle/working/kuma_utils\n",
      "Branch 'lgb_add_tqdm' set up to track remote branch 'lgb_add_tqdm' from 'origin'.\r\n",
      "Switched to a new branch 'lgb_add_tqdm'\r\n",
      "/kaggle/working\n"
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   "source": [
    "!git clone https://github.com/marketneutral/kuma_utils.git\n",
    "%cd kuma_utils\n",
    "!git checkout lgb_add_tqdm\n",
    "%cd ..\n",
    "import sys\n",
    "sys.path.append(\"kuma_utils/\")"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "from kuma_utils.preprocessing.imputer import LGBMImputer"
   ]
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   "source": [
    "input_path = Path('/kaggle/input/tabular-playground-series-jun-2022/')\n",
    "data = pd.read_csv(input_path / 'data.csv', index_col='row_id')\n",
    "submission = pd.read_csv(input_path / 'sample_submission.csv', index_col='row-col')"
   ]
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    "for col in data.columns:\n",
    "    if data[col].dtype == \"float64\":\n",
    "        data[col]=pd.to_numeric(data[col], downcast=\"float\")\n",
    "    if data[col].dtype == \"int64\":\n",
    "        data[col]=pd.to_numeric(data[col], downcast=\"integer\")"
   ]
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    "F = [[], [], [], [], []]\n",
    "features = list(data.columns)\n",
    "\n",
    "for feature in features:\n",
    "    for i in [1, 2, 3, 4]:\n",
    "        if feature.split('_')[1] == str(i):\n",
    "            F[i].append(feature)\n",
    "df = [[], [], [], [], []]\n",
    "\n",
    "for i in [1, 2, 3, 4]:\n",
    "    df[i] = data[F[i]]\n",
    "    corr = df[i].corr()"
   ]
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      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:181: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n",
      "/opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py:723: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  iloc._setitem_with_indexer(indexer, value, self.name)\n"
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    "for i in [1, 3, 4]:\n",
    "    imp = LGBMImputer(n_iter=200, verbose=True)\n",
    "    df[i][:] = imp.fit_transform(df[i])"
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      "100%|██████████| 1000000/1000000 [01:25<00:00, 11678.63it/s]\n"
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    "d = pd.concat([df[1], df[2], df[3], df[4]], axis=1)\n",
    "for i in tqdm(submission.index):\n",
    "    row = int(i.split('-')[0])\n",
    "    col = i.split('-')[1]\n",
    "    submission.loc[i, 'value'] = d.loc[row, col]\n",
    "\n",
    "submission.to_csv('sub_9.csv')"
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