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    "# WS 11 permutation_importance"
   ]
  },
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   "source": [
    "* ermitteln Sie die Importance der Features der *Rohdaten* von `melb_data.csv` unter Einsatz von `sklearn.inspection.ermutation_importance`\n",
    "* setzen Sie dazu minimales Feature Engineering wie folgt ein:\n",
    "  * entfernen fragwürdiger Variablen: 'Unnamed: 0', 'Suburb', 'Address', 'SellerG', 'Postcode', 'Bedroom2', 'Date', 'CouncilArea'\n",
    "  * One-Hot encoding aller verbleibenden kategorialen Variablen (der Parameter `dummy_na=True` von `pd.get_dummies()` erstellt auch Dummy-Variablen für NAs)\n",
    "  * einsetzen von geschätzten Werten für NAs in verbleibenden numerischen Variablen mit `sklearn.impute.KNNImputer`\n",
    "* danach:\n",
    "  * features - target - split\n",
    "  * **kein** train - test - split\n",
    "  * ermitteln der Importance unter Einsatz von \n",
    "    * `sklearn.inspection.permutation_importance`\n",
    "    * `sklearn.tree.DecisionTreeRegressor`\n",
    "  * tabellarische und graphische Darstellung der Ergebnisse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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   "source": [
    "## prepare env, read and prepare data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "codepath = '../2_code'\n",
    "datapath = '../3_data'\n",
    "from sys import path; path.insert(1, codepath)\n",
    "from os import chdir; chdir(datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## read data\n",
    "data = pd.read_csv('melb_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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   "source": [
    "## drop columns\n",
    "vars_to_drop = ['Unnamed: 0', 'Suburb', 'Address', 'SellerG', 'Postcode', 'Bedroom2', 'Date', 'CouncilArea']\n",
    "data = data.drop(vars_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## one-hot encode (incl. NAs)\n",
    "data = pd.get_dummies(data, drop_first=False, dummy_na=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "source": [
    "## KNNImputer for NAs\n",
    "## tbd\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## features - target - split\n",
    "## tbd\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## permutation_importance\n",
    "## tbd\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## collect results in a dataframe, ordered by mean\n",
    "## tbd\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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    "tags": []
   },
   "outputs": [],
   "source": [
    "## visualize results\n",
    "## tbd\n",
    "\n",
    "\n"
   ]
  }
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