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   "source": [
    "# WS 04 Vorlage - KNeighborsClassifier"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* standardisieren Sie die Features von Trainings- und Testdaten mit Hilfe von sklearn.preprocessing.StandardScaler\n",
    "* ermitteln Sie anschliessend die besten Parameterwerte für KNeighborsClassifier\n",
    "  * n_neighbors (1-10)\n",
    "  * p (z.B. 1, 2, 3)\n",
    "* vergleichen Sie die Ergebnisse ohne und mit standardisieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import libraries\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",
    "## load data\n",
    "datapath = '../3_data'\n",
    "from os import chdir; chdir(datapath)\n",
    "data = pd.read_csv('bank_data_prep.csv')\n",
    "#data.shape  ## check\n",
    "\n",
    "## features - target - split\n",
    "X = data.drop('y', axis=1)\n",
    "y = data['y']\n",
    "\n",
    "## test - train - split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test, = train_test_split(X,\n",
    "                                                     y,\n",
    "                                                     train_size=2 / 3,\n",
    "                                                     random_state=1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rem: für die obige Datenaufbereitung wird ab dem nächsten Workshop die Funktion `prep_data()` aus dem Modul `bfh_cas_pml` verwendet werden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## standardiz features (lead: train data)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "## Tune über n_neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier()\n",
    "params = range(1, 11)\n",
    "scores = []  ## scores ohne Standardisieren\n",
    "scores_sc = []  ## scores mit Standardisieren\n",
    "\n",
    "for param in params:\n",
    "    print(param)\n",
    "    ## tbd\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Tune über p\n",
    "params = range(1, 4) ## dasselbe wie [1, 2, 3]\n",
    "## tbd\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fazit**:\n",
    "* tbd\n",
    "\n",
    "\n"
   ]
  }
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