refactor: move things around

SL/aufgaben/template/4_WS/WS 04 Vorlage.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# WS 04 Vorlage - KNeighborsClassifier"
+   ]
+  },
+  {
+   "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": {},
+   "outputs": [
+    {
+     "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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+   "title_cell": "WS 07 Klassifikation - KNeighborsClassifier",
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