cas-pml/SL/aufgaben/template/4_WS/Loesungen/WS 08 Loesung.ipynb

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    "# WS 08 Regression mit Standardisieren und Logarithmieren"
   ]
  },
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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' ## for import of user defined module\n",
    "#datapath = '../3_data'\n",
    "codepath = '.././2_code' ## for import of user defined module\n",
    "datapath = '../../3_data'\n",
    "\n",
    "from sys import path; path.insert(1, codepath)\n",
    "from os import chdir; chdir(datapath)\n",
    "\n",
    "from bfh_cas_pml import prep_data\n",
    "X_train, X_test, y_train, y_test = prep_data('melb_data_prep.csv', target='Price', seed=1234)"
   ]
  },
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      "-105513873.23403685\n",
      "[ 245383.60581414 -141356.39759052  -40383.66643969  161336.03949841\n",
      "   40391.14829949   83303.27089591]\n",
      "[1331246.16325189 2557493.2373921   871684.82823291 1495633.275723\n",
      " 1549557.61151302  634348.67092323]\n",
      "0.5601419746121152\n"
     ]
    }
   ],
   "source": [
    "## baseline\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(model.intercept_)\n",
    "print(model.coef_[:6])\n",
    "print(y_pred[:6])\n",
    "print(r2_score(y_test, y_pred))"
   ]
  },
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      "1055902.69523731\n",
      "[ 235020.76662584  -96493.73493151 -243470.62893089  106305.85273776\n",
      "   35544.05464669   71047.51543032]\n",
      "[1331246.16325187 2557493.23739203  871684.82823297 1495633.27572294\n",
      " 1549557.611513    634348.67092323]\n",
      "0.5601419746121148\n"
     ]
    }
   ],
   "source": [
    "## scaled features\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_sc = scaler.transform(X_train)\n",
    "X_test_sc = scaler.transform(X_test)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_sc, y_train)\n",
    "y_pred = model.predict(X_test_sc)\n",
    "\n",
    "print(model.intercept_)\n",
    "print(model.coef_[:6])\n",
    "print(y_pred[:6])\n",
    "print(r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fazit**\n",
    "* Auswirkung von Skalieren der Features\n",
    "  * Koeffizienten und Intercept: Einfluss\n",
    "  * Prediction: kein Einfluss\n",
    "  * Score: natürlich auch kein Einfluss, wird ja aus Prediction berechnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5519266421486302\n"
     ]
    }
   ],
   "source": [
    "## log target\n",
    "y_train_log = np.log10(y_train)\n",
    "y_test_log = np.log10(y_test)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train_log)\n",
    "y_pred = model.predict(X_test)\n",
    "print(r2_score(10**y_test_log, 10**y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fazit**\n",
    "* wird sogar etwas schlechter\n",
    "* kombination mit skalierten Features erübrigt sich hier, da skalieren ja offenbar keinen Einfluss auf score hat"
   ]
  }
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