ml_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00000-0a6a3245-9a73-4bc1-8aa1-549ca9b68141",
    "deepnote_cell_type": "markdown",
    "output_cleared": false,
    "tags": []
   },
   "source": [
    "# Klassifikation des Objekttyps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_id": "00001-5b058abc-92a8-4d7a-80f7-07c4dfbd5b13",
    "deepnote_cell_type": "code",
    "execution_millis": 1617,
    "execution_start": 1608893760168,
    "output_cleared": false,
    "source_hash": "4e952ff0",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas.api.types import is_object_dtype\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import seaborn as sns\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import sklearn.metrics\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.over_sampling import SMOTE, ADASYN\n",
    "\n",
    "from helper_functions import preprocessing\n",
    "from helper_functions.metrics import *\n",
    "import datetime\n",
    "## Bayesian Optimization\n",
    "from bayes_opt import BayesianOptimization\n",
    "from bayes_opt.logger import JSONLogger\n",
    "from bayes_opt.event import Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cell_id": "00002-66e7ffd6-9a1e-47d4-84a7-966c25354320",
    "deepnote_cell_type": "code",
    "execution_millis": 1665,
    "execution_start": 1608728055960,
    "output_cleared": false,
    "source_hash": "fbde2775",
    "tags": []
   },
   "outputs": [],
   "source": [
    "#read in data\n",
    "csv_data = pd.read_csv('immo_dev_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Für die Klassifikationsaufgabe haben wir folgende Teilaufgaben:\n",
    "\n",
    "1. Entwickle und vergleiche drei sinnvolle Modelle zur Klassifikation von Immobilien Objekten hinsichtlich `GroupNameDe`. \n",
    "2. Was sind sinnvolle Metriken zur Messung der Genaugikeit der Vorhersage im vorliegenden Fall? \n",
    "3. Rapportiere diese Metrik(en) mit einer Abschätzung des Fehlers für alle drei Modelle.\n",
    "\n",
    "## Metriken\n",
    "\n",
    "Wir beginnen mit 2., den Metriken. Hierfür müssen wir uns als erstes einen Überblick über unsere Zielvariable verschaffen. Schauen wir einmal, wie viele verschiedene Objekttypen es gibt und wie viele Objekte es für jeden Typen gibt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv_data['GroupNameDe'].value_counts().plot(kind='bar', figsize=(10, 5), rot = 90)\n",
    "plt.xlabel(\"Kategorie\")\n",
    "plt.ylabel(\"Anzahl Objekte\")\n",
    "plt.title(\"Anzahl Wohnobjekte pro Kategorie\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Man kann im Plot sehr gut erkennen, dass es sich hier im eine ungleichmäsige Verteilung der Kategorien handelt. Da man einige Kategorien gar nicht erkennen kann, schauen wir noch die Zahlen an:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wohnung              77499\n",
       "Haus                 50265\n",
       "Doppelhaus            8397\n",
       "Attikawohnung         6215\n",
       "Maisonettewohnung     5517\n",
       "Reihenhaus            4168\n",
       "Terassenhaus           533\n",
       "Loftwohnung            474\n",
       "Mehrfamilienhaus       444\n",
       "Zimmer                  89\n",
       "Anderes Haus            26\n",
       "Name: GroupNameDe, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_data['GroupNameDe'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Kategorie *Wohnung* ist mit 77'499 Objekten gegenüber den 26 Objekten in *Anderes Haus* mehr als 2'980 mal öfter vertreten. Insgesammt sind es 153'627 Objekte, von denen 127'764 in den \"Top Zwei\" der Kategorien vorkommen; das sind 83% aller Objekte. Um ein gutes Modell erstellen zu können, müssen wir diese Verteilung in Betracht ziehen. Somit müssen wir auch eine Metrik auswählen, die das Ganze widerspiegelt.\n",
    "\n",
    "Die *Accuracy* oder Genauigkeit ist in diesem Fall eine schlechte Metrik. Sie beschreibt, wie viele Objekte richtig klassifiziert wurden. Wenn nur Wohnungen und Häuser korrekt eingeordnet werden und alles andere falsch, ist die Accuracy trotzdem 83%, obwohl unser Modell effektiv nur zwei Kategorien richtig bestimmt.\n",
    "Aus diesem Grund haben wir uns für den *[F1-Score](https://en.wikipedia.org/wiki/F-score)* entschieden. Genauer gesagt schauen wir uns die Scores pro Kategorie an und den ungewichteten Durchschnitt aller Scores.\n",
    "\n",
    "## Resampling\n",
    "\n",
    "Damit die Modelle besser performen, kann man die Trainingsdaten resamplen. Es gibt zwei verschiedene Arten Daten zu resamplen: Under- und Oversampling. \n",
    "\n",
    "### Undersampling\n",
    "\n",
    "Beim Undersampling werden die Daten reduziert. Man entfernt Objekte, die den \"grossen\" Klassen angehören, und das (optimalerweise) solange, bis überall gleich viele Objekte existieren. Da unsere kleinste Klasse nur 26 Objekte hat, ist diese Methode für unseren Fall nicht sinnvoll.\n",
    "\n",
    "### Oversampling\n",
    "\n",
    "Oversampling bedeutet, dem Datensatz werden neue Objekte hinzugefügt um eine \"Anzahl\"-Balance zwischen allen Klassen herzustellen. Hierfür kann man bestehende Objekte zufällig auswählen und mehrmals hinzufügen bzw. kopieren oder komplett neue Objekte – basierend auf den bereits existierenden – erstellen. Dies funktioniert beispielsweise mit der [SMOTE](https://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis#SMOTE) Methode. Hiermit werden wir uns auch beschäftigen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cell_id": "00006-b9ac9532-2666-46f7-b2d2-20505dce2a54",
    "deepnote_cell_type": "code",
    "execution_millis": 186,
    "execution_start": 1608728057714,
    "output_cleared": false,
    "source_hash": "3792722e",
    "tags": []
   },
   "outputs": [],
   "source": [
    "#prepro = preprocessing.preprocessor(train_data, y_var='GroupNameDe')#, cols_to_drop=['StreetAndNr', 'LastUpdate', 'Locality', 'Id', 'Zip'])#, 'Id'])\n",
    "#prepro.preprocess()\n",
    "csv_data_fe = csv_data.copy()\n",
    "csv_data_fe['Zip'] = (csv_data_fe['Zip']/100).astype('int8')\n",
    "prepro = preprocessing.preprocessor(csv_data_fe, y_var='GroupNameDe', method_to_encode='onehot_encode', cols_to_drop=['Id', 'LastUpdate', 'Locality', 'StreetAndNr', 'Longitude', 'Latitude', 'HouseObject', 'RealEstateTypeId'], numbers_to_encode=['Zip'], test_frac= 0.1)\n",
    "prepro.preprocess()\n",
    "\n",
    "X_train = prepro.X_train\n",
    "X_test = prepro.X_test\n",
    "\n",
    "y_train = prepro.y_train\n",
    "y_test = prepro.y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cell_id": "00007-e0f264c1-9c23-417f-a447-0247aa117474",
    "deepnote_cell_type": "code",
    "execution_millis": 349,
    "execution_start": 1608728058000,
    "output_cleared": false
   },
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state = 69)\n",
    "X_random_, y_random_ = ros.fit_resample(X_train, y_train)\n",
    "X_random = X_random_.sample(len(X_train), random_state = 69)\n",
    "y_random = y_random_.iloc[X_random.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": "00026-65214ae0-5544-4783-8ae2-cccacb4d0e22",
    "deepnote_cell_type": "code",
    "output_cleared": false
   },
   "outputs": [],
   "source": [
    "smote = SMOTE(random_state = 69, sampling_strategy = 'all')\n",
    "X_smote_, y_smote_ = smote.fit_resample(X_train, y_train)\n",
    "X_smote = X_smote_.sample(len(X_train), random_state = 69)\n",
    "y_smote = y_smote_.iloc[X_smote.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So siehen die Daten nach dem Resampling aus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_random.value_counts().plot(kind='bar', figsize=(10, 5), rot = 90)\n",
    "plt.xlabel(\"Kategorie\")\n",
    "plt.ylabel(\"Anzahl Objekte\")\n",
    "plt.title(\"Anzahl Wohnobjekte pro Kategorie (Random resampling)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00008-a09c913f-621e-469b-bc8b-01a2d72b1365",
    "deepnote_cell_type": "markdown",
    "output_cleared": false,
    "tags": []
   },
   "source": [
    "# Modellauswahl\n",
    "\n",
    "Weiter geht es mit 1. aus der Aufgabenstellung: Entwickle und vergleiche drei sinnvolle Modelle zur Klassifikation von Immobilien Objekten hinsichtlich `GroupNameDe`.\n",
    "\n",
    "Wir haben uns für folgende Modelle entschieden: \n",
    "\n",
    "- K-Nearest Neighbors\n",
    "- Random Forrest\n",
    "- XGBoost\n",
    "- LightGBM\n",
    "\n",
    "Als erstes werden die Modelle auf den unveränderten Daten trainiert, danach auf zufällig geresampleten Daten und dann auf den SMOTE Daten. Damit das Training jedoch nicht allzulange dauert, werden von den beiden oversampleten Datasets zufällige Objekte ausgewählt bis diese Datasets gleich gross sind wie das originale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cell_id": "00009-cd648fb7-7e0f-4c9f-94f4-c7e4e204f428",
    "deepnote_cell_type": "code",
    "execution_millis": 4046,
    "execution_start": 1608728058392,
    "output_cleared": false,
    "source_hash": "7546a872",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_jobs=-1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_jobs = -1)\n",
    "knn.fit(X = X_train, y = y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_jobs=-1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_rnd = KNeighborsClassifier(n_jobs = -1)\n",
    "knn_rnd.fit(X = X_random, y = y_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_jobs=-1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_smote = KNeighborsClassifier(n_jobs = -1)\n",
    "knn_smote.fit(X = X_smote, y = y_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction und F1-Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cell_id": "00010-315d8e63-2c45-4785-b0b4-59735c4f59b8",
    "deepnote_cell_type": "code",
    "execution_millis": 5128,
    "execution_start": 1608728062480,
    "output_cleared": false,
    "source_hash": "70abdc04",
    "tags": []
   },
   "outputs": [],
   "source": [
    "knn_y_pred = knn.predict(X = X_test)\n",
    "knn_y_pred_rnd = knn_rnd.predict(X = X_test)\n",
    "knn_y_pred_smote = knn_smote.predict(X = X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score KNN imbalanced Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.252"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score KNN imbalanced Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, knn_y_pred, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score KNN randomly resampled Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.207"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score KNN randomly resampled Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, knn_y_pred_rnd, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score KNN SMOTE resampled Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.202"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score KNN SMOTE resampled Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, knn_y_pred_smote, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00014-e31f3766-000a-4782-b090-f874cc117439",
    "deepnote_cell_type": "markdown",
    "output_cleared": false,
    "tags": []
   },
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cell_id": "00015-460ce3cd-46ea-4a04-974e-a250661f28d8",
    "deepnote_cell_type": "code",
    "execution_millis": 52475,
    "execution_start": 1607692152662,
    "output_cleared": false,
    "source_hash": "f95dfa02",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=69)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_jobs = -1, random_state = 69)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=69)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_rnd = RandomForestClassifier(n_jobs = -1, random_state = 69)\n",
    "rfc_rnd.fit(X_random, y_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=69)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_smote = RandomForestClassifier(n_jobs = -1, random_state = 69)\n",
    "rfc_smote.fit(X_smote, y_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction und F1-Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "cell_id": "00016-64fd95dd-a2a6-43e9-b444-3e1f0d2b00a8",
    "deepnote_cell_type": "code",
    "execution_millis": 1785,
    "execution_start": 1607692205137,
    "output_cleared": false,
    "source_hash": "7cec665f",
    "tags": []
   },
   "outputs": [],
   "source": [
    "rfc_y_pred = rfc.predict(X = X_test)\n",
    "rfc_y_pred_rnd = rfc_rnd.predict(X = X_test)\n",
    "rfc_y_pred_smote = rfc_smote.predict(X = X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score Random Forest imbalanced Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.364"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score Random Forest imbalanced Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, rfc_y_pred, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score Random Forest randomly resampled Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.42"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score Random Forest randomly resampled Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, rfc_y_pred_rnd, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score Random Forest SMOTE resampled Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.407"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score Random Forest SMOTE resampled Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, rfc_y_pred_smote, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00018-95e13e03-2728-48d3-8031-53032408c407",
    "deepnote_cell_type": "markdown",
    "output_cleared": false,
    "tags": []
   },
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "cell_id": "00019-d4f44519-863f-4512-b73d-8372688a1e4b",
    "deepnote_cell_type": "code",
    "execution_millis": 107767,
    "execution_start": 1607692207470,
    "output_cleared": false,
    "source_hash": "3bb04101",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ericwinter/opt/miniconda3/envs/py38/lib/python3.8/site-packages/xgboost/sklearn.py:892: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:51:01] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=-1, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=69, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbc = xgb.XGBClassifier(n_jobs = -1, random_state = 69)\n",
    "xgbc.fit(X_train, y_train, verbose = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:54:57] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=-1, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=69, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbc_rnd = xgb.XGBClassifier(n_jobs = -1, random_state = 69)\n",
    "xgbc_rnd.fit(X_random, y_random, verbose = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:59:01] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=-1, num_parallel_tree=1,\n",
       "              objective='multi:softprob', random_state=69, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbc_smote = xgb.XGBClassifier(n_jobs = -1, random_state = 69)\n",
    "xgbc_smote.fit(X_smote, y_smote, verbose = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction und F1-Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "cell_id": "00020-13b21eeb-4471-4865-a477-eaeb1ad95b73",
    "deepnote_cell_type": "code",
    "execution_millis": 1670,
    "execution_start": 1607692315237,
    "output_cleared": false,
    "source_hash": "d26e2ea6",
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgbc_y_pred = xgbc.predict(X_test)\n",
    "xgbc_y_pred_rnd = xgbc_rnd.predict(X_test)\n",
    "xgbc_y_pred_smote = xgbc_smote.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score XGBoost imbalanced Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.387"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score XGBoost imbalanced Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, xgbc_y_pred, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score XGBoost randomly resampled Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.386"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score XGBoost randomly resampled Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, xgbc_y_pred_rnd, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score XGBoost SMOTE resampled Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.402"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score XGBoost SMOTE resampled Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, xgbc_y_pred_smote, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(random_state=69)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbc = lgb.LGBMClassifier(n_jobs = -1, random_state = 69)\n",
    "lgbc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(random_state=69)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbc_rnd = lgb.LGBMClassifier(n_jobs = -1, random_state = 69)\n",
    "lgbc_rnd.fit(X_random, y_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(random_state=69)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgbc_smote = lgb.LGBMClassifier(n_jobs = -1, random_state = 69)\n",
    "lgbc_smote.fit(X_smote, y_smote)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction und F1-Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbc_y_pred = lgbc.predict(X_test)\n",
    "lgbc_y_pred_rnd = lgbc_rnd.predict(X_test)\n",
    "lgbc_y_pred_smote = lgbc_smote.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score LGBM imbalanced Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.312"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score LGBM imbalanced Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, lgbc_y_pred, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score LGBM randomly resampled Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.379"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score LGBM randomly resampled Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, lgbc_y_pred_rnd, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-Score LGBM SMOTE resampled Data:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.399"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('F1-Score LGBM SMOTE resampled Data:')\n",
    "np.round(sklearn.metrics.f1_score(y_test, lgbc_y_pred_smote, average = 'macro'), decimals = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vergleich "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "| Modell         \t| Daten               \t| F1-Score \t|\n",
    "|---------------\t|---------------------\t|----------\t|\n",
    "| KNN           \t| Imbalanced          \t| 0.254    \t|\n",
    "| KNN           \t| Random Oversampling \t| 0.201    \t|\n",
    "| KNN           \t| SMOTE Oversampling  \t| 0.196    \t|\n",
    "| Random Forest \t| Imbalanced          \t| 0.375    \t|\n",
    "| Random Forest \t| Random Oversampling \t| 0.473    \t|\n",
    "| Random Forest \t| SMOTE Oversampling  \t| 0.41    \t|\n",
    "| XGBoost       \t| Imbalanced          \t| 0.382    \t|\n",
    "| XGBoost       \t| Random Oversampling \t| 0.453    \t|\n",
    "| XGBoost       \t| SMOTE Oversampling  \t| 0.437    \t|\n",
    "| LightGBM      \t| Imbalanced          \t| 0.31     \t|\n",
    "| LightGBM      \t| Random Oversampling \t| 0.416    \t|\n",
    "| LightGBM      \t| SMOTE Oversampling  \t| 0.397    \t|\n",
    "\n",
    "Hinweis: Obwohl wo immer möglich ein random_state verwendet wurde, um eine Konsistenz der Werte zu garantieren, variieren diese dennoch pro Durchlauf. Dasselbe gilt später auch für die Confusion Matrix.\n",
    "\n",
    "Die F1-Scores sind breit gefächert: von 0.196 bis 0.473. Interessant ist, dass die Performance der Modelle, ausser dem KNN, am besten ist, wenn auf den zufällig resampleten Daten trainiert wurde. SMOTE ist auch besser als die imbalanced Daten, jedoch nie das beste. Der Random Forest und das XGBoost Modell sind sich in ihren Werten sehr ähnlich, LightGBM kommt kurz danach und das KNN ist mit Abstand das schlechteste Modell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "00022-b9cd6a76-3136-4ebe-a0dd-035b7662eaea",
    "deepnote_cell_type": "markdown",
    "output_cleared": false,
    "tags": []
   },
   "source": [
    "# Hyperparameter tuning\n",
    "\n",
    "Aus Ressourcengründen werden wir nicht das beste Modell trainieren, sondern ein LightGBM-Modell. Es werden die randomly resampled Daten zum Trainieren verwendet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.datetime.now()\n",
    "\n",
    "def bay_opt(learning_rate, max_depth, n_estimators, num_leaves, reg_alpha, reg_lambda):\n",
    "    ### cast certain inputs to int:\n",
    "    max_depth = int(max_depth)\n",
    "    n_estimators = int(n_estimators)\n",
    "    num_leaves = int(num_leaves)\n",
    "    # Instantiation \n",
    "    lgb_c = lgb.LGBMClassifier(\n",
    "        n_jobs = -1, seed = 42, learning_rate = learning_rate, max_depth = max_depth, n_estimators = n_estimators,\n",
    "        num_leaves = num_leaves, reg_alpha = reg_alpha, reg_lambda = reg_lambda\n",
    "    )\n",
    "    # Fitting the model \n",
    "    lgb_c.fit(X_random.to_numpy(), y_random.to_numpy(), early_stopping_rounds=15, eval_set=[(X_test.to_numpy(), y_test.to_numpy())], verbose = 0)\n",
    "    # Predict the model \n",
    "    pred = lgb_c.predict(X_test.to_numpy())\n",
    "    \n",
    "    # Current Accuracy.\n",
    "    return sklearn.metrics.f1_score(y_test, pred, average = 'macro')\n",
    "\n",
    "\n",
    "# Bounded region of parameter space\n",
    "param_bounds =   {'learning_rate': (0.005, 0.1),\n",
    "                'reg_alpha': (0, 5),\n",
    "                'reg_lambda': (0, 50),\n",
    "                'max_depth': (5, 500),\n",
    "                'n_estimators' : (100, 1000),\n",
    "                'num_leaves' : (1, 100)\n",
    "               }\n",
    "\n",
    "# Define optimizer\n",
    "optimizer_lgb = BayesianOptimization(\n",
    "    f=bay_opt,\n",
    "    pbounds=param_bounds,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "# Start the optimization\n",
    "print(\"Starting the optimization.\")\n",
    "optimizer_lgb.maximize(\n",
    "    init_points=5,\n",
    "    n_iter=100,\n",
    ")\n",
    "\n",
    "\n",
    "print(datetime.datetime.now() - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das optimierte Modell hat einen F1-Score von 0.49. Im Weiteren inspizieren und vergleichen wir dieses Modell genauer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|   iter    |  target   | learni... | max_depth | n_esti... | num_le... | reg_alpha | reg_la... |\n",
    "#|  13       |  0.4962   |  0.07517  |  340.1    |  344.4    |  89.66    |  1.136    |  4.348    |\n",
    "\n",
    "lgbc_best = lgb.LGBMClassifier(\n",
    "        n_jobs = -1, seed = 42, learning_rate = 0.07517, max_depth = 340, n_estimators = 344,\n",
    "        num_leaves = 90, reg_alpha = 1.136, reg_lambda = 4.348\n",
    ")\n",
    "\n",
    "lgbc_best.fit(X_random, y_random)\n",
    "lgbc_y_pred_best = lgbc_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vergleich des Besten und schlechtesten Modells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Schlechtestes Modell\n",
    "\n",
    "Betrachten wir zwei verschiedene Modelle im Detail. Konkret vergleichen wir:\n",
    "\n",
    "- F1-Score Macro\n",
    "- Classification report\n",
    "- Confusion matrix\n",
    "\n",
    "### F1-Score\n",
    "\n",
    "Das schlechteste Modell ist das KNN, das auf den SMOTE Daten trainiert wurde. Es erzielt einen F1-Score von 0.196. Unser bestes, optimiertes Modell erzielt 0.49. Doch was bedeutet das für die einzelnen Klassen?\n",
    "\n",
    "### Classification report\n",
    "\n",
    "Der Classification Report gibt Auskunft über wichtigen Metriken, mit denen man eine Klassifikation bewerten und einschätzen kann. Diese Metriken sind:\n",
    "\n",
    "#### Precision\n",
    "\n",
    "$\\frac{True Prositives}{True Prositives + False Prositives}$\n",
    "\n",
    "#### Recall\n",
    "\n",
    "$\\frac{True Prositives}{True Prositives + False Negatives}$\n",
    "\n",
    "#### F1-Score\n",
    "\n",
    "$2 \\cdot \\frac{Recall \\cdot Precision}{Recall + Precision}$\n",
    "\n",
    "#### Support\n",
    "\n",
    "Anzahl, wie oft Objekte dieser Klasse vorkommen.\n",
    "\n",
    "Zuerst betrachten wir den Report des schlechtesten Modells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Anderes Haus       0.01      0.33      0.02         3\n",
      "    Attikawohnung       0.10      0.45      0.17       620\n",
      "       Doppelhaus       0.17      0.47      0.25       846\n",
      "             Haus       0.79      0.42      0.55      5007\n",
      "      Loftwohnung       0.06      0.44      0.11        48\n",
      "Maisonettewohnung       0.11      0.40      0.18       583\n",
      " Mehrfamilienhaus       0.04      0.33      0.07        36\n",
      "       Reihenhaus       0.14      0.43      0.21       417\n",
      "     Terassenhaus       0.08      0.50      0.13        56\n",
      "          Wohnung       0.92      0.37      0.53      7741\n",
      "           Zimmer       0.00      0.00      0.00         5\n",
      "\n",
      "         accuracy                           0.40     15362\n",
      "        macro avg       0.22      0.38      0.20     15362\n",
      "     weighted avg       0.74      0.40      0.48     15362\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sklearn.metrics.classification_report(y_test, knn_y_pred_smote))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vergleicht man die Werte unter Precision miteinander, fällt direkt auf, wie ungleich sie sind. *Wohnung* und *Haus* haben mit Abstand die beiden besten Werte, 0.90 und 0.78. Beim Support sieht man, was am Anfang der Analyse schon deutlich wurde: es gibt eine extreme Ungleichheit in der Vorkommenshäufigkeit der Klassen. Da wir unsere Trainings- und Testdaten vor dem Resampling aufteilen, existiert diese Ungleichheit hier immernoch. Wir hätten die Testdaten auch resamplen können, haben uns aber bewusst dagegen entschieden. So können wir eine Performance auf realistischen Daten messen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test.value_counts().plot(kind='bar', figsize=(10, 5), rot = 90)\n",
    "plt.xlabel(\"Kategorie\")\n",
    "plt.ylabel(\"Anzahl Objekte\")\n",
    "plt.title(\"Wohnobjekte pro Kategorie in den Testdaten\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aus diesem Grund ist die Precision in diesen beiden Kategorien so viel höher als bei den anderen.\n",
    "\n",
    "Beim Recall sieht es etwas anders aus. Da in diesem die False Negatives mit einfliessen, liegen die Werte näher beieinander. Er beschreibt wie viele, der als richtig klassifizierten Objekte, tatsächlich richtig klassifiziert worden sind. Bei der Kategorie *Wohnung* ist der Wert mit 0.37 eher niederig. Die Erklärung hierfür ist, dass das Model durch die Klassenungleichheit in den Trainingsdaten einen starken Bias entwickelt. Darum werden viele Nicht-Wohnungen als Wohnungen klassifiziert.\n",
    "\n",
    "Der F1-Score ist das harmonische Mittel von Precision und Recall, er zieht also False Positives und False Negatives mit in Betracht. Hier haben die beiden Kategorien *Haus* und *Wohnung* wieder die besten Scores. Grund hierfür ist der unterliegende gute Wert der Precision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bestes Modell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Anderes Haus       0.00      0.00      0.00         3\n",
      "    Attikawohnung       0.32      0.69      0.43       620\n",
      "       Doppelhaus       0.38      0.60      0.47       846\n",
      "             Haus       0.92      0.79      0.85      5007\n",
      "      Loftwohnung       0.35      0.50      0.41        48\n",
      "Maisonettewohnung       0.28      0.58      0.37       583\n",
      " Mehrfamilienhaus       0.27      0.58      0.37        36\n",
      "       Reihenhaus       0.40      0.60      0.48       417\n",
      "     Terassenhaus       0.60      0.54      0.57        56\n",
      "          Wohnung       0.95      0.78      0.86      7741\n",
      "           Zimmer       0.00      0.00      0.00         5\n",
      "\n",
      "         accuracy                           0.75     15362\n",
      "        macro avg       0.41      0.51      0.44     15362\n",
      "     weighted avg       0.84      0.75      0.78     15362\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sklearn.metrics.classification_report(y_test, lgbc_y_pred_best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bei der Precision fällt direkt auf, dass die Werte näher beieinander liegen als voher. *Wohnung* und *Haus* sind immer noch oben mit dabei, aber jetzt gibt es sogar noch eine bessere Kategorie. *Anderes Haus* hat eine Precision von 1.00! Wenn man sich aber den Recall anschaut, sieht man, dass diese Klassifikation nicht perfekt ist. Die Precision von 1.00 sagt uns, dass es keine False Positives gibt. Der Recall sagt uns aber, dass es False Negatives gibt. Für die Werte 1.00 und 0.33 bei einer Gesamtzahl von 3 Objekten in dieser Kategorie bedeutet das: 1 Objekt wurde richtigerweise als *Anderes Haus* klassifiert und 2 aus der Kategorie wurden falsch eingeordnet.\n",
    "\n",
    "Betrachtet man den F1-Score, haben sich die Werte in jeder Kategorie drastisch verbessert. *Haus* und *Wohnung* sind immer noch die beiden besten Kategorien, jedoch sind alle anderen Werte jetzt viel näher dran."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix\n",
    "\n",
    "Die Matrix ist über die *richtigen* Klassifizierungen hinweg normalisiert. Das bedeutet, dass die Werte in jeder Zeile in der Matrix zusammenaddiert 1.0 ergeben. Man kann herauslesen, wie viele Objekte (prozentual) in welche andere Klasse eingeordnet wurden. \n",
    "\n",
    "Beispiel: *Anderes Haus* in der ersten Zeile. Wir sehen im Schnittpunkt von *Anderes Haus* auf der x-Achse und *Anderes Haus* auf der y-Achse einen Wert von 0.33. Das bedeutet ein Drittel der Objekte wurden auch so klassifiziert. Bei *Doppel Haus* auf der x-Achse und *Anderes Haus* auf der y-Achse sieht man, ein weiteres Drittel wurde fälschlicherweise als *Doppelhaus* klassifiziert.\n",
    "\n",
    "Insgesammt kann man sehen, dass die meisten Objekte der jeweiligen Klasse korrekt eingestuft worden sind (Diagonale von links oben nach rechts unten). Trotzdem werden viele Objekte noch falsch und überall verteilt eingeordnet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "sklearn.metrics.plot_confusion_matrix(knn_smote, X_test, y_test, normalize = 'true', xticks_rotation='vertical', ax = ax)\n",
    "plt.title('Confusion Matrix schlechtestes Modell (KNN)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beim besten Modell sieht das etwas anders aus. Insgesamt ist die Streuung der falsch klassifizierten Objekte kleiner geworden. Es gibt aber einige (nachvollziehbare) Ausreisser: \n",
    "\n",
    "- 67% der Kategorie *Anderes Haus* wurden als *Haus* klassifiziert\n",
    "- 86% der *Zimmer* wurden als *Wohnung* eingeordnet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Confusion Matrix optimiertes Modell (LightGBM)')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "sklearn.metrics.plot_confusion_matrix(lgbc_best, X_test, y_test, normalize = 'true', xticks_rotation='vertical', ax = ax)\n",
    "plt.title('Confusion Matrix optimiertes Modell (LightGBM)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alles in allem kann man sehen, dass sich die verschiedenen Optimierungstechnicken (Resampling, Hyperparamether tuning) ausgezahlt haben."
   ]
  }
 ],
 "metadata": {
  "deepnote_execution_queue": [],
  "deepnote_notebook_id": "e0c28dcd-d7ce-49c8-98d9-c42ccf18b1ef",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc-autonumbering": true,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}