08b_Tuning Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6b0fd06a-f3cb-4732-bc7d-1c7ad2d39c0f",
   "metadata": {},
   "source": [
    "# Comparison Loss Function and Validation Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bce52fe-8793-4ee5-8b75-1932fd4aa55d",
   "metadata": {},
   "source": [
    "1) Import der einzelnen Libraries. Sortieren der Daten nach der Zielkategorie für den ersten Überblick.\n",
    "2) Aufteilen der Trainings- und Testdaten. Erstellen von notwendigen Listen\n",
    "3) Erstellen von Funktionen \n",
    "4) initialisieren der einzelnen MLPClassifier Netze \n",
    "5) Ergebnisse: Textuelle Daten, geplottete Vergleiche, prozentuale Heatmaps und Reports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3990afb-733a-40a5-957e-451abceb2d5e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## imports and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9ce620e-67af-4fcb-8736-ead8893c3523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_covtype\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix, balanced_accuracy_score, roc_auc_score, top_k_accuracy_score, cohen_kappa_score, matthews_corrcoef, hinge_loss\n",
    "from sklearn.metrics import plot_confusion_matrix, multilabel_confusion_matrix\n",
    "from sklearn.metrics import RocCurveDisplay, accuracy_score, recall_score, precision_score, precision_recall_fscore_support\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.figure as fig\n",
    "from matplotlib.colors import ListedColormap\n",
    "from mpl_toolkits import mplot3d\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "covertype = fetch_covtype()\n",
    "forest = fetch_covtype(as_frame=True)\n",
    "covertype.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a574498c-78cc-4eb5-9626-ea65dab5307e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "0         5\n",
       "1         5\n",
       "2         2\n",
       "3         2\n",
       "4         5\n",
       "         ..\n",
       "581007    3\n",
       "581008    3\n",
       "581009    3\n",
       "581010    3\n",
       "581011    3\n",
       "Name: Cover_Type, Length: 581012, dtype: int32"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "forest.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a19f2f3-1f0e-43f7-88fd-f3a9b3b7bdf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "forestlist = forest.frame\n",
    "forestlistFiltered = forestlist.filter(items=['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology', 'Horizontal_Distance_To_Roadways', 'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm', 'Horizontal_Distance_To_Fire_Points', 'Wilderness_Area_0', 'Wilderness_Area_1', 'Wilderness_Area_2', 'Wilderness_Area_3', 'Cover_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ec34c71-e117-44eb-a8bb-1d7acdd756f5",
   "metadata": {
    "tags": []
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology</th>\n",
       "      <th>Vertical_Distance_To_Hydrology</th>\n",
       "      <th>Horizontal_Distance_To_Roadways</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type_31</th>\n",
       "      <th>Soil_Type_32</th>\n",
       "      <th>Soil_Type_33</th>\n",
       "      <th>Soil_Type_34</th>\n",
       "      <th>Soil_Type_35</th>\n",
       "      <th>Soil_Type_36</th>\n",
       "      <th>Soil_Type_37</th>\n",
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       "      <td>45.0</td>\n",
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      "text/plain": [
       "        Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \\\n",
       "290505     2928.0    58.0    7.0                             150.0   \n",
       "532739     3015.0   343.0   18.0                             376.0   \n",
       "532740     3018.0   341.0   18.0                             350.0   \n",
       "532741     3021.0   345.0   18.0                             324.0   \n",
       "532742     3023.0   349.0   19.0                             300.0   \n",
       "...           ...     ...    ...                               ...   \n",
       "399826     3334.0   356.0   10.0                             558.0   \n",
       "399825     3333.0     6.0    7.0                             601.0   \n",
       "399824     3334.0     3.0    5.0                             601.0   \n",
       "399812     3331.0   246.0    3.0                             660.0   \n",
       "293238     3284.0   307.0   18.0                              60.0   \n",
       "\n",
       "        Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \\\n",
       "290505                            -7.0                           1836.0   \n",
       "532739                            50.0                           3378.0   \n",
       "532740                            53.0                           3398.0   \n",
       "532741                            45.0                           3419.0   \n",
       "532742                            47.0                           3439.0   \n",
       "...                                ...                              ...   \n",
       "399826                           100.0                            765.0   \n",
       "399825                            99.0                            806.0   \n",
       "399824                            39.0                            845.0   \n",
       "399812                           117.0                            859.0   \n",
       "293238                            22.0                           3922.0   \n",
       "\n",
       "        Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \\\n",
       "290505          226.0           225.0          133.0   \n",
       "532739          181.0           211.0          166.0   \n",
       "532740          180.0           211.0          168.0   \n",
       "532741          182.0           209.0          164.0   \n",
       "532742          182.0           206.0          160.0   \n",
       "...               ...             ...            ...   \n",
       "399826          204.0           223.0          156.0   \n",
       "399825          212.0           226.0          152.0   \n",
       "399824          214.0           231.0          155.0   \n",
       "399812          213.0           241.0          166.0   \n",
       "293238          167.0           226.0          198.0   \n",
       "\n",
       "        Horizontal_Distance_To_Fire_Points  ...  Soil_Type_31  Soil_Type_32  \\\n",
       "290505                               190.0  ...           1.0           0.0   \n",
       "532739                              1982.0  ...           0.0           0.0   \n",
       "532740                              2005.0  ...           0.0           0.0   \n",
       "532741                              2029.0  ...           0.0           0.0   \n",
       "532742                              2052.0  ...           0.0           0.0   \n",
       "...                                    ...  ...           ...           ...   \n",
       "399826                              1642.0  ...           0.0           0.0   \n",
       "399825                              1583.0  ...           1.0           0.0   \n",
       "399824                              1524.0  ...           1.0           0.0   \n",
       "399812                              1113.0  ...           1.0           0.0   \n",
       "293238                              3999.0  ...           0.0           0.0   \n",
       "\n",
       "        Soil_Type_33  Soil_Type_34  Soil_Type_35  Soil_Type_36  Soil_Type_37  \\\n",
       "290505           0.0           0.0           0.0           0.0           0.0   \n",
       "532739           0.0           0.0           0.0           0.0           0.0   \n",
       "532740           0.0           0.0           0.0           0.0           0.0   \n",
       "532741           0.0           0.0           0.0           0.0           0.0   \n",
       "532742           0.0           0.0           0.0           0.0           0.0   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "399826           0.0           0.0           0.0           0.0           0.0   \n",
       "399825           0.0           0.0           0.0           0.0           0.0   \n",
       "399824           0.0           0.0           0.0           0.0           0.0   \n",
       "399812           0.0           0.0           0.0           0.0           0.0   \n",
       "293238           0.0           0.0           0.0           0.0           1.0   \n",
       "\n",
       "        Soil_Type_38  Soil_Type_39  Cover_Type  \n",
       "290505           0.0           0.0           1  \n",
       "532739           0.0           0.0           1  \n",
       "532740           0.0           0.0           1  \n",
       "532741           0.0           0.0           1  \n",
       "532742           0.0           0.0           1  \n",
       "...              ...           ...         ...  \n",
       "399826           0.0           0.0           7  \n",
       "399825           0.0           0.0           7  \n",
       "399824           0.0           0.0           7  \n",
       "399812           0.0           0.0           7  \n",
       "293238           0.0           0.0           7  \n",
       "\n",
       "[581012 rows x 55 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forestlist = forest.frame\n",
    "forestlistSorted = forestlist.sort_values(by='Cover_Type')\n",
    "forestlistSorted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad685848-0852-40ec-9224-6479edcfbadf",
   "metadata": {
    "tags": []
   },
   "source": [
    "## training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7374455-6ab6-482b-914d-7d25c028307e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(581012, 54)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covertype['data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa2226cd-ac5d-44b9-81d3-86e117c60d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_c = covertype['data']\n",
    "y_c = covertype['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e95d3764-9b2c-4c58-a730-8b7167b298a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(435759, 54)\n",
      "(145253, 54)\n"
     ]
    }
   ],
   "source": [
    "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(X_c, y_c)\n",
    "print(X_train_c.shape) \n",
    "print(X_test_c.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e55d24c-b370-4bec-95d4-52c00074d70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler_c = StandardScaler()\n",
    "# Fit only to the training data\n",
    "scaler_c.fit(X_train_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2892e8de-0ce9-4e7a-aab7-6b188f2a8c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_c = scaler_c.transform(X_train_c)\n",
    "X_test_c = scaler_c.transform(X_test_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbdcb1ab-db1f-4585-9d11-b7c56775370d",
   "metadata": {},
   "outputs": [],
   "source": [
    "classificationList = []\n",
    "curve = []\n",
    "validation = []\n",
    "iteration = []\n",
    "\n",
    "accuracyList = []\n",
    "recallList = []\n",
    "precisionList = []\n",
    "timeList = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad8a3ff-1ec6-4b47-b833-497c31f505d7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00451c60-04e7-4cb1-85f2-cc748c4d0ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotData():\n",
    "    fig, axes = plt.subplots(3,2)\n",
    "    sns.histplot(data=forestlistSorted, x=\"Elevation\", color=\"skyblue\", label=\"Elevation\", kde=True, ax=axes[0,0])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Aspect\", color=\"red\", label=\"Aspect\", kde=True, ax=axes[0,0])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Slope\", color=\"orange\", label=\"Slope\", kde=True, ax=axes[0,0])\n",
    "    plt.legend() \n",
    "\n",
    "    sns.histplot(data=forestlistSorted, x=\"Horizontal_Distance_To_Hydrology\", color=\"skyblue\", label=\"Horizontal_Distance_To_Hydrology\", kde=True, ax=axes[0,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Vertical_Distance_To_Hydrology\", color=\"red\", label=\"Vertical_Distance_To_Hydrology\", kde=True, ax=axes[0,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Horizontal_Distance_To_Roadways\", color=\"orange\", label=\"Horizontal_Distance_To_Roadways\", kde=True, ax=axes[0,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Horizontal_Distance_To_Fire_Points\", color=\"green\", label=\"Horizontal_Distance_To_Fire_Points\", kde=True, ax=axes[0,1])\n",
    "    plt.legend() \n",
    "\n",
    "    sns.histplot(data=forestlistSorted, x=\"Hillshade_9am\", color=\"skyblue\", label=\"Hillshade_9am\", kde=True, ax=axes[1,0])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Hillshade_Noon\", color=\"red\", label=\"Hillshade_Noon\", kde=True, ax=axes[1,0])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Hillshade_3pm\", color=\"orange\", label=\"Hillshade_3pm\", kde=True, ax=axes[1,0])\n",
    "    plt.legend() \n",
    "\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_31\", color=\"skyblue\", label=\"Soil_Type_31\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_32\", color=\"red\", label=\"Soil_Type_32\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_33\", color=\"orange\", label=\"Soil_Type_33\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_34\", color=\"cyan\", label=\"Soil_Type_34\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_35\", color=\"darkgreen\", label=\"Soil_Type_35\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_36\", color=\"gold\", label=\"Soil_Type_36\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_37\", color=\"purple\", label=\"Soil_Type_37\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_38\", color=\"silver\", label=\"Soil_Type_38\", kde=True, ax=axes[1,1])\n",
    "    sns.histplot(data=forestlistSorted, x=\"Soil_Type_39\", color=\"navy\", label=\"Soil_Type_39\", kde=True, ax=axes[1,1])\n",
    "    plt.legend() \n",
    "\n",
    "    sns.histplot(data=forestlistSorted, x=\"Cover_Type\", color=\"orange\", label=\"Cover_Type\",  ax=axes[2,1], discrete=True, kde=False)\n",
    "    plt.legend() \n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "207ed85a-e659-4bb9-b533-fc3dc2a39ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def addToList(mlp, liste):\n",
    "    liste.append(mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b80fe74-be82-444d-ac56-b6a8f4797089",
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeFromList(mlp, liste):\n",
    "    liste.remove(mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2cc06923-ca80-4f1a-ae11-2e5fafc52406",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitWithTime(mpl):\n",
    "    begin = time.time()\n",
    "    mlptrain = mpl.fit(X_train_c,y_train_c)\n",
    "    end = time.time()\n",
    "    timestamp = end-begin\n",
    "    timeList.append(timestamp)\n",
    "    addToList(mlptrain, classificationList)\n",
    "    print('Zeit in Sekunden: \\t{0:.2f}'.format(timestamp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8384363b-3a23-4ea7-9ddf-e8e2fb0fcd49",
   "metadata": {},
   "outputs": [],
   "source": [
    "def comparison(List):\n",
    "    for mlp in List:\n",
    "        print(mlp)\n",
    "        print('\\nparamters: ' + str(mlp.get_params(deep=True)))\n",
    "        print('\\nlast loss: ' + str(mlp.loss_))\n",
    "        print('\\nnumber of features seen during fit: ' + str(mlp.n_features_in_))\n",
    "        print('\\nnumber of layers: ' + str(mlp.n_layers_))\n",
    "        print('\\nnumber of outputs: ' + str(mlp.n_outputs_))\n",
    "        print('\\nname of output activation function: ' + str(mlp.out_activation_) )\n",
    "        print('\\nnumber of iterations: ' +str(mlp.n_iter_) )\n",
    "        print('\\nnumber training samples seen by the solver during fitting: ' +str(mlp.t_) )\n",
    "        print('****************************************************************************************')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "76d31667-65c3-4d58-89a5-0c8e333620a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def curveValidateIteration(List, c, v, i):\n",
    "    for mlp in List:\n",
    "        c.append(mlp.loss_curve_)\n",
    "        v.append(mlp.validation_scores_)\n",
    "        i.append(np.arange(1, mlp.n_iter_+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecba6578-cde2-4e01-970c-209a788ea8ed",
   "metadata": {},
   "source": [
    "__________________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e8f31d2-0b07-4411-8bc6-1c2c68b140fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = ListedColormap([\"coral\", \"azure\", \"indigo\", \"darkgreen\", \"lightblue\", \"crimson\", \"gold\", \"silver\", \"teal\"])\n",
    "def plotCurvePerInteration(List, curvy, iterate):\n",
    "    print('default: hidden_layer = (100,), activation = relu, solver = adam, alpha = 0.0001, learning_rate = constant, learning_rate_init = 0.001, max_iter = 200, shuffle = true, early_stopping & warm_start = FALSE')\n",
    "    print('Alle Infos, die davon abweichen, werden in der Legende gelistet')\n",
    "    print('****************************************************************************************')\n",
    "    for i,c,cl in zip(iterate, curvy, List):\n",
    "        plt.scatter(i,c, marker = '.', label = str(cl))\n",
    "    plt.title('Losscurve per iteration')\n",
    "    plt.xlabel(\"iteration\")\n",
    "    plt.ylabel(\"loss curve\")\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3ef3f87-42fb-46b9-96f7-7b363ec85fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotValidationPerInteration(List, validate, iterate):\n",
    "    for i,v, cl in zip(iterate, validate, List):\n",
    "        plt.scatter(i,v, marker = '.', label = str(cl))\n",
    "    plt.title('Validation per iteration')\n",
    "    plt.xlabel(\"iteration\")\n",
    "    plt.ylabel(\"validation\")\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25ada7f9-9a81-4a58-80ae-1bea09db70f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotValidationPercurve(List, validate, curvy):\n",
    "    for c,v,cl in zip(validate, curvy, List):\n",
    "        plt.scatter(c,v, marker = '.', label = str(cl))\n",
    "    plt.title('Validation per curve')\n",
    "    plt.xlabel(\"validation\")\n",
    "    plt.ylabel(\"curve\")\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fcda0d6-d713-4584-ac38-41b1814e1eec",
   "metadata": {},
   "source": [
    "__________________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ad8cf6d8-5f4f-477f-9b18-8acb297611fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def heatmaping(List):\n",
    "    for mlp in List:\n",
    "        print(mlp)\n",
    "        predictions = mlp.predict(X_test_c)   \n",
    "        con_ma = confusion_matrix(y_test_c,predictions)\n",
    "        con_ma_perc = np.empty([7, 7], dtype=float)\n",
    "\n",
    "        for i in range(0, 7):\n",
    "            number = 0.0\n",
    "            for j in range(0, 7):\n",
    "                number += con_ma[i][j]\n",
    "            for j in range(0, 7):\n",
    "                con_ma_perc[i][j] = 100/number*con_ma[i][j]\n",
    "\n",
    "        tree = ['Spruce', 'Lodgepole Pine', 'PoderosaPine', 'Cottonwood/Willow', 'Aspen', 'Douglas-fir', 'Krummholz']\n",
    "\n",
    "        # ======== Plot confuse matrix prozentual ========\n",
    "\n",
    "        ay = sns.heatmap(con_ma_perc, annot=True, cmap='coolwarm', yticklabels=tree, xticklabels=tree, fmt='.2f', vmin=0, vmax=100)\n",
    "        ay.xaxis.tick_top()\n",
    "        ay.xaxis.set_label_position('top')\n",
    "        plt.xticks(rotation=90)\n",
    "        ay.set(title=\"Confusion Matrix - Recall\",\n",
    "              xlabel=\"- Predicted Tree -\",\n",
    "              ylabel=\"- Actual Tree -\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "633860b1-fd10-4b7a-b001-8bacfb1ce97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reporting(List):\n",
    "    for mlp in List:\n",
    "        print(mlp)\n",
    "        predictions = mlp.predict(X_test_c)\n",
    "        print(classification_report(y_test_c,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1e4d2697-5eb8-4deb-a58a-a9cde3fc2599",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracyPlotting(List):\n",
    "        for mlp in List:\n",
    "            print(mlp)\n",
    "            predictions = mlp.predict(X_test_c)\n",
    "            accuracy = accuracy_score(y_test_c, predictions)\n",
    "            recall = recall_score(y_test_c, predictions, average = 'weighted')\n",
    "            precision = precision_score(y_test_c, predictions, average = 'weighted')\n",
    "            \n",
    "            addToList(accuracy, accuracyList)\n",
    "            addToList(recall, recallList)\n",
    "            addToList(precision, precisionList)\n",
    "            \n",
    "            print('Accuracy: \\t{0:.2f}'.format(accuracy * 100.0))\n",
    "            print('Recall: \\t{0:.2f}'.format(recall * 100.0))\n",
    "            print('precision: \\t{0:.2f}'.format(precision * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8e5c7d4-ba02-4cd5-8524-cd12e89acfb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTimeofMLP(List):\n",
    "    for mlp, t in zip(List,timeList):\n",
    "        print(mlp,t/60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f628e5a-26a7-4774-8cbc-9913bb044787",
   "metadata": {},
   "source": [
    "## initializing nets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ab1fd8-91f0-4757-b19e-2f0cdcdfab87",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### tanh\n",
    "<li> Aktivierungsfunktion: hyperbolic Tangens Function </li>\n",
    "<li> Berechnung: $f(x) = tanh(x)$ </li>\n",
    "<li> Andere Schreibweise: $f(x) = \\frac{e^x-e^{-x}}{e^x+e^{-x}}$</li>\n",
    "<li> Werte von -1 bis 1 </li>\n",
    "\n",
    "### Learningrate = invscaling\n",
    "<li>invscaling: $effectiveLearningRate = \\frac{learningRateInit}{t^power_t}$ $$invs = \\frac{learningRateInit}{t^{0.5}}$$</li>\n",
    "<li> Parameter zum Gradientenabstieg </li>\n",
    "\n",
    "### Solver = Adam\n",
    "<li> Stochastic Gradient Descent: Implementierung nach jedem Datensatz </li>\n",
    "<li> Kombination aus zwei Erweiterungen: Adaptive Gradient Algorithm + Root Mean Square Propagation   </li>\n",
    "<li> dadurch passt sich Lernrate an und beschleuningt den Abstieg des Gradienten </li>\n",
    "<li> \"Adaptive Moment Estimation: $$m_t = \\beta_1 * m_{t-1} * (1- \\beta_1) * \\nabla w_t $$  $$v_t = \\beta_2 * v_{t-1} * (1- \\beta_2) * \\nabla (w_t)^2 $$ $$\\widehat{m}_t = \\frac{m_t}{1-\\beta_1^t}$$ $$\\widehat{v}_t = \\frac{v_t}{1-\\beta_2^t}$$ $$ w_{t+1} = w_t - \\frac{\\eta}{sqrt(\\widehat{v}_t+ \\epsilon)}* \\widehat{w}_t$$ </li>\n",
    "\n",
    "#### Parameter\n",
    "<li> alpha: Schrittgröße/Lernrate: $0.0001$ </li>\n",
    "<li> beta1: Exponetielle Zerfallsrate des ersten Impulses </li>\n",
    "<li> beta2: Exponetielle Zerfallsrate des zweiten Impulses </li>\n",
    "<li> epsilon: Um nicht durch 0 zu teilen </li>\n",
    "\n",
    "##### beta_1\n",
    "<li>exponentielle Zerfallrate der Ersten-Moments-Schätzung</li>\n",
    "<li>Zwischen $[0,1)$</li>\n",
    "<li>Wert: $0.9$ </li>\n",
    "\n",
    "##### beta_2\n",
    "<li>exponentielle Zerfallrate der Zweiten-Moments-Schätzung</li>\n",
    "<li>Zwischen $[0,1)$</li>\n",
    "<li>Wert: $0.9$ </li>\n",
    "\n",
    "##### Epsilon\n",
    "<li>Wert für die numerische Stabilität in Adam</li>\n",
    "<li>Vermeidet eine Division durch 0</li>\n",
    "<li>Default-Wert: $1e-06$ </li>\n",
    "\n",
    "### Softmax\n",
    "<li> Aktivierungsfunktion des Outputlayers </li>\n",
    "$e^{T_k}*(\\sum_{l=1} ^{K} e^{T_l})^{-1}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92c769d6-f44b-4ff2-a3ed-32722dc3a21a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.67635071\n",
      "Validation score: 0.752639\n",
      "Iteration 2, loss = 0.55644265\n",
      "Validation score: 0.768451\n",
      "Iteration 3, loss = 0.52228192\n",
      "Validation score: 0.784124\n",
      "Iteration 4, loss = 0.50041453\n",
      "Validation score: 0.792478\n",
      "Iteration 5, loss = 0.48350492\n",
      "Validation score: 0.797595\n",
      "Iteration 6, loss = 0.47117977\n",
      "Validation score: 0.804916\n",
      "Iteration 7, loss = 0.46056726\n",
      "Validation score: 0.811984\n",
      "Iteration 8, loss = 0.45150548\n",
      "Validation score: 0.815219\n",
      "Iteration 9, loss = 0.44323984\n",
      "Validation score: 0.818019\n",
      "Iteration 10, loss = 0.43594213\n",
      "Validation score: 0.822655\n",
      "Iteration 11, loss = 0.42987723\n",
      "Validation score: 0.824582\n",
      "Iteration 12, loss = 0.42494962\n",
      "Validation score: 0.825408\n",
      "Iteration 13, loss = 0.42090064\n",
      "Validation score: 0.826694\n",
      "Iteration 14, loss = 0.41705763\n",
      "Validation score: 0.829264\n",
      "Iteration 15, loss = 0.41403200\n",
      "Validation score: 0.831559\n",
      "Iteration 16, loss = 0.41172011\n",
      "Validation score: 0.830618\n",
      "Iteration 17, loss = 0.40920620\n",
      "Validation score: 0.833027\n",
      "Iteration 18, loss = 0.40692313\n",
      "Validation score: 0.832660\n",
      "Iteration 19, loss = 0.40498590\n",
      "Validation score: 0.833670\n",
      "Iteration 20, loss = 0.40296985\n",
      "Validation score: 0.830388\n",
      "Iteration 21, loss = 0.40128934\n",
      "Validation score: 0.835552\n",
      "Iteration 22, loss = 0.39939246\n",
      "Validation score: 0.834771\n",
      "Iteration 23, loss = 0.39776159\n",
      "Validation score: 0.837043\n",
      "Iteration 24, loss = 0.39605920\n",
      "Validation score: 0.835598\n",
      "Iteration 25, loss = 0.39460194\n",
      "Validation score: 0.838558\n",
      "Iteration 26, loss = 0.39316481\n",
      "Validation score: 0.840164\n",
      "Iteration 27, loss = 0.39164597\n",
      "Validation score: 0.839659\n",
      "Iteration 28, loss = 0.38982888\n",
      "Validation score: 0.839843\n",
      "Iteration 29, loss = 0.38879960\n",
      "Validation score: 0.839200\n",
      "Iteration 30, loss = 0.38766505\n",
      "Validation score: 0.840623\n",
      "Iteration 31, loss = 0.38611319\n",
      "Validation score: 0.842344\n",
      "Iteration 32, loss = 0.38493402\n",
      "Validation score: 0.842964\n",
      "Iteration 33, loss = 0.38374596\n",
      "Validation score: 0.843630\n",
      "Iteration 34, loss = 0.38270941\n",
      "Validation score: 0.843652\n",
      "Iteration 35, loss = 0.38170206\n",
      "Validation score: 0.842390\n",
      "Iteration 36, loss = 0.38037297\n",
      "Validation score: 0.842964\n",
      "Iteration 37, loss = 0.37973602\n",
      "Validation score: 0.843836\n",
      "Iteration 38, loss = 0.37877399\n",
      "Validation score: 0.844226\n",
      "Iteration 39, loss = 0.37772724\n",
      "Validation score: 0.845878\n",
      "Iteration 40, loss = 0.37702036\n",
      "Validation score: 0.847760\n",
      "Iteration 41, loss = 0.37652197\n",
      "Validation score: 0.845993\n",
      "Iteration 42, loss = 0.37596385\n",
      "Validation score: 0.846406\n",
      "Iteration 43, loss = 0.37492575\n",
      "Validation score: 0.846429\n",
      "Iteration 44, loss = 0.37416416\n",
      "Validation score: 0.847416\n",
      "Iteration 45, loss = 0.37393908\n",
      "Validation score: 0.846819\n",
      "Iteration 46, loss = 0.37337464\n",
      "Validation score: 0.845878\n",
      "Iteration 47, loss = 0.37276027\n",
      "Validation score: 0.845970\n",
      "Iteration 48, loss = 0.37200600\n",
      "Validation score: 0.846521\n",
      "Iteration 49, loss = 0.37143905\n",
      "Validation score: 0.846246\n",
      "Iteration 50, loss = 0.37092213\n",
      "Validation score: 0.847737\n",
      "Iteration 51, loss = 0.36998779\n",
      "Validation score: 0.847095\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "Zeit in Sekunden: \t756.45\n"
     ]
    }
   ],
   "source": [
    "mlp_1 = MLPClassifier(hidden_layer_sizes=(20,20,20), max_iter = 350, beta_1 = 0.9, beta_2 = 0.9, epsilon= 1e-06, early_stopping = True, verbose = True, learning_rate = \"invscaling\", activation = 'tanh', solver = \"adam\")\n",
    "begin = time.time()\n",
    "mlp_1_train = mlp_1.fit(X_train_c,y_train_c)\n",
    "end = time.time()\n",
    "timestamp = end-begin\n",
    "timeList.append(timestamp)\n",
    "print('Zeit in Sekunden: \\t{0:.2f}'.format(timestamp))\n",
    "addToList(mlp_1_train, classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e4d2031a-0042-463b-b564-2226c486afa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.59319445\n",
      "Validation score: 0.785295\n",
      "Iteration 2, loss = 0.46704046\n",
      "Validation score: 0.817170\n",
      "Iteration 3, loss = 0.40765351\n",
      "Validation score: 0.837158\n",
      "Iteration 4, loss = 0.36657971\n",
      "Validation score: 0.856113\n",
      "Iteration 5, loss = 0.33700740\n",
      "Validation score: 0.865683\n",
      "Iteration 6, loss = 0.31390293\n",
      "Validation score: 0.876262\n",
      "Iteration 7, loss = 0.29582345\n",
      "Validation score: 0.881770\n",
      "Iteration 8, loss = 0.28026685\n",
      "Validation score: 0.887599\n",
      "Iteration 9, loss = 0.26723907\n",
      "Validation score: 0.894552\n",
      "Iteration 10, loss = 0.25616244\n",
      "Validation score: 0.896962\n",
      "Iteration 11, loss = 0.24661088\n",
      "Validation score: 0.899922\n",
      "Iteration 12, loss = 0.23839486\n",
      "Validation score: 0.900955\n",
      "Iteration 13, loss = 0.23136469\n",
      "Validation score: 0.905338\n",
      "Iteration 14, loss = 0.22483829\n",
      "Validation score: 0.907908\n",
      "Iteration 15, loss = 0.22029824\n",
      "Validation score: 0.909882\n",
      "Iteration 16, loss = 0.21502917\n",
      "Validation score: 0.913989\n",
      "Iteration 17, loss = 0.21086896\n",
      "Validation score: 0.915848\n",
      "Iteration 18, loss = 0.20709585\n",
      "Validation score: 0.914838\n",
      "Iteration 19, loss = 0.20327301\n",
      "Validation score: 0.917041\n",
      "Iteration 20, loss = 0.19993965\n",
      "Validation score: 0.917271\n",
      "Iteration 21, loss = 0.19690582\n",
      "Validation score: 0.919497\n",
      "Iteration 22, loss = 0.19359091\n",
      "Validation score: 0.920966\n",
      "Iteration 23, loss = 0.19119785\n",
      "Validation score: 0.923857\n",
      "Iteration 24, loss = 0.18849445\n",
      "Validation score: 0.920598\n",
      "Iteration 25, loss = 0.18591448\n",
      "Validation score: 0.922297\n",
      "Iteration 26, loss = 0.18397549\n",
      "Validation score: 0.924752\n",
      "Iteration 27, loss = 0.18152528\n",
      "Validation score: 0.923605\n",
      "Iteration 28, loss = 0.17975871\n",
      "Validation score: 0.924500\n",
      "Iteration 29, loss = 0.17774079\n",
      "Validation score: 0.924500\n",
      "Iteration 30, loss = 0.17610236\n",
      "Validation score: 0.924569\n",
      "Iteration 31, loss = 0.17444990\n",
      "Validation score: 0.925808\n",
      "Iteration 32, loss = 0.17312045\n",
      "Validation score: 0.926519\n",
      "Iteration 33, loss = 0.17164407\n",
      "Validation score: 0.926473\n",
      "Iteration 34, loss = 0.17007189\n",
      "Validation score: 0.927598\n",
      "Iteration 35, loss = 0.16860187\n",
      "Validation score: 0.928883\n",
      "Iteration 36, loss = 0.16737843\n",
      "Validation score: 0.929480\n",
      "Iteration 37, loss = 0.16592630\n",
      "Validation score: 0.930168\n",
      "Iteration 38, loss = 0.16483444\n",
      "Validation score: 0.930627\n",
      "Iteration 39, loss = 0.16296871\n",
      "Validation score: 0.929388\n",
      "Iteration 40, loss = 0.16274650\n",
      "Validation score: 0.933128\n",
      "Iteration 41, loss = 0.16181661\n",
      "Validation score: 0.930283\n",
      "Iteration 42, loss = 0.16079935\n",
      "Validation score: 0.934069\n",
      "Iteration 43, loss = 0.15971727\n",
      "Validation score: 0.931614\n",
      "Iteration 44, loss = 0.15820800\n",
      "Validation score: 0.933151\n",
      "Iteration 45, loss = 0.15761463\n",
      "Validation score: 0.930925\n",
      "Iteration 46, loss = 0.15663319\n",
      "Validation score: 0.933312\n",
      "Iteration 47, loss = 0.15595349\n",
      "Validation score: 0.933587\n",
      "Iteration 48, loss = 0.15512147\n",
      "Validation score: 0.934115\n",
      "Iteration 49, loss = 0.15411356\n",
      "Validation score: 0.933289\n",
      "Iteration 50, loss = 0.15350882\n",
      "Validation score: 0.935699\n",
      "Iteration 51, loss = 0.15252578\n",
      "Validation score: 0.933381\n",
      "Iteration 52, loss = 0.15183619\n",
      "Validation score: 0.934872\n",
      "Iteration 53, loss = 0.15148959\n",
      "Validation score: 0.934804\n",
      "Iteration 54, loss = 0.15058588\n",
      "Validation score: 0.935263\n",
      "Iteration 55, loss = 0.14986059\n",
      "Validation score: 0.933748\n",
      "Iteration 56, loss = 0.14942816\n",
      "Validation score: 0.932623\n",
      "Iteration 57, loss = 0.14876212\n",
      "Validation score: 0.934781\n",
      "Iteration 58, loss = 0.14754390\n",
      "Validation score: 0.934804\n",
      "Iteration 59, loss = 0.14698569\n",
      "Validation score: 0.936272\n",
      "Iteration 60, loss = 0.14644091\n",
      "Validation score: 0.935974\n",
      "Iteration 61, loss = 0.14638963\n",
      "Validation score: 0.937764\n",
      "Iteration 62, loss = 0.14584154\n",
      "Validation score: 0.935767\n",
      "Iteration 63, loss = 0.14540974\n",
      "Validation score: 0.936089\n",
      "Iteration 64, loss = 0.14507538\n",
      "Validation score: 0.937397\n",
      "Iteration 65, loss = 0.14367231\n",
      "Validation score: 0.936984\n",
      "Iteration 66, loss = 0.14300989\n",
      "Validation score: 0.935790\n",
      "Iteration 67, loss = 0.14294262\n",
      "Validation score: 0.935400\n",
      "Iteration 68, loss = 0.14243872\n",
      "Validation score: 0.937098\n",
      "Iteration 69, loss = 0.14209098\n",
      "Validation score: 0.935882\n",
      "Iteration 70, loss = 0.14215260\n",
      "Validation score: 0.937580\n",
      "Iteration 71, loss = 0.14171962\n",
      "Validation score: 0.936433\n",
      "Iteration 72, loss = 0.14120165\n",
      "Validation score: 0.935744\n",
      "Validation score did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
      "Zeit in Sekunden: \t3476.70\n"
     ]
    }
   ],
   "source": [
    "mlp_2 = MLPClassifier(hidden_layer_sizes=(70,70,70), max_iter = 350, beta_1 = 0.9, beta_2 = 0.9, epsilon= 1e-06, early_stopping = True, verbose = True, learning_rate = \"invscaling\", activation = 'tanh', solver = \"adam\")\n",
    "begin = time.time()\n",
    "mlp_2_train = mlp_2.fit(X_train_c,y_train_c)\n",
    "end = time.time()\n",
    "timestamp = end-begin\n",
    "timeList.append(timestamp)\n",
    "print('Zeit in Sekunden: \\t{0:.2f}'.format(timestamp))\n",
    "addToList(mlp_2_train, classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cd9d9bbb-d86f-4381-a472-ee7a61665261",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(20, 20, 20), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True) 12.607451768716176\n",
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(70, 70, 70), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True) 57.944978074232736\n"
     ]
    }
   ],
   "source": [
    "getTimeofMLP(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5151451f-0da7-4f6c-be79-427067a27443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
       "               hidden_layer_sizes=(20, 20, 20), learning_rate='invscaling',\n",
       "               max_iter=350, verbose=True),\n",
       " MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
       "               hidden_layer_sizes=(70, 70, 70), learning_rate='invscaling',\n",
       "               max_iter=350, verbose=True)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classificationList"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b323c8-2457-480e-958d-2acc38eec75b",
   "metadata": {},
   "source": [
    "__________________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d98e013-6397-47bf-9663-2415ad6c92f8",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2ad41f1b-5588-42f7-8797-cd2f50093c01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(20, 20, 20), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True)\n",
      "\n",
      "paramters: {'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.9, 'early_stopping': True, 'epsilon': 1e-06, 'hidden_layer_sizes': (20, 20, 20), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 350, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': True, 'warm_start': False}\n",
      "\n",
      "last loss: 3.699877917671539e-01\n",
      "\n",
      "number of features seen during fit: 54\n",
      "\n",
      "number of layers: 5\n",
      "\n",
      "number of outputs: 7\n",
      "\n",
      "name of output activation function: softmax\n",
      "\n",
      "number of iterations: 51\n",
      "\n",
      "number training samples seen by the solver during fitting: 20001333\n",
      "****************************************************************************************\n",
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(70, 70, 70), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True)\n",
      "\n",
      "paramters: {'activation': 'tanh', 'alpha': 0.0001, 'batch_size': 'auto', 'beta_1': 0.9, 'beta_2': 0.9, 'early_stopping': True, 'epsilon': 1e-06, 'hidden_layer_sizes': (70, 70, 70), 'learning_rate': 'invscaling', 'learning_rate_init': 0.001, 'max_fun': 15000, 'max_iter': 350, 'momentum': 0.9, 'n_iter_no_change': 10, 'nesterovs_momentum': True, 'power_t': 0.5, 'random_state': None, 'shuffle': True, 'solver': 'adam', 'tol': 0.0001, 'validation_fraction': 0.1, 'verbose': True, 'warm_start': False}\n",
      "\n",
      "last loss: 0.14120164520835493\n",
      "\n",
      "number of features seen during fit: 54\n",
      "\n",
      "number of layers: 5\n",
      "\n",
      "number of outputs: 7\n",
      "\n",
      "name of output activation function: softmax\n",
      "\n",
      "number of iterations: 72\n",
      "\n",
      "number training samples seen by the solver during fitting: 28237176\n",
      "****************************************************************************************\n"
     ]
    }
   ],
   "source": [
    "comparison(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "daad5cc5-0f3e-416d-bf86-f53792ab7ba2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default: hidden_layer = (100,), activation = relu, solver = adam, alpha = 0.0001, learning_rate = constant, learning_rate_init = 0.001, max_iter = 200, shuffle = true, early_stopping & warm_start = FALSE\n",
      "Alle Infos, die davon abweichen, werden in der Legende gelistet\n",
      "****************************************************************************************\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "curveValidateIteration(classificationList, curve, validation, iteration)\n",
    "plotCurvePerInteration(classificationList, curve, iteration)\n",
    "plotValidationPerInteration(classificationList, validation, iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "715808d2-3450-4561-b72a-fe134258b439",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotValidationPercurve(classificationList, validation, curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc09c0c7-abb7-44d0-bbe0-36da87b460e4",
   "metadata": {},
   "source": [
    "## Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ef05d5a2-ac58-4035-aa6c-dfa6abd61000",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(20, 20, 20), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(70, 70, 70), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmaping(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6f083132-b371-422e-9d69-2489a9f2c6db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(20, 20, 20), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.85      0.84      0.84     53246\n",
      "           2       0.86      0.89      0.87     70780\n",
      "           3       0.80      0.87      0.83      8841\n",
      "           4       0.83      0.57      0.68       682\n",
      "           5       0.75      0.45      0.56      2300\n",
      "           6       0.70      0.56      0.62      4300\n",
      "           7       0.90      0.80      0.85      5104\n",
      "\n",
      "    accuracy                           0.85    145253\n",
      "   macro avg       0.81      0.71      0.75    145253\n",
      "weighted avg       0.85      0.85      0.85    145253\n",
      "\n",
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(70, 70, 70), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.94      0.93      0.93     53246\n",
      "           2       0.94      0.95      0.95     70780\n",
      "           3       0.93      0.92      0.93      8841\n",
      "           4       0.87      0.79      0.82       682\n",
      "           5       0.84      0.78      0.81      2300\n",
      "           6       0.87      0.87      0.87      4300\n",
      "           7       0.95      0.92      0.93      5104\n",
      "\n",
      "    accuracy                           0.94    145253\n",
      "   macro avg       0.91      0.88      0.89    145253\n",
      "weighted avg       0.94      0.94      0.94    145253\n",
      "\n"
     ]
    }
   ],
   "source": [
    "reporting(classificationList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "74a399f7-9f58-4c18-9122-6cd52a869c2f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(20, 20, 20), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True)\n",
      "Accuracy: \t84.82\n",
      "Recall: \t84.82\n",
      "precision: \t84.70\n",
      "MLPClassifier(activation='tanh', beta_2=0.9, early_stopping=True, epsilon=1e-06,\n",
      "              hidden_layer_sizes=(70, 70, 70), learning_rate='invscaling',\n",
      "              max_iter=350, verbose=True)\n",
      "Accuracy: \t93.56\n",
      "Recall: \t93.56\n",
      "precision: \t93.55\n"
     ]
    }
   ],
   "source": [
    "accuracyPlotting(classificationList)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22517bb-90b7-455c-8f31-5a9068fbe6bf",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Fazit\n",
    "<li> </li>\n",
    "<li>  </li>\n",
    "<li>  </li>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}