Regression model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import validation_curve\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import random\n",
    "import math\n",
    "%matplotlib inline\n",
    "#import elm\n",
    "#from glm import GLM\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nr.1</th>\n",
       "      <th>Nr.2</th>\n",
       "      <th>Konfiguration</th>\n",
       "      <th>Versuch</th>\n",
       "      <th>Bauteil</th>\n",
       "      <th>T_O</th>\n",
       "      <th>delta_t_h</th>\n",
       "      <th>F_p</th>\n",
       "      <th>t_a</th>\n",
       "      <th>d_T_O</th>\n",
       "      <th>...</th>\n",
       "      <th>HV_MP3_sim</th>\n",
       "      <th>HV_MP4_sim</th>\n",
       "      <th>HV_MP5_sim</th>\n",
       "      <th>HV_MP6_sim</th>\n",
       "      <th>HV_MP1_delta_exp_sim</th>\n",
       "      <th>HV_MP2_delta_exp_sim</th>\n",
       "      <th>HV_MP3_delta_exp_sim</th>\n",
       "      <th>HV_MP4_delta_exp_sim</th>\n",
       "      <th>HV_MP5_delta_exp_sim</th>\n",
       "      <th>HV_MP6_delta_exp_sim</th>\n",
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       "      <td>930</td>\n",
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       "      <td>1750</td>\n",
       "      <td>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>930</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>2</td>\n",
       "      <td>-38.4</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>930</td>\n",
       "      <td>0</td>\n",
       "      <td>2250</td>\n",
       "      <td>6</td>\n",
       "      <td>-38.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>930</td>\n",
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       "      <td>-41.4</td>\n",
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       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>930</td>\n",
       "      <td>4</td>\n",
       "      <td>1750</td>\n",
       "      <td>2</td>\n",
       "      <td>-39.7</td>\n",
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       "<p>5 rows × 35 columns</p>\n",
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      ],
      "text/plain": [
       "   Nr.1  Nr.2  Konfiguration  Versuch  Bauteil  T_O  delta_t_h   F_p  t_a  \\\n",
       "0     1     1              1        1        3  930          0  1750    4   \n",
       "1     2     4              2        1        2  930          0  2000    2   \n",
       "2     3     7              3        1        1  930          0  2250    6   \n",
       "3     4    10              4        1        4  930          2  2000    6   \n",
       "4     5    13              5        1        5  930          4  1750    2   \n",
       "\n",
       "   d_T_O  ...  HV_MP3_sim  HV_MP4_sim  HV_MP5_sim  HV_MP6_sim  \\\n",
       "0  -41.7  ...         0.0         0.0         0.0         0.0   \n",
       "1  -38.4  ...         0.0         0.0         0.0         0.0   \n",
       "2  -38.1  ...         0.0         0.0         0.0         0.0   \n",
       "3  -41.4  ...         0.0         0.0         0.0         0.0   \n",
       "4  -39.7  ...         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   HV_MP1_delta_exp_sim  HV_MP2_delta_exp_sim  HV_MP3_delta_exp_sim  \\\n",
       "0                   0.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "\n",
       "   HV_MP4_delta_exp_sim  HV_MP5_delta_exp_sim  HV_MP6_delta_exp_sim  \n",
       "0                   0.0                   0.0                   0.0  \n",
       "1                   0.0                   0.0                   0.0  \n",
       "2                   0.0                   0.0                   0.0  \n",
       "3                   0.0                   0.0                   0.0  \n",
       "4                   0.0                   0.0                   0.0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting data from the CSV file\n",
    "dataset = pd.read_csv('D:/JOB, Application STUFF/Fraunhofer/Frauhofer task/Task 2/20200709_Results_Exp_Sim.csv', sep=';', decimal=',')\n",
    "\n",
    "dataset_Exp = dataset[0:0]\n",
    "dataset_Sim = dataset[40:60]\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ML_Model:\n",
    "    def __init__(self, X, y, MP):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.X, self.y, test_size=.2, shuffle= True)\n",
    "        self.Neg_Root_Mean_Square_Error={}\n",
    "        self.MP = MP\n",
    "    def Linear_regression(self):\n",
    "        regressor = LinearRegression()  \n",
    "        self.Error= cross_validate(regressor, X, y, cv=5, scoring=('neg_mean_squared_error'))\n",
    "        self.Neg_Root_Mean_Square_Error.update({'Linear_regression.................':np.sqrt(-self.Error[\"test_score\"])})\n",
    "        \n",
    "    def Gaussian_regression(self):\n",
    "        kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))\n",
    "        gp = GaussianProcessRegressor(kernel=kernel, alpha=9,n_restarts_optimizer=10)\n",
    "        self.Error= cross_validate(gp, X, y, cv=5, scoring=('neg_mean_squared_error')) \n",
    "        self.Neg_Root_Mean_Square_Error.update({'Gaussian_regression...............':np.sqrt(-self.Error[\"test_score\"])})\n",
    "        \n",
    "    def SupportVector_regression(self):\n",
    "        svr_lin = SVR(kernel='linear', C=1, gamma='auto')\n",
    "        svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)\n",
    "        svr_poly = SVR(kernel='poly', C=100, gamma= 'scale', degree=2, epsilon=.1, coef0=1)\n",
    "        self.Error = cross_validate(svr_lin, X, y, cv=5, scoring=('neg_mean_squared_error'))\n",
    "        self.Neg_Root_Mean_Square_Error.update({'SupportVector_regression_lin......':np.sqrt(-self.Error[\"test_score\"])})\n",
    "        self.Error = cross_validate(svr_rbf, X, y, cv=5, scoring=('neg_mean_squared_error'))\n",
    "        self.Neg_Root_Mean_Square_Error.update({'SupportVector_regression_rbf......':np.sqrt(-self.Error[\"test_score\"])})\n",
    "        self.Error = cross_validate(svr_poly, X, y, cv=5, scoring=('neg_mean_squared_error'))\n",
    "        self.Neg_Root_Mean_Square_Error.update({'SupportVector_regression_poly......':np.sqrt(-self.Error[\"test_score\"])})\n",
    "    \n",
    "    def DecisionTree_Regressor(self):\n",
    "        regr = DecisionTreeRegressor(max_depth=6)\n",
    "        self.Error = cross_validate(regr, X, y, cv=5, scoring=('neg_mean_squared_error')) \n",
    "        self.Neg_Root_Mean_Square_Error.update({'DecisionTree_Regressor............':np.sqrt(-self.Error[\"test_score\"])})\n",
    "        \n",
    "    \n",
    "    def covariance(self):\n",
    "        print(\"The covarience matrix is\")\n",
    "        A= np.dot(np.transpose(X),X)\n",
    "        with np.printoptions(precision=7, suppress=True):\n",
    "            print(np.linalg.inv(A))\n",
    "    \n",
    "\n",
    "        \n",
    "    #def ELM_(self):\n",
    "        #dataset = normalize(dataset)\n",
    "        #dataset.target = [1 if i == 1 else -1 for i in  dataset.target.astype(int)]\n",
    "        #elm = ELM(hid_num=10).fit(self.X_train, self.y_train)\n",
    "        #y_pred = elm.predict(self.X_test)\n",
    "        #self.Neg_Root_Mean_Square_Error.update({'ELM...............................': metrics.Neg_Root_Mean_Square_Error(self.y_test, y_pred)})\n",
    "\n",
    "        \n",
    "    \n",
    "    def Print(self):\n",
    "        print(\"The Root Mean Squared Error for\",self.MP, \"\\n\")\n",
    "        for key, value in self.Neg_Root_Mean_Square_Error.items() :\n",
    "            print (key, value, \"\\n\")\n",
    "            \n",
    "    def plot(self):\n",
    "        fig = plt.figure()\n",
    "        fig.suptitle('SIM '+self.MP+'.jpeg')\n",
    "        ax = fig.add_subplot(111)\n",
    "        plt.ylim(ymin=0, ymax=150)\n",
    "        plt.boxplot(self.Neg_Root_Mean_Square_Error.values())\n",
    "        ax.set_xticklabels(['LR','GR','SVR-l','SVR-RBF','SVR-POLY','DTR'])\n",
    "        plt.xlabel('Chosen Models')\n",
    "        plt.ylabel('Root Mean Squared Error')\n",
    "        plt.savefig('D:/JOB, Application STUFF/Fraunhofer/Frauhofer task/Task 2/SIM/SIM '+self.MP+'.jpeg')\n",
    "        plt.show()\n",
    "    \n",
    "    def func_call(self):\n",
    "        self.Linear_regression()\n",
    "        self.Gaussian_regression()\n",
    "        self.SupportVector_regression()\n",
    "        self.DecisionTree_Regressor()\n",
    "        self.covariance()\n",
    "        self.plot()\n",
    "        self.Print()\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 3)\n",
      "(20,)\n",
      "The covarience matrix is\n",
      "[[ 0.0000004 -0.0000324 -0.0000648]\n",
      " [-0.0000324  0.0147427 -0.0000538]\n",
      " [-0.0000648 -0.0000538  0.0154355]]\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Error for MP1 \n",
      "\n",
      "Linear_regression................. [53.3718358  84.16347819 64.3037097  92.65636544 32.3284926 ] \n",
      "\n",
      "Gaussian_regression............... [105.5128826   43.61893115 471.45510892  39.69219725  22.03855739] \n",
      "\n",
      "SupportVector_regression_lin...... [ 85.23239262 149.23657691  80.70597581  30.80040694  20.96211981] \n",
      "\n",
      "SupportVector_regression_rbf...... [ 41.39873585 136.63679668  62.72005897  53.94629262  24.25085268] \n",
      "\n",
      "SupportVector_regression_poly...... [ 95.25713412 173.2252754   31.28828697  29.25808253   6.83184241] \n",
      "\n",
      "DecisionTree_Regressor............ [116.08715614  18.8175442  138.70257406   7.97196473  15.04388714] \n",
      "\n",
      "The covarience matrix is\n",
      "[[ 0.0000004 -0.0000324 -0.0000648]\n",
      " [-0.0000324  0.0147427 -0.0000538]\n",
      " [-0.0000648 -0.0000538  0.0154355]]\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Error for MP2 \n",
      "\n",
      "Linear_regression................. [48.41679193 89.76715107 60.21442958 89.52935633 29.22180031] \n",
      "\n",
      "Gaussian_regression............... [106.13974339  28.10521494 385.41853163  40.19109525   5.18825064] \n",
      "\n",
      "SupportVector_regression_lin...... [ 83.50450189 148.13632911  77.12736712  42.03594727  12.49281129] \n",
      "\n",
      "SupportVector_regression_rbf...... [ 34.16263946 130.52006707  62.27286182  53.13125358  12.81058313] \n",
      "\n",
      "SupportVector_regression_poly...... [ 82.74215283 170.07434563  29.6506924   29.5527417    9.58567267] \n",
      "\n",
      "DecisionTree_Regressor............ [116.31557258  32.87724732 126.69960427   2.19758324   4.13361658] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\gaussian_process\\gpr.py:469: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-3.30416614e+02,  1.74789827e-05]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 54, 'nit': 8, 'warnflag': 2}\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The covarience matrix is\n",
      "[[ 0.0000004 -0.0000324 -0.0000648]\n",
      " [-0.0000324  0.0147427 -0.0000538]\n",
      " [-0.0000648 -0.0000538  0.0154355]]\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Error for MP3 \n",
      "\n",
      "Linear_regression................. [51.97661599 91.42322487 59.51797038 91.78443836 33.58972455] \n",
      "\n",
      "Gaussian_regression............... [109.58723343  30.14892595 472.05429657  39.91227101   4.51288665] \n",
      "\n",
      "SupportVector_regression_lin...... [ 86.10831145 149.70375915  77.83607931  42.91962573  13.00571098] \n",
      "\n",
      "SupportVector_regression_rbf...... [ 36.11003858 134.16129338  59.97311974  51.90955739  15.49050588] \n",
      "\n",
      "SupportVector_regression_poly...... [ 83.95278439 172.68552205  27.21789923  27.305287     9.33956485] \n",
      "\n",
      "DecisionTree_Regressor............ [119.59352741  30.64409312 127.56043657   8.96556252   3.07449107] \n",
      "\n",
      "The covarience matrix is\n",
      "[[ 0.0000004 -0.0000324 -0.0000648]\n",
      " [-0.0000324  0.0147427 -0.0000538]\n",
      " [-0.0000648 -0.0000538  0.0154355]]\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Error for MP4 \n",
      "\n",
      "Linear_regression................. [ 67.43730652 108.92654217  59.94086559 105.70851128  50.08816304] \n",
      "\n",
      "Gaussian_regression............... [119.37777351  44.67168754 471.15817708 245.9459449  233.35099108] \n",
      "\n",
      "SupportVector_regression_lin...... [108.62216392 179.15203149  78.54446549  64.14236889  34.97293489] \n",
      "\n",
      "SupportVector_regression_rbf...... [ 40.28030884 131.95754349  57.22974615  35.5227617   27.84728297] \n",
      "\n",
      "SupportVector_regression_poly...... [ 92.19848132 180.78416186  11.37201965   5.01311707   2.37869415] \n",
      "\n",
      "DecisionTree_Regressor............ [122.50749965  28.27103892 128.40516454   2.95904091   2.05098164] \n",
      "\n",
      "The covarience matrix is\n",
      "[[ 0.0000004 -0.0000324 -0.0000648]\n",
      " [-0.0000324  0.0147427 -0.0000538]\n",
      " [-0.0000648 -0.0000538  0.0154355]]\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Error for MP5 \n",
      "\n",
      "Linear_regression................. [ 61.92914004  93.42782107  66.48443309 100.7888793   34.9453347 ] \n",
      "\n",
      "Gaussian_regression............... [111.12303706 162.53891153 472.5207251   31.62297236 259.82611024] \n",
      "\n",
      "SupportVector_regression_lin...... [104.64829223 175.4795865   89.7191335   38.20825058  22.1314885 ] \n",
      "\n",
      "SupportVector_regression_rbf...... [ 48.14767952 141.60931126  67.05358885  51.15875539  27.00448144] \n",
      "\n",
      "SupportVector_regression_poly...... [109.04441844 190.86440713  21.60946916  18.07670678   6.34673236] \n",
      "\n",
      "DecisionTree_Regressor............ [121.21545354  20.34919332 148.12301381   6.96557126  10.89233249] \n",
      "\n",
      "The covarience matrix is\n",
      "[[ 0.0000004 -0.0000324 -0.0000648]\n",
      " [-0.0000324  0.0147427 -0.0000538]\n",
      " [-0.0000648 -0.0000538  0.0154355]]\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Root Mean Squared Error for MP6 \n",
      "\n",
      "Linear_regression................. [ 64.52078786 103.11749183  61.22113911 100.23909387  45.2652847 ] \n",
      "\n",
      "Gaussian_regression............... [119.24896241 146.8883539  474.84465842 270.96766761 252.42668482] \n",
      "\n",
      "SupportVector_regression_lin...... [102.79610456 167.56107601  79.3813086   52.68400301  23.69994845] \n",
      "\n",
      "SupportVector_regression_rbf...... [ 42.03800345 139.66642986  56.46972841  44.32791495  29.47510231] \n",
      "\n",
      "SupportVector_regression_poly...... [ 94.80286073 182.1842378   15.45898081  14.40384315   3.77874036] \n",
      "\n",
      "DecisionTree_Regressor............ [124.15877842  27.71518879 134.6098941    5.44692968   5.04495088] \n",
      "\n",
      "PCA \n",
      "\n",
      "Explained variance ratio: \n",
      " [0.97997641 0.01037999 0.0096436 ]\n",
      "Singular values: \n",
      " [80.00631188  8.23407554  7.93662775]\n"
     ]
    }
   ],
   "source": [
    "X_exp = dataset_Exp[['T_O', 'delta_t_h','t_a']].values\n",
    "X_sim = dataset_Sim[['T_O', 'delta_t_h', 't_a']].values\n",
    "X = np.concatenate([X_exp, X_sim])\n",
    "#X = np.transpose(np.array([X[:,0], X[:,1], X[:,2], X[:,1]*X[:,1], X[:,2]*X[:,2] ]))\n",
    "y = np.concatenate([dataset_Exp['HV_MP1_exp'].values, dataset_Sim['HV_MP1_sim'].values])\n",
    "print(X.shape)\n",
    "print(y.shape)\n",
    "MP1=ML_Model(X,y,'MP1')\n",
    "MP1.func_call()\n",
    "            \n",
    "y = np.concatenate([dataset_Exp['HV_MP2_exp'].values, dataset_Sim['HV_MP2_sim'].values])\n",
    "MP2=ML_Model(X,y,'MP2')\n",
    "MP2.func_call()\n",
    "\n",
    "y = np.concatenate([dataset_Exp['HV_MP3_exp'].values, dataset_Sim['HV_MP3_sim'].values])\n",
    "MP3=ML_Model(X,y,'MP3')\n",
    "MP3.func_call()\n",
    "\n",
    "y = np.concatenate([dataset_Exp['HV_MP4_exp'].values, dataset_Sim['HV_MP4_sim'].values])\n",
    "MP4=ML_Model(X,y,'MP4')\n",
    "MP4.func_call()\n",
    "\n",
    "y = np.concatenate([dataset_Exp['HV_MP5_exp'].values, dataset_Sim['HV_MP5_sim'].values])\n",
    "MP5=ML_Model(X,y,'MP5')\n",
    "MP5.func_call()\n",
    "\n",
    "y = np.concatenate([dataset_Exp['HV_MP6_exp'].values, dataset_Sim['HV_MP6_sim'].values])\n",
    "MP6=ML_Model(X,y,'MP6')\n",
    "MP6.func_call()\n",
    "\n",
    "#PCA\n",
    "pca = PCA(n_components=3, svd_solver='randomized')\n",
    "X= pca.fit_transform(X)\n",
    "print(\"PCA \\n\")\n",
    "print(\"Explained variance ratio: \\n\", pca.explained_variance_ratio_)\n",
    "print(\"Singular values: \\n\", pca.singular_values_)\n",
    "    \n",
    "#ELM\n",
    "#https://towardsdatascience.com/build-an-extreme-learning-machine-in-python-91d1e8958599\n",
    "#https://github.com/masaponto/Python-ELM\n",
    "\n",
    "\n",
    "#GLM\n",
    "#https://towardsdatascience.com/generalized-linear-models-9cbf848bb8ab            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": [
    "X_exp = dataset_Exp[['T_O', 'delta_t_h','t_a']].values\n",
    "X_sim = dataset_Sim[['T_O', 'delta_t_h', 't_a']].values\n",
    "X = np.concatenate([X_exp, X_sim])\n",
    "#X = np.transpose(np.array([X[:,0], X[:,1], X[:,2], X[:,1]*X[:,1], X[:,2]*X[:,2] ]))\n",
    "y = np.concatenate([dataset_Exp['HV_MP2_exp'].values, dataset_Sim['HV_MP2_sim'].values])\n",
    "#svr_poly = SVR(kernel='poly', C=100, gamma= 'scale', degree=2, epsilon=.1, coef0=1)\n",
    "\n",
    "train_scores, test_scores = validation_curve(DecisionTreeRegressor(), X, y,'max_depth',[x for x in range(1,10)],cv=5, scoring='neg_mean_absolute_error')\n",
    "#train_scores, test_scores = validation_curve(SVR(), X, y,'degree',[x for x in range(1,5)],cv=5, scoring='neg_mean_absolute_error')\n",
    "\n",
    "train_scores_mean = -np.mean(train_scores, axis=1)\n",
    "train_scores_std = -np.std(train_scores, axis=1)\n",
    "test_scores_mean = -np.mean(test_scores, axis=1)\n",
    "test_scores_std = -np.std(test_scores, axis=1)\n",
    "\n",
    "plt.title(\"Validation Curve with Decision tree\")\n",
    "plt.xlabel(r\"$\\gamma$\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.ylim(0.0, 80)\n",
    "plt.plot([x for x in range(1,10)], train_scores_mean, label=\"Training score\",color=\"darkorange\", lw=2)\n",
    "plt.fill_between([x for x in range(1,10)], train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.2,color=\"darkorange\", lw=2)\n",
    "plt.plot([x for x in range(1,10)], test_scores_mean, label=\"Cross-validation score\",color=\"navy\", lw=2)\n",
    "plt.fill_between([x for x in range(1,10)], test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.2, color=\"navy\", lw=2)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.savefig('D:/JOB, Application STUFF/Fraunhofer/Frauhofer task/Task 2/SIM/Validation Curve Decision tree.jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:1360: RuntimeWarning: Removed duplicate entries from 'train_sizes'. Number of ticks will be less than the size of 'train_sizes' 12 instead of 20).\n",
      "  % (train_sizes_abs.shape[0], n_ticks), RuntimeWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:1360: RuntimeWarning: Removed duplicate entries from 'train_sizes'. Number of ticks will be less than the size of 'train_sizes' 12 instead of 20).\n",
      "  % (train_sizes_abs.shape[0], n_ticks), RuntimeWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:1360: RuntimeWarning: Removed duplicate entries from 'train_sizes'. Number of ticks will be less than the size of 'train_sizes' 12 instead of 20).\n",
      "  % (train_sizes_abs.shape[0], n_ticks), RuntimeWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:1360: RuntimeWarning: Removed duplicate entries from 'train_sizes'. Number of ticks will be less than the size of 'train_sizes' 12 instead of 20).\n",
      "  % (train_sizes_abs.shape[0], n_ticks), RuntimeWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:1360: RuntimeWarning: Removed duplicate entries from 'train_sizes'. Number of ticks will be less than the size of 'train_sizes' 12 instead of 20).\n",
      "  % (train_sizes_abs.shape[0], n_ticks), RuntimeWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\regression.py:543: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\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"
    },
    {
     "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"
    },
    {
     "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": [
    "# A learning curve for d=1,2,3,4,5\n",
    "for d in [1,2,3,4,5]:\n",
    "    model = make_pipeline(PolynomialFeatures(degree=d), LinearRegression())\n",
    "\n",
    "    from sklearn.model_selection import learning_curve\n",
    "    train_sizes, train_scores, validation_scores = learning_curve(model, X, y,train_sizes=(np.logspace(-1, 0, 20)))\n",
    "    # Plot the mean train error and validation error across folds\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    plt.plot(train_sizes, validation_scores.mean(axis=1),lw=2, label='cross-validation')\n",
    "    plt.plot(train_sizes, train_scores.mean(axis=1),lw=2, label='training')\n",
    "    plt.ylim(ymin=-.1, ymax=2)\n",
    "\n",
    "    plt.legend(loc='best')\n",
    "    plt.xlabel('number of train samples')\n",
    "    plt.ylabel('explained variance')\n",
    "    plt.title('Learning curve (degree=%i)' % d)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'D:/JOB, Application STUFF/Fraunhofer/Frauhofer task/Task 2/SIM/Learning Curve Lenear Regression {d}.jpeg')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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