method-training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Method training\n",
    "\n",
    "Train a couple of models using a set of different predictors and pre-processing.\n",
    "\n",
    "The data set is comprised of one year FO flow data from the engines of the cruise ship MS Birka. The system has two flow meters for each engine side, which means 4 engines for each flow meter. On the ship there are legacy volume flow meters which are logged within the machinery logging systemn. The volume flow meter is consistent within a very small margin with the mass flow meter, which is manually recorded by the crew once each day.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading database ...\n",
      "Time to load database: 3.813807725906372\n"
     ]
    }
   ],
   "source": [
    "# Train a TPOT model\n",
    "\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import time\n",
    "import numpy as np\n",
    "from tpot import TPOTRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.externals import joblib\n",
    "#%%\n",
    "\n",
    "t1 = time.time()\n",
    "print('Loading database ...')\n",
    "df = pd.read_hdf('database/all_data_comp.h5','table')\n",
    "print('Time to load database:', time.time()-t1)\n",
    "#%%\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Variable names.\n",
    "import var_names\n",
    "d = var_names.d\n",
    "\n",
    "# Check if variables exist in the dictonary..\n",
    "# for names in d:\n",
    "#     if d[names] in list(df):\n",
    "#         pass\n",
    "#     else:\n",
    "#         print('*** VAR MISSING *** ', d[names], ' *** VAR MISSING ***')\n",
    "#%%\n",
    "\n",
    "gen = 50\n",
    "cores=-1 # -1 = use all of them, can be\n",
    "\n",
    "import train_model as tm\n",
    "#\n",
    "# def train_tpot(name,X,y,gen,cores):\n",
    "#\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features and predictions for training...:\n",
      "\n",
      "-  ae1_exh_T\n",
      "-  ae3_exh_T\n",
      "-  me1_exh_T\n",
      "-  me3_exh_T\n",
      "-  fo_booster_13\n",
      "\n",
      "Date:  180116\n",
      "Time:  11:44:10\n",
      "Training with TPOT ....  gen_50eng_13_exh_T_predictor_180116_180116\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fredde/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
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      "Generation 1 - Current best internal CV score: 0.014959897981288467\n"
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      "Optimization Progress:  97%|█████████▋| 2472/2550 [5:35:51<23:07, 17.78s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 48 - Current best internal CV score: 0.014331359386087327\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Progress:  99%|█████████▉| 2524/2550 [5:42:48<10:13, 23.59s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 49 - Current best internal CV score: 0.014331359386087327\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                  \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 50 - Current best internal CV score: 0.014331359386087327\n",
      "\n",
      "Best pipeline: RandomForestRegressor(ExtraTreesRegressor(PolynomialFeatures(input_matrix, degree=2, include_bias=False, interaction_only=False), bootstrap=False, max_features=0.45, min_samples_leaf=17, min_samples_split=3, n_estimators=100), bootstrap=False, max_features=0.1, min_samples_leaf=2, min_samples_split=3, n_estimators=100)\n",
      "0.0140278966886\n",
      "Time to train...: 20840.912600040436\n",
      "Saving the model ...\n",
      "gen_50eng_13_exh_T_predictor_180116_180116  saved ... \n",
      "Features and predictions for training...:\n",
      "\n",
      "-  ae2_exh_T\n",
      "-  ae4_exh_T\n",
      "-  me2_exh_T\n",
      "-  me4_exh_T\n",
      "-  fo_booster_24\n",
      "\n",
      "Date:  180116\n",
      "Time:  17:31:31\n",
      "Training with TPOT ....  gen_50eng24_exh_T_predictor_180116_180116\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fredde/anaconda3/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'FitnessMulti' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
      "  RuntimeWarning)\n",
      "/home/fredde/anaconda3/lib/python3.6/site-packages/deap/creator.py:141: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
      "  RuntimeWarning)\n",
      "Optimization Progress:   4%|▍         | 100/2550 [03:17<7:10:24, 10.54s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.016272481103666768\n"
     ]
    },
    {
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      "Optimization Progress:   6%|▌         | 150/2550 [05:30<13:40:24, 20.51s/pipeline]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Generation 2 - Current best internal CV score: 0.015683845661954875\n"
     ]
    },
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      "Optimization Progress:   8%|▊         | 200/2550 [08:32<13:29:04, 20.66s/pipeline]"
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    {
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      "Generation 3 - Current best internal CV score: 0.01492815305771098\n"
     ]
    },
    {
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      "Optimization Progress:  10%|▉         | 251/2550 [15:36<24:14:49, 37.97s/pipeline]"
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    },
    {
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      "Generation 4 - Current best internal CV score: 0.01492815305771098\n"
     ]
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    {
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    {
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     "output_type": "stream",
     "text": [
      "Generation 5 - Current best internal CV score: 0.014899357661953022\n"
     ]
    },
    {
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      "Optimization Progress:  14%|█▍        | 353/2550 [31:55<50:56:17, 83.47s/pipeline] "
     ]
    },
    {
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     "text": [
      "Generation 6 - Current best internal CV score: 0.014804081206529355\n"
     ]
    },
    {
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      "Optimization Progress:  16%|█▌        | 404/2550 [40:18<56:26:25, 94.68s/pipeline] "
     ]
    },
    {
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     "text": [
      "Generation 7 - Current best internal CV score: 0.014804081206529355\n"
     ]
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    {
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    },
    {
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     "text": [
      "Generation 8 - Current best internal CV score: 0.014804081206529355\n"
     ]
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    {
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    },
    {
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     "text": [
      "Generation 9 - Current best internal CV score: 0.014801486543541959\n"
     ]
    },
    {
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     "text": [
      "Optimization Progress:  22%|██▏       | 555/2550 [1:03:40<39:28:09, 71.22s/pipeline]"
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    },
    {
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     "text": [
      "Generation 10 - Current best internal CV score: 0.014801486543541959\n"
     ]
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    {
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      "Optimization Progress:  24%|██▍       | 607/2550 [1:13:56<56:20:53, 104.40s/pipeline]"
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    },
    {
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     "text": [
      "Generation 11 - Current best internal CV score: 0.014763727411597757\n"
     ]
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    {
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      "Optimization Progress:  26%|██▌       | 659/2550 [1:25:55<57:11:22, 108.87s/pipeline]"
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    {
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      "Generation 12 - Current best internal CV score: 0.014763727411597757\n"
     ]
    },
    {
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      "Optimization Progress:  28%|██▊       | 711/2550 [1:33:21<19:57:35, 39.07s/pipeline] "
     ]
    },
    {
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     "text": [
      "Generation 13 - Current best internal CV score: 0.014763727411597757\n"
     ]
    },
    {
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      "Optimization Progress:  30%|██▉       | 762/2550 [1:40:56<21:44:41, 43.78s/pipeline]"
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    {
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     "text": [
      "Generation 14 - Current best internal CV score: 0.014762297353561039\n"
     ]
    },
    {
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      "Optimization Progress:  32%|███▏      | 812/2550 [1:44:47<13:28:34, 27.91s/pipeline]"
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    {
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      "Generation 15 - Current best internal CV score: 0.014686766973617139\n"
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    {
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    {
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     "text": [
      "Generation 16 - Current best internal CV score: 0.014686766973617139\n"
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    {
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    },
    {
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     "text": [
      "Generation 17 - Current best internal CV score: 0.014600557436622742\n"
     ]
    },
    {
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     ]
    },
    {
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     "text": [
      "Generation 18 - Current best internal CV score: 0.014600557436622742\n"
     ]
    },
    {
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    },
    {
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      "Generation 19 - Current best internal CV score: 0.014600557436622742\n"
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    {
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    },
    {
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      "Generation 20 - Current best internal CV score: 0.014600557436622742\n"
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    {
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    {
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      "Generation 21 - Current best internal CV score: 0.014600557436622742\n"
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    {
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    },
    {
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      "Generation 22 - Current best internal CV score: 0.014600557436622742\n"
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    {
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    {
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      "Generation 23 - Current best internal CV score: 0.014600557436622742\n"
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    {
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    {
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      "Generation 24 - Current best internal CV score: 0.014600557436622742\n"
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    {
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    {
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      "Generation 25 - Current best internal CV score: 0.014527514184977223\n"
     ]
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    {
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    {
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      "Generation 26 - Current best internal CV score: 0.014527514184977223\n"
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    {
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      "Generation 27 - Current best internal CV score: 0.014446339583908141\n"
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    {
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    {
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      "Generation 28 - Current best internal CV score: 0.014446339583908141\n"
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    {
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    {
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      "Generation 29 - Current best internal CV score: 0.014446339583908141\n"
     ]
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    {
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    {
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      "Generation 30 - Current best internal CV score: 0.014443454824824693\n"
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    {
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    {
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      "Generation 31 - Current best internal CV score: 0.014402800949981216\n"
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    {
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      "Generation 32 - Current best internal CV score: 0.014402800949981216\n"
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    {
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      "Generation 33 - Current best internal CV score: 0.014402800949981216\n"
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    {
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      "Generation 34 - Current best internal CV score: 0.014402800949981216\n"
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    {
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    {
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      "Generation 35 - Current best internal CV score: 0.014402800949981216\n"
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    {
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    },
    {
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      "Generation 36 - Current best internal CV score: 0.014402800949981216\n"
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    {
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    {
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      "Generation 37 - Current best internal CV score: 0.0143513374725668\n"
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    {
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    {
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      "Generation 38 - Current best internal CV score: 0.0143513374725668\n"
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    {
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    },
    {
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      "Generation 39 - Current best internal CV score: 0.014305626001784888\n"
     ]
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    {
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    {
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      "Generation 40 - Current best internal CV score: 0.014305626001784888\n"
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    {
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    {
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      "Generation 41 - Current best internal CV score: 0.014305626001784888\n"
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      "Generation 42 - Current best internal CV score: 0.014305626001784888\n"
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    {
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      "Generation 43 - Current best internal CV score: 0.014305626001784888\n"
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      "Generation 44 - Current best internal CV score: 0.014305626001784888\n"
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      "Generation 45 - Current best internal CV score: 0.014305626001784888\n"
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    {
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      "Generation 46 - Current best internal CV score: 0.014305626001784888\n"
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      "Generation 47 - Current best internal CV score: 0.014298457744336907\n"
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      "Generation 48 - Current best internal CV score: 0.014298457744336907\n"
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    {
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      "Generation 49 - Current best internal CV score: 0.01428670859466906\n"
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    {
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     "text": [
      "                                                                                  \r"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 50 - Current best internal CV score: 0.01428670859466906\n",
      "\n",
      "Best pipeline: RandomForestRegressor(CombineDFs(CombineDFs(MinMaxScaler(Normalizer(input_matrix, norm=l1)), input_matrix), input_matrix), bootstrap=False, max_features=0.15, min_samples_leaf=1, min_samples_split=3, n_estimators=100)\n",
      "0.0133966106313\n",
      "Time to train...: 21321.895904541016\n",
      "Saving the model ...\n",
      "gen_50eng24_exh_T_predictor_180116_180116  saved ... \n"
     ]
    }
   ],
   "source": [
    "\n",
    "####\n",
    "#### Training the first set with only one predictor\n",
    "####\n",
    "##\n",
    "# Features and target for Eng 1/3\n",
    "\n",
    "\n",
    "test_name = str('eng_13_exh_T_predictor_'+time.strftime('%y%m%d'))\n",
    "features = [d['ae1_exh_T'],\n",
    "          d['ae3_exh_T'],\n",
    "          d['me1_exh_T'],\n",
    "          d['me3_exh_T'],\n",
    "          d['fo_booster_13']\n",
    "          ]\n",
    "\n",
    "print('Features and predictions for training...:\\n')\n",
    "for n in features:\n",
    "    print('- ',d[n])\n",
    "\n",
    "print('\\nDate: ',time.strftime('%y%m%d'))\n",
    "print('Time: ',time.strftime('%H:%M:%S'))\n",
    "\n",
    "# Drop Nan from the DataFrame.\n",
    "\n",
    "# Create training arrays, X_13 is the features for engine pair 1 and 3\n",
    "\n",
    "df_train = df[features].dropna()\n",
    "X = np.array(df_train.drop(labels=d['fo_booster_13'],axis=1))\n",
    "y = np.array(df_train[d['fo_booster_13']])\n",
    "\n",
    "tm.train_tpot(test_name,X,y,gen,cores)\n",
    "\n",
    "##\n",
    "##\n",
    "##%%\n",
    "## Training next engine pair.\n",
    "##\n",
    "# X_24 features for engine 2, 4\n",
    "\n",
    "\n",
    "test_name = str('eng24_exh_T_predictor_'+time.strftime('%y%m%d'))\n",
    "features = [d['ae2_exh_T'],\n",
    "          d['ae4_exh_T'],\n",
    "          d['me2_exh_T'],\n",
    "          d['me4_exh_T'],\n",
    "          d['fo_booster_24']\n",
    "          ]\n",
    "\n",
    "print('Features and predictions for training...:\\n')\n",
    "for n in features:\n",
    "    print('- ',d[n])\n",
    "print('\\nDate: ',time.strftime('%y%m%d'))\n",
    "print('Time: ',time.strftime('%H:%M:%S'))\n",
    "\n",
    "\n",
    "df_train = df[features].dropna()\n",
    "X = np.array(df_train.drop(labels=d['fo_booster_24'],axis=1))\n",
    "y = np.array(df_train[d['fo_booster_24']])\n",
    "\n",
    "tm.train_tpot(test_name,X,y,gen,cores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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