Search_Nwtco_Loss_Repeated_Left_Right.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from Genetic.Representation import Function\n",
    "from Genetic.NeuroEvolution import NeuroEvolution\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.losses import mse\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "import numpy as np\n",
    "from Model.model import SurvModel, FlchainModel, NwtcoModel, SupportModel\n",
    "from Data.dataset import Flchain, Support, Nwtco\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from scipy.stats import f_oneway, ttest_rel\n",
    "\n",
    "from Utils.utils import Experiment, FunctionHelper, StatsHelper\n",
    "from itertools import combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "surv_reg1_func = [1, 9, 9, 1, 5, 12, 7, 9, 7]\n",
    "surv_reg2_func = [1, 9, 12, 1, 5, 12, 7, 9, 7]\n",
    "\n",
    "surv_reg1_f = FunctionHelper(surv_reg1_func)\n",
    "surv_reg2_f = FunctionHelper(surv_reg2_func)\n",
    "\n",
    "surv_reg1_f.plot_surv_loss()\n",
    "surv_reg2_f.plot_surv_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_class = NwtcoModel\n",
    "ds = Nwtco('Data/nwtco.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, ye_train, y_train, e_train,\n",
    " x_val, ye_val, y_val, e_val,\n",
    " x_test, ye_test, y_test, e_test) = ds.get_train_val_test_from_splits(test_id=0, val_id=1)\n",
    "xy = (x_train, ye_train), (x_val, ye_val), (x_test, ye_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_fix_left = [    \n",
    "    [\n",
    "    [6, 8, 24, 1, 6, 23, 27, 27, 13],\n",
    "    [0.7159451, 0.7147979, 0.70356005, 0.654633, 0.6085865]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 13, 17, 3, 3, 16, 24, 27, 26],\n",
    "    [0.7398461, 0.7456392, 0.7024854, 0.6869316, 0.6461919]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 29, 17, 4, 2, 22, 23, 25, 28],\n",
    "    [0.7532277, 0.74121404, 0.71607333, 0.69473165, 0.65950316]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 15, 24, 1, 4, 27, 7, 17, 28],\n",
    "    [0.75763243, 0.75088567, 0.72227436, 0.6933645, 0.6526281]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 14, 12, 4, 6, 9, 17, 26, 15],\n",
    "    [0.75110894, 0.7523076, 0.7198409, 0.69285184, 0.6572115]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [5, 9, 19, 2, 3, 28, 12, 17, 26],\n",
    "    [0.75421894, 0.73794115, 0.72266966, 0.688372, 0.6419499]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 8, 17, 2, 2, 27, 22, 25, 23],\n",
    "    [0.7558049, 0.7504811, 0.7117684, 0.6929495, 0.6526281]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [5, 17, 26, 4, 2, 15, 23, 9, 7],\n",
    "    [0.7562076, 0.75126565, 0.712769, 0.6977955, 0.64961725]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 24, 26, 2, 5, 28, 28, 14, 21],\n",
    "    [0.73821056, 0.7467547, 0.6965128, 0.68730396, 0.6504461]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 26, 14, 2, 1, 21, 30, 15, 28],\n",
    "    [0.75919986, 0.7484708, 0.71755564, 0.6922049, 0.6469599]\n",
    "    ]\n",
    "]\n",
    "\n",
    "\n",
    "exp_fix_right = [\n",
    "    [\n",
    "    [5, 12, 19, 4, 2, 8, 26, 26, 11],\n",
    "    [0.7361538, 0.7425992, 0.7248561, 0.68159735, 0.62622505]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [6, 13, 28, 1, 2, 10, 23, 7, 13],\n",
    "    [0.729463, 0.7391547, 0.72110087, 0.67584807, 0.6316861]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 15, 27, 2, 1, 23, 19, 25, 30],\n",
    "    [0.73288274, 0.7435921, 0.70992166, 0.6748227, 0.6335755]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [6, 15, 19, 1, 4, 10, 10, 21, 13],\n",
    "    [0.7278275, 0.7419373, 0.7047212, 0.68137765, 0.6285777]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 16, 21, 1, 6, 17, 18, 13, 22],\n",
    "    [0.70629305, 0.7112431, 0.69296145, 0.655048, 0.6129504]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 15, 11, 4, 5, 8, 15, 12, 10],\n",
    "    [0.73204017, 0.74229276, 0.711824, 0.68471, 0.62851673]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 12, 17, 2, 6, 23, 30, 13, 8],\n",
    "    [0.7154, 0.73790437, 0.7129234, 0.67244244, 0.628468]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 15, 27, 2, 1, 23, 27, 23, 8],\n",
    "    [0.72980994, 0.74311405, 0.7128369, 0.67825276, 0.62922376]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 12, 8, 2, 6, 23, 27, 17, 10],\n",
    "    [0.7260928, 0.74266046, 0.7048941, 0.6721861, 0.6283461]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [5, 10, 10, 1, 5, 25, 20, 30, 22],\n",
    "    [0.7260433, 0.7368869, 0.70520294, 0.6612124, 0.6207397]\n",
    "    ]\n",
    "]\n",
    "\n",
    "exp_opt_both = [\n",
    "    [\n",
    "    [2, 24, 26, 3, 5, 23, 8, 26, 20],\n",
    "    [0.75557566, 0.74896115, 0.6931529, 0.68972695, 0.6355624]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 24, 9, 2, 3, 10, 25, 23, 26],\n",
    "    [0.7465369, 0.75015014, 0.708952, 0.6798945, 0.62873614]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 13, 17, 5, 3, 11, 23, 23, 20],\n",
    "    [0.74186575, 0.74427855, 0.7164933, 0.6981006, 0.6556634]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 12, 9, 3, 3, 27, 23, 26, 19],\n",
    "    [0.74873, 0.73890954, 0.7080811, 0.68436825, 0.6495441]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [6, 10, 11, 2, 4, 28, 11, 15, 17],\n",
    "    [0.7571554, 0.74746567, 0.72580105, 0.68065745, 0.6355624]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 11, 19, 1, 3, 20, 29, 28, 30],\n",
    "    [0.71743816, 0.7270805, 0.6929923, 0.6705809, 0.6268102]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 10, 15, 1, 6, 25, 9, 22, 21],\n",
    "    [0.74350125, 0.7491695, 0.701911, 0.6761471, 0.6336547]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 19, 12, 6, 5, 12, 12, 13, 26],\n",
    "    [0.7536118, 0.7524179, 0.70849496, 0.6903617, 0.654676]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [5, 11, 30, 1, 4, 26, 25, 19, 23],\n",
    "    [0.7558606, 0.7575663, 0.7106876, 0.6742978, 0.6363791]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [6, 25, 17, 2, 1, 12, 24, 24, 7],\n",
    "    [0.7357821, 0.7412753, 0.723522, 0.6844415, 0.6525184]\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Folds CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        func_l, folds5_l = exp_fix_left[i]\n",
    "        func_r, folds5_r = exp_fix_right[j]\n",
    "        StatsHelper.plot_box(folds1=folds5_l, name1=f'fix_left_{i}', folds2=folds5_r, name2=f'fix_right_{j}')\n",
    "    print('--------------------------------------')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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