Search_Support_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 = SupportModel\n",
    "ds = Support('Data/support2.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",
    "    [4, 24, 26, 1, 5, 8, 10, 18, 26],\n",
    "    [0.8772409, 0.86199677, 0.87765175, 0.8729801, 0.86985534]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 13, 21, 6, 1, 17, 16, 26, 23],\n",
    "    [0.8761697, 0.86079067, 0.8799163, 0.87499064, 0.8691531]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 30, 17, 2, 2, 12, 7, 27, 21],\n",
    "    [0.87695175, 0.8607952, 0.8758574, 0.8746895, 0.86952513]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [5, 16, 26, 2, 1, 23, 21, 26, 13],\n",
    "    [0.8772601, 0.8598947, 0.8762512, 0.8748148, 0.8704117]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 19, 24, 4, 2, 9, 7, 26, 22],\n",
    "    [0.8768213, 0.8613667, 0.87783444, 0.8758533, 0.8684299]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 25, 19, 1, 2, 22, 11, 27, 20],\n",
    "    [0.87629455, 0.85960054, 0.87423044, 0.8746452, 0.87202144]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 29, 23, 1, 5, 28, 10, 12, 26],\n",
    "    [0.87911654, 0.8607826, 0.8786061, 0.8766557, 0.87129545]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [6, 19, 28, 3, 2, 27, 18, 25, 26],\n",
    "    [0.87640166, 0.8616734, 0.8762457, 0.87446105, 0.8708326]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [6, 20, 26, 5, 6, 15, 15, 26, 8],\n",
    "    [0.8772409, 0.86199677, 0.87765175, 0.8729801, 0.86985534]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 16, 17, 5, 5, 11, 14, 29, 26],\n",
    "    [0.87617105, 0.8618076, 0.8731987, 0.8757993, 0.8702609]\n",
    "    ]\n",
    "]\n",
    "\n",
    "\n",
    "exp_fix_right = [\n",
    "    [\n",
    "    [2, 12, 18, 5, 5, 10, 21, 30, 13],\n",
    "    [0.87411094, 0.8588948, 0.87764794, 0.8668676, 0.86477727]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 12, 30, 3, 5, 18, 11, 17, 8],\n",
    "    [0.87414896, 0.85872793, 0.8756539, 0.8677102, 0.8669584]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 24, 22, 2, 5, 11, 19, 9, 21],\n",
    "    [0.86574346, 0.85022885, 0.863207, 0.85962385, 0.8488512]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 17, 18, 5, 4, 28, 23, 27, 26],\n",
    "    [0.87232566, 0.8537298, 0.8730966, 0.8613159, 0.860814]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 28, 19, 2, 3, 11, 17, 7, 20],\n",
    "    [0.87635666, 0.86017835, 0.8806345, 0.8711718, 0.870226]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 29, 25, 2, 6, 13, 30, 20, 14],\n",
    "    [0.8657668, 0.84920144, 0.8708314, 0.8643102, 0.8586067]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 12, 11, 5, 5, 11, 30, 23, 21],\n",
    "    [0.8757766, 0.8564313, 0.8773052, 0.8652497, 0.8636394]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 20, 7, 2, 3, 13, 17, 8, 7],\n",
    "    [0.866109, 0.84694463, 0.8608435, 0.8594902, 0.8521]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 11, 25, 2, 6, 29, 20, 14, 25],\n",
    "    [0.86353123, 0.84722203, 0.86175334, 0.8571675, 0.8485608]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 10, 12, 6, 3, 15, 16, 15, 30],\n",
    "    [0.8768931, 0.8579675, 0.8747246, 0.8729199, 0.869685]\n",
    "    ]\n",
    "]\n",
    "\n",
    "\n",
    "exp_opt_both = [\n",
    "    [\n",
    "    [5, 16, 25, 4, 3, 27, 10, 22, 12],\n",
    "    [0.8672793, 0.84597194, 0.8634279, 0.8487187, 0.84988296]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [5, 22, 28, 6, 6, 17, 16, 22, 15],\n",
    "    [0.8764243, 0.86214036, 0.8789259, 0.87793785, 0.87365144]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [4, 29, 17, 5, 1, 9, 10, 24, 29],\n",
    "    [0.8819946, 0.8634803, 0.882649, 0.87231207, 0.87245286]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [3, 20, 18, 2, 6, 11, 28, 17, 16],\n",
    "    [0.8812691, 0.8650252, 0.88095015, 0.8795607, 0.8745582]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 10, 13, 2, 3, 16, 7, 26, 17],\n",
    "    [0.87773484, 0.8610649, 0.8783397, 0.875197, 0.86913913]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [5, 17, 25, 5, 2, 11, 21, 26, 16],\n",
    "    [0.88046366, 0.86280876, 0.8812849, 0.8716575, 0.87339664]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [2, 11, 24, 5, 2, 30, 24, 25, 8],\n",
    "    [0.8798274, 0.8637887, 0.8800903, 0.8772788, 0.87508523]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [5, 14, 17, 3, 5, 8, 12, 18, 20],\n",
    "    [0.8777477, 0.8639257, 0.8754931, 0.8790705, 0.87415475]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 11, 23, 2, 3, 25, 10, 22, 15],\n",
    "    [0.8809953, 0.8639982, 0.882283, 0.87423015, 0.87411565]\n",
    "    ]\n",
    "    ,\n",
    "    [\n",
    "    [1, 18, 26, 5, 1, 28, 23, 12, 15],\n",
    "    [0.87382, 0.858814, 0.8776472, 0.87495846, 0.8695995]\n",
    "    ]\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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