All_eval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "389621ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'Padgan_variants' from 'C:\\\\Users\\\\Lyle\\\\Documents\\\\Files\\\\DeCoDE\\\\Bike Project\\\\DeCoDE-Bike-Project-Git\\\\Structural Analysis\\\\Padgan_variants.py'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "from tqdm.autonotebook import tqdm, trange\n",
    "import itertools\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import sklearn \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras import layers\n",
    "import random\n",
    "\n",
    "import importlib\n",
    "import evaluation\n",
    "import load_data\n",
    "import Padgan_variants\n",
    "# import GANs\n",
    "\n",
    "importlib.reload(evaluation)\n",
    "importlib.reload(load_data)\n",
    "# importlib.reload(GANs)\n",
    "importlib.reload(Padgan_variants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8d75276",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(ax, rangearr, xx, yy, Z, x, y, title, boundary=0.0, color=\"blue\", plottype = 1):\n",
    "    if plottype==1:\n",
    "        ax.contourf(xx, yy, Z, cmap=\"RdBu\", alpha=0.1)\n",
    "        ax.scatter(x,y, s=3, c=color)\n",
    "    elif plottype==2:\n",
    "        ax.imshow(Z.T, cmap=\"viridis\", alpha=0.5, origin='lower', extent = [-2,2,-2,2])\n",
    "        ax.scatter(x,y, s=3, c=color, alpha = 0.1)\n",
    "    else:\n",
    "        ax.scatter(x,y, s=3, c=color)\n",
    "    ax.set_title(title)\n",
    "    xlen = rangearr[0,1]-rangearr[0,0]\n",
    "    ylen = rangearr[1,1]-rangearr[1,0]\n",
    "    ax.set_xlim(rangearr[0,0]-xlen*boundary, rangearr[0,1]+xlen*boundary)\n",
    "    ax.set_ylim(rangearr[1,0]-ylen*boundary, rangearr[1,1]+ylen*boundary)\n",
    "    ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dfca0bda",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lyle\\AppData\\Local\\Temp/ipykernel_28388/1472824196.py:46: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  methods=pd.Series()\n",
      "C:\\Users\\Lyle\\AppData\\Local\\Temp/ipykernel_28388/1472824196.py:82: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  metrics=pd.Series()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No classifier provided! Fitting DNN Classifier using provided Parameters...\n",
      "Epoch 1/2000\n",
      "160/160 [==============================] - 0s 1ms/step - loss: 0.6911 - accuracy: 0.4806 - val_loss: 0.6891 - val_accuracy: 0.4187\n",
      "Epoch 2/2000\n",
      "160/160 [==============================] - 0s 851us/step - loss: 0.6889 - accuracy: 0.4821 - val_loss: 0.6864 - val_accuracy: 0.4415\n",
      "Epoch 3/2000\n",
      "160/160 [==============================] - 0s 848us/step - loss: 0.6863 - accuracy: 0.5052 - val_loss: 0.6835 - val_accuracy: 0.5405\n",
      "Epoch 4/2000\n",
      "160/160 [==============================] - 0s 832us/step - loss: 0.6843 - accuracy: 0.5487 - val_loss: 0.6807 - val_accuracy: 0.5585\n",
      "Epoch 5/2000\n",
      "160/160 [==============================] - 0s 838us/step - loss: 0.6819 - accuracy: 0.5674 - val_loss: 0.6783 - val_accuracy: 0.5770\n",
      "Epoch 6/2000\n",
      "160/160 [==============================] - 0s 838us/step - loss: 0.6790 - accuracy: 0.5857 - val_loss: 0.6747 - val_accuracy: 0.6168\n",
      "Epoch 7/2000\n",
      "160/160 [==============================] - 0s 841us/step - loss: 0.6762 - accuracy: 0.6009 - val_loss: 0.6710 - val_accuracy: 0.6650\n",
      "Epoch 8/2000\n",
      "160/160 [==============================] - 0s 829us/step - loss: 0.6730 - accuracy: 0.6339 - val_loss: 0.6674 - val_accuracy: 0.6605\n",
      "Epoch 9/2000\n",
      "160/160 [==============================] - 0s 848us/step - loss: 0.6692 - accuracy: 0.6437 - val_loss: 0.6631 - val_accuracy: 0.6793\n",
      "Epoch 10/2000\n",
      "160/160 [==============================] - 0s 848us/step - loss: 0.6660 - accuracy: 0.6537 - val_loss: 0.6579 - val_accuracy: 0.7218\n",
      "Epoch 11/2000\n",
      "160/160 [==============================] - 0s 844us/step - loss: 0.6619 - accuracy: 0.6714 - val_loss: 0.6533 - val_accuracy: 0.6927\n",
      "Epoch 12/2000\n",
      "160/160 [==============================] - 0s 848us/step - loss: 0.6571 - accuracy: 0.6745 - val_loss: 0.6474 - val_accuracy: 0.7265\n",
      "Epoch 13/2000\n",
      "160/160 [==============================] - 0s 844us/step - loss: 0.6515 - accuracy: 0.6835 - val_loss: 0.6418 - val_accuracy: 0.7400\n",
      "Epoch 14/2000\n",
      "160/160 [==============================] - 0s 857us/step - loss: 0.6473 - accuracy: 0.6860 - val_loss: 0.6368 - val_accuracy: 0.7383\n",
      "Epoch 15/2000\n",
      "160/160 [==============================] - 0s 844us/step - loss: 0.6424 - accuracy: 0.6971 - val_loss: 0.6315 - val_accuracy: 0.7500\n",
      "Epoch 16/2000\n",
      "160/160 [==============================] - 0s 848us/step - loss: 0.6385 - accuracy: 0.6948 - val_loss: 0.6253 - val_accuracy: 0.7477\n",
      "Epoch 17/2000\n",
      "160/160 [==============================] - 0s 835us/step - loss: 0.6342 - accuracy: 0.6982 - val_loss: 0.6211 - val_accuracy: 0.7285\n",
      "Epoch 18/2000\n",
      "160/160 [==============================] - 0s 838us/step - loss: 0.6280 - accuracy: 0.7026 - val_loss: 0.6143 - val_accuracy: 0.7510\n",
      "Epoch 19/2000\n",
      "160/160 [==============================] - 0s 854us/step - loss: 0.6265 - accuracy: 0.6963 - val_loss: 0.6095 - val_accuracy: 0.7815\n",
      "Epoch 20/2000\n",
      "160/160 [==============================] - 0s 844us/step - loss: 0.6198 - accuracy: 0.7079 - val_loss: 0.6034 - val_accuracy: 0.7782\n",
      "Epoch 21/2000\n",
      "160/160 [==============================] - 0s 851us/step - loss: 0.6170 - accuracy: 0.7061 - val_loss: 0.5984 - val_accuracy: 0.7732\n",
      "Epoch 22/2000\n",
      "160/160 [==============================] - 0s 841us/step - loss: 0.6108 - accuracy: 0.7079 - val_loss: 0.5926 - val_accuracy: 0.7713\n",
      "Epoch 23/2000\n",
      "160/160 [==============================] - 0s 851us/step - loss: 0.6067 - accuracy: 0.7110 - val_loss: 0.5871 - val_accuracy: 0.7760\n",
      "Epoch 24/2000\n",
      "160/160 [==============================] - 0s 841us/step - loss: 0.6020 - accuracy: 0.7139 - val_loss: 0.5823 - val_accuracy: 0.7615\n",
      "Epoch 25/2000\n",
      "160/160 [==============================] - 0s 857us/step - loss: 0.5974 - accuracy: 0.7120 - val_loss: 0.5753 - val_accuracy: 0.7790\n",
      "Epoch 26/2000\n",
      "160/160 [==============================] - 0s 841us/step - loss: 0.5945 - accuracy: 0.7078 - val_loss: 0.5697 - val_accuracy: 0.7855\n",
      "Epoch 27/2000\n",
      "160/160 [==============================] - 0s 841us/step - loss: 0.5897 - accuracy: 0.7088 - val_loss: 0.5643 - val_accuracy: 0.7870\n",
      "Epoch 28/2000\n",
      "160/160 [==============================] - 0s 838us/step - loss: 0.5809 - accuracy: 0.7166 - val_loss: 0.5585 - val_accuracy: 0.7835\n",
      "Epoch 29/2000\n",
      "160/160 [==============================] - 0s 838us/step - loss: 0.5800 - accuracy: 0.7166 - val_loss: 0.5526 - val_accuracy: 0.7763\n",
      "Epoch 30/2000\n",
      "160/160 [==============================] - 0s 838us/step - loss: 0.5743 - accuracy: 0.7166 - val_loss: 0.5462 - val_accuracy: 0.7968\n",
      "Epoch 31/2000\n",
      "  1/160 [..............................] - ETA: 0s - loss: 0.5633 - accuracy: 0.7100"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_28388/1472824196.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethods\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethods\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 142\u001b[1;33m             \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minvalid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_valid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    143\u001b[0m             \u001b[0mx_fake\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m             \u001b[0my_fake\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval_obj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_fake\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobjectives\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\Files\\DeCoDE\\Bike Project\\DeCoDE-Bike-Project-Git\\Structural Analysis\\Padgan_variants.py\u001b[0m in \u001b[0;36mmodel\u001b[1;34m(X, N, Y)\u001b[0m\n\u001b[0;32m    294\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpadgan_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDTAI_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclassifier_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mregressor_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mtrain_padgan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconfig_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDTAI_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTAI_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclassifier_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclassifier_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mregressor_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mregressor_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\Files\\DeCoDE\\Bike Project\\DeCoDE-Bike-Project-Git\\Structural Analysis\\Padgan_variants.py\u001b[0m in \u001b[0;36mtrain_padgan\u001b[1;34m(X, N, Y, config_params, train_params, DTAI_params, classifier_params, regressor_params)\u001b[0m\n\u001b[0;32m    280\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mclassifier\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"auto\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No classifier provided! Fitting DNN Classifier using provided Parameters...\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 282\u001b[1;33m             \u001b[0mclassifier\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclassifier_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    283\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    284\u001b[0m         \u001b[0mclassifier\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf_trivial\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\Files\\DeCoDE\\Bike Project\\DeCoDE-Bike-Project-Git\\Structural Analysis\\Padgan_variants.py\u001b[0m in \u001b[0;36mfit_classifier\u001b[1;34m(X, N, clfparams)\u001b[0m\n\u001b[0;32m    235\u001b[0m     \u001b[0mcallback\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEarlyStopping\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmonitor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'val_loss'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpatience\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpatience\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'binary_crossentropy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 237\u001b[1;33m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatchsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    238\u001b[0m     \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m     \u001b[0mf1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf1_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    106\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 108\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m     \u001b[1;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[0;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1098\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1099\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    778\u001b[0m       \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m         \u001b[0mcompiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"nonXla\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    782\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    805\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    806\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 807\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    808\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    809\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2827\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2828\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2829\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2830\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2831\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[1;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1841\u001b[0m       \u001b[0;31m`\u001b[0m\u001b[0margs\u001b[0m\u001b[0;31m`\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1842\u001b[0m     \"\"\"\n\u001b[1;32m-> 1843\u001b[1;33m     return self._call_flat(\n\u001b[0m\u001b[0;32m   1844\u001b[0m         [t for t in nest.flatten((args, kwargs), expand_composites=True)\n\u001b[0;32m   1845\u001b[0m          if isinstance(t, (ops.Tensor,\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1921\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1922\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1923\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1924\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1925\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    543\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 545\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    546\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\tf27\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1065.6x849.6 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importlib.reload(evaluation)\n",
    "# importlib.reload(load_data)\n",
    "# importlib.reload(GANs)\n",
    "# importlib.reload(Padgan_variants)\n",
    "\n",
    "numgen = 500 #Number of samples to generate\n",
    "numinst = 10 #Number of instantiations to test\n",
    "scaling = True #Scale or not\n",
    "scorebars = True #Print progress bars for scoring functions\n",
    "\n",
    "#Select problems to test\n",
    "functions=[]\n",
    "\n",
    "sampling_func_1 = load_data.sample_uniform_wrapper(10000, 10000) #Uniform Sampling with Number of positive samples & Negative Samples\n",
    "validity_func_1 = load_data.circles_val_wrapper(0.3)\n",
    "# objs_1 = [load_data.uniform_obj_wrapper(), load_data.uniform_obj_wrapper()]\n",
    "objs_1 = [load_data.circle_obj_wrapper(0), load_data.rectangles_obj_wrapper(2, 2)]\n",
    "rangearr_1 = np.array([[-1,1], [-1,1]])\n",
    "functions.append([sampling_func_1, validity_func_1, objs_1, rangearr_1])\n",
    "\n",
    "sampling_func_2 = load_data.sample_uniform_wrapper(10000, 10000) #Uniform Sampling with Number of positive samples & Negative Samples\n",
    "validity_func_2 = load_data.concentric_circles_val_wrapper(2)\n",
    "objs_2 = [load_data.circle_obj_wrapper(0), load_data.rectangles_obj_wrapper(2, 2)]\n",
    "rangearr_2 = np.array([[-1,1], [-1,1]])\n",
    "functions.append([sampling_func_2, validity_func_2, objs_2, rangearr_2])\n",
    "\n",
    "sampling_func_3 = load_data.sample_uniform_wrapper(10000, 10000) #Uniform Sampling with Number of positive samples & Negative Samples\n",
    "validity_func_3 = load_data.rectangles_val_wrapper(1, 1)\n",
    "objs_3 = [load_data.circle_obj_wrapper(0), load_data.rectangles_obj_wrapper(2, 2)]\n",
    "rangearr_3 = np.array([[-2,2], [-2,2]])\n",
    "functions.append([sampling_func_3, validity_func_3, objs_3, rangearr_3])\n",
    "\n",
    "sampling_func_4 = load_data.sample_uniform_wrapper(10000, 10000) #Uniform Sampling with Number of positive samples & Negative Samples\n",
    "validity_func_4 = load_data.radial_circles_val_wrapper(1.3, 0.45, 6)\n",
    "objs_4 = [load_data.circle_obj_wrapper(0), load_data.rectangles_obj_wrapper(2, 2)]\n",
    "rangearr_4 = np.array([[-2,2], [-2,2]])\n",
    "functions.append([sampling_func_4, validity_func_4, objs_4, rangearr_4])\n",
    "\n",
    "#Find the highest number of objectives in any problem\n",
    "max_obj = 0\n",
    "for i in range(len(functions)):\n",
    "    if len(functions[i][2])>max_obj:\n",
    "        max_obj = len(functions[i][2])\n",
    "\n",
    "#Select methods to test\n",
    "methods=pd.Series()\n",
    "\n",
    "# Train params: [def_batch_size, def_disc_lr, def_disc_aux_lr, def_gen_lr, def_noise_dim, def_disc_dim, def_steps]\n",
    "def_train_params = [32, 1e-3, 1e-4, 1e-3, 4, 1, 10000]\n",
    "gram_train_params = [100, 1e-3, 1e-4, 1e-3, 20, 10, 10000]\n",
    "\n",
    "# Config Params: = [cont_delay, validity_weight, pretrain_aux, normal_train_aux, def_gram, diversity]\n",
    "GAN_config_params = [None, 0, False, False, 0]\n",
    "ContGAN_config_params = [0, 0.4, False, False, 0]\n",
    "gram_config = [None, 0, False, True, 0]\n",
    "ContGAN_VW1 = [0, 0.2, False, False, 0.03]\n",
    "ContGAN_VW2 = [0, 0.6, False, False, 0.1]\n",
    "# ContGAN_Div1 = [0, 0.4, False, False, 0.03]\n",
    "# ContGAN_Div2 = [0, 0.4, False, False, 0.1]\n",
    "\n",
    "# methods[\"GAN\"] = GANs.contrastive_wrapper(def_train_params, GAN_config_params)\n",
    "# methods[\"GRAM-Net\"] = GANs.contrastive_wrapper(gram_train_params, gram_config)\n",
    "# methods[\"ContGAN_VW=0.2\"] = GANs.contrastive_wrapper(def_train_params, ContGAN_VW1)\n",
    "# methods[\"ContGAN_VW=0.4\"] = GANs.contrastive_wrapper(def_train_params, ContGAN_config_params)\n",
    "# methods[\"ContGAN_VW=0.6\"] = GANs.contrastive_wrapper(def_train_params, ContGAN_VW2)\n",
    "# methods[\"ContGAN+0.1_Div\"] = GANs.contrastive_wrapper(def_train_params, ContGAN_VW2)\n",
    "# methods[\"ContGAN+0.03_Div\"] = GANs.contrastive_wrapper(def_train_params, ContGAN_Div1)\n",
    "# methods[\"ContGAN+0.1_Div\"] = GANs.contrastive_wrapper(def_train_params, ContGAN_Div2)\n",
    "# methods[\"GRAM+Cont\"] = GANs.contrastive_wrapper(gram_train_params, ContGAN_config_params)\n",
    "\n",
    "# Regressor/Classifier params: [dropout, layers, layersize, batchnorm, activation, patience, lr, batchsize, epochs]\n",
    "reg_clf_params = [0.1, 2, 100, True, \"Leaky ReLU\", 30, 1e-4, 100, 2000]\n",
    "config_params = [True, True, True, \"auto\", \"auto\"]\n",
    "train_params = [5, 0.5, 4, 5000]\n",
    "DTAI_params= [\"auto\", \"auto\", \"auto\"]\n",
    "methods[\"PaDGAN+DTAI+CLF\"] = Padgan_variants.padgan_wrapper(config_params, train_params, DTAI_params, reg_clf_params, reg_clf_params)\n",
    "\n",
    "#Select Metrics to evaluate\n",
    "target = np.array([0.5, 0.5])\n",
    "a_ = np.array([1, 1])\n",
    "p_ = np.array([1, 1])\n",
    "metrics=pd.Series()\n",
    "metrics[\"Gen_gen_distance\"] = evaluation.gen_gen_distance_wrapper(\"x\", \"min\")\n",
    "metrics[\"Gen_data_distance\"] = evaluation.gen_data_distance_wrapper(\"x\", \"min\")\n",
    "metrics[\"Data_gen_distance\"] = evaluation.data_gen_distance_wrapper(\"x\", \"min\")\n",
    "metrics[\"Gen_neg_distance\"] = evaluation.gen_neg_distance_wrapper(\"min\")\n",
    "# metrics[\"Minimum Target Ratio\"] = evaluation.minimum_target_ratio_wrapper(target)\n",
    "# metrics[\"DTAI\"] = evaluation.DTAI_wrapper(target, a_, p_)\n",
    "# metrics[\"Hypervolume\"] = evaluation.Hypervolume_wrapper()\n",
    "# metrics[\"Weighted Target Success Rate\"] = evaluation.weighted_target_success_rate_wrapper(target, p_)\n",
    "metrics[\"Recall\"] = evaluation.recall_wrapper(\"x\")\n",
    "metrics[\"Precison\"] = evaluation.precision_wrapper(\"x\")\n",
    "metrics[\"F1\"] = evaluation.F_wrapper(\"x\", 1)\n",
    "metrics[\"F10\"] = evaluation.F_wrapper(\"x\", 10)\n",
    "metrics[\"F0.1\"] = evaluation.F_wrapper(\"x\", 0.1)\n",
    "metrics[\"AUC-PR\"] = evaluation.AUC_wrapper(\"x\")\n",
    "metrics[\"MMD\"] = evaluation.MMD_wrapper()\n",
    "metrics[\"Target Threshold Distance\"] = evaluation.target_threshold_distance_wrapper(target, a_)\n",
    "\n",
    "metrics[\"Validity\"] = \"valflag\"\n",
    "\n",
    "scores=np.zeros((len(functions), len(methods), len(metrics), numinst))\n",
    "\n",
    "#Set up plots\n",
    "validity_status = 1\n",
    "obj_status = 1\n",
    "plots_in_row = 1 + validity_status +(len(methods)+max_obj*obj_status)\n",
    "#Loop over training instances\n",
    "for inst in range(numinst):\n",
    "    fig, ax = plt.subplots(len(functions), plots_in_row, figsize=(3*plots_in_row-0.2, 3*len(functions)-0.2))\n",
    "    \n",
    "    #Loop over problems to test\n",
    "    for func in range(len(functions)):\n",
    "        samplingfunction, validityfunction, objectives, rangearr = functions[func]\n",
    "        valid, invalid, scaler = load_data.gen_toy_dataset(samplingfunction, validityfunction, objectives, rangearr, scaling)\n",
    "        y_valid = load_data.eval_obj(valid, objectives)\n",
    "        #Generate Colored Background\n",
    "        \n",
    "        \n",
    "        if scaling: \n",
    "            v_s = scaler.inverse_transform(valid)\n",
    "            try:\n",
    "                i_s = scaler.inverse_transform(invalid)\n",
    "            except:\n",
    "                i_s = np.array([[None,None]])\n",
    "        else:\n",
    "            v_s = valid\n",
    "            i_s = invalid\n",
    "        xx, yy, Z = load_data.gen_background_plot(validityfunction, rangearr)\n",
    "        plot(fig.axes[plots_in_row*func], rangearr, xx, yy, Z, v_s[:,0], v_s[:,1], \"original valid data\", plottype=validity_status)\n",
    "        if validity_status==1:\n",
    "            plot(fig.axes[plots_in_row*func+1], rangearr, xx, yy, Z, i_s[:,0], i_s[:,1], \"original invalid data\", color=\"red\")\n",
    "        if obj_status==1:\n",
    "            for i in range(len(objectives)):\n",
    "                xx_o, yy_o, Z_o = load_data.gen_background_plot(objectives[i], rangearr)\n",
    "                obj_idx = plots_in_row*func+1+validity_status+i\n",
    "                plot(fig.axes[obj_idx], rangearr, xx_o, yy_o, Z_o, v_s[:,0], v_s[:,1], \"Objective \" +str(i+1), color=\"black\", plottype = 2)\n",
    "\n",
    "        #Loop over methods to test\n",
    "        for i in range(len(methods)): \n",
    "            method = methods.values[i]\n",
    "            model = method(valid, invalid, y_valid)\n",
    "            x_fake = model.generate(numgen)\n",
    "            y_fake = load_data.eval_obj(x_fake, objectives)\n",
    "            if scaling==True:\n",
    "                x_fake = scaler.inverse_transform(x_fake)\n",
    "\n",
    "\n",
    "            for j in range(len(metrics)):\n",
    "                if metrics.values[j]==\"valflag\":\n",
    "                    allscores, meanscore = evaluation.evaluate_validity(x_fake, validityfunction)\n",
    "                else:\n",
    "                    allscores, meanscore = metrics.values[j](x_fake, y_fake, valid, y_valid, invalid, scorebars)\n",
    "                scores[func, i, j, inst] = meanscore\n",
    "            res_idx=plots_in_row*func+1+validity_status+len(objectives)*obj_status+i\n",
    "            plot(fig.axes[res_idx], rangearr, xx, yy, Z, x_fake[:,0], x_fake[:,1], methods.index[i], plottype=validity_status)\n",
    "    plt.show()\n",
    "    fig.savefig(\"Contrastive_Results_\" + str(inst) + \".png\", dpi=400)\n",
    "    \n",
    "for i in range(np.shape(scores)[0]):\n",
    "    meanscores = np.mean(scores[i], axis=(2))\n",
    "    scoredf = pd.DataFrame(meanscores, index=methods.index, columns = metrics.index).transpose()\n",
    "    scoredf.columns.name=\"Problem \" + str(i+1) + \" Scores:\"\n",
    "    print(scoredf)\n",
    "#     scoredf.to_csv(\"Contrastive_scores.csv\", index_label=scoredf.columns.name)\n",
    "\n",
    "#average scores\n",
    "\n",
    "meanscores = np.mean(scores, axis=(0,3))\n",
    "scoredf = pd.DataFrame(meanscores, index=methods.index, columns = metrics.index).transpose()\n",
    "scoredf.columns.name=\"Average scores:\"\n",
    "print(scoredf)\n",
    "scoredf.to_csv(\"Contrastive_scores.csv\", index_label=scoredf.columns.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dfde643",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c37ff80",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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