{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d69d2abf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-24 12:30:42.246729: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-03-24 12:30:42.246750: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import library.analysis as A \n",
    "from library.exercise import plotCloud\n",
    "from library.generators import *\n",
    "from library.dataset import DataSet\n",
    "from library.timing import timing\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3364eda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load 'data_input/folding_abalone9-18'\n",
      "from pickle file\n",
      "Data loaded.\n"
     ]
    }
   ],
   "source": [
    "dataSetName = \"imblearn_ozone_level\"\n",
    "dataSetName = \"folding_abalone_17_vs_7_8_9_10\"\n",
    "dataSetName = \"folding_abalone9-18\"\n",
    "#dataSetName = \"folding_yeast4\"\n",
    "#dataSetName = \"folding_car_good\"\n",
    "data = A.loadDataset(\"data_input/\" + dataSetName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0ea209e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def avg(x):\n",
    "    return sum(x) / len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a11a121e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def testHisto(descTrainCount):\n",
    "    print(f\"======[ {descTrainCount} ]======\")\n",
    "    t = timing(f\"train with {descTrainCount} extra rounds\")\n",
    "    t.start()\n",
    "    g = ConvGAN(data.data1.shape[1], neb_epochs=10, withMajorhoodNbSearch=True)\n",
    "    g.reset()\n",
    "    g.train(data, descTrainCount)\n",
    "    t.stop()\n",
    "    print(t)\n",
    "    t0 = g.predictReal(data.data0)\n",
    "    print(\"majority \" + str((min(t0), avg(t0), max(t0))))\n",
    "    t1 = g.predictReal(data.data1)\n",
    "    print(\"minority \" + str((min(t1), avg(t1), max(t1))))\n",
    "    t2 = g.predictReal(g.generateData(data.data0.shape[0]))\n",
    "    print(\"synthetic \" + str((min(t2), avg(t2), max(t2))))\n",
    "    plt.hist(t0, label=\"majority\")\n",
    "    #plt.hist(t1, label=\"minority\")\n",
    "    plt.hist(t2, label=\"synthetic\")\n",
    "    plt.show()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "662817c4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======[ 0 ]======\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-24 12:30:44.032119: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-03-24 12:30:44.032141: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-03-24 12:30:44.032157: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (sbi-klabautermann): /proc/driver/nvidia/version does not exist\n",
      "2022-03-24 12:30:44.032352: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train with 0 extra rounds: #1 44.1003s\n",
      "majority (0.36754203, 0.431634900921142, 0.60214233)\n",
      "minority (0.41898528, 0.5370993649675733, 0.8336862)\n",
      "synthetic (0.42654964, 0.5366197719369814, 0.8290475)\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": [
      "\n",
      "======[ 1 ]======\n",
      "train with 1 extra rounds: #1 72.5592s\n",
      "majority (0.054442644, 0.27122818303385043, 0.84951097)\n",
      "minority (0.2057668, 0.6238270089739845, 0.9983149)\n",
      "synthetic (0.20576864, 0.635948086987386, 0.99831486)\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": [
      "\n",
      "======[ 2 ]======\n",
      "train with 2 extra rounds: #1 101.5201s\n",
      "majority (0.00344944, 0.15321989588882823, 0.94366026)\n",
      "minority (0.10768956, 0.6254928175892148, 0.9991397)\n",
      "synthetic (0.20021954, 0.6810118817712122, 0.9968319)\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": [
      "\n",
      "======[ 3 ]======\n",
      "train with 3 extra rounds: #1 129.5592s\n",
      "majority (0.0005763471, 0.10390776793738754, 0.9897444)\n",
      "minority (0.04233092, 0.6437406504438037, 0.9999985)\n",
      "synthetic (0.15036112, 0.7553514468825607, 0.99995005)\n"
     ]
    },
    {
     "data": {
      "image/png": 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qOrvchQ7RgmNOciNwP/C2Mb6LG7TgmKtqS1VNVtUk8JfAr49xsEO/3+0HgJ9NsjrJ9zD7htUnlrnOYeoz5tPM/pcKSdYDPwJ8ZVmrXF5Dz6+xuHOveV5nkOTXuv0fYPbJiduBaeB/mP3LP7Z6jvl3ge8H7uvuZC/UGL9Rr+eYm9JnzFX1RJJPAY8A32b2/2w25yN146Dnz/n3gA8neZTZKYt3VdXYvgo4yUeB1wNrk8wA7wFeDKPLL18/IEkNGpdpGUnSVTDcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP+H9hB3cKxZ3YmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======[ 4 ]======\n",
      "train with 4 extra rounds: #1 158.6489s\n",
      "majority (0.0010927618, 0.09106668258959047, 0.99234223)\n",
      "minority (0.032084554, 0.652064446182478, 1.0)\n",
      "synthetic (0.10200524, 0.7617625614036151, 0.9999969)\n"
     ]
    },
    {
     "data": {
      "image/png": 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1taq2An8B/MYYBzv0+7t9H/BzSdYm+R7mnrD62ArXOUx9xnyGuX+pkGQj8KPAV1a0ypU19Pwai5l7LfA4gyS/3p3/AHOfnLgNmAb+m7nf/GOr55h/D/h+4N5uJnuhxviJej3H3JQ+Y66qx5J8GngI+Dbwwaqa9yN146Dnz/n3gQ8neZi5JYt3VtXYPgo4yUeB1wLrk8wA7waeD6PLLx8/IEkNGpdlGUnSVTDcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP+DxdG16k9VSowAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======[ 5 ]======\n",
      "train with 5 extra rounds: #1 188.0797s\n",
      "majority (0.00028133392, 0.10637742319889096, 0.9951868)\n",
      "minority (0.039069414, 0.6762245524497259, 1.0)\n",
      "synthetic (0.039138883, 0.7555334710763397, 0.9999926)\n"
     ]
    },
    {
     "data": {
      "image/png": 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VNVlVk8BfAr85xsEO/f5u3w/8QpLVSb6P2SesPr7MdQ5TnzGfZvZfKiRZD/w48NVlrXJ5DT2/xmLmXvM8ziDJb3TnP8jsJyduA6aB/2H2N//Y6jnm3wd+ELivm8leqDF+ol7PMTelz5ir6vEknwEeBr4DfKiq5vxI3Tjo+XP+A+AjSR5hdsni3VU1to8CTvIx4HXA2iQzwHuBF8Lo8svHD0hSg8ZlWUaSdBUMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktSg/wc97tY9N5lwvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======[ 6 ]======\n",
      "train with 6 extra rounds: #1 217.5180s\n",
      "majority (0.00011443569, 0.10461087292720267, 0.98728174)\n",
      "minority (0.028799742, 0.687773618669737, 0.99999905)\n",
      "synthetic (0.036519766, 0.728148201769772, 0.99999905)\n"
     ]
    },
    {
     "data": {
      "image/png": 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0iBYdc5JXAB8H3jrGs7hBi465qm6qqq1VtRX4K+C3xjjYod/P9gPALyRZm+T7mHvD6hMrXOcw9Rnzaeb+pUKSG4EfB766olWurKHn11jM3GuB1xkk+c1u/weYe3LiDmAa+F/mfvOPrZ5j/gPgB4H7u5ns+RrjN+r1HHNT+oy5qp5I8mngEeA7wAerat5H6sZBz7/nPwQ+nORR5m5ZvLOqxvZVwEk+CrwOWJ9kBng38EIYXX75+gFJatC43JaRJF0Bw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ16P8B9GjeMYIXrrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======[ 7 ]======\n",
      "train with 7 extra rounds: #1 247.2812s\n",
      "majority (5.7400057e-06, 0.07812106153224706, 0.995649)\n",
      "minority (0.017223567, 0.6630145311355591, 0.9999995)\n",
      "synthetic (0.09074798, 0.7825938416928788, 0.9999931)\n"
     ]
    },
    {
     "data": {
      "image/png": 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DfnOCgx36fW/fD/x8ktVJvo/5N6w+PuY6h6nPmE8x/z8VkqwHfgz42lirHK+h59dEzNxrkdcZJPmNbv8Hmb9z4nZgFvhf5n/yT6yeY/594AeB+7qZ7Pma4Dfq9RxzU/qMuaoeT/IZ4GHgu8CHqmrBW+omQc+v8x8AH0nyCPOXLN5dVRP7KuAkHwVeD6xNchp4L/BCGF1++foBSWrQpFyWkSRdAcNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNej/AbMM2jbteNZvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======[ 8 ]======\n",
      "train with 8 extra rounds: #1 274.0284s\n",
      "majority (0.0003386438, 0.10330177780851743, 0.9970418)\n",
      "minority (0.02112639, 0.6864672005176544, 1.0)\n",
      "synthetic (0.06407535, 0.8349364148169021, 0.99999785)\n"
     ]
    },
    {
     "data": {
      "image/png": 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3jvEsbtCSY66q7VU1WVWTwF8CvzHGwQ79vrfvB34uyXVJvpf5J6w+vsp1DlOfMZ9h/n8qJNkE/CjwlVWtcnUNPb/GYuZeizzOIMmvd+ffz/w7J24HZoD/Yf4n/9jqOebfA34AuK+byV6oMX6iXs8xN6XPmKvq8SSfAh4Gvg18oKoWfEvdOOj5df594ENJHmF+yeKdVTW2jwJO8hHgtcCGJLPAu4EXwOjyy8cPSFKDxmVZRpJ0FQx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KD/B1Yu3Zo0tfibAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======[ 9 ]======\n",
      "train with 9 extra rounds: #1 306.6393s\n",
      "majority (4.8418256e-05, 0.08757061694664493, 0.9976011)\n",
      "minority (0.03271407, 0.6800935970885413, 1.0)\n",
      "synthetic (0.12129837, 0.7845661920023588, 0.9999973)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5qjYDHwd+c4KDHfr9bj8A/HySa5J8H3NPWH1yzHUOU58xn2LufyokWQf8GPDVsVY5XkPPr4mYudcCjzNI8hvd/g8xd+fEbcAM8L/M/eWfWD3H/PvADwL3dTPZ8zXBT9TrOeam9BlzVT2Z5DPAo8B3gA9X1by31E2Cnj/nPwA+muQx5i5ZvLeqJvZRwEk+BrwRWJNkFng/8FIYXX75+AFJatCkXJaRJF0Bw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ16P8BnV3V0HjLyDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for n in range(10):\n",
    "    testHisto(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19babca7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05929c92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A.runExercise(dataSetName, None, \"test\", A.generators[\"convGAN\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}