use_fedn_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate a model from the FEDn model trail "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor Flow Version: 2.3.2\n",
      "Keras Version: 2.4.0\n",
      "\n",
      "Python 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27) \n",
      "[GCC 9.3.0]\n",
      "GPU is NOT AVAILABLE\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "from stackn import stackn\n",
    "from fedn.utils.kerashelper import KerasHelper\n",
    "import tensorflow\n",
    "from client.models.AMLmodel import construct_model\n",
    "import sys\n",
    "# Before using tensorflow:\n",
    "import os\n",
    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as KB\n",
    "from matplotlib import pylab as plt\n",
    "import tensorflow.keras\n",
    "\n",
    "import pickle\n",
    "import numpy as np\n",
    "print(f\"Tensor Flow Version: {tf.__version__}\")\n",
    "print(f\"Keras Version: {tensorflow.keras.__version__}\")\n",
    "print()\n",
    "print(f\"Python {sys.version}\")\n",
    "\n",
    "gpu = len(tf.config.list_physical_devices('GPU'))>0\n",
    "print(\"GPU is\", \"available\" if gpu else \"NOT AVAILABLE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a given FEDn model version as a native Keras model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This asssumes that we are storing the model trail in Studio Minio, \n",
    "# and that we have mounted the minio-vol volume when staring the lab session. \n",
    "model_id = '044e6d5c-f187-4805-af04-127f11f01460'\n",
    "fedn_model = '/home/jovyan/work/minio-vol/fedn-models/{}'.format(model_id)\n",
    "helper = KerasHelper()\n",
    "weights = helper.load_model(fedn_model)\n",
    "model = construct_model()\n",
    "model.set_weights(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BAS_0001', 'BAS_0028', 'BAS_0030', 'BAS_0031', 'BAS_0044',\n",
       "       'BAS_0052', 'EBO_0008', 'EOS_0018', 'EOS_0051', 'EOS_0069',\n",
       "       'EOS_0100', 'EOS_0138', 'EOS_0162', 'EOS_0182', 'EOS_0198',\n",
       "       'EOS_0236', 'EOS_0247', 'EOS_0259', 'EOS_0276', 'EOS_0283',\n",
       "       'EOS_0308', 'EOS_0334', 'EOS_0346', 'EOS_0355', 'EOS_0375',\n",
       "       'EOS_0421', 'LYA_0009', 'LYT_0007', 'LYT_0008', 'LYT_0015',\n",
       "       'LYT_0060', 'LYT_0082', 'LYT_0180', 'LYT_0182', 'LYT_0240',\n",
       "       'LYT_0295', 'LYT_0308', 'LYT_0351', 'LYT_0380', 'LYT_0450',\n",
       "       'LYT_0518', 'LYT_0572', 'LYT_0615', 'LYT_0656', 'LYT_0675',\n",
       "       'LYT_0687', 'LYT_0692', 'LYT_0746', 'LYT_0753', 'LYT_0755',\n",
       "       'LYT_0767', 'LYT_0815', 'LYT_0831', 'LYT_0945', 'LYT_0955',\n",
       "       'LYT_0966', 'LYT_1008', 'LYT_1074', 'LYT_1093', 'LYT_1274',\n",
       "       'LYT_1477', 'LYT_1513', 'LYT_1524', 'LYT_1549', 'LYT_1573',\n",
       "       'LYT_1587', 'LYT_1590', 'LYT_1629', 'LYT_1637', 'LYT_1674',\n",
       "       'LYT_1678', 'LYT_1683', 'LYT_1694', 'LYT_1721', 'LYT_1737',\n",
       "       'LYT_1745', 'LYT_1761', 'LYT_1862', 'LYT_1904', 'LYT_2075',\n",
       "       'LYT_2081', 'LYT_2129', 'LYT_2188', 'LYT_2203', 'LYT_2298',\n",
       "       'LYT_2323', 'LYT_2339', 'LYT_2340', 'LYT_2413', 'LYT_2446',\n",
       "       'LYT_2498', 'LYT_2505', 'LYT_2509', 'LYT_2563', 'LYT_2582',\n",
       "       'LYT_2648', 'LYT_2724', 'LYT_2751', 'LYT_2822', 'LYT_2841',\n",
       "       'LYT_2848', 'LYT_2874', 'LYT_2919', 'LYT_2935', 'LYT_2972',\n",
       "       'LYT_2978', 'LYT_2995', 'LYT_3038', 'LYT_3046', 'LYT_3053',\n",
       "       'LYT_3085', 'LYT_3094', 'LYT_3104', 'LYT_3145', 'LYT_3181',\n",
       "       'LYT_3224', 'LYT_3277', 'LYT_3333', 'LYT_3343', 'LYT_3380',\n",
       "       'LYT_3387', 'LYT_3447', 'LYT_3451', 'LYT_3535', 'LYT_3565',\n",
       "       'LYT_3573', 'LYT_3605', 'LYT_3667', 'LYT_3685', 'LYT_3703',\n",
       "       'LYT_3707', 'LYT_3761', 'LYT_3818', 'LYT_3908', 'LYT_3931',\n",
       "       'LYT_3933', 'MMZ_0003', 'MOB_0004', 'MON_0020', 'MON_0022',\n",
       "       'MON_0056', 'MON_0104', 'MON_0132', 'MON_0152', 'MON_0191',\n",
       "       'MON_0275', 'MON_0296', 'MON_0340', 'MON_0435', 'MON_0495',\n",
       "       'MON_0510', 'MON_0537', 'MON_0619', 'MON_0649', 'MON_0671',\n",
       "       'MON_0697', 'MON_0726', 'MON_0773', 'MON_0778', 'MON_0801',\n",
       "       'MON_0817', 'MON_0866', 'MON_0874', 'MON_0916', 'MON_0969',\n",
       "       'MON_1035', 'MON_1044', 'MON_1046', 'MON_1089', 'MON_1143',\n",
       "       'MON_1225', 'MON_1248', 'MON_1337', 'MON_1370', 'MON_1388',\n",
       "       'MON_1465', 'MON_1479', 'MON_1545', 'MON_1618', 'MON_1633',\n",
       "       'MON_1650', 'MON_1657', 'MON_1659', 'MON_1662', 'MON_1691',\n",
       "       'MON_1725', 'MON_1744', 'MON_1752', 'MON_1768', 'MYB_0010',\n",
       "       'MYB_0021', 'MYO_0009', 'MYO_0021', 'MYO_0032', 'MYO_0116',\n",
       "       'MYO_0133', 'MYO_0158', 'MYO_0173', 'MYO_0202', 'MYO_0205',\n",
       "       'MYO_0211', 'MYO_0218', 'MYO_0223', 'MYO_0254', 'MYO_0256',\n",
       "       'MYO_0272', 'MYO_0330', 'MYO_0349', 'MYO_0356', 'MYO_0492',\n",
       "       'MYO_0626', 'MYO_0656', 'MYO_0754', 'MYO_0781', 'MYO_0823',\n",
       "       'MYO_0833', 'MYO_0842', 'MYO_0904', 'MYO_0920', 'MYO_0940',\n",
       "       'MYO_0961', 'MYO_0975', 'MYO_0996', 'MYO_1050', 'MYO_1172',\n",
       "       'MYO_1242', 'MYO_1275', 'MYO_1312', 'MYO_1355', 'MYO_1379',\n",
       "       'MYO_1434', 'MYO_1452', 'MYO_1461', 'MYO_1469', 'MYO_1481',\n",
       "       'MYO_1484', 'MYO_1488', 'MYO_1506', 'MYO_1523', 'MYO_1563',\n",
       "       'MYO_1676', 'MYO_1710', 'MYO_1733', 'MYO_1761', 'MYO_1805',\n",
       "       'MYO_1807', 'MYO_1846', 'MYO_1864', 'MYO_1876', 'MYO_1892',\n",
       "       'MYO_1972', 'MYO_1988', 'MYO_1997', 'MYO_2042', 'MYO_2058',\n",
       "       'MYO_2074', 'MYO_2083', 'MYO_2113', 'MYO_2119', 'MYO_2161',\n",
       "       'MYO_2167', 'MYO_2200', 'MYO_2247', 'MYO_2306', 'MYO_2336',\n",
       "       'MYO_2349', 'MYO_2382', 'MYO_2384', 'MYO_2388', 'MYO_2408',\n",
       "       'MYO_2437', 'MYO_2440', 'MYO_2520', 'MYO_2564', 'MYO_2573',\n",
       "       'MYO_2601', 'MYO_2628', 'MYO_2647', 'MYO_2655', 'MYO_2673',\n",
       "       'MYO_2675', 'MYO_2702', 'MYO_2721', 'MYO_2803', 'MYO_2872',\n",
       "       'MYO_2944', 'MYO_2998', 'MYO_3009', 'MYO_3036', 'MYO_3039',\n",
       "       'MYO_3074', 'MYO_3090', 'MYO_3095', 'MYO_3113', 'MYO_3159',\n",
       "       'MYO_3162', 'MYO_3193', 'MYO_3220', 'MYO_3243', 'MYO_3245',\n",
       "       'NGB_0057', 'NGB_0102', 'NGS_0011', 'NGS_0097', 'NGS_0103',\n",
       "       'NGS_0135', 'NGS_0149', 'NGS_0171', 'NGS_0177', 'NGS_0185',\n",
       "       'NGS_0201', 'NGS_0231', 'NGS_0239', 'NGS_0263', 'NGS_0279',\n",
       "       'NGS_0303', 'NGS_0305', 'NGS_0390', 'NGS_0402', 'NGS_0456',\n",
       "       'NGS_0475', 'NGS_0532', 'NGS_0558', 'NGS_0687', 'NGS_0744',\n",
       "       'NGS_0761', 'NGS_0769', 'NGS_0796', 'NGS_0801', 'NGS_0802',\n",
       "       'NGS_0816', 'NGS_0873', 'NGS_0885', 'NGS_0940', 'NGS_0943',\n",
       "       'NGS_0962', 'NGS_0963', 'NGS_0999', 'NGS_1002', 'NGS_1011',\n",
       "       'NGS_1033', 'NGS_1038', 'NGS_1073', 'NGS_1157', 'NGS_1160',\n",
       "       'NGS_1163', 'NGS_1197', 'NGS_1224', 'NGS_1228', 'NGS_1273',\n",
       "       'NGS_1280', 'NGS_1291', 'NGS_1343', 'NGS_1356', 'NGS_1360',\n",
       "       'NGS_1371', 'NGS_1390', 'NGS_1391', 'NGS_1414', 'NGS_1415',\n",
       "       'NGS_1416', 'NGS_1428', 'NGS_1454', 'NGS_1505', 'NGS_1632',\n",
       "       'NGS_1644', 'NGS_1656', 'NGS_1716', 'NGS_1717', 'NGS_1737',\n",
       "       'NGS_1778', 'NGS_1782', 'NGS_1785', 'NGS_1883', 'NGS_1895',\n",
       "       'NGS_1931', 'NGS_1955', 'NGS_1968', 'NGS_2002', 'NGS_2040',\n",
       "       'NGS_2049', 'NGS_2062', 'NGS_2109', 'NGS_2114', 'NGS_2149',\n",
       "       'NGS_2150', 'NGS_2161', 'NGS_2168', 'NGS_2174', 'NGS_2225',\n",
       "       'NGS_2228', 'NGS_2271', 'NGS_2273', 'NGS_2282', 'NGS_2524',\n",
       "       'NGS_2540', 'NGS_2559', 'NGS_2646', 'NGS_2672', 'NGS_2675',\n",
       "       'NGS_2682', 'NGS_2694', 'NGS_2698', 'NGS_2751', 'NGS_2850',\n",
       "       'NGS_2892', 'NGS_2898', 'NGS_2904', 'NGS_2910', 'NGS_2912',\n",
       "       'NGS_2928', 'NGS_2938', 'NGS_3010', 'NGS_3055', 'NGS_3089',\n",
       "       'NGS_3140', 'NGS_3160', 'NGS_3199', 'NGS_3223', 'NGS_3254',\n",
       "       'NGS_3278', 'NGS_3281', 'NGS_3308', 'NGS_3325', 'NGS_3445',\n",
       "       'NGS_3450', 'NGS_3507', 'NGS_3516', 'NGS_3543', 'NGS_3564',\n",
       "       'NGS_3599', 'NGS_3605', 'NGS_3623', 'NGS_3647', 'NGS_3697',\n",
       "       'NGS_3719', 'NGS_3720', 'NGS_3742', 'NGS_3819', 'NGS_3875',\n",
       "       'NGS_3892', 'NGS_3896', 'NGS_3901', 'NGS_3915', 'NGS_3971',\n",
       "       'NGS_3979', 'NGS_4016', 'NGS_4031', 'NGS_4044', 'NGS_4074',\n",
       "       'NGS_4091', 'NGS_4164', 'NGS_4166', 'NGS_4215', 'NGS_4226',\n",
       "       'NGS_4229', 'NGS_4286', 'NGS_4309', 'NGS_4313', 'NGS_4317',\n",
       "       'NGS_4345', 'NGS_4365', 'NGS_4511', 'NGS_4521', 'NGS_4566',\n",
       "       'NGS_4600', 'NGS_4616', 'NGS_4620', 'NGS_4630', 'NGS_4668',\n",
       "       'NGS_4671', 'NGS_4680', 'NGS_4741', 'NGS_4769', 'NGS_4839',\n",
       "       'NGS_4842', 'NGS_4856', 'NGS_4898', 'NGS_4909', 'NGS_4957',\n",
       "       'NGS_5055', 'NGS_5172', 'NGS_5211', 'NGS_5300', 'NGS_5301',\n",
       "       'NGS_5303', 'NGS_5321', 'NGS_5342', 'NGS_5348', 'NGS_5354',\n",
       "       'NGS_5387', 'NGS_5396', 'NGS_5428', 'NGS_5461', 'NGS_5487',\n",
       "       'NGS_5554', 'NGS_5561', 'NGS_5571', 'NGS_5628', 'NGS_5660',\n",
       "       'NGS_5687', 'NGS_5751', 'NGS_5758', 'NGS_5804', 'NGS_5812',\n",
       "       'NGS_5817', 'NGS_5837', 'NGS_5867', 'NGS_5895', 'NGS_5930',\n",
       "       'NGS_5933', 'NGS_5944', 'NGS_5945', 'NGS_5976', 'NGS_6049',\n",
       "       'NGS_6068', 'NGS_6076', 'NGS_6091', 'NGS_6121', 'NGS_6142',\n",
       "       'NGS_6175', 'NGS_6189', 'NGS_6197', 'NGS_6243', 'NGS_6280',\n",
       "       'NGS_6288', 'NGS_6292', 'NGS_6297', 'NGS_6307', 'NGS_6375',\n",
       "       'NGS_6425', 'NGS_6472', 'NGS_6513', 'NGS_6530', 'NGS_6532',\n",
       "       'NGS_6577', 'NGS_6628', 'NGS_6757', 'NGS_6772', 'NGS_6808',\n",
       "       'NGS_6820', 'NGS_6831', 'NGS_6853', 'NGS_6882', 'NGS_6909',\n",
       "       'NGS_6929', 'NGS_6942', 'NGS_6955', 'NGS_6959', 'NGS_7016',\n",
       "       'NGS_7040', 'NGS_7051', 'NGS_7055', 'NGS_7063', 'NGS_7071',\n",
       "       'NGS_7078', 'NGS_7112', 'NGS_7277', 'NGS_7326', 'NGS_7338',\n",
       "       'NGS_7389', 'NGS_7448', 'NGS_7522', 'NGS_7538', 'NGS_7547',\n",
       "       'NGS_7559', 'NGS_7568', 'NGS_7572', 'NGS_7584', 'NGS_7604',\n",
       "       'NGS_7628', 'NGS_7635', 'NGS_7653', 'NGS_7654', 'NGS_7665',\n",
       "       'NGS_7754', 'NGS_7771', 'NGS_7774', 'NGS_7789', 'NGS_7803',\n",
       "       'NGS_7804', 'NGS_7822', 'NGS_7871', 'NGS_7876', 'NGS_7903',\n",
       "       'NGS_7970', 'NGS_8002', 'NGS_8030', 'NGS_8031', 'NGS_8044',\n",
       "       'NGS_8052', 'NGS_8091', 'NGS_8159', 'NGS_8173', 'NGS_8178',\n",
       "       'NGS_8183', 'NGS_8195', 'NGS_8205', 'NGS_8232', 'NGS_8240',\n",
       "       'NGS_8242', 'NGS_8246', 'NGS_8269', 'NGS_8276', 'NGS_8278',\n",
       "       'NGS_8297', 'NGS_8439', 'NGS_8473', 'NGS_8475', 'PMB_0001',\n",
       "       'PMO_0012', 'PMO_0057'], dtype='<U8')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from client.data.datagenerator import DataGenerator\n",
    "labels1 = np.load('dataset/processed/data_partitions/partition1/labels.npy', allow_pickle=True).item()\n",
    "data_path1 = 'dataset/processed/data_partitions/partition1/data_singlets'\n",
    "\n",
    "ids1 = [l for l in labels1]\n",
    "np.random.shuffle(ids1)\n",
    "\n",
    "test_gen = DataGenerator(ids1,labels1, data_path1, dim=(100,100), batch_size=32)\n",
    "labels, preds = test_gen.predict_all(model)\n",
    "np.sort([k for k in labels1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def confusion_matrix(model, data_gen):\n",
    "    \n",
    "    labels, preds = data_gen.predict_all(model)\n",
    "\n",
    "    y_pred = np.argmax(preds,1)\n",
    "    y_data = np.argmax(labels,1)\n",
    "    M = np.zeros((16,16))\n",
    "\n",
    "    for pred_, true_ in zip(y_pred,y_data):\n",
    "        M[true_,pred_] +=1\n",
    "    \n",
    "    M[15,:15] = np.sum(M[:15,:15],0)\n",
    "    M[:15,15] = np.sum(M[:15,:15],1)\n",
    "    M[15,15] = np.sum(M[:15,:15])\n",
    "        \n",
    "    return M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "from client.models.AMLmodel import classes as aml_classes\n",
    "\n",
    "class_names_y = [aml_classes[k] for k in aml_classes] + ['Total']\n",
    "class_names_x = [k for k in aml_classes] + ['Total']\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(matrix, title='Confusion matrix', cmap=plt.cm.autumn):\n",
    "    \n",
    "    f, ax = plt.subplots(1,1,figsize=(20,14))\n",
    "    color_mat = np.zeros(matrix.shape)\n",
    "    color_mat[:15,:15] = matrix[:15,:15]/np.maximum(1,np.sum(matrix[:15,:15],0))\n",
    "    ax.matshow(color_mat, cmap=cmap, norm=matplotlib.colors.LogNorm()) \n",
    "\n",
    "    tick_mark = np.arange(16)\n",
    "    ax.set_xticks(tick_mark)\n",
    "    ax.set_yticks(tick_mark)\n",
    "\n",
    "    ax.set_xticklabels(class_names_x)\n",
    "    ax.set_yticklabels(class_names_y)\n",
    "    \n",
    "    for (i, j), z in np.ndenumerate(matrix):\n",
    "        \n",
    "        ax.text(j, i, '{:0.0f}'.format(z), ha='center', va='center')\n",
    "\n",
    "    ax.axvline(x=14.5,color='black',lw=5)\n",
    "    ax.axhline(y=14.5,color='black', lw=5)\n",
    "    ax.set_xlabel('Predicted class', fontsize=20)\n",
    "    ax.set_ylabel('True class', fontsize=20)\n",
    "\n",
    "M = confusion_matrix(model, test_gen)\n",
    "plot_confusion_matrix(M)\n",
    "plt.savefig('confusion_sample.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}