custom_experiments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58ac3252",
   "metadata": {},
   "source": [
    "# Experiments with custom models and datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0747627e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# import our utils\n",
    "from utils import datasets, metrics, core_models, cpd_models, klcpd, tscp\n",
    "from utils.model_utils import fix_seeds\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e8c427",
   "metadata": {},
   "source": [
    "## Fix seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59c0d822",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 42\n",
    "fix_seeds(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497fbf62",
   "metadata": {},
   "source": [
    "## Create dataset\n",
    "\n",
    "You should define your own CustomDataset class with self.data and self.labels and then use our CPDDatasets wrapper. For example, we take our SyntheticNormalDataset (100D) defined in utils/datasets.py."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0100e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments_name = \"synthetic_100D\"\n",
    "train_dataset, test_dataset = datasets.CPDDatasets(experiments_name=experiments_name).get_dataset_()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8435185",
   "metadata": {},
   "source": [
    "## Create custom core model\n",
    "\n",
    "You should use torch.nn.Module wrapper for your core model and define self.forward method. For example, we take our BaseRnn model defined in utils/core_models.py. We use default parameters (specified in configs/synthetic_100D_seq2seq.yaml file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f0b9741",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eromanenkova/stash/eromanenkova/miniconda3/lib/python3.9/site-packages/torch/nn/modules/rnn.py:67: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.5 and num_layers=1\n",
      "  warnings.warn(\"dropout option adds dropout after all but last \"\n"
     ]
    }
   ],
   "source": [
    "# define core model for an experiment with our 'indid' loss\n",
    "core_model_indid = core_models.BaseRnn(\n",
    "    input_size=100,\n",
    "    hidden_dim=8,\n",
    "    n_layers=1,\n",
    "    drop_prob=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4593b40d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary with default arguments for consistency with our interface\n",
    "# define arguments as they are set in the corresponding config file\n",
    "learning = dict()\n",
    "learning[\"batch_size\"] = 64\n",
    "learning[\"lr\"] = 0.001\n",
    "learning[\"grad_clip\"] = 0.0\n",
    "\n",
    "loss = dict()\n",
    "loss[\"T\"] = 32\n",
    "\n",
    "args = dict()\n",
    "args[\"learning\"] = learning\n",
    "args[\"loss\"] = loss\n",
    "\n",
    "args[\"experiments_name\"] = experiments_name\n",
    "args[\"num_workers\"] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea1de514",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define CPDModel with our 'indid' loss\n",
    "indid_model = cpd_models.CPDModel(\n",
    "    loss_type=\"indid\",\n",
    "    args=args,\n",
    "    model=core_model_indid,\n",
    "    train_dataset=train_dataset,\n",
    "    test_dataset=test_dataset\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c17c3a",
   "metadata": {},
   "source": [
    "## Train the model using pytorch_lightning trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2670f554",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eromanenkova/stash/eromanenkova/miniconda3/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:441: LightningDeprecationWarning: Setting `Trainer(gpus=1)` is deprecated in v1.7 and will be removed in v2.0. Please use `Trainer(accelerator='gpu', devices=1)` instead.\n",
      "  rank_zero_deprecation(\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name  | Type    | Params\n",
      "----------------------------------\n",
      "0 | model | BaseRnn | 3.5 K \n",
      "1 | loss  | CPDLoss | 0     \n",
      "----------------------------------\n",
      "3.5 K     Trainable params\n",
      "0         Non-trainable params\n",
      "3.5 K     Total params\n",
      "0.014     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8378490d961a4b78a333bf0c1888bd19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
     ]
    }
   ],
   "source": [
    "# use logger\n",
    "model_name = f'seq2seq_indid_seed_{SEED}'\n",
    "logger = TensorBoardLogger(save_dir=f'logs/{experiments_name}', name=model_name)\n",
    "\n",
    "# define trainer with custom parameters\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=30,\n",
    "    gpus=1,\n",
    "    benchmark=True,\n",
    "    check_val_every_n_epoch=1,\n",
    "    gradient_clip_val=0.,\n",
    "    logger=logger,\n",
    "    \n",
    "    # use early stopping\n",
    "    callbacks=EarlyStopping(monitor=\"val_loss\", min_delta=0, patience=10)\n",
    ")\n",
    "\n",
    "trainer.fit(indid_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e87f0ff",
   "metadata": {},
   "source": [
    "## Evaluate your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1660d56c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|▊                                                                             | 1/102 [00:01<01:45,  1.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|█▌                                                                            | 2/102 [00:01<01:27,  1.15it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|██▎                                                                           | 3/102 [00:02<01:18,  1.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|███                                                                           | 4/102 [00:03<01:17,  1.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|███▊                                                                          | 5/102 [00:04<01:15,  1.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|████▌                                                                         | 6/102 [00:04<01:10,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|█████▎                                                                        | 7/102 [00:05<01:09,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|██████                                                                        | 8/102 [00:06<01:08,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|██████▉                                                                       | 9/102 [00:06<01:05,  1.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|███████▌                                                                     | 10/102 [00:07<01:07,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 11%|████████▎                                                                    | 11/102 [00:08<01:06,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█████████                                                                    | 12/102 [00:09<01:06,  1.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█████████▊                                                                   | 13/102 [00:09<01:06,  1.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 1, FP: 298, FN: 0, TP: 1, DELAY:0.0, FP_DELAY:0.7733333110809326, COVER: 0.8236637369791666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|██████████▌                                                                  | 14/102 [00:10<01:07,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 1, FP: 298, FN: 0, TP: 1, DELAY:0.0, FP_DELAY:0.7733333110809326, COVER: 0.8236637369791666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 15%|███████████▎                                                                 | 15/102 [00:11<01:07,  1.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 1, FP: 297, FN: 0, TP: 2, DELAY:0.0, FP_DELAY:0.9566666483879089, COVER: 0.8252974446614584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|████████████                                                                 | 16/102 [00:12<01:05,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 3, FP: 292, FN: 0, TP: 5, DELAY:0.0, FP_DELAY:2.4166667461395264, COVER: 0.8292826334635417\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|████████████▊                                                                | 17/102 [00:12<01:03,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 7, FP: 287, FN: 0, TP: 6, DELAY:0.0, FP_DELAY:4.163333415985107, COVER: 0.8298490397135416\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|█████████████▌                                                               | 18/102 [00:13<01:04,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 11, FP: 278, FN: 0, TP: 11, DELAY:0.0033333334140479565, FP_DELAY:6.853333473205566, COVER: 0.8345673101233818\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 19%|██████████████▎                                                              | 19/102 [00:14<01:01,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 20, FP: 257, FN: 0, TP: 23, DELAY:0.0033333334140479565, FP_DELAY:12.876667022705078, COVER: 0.8460317469723402\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|███████████████                                                              | 20/102 [00:15<01:00,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 29, FP: 246, FN: 0, TP: 25, DELAY:0.0033333334140479565, FP_DELAY:17.0766658782959, COVER: 0.8477081792640069\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 21%|███████████████▊                                                             | 21/102 [00:15<00:57,  1.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 40, FP: 227, FN: 0, TP: 33, DELAY:0.0033333334140479565, FP_DELAY:23.486665725708008, COVER: 0.8553078700192153\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|████████████████▌                                                            | 22/102 [00:16<00:57,  1.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 59, FP: 201, FN: 0, TP: 40, DELAY:0.0033333334140479565, FP_DELAY:33.573333740234375, COVER: 0.863942310123382\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|█████████████████▎                                                           | 23/102 [00:17<00:55,  1.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 72, FP: 179, FN: 0, TP: 49, DELAY:0.006666666828095913, FP_DELAY:41.04999923706055, COVER: 0.8718528082431891\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██████████████████                                                           | 24/102 [00:17<00:56,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 82, FP: 151, FN: 0, TP: 67, DELAY:0.006666666828095913, FP_DELAY:48.46666717529297, COVER: 0.8901523687900641\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██████████████████▊                                                          | 25/102 [00:18<00:56,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 91, FP: 129, FN: 1, TP: 79, DELAY:0.07000000029802322, FP_DELAY:55.25666809082031, COVER: 0.9040235198076181\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|███████████████████▋                                                         | 26/102 [00:19<00:56,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 98, FP: 113, FN: 1, TP: 88, DELAY:0.07333333045244217, FP_DELAY:59.836666107177734, COVER: 0.9153364853781123\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|████████████████████▍                                                        | 27/102 [00:20<00:56,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 102, FP: 97, FN: 2, TP: 99, DELAY:0.14000000059604645, FP_DELAY:64.33333587646484, COVER: 0.9284230466188743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|█████████████████████▏                                                       | 28/102 [00:21<00:56,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 110, FP: 75, FN: 2, TP: 113, DELAY:0.14000000059604645, FP_DELAY:70.65666961669922, COVER: 0.946130973051166\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|█████████████████████▉                                                       | 29/102 [00:21<00:56,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 115, FP: 59, FN: 2, TP: 124, DELAY:0.14000000059604645, FP_DELAY:75.44000244140625, COVER: 0.9595998044313744\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 29%|██████████████████████▋                                                      | 30/102 [00:22<00:53,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 126, FP: 39, FN: 2, TP: 133, DELAY:0.15333333611488342, FP_DELAY:82.5, COVER: 0.96947569335365\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███████████████████████▍                                                     | 31/102 [00:23<00:52,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 132, FP: 25, FN: 2, TP: 141, DELAY:0.15333333611488342, FP_DELAY:87.0, COVER: 0.97963194335365\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 31%|████████████████████████▏                                                    | 32/102 [00:24<00:53,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 138, FP: 16, FN: 2, TP: 144, DELAY:0.15333333611488342, FP_DELAY:90.4566650390625, COVER: 0.9837705338484416\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32%|████████████████████████▉                                                    | 33/102 [00:24<00:51,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 139, FP: 9, FN: 3, TP: 149, DELAY:0.17666666209697723, FP_DELAY:91.90333557128906, COVER: 0.9885765776852473\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|█████████████████████████▋                                                   | 34/102 [00:25<00:48,  1.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 139, FP: 7, FN: 3, TP: 151, DELAY:0.18000000715255737, FP_DELAY:92.28666687011719, COVER: 0.9912966020938296\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 34%|██████████████████████████▍                                                  | 35/102 [00:26<00:47,  1.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 140, FP: 4, FN: 3, TP: 153, DELAY:0.18333333730697632, FP_DELAY:93.15333557128906, COVER: 0.994535951123706\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 35%|███████████████████████████▏                                                 | 36/102 [00:26<00:46,  1.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 140, FP: 3, FN: 3, TP: 154, DELAY:0.18666666746139526, FP_DELAY:93.25, COVER: 0.9956527845098264\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███████████████████████████▉                                                 | 37/102 [00:27<00:44,  1.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 141, FP: 2, FN: 3, TP: 154, DELAY:0.1899999976158142, FP_DELAY:93.67666625976562, COVER: 0.9956011145362815\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|████████████████████████████▋                                                | 38/102 [00:28<00:46,  1.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 1, FN: 3, TP: 154, DELAY:0.1966666728258133, FP_DELAY:94.10333251953125, COVER: 0.995503685059169\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|█████████████████████████████▍                                               | 39/102 [00:28<00:45,  1.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 1, FN: 4, TP: 153, DELAY:0.3266666531562805, FP_DELAY:94.26333618164062, COVER: 0.9943862342164723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 39%|██████████████████████████████▏                                              | 40/102 [00:29<00:46,  1.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 1, FN: 4, TP: 153, DELAY:0.3266666531562805, FP_DELAY:94.26333618164062, COVER: 0.9943862342164723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|██████████████████████████████▉                                              | 41/102 [00:30<00:46,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 1, FN: 4, TP: 153, DELAY:0.33000001311302185, FP_DELAY:94.26333618164062, COVER: 0.9943347427391995\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 41%|███████████████████████████████▋                                             | 42/102 [00:31<00:45,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 1, FN: 4, TP: 153, DELAY:0.34333333373069763, FP_DELAY:94.26333618164062, COVER: 0.9941421691280367\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████████████████████████████████▍                                            | 43/102 [00:32<00:45,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 4, TP: 154, DELAY:0.3633333444595337, FP_DELAY:94.44999694824219, COVER: 0.9954915097674245\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 43%|█████████████████████████████████▏                                           | 44/102 [00:32<00:43,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 4, TP: 154, DELAY:0.3700000047683716, FP_DELAY:94.44999694824219, COVER: 0.9953937082740625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|█████████████████████████████████▉                                           | 45/102 [00:33<00:42,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 4, TP: 154, DELAY:0.4233333468437195, FP_DELAY:94.44999694824219, COVER: 0.9946534135889415\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 45%|██████████████████████████████████▋                                          | 46/102 [00:34<00:41,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 4, TP: 154, DELAY:0.4300000071525574, FP_DELAY:94.44999694824219, COVER: 0.9945625447946155\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|███████████████████████████████████▍                                         | 47/102 [00:35<00:40,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 4, TP: 154, DELAY:0.4399999976158142, FP_DELAY:94.44999694824219, COVER: 0.9944107091840717\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|████████████████████████████████████▏                                        | 48/102 [00:35<00:38,  1.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 5, TP: 153, DELAY:0.6433333158493042, FP_DELAY:94.70333099365234, COVER: 0.9934964329091961\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 48%|████████████████████████████████████▉                                        | 49/102 [00:36<00:38,  1.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 5, TP: 153, DELAY:0.6866666674613953, FP_DELAY:94.70333099365234, COVER: 0.9928876163511607\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 49%|█████████████████████████████████████▋                                       | 50/102 [00:37<00:37,  1.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 7, TP: 151, DELAY:1.0133333206176758, FP_DELAY:94.90666961669922, COVER: 0.9895016385733273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|██████████████████████████████████████▌                                      | 51/102 [00:37<00:35,  1.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 156, TP: 2, DELAY:33.61333465576172, FP_DELAY:127.2699966430664, COVER: 0.8225516481179795\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 51%|███████████████████████████████████████▎                                     | 52/102 [00:38<00:36,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|████████████████████████████████████████                                     | 53/102 [00:39<00:35,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|████████████████████████████████████████▊                                    | 54/102 [00:40<00:35,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 54%|█████████████████████████████████████████▌                                   | 55/102 [00:40<00:35,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|██████████████████████████████████████████▎                                  | 56/102 [00:41<00:34,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 56%|███████████████████████████████████████████                                  | 57/102 [00:42<00:34,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 57%|███████████████████████████████████████████▊                                 | 58/102 [00:43<00:33,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|████████████████████████████████████████████▌                                | 59/102 [00:43<00:32,  1.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 59%|█████████████████████████████████████████████▎                               | 60/102 [00:44<00:32,  1.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████████████████████████████████████████████                               | 61/102 [00:45<00:30,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 61%|██████████████████████████████████████████████▊                              | 62/102 [00:46<00:29,  1.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|███████████████████████████████████████████████▌                             | 63/102 [00:46<00:27,  1.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 63%|████████████████████████████████████████████████▎                            | 64/102 [00:47<00:27,  1.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|█████████████████████████████████████████████████                            | 65/102 [00:48<00:25,  1.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 65%|█████████████████████████████████████████████████▊                           | 66/102 [00:48<00:26,  1.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|██████████████████████████████████████████████████▌                          | 67/102 [00:49<00:25,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|███████████████████████████████████████████████████▎                         | 68/102 [00:50<00:25,  1.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|████████████████████████████████████████████████████                         | 69/102 [00:51<00:25,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 69%|████████████████████████████████████████████████████▊                        | 70/102 [00:52<00:24,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|█████████████████████████████████████████████████████▌                       | 71/102 [00:52<00:23,  1.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 71%|██████████████████████████████████████████████████████▎                      | 72/102 [00:53<00:22,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|███████████████████████████████████████████████████████                      | 73/102 [00:54<00:21,  1.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|███████████████████████████████████████████████████████▊                     | 74/102 [00:55<00:21,  1.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 74%|████████████████████████████████████████████████████████▌                    | 75/102 [00:55<00:19,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|█████████████████████████████████████████████████████████▎                   | 76/102 [00:56<00:18,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|██████████████████████████████████████████████████████████▏                  | 77/102 [00:57<00:17,  1.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|██████████████████████████████████████████████████████████▉                  | 78/102 [00:57<00:16,  1.43it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████████████████████████████████████████████████████████▋                 | 79/102 [00:58<00:16,  1.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 78%|████████████████████████████████████████████████████████████▍                | 80/102 [00:59<00:16,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 79%|█████████████████████████████████████████████████████████████▏               | 81/102 [01:00<00:15,  1.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|█████████████████████████████████████████████████████████████▉               | 82/102 [01:00<00:14,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 81%|██████████████████████████████████████████████████████████████▋              | 83/102 [01:01<00:14,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|███████████████████████████████████████████████████████████████▍             | 84/102 [01:02<00:13,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████████████████████████████████████████████████████████████▏            | 85/102 [01:03<00:12,  1.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 84%|████████████████████████████████████████████████████████████████▉            | 86/102 [01:03<00:11,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 85%|█████████████████████████████████████████████████████████████████▋           | 87/102 [01:04<00:11,  1.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|██████████████████████████████████████████████████████████████████▍          | 88/102 [01:05<00:10,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|███████████████████████████████████████████████████████████████████▏         | 89/102 [01:06<00:09,  1.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|███████████████████████████████████████████████████████████████████▉         | 90/102 [01:06<00:08,  1.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 89%|████████████████████████████████████████████████████████████████████▋        | 91/102 [01:07<00:07,  1.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████████████████████████████████████████████████████████████████▍       | 92/102 [01:08<00:06,  1.44it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 91%|██████████████████████████████████████████████████████████████████████▏      | 93/102 [01:08<00:06,  1.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|██████████████████████████████████████████████████████████████████████▉      | 94/102 [01:09<00:05,  1.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|███████████████████████████████████████████████████████████████████████▋     | 95/102 [01:10<00:05,  1.39it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|████████████████████████████████████████████████████████████████████████▍    | 96/102 [01:11<00:04,  1.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████████████████████████████████████████████████████████████████████▏   | 97/102 [01:11<00:03,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████████████████████████████████████████████████████████████████████▉   | 98/102 [01:12<00:03,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|██████████████████████████████████████████████████████████████████████████▋  | 99/102 [01:13<00:02,  1.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|██████████████████████████████████████████████████████████████████████████▌ | 100/102 [01:14<00:01,  1.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|███████████████████████████████████████████████████████████████████████████▎| 101/102 [01:14<00:00,  1.34it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 102/102 [01:15<00:00,  1.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n",
      "AUC: 591.0524\n",
      "Time to FA 94.1033, delay detection 0.1967 for best-F1 threshold: 0.2036\n",
      "TN 142, FP 1, FN 3, TP 154 for best-F1 threshold: 0.2036\n",
      "Max F1 0.9872: for best-F1 threshold 0.2036\n",
      "COVER 0.9955: for best-F1 threshold 0.2036\n",
      "Max COVER 0.9957: for threshold 0.1728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# create list of thresholds from [0, 1]\n",
    "threshold_number = 100\n",
    "threshold_list = np.linspace(-5, 5, threshold_number)\n",
    "threshold_list = 1 / (1 + np.exp(-threshold_list))\n",
    "threshold_list = [-0.001] + list(threshold_list) + [1.001]\n",
    "\n",
    "metrics_local, delay_list, fp_delay_list = \\\n",
    "    metrics.evaluation_pipeline(indid_model,\n",
    "                                indid_model.val_dataloader(),\n",
    "                                threshold_list,\n",
    "                                device=\"cuda\", # choose 'cpu' or 'cuda' if available\n",
    "                                model_type=\"seq2seq\",\n",
    "                                verbose=True\n",
    "                               )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9395a582",
   "metadata": {},
   "source": [
    "## Draw detection curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9284ee04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "plt.plot(fp_delay_list.values(), delay_list.values(), '-o', markersize=8, label=\"InDiD\")\n",
    "plt.xlabel('Mean Time to False Alarm', fontsize=28)\n",
    "plt.ylabel('Mean Detection Delay', fontsize=28)\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=24)\n",
    "plt.legend(loc='upper left', fontsize=26)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90f3408",
   "metadata": {},
   "source": [
    "## The same experiment with 'BCE' loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6fe1fcfc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "Missing logger folder: logs/synthetic_100D/seq2seq_bce_seed_42\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name  | Type    | Params\n",
      "----------------------------------\n",
      "0 | model | BaseRnn | 3.5 K \n",
      "1 | loss  | BCELoss | 0     \n",
      "----------------------------------\n",
      "3.5 K     Trainable params\n",
      "0         Non-trainable params\n",
      "3.5 K     Total params\n",
      "0.014     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9a1fc6c3a944ed79170e03397f01308",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=30` reached.\n",
      "\n",
      "  0%|                                                                                      | 0/102 [00:00<?, ?it/s]\u001b[A\n",
      "  1%|▊                                                                             | 1/102 [00:01<02:21,  1.40s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  2%|█▌                                                                            | 2/102 [00:02<02:17,  1.38s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  3%|██▎                                                                           | 3/102 [00:04<02:16,  1.38s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  4%|███                                                                           | 4/102 [00:05<02:14,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  5%|███▊                                                                          | 5/102 [00:06<02:01,  1.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  6%|████▌                                                                         | 6/102 [00:07<02:02,  1.27s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  7%|█████▎                                                                        | 7/102 [00:09<02:01,  1.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  8%|██████                                                                        | 8/102 [00:10<02:02,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  9%|██████▉                                                                       | 9/102 [00:11<02:01,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 10%|███████▌                                                                     | 10/102 [00:13<02:00,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 11%|████████▎                                                                    | 11/102 [00:14<02:02,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 12%|█████████                                                                    | 12/102 [00:15<02:01,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 13%|█████████▊                                                                   | 13/102 [00:17<01:58,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 14%|██████████▌                                                                  | 14/102 [00:18<01:53,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 15%|███████████▎                                                                 | 15/102 [00:19<01:51,  1.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 16%|████████████                                                                 | 16/102 [00:20<01:49,  1.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 17%|████████████▊                                                                | 17/102 [00:22<01:50,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 18%|█████████████▌                                                               | 18/102 [00:23<01:51,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 19%|██████████████▎                                                              | 19/102 [00:25<01:50,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 20%|███████████████                                                              | 20/102 [00:26<01:50,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 21%|███████████████▊                                                             | 21/102 [00:27<01:50,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 22%|████████████████▌                                                            | 22/102 [00:29<01:49,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 23%|█████████████████▎                                                           | 23/102 [00:30<01:46,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 24%|██████████████████                                                           | 24/102 [00:31<01:40,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 25%|██████████████████▊                                                          | 25/102 [00:32<01:37,  1.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 25%|███████████████████▋                                                         | 26/102 [00:34<01:35,  1.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 26%|████████████████████▍                                                        | 27/102 [00:35<01:36,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 27%|█████████████████████▏                                                       | 28/102 [00:36<01:38,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 28%|█████████████████████▉                                                       | 29/102 [00:38<01:37,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 29%|██████████████████████▋                                                      | 30/102 [00:39<01:37,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 30%|███████████████████████▍                                                     | 31/102 [00:41<01:36,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 31%|████████████████████████▏                                                    | 32/102 [00:42<01:32,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 32%|████████████████████████▉                                                    | 33/102 [00:43<01:29,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 33%|█████████████████████████▋                                                   | 34/102 [00:44<01:26,  1.27s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 34%|██████████████████████████▍                                                  | 35/102 [00:45<01:24,  1.27s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 1, FP: 298, FN: 0, TP: 1, DELAY:0.0, FP_DELAY:0.5199999809265137, COVER: 0.823731689453125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 35%|███████████████████████████▏                                                 | 36/102 [00:47<01:26,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 2, FP: 297, FN: 0, TP: 1, DELAY:0.0, FP_DELAY:0.9466666579246521, COVER: 0.823731689453125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 36%|███████████████████████████▉                                                 | 37/102 [00:48<01:25,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 4, FP: 294, FN: 0, TP: 2, DELAY:0.0, FP_DELAY:1.9366666078567505, COVER: 0.8252254263810286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 37%|████████████████████████████▋                                                | 38/102 [00:50<01:24,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 8, FP: 287, FN: 0, TP: 5, DELAY:0.0033333334140479565, FP_DELAY:4.246666431427002, COVER: 0.8300598241158169\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 38%|█████████████████████████████▍                                               | 39/102 [00:51<01:25,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 12, FP: 280, FN: 0, TP: 8, DELAY:0.0033333334140479565, FP_DELAY:6.4066667556762695, COVER: 0.8325961087953199\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 39%|██████████████████████████████▏                                              | 40/102 [00:52<01:23,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 16, FP: 268, FN: 0, TP: 16, DELAY:0.006666666828095913, FP_DELAY:9.513333320617676, COVER: 0.8398203461465104\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 40%|██████████████████████████████▉                                              | 41/102 [00:53<01:18,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 31, FP: 248, FN: 0, TP: 21, DELAY:0.006666666828095913, FP_DELAY:17.3266658782959, COVER: 0.8466343109902603\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 41%|███████████████████████████████▋                                             | 42/102 [00:55<01:17,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 46, FP: 218, FN: 2, TP: 34, DELAY:0.12666666507720947, FP_DELAY:27.633333206176758, COVER: 0.863131138890324\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 42%|████████████████████████████████▍                                            | 43/102 [00:56<01:12,  1.23s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 54, FP: 196, FN: 2, TP: 48, DELAY:0.1599999964237213, FP_DELAY:34.23666763305664, COVER: 0.8810468386123269\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 43%|█████████████████████████████████▏                                           | 44/102 [00:57<01:10,  1.21s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 65, FP: 162, FN: 3, TP: 70, DELAY:0.27000001072883606, FP_DELAY:43.776668548583984, COVER: 0.9043586192673225\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 44%|█████████████████████████████████▉                                           | 45/102 [00:58<01:06,  1.16s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 81, FP: 125, FN: 3, TP: 91, DELAY:0.4033333361148834, FP_DELAY:55.4566650390625, COVER: 0.9245072696975486\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 45%|██████████████████████████████████▋                                          | 46/102 [00:59<01:09,  1.24s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 93, FP: 95, FN: 3, TP: 109, DELAY:0.5333333611488342, FP_DELAY:63.94333267211914, COVER: 0.9455140097341587\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 46%|███████████████████████████████████▍                                         | 47/102 [01:01<01:10,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 111, FP: 64, FN: 3, TP: 122, DELAY:0.6366666555404663, FP_DELAY:74.2699966430664, COVER: 0.9576038761406691\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 47%|████████████████████████████████████▏                                        | 48/102 [01:02<01:10,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 124, FP: 41, FN: 3, TP: 132, DELAY:0.746666669845581, FP_DELAY:82.12000274658203, COVER: 0.9660016588233012\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 48%|████████████████████████████████████▉                                        | 49/102 [01:04<01:09,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 132, FP: 25, FN: 4, TP: 139, DELAY:0.8633333444595337, FP_DELAY:87.14666748046875, COVER: 0.9709671995484662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 49%|█████████████████████████████████████▋                                       | 50/102 [01:05<01:10,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 135, FP: 16, FN: 4, TP: 145, DELAY:0.9766666889190674, FP_DELAY:89.58333587646484, COVER: 0.9762903760495564\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 50%|██████████████████████████████████████▌                                      | 51/102 [01:06<01:09,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 140, FP: 5, FN: 4, TP: 151, DELAY:1.0866667032241821, FP_DELAY:92.87999725341797, COVER: 0.9795725452602194\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 51%|███████████████████████████████████████▎                                     | 52/102 [01:08<01:06,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 4, TP: 154, DELAY:1.2599999904632568, FP_DELAY:94.29666900634766, COVER: 0.9809753600759372\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 52%|████████████████████████████████████████                                     | 53/102 [01:09<01:03,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 4, TP: 154, DELAY:1.3600000143051147, FP_DELAY:94.29666900634766, COVER: 0.9794879949243489\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 53%|████████████████████████████████████████▊                                    | 54/102 [01:10<00:56,  1.17s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 4, TP: 154, DELAY:1.4733333587646484, FP_DELAY:94.29666900634766, COVER: 0.9778304372839047\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 54%|█████████████████████████████████████████▌                                   | 55/102 [01:11<00:56,  1.21s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 5, TP: 153, DELAY:1.5733333826065063, FP_DELAY:94.30333709716797, COVER: 0.9763582487458905\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 55%|██████████████████████████████████████████▎                                  | 56/102 [01:12<00:57,  1.25s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 5, TP: 153, DELAY:1.6466666460037231, FP_DELAY:94.30333709716797, COVER: 0.975272700999797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 56%|███████████████████████████████████████████                                  | 57/102 [01:14<00:57,  1.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 5, TP: 153, DELAY:1.7833333015441895, FP_DELAY:94.30333709716797, COVER: 0.9732903709374199\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 57%|███████████████████████████████████████████▊                                 | 58/102 [01:15<00:57,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 5, TP: 153, DELAY:1.9733333587646484, FP_DELAY:94.30333709716797, COVER: 0.9705634538153682\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 58%|████████████████████████████████████████████▌                                | 59/102 [01:17<00:57,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 6, TP: 152, DELAY:2.4033334255218506, FP_DELAY:94.46333312988281, COVER: 0.965140372405081\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 59%|█████████████████████████████████████████████▎                               | 60/102 [01:18<00:55,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 60%|██████████████████████████████████████████████                               | 61/102 [01:19<00:54,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 61%|██████████████████████████████████████████████▊                              | 62/102 [01:21<00:54,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 62%|███████████████████████████████████████████████▌                             | 63/102 [01:22<00:50,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 63%|████████████████████████████████████████████████▎                            | 64/102 [01:23<00:48,  1.27s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 64%|█████████████████████████████████████████████████                            | 65/102 [01:24<00:48,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 65%|█████████████████████████████████████████████████▊                           | 66/102 [01:26<00:47,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 66%|██████████████████████████████████████████████████▌                          | 67/102 [01:27<00:45,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 67%|███████████████████████████████████████████████████▎                         | 68/102 [01:28<00:44,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 68%|████████████████████████████████████████████████████                         | 69/102 [01:30<00:44,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 69%|████████████████████████████████████████████████████▊                        | 70/102 [01:31<00:43,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 70%|█████████████████████████████████████████████████████▌                       | 71/102 [01:32<00:41,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 71%|██████████████████████████████████████████████████████▎                      | 72/102 [01:34<00:39,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 72%|███████████████████████████████████████████████████████                      | 73/102 [01:35<00:36,  1.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 73%|███████████████████████████████████████████████████████▊                     | 74/102 [01:36<00:35,  1.27s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 74%|████████████████████████████████████████████████████████▌                    | 75/102 [01:38<00:35,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 75%|█████████████████████████████████████████████████████████▎                   | 76/102 [01:39<00:34,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 75%|██████████████████████████████████████████████████████████▏                  | 77/102 [01:40<00:33,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 76%|██████████████████████████████████████████████████████████▉                  | 78/102 [01:42<00:31,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 77%|███████████████████████████████████████████████████████████▋                 | 79/102 [01:43<00:30,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 78%|████████████████████████████████████████████████████████████▍                | 80/102 [01:44<00:29,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 79%|█████████████████████████████████████████████████████████████▏               | 81/102 [01:45<00:26,  1.27s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 80%|█████████████████████████████████████████████████████████████▉               | 82/102 [01:47<00:25,  1.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 81%|██████████████████████████████████████████████████████████████▋              | 83/102 [01:48<00:24,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 82%|███████████████████████████████████████████████████████████████▍             | 84/102 [01:49<00:24,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 83%|████████████████████████████████████████████████████████████████▏            | 85/102 [01:51<00:22,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 84%|████████████████████████████████████████████████████████████████▉            | 86/102 [01:52<00:21,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 85%|█████████████████████████████████████████████████████████████████▋           | 87/102 [01:54<00:20,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 86%|██████████████████████████████████████████████████████████████████▍          | 88/102 [01:55<00:19,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 87%|███████████████████████████████████████████████████████████████████▏         | 89/102 [01:56<00:17,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 88%|███████████████████████████████████████████████████████████████████▉         | 90/102 [01:58<00:16,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 89%|████████████████████████████████████████████████████████████████████▋        | 91/102 [01:59<00:14,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 90%|█████████████████████████████████████████████████████████████████████▍       | 92/102 [02:00<00:13,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 91%|██████████████████████████████████████████████████████████████████████▏      | 93/102 [02:02<00:12,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 92%|██████████████████████████████████████████████████████████████████████▉      | 94/102 [02:03<00:10,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 93%|███████████████████████████████████████████████████████████████████████▋     | 95/102 [02:04<00:09,  1.38s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 94%|████████████████████████████████████████████████████████████████████████▍    | 96/102 [02:06<00:07,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 95%|█████████████████████████████████████████████████████████████████████████▏   | 97/102 [02:07<00:06,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 96%|█████████████████████████████████████████████████████████████████████████▉   | 98/102 [02:08<00:05,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 97%|██████████████████████████████████████████████████████████████████████████▋  | 99/102 [02:10<00:03,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 98%|██████████████████████████████████████████████████████████████████████████▌ | 100/102 [02:11<00:02,  1.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 99%|███████████████████████████████████████████████████████████████████████████▎| 101/102 [02:12<00:01,  1.24s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 102/102 [02:13<00:00,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n",
      "AUC: 642.2576\n",
      "Time to FA 94.2967, delay detection 1.26 for best-F1 threshold: 0.5126\n",
      "TN 142, FP 0, FN 4, TP 154 for best-F1 threshold: 0.5126\n",
      "Max F1 0.9872: for best-F1 threshold 0.5126\n",
      "COVER 0.981: for best-F1 threshold 0.5126\n",
      "Max COVER 0.981: for threshold 0.5126\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define core model for an experiment with 'bce' loss\n",
    "core_model_bce = core_models.BaseRnn(\n",
    "    input_size=100,\n",
    "    hidden_dim=8,\n",
    "    n_layers=1,\n",
    "    drop_prob=0.5\n",
    ")\n",
    "\n",
    "# define CPDModel with 'bce' loss\n",
    "bce_model = cpd_models.CPDModel(\n",
    "    loss_type=\"bce\",\n",
    "    args=args,\n",
    "    model=core_model_bce,\n",
    "    train_dataset=train_dataset,\n",
    "    test_dataset=test_dataset\n",
    ")\n",
    "\n",
    "# use logger\n",
    "model_name = f'seq2seq_bce_seed_{SEED}'\n",
    "logger = TensorBoardLogger(save_dir=f'logs/{experiments_name}', name=model_name)\n",
    "\n",
    "# define trainer with custom parameters\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=30,\n",
    "    gpus=1,\n",
    "    benchmark=True,\n",
    "    check_val_every_n_epoch=1,\n",
    "    gradient_clip_val=0.,\n",
    "    logger=logger,\n",
    "    \n",
    "    # use early stopping\n",
    "    callbacks=EarlyStopping(monitor=\"val_loss\", min_delta=0, patience=10)\n",
    ")\n",
    "\n",
    "trainer.fit(bce_model)\n",
    "\n",
    "threshold_number = 100\n",
    "threshold_list = np.linspace(-5, 5, threshold_number)\n",
    "threshold_list = 1 / (1 + np.exp(-threshold_list))\n",
    "threshold_list = [-0.001] + list(threshold_list) + [1.001]\n",
    "\n",
    "# evaluate model\n",
    "metrics_local, delay_list, fp_delay_list = \\\n",
    "    metrics.evaluation_pipeline(bce_model,\n",
    "                                bce_model.val_dataloader(),\n",
    "                                threshold_list,\n",
    "                                device=\"cpu\", # choose 'cpu' or 'cuda' if available\n",
    "                                model_type=\"seq2seq\",\n",
    "                                verbose=True\n",
    "                               )\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.plot(fp_delay_list.values(), delay_list.values(), '-o', markersize=8, label=\"BCE\")\n",
    "plt.xlabel('Mean Time to False Alarm', fontsize=28)\n",
    "plt.ylabel('Mean Detection Delay', fontsize=28)\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=24)\n",
    "plt.legend(loc='upper left', fontsize=26)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7df4ec0",
   "metadata": {},
   "source": [
    "## The same experiment with 'Combined' loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2c3e778b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name  | Type    | Params\n",
      "----------------------------------\n",
      "0 | model | BaseRnn | 3.5 K \n",
      "1 | loss  | BCELoss | 0     \n",
      "----------------------------------\n",
      "3.5 K     Trainable params\n",
      "0         Non-trainable params\n",
      "3.5 K     Total params\n",
      "0.014     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "865c7e7328924df58bbf60e4481c67ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=15` reached.\n",
      "/home/eromanenkova/stash/eromanenkova/miniconda3/lib/python3.9/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:604: UserWarning: Checkpoint directory logs/synthetic_100D/seq2seq_combined_seed_42/version_1/checkpoints exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name  | Type    | Params\n",
      "----------------------------------\n",
      "0 | model | BaseRnn | 3.5 K \n",
      "1 | loss  | CPDLoss | 0     \n",
      "----------------------------------\n",
      "3.5 K     Trainable params\n",
      "0         Non-trainable params\n",
      "3.5 K     Total params\n",
      "0.014     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "029f3596612946e5bde0df2433af2b21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=15` reached.\n",
      "\n",
      "  0%|                                                                                      | 0/102 [00:00<?, ?it/s]\u001b[A\n",
      "  1%|▊                                                                             | 1/102 [00:01<02:01,  1.20s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  2%|█▌                                                                            | 2/102 [00:02<02:00,  1.21s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  3%|██▎                                                                           | 3/102 [00:03<02:10,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  4%|███                                                                           | 4/102 [00:05<02:10,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  5%|███▊                                                                          | 5/102 [00:06<02:10,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  6%|████▌                                                                         | 6/102 [00:08<02:13,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  7%|█████▎                                                                        | 7/102 [00:09<02:12,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  8%|██████                                                                        | 8/102 [00:10<02:07,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  9%|██████▉                                                                       | 9/102 [00:12<02:04,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 10%|███████▌                                                                     | 10/102 [00:13<01:58,  1.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 11%|████████▎                                                                    | 11/102 [00:14<01:51,  1.22s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 12%|█████████                                                                    | 12/102 [00:15<01:52,  1.25s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 13%|█████████▊                                                                   | 13/102 [00:17<01:56,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 14%|██████████▌                                                                  | 14/102 [00:18<01:56,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 15%|███████████▎                                                                 | 15/102 [00:19<01:55,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 16%|████████████                                                                 | 16/102 [00:21<01:55,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 17%|████████████▊                                                                | 17/102 [00:22<01:57,  1.38s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 18%|█████████████▌                                                               | 18/102 [00:23<01:51,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 19%|██████████████▎                                                              | 19/102 [00:25<01:52,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 20%|███████████████                                                              | 20/102 [00:26<01:46,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 299, FN: 0, TP: 1, DELAY:0.0, FP_DELAY:0.1899999976158142, COVER: 0.8242948404947916\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 21%|███████████████▊                                                             | 21/102 [00:27<01:44,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 298, FN: 0, TP: 2, DELAY:0.0, FP_DELAY:0.40666666626930237, COVER: 0.8259611002604167\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 22%|████████████████▌                                                            | 22/102 [00:29<01:45,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 2, FP: 294, FN: 0, TP: 4, DELAY:0.0, FP_DELAY:1.7699999809265137, COVER: 0.8274076334635416\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 23%|█████████████████▎                                                           | 23/102 [00:30<01:46,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 4, FP: 285, FN: 1, TP: 10, DELAY:0.019999999552965164, FP_DELAY:4.236666679382324, COVER: 0.8351900227864584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 24%|██████████████████                                                           | 24/102 [00:31<01:45,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 14, FP: 270, FN: 1, TP: 15, DELAY:0.019999999552965164, FP_DELAY:10.053333282470703, COVER: 0.8387215169270833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 25%|██████████████████▊                                                          | 25/102 [00:33<01:47,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 22, FP: 253, FN: 1, TP: 24, DELAY:0.023333333432674408, FP_DELAY:15.383333206176758, COVER: 0.8466480848524305\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 25%|███████████████████▋                                                         | 26/102 [00:34<01:47,  1.42s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 32, FP: 233, FN: 1, TP: 34, DELAY:0.023333333432674408, FP_DELAY:21.549999237060547, COVER: 0.8592327202690971\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 26%|████████████████████▍                                                        | 27/102 [00:35<01:41,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 48, FP: 205, FN: 2, TP: 45, DELAY:0.07999999821186066, FP_DELAY:30.866666793823242, COVER: 0.8747010633680554\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 27%|█████████████████████▏                                                       | 28/102 [00:37<01:37,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 58, FP: 183, FN: 2, TP: 57, DELAY:0.1366666704416275, FP_DELAY:37.46666717529297, COVER: 0.8903556371035583\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 28%|█████████████████████▉                                                       | 29/102 [00:38<01:35,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 70, FP: 147, FN: 3, TP: 80, DELAY:0.23999999463558197, FP_DELAY:47.34333419799805, COVER: 0.9156414116753471\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 29%|██████████████████████▋                                                      | 30/102 [00:39<01:35,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 85, FP: 116, FN: 3, TP: 96, DELAY:0.25, FP_DELAY:57.51333236694336, COVER: 0.9343600461495968\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 30%|███████████████████████▍                                                     | 31/102 [00:41<01:35,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 100, FP: 80, FN: 3, TP: 117, DELAY:0.34333333373069763, FP_DELAY:68.73999786376953, COVER: 0.9527351848194496\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 31%|████████████████████████▏                                                    | 32/102 [00:42<01:35,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 114, FP: 55, FN: 3, TP: 128, DELAY:0.36666667461395264, FP_DELAY:77.18000030517578, COVER: 0.9664369086276137\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 32%|████████████████████████▉                                                    | 33/102 [00:44<01:34,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 122, FP: 39, FN: 3, TP: 136, DELAY:0.43666666746139526, FP_DELAY:81.7933349609375, COVER: 0.9749665940808259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 33%|█████████████████████████▋                                                   | 34/102 [00:45<01:34,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 130, FP: 24, FN: 4, TP: 142, DELAY:0.5366666913032532, FP_DELAY:86.7300033569336, COVER: 0.9790553528128726\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 34%|██████████████████████████▍                                                  | 35/102 [00:46<01:31,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 136, FP: 14, FN: 5, TP: 145, DELAY:0.5699999928474426, FP_DELAY:90.68000030517578, COVER: 0.9827756490368309\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 35%|███████████████████████████▏                                                 | 36/102 [00:48<01:28,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 140, FP: 5, FN: 5, TP: 150, DELAY:0.6133333444595337, FP_DELAY:93.1866683959961, COVER: 0.9892870216343829\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 36%|███████████████████████████▉                                                 | 37/102 [00:49<01:25,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 141, FP: 4, FN: 5, TP: 150, DELAY:0.6299999952316284, FP_DELAY:93.61333465576172, COVER: 0.9890398454791756\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 37%|████████████████████████████▋                                                | 38/102 [00:50<01:24,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 141, FP: 1, FN: 7, TP: 151, DELAY:0.7933333516120911, FP_DELAY:94.4800033569336, COVER: 0.9918692215891785\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 38%|█████████████████████████████▍                                               | 39/102 [00:52<01:23,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 141, FP: 1, FN: 7, TP: 151, DELAY:0.8166666626930237, FP_DELAY:94.4800033569336, COVER: 0.9915103191005975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 39%|██████████████████████████████▏                                              | 40/102 [00:53<01:24,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 141, FP: 1, FN: 7, TP: 151, DELAY:0.8866666555404663, FP_DELAY:94.4800033569336, COVER: 0.990600354460743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 40%|██████████████████████████████▉                                              | 41/102 [00:54<01:23,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 7, TP: 151, DELAY:0.9933333396911621, FP_DELAY:94.90666961669922, COVER: 0.9891064608118991\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 41%|███████████████████████████████▋                                             | 42/102 [00:56<01:23,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 9, TP: 149, DELAY:1.1033333539962769, FP_DELAY:94.94000244140625, COVER: 0.9879241673523541\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 42%|████████████████████████████████▍                                            | 43/102 [00:57<01:23,  1.42s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 13, TP: 145, DELAY:1.9833333492279053, FP_DELAY:95.90666961669922, COVER: 0.9835885287002759\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 43%|█████████████████████████████████▏                                           | 44/102 [00:59<01:20,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 14, TP: 144, DELAY:2.8333332538604736, FP_DELAY:96.19000244140625, COVER: 0.9778870751441002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 44%|█████████████████████████████████▉                                           | 45/102 [01:00<01:17,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 22, TP: 136, DELAY:4.276666641235352, FP_DELAY:98.163330078125, COVER: 0.9720220826565281\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 45%|██████████████████████████████████▋                                          | 46/102 [01:01<01:14,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 46%|███████████████████████████████████▍                                         | 47/102 [01:03<01:14,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 47%|████████████████████████████████████▏                                        | 48/102 [01:04<01:11,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 48%|████████████████████████████████████▉                                        | 49/102 [01:05<01:11,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 49%|█████████████████████████████████████▋                                       | 50/102 [01:07<01:10,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 50%|██████████████████████████████████████▌                                      | 51/102 [01:08<01:11,  1.40s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 51%|███████████████████████████████████████▎                                     | 52/102 [01:09<01:09,  1.40s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 52%|████████████████████████████████████████                                     | 53/102 [01:11<01:07,  1.38s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 53%|████████████████████████████████████████▊                                    | 54/102 [01:12<01:03,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 54%|█████████████████████████████████████████▌                                   | 55/102 [01:13<01:01,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 55%|██████████████████████████████████████████▎                                  | 56/102 [01:15<01:00,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 56%|███████████████████████████████████████████                                  | 57/102 [01:16<00:58,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 57%|███████████████████████████████████████████▊                                 | 58/102 [01:17<00:58,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 58%|████████████████████████████████████████████▌                                | 59/102 [01:19<00:57,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 59%|█████████████████████████████████████████████▎                               | 60/102 [01:20<00:57,  1.36s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 60%|██████████████████████████████████████████████                               | 61/102 [01:21<00:55,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 61%|██████████████████████████████████████████████▊                              | 62/102 [01:23<00:54,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 62%|███████████████████████████████████████████████▌                             | 63/102 [01:24<00:53,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 63%|████████████████████████████████████████████████▎                            | 64/102 [01:25<00:50,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 64%|█████████████████████████████████████████████████                            | 65/102 [01:27<00:49,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 65%|█████████████████████████████████████████████████▊                           | 66/102 [01:28<00:47,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 66%|██████████████████████████████████████████████████▌                          | 67/102 [01:29<00:46,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 67%|███████████████████████████████████████████████████▎                         | 68/102 [01:31<00:46,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 68%|████████████████████████████████████████████████████                         | 69/102 [01:32<00:45,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 69%|████████████████████████████████████████████████████▊                        | 70/102 [01:34<00:44,  1.40s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 70%|█████████████████████████████████████████████████████▌                       | 71/102 [01:35<00:43,  1.39s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 71%|██████████████████████████████████████████████████████▎                      | 72/102 [01:36<00:41,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 72%|███████████████████████████████████████████████████████                      | 73/102 [01:38<00:39,  1.38s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 73%|███████████████████████████████████████████████████████▊                     | 74/102 [01:39<00:37,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 74%|████████████████████████████████████████████████████████▌                    | 75/102 [01:40<00:34,  1.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 75%|█████████████████████████████████████████████████████████▎                   | 76/102 [01:41<00:33,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 75%|██████████████████████████████████████████████████████████▏                  | 77/102 [01:43<00:32,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 76%|██████████████████████████████████████████████████████████▉                  | 78/102 [01:44<00:31,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 77%|███████████████████████████████████████████████████████████▋                 | 79/102 [01:46<00:30,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 78%|████████████████████████████████████████████████████████████▍                | 80/102 [01:47<00:29,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 79%|█████████████████████████████████████████████████████████████▏               | 81/102 [01:48<00:28,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 80%|█████████████████████████████████████████████████████████████▉               | 82/102 [01:49<00:25,  1.27s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 81%|██████████████████████████████████████████████████████████████▋              | 83/102 [01:51<00:24,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 82%|███████████████████████████████████████████████████████████████▍             | 84/102 [01:52<00:22,  1.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 83%|████████████████████████████████████████████████████████████████▏            | 85/102 [01:53<00:22,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 84%|████████████████████████████████████████████████████████████████▉            | 86/102 [01:55<00:21,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 85%|█████████████████████████████████████████████████████████████████▋           | 87/102 [01:56<00:19,  1.32s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 86%|██████████████████████████████████████████████████████████████████▍          | 88/102 [01:57<00:19,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 87%|███████████████████████████████████████████████████████████████████▏         | 89/102 [01:59<00:18,  1.41s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 88%|███████████████████████████████████████████████████████████████████▉         | 90/102 [02:00<00:16,  1.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 89%|████████████████████████████████████████████████████████████████████▋        | 91/102 [02:01<00:14,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 90%|█████████████████████████████████████████████████████████████████████▍       | 92/102 [02:03<00:13,  1.30s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 91%|██████████████████████████████████████████████████████████████████████▏      | 93/102 [02:04<00:11,  1.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 92%|██████████████████████████████████████████████████████████████████████▉      | 94/102 [02:05<00:10,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 93%|███████████████████████████████████████████████████████████████████████▋     | 95/102 [02:07<00:09,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 94%|████████████████████████████████████████████████████████████████████████▍    | 96/102 [02:08<00:07,  1.33s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 95%|█████████████████████████████████████████████████████████████████████████▏   | 97/102 [02:09<00:06,  1.35s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 96%|█████████████████████████████████████████████████████████████████████████▉   | 98/102 [02:11<00:05,  1.38s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 97%|██████████████████████████████████████████████████████████████████████████▋  | 99/102 [02:12<00:03,  1.31s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 98%|██████████████████████████████████████████████████████████████████████████▌ | 100/102 [02:13<00:02,  1.29s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 99%|███████████████████████████████████████████████████████████████████████████▎| 101/102 [02:15<00:01,  1.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 102/102 [02:16<00:00,  1.34s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n",
      "AUC: 599.0459\n",
      "Time to FA 94.9067, delay detection 0.9933 for best-F1 threshold: 0.2572\n",
      "TN 142, FP 0, FN 7, TP 151 for best-F1 threshold: 0.2572\n",
      "Max F1 0.9773: for best-F1 threshold 0.2572\n",
      "COVER 0.9891: for best-F1 threshold 0.2572\n",
      "Max COVER 0.9919: for threshold 0.2036\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define core model for an experiment with 'combined' loss\n",
    "core_model_combined = core_models.BaseRnn(\n",
    "    input_size=100,\n",
    "    hidden_dim=8,\n",
    "    n_layers=1,\n",
    "    drop_prob=0.5\n",
    ")\n",
    "\n",
    "# define 2 CPDModels for this experiment\n",
    "# note that they share the one and the same core model \n",
    "combined_model_1 = cpd_models.CPDModel(\n",
    "    loss_type=\"bce\",\n",
    "    args=args,\n",
    "    model=core_model_combined,\n",
    "    train_dataset=train_dataset,\n",
    "    test_dataset=test_dataset\n",
    ")\n",
    "\n",
    "combined_model_2 = cpd_models.CPDModel(\n",
    "    loss_type=\"indid\",\n",
    "    args=args,\n",
    "    model=core_model_combined,\n",
    "    train_dataset=train_dataset,\n",
    "    test_dataset=test_dataset\n",
    ")\n",
    "\n",
    "# use logger\n",
    "model_name = f'seq2seq_combined_seed_{SEED}'\n",
    "logger = TensorBoardLogger(save_dir=f'logs/{experiments_name}', name=model_name)\n",
    "\n",
    "# define trainer with custom parameters\n",
    "trainer_1 = pl.Trainer(\n",
    "    max_epochs=15,\n",
    "    gpus=1,\n",
    "    benchmark=True,\n",
    "    check_val_every_n_epoch=1,\n",
    "    gradient_clip_val=0.,\n",
    "    logger=logger,\n",
    "    \n",
    "    # use early stopping\n",
    "    callbacks=EarlyStopping(monitor=\"val_loss\", min_delta=0, patience=10)\n",
    ")\n",
    "\n",
    "trainer_2 = pl.Trainer(\n",
    "    max_epochs=15,\n",
    "    gpus=1,\n",
    "    benchmark=True,\n",
    "    check_val_every_n_epoch=1,\n",
    "    gradient_clip_val=0.,\n",
    "    logger=logger,\n",
    "    \n",
    "    # use early stopping\n",
    "    callbacks=EarlyStopping(monitor=\"val_loss\", min_delta=0, patience=10)\n",
    ")\n",
    "\n",
    "trainer_1.fit(combined_model_1)\n",
    "trainer_2.fit(combined_model_2)\n",
    "\n",
    "threshold_number = 100\n",
    "threshold_list = np.linspace(-5, 5, threshold_number)\n",
    "threshold_list = 1 / (1 + np.exp(-threshold_list))\n",
    "threshold_list = [-0.001] + list(threshold_list) + [1.001]\n",
    "\n",
    "# evaluate model\n",
    "metrics_local, delay_list, fp_delay_list = \\\n",
    "    metrics.evaluation_pipeline(combined_model_2, # use the second model for evaluation\n",
    "                                combined_model_2.val_dataloader(),\n",
    "                                threshold_list,\n",
    "                                device=\"cpu\", # choose 'cpu' or 'cuda' if available\n",
    "                                model_type=\"seq2seq\",\n",
    "                                verbose=True\n",
    "                               )\n",
    "# plot detection curve\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.plot(fp_delay_list.values(), delay_list.values(), '-o', markersize=8, label=\"Combined\")\n",
    "plt.xlabel('Mean Time to False Alarm', fontsize=28)\n",
    "plt.ylabel('Mean Detection Delay', fontsize=28)\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=24)\n",
    "plt.legend(loc='upper left', fontsize=26)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d80a761",
   "metadata": {},
   "source": [
    "## Experiments with KL-CPD baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "51c1c167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary with default arguments for consistency with our interface\n",
    "# define arguments as they are set in the corresponding config file\n",
    "model = dict()\n",
    "model[\"input_dim\"] = 100\n",
    "model[\"rnn_hid_dim\"] = 16\n",
    "model[\"num_layers\"] = 1\n",
    "model[\"wnd_dim\"] = 8\n",
    "model[\"window_1\"] = 8\n",
    "model[\"window_2\"] = 8\n",
    "\n",
    "\n",
    "learning = dict()\n",
    "learning[\"batch_size\"] = 64\n",
    "learning[\"lr\"] = 0.001\n",
    "learning[\"grad_clip\"] = 0.0\n",
    "learning[\"critic_iters\"] = 5\n",
    "learning[\"weight_decay\"] = 0.\n",
    "learning[\"grad_clip\"] = 10.\n",
    "learning[\"weight_clip\"] = 0.1\n",
    "\n",
    "loss = dict()\n",
    "loss[\"lambda_real\"] = 10\n",
    "loss[\"lambda_ae\"] = 0.1\n",
    "loss[\"sqdist\"] = 3\n",
    "\n",
    "args = dict()\n",
    "args[\"model\"] = model\n",
    "args[\"learning\"] = learning\n",
    "args[\"loss\"] = loss\n",
    "\n",
    "args[\"experiments_name\"] = experiments_name\n",
    "args[\"num_workers\"] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4d4044a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name              | Type | Params\n",
      "-------------------------------------------\n",
      "0 | net_generator     | NetG | 13.0 K\n",
      "1 | net_discriminator | NetD | 41.1 K\n",
      "-------------------------------------------\n",
      "54.1 K    Trainable params\n",
      "0         Non-trainable params\n",
      "54.1 K    Total params\n",
      "0.216     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b649eae7cab4858984d7c9ca3f013c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|                                                                                       | 0/27 [00:00<?, ?it/s]\u001b[A\n",
      "  4%|██▉                                                                            | 1/27 [00:21<09:09, 21.13s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  7%|█████▊                                                                         | 2/27 [00:43<09:01, 21.66s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 0, TP: 18, DELAY:0.3766666650772095, FP_DELAY:14.623332977294922, COVER: 0.76841348155574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 11%|████████▊                                                                      | 3/27 [01:05<08:45, 21.90s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 0, TP: 18, DELAY:0.3766666650772095, FP_DELAY:14.623332977294922, COVER: 0.76841348155574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 15%|███████████▋                                                                   | 4/27 [01:26<08:17, 21.63s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 0, TP: 18, DELAY:0.3766666650772095, FP_DELAY:14.623332977294922, COVER: 0.76841348155574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 19%|██████████████▋                                                                | 5/27 [01:48<07:59, 21.78s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 0, TP: 18, DELAY:0.3766666650772095, FP_DELAY:14.623332977294922, COVER: 0.76841348155574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 22%|█████████████████▌                                                             | 6/27 [02:10<07:39, 21.87s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 0, TP: 18, DELAY:0.3766666650772095, FP_DELAY:14.623332977294922, COVER: 0.76841348155574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 26%|████████████████████▍                                                          | 7/27 [02:32<07:18, 21.93s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 0, TP: 18, DELAY:0.3766666650772095, FP_DELAY:14.623332977294922, COVER: 0.76841348155574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 30%|███████████████████████▍                                                       | 8/27 [02:54<06:57, 21.97s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 0, TP: 18, DELAY:0.3766666650772095, FP_DELAY:14.630000114440918, COVER: 0.7685102242074466\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 33%|██████████████████████████▎                                                    | 9/27 [03:16<06:34, 21.89s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 0, TP: 18, DELAY:0.3766666650772095, FP_DELAY:14.636666297912598, COVER: 0.76846420889521\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 37%|████████████████████████████▉                                                 | 10/27 [03:38<06:12, 21.89s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 282, FN: 1, TP: 17, DELAY:0.7566666603088379, FP_DELAY:15.08666706085205, COVER: 0.7686541655007556\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 41%|███████████████████████████████▊                                              | 11/27 [03:59<05:48, 21.79s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 281, FN: 1, TP: 18, DELAY:0.7566666603088379, FP_DELAY:15.146666526794434, COVER: 0.7686080579439911\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 44%|██████████████████████████████████▋                                           | 12/27 [04:21<05:25, 21.72s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 281, FN: 1, TP: 18, DELAY:0.7633333206176758, FP_DELAY:15.266666412353516, COVER: 0.7681834127840258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 48%|█████████████████████████████████████▌                                        | 13/27 [04:43<05:05, 21.79s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 281, FN: 2, TP: 17, DELAY:1.1399999856948853, FP_DELAY:15.966666221618652, COVER: 0.7677570641853722\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 52%|████████████████████████████████████████▍                                     | 14/27 [05:04<04:41, 21.66s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 281, FN: 2, TP: 17, DELAY:1.1433333158493042, FP_DELAY:16.30666732788086, COVER: 0.7668928762708356\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 56%|███████████████████████████████████████████▎                                  | 15/27 [05:26<04:21, 21.76s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 280, FN: 3, TP: 17, DELAY:1.5666667222976685, FP_DELAY:17.34000015258789, COVER: 0.7647045993323999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 59%|██████████████████████████████████████████████▏                               | 16/27 [05:48<03:59, 21.81s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 278, FN: 3, TP: 19, DELAY:1.5833333730697632, FP_DELAY:17.953332901000977, COVER: 0.7637855338503741\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 63%|█████████████████████████████████████████████████                             | 17/27 [06:10<03:38, 21.88s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 273, FN: 3, TP: 24, DELAY:1.6633332967758179, FP_DELAY:18.889999389648438, COVER: 0.7618704173219064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 67%|████████████████████████████████████████████████████                          | 18/27 [06:32<03:15, 21.75s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 273, FN: 3, TP: 24, DELAY:1.746666669845581, FP_DELAY:20.030000686645508, COVER: 0.7579507027801753\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 70%|██████████████████████████████████████████████████████▉                       | 19/27 [06:53<02:53, 21.74s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 273, FN: 4, TP: 23, DELAY:2.1500000953674316, FP_DELAY:21.816667556762695, COVER: 0.753597463705728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 74%|█████████████████████████████████████████████████████████▊                    | 20/27 [07:15<02:32, 21.84s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 269, FN: 4, TP: 27, DELAY:2.259999990463257, FP_DELAY:23.763334274291992, COVER: 0.7487095574836441\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 78%|████████████████████████████████████████████████████████████▋                 | 21/27 [07:37<02:11, 21.85s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 265, FN: 6, TP: 29, DELAY:2.9166667461395264, FP_DELAY:26.226667404174805, COVER: 0.7417311355457743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 81%|███████████████████████████████████████████████████████████████▌              | 22/27 [07:59<01:48, 21.75s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 264, FN: 7, TP: 29, DELAY:3.2766666412353516, FP_DELAY:30.3799991607666, COVER: 0.7380579530905552\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 85%|██████████████████████████████████████████████████████████████████▍           | 23/27 [08:20<01:26, 21.55s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 256, FN: 15, TP: 29, DELAY:5.643333435058594, FP_DELAY:35.400001525878906, COVER: 0.7343173534858896\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 89%|█████████████████████████████████████████████████████████████████████▎        | 24/27 [08:42<01:05, 21.71s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 2, FP: 252, FN: 19, TP: 27, DELAY:6.7166666984558105, FP_DELAY:40.34000015258789, COVER: 0.7316091917943143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 93%|████████████████████████████████████████████████████████████████████████▏     | 25/27 [09:04<00:43, 21.80s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 5, FP: 242, FN: 28, TP: 25, DELAY:9.306666374206543, FP_DELAY:46.81999969482422, COVER: 0.7316535584744785\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 96%|███████████████████████████████████████████████████████████████████████████   | 26/27 [09:26<00:21, 21.71s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 10, FP: 231, FN: 35, TP: 24, DELAY:11.079999923706055, FP_DELAY:52.40999984741211, COVER: 0.7319535860371597\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 27/27 [09:48<00:00, 21.78s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n",
      "AUC: 1895.8565\n",
      "Time to FA 26.2267, delay detection 2.9167 for best-F1 threshold: 0.9487\n",
      "TN 0, FP 265, FN 6, TP 29 for best-F1 threshold: 0.9487\n",
      "Max F1 0.1763: for best-F1 threshold 0.9487\n",
      "COVER 0.7417: for best-F1 threshold 0.9487\n",
      "Max COVER 0.8226: for threshold -0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define core models (discriminator and generator) for KL-CPD model\n",
    "net_discriminator = klcpd.NetD(args=args)\n",
    "net_generator = klcpd.NetG(args=args)\n",
    "\n",
    "# create KL-CPD model (our pytorch_lightning wrapper)\n",
    "klcpd_model = klcpd.KLCPD(\n",
    "    args=args,\n",
    "    net_discriminator=net_discriminator,\n",
    "    net_generator=net_generator,\n",
    "    train_dataset=train_dataset,\n",
    "    test_dataset=test_dataset\n",
    ")\n",
    "\n",
    "# use logger\n",
    "model_name = f'kl_cpd_seed_{SEED}'\n",
    "logger = TensorBoardLogger(save_dir=f'logs/{experiments_name}', name=model_name)\n",
    "\n",
    "# define trainer with custom parameters\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=30,\n",
    "    gpus=1,\n",
    "    benchmark=True,\n",
    "    check_val_every_n_epoch=1,\n",
    "    gradient_clip_val=0.,\n",
    "    logger=logger,\n",
    "    \n",
    "    # use early stopping\n",
    "    callbacks=EarlyStopping(monitor=\"val_mmd2_real_D\", min_delta=0.00001, patience=5)\n",
    ")\n",
    "\n",
    "trainer.fit(klcpd_model)\n",
    "\n",
    "threshold_number = 25\n",
    "threshold_list = np.linspace(-5, 5, threshold_number)\n",
    "threshold_list = 1 / (1 + np.exp(-threshold_list))\n",
    "threshold_list = [-0.001] + list(threshold_list) + [1.001]\n",
    "\n",
    "# evaluate model\n",
    "metrics_local, delay_list, fp_delay_list = \\\n",
    "    metrics.evaluation_pipeline(klcpd_model, # use the second model for evaluation\n",
    "                                klcpd_model.val_dataloader(),\n",
    "                                threshold_list,\n",
    "                                device=\"cuda\", # choose 'cpu' or 'cuda' if available\n",
    "                                model_type=\"kl_cpd\",\n",
    "                                scale=100,\n",
    "                                verbose=True\n",
    "                               )\n",
    "\n",
    "# plot detection curve\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.plot(fp_delay_list.values(), delay_list.values(), '-o', markersize=8, label=\"KL-CPD\")\n",
    "plt.xlabel('Mean Time to False Alarm', fontsize=28)\n",
    "plt.ylabel('Mean Detection Delay', fontsize=28)\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=24)\n",
    "plt.legend(loc='upper left', fontsize=26)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d762cf",
   "metadata": {},
   "source": [
    "## Experiments with TS-CP2 baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e43ab8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dictionary with default arguments for consistency with our interface\n",
    "# define arguments as they are set in the corresponding config file\n",
    "model = dict()\n",
    "model[\"c_in\"] = 100\n",
    "model[\"nb_filters\"] = 4\n",
    "model[\"nb_stacks\"] = 2\n",
    "model[\"kernel_size\"] = 5\n",
    "model[\"dilations\"] = [1, 2, 4, 8]\n",
    "model[\"use_skip_connections\"] = True\n",
    "model[\"use_batch_norm\"] = False\n",
    "model[\"use_layer_norm\"] = False\n",
    "model[\"use_weight_norm\"] = False\n",
    "model[\"dropout_rate\"] = 0.\n",
    "model[\"seq_len\"] = 16\n",
    "model[\"n_steps\"] = 4\n",
    "model[\"code_size\"] = 4\n",
    "model[\"window\"] = 16\n",
    "model[\"window_1\"] = 16\n",
    "model[\"window_2\"] = 16\n",
    "\n",
    "\n",
    "learning = dict()\n",
    "learning[\"batch_size\"] = 4\n",
    "learning[\"lr\"] = 0.0001\n",
    "learning[\"grad_clip\"] = 0.0\n",
    "learning[\"decay_steps\"] = 1000\n",
    "\n",
    "loss = dict()\n",
    "loss[\"temperature\"] = 0.5\n",
    "\n",
    "args = dict()\n",
    "args[\"model\"] = model\n",
    "args[\"learning\"] = learning\n",
    "args[\"loss\"] = loss\n",
    "\n",
    "args[\"experiments_name\"] = experiments_name\n",
    "args[\"num_workers\"] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "49a18dca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "Missing logger folder: logs/synthetic_100D/tscp_seed_42\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name  | Type            | Params\n",
      "------------------------------------------\n",
      "0 | model | BaseTSCPEncoder | 4.5 K \n",
      "------------------------------------------\n",
      "4.5 K     Trainable params\n",
      "0         Non-trainable params\n",
      "4.5 K     Total params\n",
      "0.018     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c55f1e6f5fc74542b2cd2e81ea541983",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=30` reached.\n",
      "\n",
      "  0%|                                                                                       | 0/27 [00:00<?, ?it/s]\u001b[A\n",
      "  4%|██▉                                                                            | 1/27 [01:00<26:14, 60.56s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  7%|█████▊                                                                         | 2/27 [01:59<24:44, 59.37s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 11%|████████▊                                                                      | 3/27 [03:00<24:02, 60.10s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 15%|███████████▋                                                                   | 4/27 [03:59<22:54, 59.75s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 19%|██████████████▋                                                                | 5/27 [05:00<22:02, 60.10s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 22%|█████████████████▌                                                             | 6/27 [06:01<21:12, 60.60s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 26%|████████████████████▍                                                          | 7/27 [07:00<20:01, 60.07s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 30%|███████████████████████▍                                                       | 8/27 [08:02<19:11, 60.60s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 33%|██████████████████████████▎                                                    | 9/27 [09:03<18:15, 60.87s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 37%|████████████████████████████▉                                                 | 10/27 [10:04<17:15, 60.89s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 41%|███████████████████████████████▊                                              | 11/27 [11:05<16:12, 60.79s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 300, FN: 0, TP: 0, DELAY:0.0, FP_DELAY:0.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 44%|██████████████████████████████████▋                                           | 12/27 [12:03<14:58, 59.91s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 261, FN: 18, TP: 21, DELAY:7.599999904632568, FP_DELAY:36.66999816894531, COVER: 0.7195803136661221\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 48%|█████████████████████████████████████▌                                        | 13/27 [13:02<13:55, 59.67s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 258, FN: 19, TP: 23, DELAY:7.929999828338623, FP_DELAY:37.43000030517578, COVER: 0.7181332905024344\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 52%|████████████████████████████████████████▍                                     | 14/27 [14:03<13:03, 60.25s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 258, FN: 19, TP: 23, DELAY:7.9633331298828125, FP_DELAY:37.849998474121094, COVER: 0.715896629704182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 56%|███████████████████████████████████████████▎                                  | 15/27 [15:05<12:06, 60.54s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 256, FN: 20, TP: 24, DELAY:8.356666564941406, FP_DELAY:38.86666488647461, COVER: 0.7127531593675542\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 59%|██████████████████████████████████████████████▏                               | 16/27 [16:05<11:06, 60.61s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 0, FP: 253, FN: 28, TP: 19, DELAY:11.083333015441895, FP_DELAY:44.72666549682617, COVER: 0.6978596986251635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 63%|█████████████████████████████████████████████████                             | 17/27 [17:07<10:08, 60.83s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 67%|████████████████████████████████████████████████████                          | 18/27 [18:08<09:07, 60.87s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 70%|██████████████████████████████████████████████████████▉                       | 19/27 [19:07<08:02, 60.28s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 74%|█████████████████████████████████████████████████████████▊                    | 20/27 [20:04<06:56, 59.54s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 78%|████████████████████████████████████████████████████████████▋                 | 21/27 [21:06<06:01, 60.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 81%|███████████████████████████████████████████████████████████████▌              | 22/27 [22:08<05:03, 60.69s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 85%|██████████████████████████████████████████████████████████████████▍           | 23/27 [23:07<04:01, 60.26s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 89%|█████████████████████████████████████████████████████████████████████▎        | 24/27 [24:07<03:00, 60.12s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 93%|████████████████████████████████████████████████████████████████████████▏     | 25/27 [25:07<02:00, 60.02s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 96%|███████████████████████████████████████████████████████████████████████████   | 26/27 [26:06<00:59, 59.79s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 27/27 [27:06<00:00, 60.24s/it]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TN: 142, FP: 0, FN: 158, TP: 0, DELAY:33.81999969482422, FP_DELAY:128.0, COVER: 0.8226481119791667\n",
      "AUC: 2083.4653\n",
      "Time to FA 38.8667, delay detection 8.3567 for best-F1 threshold: 0.6027\n",
      "TN 0, FP 256, FN 20, TP 24 for best-F1 threshold: 0.6027\n",
      "Max F1 0.1481: for best-F1 threshold 0.6027\n",
      "COVER 0.7128: for best-F1 threshold 0.6027\n",
      "Max COVER 0.8226: for threshold -0.001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define encoder for TS-CP2 model\n",
    "har_encoder = tscp.BaseTSCPEncoder(args=args)\n",
    "\n",
    "# create KL-CPD model (our pytorch_lightning wrapper)\n",
    "tscp_model = tscp.TSCP_model(\n",
    "    args=args,\n",
    "    model=har_encoder,\n",
    "    train_dataset=train_dataset,\n",
    "    test_dataset=test_dataset\n",
    ")\n",
    "\n",
    "# use logger\n",
    "model_name = f'tscp_seed_{SEED}'\n",
    "logger = TensorBoardLogger(save_dir=f'logs/{experiments_name}', name=model_name)\n",
    "\n",
    "# define trainer with custom parameters\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=30,\n",
    "    gpus=1,\n",
    "    benchmark=True,\n",
    "    check_val_every_n_epoch=1,\n",
    "    gradient_clip_val=0.,\n",
    "    logger=logger,\n",
    "    \n",
    "    # use early stopping\n",
    "    callbacks=EarlyStopping(monitor=\"train_loss_epoch\", min_delta=0.00001, patience=5)\n",
    ")\n",
    "\n",
    "trainer.fit(tscp_model)\n",
    "\n",
    "# evaluate model\n",
    "metrics_local, delay_list, fp_delay_list = \\\n",
    "    metrics.evaluation_pipeline(tscp_model, # use the second model for evaluation\n",
    "                                tscp_model.val_dataloader(),\n",
    "                                threshold_list,\n",
    "                                device=\"cuda\", # choose 'cpu' or 'cuda' if available\n",
    "                                model_type=\"tscp\",\n",
    "                                scale=1,\n",
    "                                verbose=True\n",
    "                               )\n",
    "\n",
    "# plot detection curve\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.plot(fp_delay_list.values(), delay_list.values(), '-o', markersize=8, label=\"TS-CP2\")\n",
    "plt.xlabel('Mean Time to False Alarm', fontsize=28)\n",
    "plt.ylabel('Mean Detection Delay', fontsize=28)\n",
    "plt.xticks(fontsize=24)\n",
    "plt.yticks(fontsize=24)\n",
    "plt.legend(loc='upper left', fontsize=26)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}