icm_mds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import network_diffusion as nd\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from tqdm import tqdm\n",
    "from utils import functions, networks, berahmand_centrality\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "functions.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_dir = \"icm_mds\"\n",
    "\n",
    "max_epochs = 50\n",
    "n_runs = 100\n",
    "patience = 1\n",
    "\n",
    "protocol = \"AND\"\n",
    "probability = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ICM on Multilayer Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "aucs = networks.get_aucs_network()\n",
    "lazega = networks.get_lazega_network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================\n",
      "network parameters\n",
      "--------------------------------------------\n",
      "general parameters:\n",
      "\tnumber of layers: 5\n",
      "\tnumber of actors: 61\n",
      "\n",
      "layer 'facebook' parameters:\n",
      "\tgraph type - <class 'networkx.classes.graph.Graph'>\n",
      "\tnumber of nodes - 32\n",
      "\tnumber of edges - 124\n",
      "\taverage degree - 7.75\n",
      "\tclustering coefficient - 0.5404\n",
      "\n",
      "layer 'lunch' parameters:\n",
      "\tgraph type - <class 'networkx.classes.graph.Graph'>\n",
      "\tnumber of nodes - 60\n",
      "\tnumber of edges - 193\n",
      "\taverage degree - 6.4333\n",
      "\tclustering coefficient - 0.6733\n",
      "\n",
      "layer 'coauthor' parameters:\n",
      "\tgraph type - <class 'networkx.classes.graph.Graph'>\n",
      "\tnumber of nodes - 25\n",
      "\tnumber of edges - 21\n",
      "\taverage degree - 1.68\n",
      "\tclustering coefficient - 0.268\n",
      "\n",
      "layer 'leisure' parameters:\n",
      "\tgraph type - <class 'networkx.classes.graph.Graph'>\n",
      "\tnumber of nodes - 47\n",
      "\tnumber of edges - 88\n",
      "\taverage degree - 3.7447\n",
      "\tclustering coefficient - 0.3925\n",
      "\n",
      "layer 'work' parameters:\n",
      "\tgraph type - <class 'networkx.classes.graph.Graph'>\n",
      "\tnumber of nodes - 60\n",
      "\tnumber of edges - 194\n",
      "\taverage degree - 6.4667\n",
      "\tclustering coefficient - 0.6396\n",
      "============================================\n"
     ]
    }
   ],
   "source": [
    "print(aucs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================\n",
      "network parameters\n",
      "--------------------------------------------\n",
      "general parameters:\n",
      "\tnumber of layers: 3\n",
      "\tnumber of actors: 71\n",
      "\n",
      "layer 'advice' parameters:\n",
      "\tgraph type - <class 'networkx.classes.graph.Graph'>\n",
      "\tnumber of nodes - 71\n",
      "\tnumber of edges - 556\n",
      "\taverage degree - 15.662\n",
      "\tclustering coefficient - 0.376\n",
      "\n",
      "layer 'friendship' parameters:\n",
      "\tgraph type - <class 'networkx.classes.graph.Graph'>\n",
      "\tnumber of nodes - 71\n",
      "\tnumber of edges - 725\n",
      "\taverage degree - 20.4225\n",
      "\tclustering coefficient - 0.4493\n",
      "\n",
      "layer 'co-work' parameters:\n",
      "\tgraph type - <class 'networkx.classes.graph.Graph'>\n",
      "\tnumber of nodes - 70\n",
      "\tnumber of edges - 378\n",
      "\taverage degree - 10.8\n",
      "\tclustering coefficient - 0.3908\n",
      "============================================\n"
     ]
    }
   ],
   "source": [
    "print(lazega)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# propagation prob: 0.1, buget 2% active\n",
    "def create_mic_model(seeding_budget, seed_selector, protocol, probability):\n",
    "    return nd.models.MICModel(\n",
    "        seeding_budget=seeding_budget,\n",
    "        seed_selector=seed_selector,\n",
    "        protocol=protocol,\n",
    "        probability=probability,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_degree_01 = create_mic_model([95, 5, 0], nd.seeding.DegreeCentralitySelector(), protocol, probability)\n",
    "model_btw_01 = create_mic_model([95, 5, 0], nd.seeding.BetweennessSelector(), protocol, probability)\n",
    "model_close_01 = create_mic_model([95, 5, 0], nd.seeding.ClosenessSelector(), protocol, probability)\n",
    "model_VR_01 = create_mic_model([95, 5, 0], nd.seeding.VoteRankMLNSeedSelector(), protocol, probability)\n",
    "model_berahmand_01 = create_mic_model([95, 5, 0], berahmand_centrality.BerahmandCentralitySelector(), protocol, probability)\n",
    "model_driver_01 = create_mic_model([95, 5, 0], nd.seeding.DriverActorSelector(nd.seeding.DegreeCentralitySelector()), protocol, probability)\n",
    "\n",
    "model_degree_02 = create_mic_model([90, 10, 0], nd.seeding.DegreeCentralitySelector(), protocol, probability)\n",
    "model_btw_02 = create_mic_model([90, 10, 0], nd.seeding.BetweennessSelector(), protocol, probability)\n",
    "model_close_02 = create_mic_model([90, 10, 0], nd.seeding.ClosenessSelector(), protocol, probability)\n",
    "model_VR_02 = create_mic_model([90, 10, 0], nd.seeding.VoteRankMLNSeedSelector(), protocol, probability)\n",
    "model_berahmand_02 = create_mic_model([90, 10, 0], berahmand_centrality.BerahmandCentralitySelector(), protocol, probability)\n",
    "model_driver_02 = create_mic_model([90, 10, 0], nd.seeding.DriverActorSelector(nd.seeding.DegreeCentralitySelector()), protocol, probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"degree_05\": model_degree_01,\n",
    "    \"degree_10\": model_degree_02,\n",
    "    \"btw_05\": model_btw_01,\n",
    "    \"btw_10\": model_btw_02,\n",
    "    \"close_05\": model_close_01,\n",
    "    \"close_10\": model_close_02,\n",
    "    \"VR_05\": model_VR_01,\n",
    "    \"VR_10\": model_VR_02,\n",
    "    \"Brh_05\": model_berahmand_01,\n",
    "    \"Brh_10\": model_berahmand_02,\n",
    "    \"driver_05\": model_driver_01,\n",
    "    \"driver_10\": model_driver_02\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "logs_dict = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lazega"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = lazega\n",
    "nb_actors = network.get_actors_num()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running experiment for model degree_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 39.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 56.21, Std = 12.59467744723937\n",
      "Epochs executed: Avg = 6.01, Std = 0.9949371839468059\n",
      "Running experiment for model degree_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 45.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 62.76, Std = 2.5024787711387284\n",
      "Epochs executed: Avg = 4.37, Std = 0.5225897052181568\n",
      "Running experiment for model btw_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:05<00:00, 19.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 55.4, Std = 11.028145809699835\n",
      "Epochs executed: Avg = 6.02, Std = 0.9589577675789482\n",
      "Running experiment for model btw_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:04<00:00, 21.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 63.42, Std = 2.4132136250236944\n",
      "Epochs executed: Avg = 4.27, Std = 0.4439594576084623\n",
      "Running experiment for model close_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:03<00:00, 30.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 55.77, Std = 9.279929956632216\n",
      "Epochs executed: Avg = 6.17, Std = 0.8950418984606252\n",
      "Running experiment for model close_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 35.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 63.26, Std = 2.381680079271773\n",
      "Epochs executed: Avg = 4.43, Std = 0.5701754116059373\n",
      "Running experiment for model VR_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [01:04<00:00,  1.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 58.29, Std = 9.443828672736498\n",
      "Epochs executed: Avg = 5.84, Std = 0.9024411338142783\n",
      "Running experiment for model VR_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [01:04<00:00,  1.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 63.21, Std = 2.4136901209558776\n",
      "Epochs executed: Avg = 4.44, Std = 0.49638694583963433\n",
      "Running experiment for model Brh_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:03<00:00, 30.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 59.07, Std = 8.46434285694997\n",
      "Epochs executed: Avg = 5.83, Std = 1.0203430795570674\n",
      "Running experiment for model Brh_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 33.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 63.28, Std = 2.2675096471680116\n",
      "Epochs executed: Avg = 4.31, Std = 0.5038849074937649\n",
      "Running experiment for model driver_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 38.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 60.28, Std = 4.2897086148128984\n",
      "Epochs executed: Avg = 5.77, Std = 0.8468175718535841\n",
      "Running experiment for model driver_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 43.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 63.39, Std = 2.331930530697688\n",
      "Epochs executed: Avg = 4.28, Std = 0.44899888641287294\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for model_name, model in models.items():\n",
    "    print(f\"Running experiment for model {model_name}...\")\n",
    "\n",
    "    nb_activated_actors_model = []\n",
    "    nb_epochs_model = []\n",
    "\n",
    "    for run in tqdm(range(n_runs)):\n",
    "        experiment = nd.Simulator(model, network)\n",
    "        run_logs = experiment.perform_propagation(n_epochs=max_epochs, patience=patience)\n",
    "        nb_activated_actors_run, nb_epochs_run = functions.convert_micm_logs(run_logs, patience)\n",
    "        nb_activated_actors_model.append(nb_activated_actors_run)\n",
    "        nb_epochs_model.append(nb_epochs_run)\n",
    "\n",
    "    avg_activated_actors_model = np.mean(nb_activated_actors_model)\n",
    "    avg_epochs_model = np.mean(nb_epochs_model)\n",
    "    std_activated_actors_model = np.std(nb_activated_actors_model)\n",
    "    std_epochs_model = np.std(nb_epochs_model)\n",
    "\n",
    "    print(f\"Activated actors: Avg = {avg_activated_actors_model}, Std = {std_activated_actors_model}\")\n",
    "    print(f\"Epochs executed: Avg = {avg_epochs_model}, Std = {std_epochs_model}\")\n",
    "\n",
    "    logs_dict[f\"logs_lazega_{model_name}\"] = (\n",
    "        100 * avg_activated_actors_model / nb_actors,\n",
    "        avg_epochs_model,\n",
    "        100 * std_activated_actors_model / nb_actors,\n",
    "        std_epochs_model\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aucs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = aucs\n",
    "nb_actors = network.get_actors_num()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running experiment for model degree_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 59.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 7.1, Std = 1.9364916731037087\n",
      "Epochs executed: Avg = 2.82, Std = 0.766550715869472\n",
      "Running experiment for model degree_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 55.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 12.75, Std = 1.6209565077447328\n",
      "Epochs executed: Avg = 2.79, Std = 0.4958830507286975\n",
      "Running experiment for model btw_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:03<00:00, 33.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 5.97, Std = 1.5326773959317075\n",
      "Epochs executed: Avg = 2.54, Std = 0.6843975452907468\n",
      "Running experiment for model btw_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:03<00:00, 31.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 11.05, Std = 1.8405162319305959\n",
      "Epochs executed: Avg = 2.76, Std = 0.5499090833947009\n",
      "Running experiment for model close_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 44.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 5.36, Std = 1.118212859879549\n",
      "Epochs executed: Avg = 2.33, Std = 0.6009159675029446\n",
      "Running experiment for model close_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 43.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 10.06, Std = 1.482025640803829\n",
      "Epochs executed: Avg = 2.46, Std = 0.5370288632839021\n",
      "Running experiment for model VR_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:21<00:00,  4.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 6.7, Std = 2.3811761799581315\n",
      "Epochs executed: Avg = 2.76, Std = 0.9068627239003708\n",
      "Running experiment for model VR_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:21<00:00,  4.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 12.38, Std = 2.32714417258579\n",
      "Epochs executed: Avg = 2.88, Std = 0.515363949069005\n",
      "Running experiment for model Brh_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 53.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 4.71, Std = 1.5575300960174092\n",
      "Epochs executed: Avg = 2.1, Std = 0.8426149773176358\n",
      "Running experiment for model Brh_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 49.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 8.27, Std = 1.164946350696031\n",
      "Epochs executed: Avg = 2.38, Std = 0.5793099343184096\n",
      "Running experiment for model driver_05...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 49.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 7.51, Std = 1.9209112420931898\n",
      "Epochs executed: Avg = 2.84, Std = 0.7310266752998825\n",
      "Running experiment for model driver_10...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 51.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activated actors: Avg = 11.87, Std = 2.343736333293487\n",
      "Epochs executed: Avg = 2.8, Std = 0.5099019513592784\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for model_name, model in models.items():\n",
    "    print(f\"Running experiment for model {model_name}...\")\n",
    "\n",
    "    nb_activated_actors_model = []\n",
    "    nb_epochs_model = []\n",
    "\n",
    "    for run in tqdm(range(n_runs)):\n",
    "        experiment = nd.Simulator(model, network)\n",
    "        run_logs = experiment.perform_propagation(n_epochs=max_epochs, patience=patience)\n",
    "        nb_activated_actors_run, nb_epochs_run = functions.convert_micm_logs(run_logs, patience)\n",
    "        nb_activated_actors_model.append(nb_activated_actors_run)\n",
    "        nb_epochs_model.append(nb_epochs_run)\n",
    "\n",
    "    avg_activated_actors_model = np.mean(nb_activated_actors_model)\n",
    "    avg_epochs_model = np.mean(nb_epochs_model)\n",
    "    std_activated_actors_model = np.std(nb_activated_actors_model)\n",
    "    std_epochs_model = np.std(nb_epochs_model)\n",
    "\n",
    "    print(f\"Activated actors: Avg = {avg_activated_actors_model}, Std = {std_activated_actors_model}\")\n",
    "    print(f\"Epochs executed: Avg = {avg_epochs_model}, Std = {std_epochs_model}\")\n",
    "\n",
    "    logs_dict[f\"logs_aucs_{model_name}\"] = (\n",
    "        100 * avg_activated_actors_model / nb_actors,\n",
    "        avg_epochs_model,\n",
    "        100 * std_activated_actors_model / nb_actors,\n",
    "        std_epochs_model\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data concatenation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_activated_actors_model</th>\n",
       "      <th>avg_epochs_model</th>\n",
       "      <th>std_activated_actors_model</th>\n",
       "      <th>std_epochs_model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>logs_lazega_degree_05</th>\n",
       "      <td>79.169014</td>\n",
       "      <td>6.01</td>\n",
       "      <td>17.738982</td>\n",
       "      <td>0.994937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_degree_10</th>\n",
       "      <td>88.394366</td>\n",
       "      <td>4.37</td>\n",
       "      <td>3.524618</td>\n",
       "      <td>0.522590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_btw_05</th>\n",
       "      <td>78.028169</td>\n",
       "      <td>6.02</td>\n",
       "      <td>15.532600</td>\n",
       "      <td>0.958958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_btw_10</th>\n",
       "      <td>89.323944</td>\n",
       "      <td>4.27</td>\n",
       "      <td>3.398892</td>\n",
       "      <td>0.443959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_close_05</th>\n",
       "      <td>78.549296</td>\n",
       "      <td>6.17</td>\n",
       "      <td>13.070324</td>\n",
       "      <td>0.895042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_close_10</th>\n",
       "      <td>89.098592</td>\n",
       "      <td>4.43</td>\n",
       "      <td>3.354479</td>\n",
       "      <td>0.570175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_VR_05</th>\n",
       "      <td>82.098592</td>\n",
       "      <td>5.84</td>\n",
       "      <td>13.301167</td>\n",
       "      <td>0.902441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_VR_10</th>\n",
       "      <td>89.028169</td>\n",
       "      <td>4.44</td>\n",
       "      <td>3.399564</td>\n",
       "      <td>0.496387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_Brh_05</th>\n",
       "      <td>83.197183</td>\n",
       "      <td>5.83</td>\n",
       "      <td>11.921610</td>\n",
       "      <td>1.020343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_Brh_10</th>\n",
       "      <td>89.126761</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3.193676</td>\n",
       "      <td>0.503885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_driver_05</th>\n",
       "      <td>84.901408</td>\n",
       "      <td>5.77</td>\n",
       "      <td>6.041843</td>\n",
       "      <td>0.846818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_driver_10</th>\n",
       "      <td>89.281690</td>\n",
       "      <td>4.28</td>\n",
       "      <td>3.284409</td>\n",
       "      <td>0.448999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_degree_05</th>\n",
       "      <td>11.639344</td>\n",
       "      <td>2.82</td>\n",
       "      <td>3.174577</td>\n",
       "      <td>0.766551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_degree_10</th>\n",
       "      <td>20.901639</td>\n",
       "      <td>2.79</td>\n",
       "      <td>2.657306</td>\n",
       "      <td>0.495883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_btw_05</th>\n",
       "      <td>9.786885</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.512586</td>\n",
       "      <td>0.684398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_btw_10</th>\n",
       "      <td>18.114754</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.017240</td>\n",
       "      <td>0.549909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_close_05</th>\n",
       "      <td>8.786885</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1.833136</td>\n",
       "      <td>0.600916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_close_10</th>\n",
       "      <td>16.491803</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.429550</td>\n",
       "      <td>0.537029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_VR_05</th>\n",
       "      <td>10.983607</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.903568</td>\n",
       "      <td>0.906863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_VR_10</th>\n",
       "      <td>20.295082</td>\n",
       "      <td>2.88</td>\n",
       "      <td>3.814990</td>\n",
       "      <td>0.515364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_Brh_05</th>\n",
       "      <td>7.721311</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.553328</td>\n",
       "      <td>0.842615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_Brh_10</th>\n",
       "      <td>13.557377</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1.909748</td>\n",
       "      <td>0.579310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_driver_05</th>\n",
       "      <td>12.311475</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.149035</td>\n",
       "      <td>0.731027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_driver_10</th>\n",
       "      <td>19.459016</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.842191</td>\n",
       "      <td>0.509902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       avg_activated_actors_model  avg_epochs_model  \\\n",
       "logs_lazega_degree_05                   79.169014              6.01   \n",
       "logs_lazega_degree_10                   88.394366              4.37   \n",
       "logs_lazega_btw_05                      78.028169              6.02   \n",
       "logs_lazega_btw_10                      89.323944              4.27   \n",
       "logs_lazega_close_05                    78.549296              6.17   \n",
       "logs_lazega_close_10                    89.098592              4.43   \n",
       "logs_lazega_VR_05                       82.098592              5.84   \n",
       "logs_lazega_VR_10                       89.028169              4.44   \n",
       "logs_lazega_Brh_05                      83.197183              5.83   \n",
       "logs_lazega_Brh_10                      89.126761              4.31   \n",
       "logs_lazega_driver_05                   84.901408              5.77   \n",
       "logs_lazega_driver_10                   89.281690              4.28   \n",
       "logs_aucs_degree_05                     11.639344              2.82   \n",
       "logs_aucs_degree_10                     20.901639              2.79   \n",
       "logs_aucs_btw_05                         9.786885              2.54   \n",
       "logs_aucs_btw_10                        18.114754              2.76   \n",
       "logs_aucs_close_05                       8.786885              2.33   \n",
       "logs_aucs_close_10                      16.491803              2.46   \n",
       "logs_aucs_VR_05                         10.983607              2.76   \n",
       "logs_aucs_VR_10                         20.295082              2.88   \n",
       "logs_aucs_Brh_05                         7.721311              2.10   \n",
       "logs_aucs_Brh_10                        13.557377              2.38   \n",
       "logs_aucs_driver_05                     12.311475              2.84   \n",
       "logs_aucs_driver_10                     19.459016              2.80   \n",
       "\n",
       "                       std_activated_actors_model  std_epochs_model  \n",
       "logs_lazega_degree_05                   17.738982          0.994937  \n",
       "logs_lazega_degree_10                    3.524618          0.522590  \n",
       "logs_lazega_btw_05                      15.532600          0.958958  \n",
       "logs_lazega_btw_10                       3.398892          0.443959  \n",
       "logs_lazega_close_05                    13.070324          0.895042  \n",
       "logs_lazega_close_10                     3.354479          0.570175  \n",
       "logs_lazega_VR_05                       13.301167          0.902441  \n",
       "logs_lazega_VR_10                        3.399564          0.496387  \n",
       "logs_lazega_Brh_05                      11.921610          1.020343  \n",
       "logs_lazega_Brh_10                       3.193676          0.503885  \n",
       "logs_lazega_driver_05                    6.041843          0.846818  \n",
       "logs_lazega_driver_10                    3.284409          0.448999  \n",
       "logs_aucs_degree_05                      3.174577          0.766551  \n",
       "logs_aucs_degree_10                      2.657306          0.495883  \n",
       "logs_aucs_btw_05                         2.512586          0.684398  \n",
       "logs_aucs_btw_10                         3.017240          0.549909  \n",
       "logs_aucs_close_05                       1.833136          0.600916  \n",
       "logs_aucs_close_10                       2.429550          0.537029  \n",
       "logs_aucs_VR_05                          3.903568          0.906863  \n",
       "logs_aucs_VR_10                          3.814990          0.515364  \n",
       "logs_aucs_Brh_05                         2.553328          0.842615  \n",
       "logs_aucs_Brh_10                         1.909748          0.579310  \n",
       "logs_aucs_driver_05                      3.149035          0.731027  \n",
       "logs_aucs_driver_10                      3.842191          0.509902  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logs_df = pd.DataFrame(logs_dict).T.rename(\n",
    "    columns={0: \"avg_activated_actors_model\", 1: \"avg_epochs_model\", 2: \"std_activated_actors_model\", 3: \"std_epochs_model\"}\n",
    ")\n",
    "logs_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_activated_actors_model</th>\n",
       "      <th>avg_epochs_model</th>\n",
       "      <th>std_activated_actors_model</th>\n",
       "      <th>std_epochs_model</th>\n",
       "      <th>avg_std_activated_actors_model</th>\n",
       "      <th>avg_std_epochs_model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>logs_lazega_degree_05</th>\n",
       "      <td>79.169014</td>\n",
       "      <td>6.01</td>\n",
       "      <td>17.738982</td>\n",
       "      <td>0.994937</td>\n",
       "      <td>79.17±17.74%</td>\n",
       "      <td>6.01±0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_degree_10</th>\n",
       "      <td>88.394366</td>\n",
       "      <td>4.37</td>\n",
       "      <td>3.524618</td>\n",
       "      <td>0.522590</td>\n",
       "      <td>88.39±3.52%</td>\n",
       "      <td>4.37±0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_btw_05</th>\n",
       "      <td>78.028169</td>\n",
       "      <td>6.02</td>\n",
       "      <td>15.532600</td>\n",
       "      <td>0.958958</td>\n",
       "      <td>78.03±15.53%</td>\n",
       "      <td>6.02±0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_btw_10</th>\n",
       "      <td>89.323944</td>\n",
       "      <td>4.27</td>\n",
       "      <td>3.398892</td>\n",
       "      <td>0.443959</td>\n",
       "      <td>89.32±3.4%</td>\n",
       "      <td>4.27±0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_close_05</th>\n",
       "      <td>78.549296</td>\n",
       "      <td>6.17</td>\n",
       "      <td>13.070324</td>\n",
       "      <td>0.895042</td>\n",
       "      <td>78.55±13.07%</td>\n",
       "      <td>6.17±0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_close_10</th>\n",
       "      <td>89.098592</td>\n",
       "      <td>4.43</td>\n",
       "      <td>3.354479</td>\n",
       "      <td>0.570175</td>\n",
       "      <td>89.1±3.35%</td>\n",
       "      <td>4.43±0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_VR_05</th>\n",
       "      <td>82.098592</td>\n",
       "      <td>5.84</td>\n",
       "      <td>13.301167</td>\n",
       "      <td>0.902441</td>\n",
       "      <td>82.1±13.3%</td>\n",
       "      <td>5.84±0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_VR_10</th>\n",
       "      <td>89.028169</td>\n",
       "      <td>4.44</td>\n",
       "      <td>3.399564</td>\n",
       "      <td>0.496387</td>\n",
       "      <td>89.03±3.4%</td>\n",
       "      <td>4.44±0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_Brh_05</th>\n",
       "      <td>83.197183</td>\n",
       "      <td>5.83</td>\n",
       "      <td>11.921610</td>\n",
       "      <td>1.020343</td>\n",
       "      <td>83.2±11.92%</td>\n",
       "      <td>5.83±1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_Brh_10</th>\n",
       "      <td>89.126761</td>\n",
       "      <td>4.31</td>\n",
       "      <td>3.193676</td>\n",
       "      <td>0.503885</td>\n",
       "      <td>89.13±3.19%</td>\n",
       "      <td>4.31±0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_driver_05</th>\n",
       "      <td>84.901408</td>\n",
       "      <td>5.77</td>\n",
       "      <td>6.041843</td>\n",
       "      <td>0.846818</td>\n",
       "      <td>84.9±6.04%</td>\n",
       "      <td>5.77±0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_lazega_driver_10</th>\n",
       "      <td>89.281690</td>\n",
       "      <td>4.28</td>\n",
       "      <td>3.284409</td>\n",
       "      <td>0.448999</td>\n",
       "      <td>89.28±3.28%</td>\n",
       "      <td>4.28±0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_degree_05</th>\n",
       "      <td>11.639344</td>\n",
       "      <td>2.82</td>\n",
       "      <td>3.174577</td>\n",
       "      <td>0.766551</td>\n",
       "      <td>11.64±3.17%</td>\n",
       "      <td>2.82±0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_degree_10</th>\n",
       "      <td>20.901639</td>\n",
       "      <td>2.79</td>\n",
       "      <td>2.657306</td>\n",
       "      <td>0.495883</td>\n",
       "      <td>20.9±2.66%</td>\n",
       "      <td>2.79±0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_btw_05</th>\n",
       "      <td>9.786885</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.512586</td>\n",
       "      <td>0.684398</td>\n",
       "      <td>9.79±2.51%</td>\n",
       "      <td>2.54±0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_btw_10</th>\n",
       "      <td>18.114754</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.017240</td>\n",
       "      <td>0.549909</td>\n",
       "      <td>18.11±3.02%</td>\n",
       "      <td>2.76±0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_close_05</th>\n",
       "      <td>8.786885</td>\n",
       "      <td>2.33</td>\n",
       "      <td>1.833136</td>\n",
       "      <td>0.600916</td>\n",
       "      <td>8.79±1.83%</td>\n",
       "      <td>2.33±0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_close_10</th>\n",
       "      <td>16.491803</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.429550</td>\n",
       "      <td>0.537029</td>\n",
       "      <td>16.49±2.43%</td>\n",
       "      <td>2.46±0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_VR_05</th>\n",
       "      <td>10.983607</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.903568</td>\n",
       "      <td>0.906863</td>\n",
       "      <td>10.98±3.9%</td>\n",
       "      <td>2.76±0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_VR_10</th>\n",
       "      <td>20.295082</td>\n",
       "      <td>2.88</td>\n",
       "      <td>3.814990</td>\n",
       "      <td>0.515364</td>\n",
       "      <td>20.3±3.81%</td>\n",
       "      <td>2.88±0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_Brh_05</th>\n",
       "      <td>7.721311</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.553328</td>\n",
       "      <td>0.842615</td>\n",
       "      <td>7.72±2.55%</td>\n",
       "      <td>2.1±0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_Brh_10</th>\n",
       "      <td>13.557377</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1.909748</td>\n",
       "      <td>0.579310</td>\n",
       "      <td>13.56±1.91%</td>\n",
       "      <td>2.38±0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_driver_05</th>\n",
       "      <td>12.311475</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.149035</td>\n",
       "      <td>0.731027</td>\n",
       "      <td>12.31±3.15%</td>\n",
       "      <td>2.84±0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logs_aucs_driver_10</th>\n",
       "      <td>19.459016</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.842191</td>\n",
       "      <td>0.509902</td>\n",
       "      <td>19.46±3.84%</td>\n",
       "      <td>2.8±0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       avg_activated_actors_model  avg_epochs_model  \\\n",
       "logs_lazega_degree_05                   79.169014              6.01   \n",
       "logs_lazega_degree_10                   88.394366              4.37   \n",
       "logs_lazega_btw_05                      78.028169              6.02   \n",
       "logs_lazega_btw_10                      89.323944              4.27   \n",
       "logs_lazega_close_05                    78.549296              6.17   \n",
       "logs_lazega_close_10                    89.098592              4.43   \n",
       "logs_lazega_VR_05                       82.098592              5.84   \n",
       "logs_lazega_VR_10                       89.028169              4.44   \n",
       "logs_lazega_Brh_05                      83.197183              5.83   \n",
       "logs_lazega_Brh_10                      89.126761              4.31   \n",
       "logs_lazega_driver_05                   84.901408              5.77   \n",
       "logs_lazega_driver_10                   89.281690              4.28   \n",
       "logs_aucs_degree_05                     11.639344              2.82   \n",
       "logs_aucs_degree_10                     20.901639              2.79   \n",
       "logs_aucs_btw_05                         9.786885              2.54   \n",
       "logs_aucs_btw_10                        18.114754              2.76   \n",
       "logs_aucs_close_05                       8.786885              2.33   \n",
       "logs_aucs_close_10                      16.491803              2.46   \n",
       "logs_aucs_VR_05                         10.983607              2.76   \n",
       "logs_aucs_VR_10                         20.295082              2.88   \n",
       "logs_aucs_Brh_05                         7.721311              2.10   \n",
       "logs_aucs_Brh_10                        13.557377              2.38   \n",
       "logs_aucs_driver_05                     12.311475              2.84   \n",
       "logs_aucs_driver_10                     19.459016              2.80   \n",
       "\n",
       "                       std_activated_actors_model  std_epochs_model  \\\n",
       "logs_lazega_degree_05                   17.738982          0.994937   \n",
       "logs_lazega_degree_10                    3.524618          0.522590   \n",
       "logs_lazega_btw_05                      15.532600          0.958958   \n",
       "logs_lazega_btw_10                       3.398892          0.443959   \n",
       "logs_lazega_close_05                    13.070324          0.895042   \n",
       "logs_lazega_close_10                     3.354479          0.570175   \n",
       "logs_lazega_VR_05                       13.301167          0.902441   \n",
       "logs_lazega_VR_10                        3.399564          0.496387   \n",
       "logs_lazega_Brh_05                      11.921610          1.020343   \n",
       "logs_lazega_Brh_10                       3.193676          0.503885   \n",
       "logs_lazega_driver_05                    6.041843          0.846818   \n",
       "logs_lazega_driver_10                    3.284409          0.448999   \n",
       "logs_aucs_degree_05                      3.174577          0.766551   \n",
       "logs_aucs_degree_10                      2.657306          0.495883   \n",
       "logs_aucs_btw_05                         2.512586          0.684398   \n",
       "logs_aucs_btw_10                         3.017240          0.549909   \n",
       "logs_aucs_close_05                       1.833136          0.600916   \n",
       "logs_aucs_close_10                       2.429550          0.537029   \n",
       "logs_aucs_VR_05                          3.903568          0.906863   \n",
       "logs_aucs_VR_10                          3.814990          0.515364   \n",
       "logs_aucs_Brh_05                         2.553328          0.842615   \n",
       "logs_aucs_Brh_10                         1.909748          0.579310   \n",
       "logs_aucs_driver_05                      3.149035          0.731027   \n",
       "logs_aucs_driver_10                      3.842191          0.509902   \n",
       "\n",
       "                      avg_std_activated_actors_model avg_std_epochs_model  \n",
       "logs_lazega_degree_05                   79.17±17.74%            6.01±0.99  \n",
       "logs_lazega_degree_10                    88.39±3.52%            4.37±0.52  \n",
       "logs_lazega_btw_05                      78.03±15.53%            6.02±0.96  \n",
       "logs_lazega_btw_10                        89.32±3.4%            4.27±0.44  \n",
       "logs_lazega_close_05                    78.55±13.07%             6.17±0.9  \n",
       "logs_lazega_close_10                      89.1±3.35%            4.43±0.57  \n",
       "logs_lazega_VR_05                         82.1±13.3%             5.84±0.9  \n",
       "logs_lazega_VR_10                         89.03±3.4%             4.44±0.5  \n",
       "logs_lazega_Brh_05                       83.2±11.92%            5.83±1.02  \n",
       "logs_lazega_Brh_10                       89.13±3.19%             4.31±0.5  \n",
       "logs_lazega_driver_05                     84.9±6.04%            5.77±0.85  \n",
       "logs_lazega_driver_10                    89.28±3.28%            4.28±0.45  \n",
       "logs_aucs_degree_05                      11.64±3.17%            2.82±0.77  \n",
       "logs_aucs_degree_10                       20.9±2.66%             2.79±0.5  \n",
       "logs_aucs_btw_05                          9.79±2.51%            2.54±0.68  \n",
       "logs_aucs_btw_10                         18.11±3.02%            2.76±0.55  \n",
       "logs_aucs_close_05                        8.79±1.83%             2.33±0.6  \n",
       "logs_aucs_close_10                       16.49±2.43%            2.46±0.54  \n",
       "logs_aucs_VR_05                           10.98±3.9%            2.76±0.91  \n",
       "logs_aucs_VR_10                           20.3±3.81%            2.88±0.52  \n",
       "logs_aucs_Brh_05                          7.72±2.55%             2.1±0.84  \n",
       "logs_aucs_Brh_10                         13.56±1.91%            2.38±0.58  \n",
       "logs_aucs_driver_05                      12.31±3.15%            2.84±0.73  \n",
       "logs_aucs_driver_10                      19.46±3.84%             2.8±0.51  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def aggregate_avg_activated(row):\n",
    "    return f\"{round(row['avg_activated_actors_model'], 2)}±{round(row['std_activated_actors_model'], 2)}%\"\n",
    "\n",
    "def aggregate_avg_epochs(row):\n",
    "    return f\"{round(row['avg_epochs_model'], 2)}±{round(row['std_epochs_model'], 2)}\"\n",
    "\n",
    "logs_df['avg_std_activated_actors_model'] = logs_df.apply(aggregate_avg_activated, axis=1)\n",
    "logs_df['avg_std_epochs_model'] = logs_df.apply(aggregate_avg_epochs, axis=1)\n",
    "\n",
    "logs_df.to_csv(f\"{out_dir}/logs.csv\")\n",
    "logs_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                          ?, ?it/s]\r"
     ]
    }
   ],
   "source": [
    "model_driver_10_OR = create_mic_model(\n",
    "    [90, 10, 0], nd.seeding.DriverActorSelector(nd.seeding.DegreeCentralitySelector()), \"OR\", probability\n",
    ")\n",
    "experiment = nd.Simulator(model_driver_10_OR, aucs)\n",
    "logs = experiment.perform_propagation(n_epochs=10, patience=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'facebook': (('0', 26), ('1', 6)),\n",
       "  'lunch': (('0', 54), ('1', 6)),\n",
       "  'coauthor': (('0', 23), ('1', 2)),\n",
       "  'leisure': (('0', 43), ('1', 4)),\n",
       "  'work': (('0', 54), ('1', 6))},\n",
       " {'facebook': (('1', 24), ('0', 2), ('-1', 6)),\n",
       "  'lunch': (('0', 9), ('1', 45), ('-1', 6)),\n",
       "  'coauthor': (('1', 21), ('0', 2), ('-1', 2)),\n",
       "  'leisure': (('1', 35), ('0', 8), ('-1', 4)),\n",
       "  'work': (('1', 45), ('0', 9), ('-1', 6))},\n",
       " {'facebook': (('-1', 30), ('1', 2)),\n",
       "  'lunch': (('1', 9), ('-1', 51)),\n",
       "  'coauthor': (('-1', 23), ('1', 2)),\n",
       "  'leisure': (('-1', 39), ('1', 8)),\n",
       "  'work': (('-1', 51), ('1', 8), ('0', 1))},\n",
       " {'facebook': (('-1', 32),),\n",
       "  'lunch': (('-1', 60),),\n",
       "  'coauthor': (('-1', 25),),\n",
       "  'leisure': (('-1', 47),),\n",
       "  'work': (('-1', 59), ('0', 1))},\n",
       " {'facebook': (('-1', 32),),\n",
       "  'lunch': (('-1', 60),),\n",
       "  'coauthor': (('-1', 25),),\n",
       "  'leisure': (('-1', 47),),\n",
       "  'work': (('-1', 59), ('0', 1))}]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bulk logs from entire simulation that concatenate states of agents per each layer of the network\n",
    "exp_01 = logs._global_stats\n",
    "exp_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the data to a JSON-compatible format\n",
    "json_compatible_data = []\n",
    "for record in exp_01:\n",
    "    new_record = {}\n",
    "    for key, value in record.items():\n",
    "        new_record[key] = [list(item) for item in value]\n",
    "    json_compatible_data.append(new_record)\n",
    "\n",
    "# Write to a JSON file\n",
    "file_path = f\"{out_dir}/exp_visualise.json\"\n",
    "with open(file_path, 'w') as f:\n",
    "    json.dump(json_compatible_data, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data from a JSON file\n",
    "file_path = f\"{out_dir}/exp_visualise.json\"\n",
    "with open(file_path, 'r') as f:\n",
    "    loaded_data = json.load(f)\n",
    "\n",
    "# Convert the data back to its original form\n",
    "parsed_data = []\n",
    "for record in loaded_data:\n",
    "    new_record = {}\n",
    "    for key, value in record.items():\n",
    "        new_record[key] = [tuple(item) for item in value]\n",
    "    parsed_data.append(new_record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x430 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layers = ['facebook', 'lunch', 'coauthor', 'leisure', 'work']\n",
    "statuses = ['0', '1', '-1']\n",
    "status_labels = {'0': 'Inactive', '1': 'Active', '-1': 'Activated'}  # Mapping from status to label\n",
    "plot_data = {layer: {status: [] for status in statuses} for layer in layers}\n",
    "\n",
    "# Populate the plot data\n",
    "for epoch, record in enumerate(parsed_data):\n",
    "    for layer in layers:\n",
    "        layer_data = dict(record.get(layer, []))\n",
    "        for status in statuses:\n",
    "            plot_data[layer][status].append(layer_data.get(status, 0))\n",
    "\n",
    "# Generate plots\n",
    "fig, axes = plt.subplots(1, len(layers), figsize=(20, 4.3), sharey=True)\n",
    "\n",
    "for ax, layer in zip(axes, layers):\n",
    "    for status, color in zip(statuses, ['r', 'g', 'b']):\n",
    "        label = status_labels[status]  # Get the label from the mapping\n",
    "        ax.plot(plot_data[layer][status], color=color, label=label)  # Use the label in the plot\n",
    "    ax.set_title(layer.capitalize(), fontsize=16)\n",
    "    ax.set_xlabel('Epoch', fontsize=14)\n",
    "    ax.legend(fontsize=11)\n",
    "\n",
    "axes[0].set_ylabel('Number of Nodes', fontsize=14)\n",
    "plt.savefig(f\"{out_dir}/spreading.pdf\", format=\"pdf\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bdma",
   "language": "python",
   "name": "bdma"
  },
  "language_info": {
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