notebooks/example-sis-ba.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of dynamics learning of SIS on BA networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we show an example of dynamics learning of SIS dynamics on Barabasi-Albert networks. We first start by gathering the configuration of the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "from dynalearn.config import ExperimentConfig\n",
    "\n",
    "config = ExperimentConfig.default(\n",
    "    \"example-sis-ba\", \n",
    "    \"sis\", \n",
    "    \"ba\", \n",
    "    path_to_data=\"./examples/data\", \n",
    "    path_to_summary=\"./examples/summaries\", \n",
    "    path_to_best=\"./examples/best\", \n",
    "    seed=0\n",
    ")\n",
    "config.metrics.names = (\"TrueLTPMetrics\",\"GNNLTPMetrics\",\"MLELTPMetrics\")\n",
    "config.train_details.num_samples = 1000\n",
    "config.train_details.epochs = 10\n",
    "config.dataset.use_groundtruth = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we define the experiment from this configuration.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dynalearn.experiments import Experiment\n",
    "exp = Experiment(config, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can run the experiment. We must perform certain tasks before the experiment is completed: 1) we generate the training and validation datasets, 2) we train the model using these datasets and finally 3) we compute the transition probabilities computed by the trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Experiment example-sis-ba---\n",
      "Current time: 2023-11-04 15:28:23\n",
      "\n",
      "---Config---\n",
      "name: example-sis-ba\n",
      "path_to_data: ./examples/data/example-sis-ba\n",
      "path_to_best: ./examples/best/example-sis-ba.pt\n",
      "path_to_summary: ./examples/summaries\n",
      "dynamics:\n",
      "\tname: SIS\n",
      "\tinfection: 0.04\n",
      "\trecovery: 0.08\n",
      "\tinit_param: None\n",
      "\tis_weighted: False\n",
      "\tis_multiplex: False\n",
      "\tlag: 1\n",
      "\tlagstep: 1\n",
      "\n",
      "networks:\n",
      "\tname: BANetworkGenerator\n",
      "\tnum_nodes: 1000\n",
      "\tm: 2\n",
      "\n",
      "model:\n",
      "\tname: GNNSEDynamics\n",
      "\tgnn_name: DynamicsGATConv\n",
      "\ttype: linear\n",
      "\tnum_states: 2\n",
      "\toptimizer:\n",
      "\t\tname: RAdam\n",
      "\t\tlr: 0.001\n",
      "\t\tweight_decay: 0.0001\n",
      "\t\tbetas: (0.9, 0.999)\n",
      "\t\teps: 1e-08\n",
      "\t\tamsgrad: False\n",
      "\n",
      "\tin_activation: relu\n",
      "\tgnn_activation: relu\n",
      "\tout_activation: relu\n",
      "\tin_channels: [32, 32]\n",
      "\tgnn_channels: 32\n",
      "\tout_channels: [32, 32]\n",
      "\theads: 2\n",
      "\tconcat: True\n",
      "\tbias: True\n",
      "\tself_attention: True\n",
      "\tis_weighted: False\n",
      "\tis_multiplex: False\n",
      "\tlag: 1\n",
      "\tlagstep: 1\n",
      "\tnetwork_layers: None\n",
      "\tin_size: 1\n",
      "\tout_size: 2\n",
      "\tout_act: softmax\n",
      "\tnode_channels: [0]\n",
      "\tnum_params: 8810\n",
      "\n",
      "dataset:\n",
      "\tname: DiscreteStateWeightDataset\n",
      "\tmodes: ['main']\n",
      "\tbias: 0\n",
      "\treplace: True\n",
      "\tuse_groundtruth: True\n",
      "\tuse_strength: True\n",
      "\tcompounded: True\n",
      "\n",
      "train_details:\n",
      "\tval_fraction: 0.01\n",
      "\tval_bias: 0.8\n",
      "\tepochs: 10\n",
      "\tbatch_size: 1\n",
      "\tnum_networks: 1\n",
      "\tnum_samples: 1000\n",
      "\tresampling: 2\n",
      "\tmaxlag: 1\n",
      "\tresample_when_dead: True\n",
      "\n",
      "metrics:\n",
      "\tnames: ('TrueLTPMetrics', 'GNNLTPMetrics', 'MLELTPMetrics')\n",
      "\tltp:\n",
      "\t\tmax_num_sample: 1000\n",
      "\t\tmax_num_points: -1\n",
      "\n",
      "\tprediciton:\n",
      "\t\tmax_num_points: 10000.0\n",
      "\n",
      "\tstatistics:\n",
      "\t\tmax_num_points: 10000\n",
      "\t\tmaxlag: 1\n",
      "\n",
      "\tstationary:\n",
      "\t\tadaptive: True\n",
      "\t\tnum_nodes: 1000\n",
      "\t\tinit_param: {'absorbing': array([0.999, 0.001])}\n",
      "\t\tsampler: SteadyStateSampler\n",
      "\t\tburn: 1\n",
      "\t\tT: 1000\n",
      "\t\ttol: 500\n",
      "\t\tnum_samples: 5\n",
      "\t\tstatistics: MeanVarStatistics\n",
      "\t\tinit_epsilon: 0.001\n",
      "\t\tparameters: {'absorbing': array([0.1       , 0.24081633, 0.38163265, 0.52244898, 0.66326531,\n",
      "       0.80408163, 0.94489796, 1.08571429, 1.22653061, 1.36734694,\n",
      "       1.50816327, 1.64897959, 1.78979592, 1.93061224, 2.07142857,\n",
      "       2.2122449 , 2.35306122, 2.49387755, 2.63469388, 2.7755102 ,\n",
      "       2.91632653, 3.05714286, 3.19795918, 3.33877551, 3.47959184,\n",
      "       3.62040816, 3.76122449, 3.90204082, 4.04285714, 4.18367347,\n",
      "       4.3244898 , 4.46530612, 4.60612245, 4.74693878, 4.8877551 ,\n",
      "       5.02857143, 5.16938776, 5.31020408, 5.45102041, 5.59183673,\n",
      "       5.73265306, 5.87346939, 6.01428571, 6.15510204, 6.29591837,\n",
      "       6.43673469, 6.57755102, 6.71836735, 6.85918367, 7.        ])}\n",
      "\n",
      "\tattention:\n",
      "\t\tmax_num_points: 100\n",
      "\n",
      "\n",
      "train_metrics: ['jensenshannon', 'model_entropy']\n",
      "callbacks:\n",
      "\tnames: ['ModelCheckpoint', 'StepLR']\n",
      "\tstep_size: 20\n",
      "\tgamma: 0.5\n",
      "\tpath_to_best: ./examples/best/example-sis-ba.pt\n",
      "\n",
      "seed: 0\n",
      "\n",
      "\n",
      "---Generating data---\n",
      "Generating training set\n",
      "Computing weights\n",
      "\n",
      "---Partitioning val-data---\n",
      "Fraction of partitionned samples: 0.0099\n",
      "\n",
      "---Training model---\n",
      "Epoch 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wuqiang/zjy/projects/code-dynalearn/notebooks/../dynalearn/nn/optimizers/radam.py:112: UserWarning: This overload of add_ is deprecated:\n",
      "\tadd_(Number alpha, Tensor other)\n",
      "Consider using one of the following signatures instead:\n",
      "\tadd_(Tensor other, *, Number alpha) (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/torch/csrc/utils/python_arg_parser.cpp:1485.)\n",
      "  p_data_fp32.add_(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating train\n",
      "Evaluating val\n",
      "val_loss improved from inf to 0.23897, saving file to ./examples/best/example-sis-ba.pt\n",
      "\ttime: 22.3112\tjensenshannon: 0.0009\tmodel_entropy: 0.2521\tloss: 0.2371\tval_jensenshannon: 0.0009\tval_model_entropy: 0.2538\tval_loss: 0.2390\t\n",
      "Epoch 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wuqiang/zjy/projects/code-dynalearn/notebooks/../dynalearn/nn/callbacks/util.py:23: UserWarning: Impossible to move the file to its final destination: os.replace(/tmp/tmpzaeqekvd, ./examples/best/example-sis-ba.pt) -> [Errno 18] Invalid cross-device link: '/tmp/tmpzaeqekvd' -> './examples/best/example-sis-ba.pt'\n",
      "  warnings.warn(\"Impossible to move the file to its final destination: \"\n",
      "/home/wuqiang/zjy/projects/code-dynalearn/notebooks/../dynalearn/nn/callbacks/util.py:27: UserWarning: Saving ./examples/best/example-sis-ba.pt non-atomically instead.\n",
      "  warnings.warn('Saving %s non-atomically instead.' % filename)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating train\n",
      "Evaluating val\n",
      "val_loss improved from 0.23897 to 0.22871, saving file to ./examples/best/example-sis-ba.pt\n",
      "\ttime: 16.5214\tjensenshannon: 0.0000\tmodel_entropy: 0.2296\tloss: 0.2299\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2284\tval_loss: 0.2287\t\n",
      "Epoch 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wuqiang/zjy/projects/code-dynalearn/notebooks/../dynalearn/nn/callbacks/util.py:23: UserWarning: Impossible to move the file to its final destination: os.replace(/tmp/tmpik9kad5t, ./examples/best/example-sis-ba.pt) -> [Errno 18] Invalid cross-device link: '/tmp/tmpik9kad5t' -> './examples/best/example-sis-ba.pt'\n",
      "  warnings.warn(\"Impossible to move the file to its final destination: \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating train\n",
      "Evaluating val\n",
      "val_loss improved from 0.22871 to 0.22853, saving file to ./examples/best/example-sis-ba.pt\n",
      "\ttime: 16.4779\tjensenshannon: 0.0000\tmodel_entropy: 0.2325\tloss: 0.2320\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2291\tval_loss: 0.2285\t\n",
      "Epoch 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wuqiang/zjy/projects/code-dynalearn/notebooks/../dynalearn/nn/callbacks/util.py:23: UserWarning: Impossible to move the file to its final destination: os.replace(/tmp/tmpfr2n2abh, ./examples/best/example-sis-ba.pt) -> [Errno 18] Invalid cross-device link: '/tmp/tmpfr2n2abh' -> './examples/best/example-sis-ba.pt'\n",
      "  warnings.warn(\"Impossible to move the file to its final destination: \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating train\n",
      "Evaluating val\n",
      "val_loss improved from 0.22853 to 0.22764, saving file to ./examples/best/example-sis-ba.pt\n",
      "\ttime: 16.4917\tjensenshannon: 0.0000\tmodel_entropy: 0.2285\tloss: 0.2284\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2278\tval_loss: 0.2276\t\n",
      "Epoch 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wuqiang/zjy/projects/code-dynalearn/notebooks/../dynalearn/nn/callbacks/util.py:23: UserWarning: Impossible to move the file to its final destination: os.replace(/tmp/tmpekz5qaao, ./examples/best/example-sis-ba.pt) -> [Errno 18] Invalid cross-device link: '/tmp/tmpekz5qaao' -> './examples/best/example-sis-ba.pt'\n",
      "  warnings.warn(\"Impossible to move the file to its final destination: \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating train\n",
      "Evaluating val\n",
      "\ttime: 16.4707\tjensenshannon: 0.0000\tmodel_entropy: 0.2337\tloss: 0.2336\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2304\tval_loss: 0.2303\t\n",
      "Epoch 5\n",
      "Evaluating train\n",
      "Evaluating val\n",
      "\ttime: 16.4798\tjensenshannon: 0.0000\tmodel_entropy: 0.2336\tloss: 0.2322\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2327\tval_loss: 0.2313\t\n",
      "Epoch 6\n",
      "Evaluating train\n",
      "Evaluating val\n",
      "\ttime: 16.4830\tjensenshannon: 0.0000\tmodel_entropy: 0.2300\tloss: 0.2292\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2307\tval_loss: 0.2299\t\n",
      "Epoch 7\n",
      "Evaluating train\n",
      "Evaluating val\n",
      "val_loss improved from 0.22764 to 0.22509, saving file to ./examples/best/example-sis-ba.pt\n",
      "\ttime: 16.3824\tjensenshannon: 0.0000\tmodel_entropy: 0.2312\tloss: 0.2306\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2257\tval_loss: 0.2251\t\n",
      "Epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wuqiang/zjy/projects/code-dynalearn/notebooks/../dynalearn/nn/callbacks/util.py:23: UserWarning: Impossible to move the file to its final destination: os.replace(/tmp/tmpl8a5k0eb, ./examples/best/example-sis-ba.pt) -> [Errno 18] Invalid cross-device link: '/tmp/tmpl8a5k0eb' -> './examples/best/example-sis-ba.pt'\n",
      "  warnings.warn(\"Impossible to move the file to its final destination: \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating train\n",
      "Evaluating val\n",
      "\ttime: 16.2391\tjensenshannon: 0.0000\tmodel_entropy: 0.2292\tloss: 0.2290\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2316\tval_loss: 0.2314\t\n",
      "Epoch 9\n",
      "Evaluating train\n",
      "Evaluating val\n",
      "\ttime: 16.2462\tjensenshannon: 0.0000\tmodel_entropy: 0.2341\tloss: 0.2335\tval_jensenshannon: 0.0000\tval_model_entropy: 0.2288\tval_loss: 0.2282\t\n",
      "\n",
      "---Computing metrics---\n",
      "TrueLTPMetrics\n",
      "GNNLTPMetrics\n",
      "MLELTPMetrics\n",
      "\n",
      "---Finished example-sis-ba---\n",
      "Current time: 2023-11-04 15:28:23\n",
      "Computation time: 00-00:04:01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "exp.run([\"generate_data\", \"partition_val_dataset\", \"train_model\", \"compute_metrics\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "from dynalearn.experiments import LTPMetrics\n",
    "from dynalearn.util.display import *\n",
    "transitions = [(0, 1), (1, 0)]\n",
    "colors = {\n",
    "    \"true\": [color_pale[\"blue\"], color_pale[\"red\"]],\n",
    "    \"gnn\": [color_dark[\"blue\"], color_dark[\"red\"]],\n",
    "    \"mle\": [color_dark[\"blue\"], color_dark[\"red\"]],\n",
    "}\n",
    "linestyles = {\n",
    "    \"true\": [\"-\", \"-\"],\n",
    "    \"gnn\": [\"--\", \"--\"],\n",
    "    \"mle\": [\"None\", \"None\"],\n",
    "}\n",
    "markers = {\n",
    "    \"true\": [\"None\", \"None\"],\n",
    "    \"gnn\": [\"None\", \"None\"],\n",
    "    \"mle\": [\"o\", \"^\"],\n",
    "}\n",
    "plt.rcParams.update({'text.usetex': False})\n",
    "\n",
    "def plot_ltp(experiment, ax):\n",
    "    summary = experiment.metrics[\"TrueLTPMetrics\"].data[\"summaries\"]\n",
    "    true_ltp = experiment.metrics[\"TrueLTPMetrics\"].data[\"ltp\"]\n",
    "    gnn_ltp = experiment.metrics[\"GNNLTPMetrics\"].data[\"ltp\"]\n",
    "    mle_ltp = experiment.metrics[\"MLELTPMetrics\"].data[\"ltp\"]\n",
    "    agg = lambda ltp, in_s, out_s: LTPMetrics.aggregate(\n",
    "            ltp, summary, \n",
    "            in_state=in_s, \n",
    "            out_state=out_s,\n",
    "            axis=1, \n",
    "            reduce=\"mean\", \n",
    "            err_reduce=\"percentile\"\n",
    "        )\n",
    "    x_min, x_max = -np.inf, np.inf\n",
    "    for i, (in_s, out_s) in enumerate(transitions):\n",
    "        x, y, yl, yh = agg(true_ltp, in_s, out_s)\n",
    "        ax.plot(\n",
    "            x, y, color=colors[\"true\"][i], linestyle=linestyles[\"true\"][i],marker=markers[\"true\"][i],linewidth=3\n",
    "        )\n",
    "        ax.fill_between(x, yl, yh, color=colors[\"true\"][i], alpha=0.3)\n",
    "        \n",
    "        x, y, yl, yh = agg(gnn_ltp, in_s, out_s)\n",
    "        ax.plot(\n",
    "            x, y, color=colors[\"gnn\"][i], linestyle=linestyles[\"gnn\"][i],marker=markers[\"gnn\"][i],linewidth=3\n",
    "        )\n",
    "        ax.fill_between(x, yl, yh, color=colors[\"gnn\"][i], alpha=0.3)\n",
    "        \n",
    "        x, y, yl, yh = agg(mle_ltp, in_s, out_s)\n",
    "        yerr = np.concatenate([np.expand_dims(y-yl,0), np.expand_dims(yh-y,0)], axis=0)\n",
    "        # 将 yerr 中的负数替换为零\n",
    "        yerr_non_negative = np.where(yerr < 0, 0, yerr)\n",
    "        ax.errorbar(\n",
    "            x, \n",
    "            y, \n",
    "            yerr=yerr_non_negative,\n",
    "            color=colors[\"mle\"][i], \n",
    "            linestyle=linestyles[\"mle\"][i], \n",
    "            marker=markers[\"mle\"][i], \n",
    "            alpha=0.3\n",
    "        )\n",
    "#         ax.plot(\n",
    "#             x, y, color=colors[\"mle\"][i], linestyle=linestyles[\"mle\"][i], marker=markers[\"mle\"][i], alpha=0.5\n",
    "#         )\n",
    "#         ax.fill_between(x, yl, yh, color=colors[\"mle\"][i], alpha=0.3)\n",
    "        \n",
    "        if x.min() > x_min:\n",
    "            x_min = x.min()\n",
    "        if x.max() < x_max:\n",
    "            x_max = x.max()\n",
    "    ax.set_xlim([x_min, x_max])\n",
    "    ax.set_ylim([0, 1.1])\n",
    "    return ax\n",
    "\n",
    "fig, ax = plt.subplots(1, 1,figsize=(15, 10))\n",
    "\n",
    "plot_ltp(exp, ax)\n",
    "ax.set_xlabel(\"Number of infected neighbors\", fontsize=18)\n",
    "ax.set_ylabel(\"Transition probability\", fontsize=18)\n",
    "handles = []\n",
    "\n",
    "handles.append(Line2D([-1], [-1], linestyle=\"-\", marker=\"None\", linewidth=3,\n",
    "                     color=color_pale[\"grey\"], \n",
    "                     label=r\"True\")\n",
    "             )\n",
    "handles.append(Line2D([-1], [-1], linestyle=\"--\", marker=\"None\", linewidth=3,\n",
    "                     color=color_dark[\"grey\"], \n",
    "                     label=r\"GNN\")\n",
    "             )\n",
    "handles.append((Line2D([-1], [-1], linestyle=\"None\", marker=\"o\", markersize=5, markeredgewidth=1,\n",
    "                      markeredgecolor='k', color=color_dark[\"grey\"], alpha=0.3),\n",
    "                Line2D([-1], [-1], linestyle=\"None\", marker=\"^\", markersize=5, markeredgewidth=1,\n",
    "                      markeredgecolor='k', color=color_dark[\"grey\"], alpha=0.3))\n",
    "             )\n",
    "handles.append(Line2D([-1], [-1], linestyle=\"None\", marker=\"s\", markersize=12,\n",
    "                     color=color_pale[\"blue\"])\n",
    "             )\n",
    "handles.append(Line2D([-1], [-1], linestyle=\"None\", marker=\"s\", markersize=12,\n",
    "                     color=color_pale[\"red\"])\n",
    "             )\n",
    "ax.legend(handles=handles, \n",
    "             labels=[r\"GT\", r\"GNN\", r\"MLE\", \"Infection\", \"Recovery\"],\n",
    "             handler_map={tuple: HandlerTuple(ndivide=None)},\n",
    "             loc=\"upper right\", fancybox=True, fontsize=14, framealpha=0.75, ncol=1\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "juyuan",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}