doc/example/petab_import.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model import using the Petab format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we illustrate how to use [pyPESTO](https://github.com/icb-dcm/pypesto.git) together with [PEtab](https://github.com/petab-dev/petab.git) and [AMICI](https://github.com/icb-dcm/amici.git). We employ models from the [benchmark collection](https://github.com/benchmarking-initiative/benchmark-models-petab), which we first download:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'tmp/benchmark-models' already exists and is not an empty directory.\n",
      "Already up to date.\n"
     ]
    }
   ],
   "source": [
    "import pypesto\n",
    "import amici\n",
    "import petab\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "!git clone --depth 1 https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git tmp/benchmark-models || (cd tmp/benchmark-models && git pull)\n",
    "\n",
    "folder_base = \"tmp/benchmark-models/Benchmark-Models/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manage PEtab model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A PEtab problem comprises all the information on the model, the data and the parameters to perform parameter estimation. We import a model as a `petab.Problem`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# a collection of models that can be simulated\n",
    "\n",
    "#model_name = \"Zheng_PNAS2012\"\n",
    "model_name = \"Boehm_JProteomeRes2014\"\n",
    "#model_name = \"Fujita_SciSignal2010\"\n",
    "#model_name = \"Sneyd_PNAS2002\"\n",
    "#model_name = \"Borghans_BiophysChem1997\"\n",
    "#model_name = \"Elowitz_Nature2000\"\n",
    "#model_name = \"Crauste_CellSystems2017\"\n",
    "#model_name = \"Lucarelli_CellSystems2018\"\n",
    "#model_name = \"Schwen_PONE2014\"\n",
    "#model_name = \"Blasi_CellSystems2016\"\n",
    "\n",
    "# the yaml configuration file links to all needed files\n",
    "yaml_config = os.path.join(folder_base, model_name, model_name + '.yaml')\n",
    "\n",
    "# create a petab problem\n",
    "petab_problem = petab.Problem.from_yaml(yaml_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import model to AMICI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model must be imported to pyPESTO and AMICI. Therefore, we create a `pypesto.PetabImporter` from the problem, and create an AMICI model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model parameters: ['Epo_degradation_BaF3', 'k_exp_hetero', 'k_exp_homo', 'k_imp_hetero', 'k_imp_homo', 'k_phos', 'ratio', 'specC17', 'noiseParameter1_pSTAT5A_rel', 'noiseParameter1_pSTAT5B_rel', 'noiseParameter1_rSTAT5A_rel'] \n",
      "\n",
      "Model const parameters: [] \n",
      "\n",
      "Model outputs:    ['pSTAT5A_rel', 'pSTAT5B_rel', 'rSTAT5A_rel'] \n",
      "\n",
      "Model states:     ['STAT5A', 'STAT5B', 'pApB', 'pApA', 'pBpB', 'nucpApA', 'nucpApB', 'nucpBpB'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "importer = pypesto.PetabImporter(petab_problem)\n",
    "\n",
    "model = importer.create_model()\n",
    "\n",
    "# some model properties\n",
    "print(\"Model parameters:\", list(model.getParameterIds()), '\\n')\n",
    "print(\"Model const parameters:\", list(model.getFixedParameterIds()), '\\n')\n",
    "print(\"Model outputs:   \", list(model.getObservableIds()), '\\n')\n",
    "print(\"Model states:    \", list(model.getStateIds()), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create objective function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To perform parameter estimation, we need to define an objective function, which integrates the model, data, and noise model defined in the PEtab problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import libsbml\n",
    "converter_config = libsbml.SBMLLocalParameterConverter()\\\n",
    "    .getDefaultProperties()\n",
    "petab_problem.sbml_document.convert(converter_config)\n",
    "\n",
    "obj = importer.create_objective()\n",
    "\n",
    "# for some models, hyperparamters need to be adjusted\n",
    "#obj.amici_solver.setMaxSteps(10000)\n",
    "#obj.amici_solver.setRelativeTolerance(1e-7)\n",
    "#obj.amici_solver.setAbsoluteTolerance(1e-7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can request variable derivatives via `sensi_orders`, or function values or residuals as specified via `mode`. Passing `return_dict`, we obtain the direct result of the AMICI simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fval': 138.22199677513575, 'grad': array([ 2.20386015e-02,  5.53227506e-02,  5.78886452e-03,  5.40656415e-03,\n",
      "       -4.51595809e-05,  7.91163446e-03,  0.00000000e+00,  1.07840959e-02,\n",
      "        2.40378735e-02,  1.91919657e-02,  0.00000000e+00]), 'hess': array([[ 2.11105595e+03,  5.89390039e-01,  1.07159910e+02,\n",
      "         2.81393973e+03,  8.94333861e-06, -7.86055092e+02,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 5.89390039e-01,  1.91513744e-03, -1.72774945e-01,\n",
      "         7.12558479e-01, -3.69774927e-08, -3.20531692e-01,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 1.07159910e+02, -1.72774945e-01,  6.99839693e+01,\n",
      "         1.61497679e+02,  7.16323554e-06, -8.83572656e+01,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 2.81393973e+03,  7.12558479e-01,  1.61497679e+02,\n",
      "         3.76058352e+03,  8.40044683e-06, -1.04136909e+03,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 8.94333861e-06, -3.69774927e-08,  7.16323554e-06,\n",
      "         8.40044683e-06,  2.86438192e-10, -2.24927732e-04,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [-7.86055092e+02, -3.20531692e-01, -8.83572656e+01,\n",
      "        -1.04136909e+03, -2.24927732e-04,  9.29902113e+02,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00],\n",
      "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
      "         0.00000000e+00,  0.00000000e+00]]), 'res': array([], dtype=float64), 'sres': array([], shape=(0, 11), dtype=float64), 'rdatas': [<amici.numpy.ReturnDataView object at 0x7f7802f86610>]}\n"
     ]
    }
   ],
   "source": [
    "ret = obj(petab_problem.x_nominal_scaled, mode='mode_fun', sensi_orders=(0,1), return_dict=True)\n",
    "print(ret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem defined in PEtab also defines the fixing of parameters, and parameter bounds. This information is contained in a `pypesto.Problem`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = importer.create_problem(obj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In particular, the problem accounts for the fixing of parametes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 10] [0, 1, 2, 3, 4, 5, 7, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "print(problem.x_fixed_indices, problem.x_free_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem creates a copy of he objective function that takes into account the fixed parameters. The objective function is able to calculate function values and derivatives. A finite difference check whether the computed gradient is accurate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(138.22199677513575, array([ 2.20386015e-02,  5.53227506e-02,  5.78886452e-03,  5.40656415e-03,\n",
      "       -4.51595809e-05,  7.91163446e-03,  1.07840959e-02,  2.40378735e-02,\n",
      "        1.91919657e-02]))\n"
     ]
    }
   ],
   "source": [
    "objective = problem.objective\n",
    "ret = objective(petab_problem.x_nominal_free_scaled, sensi_orders=(0,1))\n",
    "print(ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fd:  [0.02493368 0.05309659 0.00530587 0.01291083 0.00587754 0.01473653\n",
      " 0.01078279 0.02403657 0.01919066]\n",
      "l2 difference:  0.012310244824532144\n"
     ]
    }
   ],
   "source": [
    "eps = 1e-4\n",
    "\n",
    "def fd(x):\n",
    "    grad = np.zeros_like(x)\n",
    "    j = 0\n",
    "    for i, xi in enumerate(x):\n",
    "        mask = np.zeros_like(x)\n",
    "        mask[i] += eps\n",
    "        valinc, _ = objective(x+mask, sensi_orders=(0,1))\n",
    "        valdec, _ = objective(x-mask, sensi_orders=(0,1))\n",
    "        grad[j] = (valinc - valdec) / (2*eps)\n",
    "        j += 1\n",
    "    return grad\n",
    "\n",
    "fdval = fd(petab_problem.x_nominal_free_scaled)\n",
    "print(\"fd: \", fdval)\n",
    "print(\"l2 difference: \", np.linalg.norm(ret[1] - fdval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In short"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the previous steps can be shortened by directly creating an importer object and then a problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "importer = pypesto.PetabImporter.from_yaml(yaml_config)\n",
    "problem = importer.create_problem()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the problem, we can perform optimization. We can specify an optimizer to use, and a parallelization engine to speed things up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Engine set up to use up to 4 processes in total. The number was automatically determined and might not be appropriate on some systems.\n",
      "[Warning] AMICI:CVODES:CVode:ERR_FAILURE: AMICI ERROR: in module CVODES in function CVode : At t = 38.1195 and h = 5.55541e-06, the error test failed repeatedly or with |h| = hmin. \n",
      "[Warning] AMICI:simulation: AMICI forward simulation failed at t = 38.119511:\n",
      "AMICI failed to integrate the forward problem\n",
      "\n",
      "[Warning] AMICI:CVODES:CVode:ERR_FAILURE: AMICI ERROR: in module CVODES in function CVode : At t = 88.9211 and h = 2.14177e-05, the error test failed repeatedly or with |h| = hmin. \n",
      "[Warning] AMICI:simulation: AMICI forward simulation failed at t = 88.921131:\n",
      "AMICI failed to integrate the forward problem\n",
      "\n",
      "[Warning] AMICI:CVODES:CVode:ERR_FAILURE: AMICI ERROR: in module CVODES in function CVode : At t = 88.9211 and h = 2.14177e-05, the error test failed repeatedly or with |h| = hmin. \n",
      "[Warning] AMICI:simulation: AMICI forward simulation failed at t = 88.921131:\n",
      "AMICI failed to integrate the forward problem\n",
      "\n",
      "[Warning] AMICI:CVODES:CVode:ERR_FAILURE: AMICI ERROR: in module CVODES in function CVode : At t = 88.9211 and h = 2.14177e-05, the error test failed repeatedly or with |h| = hmin. \n",
      "[Warning] AMICI:simulation: AMICI forward simulation failed at t = 88.921131:\n",
      "AMICI failed to integrate the forward problem\n",
      "\n",
      "[Warning] AMICI:CVODES:CVode:ERR_FAILURE: AMICI ERROR: in module CVODES in function CVode : At t = 145.551 and h = 1.32433e-05, the error test failed repeatedly or with |h| = hmin. \n",
      "[Warning] AMICI:simulation: AMICI forward simulation failed at t = 145.550813:\n",
      "AMICI failed to integrate the forward problem\n",
      "\n",
      "[Warning] AMICI:CVODES:CVode:ERR_FAILURE: AMICI ERROR: in module CVODES in function CVode : At t = 145.551 and h = 1.32433e-05, the error test failed repeatedly or with |h| = hmin. \n",
      "[Warning] AMICI:simulation: AMICI forward simulation failed at t = 145.550813:\n",
      "AMICI failed to integrate the forward problem\n",
      "\n",
      "[Warning] AMICI:CVODES:CVode:ERR_FAILURE: AMICI ERROR: in module CVODES in function CVode : At t = 145.551 and h = 1.32433e-05, the error test failed repeatedly or with |h| = hmin. \n",
      "[Warning] AMICI:simulation: AMICI forward simulation failed at t = 145.550813:\n",
      "AMICI failed to integrate the forward problem\n",
      "\n"
     ]
    }
   ],
   "source": [
    "optimizer = pypesto.ScipyOptimizer()\n",
    "\n",
    "# engine = pypesto.SingleCoreEngine()\n",
    "engine = pypesto.MultiProcessEngine()\n",
    "\n",
    "# do the optimization\n",
    "result = pypesto.minimize(problem=problem, optimizer=optimizer,\n",
    "                          n_starts=10, engine=engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are contained in a `pypesto.Result` object. It contains e.g. the optimal function values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[138.2219740350346,\n",
       " 138.22404611978106,\n",
       " 145.7594099868979,\n",
       " 147.54397516143254,\n",
       " 149.58782926326572,\n",
       " 151.16644923400784,\n",
       " 154.73312826411254,\n",
       " 205.61953652493594,\n",
       " 249.27713115708494,\n",
       " 249.7459974433355]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.optimize_result.get_for_key('fval')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the standard pyPESTO plotting routines to visualize and analyze the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7802e43510>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pypesto.visualize\n",
    "\n",
    "ref = pypesto.visualize.create_references(x=petab_problem.x_nominal_scaled, fval=obj(petab_problem.x_nominal_scaled))\n",
    "\n",
    "pypesto.visualize.waterfall(result, reference=ref, scale_y='lin')\n",
    "pypesto.visualize.parameters(result, reference=ref)"
   ]
  }
 ],
 "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": "3.7.4"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}