docs/notebooks/Setting_up_Parameter_Estimation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter estimation setup\n",
    "This example goes through the steps of setting up the parameter estimation task directly from basiCO. Experimental data is provided through pandas dataframes, and mapping of the columns will be done by convention using specially crafted column names. \n",
    "\n",
    "We start as usual: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "if '../..' not in sys.path:\n",
    "    sys.path.append('../..')\n",
    "from basico import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### preparing the data\n",
    "In this example we generate the data set by modifying the brusselator model, to produce some noisy data, and we will take that data set as a starting point for the parameter estimation later on. So lets start loading the brusselator model, and adding two observable variables that follow the model species with some noise added: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_example('brusselator')\n",
    "add_parameter('obs_x', type='assignment', expression='[X] + UNIFORM(0,1) - 0.5')\n",
    "add_parameter('obs_y', type='assignment', expression='[Y] + UNIFORM(0,1) - 0.5');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we simulate the model, ensuring to return all result columns (not just the concentration ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = run_time_course(start_time=0, use_number=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Values[obs_x]</th>\n",
       "      <th>Values[obs_y]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>2.999996</td>\n",
       "      <td>2.999996</td>\n",
       "      <td>2.795629</td>\n",
       "      <td>3.114868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>3.408155</td>\n",
       "      <td>0.817484</td>\n",
       "      <td>3.086833</td>\n",
       "      <td>1.012499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1.896454</td>\n",
       "      <td>1.276790</td>\n",
       "      <td>1.456064</td>\n",
       "      <td>1.714168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>0.876253</td>\n",
       "      <td>1.872929</td>\n",
       "      <td>0.579525</td>\n",
       "      <td>2.232427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.345934</td>\n",
       "      <td>2.368188</td>\n",
       "      <td>0.448776</td>\n",
       "      <td>2.465324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             X         Y  Values[obs_x]  Values[obs_y]\n",
       "Time                                                  \n",
       "0.0   2.999996  2.999996       2.795629       3.114868\n",
       "0.5   3.408155  0.817484       3.086833       1.012499\n",
       "1.0   1.896454  1.276790       1.456064       1.714168\n",
       "1.5   0.876253  1.872929       0.579525       2.232427\n",
       "2.0   0.345934  2.368188       0.448776       2.465324"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets plot the simulation data (first plot) and the noisy data (second plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = result.plot(y='X')\n",
    "result.plot(y='Y', ax=ax)\n",
    "\n",
    "ax = result.plot(y='Values[obs_x]', style='o')\n",
    "result.plot(y='Values[obs_y]', style='o' , ax=ax);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so our experimental data is the data frame with just the 2 last columns. I also rename the columns, so it makes it clear that i want to map it later to the transient concentrations. I'll also reset the index, so Time is a separate column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = result.drop(columns=['X', 'Y'])\n",
    "data.rename(columns = {'Values[obs_x]':'[X]', 'Values[obs_y]':'[Y]'}, inplace=True)\n",
    "data = data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Time</th>\n",
       "      <th>[X]</th>\n",
       "      <th>[Y]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.795629</td>\n",
       "      <td>3.114868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>3.086833</td>\n",
       "      <td>1.012499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.456064</td>\n",
       "      <td>1.714168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.579525</td>\n",
       "      <td>2.232427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.448776</td>\n",
       "      <td>2.465324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time       [X]       [Y]\n",
       "0   0.0  2.795629  3.114868\n",
       "1   0.5  3.086833  1.012499\n",
       "2   1.0  1.456064  1.714168\n",
       "3   1.5  0.579525  2.232427\n",
       "4   2.0  0.448776  2.465324"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the parameters that gave rise to the solution were: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>value</th>\n",
       "      <th>reaction</th>\n",
       "      <th>type</th>\n",
       "      <th>mapped_to</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(R1).k1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>R1</td>\n",
       "      <td>local</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R2).k1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>R2</td>\n",
       "      <td>local</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R3).k1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>R3</td>\n",
       "      <td>local</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R4).k1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>R4</td>\n",
       "      <td>local</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         value reaction   type mapped_to\n",
       "name                                    \n",
       "(R1).k1    1.0       R1  local          \n",
       "(R2).k1    1.0       R2  local          \n",
       "(R3).k1    1.0       R3  local          \n",
       "(R4).k1    1.0       R4  local          "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_reaction_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets set them to something, else, so that the parameter estimation task will have something to do to find them again (we also remove the observable variables as we won't need them anymore): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set_reaction_parameters(['(R1).k1', '(R2).k1', '(R3).k1', '(R4).k1'], value=0.1)\n",
    "remove_parameter('obs_x')\n",
    "remove_parameter('obs_y')\n",
    "run_time_course(start_time=0).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>value</th>\n",
       "      <th>reaction</th>\n",
       "      <th>type</th>\n",
       "      <th>mapped_to</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(R1).k1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>R1</td>\n",
       "      <td>local</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R2).k1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>R2</td>\n",
       "      <td>local</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R3).k1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>R3</td>\n",
       "      <td>local</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R4).k1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>R4</td>\n",
       "      <td>local</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         value reaction   type mapped_to\n",
       "name                                    \n",
       "(R1).k1    0.1       R1  local          \n",
       "(R2).k1    0.1       R2  local          \n",
       "(R3).k1    0.1       R3  local          \n",
       "(R4).k1    0.1       R4  local          "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_reaction_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### setting up the parameter estimation\n",
    "the next step is to setup the paramter estimation task, first we add the experiment, and it will tell us where the experiment file has been created in case you want to delete it later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/frank/Development/basico/docs/notebooks/exp1.txt'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_experiment('exp1', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now lets verify that the experiment is there: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<CExperiment \"exp1\">"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_experiment('exp1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we have the mapping we were expecting: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>type</th>\n",
       "      <th>mapping</th>\n",
       "      <th>cn</th>\n",
       "      <th>column_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>column</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>time</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dependent</td>\n",
       "      <td>[X]</td>\n",
       "      <td>CN=Root,Model=The Brusselator,Vector=Compartme...</td>\n",
       "      <td>[X]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dependent</td>\n",
       "      <td>[Y]</td>\n",
       "      <td>CN=Root,Model=The Brusselator,Vector=Compartme...</td>\n",
       "      <td>[Y]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             type mapping                                                 cn  \\\n",
       "column                                                                         \n",
       "0            time                                                              \n",
       "1       dependent     [X]  CN=Root,Model=The Brusselator,Vector=Compartme...   \n",
       "2       dependent     [Y]  CN=Root,Model=The Brusselator,Vector=Compartme...   \n",
       "\n",
       "       column_name  \n",
       "column              \n",
       "0             Time  \n",
       "1              [X]  \n",
       "2              [Y]  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_experiment_mapping('exp1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we are ready to add the parameters we want to fit, in our case this will be the reaction parameters: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_items = [\n",
    "            {'name': '(R1).k1', 'lower': 0.001, 'upper': 2},\n",
    "            {'name': '(R2).k1', 'lower': 0.001, 'upper': 2},\n",
    "            {'name': '(R3).k1', 'lower': 0.001, 'upper': 2},\n",
    "            {'name': '(R4).k1', 'lower': 0.001, 'upper': 2},\n",
    "        ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_fit_parameters(fit_items)"
   ]
  },
  {
   "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>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>start</th>\n",
       "      <th>affected</th>\n",
       "      <th>cn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(R1).k1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[]</td>\n",
       "      <td>CN=Root,Model=The Brusselator,Vector=Reactions...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R2).k1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[]</td>\n",
       "      <td>CN=Root,Model=The Brusselator,Vector=Reactions...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R3).k1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[]</td>\n",
       "      <td>CN=Root,Model=The Brusselator,Vector=Reactions...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R4).k1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>[]</td>\n",
       "      <td>CN=Root,Model=The Brusselator,Vector=Reactions...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lower upper  start affected  \\\n",
       "name                                   \n",
       "(R1).k1  0.001     2    0.1       []   \n",
       "(R2).k1  0.001     2    0.1       []   \n",
       "(R3).k1  0.001     2    0.1       []   \n",
       "(R4).k1  0.001     2    0.1       []   \n",
       "\n",
       "                                                        cn  \n",
       "name                                                        \n",
       "(R1).k1  CN=Root,Model=The Brusselator,Vector=Reactions...  \n",
       "(R2).k1  CN=Root,Model=The Brusselator,Vector=Reactions...  \n",
       "(R3).k1  CN=Root,Model=The Brusselator,Vector=Reactions...  \n",
       "(R4).k1  CN=Root,Model=The Brusselator,Vector=Reactions...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_fit_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and with that we are ready to run the parameter estimation. Lets see how the fit looks now, it should be bad, since we set the parameters way off: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_per_experiment();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets run the parameter estimation, all the task names from the COPASI GUI are valid, algorithms here: \n",
    "\n",
    "Current Solution:\n",
    "\n",
    "* `Current Solution Statistics`,\n",
    "\n",
    "Global Methods: \n",
    "\n",
    "* `Random Search`,\n",
    "* `Simulated Annealing`,\n",
    "* `Differential Evolution`,\n",
    "* `Scatter Search`,\n",
    "* `Genetic Algorithm`,\n",
    "* `Evolutionary Programming`,\n",
    "* `Genetic Algorithm SR`,\n",
    "* `Evolution Strategy (SRES)`,\n",
    "* `Particle Swarm`,\n",
    "\n",
    "Local Methods:\n",
    "\n",
    "* `Levenberg - Marquardt`,\n",
    "* `Hooke & Jeeves`,\n",
    "* `Nelder - Mead`,\n",
    "* `Steepest Descent`,\n",
    "* `NL2SOL`,\n",
    "* `Praxis`,\n",
    "* `Truncated Newton`,\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>sol</th>\n",
       "      <th>affected</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(R1).k1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.908366</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R2).k1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R3).k1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.025244</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(R4).k1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>2</td>\n",
       "      <td>0.902824</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lower upper       sol affected\n",
       "name                                   \n",
       "(R1).k1  0.001     2  0.908366       []\n",
       "(R2).k1  0.001     2  0.001000       []\n",
       "(R3).k1  0.001     2  0.025244       []\n",
       "(R4).k1  0.001     2  0.902824       []"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_parameter_estimation(method='Evolution Strategy (SRES)', update_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_per_dependent_variable();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constraints\n",
    "Additionally, you can add constraints to the parameter estimation problem. These constraint will be evaluated after each simulation, and allow to ensure, for example that solutions with concentrations outside a certain range are rejected. \n",
    "\n",
    "Constraints are defined, just as the fit parameters specified above, when using `set_fit_constraints`. To see the constraints defined, you would use `get_fit_constraints`. \n",
    "\n",
    "To demonstrate, lets create a constraint for this example, that rejects solutions, where the concentration of `[Y]` is outside the range of 2...10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_fit_constraints([\n",
    "    {'name': 'Y', 'lower': 2, 'upper': 10}\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`get_fit_constraints` returns a pandas dataframe of the constraints, and as usual we can trasnform them to a dictionary if needed using `as_dict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>lower</th>\n",
       "      <th>upper</th>\n",
       "      <th>start</th>\n",
       "      <th>affected</th>\n",
       "      <th>cn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>6.597017</td>\n",
       "      <td>[]</td>\n",
       "      <td>CN=Root,Model=The Brusselator,Vector=Compartme...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lower upper     start affected  \\\n",
       "name                                  \n",
       "Y        2    10  6.597017       []   \n",
       "\n",
       "                                                     cn  \n",
       "name                                                     \n",
       "Y     CN=Root,Model=The Brusselator,Vector=Compartme...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_fit_constraints()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Y',\n",
       " 'lower': '2',\n",
       " 'upper': '10',\n",
       " 'start': 6.597017117106328,\n",
       " 'affected': [],\n",
       " 'cn': CN=Root,Model=The Brusselator,Vector=Compartments[compartment],Vector=Metabolites[Y],Reference=Concentration}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "as_dict(get_fit_constraints())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now lets try to run the parameter estimation again, using this constraint: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'obj': 295.53087769380215,\n",
       " 'rms': 0.8574097253059701,\n",
       " 'sd': 0.8617075453441385,\n",
       " 'f_evals': 147,\n",
       " 'failed_evals_exception': 0,\n",
       " 'failed_evals_nan': 0,\n",
       " 'constraint_evals': 2,\n",
       " 'failed_constraint_evals': 0,\n",
       " 'cpu_time': 0.028593999999999998,\n",
       " 'data_points': 402,\n",
       " 'valid_data_points': 402,\n",
       " 'evals_per_sec': 0.00019451700680272107}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_parameter_estimation(method=PE.LEVENBERG_MARQUARDT)\n",
    "get_fit_statistic()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look at the fit statistic, we see that the evalations failed. This happened, because the allowed interval in the constraint was too small. Setting the lower bound of the constraint, fixes that issue: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'obj': 295.53087769380215,\n",
       " 'rms': 0.8574097253059701,\n",
       " 'sd': 0.8617075453441385,\n",
       " 'f_evals': 147,\n",
       " 'failed_evals_exception': 0,\n",
       " 'failed_evals_nan': 0,\n",
       " 'constraint_evals': 2,\n",
       " 'failed_constraint_evals': 0,\n",
       " 'cpu_time': 0.026792,\n",
       " 'data_points': 402,\n",
       " 'valid_data_points': 402,\n",
       " 'evals_per_sec': 0.00018225850340136056}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_fit_constraints([\n",
    "    {'name': 'Y', 'lower': 0, 'upper': 10}\n",
    "])\n",
    "run_parameter_estimation(method=PE.LEVENBERG_MARQUARDT)\n",
    "get_fit_statistic()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've seen how to add constraints to the parameter estimation problem, and how it would look if the constraint causes the parameter estimation procedure to fail. "
   ]
  }
 ],
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