sciope-explore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib widget\n",
    "import gillespy2\n",
    "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n",
    "from gillespy2 import EventAssignment, EventTrigger, Event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Dimerization(Model):\n",
    "    def __init__(self, parameter_values=None):\n",
    "        Model.__init__(self, name=\"Dimerization\")\n",
    "        self.volume = 1\n",
    "\n",
    "        # Parameters\n",
    "        self.add_parameter(Parameter(name=\"k_c\", expression=0.005))\n",
    "        self.add_parameter(Parameter(name=\"k_d\", expression=0.08))\n",
    "\n",
    "        # Species\n",
    "        self.add_species(Species(name=\"Monomer\", initial_value=30, mode=\"discrete\"))\n",
    "        self.add_species(Species(name=\"Dimer\", initial_value=0, mode=\"discrete\"))\n",
    "\n",
    "        # Reactions\n",
    "        self.add_reaction(Reaction(name=\"r_creation\", reactants={'Monomer': 2}, products={'Dimer': 1}, rate=self.listOfParameters[\"k_c\"]))\n",
    "        self.add_reaction(Reaction(name=\"r_dissociation\", reactants={'Dimer': 1}, products={'Monomer': 2}, rate=self.listOfParameters[\"k_d\"]))\n",
    "\n",
    "        # Timespan\n",
    "        self.timespan(np.linspace(0, 100, 101))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Dimerization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sciope.utilities.gillespy2 import wrapper\n",
    "settings = {\"number_of_trajectories\":1}\n",
    "simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['Monomer', 'Dimer'])\n",
    "expression_array = wrapper.get_parameter_expression_array(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client\n",
    "from sciope.designs import latin_hypercube_sampling\n",
    "from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n",
    "\n",
    "c = Client()\n",
    "lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n",
    "lhc.generate_array(1000) #creates a LHD of size 1000\n",
    "\n",
    "#will use default minimal set of features\n",
    "summary_stats = SummariesTSFRESH()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgeiger/.pyenv/versions/3.8.2/envs/dash-playtest/lib/python3.8/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.manifold.t_sne module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.manifold. Anything that cannot be imported from sklearn.manifold is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sciope.stochmet.stochmet import StochMET\n",
    "\n",
    "met = StochMET(simulator, lhc, summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "met.compute(n_points=50, chunk_size=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First lets add some appropiate information about the model and features\n",
    "met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n",
    "met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n",
    "met.data.configurations['listOfSummaries'] = met.summaries.features\n",
    "met.data.configurations['timepoints'] = model.tspan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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       "Button(description='Clear all', style=ButtonStyle())"
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       "IntSlider(value=0, max=10, min=-1)"
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      "text/plain": [
       "Dropdown(description='Species plot 1', options={'Monomer': 0, 'Dimer': 1}, value=0)"
      ]
     },
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    },
    {
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       "Dropdown(description='Species plot 2', index=1, options={'Monomer': 0, 'Dimer': 1}, value=1)"
      ]
     },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k_c</th>\n",
       "      <th>k_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009565</td>\n",
       "      <td>0.089449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.009735</td>\n",
       "      <td>0.213413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.188909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012888</td>\n",
       "      <td>0.162963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.008053</td>\n",
       "      <td>0.157678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.013038</td>\n",
       "      <td>0.100501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.008043</td>\n",
       "      <td>0.150310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.007452</td>\n",
       "      <td>0.226547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.007122</td>\n",
       "      <td>0.149990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.221902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.007142</td>\n",
       "      <td>0.164725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.013719</td>\n",
       "      <td>0.104344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.005010</td>\n",
       "      <td>0.112352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.005080</td>\n",
       "      <td>0.163924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.216617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.012928</td>\n",
       "      <td>0.192432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.008624</td>\n",
       "      <td>0.235836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.012608</td>\n",
       "      <td>0.122442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.011386</td>\n",
       "      <td>0.236797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.014890</td>\n",
       "      <td>0.126927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.150470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.202042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.010285</td>\n",
       "      <td>0.106907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.012117</td>\n",
       "      <td>0.100180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.005020</td>\n",
       "      <td>0.119720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.011577</td>\n",
       "      <td>0.214054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.007803</td>\n",
       "      <td>0.146466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.012758</td>\n",
       "      <td>0.231512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.014780</td>\n",
       "      <td>0.167447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.014299</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.008393</td>\n",
       "      <td>0.238398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.159279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.006131</td>\n",
       "      <td>0.098098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.008293</td>\n",
       "      <td>0.168729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.010656</td>\n",
       "      <td>0.213734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.006772</td>\n",
       "      <td>0.229429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.186827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.012027</td>\n",
       "      <td>0.206847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.012708</td>\n",
       "      <td>0.196116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.010966</td>\n",
       "      <td>0.103383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.007312</td>\n",
       "      <td>0.124204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.221101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.007753</td>\n",
       "      <td>0.109630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.007863</td>\n",
       "      <td>0.190671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.011917</td>\n",
       "      <td>0.125806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.013799</td>\n",
       "      <td>0.163283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.005420</td>\n",
       "      <td>0.164084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.012968</td>\n",
       "      <td>0.221742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.005110</td>\n",
       "      <td>0.186026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.011977</td>\n",
       "      <td>0.170010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         k_c       k_d\n",
       "0   0.009565  0.089449\n",
       "1   0.009735  0.213413\n",
       "2   0.013158  0.188909\n",
       "3   0.012888  0.162963\n",
       "4   0.008053  0.157678\n",
       "5   0.013038  0.100501\n",
       "6   0.008043  0.150310\n",
       "7   0.007452  0.226547\n",
       "8   0.007122  0.149990\n",
       "9   0.013428  0.221902\n",
       "10  0.007142  0.164725\n",
       "11  0.013719  0.104344\n",
       "12  0.005010  0.112352\n",
       "13  0.005080  0.163924\n",
       "14  0.008594  0.216617\n",
       "15  0.012928  0.192432\n",
       "16  0.008624  0.235836\n",
       "17  0.012608  0.122442\n",
       "18  0.011386  0.236797\n",
       "19  0.014890  0.126927\n",
       "20  0.008504  0.150470\n",
       "21  0.008574  0.202042\n",
       "22  0.010285  0.106907\n",
       "23  0.012117  0.100180\n",
       "24  0.005020  0.119720\n",
       "25  0.011577  0.214054\n",
       "26  0.007803  0.146466\n",
       "27  0.012758  0.231512\n",
       "28  0.014780  0.167447\n",
       "29  0.014299  0.222222\n",
       "30  0.008393  0.238398\n",
       "31  0.012658  0.159279\n",
       "32  0.006131  0.098098\n",
       "33  0.008293  0.168729\n",
       "34  0.010656  0.213734\n",
       "35  0.006772  0.229429\n",
       "36  0.007172  0.186827\n",
       "37  0.012027  0.206847\n",
       "38  0.012708  0.196116\n",
       "39  0.010966  0.103383\n",
       "40  0.007312  0.124204\n",
       "41  0.011126  0.221101\n",
       "42  0.007753  0.109630\n",
       "43  0.007863  0.190671\n",
       "44  0.011917  0.125806\n",
       "45  0.013799  0.163283\n",
       "46  0.005420  0.164084\n",
       "47  0.012968  0.221742\n",
       "48  0.005110  0.186026\n",
       "49  0.011977  0.170010"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we use UMAP for dimension reduction\n",
    "met.explore(dr_method='umap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method train in module sciope.models.label_propagation:\n",
      "\n",
      "train(inputs, targets, min_=0.01, max_=30, niter=10, stepsize=0.1) method of sciope.models.label_propagation.LPModel instance\n",
      "    Train the LP model given the data\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    inputs : nd-array\n",
      "        independent variables\n",
      "    targets : vector\n",
      "        dependent variable\n",
      "    min : float\n",
      "        []\n",
      "    max : float\n",
      "        []\n",
      "    niter : int\n",
      "        number of training iterations\n",
      "    stepsize : float\n",
      "        []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(model_lp.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgeiger/.pyenv/versions/3.8.2/envs/dash-playtest/lib/python3.8/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.semi_supervised.label_propagation module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.semi_supervised. Anything that cannot be imported from sklearn.semi_supervised is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basinhopping step 0: f 0.0937494\n",
      "basinhopping step 1: f 0.0932612 trial_f 0.0932612 accepted 1  lowest_f 0.0932612\n",
      "found new global minimum on step 1 with function value 0.0932612\n",
      "basinhopping step 2: f 0.0932705 trial_f 0.0932705 accepted 1  lowest_f 0.0932612\n",
      "basinhopping step 3: f 0.09326 trial_f 0.09326 accepted 1  lowest_f 0.09326\n",
      "found new global minimum on step 3 with function value 0.09326\n",
      "basinhopping step 4: f 0.0932612 trial_f 0.0932612 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 5: f 0.09326 trial_f 0.09326 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 6: f 0.0932607 trial_f 0.0932607 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 7: f 0.0933522 trial_f 0.0933522 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 8: f 0.0933041 trial_f 0.0933041 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 9: f 0.0934684 trial_f 0.0934684 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 10: f 0.093261 trial_f 0.093261 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 11: f 0.0932984 trial_f 0.0932984 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 12: f 0.0933427 trial_f 0.0933427 accepted 1  lowest_f 0.09326\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 13: f 0.0936866 trial_f 0.0936866 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 14: f 0.093277 trial_f 0.093277 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 15: f 0.0934195 trial_f 0.0934195 accepted 1  lowest_f 0.09326\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 16: f 0.099711 trial_f 0.099711 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 17: f 0.093271 trial_f 0.093271 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 18: f 0.0932661 trial_f 0.0932661 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 19: f 0.0932636 trial_f 0.0932636 accepted 1  lowest_f 0.09326\n",
      "basinhopping step 20: f 0.0932599 trial_f 0.0932599 accepted 1  lowest_f 0.0932599\n",
      "found new global minimum on step 20 with function value 0.0932599\n",
      "basinhopping step 21: f 0.0932637 trial_f 0.0932637 accepted 1  lowest_f 0.0932599\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 22: f 0.0932668 trial_f 0.0932668 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 23: f 0.0932844 trial_f 0.0932844 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 24: f 0.0934127 trial_f 0.0934127 accepted 1  lowest_f 0.0932599\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 25: f 0.0932779 trial_f 0.0932779 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 26: f 0.0932654 trial_f 0.0932654 accepted 1  lowest_f 0.0932599\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 27: f 0.0932604 trial_f 0.0932604 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 28: f 0.0933293 trial_f 0.0933293 accepted 1  lowest_f 0.0932599\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 29: f 0.0932602 trial_f 0.0932602 accepted 1  lowest_f 0.0932599\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 30: f 0.093311 trial_f 0.093311 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 31: f 0.0932609 trial_f 0.0932609 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 32: f 0.0932846 trial_f 0.0932846 accepted 1  lowest_f 0.0932599\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 33: f 0.0932768 trial_f 0.0932768 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 34: f 0.0932627 trial_f 0.0932627 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 35: f 0.0956646 trial_f 0.0956646 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 36: f 0.0945725 trial_f 0.0945725 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 37: f 0.0944777 trial_f 0.0944777 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 38: f 0.0932604 trial_f 0.0932604 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 39: f 0.0932599 trial_f 0.0932599 accepted 1  lowest_f 0.0932599\n",
      "found new global minimum on step 39 with function value 0.0932599\n",
      "basinhopping step 40: f 0.0932599 trial_f 0.0932599 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 41: f 0.0932613 trial_f 0.0932613 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 42: f 0.0932634 trial_f 0.0932634 accepted 1  lowest_f 0.0932599\n",
      "warning: basinhopping: local minimization failure\n",
      "basinhopping step 43: f 0.0933436 trial_f 0.0933436 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 44: f 0.0932814 trial_f 0.0932814 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 45: f 0.093308 trial_f 0.093308 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 46: f 0.0953836 trial_f 0.0953836 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 47: f 0.0938049 trial_f 0.0938049 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 48: f 0.0932632 trial_f 0.0932632 accepted 1  lowest_f 0.0932599\n",
      "basinhopping step 49: f 0.0934068 trial_f 0.0934068 accepted 1  lowest_f 0.0932599\n",
      "adaptive stepsize: acceptance rate 0.980000 target 0.500000 new stepsize 0.111111 old stepsize 0.1\n",
      "basinhopping step 50: f 0.0932905 trial_f 0.0932905 accepted 1  lowest_f 0.0932599\n"
     ]
    }
   ],
   "source": [
    "from sciope.models.label_propagation import LPModel\n",
    "#here lets use the dimension reduction embedding as input data\n",
    "data = met.dr_model.embedding_\n",
    "\n",
    "model_lp = LPModel()\n",
    "#train using basinhopping\n",
    "model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just to vislualize the result we will map the label distribution to the user_labels (will enable us to see the LP model \n",
    "# output when using method \"explore\")\n",
    "\n",
    "user_labels = np.copy(met.data.user_labels)\n",
    "#takes the label corresponding to index 0\n",
    "met.data.user_labels = model_lp.model.label_distributions_[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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     "output_type": "display_data"
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      "text/plain": [
       "Button(description='Clear all', style=ButtonStyle())"
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     },
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       "Button(description='Submit', style=ButtonStyle())"
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      "text/plain": [
       "IntSlider(value=0, max=10, min=-1)"
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      "text/plain": [
       "Dropdown(description='Species plot 1', options={'Monomer': 0, 'Dimer': 1}, value=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "Dropdown(description='Species plot 2', index=1, options={'Monomer': 0, 'Dimer': 1}, value=1)"
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     "output_type": "display_data"
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     "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>k_c</th>\n",
       "      <th>k_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009565</td>\n",
       "      <td>0.089449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.009735</td>\n",
       "      <td>0.213413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.188909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012888</td>\n",
       "      <td>0.162963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.008053</td>\n",
       "      <td>0.157678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.013038</td>\n",
       "      <td>0.100501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.008043</td>\n",
       "      <td>0.150310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.007452</td>\n",
       "      <td>0.226547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.007122</td>\n",
       "      <td>0.149990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.013428</td>\n",
       "      <td>0.221902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.007142</td>\n",
       "      <td>0.164725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.013719</td>\n",
       "      <td>0.104344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.005010</td>\n",
       "      <td>0.112352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.005080</td>\n",
       "      <td>0.163924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.216617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.012928</td>\n",
       "      <td>0.192432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.008624</td>\n",
       "      <td>0.235836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.012608</td>\n",
       "      <td>0.122442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.011386</td>\n",
       "      <td>0.236797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.014890</td>\n",
       "      <td>0.126927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.008504</td>\n",
       "      <td>0.150470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.202042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.010285</td>\n",
       "      <td>0.106907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.012117</td>\n",
       "      <td>0.100180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.005020</td>\n",
       "      <td>0.119720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.011577</td>\n",
       "      <td>0.214054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.007803</td>\n",
       "      <td>0.146466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.012758</td>\n",
       "      <td>0.231512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.014780</td>\n",
       "      <td>0.167447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.014299</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.008393</td>\n",
       "      <td>0.238398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.159279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.006131</td>\n",
       "      <td>0.098098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.008293</td>\n",
       "      <td>0.168729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.010656</td>\n",
       "      <td>0.213734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.006772</td>\n",
       "      <td>0.229429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.186827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.012027</td>\n",
       "      <td>0.206847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.012708</td>\n",
       "      <td>0.196116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.010966</td>\n",
       "      <td>0.103383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.007312</td>\n",
       "      <td>0.124204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.221101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.007753</td>\n",
       "      <td>0.109630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.007863</td>\n",
       "      <td>0.190671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.011917</td>\n",
       "      <td>0.125806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.013799</td>\n",
       "      <td>0.163283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.005420</td>\n",
       "      <td>0.164084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.012968</td>\n",
       "      <td>0.221742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.005110</td>\n",
       "      <td>0.186026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.011977</td>\n",
       "      <td>0.170010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         k_c       k_d\n",
       "0   0.009565  0.089449\n",
       "1   0.009735  0.213413\n",
       "2   0.013158  0.188909\n",
       "3   0.012888  0.162963\n",
       "4   0.008053  0.157678\n",
       "5   0.013038  0.100501\n",
       "6   0.008043  0.150310\n",
       "7   0.007452  0.226547\n",
       "8   0.007122  0.149990\n",
       "9   0.013428  0.221902\n",
       "10  0.007142  0.164725\n",
       "11  0.013719  0.104344\n",
       "12  0.005010  0.112352\n",
       "13  0.005080  0.163924\n",
       "14  0.008594  0.216617\n",
       "15  0.012928  0.192432\n",
       "16  0.008624  0.235836\n",
       "17  0.012608  0.122442\n",
       "18  0.011386  0.236797\n",
       "19  0.014890  0.126927\n",
       "20  0.008504  0.150470\n",
       "21  0.008574  0.202042\n",
       "22  0.010285  0.106907\n",
       "23  0.012117  0.100180\n",
       "24  0.005020  0.119720\n",
       "25  0.011577  0.214054\n",
       "26  0.007803  0.146466\n",
       "27  0.012758  0.231512\n",
       "28  0.014780  0.167447\n",
       "29  0.014299  0.222222\n",
       "30  0.008393  0.238398\n",
       "31  0.012658  0.159279\n",
       "32  0.006131  0.098098\n",
       "33  0.008293  0.168729\n",
       "34  0.010656  0.213734\n",
       "35  0.006772  0.229429\n",
       "36  0.007172  0.186827\n",
       "37  0.012027  0.206847\n",
       "38  0.012708  0.196116\n",
       "39  0.010966  0.103383\n",
       "40  0.007312  0.124204\n",
       "41  0.011126  0.221101\n",
       "42  0.007753  0.109630\n",
       "43  0.007863  0.190671\n",
       "44  0.011917  0.125806\n",
       "45  0.013799  0.163283\n",
       "46  0.005420  0.164084\n",
       "47  0.012968  0.221742\n",
       "48  0.005110  0.186026\n",
       "49  0.011977  0.170010"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "met.explore(dr_method='umap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "met.data.user_labels = user_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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