tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this notebook is to provide an easy tutorial showcasing the different features of the BioFuzzNet module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\clegac\\Documents\\Code\\venv\\biofuzznet\\lib\\site-packages\\torch\\__init__.py:614: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at ..\\torch\\csrc\\tensor\\python_tensor.cpp:453.)\n",
      "  _C._set_default_tensor_type(t)\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "# External imports\n",
    "import torch\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Module imports\n",
    "import biological_fuzzy_logic_networks.biofuzznet as biofuzznet\n",
    "import biological_fuzzy_logic_networks.biofuzzdataset as biofuzzdataset\n",
    "import biological_fuzzy_logic_networks.Hill_function as Hill_function\n",
    "import biological_fuzzy_logic_networks.utils as utils\n",
    "import biological_fuzzy_logic_networks.draw as draw\n",
    "import biological_fuzzy_logic_networks.biomixnet as biomixnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1a1bd4c72f0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a BioFuzzNet or a BioMixNet\n",
    "\n",
    "## From scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an empty BioFuzzNet\n",
    "G_not = biofuzznet.BioFuzzNet(None, None)\n",
    "\n",
    "# Add nodes\n",
    "G_not.add_fuzzy_node(\"A\", \"BIO\")  # A biological node\n",
    "G_not.add_fuzzy_node(\"B\", \"BIO\")\n",
    "G_not.add_fuzzy_node(\"NOT A\", \"NOT\")  # A logical not gate\n",
    "# Add edges\n",
    "G_not.add_transfer_edge(\"A\", \"NOT A\")  # A transfer edge downstream of a biological node\n",
    "G_not.add_simple_edge(\"NOT A\", \"B\")  # A simple edge downstream of a logical node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates a BioFuzzNet from a list of nodes and a list of edges\n",
    "# If a node has more than 2 incoming edges, it assumes those are integrated via an OR gate\n",
    "# This is the same network as above\n",
    "\n",
    "node_list = [\"A\", \"B\"]\n",
    "edge_dic = {(\"A\", \"B\"): -1}\n",
    "G_not_from_list = biofuzznet.BioFuzzNet(node_list, edge_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw the networks to compare them\n",
    "# Color schemes for the drawing\n",
    "general_edge_color_scheme = {\"simple\": \"b\", \"transfer_function\": \"r\"}\n",
    "general_node_shape_scheme = {\n",
    "    \"biological\": \"s\",\n",
    "    \"logic_gate_AND\": \"o\",\n",
    "    \"logic_gate_OR\": \"d\",\n",
    "    \"logic_gate_NOT\": \"^\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': array([ 0., -2.]),\n",
       " 'B': array([0.        , 1.42857143]),\n",
       " 'NOT A': array([0.        , 0.57142857])}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw.draw_BioFuzzNet(G_not, general_edge_color_scheme, general_node_shape_scheme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': array([ 0., -2.]),\n",
       " 'B': array([0.        , 1.42857143]),\n",
       " 'not1': array([0.        , 0.57142857])}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw.draw_BioFuzzNet(\n",
    "    G_not_from_list, general_edge_color_scheme, general_node_shape_scheme\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the BioFuzzNet from file\n",
    "BFZ_from_file = biofuzznet.BioFuzzNet.build_BioFuzzNet_from_file(\n",
    "    \"biological_fuzzy_logic_networks/example_networks/Network_without_GRN.tsv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the BioMixNet from file\n",
    "BMX = biomixnet.BioMixNet.build_BioMixNet_from_file(\n",
    "    \"biological_fuzzy_logic_networks/example_networks/Network_without_GRN.tsv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CD45': array([-0.00881088, -0.9322599 ]),\n",
       " 'IL15Ra': array([-0.00528653, -0.9322599 ]),\n",
       " 'CTLA4': array([-0.00176218, -0.9322599 ]),\n",
       " 'CD40': array([ 0.00176218, -0.9322599 ]),\n",
       " '41BB': array([ 0.00528653, -0.9322599 ]),\n",
       " 'CD28': array([ 0.00881088, -0.9322599 ]),\n",
       " 'GSK3b': array([-0.00176218,  0.19553237]),\n",
       " 'PI3K': array([0.00176218, 0.19553237]),\n",
       " 'JAK3': array([-0.00528653, -0.9040651 ]),\n",
       " 'TRAF3': array([-0.00176218, -0.9040651 ]),\n",
       " 'TRAF2': array([ 0.00176218, -0.9040651 ]),\n",
       " 'Lck': array([ 0.00528653, -0.9040651 ]),\n",
       " 'CD3z': array([-0.00176218, -0.73489626]),\n",
       " 'Grb2': array([ 0.00176218, -0.73489626]),\n",
       " 'JAK1': array([-0.01233523, -0.9252112 ]),\n",
       " 'Shc': array([-0.00881088, -0.9252112 ]),\n",
       " 'SHP1': array([-0.00528653, -0.9252112 ]),\n",
       " 'SHP2': array([-0.00176218, -0.9252112 ]),\n",
       " 'PP2A': array([ 0.00176218, -0.9252112 ]),\n",
       " 'TRAF6': array([ 0.00528653, -0.9252112 ]),\n",
       " 'TRAF1': array([ 0.00881088, -0.9252112 ]),\n",
       " 'GADS': array([ 0.01233523, -0.9252112 ]),\n",
       " 'MAPK': array([-0.00176218,  0.64664928]),\n",
       " 'Akt': array([0.00176218, 0.64664928]),\n",
       " 'AP1': array([0.        , 1.54888309]),\n",
       " 'ERK12': array([0.        , 1.32332464]),\n",
       " 'PLCg1': array([ 0.        , -0.65031184]),\n",
       " 'mTOR': array([0.        , 0.87220773]),\n",
       " 'p38': array([-0.00528653,  1.09776618]),\n",
       " 'NFAT': array([-0.00176218,  1.09776618]),\n",
       " 'cNFkB': array([0.00176218, 1.09776618]),\n",
       " 'Bad': array([0.00528653, 1.09776618]),\n",
       " 'NFkB': array([-0.00352435,  1.77444155]),\n",
       " 'BclXL': array([0.        , 1.77444155]),\n",
       " 'Bcl2': array([0.00352435, 1.77444155]),\n",
       " 'IL2': array([0., 2.]),\n",
       " 'TRAF1_and_TRAF2': array([ 0.        , -0.89349204]),\n",
       " 'not2': array([-0.00176218, -0.87939464]),\n",
       " 'TRAF2_and_TRAF3': array([ 0.00176218, -0.87939464]),\n",
       " 'IFNG': array([-0.00528653, -0.87587029]),\n",
       " 'IL6': array([-0.00176218, -0.87587029]),\n",
       " 'TNFa': array([ 0.00176218, -0.87587029]),\n",
       " 'ncNFkB': array([ 0.00528653, -0.87587029]),\n",
       " 'STAT5': array([ 0.        , -0.88996769]),\n",
       " 'STAT3_and_STAT5': array([ 0.        , -0.86529724]),\n",
       " 'STAT3': array([ 0.       , -0.9181625]),\n",
       " 'cmyc': array([ 0.        , -0.86177289]),\n",
       " 'or29': array([-0.01057305,  0.64312492]),\n",
       " 'or26': array([-0.0070487 ,  0.64312492]),\n",
       " 'or25': array([-0.00352435,  0.64312492]),\n",
       " 'or9': array([0.        , 0.64312492]),\n",
       " 'or8': array([0.00352435, 0.64312492]),\n",
       " 'not5': array([0.0070487 , 0.64312492]),\n",
       " 'not1': array([0.01057305, 0.64312492]),\n",
       " 'or32': array([-0.01057305,  1.09424183]),\n",
       " 'or31': array([-0.0070487 ,  1.09424183]),\n",
       " 'or28': array([-0.00352435,  1.09424183]),\n",
       " 'or15': array([0.        , 1.09424183]),\n",
       " 'or11': array([0.00352435, 1.09424183]),\n",
       " 'not4': array([0.0070487 , 1.09424183]),\n",
       " 'not3': array([0.01057305, 1.09424183]),\n",
       " 'or35': array([-0.00528653,  1.54535874]),\n",
       " 'or13': array([-0.00176218,  1.54535874]),\n",
       " 'not7': array([0.00176218, 1.54535874]),\n",
       " 'not6': array([0.00528653, 1.54535874]),\n",
       " 'not11': array([-0.00528653, -0.92168685]),\n",
       " 'not10': array([-0.00176218, -0.92168685]),\n",
       " 'not9': array([ 0.00176218, -0.92168685]),\n",
       " 'not8': array([ 0.00528653, -0.92168685]),\n",
       " 'not17': array([-0.00881088, -0.91463815]),\n",
       " 'not16': array([-0.00528653, -0.91463815]),\n",
       " 'not15': array([-0.00176218, -0.91463815]),\n",
       " 'not14': array([ 0.00176218, -0.91463815]),\n",
       " 'not13': array([ 0.00528653, -0.91463815]),\n",
       " 'not12': array([ 0.00881088, -0.91463815]),\n",
       " 'or1': array([ 0.        , -0.68203099]),\n",
       " 'or2': array([ 0.        , -0.59744657]),\n",
       " 'or3': array([ 0.        , -0.03355044]),\n",
       " 'or23': array([-0.00352435,  0.19200802]),\n",
       " 'or17': array([0.        , 0.19200802]),\n",
       " 'or4': array([0.00352435, 0.19200802]),\n",
       " 'or39': array([-0.00528653, -0.90758945]),\n",
       " 'or38': array([-0.00176218, -0.90758945]),\n",
       " 'or37': array([ 0.00176218, -0.90758945]),\n",
       " 'or5': array([ 0.00528653, -0.90758945]),\n",
       " 'or6': array([ 0.        , -0.85119983]),\n",
       " 'or21': array([-0.00176218, -0.73842061]),\n",
       " 'or7': array([ 0.00176218, -0.73842061]),\n",
       " 'or30': array([-0.00528653,  0.86868338]),\n",
       " 'or27': array([-0.00176218,  0.86868338]),\n",
       " 'or14': array([0.00176218, 0.86868338]),\n",
       " 'or10': array([0.00528653, 0.86868338]),\n",
       " 'or16': array([-0.00176218,  1.31980029]),\n",
       " 'or12': array([0.00176218, 1.31980029]),\n",
       " 'or18': array([ 0.        , -0.82300503]),\n",
       " 'or19': array([ 0.        , -0.79481022]),\n",
       " 'or20': array([ 0.        , -0.76661541]),\n",
       " 'or22': array([ 0.        , -0.65383619]),\n",
       " 'or24': array([0.        , 0.41756647]),\n",
       " 'or36': array([-0.00352435,  1.7709172 ]),\n",
       " 'or34': array([0.       , 1.7709172]),\n",
       " 'or33': array([0.00352435, 1.7709172 ])}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can also draw BioMixNet with the draw_BioFuzzNet function\n",
    "# But this one is big so it won't look good\n",
    "draw.draw_BioFuzzNet(BMX, general_edge_color_scheme, general_node_shape_scheme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'node_type': 'biological', 'output_state': None, 'ground_truth': None}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is what a biological node looks like\n",
    "BFZ_from_file.nodes()[\"41BB\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'node_type': 'logic_gate_OR', 'output_state': None}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is what a logical node looks like\n",
    "BFZ_from_file.nodes()[\"or1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'edge_type': 'transfer_function',\n",
       " 'layer': HillTransferFunction(),\n",
       " 'weight': 1}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is what a transfer edge looks like\n",
    "BFZ_from_file.edges()[(\"CD28\", \"GADS\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([0.0050], requires_grad=True)\n",
      "Parameter containing:\n",
      "tensor([0.0729], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "# and its parameters are accessed via\n",
    "hill = BFZ_from_file.edges()[(\"CD28\", \"GADS\")][\"layer\"]\n",
    "print(hill.n)\n",
    "print(hill.K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# And you can plot the function at this particular edge\n",
    "\n",
    "utils.plot_edge_function(BFZ_from_file.edges()[(\"CD28\", \"GADS\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulating a BioFuzzNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's use the CAR domain signalling network\n",
    "my_model = biofuzznet.BioFuzzNet.build_BioFuzzNet_from_file(\n",
    "    \"biological_fuzzy_logic_networks/example_networks/Network_without_GRN.tsv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The edge ('CD28', 'GADS') has Hill parameters n = tensor([3.6013], grad_fn=<AddBackward0>) and K = tensor([0.8874], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD28', 'or1') has Hill parameters n = tensor([2.4057], grad_fn=<AddBackward0>) and K = tensor([0.6333], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD28', 'or5') has Hill parameters n = tensor([1.4274], grad_fn=<AddBackward0>) and K = tensor([0.3164], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD28', 'or6') has Hill parameters n = tensor([2.1814], grad_fn=<AddBackward0>) and K = tensor([0.7441], grad_fn=<ExpBackward0>)\n",
      "The edge ('PI3K', 'not1') has Hill parameters n = tensor([1.5468], grad_fn=<AddBackward0>) and K = tensor([1.4021], grad_fn=<ExpBackward0>)\n",
      "The edge ('PI3K', 'or8') has Hill parameters n = tensor([1.0688], grad_fn=<AddBackward0>) and K = tensor([2.7070], grad_fn=<ExpBackward0>)\n",
      "The edge ('PI3K', 'or9') has Hill parameters n = tensor([1.0333], grad_fn=<AddBackward0>) and K = tensor([0.6389], grad_fn=<ExpBackward0>)\n",
      "The edge ('Lck', 'not2') has Hill parameters n = tensor([3.2817], grad_fn=<AddBackward0>) and K = tensor([1.7400], grad_fn=<ExpBackward0>)\n",
      "The edge ('Lck', 'or18') has Hill parameters n = tensor([1.8691], grad_fn=<AddBackward0>) and K = tensor([0.9822], grad_fn=<ExpBackward0>)\n",
      "The edge ('Grb2', 'or1') has Hill parameters n = tensor([1.7139], grad_fn=<AddBackward0>) and K = tensor([0.4861], grad_fn=<ExpBackward0>)\n",
      "The edge ('Grb2', 'or22') has Hill parameters n = tensor([1.4802], grad_fn=<AddBackward0>) and K = tensor([1.3392], grad_fn=<ExpBackward0>)\n",
      "The edge ('Grb2', 'or23') has Hill parameters n = tensor([1.6595], grad_fn=<AddBackward0>) and K = tensor([0.6294], grad_fn=<ExpBackward0>)\n",
      "The edge ('Akt', 'mTOR') has Hill parameters n = tensor([2.3205], grad_fn=<AddBackward0>) and K = tensor([4.9427], grad_fn=<ExpBackward0>)\n",
      "The edge ('Akt', 'not3') has Hill parameters n = tensor([1.6900], grad_fn=<AddBackward0>) and K = tensor([0.2554], grad_fn=<ExpBackward0>)\n",
      "The edge ('Akt', 'not4') has Hill parameters n = tensor([1.4166], grad_fn=<AddBackward0>) and K = tensor([6.8044], grad_fn=<ExpBackward0>)\n",
      "The edge ('Akt', 'or26') has Hill parameters n = tensor([3.3449], grad_fn=<AddBackward0>) and K = tensor([1.6545], grad_fn=<ExpBackward0>)\n",
      "The edge ('ERK12', 'or10') has Hill parameters n = tensor([1.0995], grad_fn=<AddBackward0>) and K = tensor([0.3824], grad_fn=<ExpBackward0>)\n",
      "The edge ('GSK3b', 'not5') has Hill parameters n = tensor([1.6481], grad_fn=<AddBackward0>) and K = tensor([1.7471], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD3z', 'or6') has Hill parameters n = tensor([3.5678], grad_fn=<AddBackward0>) and K = tensor([0.3109], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD3z', 'or22') has Hill parameters n = tensor([4.4071], grad_fn=<AddBackward0>) and K = tensor([1.3836], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD3z', 'or32') has Hill parameters n = tensor([1.2562], grad_fn=<AddBackward0>) and K = tensor([0.4916], grad_fn=<ExpBackward0>)\n",
      "The edge ('PLCg1', 'or26') has Hill parameters n = tensor([2.4303], grad_fn=<AddBackward0>) and K = tensor([1.0787], grad_fn=<ExpBackward0>)\n",
      "The edge ('PLCg1', 'or29') has Hill parameters n = tensor([1.0592], grad_fn=<AddBackward0>) and K = tensor([1.0035], grad_fn=<ExpBackward0>)\n",
      "The edge ('MAPK', 'or9') has Hill parameters n = tensor([1.2138], grad_fn=<AddBackward0>) and K = tensor([3.5550], grad_fn=<ExpBackward0>)\n",
      "The edge ('MAPK', 'or14') has Hill parameters n = tensor([1.9585], grad_fn=<AddBackward0>) and K = tensor([0.4457], grad_fn=<ExpBackward0>)\n",
      "The edge ('MAPK', 'or32') has Hill parameters n = tensor([1.2551], grad_fn=<AddBackward0>) and K = tensor([1.8410], grad_fn=<ExpBackward0>)\n",
      "The edge ('Bad', 'not6') has Hill parameters n = tensor([2.1150], grad_fn=<AddBackward0>) and K = tensor([1.8363], grad_fn=<ExpBackward0>)\n",
      "The edge ('Bad', 'not7') has Hill parameters n = tensor([2.0845], grad_fn=<AddBackward0>) and K = tensor([2.0687], grad_fn=<ExpBackward0>)\n",
      "The edge ('cNFkB', 'or35') has Hill parameters n = tensor([3.7663], grad_fn=<AddBackward0>) and K = tensor([0.4603], grad_fn=<ExpBackward0>)\n",
      "The edge ('p38', 'or30') has Hill parameters n = tensor([1.7657], grad_fn=<AddBackward0>) and K = tensor([0.6376], grad_fn=<ExpBackward0>)\n",
      "The edge ('NFkB', 'IL2') has Hill parameters n = tensor([1.1485], grad_fn=<AddBackward0>) and K = tensor([3.6312], grad_fn=<ExpBackward0>)\n",
      "The edge ('NFkB', 'or34') has Hill parameters n = tensor([1.5577], grad_fn=<AddBackward0>) and K = tensor([3.5137], grad_fn=<ExpBackward0>)\n",
      "The edge ('41BB', 'TRAF1') has Hill parameters n = tensor([1.8456], grad_fn=<AddBackward0>) and K = tensor([1.3413], grad_fn=<ExpBackward0>)\n",
      "The edge ('41BB', 'or2') has Hill parameters n = tensor([2.3891], grad_fn=<AddBackward0>) and K = tensor([0.9732], grad_fn=<ExpBackward0>)\n",
      "The edge ('41BB', 'or37') has Hill parameters n = tensor([3.1224], grad_fn=<AddBackward0>) and K = tensor([3.3853], grad_fn=<ExpBackward0>)\n",
      "The edge ('41BB', 'or38') has Hill parameters n = tensor([1.1643], grad_fn=<AddBackward0>) and K = tensor([0.2387], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF1', 'TRAF1_and_TRAF2') has Hill parameters n = tensor([2.3592], grad_fn=<AddBackward0>) and K = tensor([0.1007], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF2', 'TRAF1_and_TRAF2') has Hill parameters n = tensor([2.7068], grad_fn=<AddBackward0>) and K = tensor([1.1945], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF2', 'TRAF2_and_TRAF3') has Hill parameters n = tensor([3.0210], grad_fn=<AddBackward0>) and K = tensor([0.9051], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF2', 'or8') has Hill parameters n = tensor([1.8352], grad_fn=<AddBackward0>) and K = tensor([1.9553], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF2', 'or23') has Hill parameters n = tensor([1.6798], grad_fn=<AddBackward0>) and K = tensor([0.7851], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF3', 'TRAF2_and_TRAF3') has Hill parameters n = tensor([2.9591], grad_fn=<AddBackward0>) and K = tensor([1.6507], grad_fn=<ExpBackward0>)\n",
      "The edge ('ncNFkB', 'or35') has Hill parameters n = tensor([1.2261], grad_fn=<AddBackward0>) and K = tensor([0.2851], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD40', 'TRAF6') has Hill parameters n = tensor([3.2850], grad_fn=<AddBackward0>) and K = tensor([1.1801], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD40', 'or37') has Hill parameters n = tensor([1.3936], grad_fn=<AddBackward0>) and K = tensor([1.2980], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD40', 'or38') has Hill parameters n = tensor([1.0784], grad_fn=<AddBackward0>) and K = tensor([0.3274], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD40', 'or39') has Hill parameters n = tensor([1.3328], grad_fn=<AddBackward0>) and K = tensor([2.1642], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF6', 'or3') has Hill parameters n = tensor([1.3329], grad_fn=<AddBackward0>) and K = tensor([1.1846], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF6', 'or25') has Hill parameters n = tensor([2.4109], grad_fn=<AddBackward0>) and K = tensor([0.9743], grad_fn=<ExpBackward0>)\n",
      "The edge ('TRAF6', 'or28') has Hill parameters n = tensor([1.0658], grad_fn=<AddBackward0>) and K = tensor([1.3031], grad_fn=<ExpBackward0>)\n",
      "The edge ('JAK3', 'STAT5') has Hill parameters n = tensor([1.1233], grad_fn=<AddBackward0>) and K = tensor([0.4278], grad_fn=<ExpBackward0>)\n",
      "The edge ('STAT5', 'TNFa') has Hill parameters n = tensor([1.1894], grad_fn=<AddBackward0>) and K = tensor([1.0424], grad_fn=<ExpBackward0>)\n",
      "The edge ('STAT5', 'IL6') has Hill parameters n = tensor([1.3270], grad_fn=<AddBackward0>) and K = tensor([0.9164], grad_fn=<ExpBackward0>)\n",
      "The edge ('STAT5', 'IFNG') has Hill parameters n = tensor([1.5622], grad_fn=<AddBackward0>) and K = tensor([2.2392], grad_fn=<ExpBackward0>)\n",
      "The edge ('STAT5', 'STAT3_and_STAT5') has Hill parameters n = tensor([2.8983], grad_fn=<AddBackward0>) and K = tensor([0.3910], grad_fn=<ExpBackward0>)\n",
      "The edge ('CTLA4', 'not8') has Hill parameters n = tensor([2.1467], grad_fn=<AddBackward0>) and K = tensor([0.9339], grad_fn=<ExpBackward0>)\n",
      "The edge ('CTLA4', 'PP2A') has Hill parameters n = tensor([1.0207], grad_fn=<AddBackward0>) and K = tensor([1.1806], grad_fn=<ExpBackward0>)\n",
      "The edge ('CTLA4', 'SHP2') has Hill parameters n = tensor([1.9352], grad_fn=<AddBackward0>) and K = tensor([0.2641], grad_fn=<ExpBackward0>)\n",
      "The edge ('CTLA4', 'not9') has Hill parameters n = tensor([1.1940], grad_fn=<AddBackward0>) and K = tensor([1.0669], grad_fn=<ExpBackward0>)\n",
      "The edge ('CTLA4', 'not10') has Hill parameters n = tensor([3.3192], grad_fn=<AddBackward0>) and K = tensor([2.3415], grad_fn=<ExpBackward0>)\n",
      "The edge ('CTLA4', 'not11') has Hill parameters n = tensor([1.0782], grad_fn=<AddBackward0>) and K = tensor([1.0292], grad_fn=<ExpBackward0>)\n",
      "The edge ('CTLA4', 'SHP1') has Hill parameters n = tensor([2.1171], grad_fn=<AddBackward0>) and K = tensor([0.2725], grad_fn=<ExpBackward0>)\n",
      "The edge ('CTLA4', 'or4') has Hill parameters n = tensor([1.2545], grad_fn=<AddBackward0>) and K = tensor([0.8391], grad_fn=<ExpBackward0>)\n",
      "The edge ('PP2A', 'not12') has Hill parameters n = tensor([1.6820], grad_fn=<AddBackward0>) and K = tensor([0.4026], grad_fn=<ExpBackward0>)\n",
      "The edge ('PP2A', 'not13') has Hill parameters n = tensor([1.2049], grad_fn=<AddBackward0>) and K = tensor([2.4243], grad_fn=<ExpBackward0>)\n",
      "The edge ('PP2A', 'not14') has Hill parameters n = tensor([1.2487], grad_fn=<AddBackward0>) and K = tensor([0.3775], grad_fn=<ExpBackward0>)\n",
      "The edge ('SHP2', 'not15') has Hill parameters n = tensor([2.5715], grad_fn=<AddBackward0>) and K = tensor([0.6094], grad_fn=<ExpBackward0>)\n",
      "The edge ('SHP2', 'not16') has Hill parameters n = tensor([1.1358], grad_fn=<AddBackward0>) and K = tensor([1.1248], grad_fn=<ExpBackward0>)\n",
      "The edge ('SHP1', 'not17') has Hill parameters n = tensor([1.3586], grad_fn=<AddBackward0>) and K = tensor([0.5077], grad_fn=<ExpBackward0>)\n",
      "The edge ('IL15Ra', 'Shc') has Hill parameters n = tensor([2.1017], grad_fn=<AddBackward0>) and K = tensor([0.7028], grad_fn=<ExpBackward0>)\n",
      "The edge ('IL15Ra', 'JAK1') has Hill parameters n = tensor([1.7089], grad_fn=<AddBackward0>) and K = tensor([0.5874], grad_fn=<ExpBackward0>)\n",
      "The edge ('IL15Ra', 'or39') has Hill parameters n = tensor([7.], grad_fn=<AddBackward0>) and K = tensor([2.9494], grad_fn=<ExpBackward0>)\n",
      "The edge ('Shc', 'or7') has Hill parameters n = tensor([2.5147], grad_fn=<AddBackward0>) and K = tensor([0.3827], grad_fn=<ExpBackward0>)\n",
      "The edge ('JAK1', 'STAT3') has Hill parameters n = tensor([1.4350], grad_fn=<AddBackward0>) and K = tensor([0.8575], grad_fn=<ExpBackward0>)\n",
      "The edge ('STAT3', 'STAT3_and_STAT5') has Hill parameters n = tensor([1.0564], grad_fn=<AddBackward0>) and K = tensor([1.1186], grad_fn=<ExpBackward0>)\n",
      "The edge ('CD45', 'or5') has Hill parameters n = tensor([5.8912], grad_fn=<AddBackward0>) and K = tensor([0.1687], grad_fn=<ExpBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# It is initialised with random transfer function parameters\n",
    "for edge in my_model.transfer_edges:\n",
    "    # Each transfer edge has a layer\n",
    "    layer = my_model.edges()[edge][\"layer\"]\n",
    "    # Each layer has a n and an K parameter\n",
    "    print(\n",
    "        f\"The edge {edge} has Hill parameters n = {1 + torch.exp(layer.n)} and K = {torch.exp(layer.K)}\"\n",
    "    )\n",
    "    # Those parameters are torch.nn.Parameters and thus have a grad_fn which corresponds to their gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CD28', '41BB', 'CD40', 'CTLA4', 'IL15Ra', 'CD45']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are its root nodes?\n",
    "my_model.root_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current output state at node 41BB is None and the current ground truth is None\n"
     ]
    }
   ],
   "source": [
    "# Currently the network has None values at the output state and ground truth tensors\n",
    "print(\n",
    "    f\"The current output state at node 41BB is {my_model.nodes()['41BB']['output_state']} and the current ground truth is {my_model.nodes()['41BB']['ground_truth']}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current output state at node 41BB is tensor([0.2102, 0.6449, 0.0293,  ..., 0.8458, 0.3625, 0.6541]) and the current ground truth is tensor([0.7739, 0.4373, 0.4319,  ..., 0.4872, 0.6610, 0.0604])\n"
     ]
    }
   ],
   "source": [
    "# We can give both of those fields a random value\n",
    "my_model.initialise_random_truth_and_output(3000)\n",
    "print(\n",
    "    f\"The current output state at node 41BB is {my_model.nodes()['41BB']['output_state']} and the current ground truth is {my_model.nodes()['41BB']['ground_truth']}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current output state at node 41BB is tensor([1., 1., 1.,  ..., 1., 1., 1.]) and the current ground truth is tensor([1., 1., 1.,  ..., 1., 1., 1.])\n",
      "The current output state at node ERK12 is tensor([0.1154, 0.8781, 0.7608,  ..., 0.3120, 0.5101, 0.1952]) and the current ground truth is tensor([0.8676, 0.6409, 0.3770,  ..., 0.6286, 0.6878, 0.5234])\n"
     ]
    }
   ],
   "source": [
    "# That works!\n",
    "# But what if we want to specify a given value as the ground truth?\n",
    "# Let's say that we know the value at the root nodes\n",
    "\n",
    "tensor_list = [torch.ones(3000) for i in range(6)]\n",
    "ground_truth = {\n",
    "    my_model.root_nodes[i]: tensor_list[i] for i in range(len(my_model.root_nodes))\n",
    "}\n",
    "\n",
    "# We can set the ground truth at the root nodes\n",
    "my_model.set_network_ground_truth(ground_truth)\n",
    "print(\n",
    "    f\"The current output state at node 41BB is {my_model.nodes()['41BB']['output_state']} and the current ground truth is {my_model.nodes()['41BB']['ground_truth']}\"\n",
    ")\n",
    "\n",
    "# But careful! The nodes that are not in ground_truth are not modified\n",
    "# (If the warnings were not enough to warn you)\n",
    "# (I do know they get pretty annoying, you can disable them during optimisation ...\n",
    "# ... but I suggest keeping them when you're setting up your network so you are aware of possible misbehaviors)\n",
    "print(\n",
    "    f\"The current output state at node ERK12 is {my_model.nodes()['ERK12']['output_state']} and the current ground truth is {my_model.nodes()['ERK12']['ground_truth']}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's now simulate our model\n",
    "# For that you need to tell it what nodes you give as input\n",
    "# Here it's easy, there's no cycle, it's just the root nodes\n",
    "\n",
    "my_model.sequential_update(my_model.root_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current output state at node ERK12 is tensor([1., 1., 1.,  ..., 1., 1., 1.], grad_fn=<IndexPutBackward0>) and the current ground truth is tensor([0.8676, 0.6409, 0.3770,  ..., 0.6286, 0.6878, 0.5234])\n"
     ]
    }
   ],
   "source": [
    "# Let's see those results\n",
    "print(\n",
    "    f\"The current output state at node ERK12 is {my_model.nodes()['ERK12']['output_state']} and the current ground truth is {my_model.nodes()['ERK12']['ground_truth']}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The simulation did work well: the output state tensor has only one value,\n",
    "#  which makes sense since all 3000 cells had the same value 1 at all inputs\n",
    "# Notice that the ground truth hasn't changed\n",
    "# Which is reassuring, we don't want our measuremets to change when the model is simulated\n",
    "# Now let's try to optimise, shall we?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimisation of a BioFuzzNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current output state at node 41BB is tensor([1., 1., 0.,  ..., 1., 0., 0.]) and the current ground truth is tensor([1., 1., 0.,  ..., 1., 0., 0.])\n",
      "The current output state at node ERK12 is tensor([1., 1., 1.,  ..., 1., 1., 1.], grad_fn=<IndexPutBackward0>) and the current ground truth is tensor([0.8676, 0.6409, 0.3770,  ..., 0.6286, 0.6878, 0.5234])\n"
     ]
    }
   ],
   "source": [
    "# For a measurement, we need input values and ground truth values (ie the values we're trying to fit)\n",
    "# for a training set and a validation set.\n",
    "# We'll simulate a dataset that is a bit more complex\n",
    "tensor_list = [torch.ones(3000) for i in range(6)]\n",
    "# Select some indices to set to 0\n",
    "indices = np.random.randint(low=0, high=3000, size=1500)\n",
    "for c in indices:\n",
    "    for i in range(6):\n",
    "        tensor_list[i][c] = 0\n",
    "ground_truth = {\n",
    "    my_model.root_nodes[i]: tensor_list[i] for i in range(len(my_model.root_nodes))\n",
    "}\n",
    "\n",
    "# We can set the ground truth at the root nodes\n",
    "my_model.set_network_ground_truth(ground_truth)\n",
    "print(\n",
    "    f\"The current output state at node 41BB is {my_model.nodes()['41BB']['output_state']} and the current ground truth is {my_model.nodes()['41BB']['ground_truth']}\"\n",
    ")\n",
    "\n",
    "# But careful! The nodes that are not in ground_truth are not modified\n",
    "# (If the warnings were not enough to warn you)\n",
    "# (I do know they get pretty annoying, you can disable them during optimisation ...\n",
    "# ... but I suggest keeping them when you're setting up your network so you are aware of possible misbehaviors)\n",
    "print(\n",
    "    f\"The current output state at node ERK12 is {my_model.nodes()['ERK12']['output_state']} and the current ground truth is {my_model.nodes()['ERK12']['ground_truth']}\"\n",
    ")\n",
    "\n",
    "# Then we simulate again\n",
    "my_model.sequential_update(my_model.root_nodes)\n",
    "\n",
    "# Let's define the ground truth: it's the result of the previous simulation, so it's already in the output states\n",
    "ground_truth_to_find = my_model.output_states\n",
    "\n",
    "# Let's define the inputs and ground_truth for the training and validation set\n",
    "ground_truth_train = {\n",
    "    node: ground_truth_to_find[node][0:2000] for node in ground_truth_to_find.keys()\n",
    "}\n",
    "ground_truth_test = {\n",
    "    node: ground_truth_to_find[node][2000:3000] for node in ground_truth_to_find.keys()\n",
    "}\n",
    "\n",
    "# The input is just the ground truth values at the root nodes\n",
    "input_train = {node: ground_truth_train[node] for node in my_model.root_nodes}\n",
    "input_test = {node: ground_truth_test[node] for node in my_model.root_nodes}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then we create a model to optimise: same topology, but different parameters at the transfer function\n",
    "model_to_fit = biofuzznet.BioFuzzNet.build_BioFuzzNet_from_file(\n",
    "    \"biological_fuzzy_logic_networks/example_networks/Network_without_GRN.tsv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we just need to specify some optimisation parameters\n",
    "\n",
    "learning_rate = 5e-3\n",
    "epochs = 2  # That seems like a lot, but 1 epoch is basically one simulation of the network on all datapoints, so we need a lot of them\n",
    "batch_size = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [05:33<00:00, 166.56s/it]\n"
     ]
    }
   ],
   "source": [
    "# Now let's optimise\n",
    "\n",
    "loss = model_to_fit.conduct_optimisation(\n",
    "    input=input_train,\n",
    "    ground_truth=ground_truth_train,\n",
    "    test_input=input_test,\n",
    "    test_ground_truth=ground_truth_test,\n",
    "    epochs=epochs,\n",
    "    learning_rate=learning_rate,\n",
    "    batch_size=batch_size,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>loss</th>\n",
       "      <th>phase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-11-02 15:36:55.479542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-11-02 15:37:21.311825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-11-02 15:37:46.378353</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-11-02 15:38:11.727781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-11-02 15:38:36.518738</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-11-02 15:39:01.659176</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-11-02 15:39:19.311929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-11-02 15:39:19.625781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-11-02 15:39:44.378663</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-11-02 15:40:09.774973</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-11-02 15:40:34.660508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023-11-02 15:40:59.974776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2023-11-02 15:41:24.709566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2023-11-02 15:41:50.955964</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2023-11-02 15:42:08.521383</td>\n",
       "      <td>0.0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023-11-02 15:42:08.842855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          time loss  phase\n",
       "0   2023-11-02 15:36:55.479542  0.0  train\n",
       "1   2023-11-02 15:37:21.311825  0.0  train\n",
       "2   2023-11-02 15:37:46.378353  0.0  train\n",
       "3   2023-11-02 15:38:11.727781  0.0  train\n",
       "4   2023-11-02 15:38:36.518738  0.0  train\n",
       "5   2023-11-02 15:39:01.659176  0.0  train\n",
       "6   2023-11-02 15:39:19.311929  0.0  train\n",
       "7   2023-11-02 15:39:19.625781  0.0   test\n",
       "8   2023-11-02 15:39:44.378663  0.0  train\n",
       "9   2023-11-02 15:40:09.774973  0.0  train\n",
       "10  2023-11-02 15:40:34.660508  0.0  train\n",
       "11  2023-11-02 15:40:59.974776  0.0  train\n",
       "12  2023-11-02 15:41:24.709566  0.0  train\n",
       "13  2023-11-02 15:41:50.955964  0.0  train\n",
       "14  2023-11-02 15:42:08.521383  0.0  train\n",
       "15  2023-11-02 15:42:08.842855  0.0   test"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You get the loss as the output of the optimisation\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can plot it to check that it converges\n",
    "# It probably won't because we did very few epochs\n",
    "sns.lineplot(data=loss, x=\"time\", y=\"loss\", hue=\"phase\")\n",
    "plt.show()\n",
    "\n",
    "# Here since we used 0 and 1 as input the Hill functions do not matter so much and we are already so close to the truth that there is not much to see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current output state at node 41BB is tensor([0.2577, 0.6366, 0.5163,  ..., 0.6653, 0.3529, 0.4909]) and the current ground truth is tensor([0.2577, 0.6366, 0.5163,  ..., 0.6653, 0.3529, 0.4909])\n",
      "The current output state at node ERK12 is tensor([1., 1., 1.,  ..., 1., 1., 1.], grad_fn=<IndexPutBackward0>) and the current ground truth is tensor([0.8676, 0.6409, 0.3770,  ..., 0.6286, 0.6878, 0.5234])\n"
     ]
    }
   ],
   "source": [
    "# Let's try with a more interesting input\n",
    "# For a measurement, we need input values and ground truth values (ie the values we're trying to fit)\n",
    "# for a training set and a validation set.\n",
    "# We'll simulate a dataset that is a bit more complex\n",
    "tensor_list = [torch.Tensor(np.random.rand(3000))] * 6\n",
    "ground_truth = {\n",
    "    my_model.root_nodes[i]: tensor_list[i] for i in range(len(my_model.root_nodes))\n",
    "}\n",
    "\n",
    "# We can set the ground truth at the root nodes\n",
    "my_model.set_network_ground_truth(ground_truth)\n",
    "print(\n",
    "    f\"The current output state at node 41BB is {my_model.nodes()['41BB']['output_state']} and the current ground truth is {my_model.nodes()['41BB']['ground_truth']}\"\n",
    ")\n",
    "\n",
    "# But careful! The nodes that are not in ground_truth are not modified\n",
    "# (If the warnings were not enough to warn you)\n",
    "# (I do know they get pretty annoying, you can disable them during optimisation ...\n",
    "# ... but I suggest keeping them when you're setting up your network so you are aware of possible misbehaviors)\n",
    "print(\n",
    "    f\"The current output state at node ERK12 is {my_model.nodes()['ERK12']['output_state']} and the current ground truth is {my_model.nodes()['ERK12']['ground_truth']}\"\n",
    ")\n",
    "\n",
    "# Then we simulate again\n",
    "my_model.sequential_update(my_model.root_nodes)\n",
    "\n",
    "# Let's define the ground truth: it's the result of the previous simulation, so it's already in the output states\n",
    "ground_truth_to_find = my_model.output_states\n",
    "\n",
    "# Let's define the inputs and ground_truth for the training and validation set\n",
    "ground_truth_train = {\n",
    "    node: ground_truth_to_find[node][0:2000] for node in ground_truth_to_find.keys()\n",
    "}\n",
    "ground_truth_test = {\n",
    "    node: ground_truth_to_find[node][2000:3000] for node in ground_truth_to_find.keys()\n",
    "}\n",
    "\n",
    "# The input is just the ground truth values at the root nodes\n",
    "input_train = {node: ground_truth_train[node] for node in my_model.root_nodes}\n",
    "input_test = {node: ground_truth_test[node] for node in my_model.root_nodes}\n",
    "\n",
    "# Let's get more epochs to actually fit\n",
    "epochs = 15\n",
    "batch_size = 300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimization is here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/15 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [26:04<00:00, 104.33s/it]\n"
     ]
    }
   ],
   "source": [
    "loss = model_to_fit.conduct_optimisation(\n",
    "    input=input_train,\n",
    "    ground_truth=ground_truth_train,\n",
    "    test_input=input_test,\n",
    "    test_ground_truth=ground_truth_test,\n",
    "    epochs=epochs,\n",
    "    learning_rate=learning_rate,\n",
    "    batch_size=batch_size,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The edge ('CD28', 'GADS') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.8688], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.2807], requires_grad=True)\n",
      "The edge ('CD28', 'or1') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.7145], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.3492], requires_grad=True)\n",
      "The edge ('CD28', 'or5') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.1658], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.1557], requires_grad=True)\n",
      "The edge ('CD28', 'or6') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.6291], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.7337], requires_grad=True)\n",
      "The edge ('PI3K', 'not1') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.4209], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-1.1378], requires_grad=True)\n",
      "The edge ('PI3K', 'or8') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.3701], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.7232], requires_grad=True)\n",
      "The edge ('PI3K', 'or9') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.1177], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([1.0102], requires_grad=True)\n",
      "The edge ('Lck', 'not2') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.5357], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-1.0072], requires_grad=True)\n",
      "The edge ('Lck', 'or18') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.9681], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.3829], requires_grad=True)\n",
      "The edge ('Grb2', 'or1') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.0630], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.7488], requires_grad=True)\n",
      "The edge ('Grb2', 'or22') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.1484], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.7754], requires_grad=True)\n",
      "The edge ('Grb2', 'or23') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.1363], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-1.0365], requires_grad=True)\n",
      "The edge ('Akt', 'mTOR') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.6896], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.2880], requires_grad=True)\n",
      "The edge ('Akt', 'not3') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.1934], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.8412], requires_grad=True)\n",
      "The edge ('Akt', 'not4') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-2.6305], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.6732], requires_grad=True)\n",
      "The edge ('Akt', 'or26') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.4613], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.2058], requires_grad=True)\n",
      "The edge ('ERK12', 'or10') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.9641], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.2316], requires_grad=True)\n",
      "The edge ('GSK3b', 'not5') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.0751], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.9330], requires_grad=True)\n",
      "The edge ('CD3z', 'or6') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.9578], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.2045], requires_grad=True)\n",
      "The edge ('CD3z', 'or22') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([1.3511], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.5314], requires_grad=True)\n",
      "The edge ('CD3z', 'or32') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.9086], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.1070], requires_grad=True)\n",
      "The edge ('PLCg1', 'or26') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.6816], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.5970], requires_grad=True)\n",
      "The edge ('PLCg1', 'or29') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-2.3125], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.6708], requires_grad=True)\n",
      "The edge ('MAPK', 'or9') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.4452], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([1.1853], requires_grad=True)\n",
      "The edge ('MAPK', 'or14') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.1918], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([1.8507], requires_grad=True)\n",
      "The edge ('MAPK', 'or32') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-2.2084], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.2846], requires_grad=True)\n",
      "The edge ('Bad', 'not6') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([1.1676], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.0886], requires_grad=True)\n",
      "The edge ('Bad', 'not7') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.6502], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.6375], requires_grad=True)\n",
      "The edge ('cNFkB', 'or35') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.5774], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.2018], requires_grad=True)\n",
      "The edge ('p38', 'or30') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.3269], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.1573], requires_grad=True)\n",
      "The edge ('NFkB', 'IL2') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.2740], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.0352], requires_grad=True)\n",
      "The edge ('NFkB', 'or34') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.8677], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-1.6741], requires_grad=True)\n",
      "The edge ('41BB', 'TRAF1') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.1869], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.3191], requires_grad=True)\n",
      "The edge ('41BB', 'or2') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.0712], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.2053], requires_grad=True)\n",
      "The edge ('41BB', 'or37') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.1443], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.2261], requires_grad=True)\n",
      "The edge ('41BB', 'or38') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.4169], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.5176], requires_grad=True)\n",
      "The edge ('TRAF1', 'TRAF1_and_TRAF2') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.6585], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([1.0211], requires_grad=True)\n",
      "The edge ('TRAF2', 'TRAF1_and_TRAF2') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.7350], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.4790], requires_grad=True)\n",
      "The edge ('TRAF2', 'TRAF2_and_TRAF3') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.4545], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.8184], requires_grad=True)\n",
      "The edge ('TRAF2', 'or8') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([1.4289], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([1.6346], requires_grad=True)\n",
      "The edge ('TRAF2', 'or23') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.2286], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.5028], requires_grad=True)\n",
      "The edge ('TRAF3', 'TRAF2_and_TRAF3') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([1.2279], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.6846], requires_grad=True)\n",
      "The edge ('ncNFkB', 'or35') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.0192], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([1.2164], requires_grad=True)\n",
      "The edge ('CD40', 'TRAF6') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.4391], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.0012], requires_grad=True)\n",
      "The edge ('CD40', 'or37') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.1950], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.8526], requires_grad=True)\n",
      "The edge ('CD40', 'or38') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.2071], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.9215], requires_grad=True)\n",
      "The edge ('CD40', 'or39') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([1.1865], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.0534], requires_grad=True)\n",
      "The edge ('TRAF6', 'or3') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.1271], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.0227], requires_grad=True)\n",
      "The edge ('TRAF6', 'or25') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.1518], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.3548], requires_grad=True)\n",
      "The edge ('TRAF6', 'or28') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-4.1641], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.3675], requires_grad=True)\n",
      "The edge ('JAK3', 'STAT5') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.7008], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-1.0247], requires_grad=True)\n",
      "The edge ('STAT5', 'TNFa') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-3.1217], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.1561], requires_grad=True)\n",
      "The edge ('STAT5', 'IL6') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-3.0912], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.5090], requires_grad=True)\n",
      "The edge ('STAT5', 'IFNG') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.1014], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([1.8808], requires_grad=True)\n",
      "The edge ('STAT5', 'STAT3_and_STAT5') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.3080], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.3927], requires_grad=True)\n",
      "The edge ('CTLA4', 'not8') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.6734], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.4015], requires_grad=True)\n",
      "The edge ('CTLA4', 'PP2A') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.6777], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.4363], requires_grad=True)\n",
      "The edge ('CTLA4', 'SHP2') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-2.8248], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.1976], requires_grad=True)\n",
      "The edge ('CTLA4', 'not9') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.2661], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.1244], requires_grad=True)\n",
      "The edge ('CTLA4', 'not10') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.3042], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.8007], requires_grad=True)\n",
      "The edge ('CTLA4', 'not11') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.4382], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.2609], requires_grad=True)\n",
      "The edge ('CTLA4', 'SHP1') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.9327], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.1734], requires_grad=True)\n",
      "The edge ('CTLA4', 'or4') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.6157], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.3432], requires_grad=True)\n",
      "The edge ('PP2A', 'not12') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.0440], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.7649], requires_grad=True)\n",
      "The edge ('PP2A', 'not13') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-2.2199], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.5141], requires_grad=True)\n",
      "The edge ('PP2A', 'not14') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.2314], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-1.2566], requires_grad=True)\n",
      "The edge ('SHP2', 'not15') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.0681], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.3080], requires_grad=True)\n",
      "The edge ('SHP2', 'not16') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.1724], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.8324], requires_grad=True)\n",
      "The edge ('SHP1', 'not17') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([0.5172], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.7756], requires_grad=True)\n",
      "The edge ('IL15Ra', 'Shc') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.1055], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.7649], requires_grad=True)\n",
      "The edge ('IL15Ra', 'JAK1') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.5127], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.1999], requires_grad=True)\n",
      "The edge ('IL15Ra', 'or39') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([1.1000], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.2584], requires_grad=True)\n",
      "The edge ('Shc', 'or7') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.2470], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([0.5035], requires_grad=True)\n",
      "The edge ('JAK1', 'STAT3') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.1267], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.2343], requires_grad=True)\n",
      "The edge ('STAT3', 'STAT3_and_STAT5') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-0.7320], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.8145], requires_grad=True)\n",
      "The edge ('CD45', 'or5') has parameter\n",
      " n = Parameter containing:\n",
      "tensor([-1.3972], requires_grad=True) and \n",
      " K = Parameter containing:\n",
      "tensor([-0.8391], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "for e in model_to_fit.transfer_edges:\n",
    "    layer = model_to_fit.edges()[e][\"layer\"]\n",
    "    # Each layer has a n and an K parameter\n",
    "    print(f\"The edge {e} has parameter\\n n = {layer.n} and \\n K = {layer.K}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>loss</th>\n",
       "      <th>phase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-11-02 15:42:39.129115</td>\n",
       "      <td>0.017638</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-11-02 15:43:04.605436</td>\n",
       "      <td>0.016875</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-11-02 15:43:29.486202</td>\n",
       "      <td>0.016755</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-11-02 15:43:55.153941</td>\n",
       "      <td>0.016816</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-11-02 15:44:20.543563</td>\n",
       "      <td>0.017035</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2023-11-02 16:07:41.321018</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2023-11-02 16:07:55.597931</td>\n",
       "      <td>0.005535</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2023-11-02 16:08:06.199336</td>\n",
       "      <td>0.004844</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2023-11-02 16:08:18.307672</td>\n",
       "      <td>0.004926</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2023-11-02 16:08:18.705331</td>\n",
       "      <td>0.004961</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           time      loss  phase\n",
       "0    2023-11-02 15:42:39.129115  0.017638  train\n",
       "1    2023-11-02 15:43:04.605436  0.016875  train\n",
       "2    2023-11-02 15:43:29.486202  0.016755  train\n",
       "3    2023-11-02 15:43:55.153941  0.016816  train\n",
       "4    2023-11-02 15:44:20.543563  0.017035  train\n",
       "..                          ...       ...    ...\n",
       "115  2023-11-02 16:07:41.321018  0.005181  train\n",
       "116  2023-11-02 16:07:55.597931  0.005535  train\n",
       "117  2023-11-02 16:08:06.199336  0.004844  train\n",
       "118  2023-11-02 16:08:18.307672  0.004926  train\n",
       "119  2023-11-02 16:08:18.705331  0.004961   test\n",
       "\n",
       "[120 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You get the loss as the output of the optimisation\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can plot it to check that it converges\n",
    "# It probably won't because we did very few epochs\n",
    "sns.lineplot(data=loss, x=\"time\", y=\"loss\", hue=\"phase\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you do enough epochs the loss should converge and you should have a model that fits your data\n",
    "# To simulate using this model, you just need to go through the simulation part of the model\n",
    "# Keep the parameters that you obtained after optimisation\n",
    "# Feed in the inputs, and simulate!\n",
    "# I suggest you try it by yourself on a simpler model that you can run for several 100 of epochs in an acceptable time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What about BioMixNets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}, {'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}, {'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}, {'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}, {'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}, {'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}, {'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}, {'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}, {'node_type': 'logic_gate_MIXED', 'output_state': None, 'gate': MixedGate()}]\n"
     ]
    }
   ],
   "source": [
    "# It's exactly the same process\n",
    "# Except you have an additinal type of gates; the MIXED gate\n",
    "# That has a parameter determining if it's an AND gate, an OR gate, or a mix of the 2 (not good if that happens after optimisation)\n",
    "\n",
    "BMN = biomixnet.BioMixNet.build_BioMixNet_from_file(\n",
    "    \"biological_fuzzy_logic_networks/example_networks/manual_network_reduced.tsv\"\n",
    ")\n",
    "# Careful; they're called or, but they're actually mixed\n",
    "BMN.mixed_gates\n",
    "print([BMN.nodes()[m] for m in BMN.mixed_gates])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the value of the AND parameter at gate or1: 0.5. Do not forget that it needs to go through a sigmoid.\n"
     ]
    }
   ],
   "source": [
    "# And this is how you access the gate parameter\n",
    "print(\n",
    "    f\"This is the value of the AND parameter at gate or1: {torch.sigmoid(BMN.nodes()['or1']['gate'].AND_param)}. Do not forget that it needs to go through a sigmoid.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If the gate parameter is close to 1, then the predicted gate is an AND gate\n",
    "# If the gate parameter is close to 0, then the predicted gate is an OR gate\n",
    "# Else: then we have a problem\n",
    "# Here it's 0.5 because we have not optimised the network yet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE END\n",
    "\n",
    "I think that's it. Good luck with the BioFuzzNet module!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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