season-1/cadcad-hack-11-networkx.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"display:block;text-align:center;margin-right:105px\"><img src=\"../media/logos/hacks-logo.png\" width=\"200\"/></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "<ul>\n",
    "    <li><a href='#Dependencies'>Dependencies</a></li>\n",
    "    <li><a href='#Setup-/-Preparatory-Steps'>Setup / Preparatory Steps</a>\n",
    "        <ul style='margin-top: 0em;'>\n",
    "            <li><a href='#Initializing-Agents-Network'>Initializing Agents Network</a></li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li><a href='#Modelling'>Modelling</a>\n",
    "        <ol style='margin-top: 0em;'>\n",
    "            <li><a href='#1.-State-Variables'>State Variables</a></li>\n",
    "            <li><a href='#2.-System-Parameters'>System Parameters</a></li>\n",
    "            <li><a href='#3.-Policy-Functions'>Policy Functions</a></li>\n",
    "            <li><a href='#4.-State-Update-Functions'>State Update Functions</a></li>\n",
    "            <li><a href='#5.-Partial-State-Update-Blocks'>Partial State Update Blocks</a></li>\n",
    "        </ol>\n",
    "    </li>\n",
    "    <li><a href='#Simulation'>Simulation</a>\n",
    "        <ol style='margin-top: 0em;' start=\"6\">\n",
    "            <li><a href='#6.-Configuration'>Configuration</a></li>\n",
    "            <li><a href='#7.-Execution'>Execution</a></li>\n",
    "            <li><a href='#8.-Output-Preparation'>Output Preparation</a></li>\n",
    "            <li><a href='#9.-Analysis'>Analysis</a></li>\n",
    "        </ol>\n",
    "    </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cadCAD standard dependencies\n",
    "\n",
    "# cadCAD configuration modules\n",
    "from cadCAD.configuration.utils import config_sim\n",
    "from cadCAD.configuration import Experiment\n",
    "\n",
    "# cadCAD simulation engine modules\n",
    "from cadCAD.engine import ExecutionMode, ExecutionContext\n",
    "from cadCAD.engine import Executor\n",
    "\n",
    "# cadCAD global simulation configuration list\n",
    "from cadCAD import configs\n",
    "\n",
    "# Included with cadCAD\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional dependencies\n",
    "\n",
    "# For networks\n",
    "import networkx as nx\n",
    "\n",
    "# For analytics\n",
    "import numpy as np\n",
    "\n",
    "# For visualization\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup / Preparatory Steps\n",
    "\n",
    "## Initializing Agents Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "agents_network:\n",
      "[(0, {'balance': 1194.1015993610738}), (1, {'balance': 1744.067353228579}), (2, {'balance': 1319.2285306804179}), (3, {'balance': 1343.4921278418767}), (4, {'balance': 1832.6208480478863}), (5, {'balance': 1843.609602318998}), (6, {'balance': 1295.9790612131849}), (7, {'balance': 1623.6981241043727}), (8, {'balance': 1808.975657700642}), (9, {'balance': 1548.935166735794})]\n"
     ]
    }
   ],
   "source": [
    "# Generate a empty directional graph\n",
    "G = nx.MultiDiGraph()\n",
    "\n",
    "# Number of agents\n",
    "NUMBER_AGENTS = 10\n",
    "\n",
    "# Max / Min balance for each agent\n",
    "MAX_AGENT_BALANCE = 2000\n",
    "MIN_AGENT_BALANCE = 1000\n",
    "\n",
    "for agent in range(NUMBER_AGENTS):\n",
    "    # Generate random balance bounded between MIN_AGENT_BALANCE and MAX_AGENT_BALANCE\n",
    "    agent_balance = MIN_AGENT_BALANCE + np.random.rand() * (MAX_AGENT_BALANCE - MIN_AGENT_BALANCE)\n",
    "    \n",
    "    # Add agent to the graph\n",
    "    G.add_node(agent, balance=agent_balance)\n",
    "\n",
    "print(\"agents_network:\")\n",
    "print(G.nodes(data=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. State Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial State: {'agents_network': <networkx.classes.multidigraph.MultiDiGraph object at 0x133260880>}\n"
     ]
    }
   ],
   "source": [
    "initial_state = {\n",
    "    'agents_network': G\n",
    "}\n",
    "\n",
    "print(f\"Initial State: {initial_state}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. System Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_params = {\n",
    "    # Pair-wise interaction probability\n",
    "    'interaction_probability': [0.05], \n",
    "    \n",
    "    # Fraction of the agent balance to stake\n",
    "    'balance_fraction_to_stake': [0.2] \n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Policy Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_interact(params, substep, state_history, previous_state):\n",
    "    \"\"\"\n",
    "    Control agent interactions.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Variables and parameters to be used\n",
    "    G = previous_state['agents_network']\n",
    "    interaction_probability = params['interaction_probability']\n",
    "    balance_fraction_to_transfer = params['balance_fraction_to_stake']\n",
    "    \n",
    "    # List of agent transactions\n",
    "    transactions = []\n",
    "    \n",
    "    for agent_1 in G.nodes:\n",
    "        for agent_2 in G.nodes:\n",
    "            \n",
    "            # Skip self-transactions\n",
    "            if agent_1 == agent_2:\n",
    "                continue\n",
    "            else:\n",
    "                # Determine if the agents are going to interact\n",
    "                will_interact = (np.random.rand() < interaction_probability)\n",
    "                \n",
    "                if will_interact is True:\n",
    "                    # Get agent balances\n",
    "                    agent_1_balance = G.nodes[agent_1]['balance']\n",
    "                    agent_2_balance = G.nodes[agent_2]['balance']\n",
    "                    \n",
    "                    \n",
    "                    # Get exchange values\n",
    "                    agent_1_exchange = agent_1_balance * balance_fraction_to_transfer\n",
    "                    agent_2_exchange = agent_2_balance * balance_fraction_to_transfer\n",
    "                    \n",
    "                    # Set the final exchange as being the minimum of the two                \n",
    "                    exchange = min(agent_1_exchange, agent_2_exchange)\n",
    "                    \n",
    "                    # Generate a transaction\n",
    "                    transaction = {'source': agent_1,\n",
    "                                   'destination': agent_2,\n",
    "                                   'value': exchange}\n",
    "                    \n",
    "                    transactions.append(transaction)\n",
    "                else:\n",
    "                    continue\n",
    "                    \n",
    "    # Return agent interactions\n",
    "    return {'transactions': transactions}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. State Update Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def s_agents_network(params, substep, state_history, previous_state, policy_input):\n",
    "    # Retrieve used variables and policy inputs\n",
    "    G_new = previous_state['agents_network'].copy()\n",
    "    transactions = policy_input['transactions']\n",
    "    \n",
    "    for transaction in transactions:\n",
    "        # Retrieve transaction details\n",
    "        source = transaction['source']\n",
    "        destination = transaction['destination']\n",
    "        amount = transaction['value']\n",
    "        \n",
    "        # Add / remove the amount from the agents balance\n",
    "        G_new.nodes[source]['balance'] -= amount\n",
    "        G_new.nodes[destination]['balance'] += amount\n",
    "        \n",
    "        # Add the transaction as being a graph edge\n",
    "        G_new.add_edge(source, destination, amount=amount)\n",
    "        \n",
    "    return ('agents_network', G_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Partial State Update Blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "partial_state_update_blocks = [\n",
    "    {   \n",
    "        # Configure the model Policy Functions\n",
    "        'policies': {\n",
    "            'p_interact': p_interact\n",
    "        },\n",
    "        # Configure the model State Update Functions\n",
    "        'variables': {\n",
    "            'agents_network': s_agents_network\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_config = config_sim({\n",
    "    \"N\": 1, # the number of times we'll run the simulation (\"Monte Carlo runs\")\n",
    "    \"T\": range(300), # the number of timesteps the simulation will run for\n",
    "    \"M\": system_params # the parameters of the system\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "del configs[:] # Clear any prior configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = Experiment()\n",
    "experiment.append_configs(\n",
    "    initial_state = initial_state,\n",
    "    partial_state_update_blocks = partial_state_update_blocks,\n",
    "    sim_configs = sim_config\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                  ___________    ____\n",
      "  ________ __ ___/ / ____/   |  / __ \\\n",
      " / ___/ __` / __  / /   / /| | / / / /\n",
      "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
      "\\___/\\__,_/\\__,_/\\____/_/  |_/_____/\n",
      "by cadCAD\n",
      "\n",
      "Execution Mode: local_proc\n",
      "Configuration Count: 1\n",
      "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (300, 2, 1, 1)\n",
      "Execution Method: local_simulations\n",
      "SimIDs   : [0]\n",
      "SubsetIDs: [0]\n",
      "Ns       : [0]\n",
      "ExpIDs   : [0]\n",
      "Execution Mode: single_threaded\n",
      "Total execution time: 1.45s\n"
     ]
    }
   ],
   "source": [
    "exec_context = ExecutionContext()\n",
    "simulation = Executor(exec_context=exec_context, configs=configs)\n",
    "raw_result, tensor_field, sessions = simulation.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Output Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agents_network</th>\n",
       "      <th>simulation</th>\n",
       "      <th>subset</th>\n",
       "      <th>run</th>\n",
       "      <th>substep</th>\n",
       "      <th>timestep</th>\n",
       "      <th>interaction_probability</th>\n",
       "      <th>balance_fraction_to_stake</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   agents_network  simulation  subset  run  substep  timestep  \\\n",
       "0  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)           0       0    1        0         0   \n",
       "1  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)           0       0    1        1         1   \n",
       "2  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)           0       0    1        1         2   \n",
       "3  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)           0       0    1        1         3   \n",
       "4  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)           0       0    1        1         4   \n",
       "\n",
       "   interaction_probability  balance_fraction_to_stake  \n",
       "0                     0.05                        0.2  \n",
       "1                     0.05                        0.2  \n",
       "2                     0.05                        0.2  \n",
       "3                     0.05                        0.2  \n",
       "4                     0.05                        0.2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert raw results to a Pandas DataFrame\n",
    "df = pd.DataFrame(raw_result)\n",
    "\n",
    "# Insert cadCAD parameters for each configuration into DataFrame\n",
    "for config in configs:\n",
    "    # Get parameters from configuration\n",
    "    parameters = config.sim_config['M']\n",
    "    # Get subset index from configuration\n",
    "    subset_index = config.subset_id\n",
    "    \n",
    "    # For each parameter key value pair\n",
    "    for (key, value) in parameters.items():\n",
    "        # Select all DataFrame indices where subset == subset_index\n",
    "        dataframe_indices = df.eval(f'subset == {subset_index}')\n",
    "        # Assign each parameter key value pair to the DataFrame for the corresponding subset\n",
    "        df.loc[dataframe_indices, key] = value\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the agents network at the beginning\n",
    "G = df.agents_network.iloc[0]\n",
    "\n",
    "# Get the agents balance\n",
    "node_balances = nx.get_node_attributes(G,'balance')\n",
    "node_labels = {node: f\"{value :.0f}\"\n",
    "               for node, value\n",
    "               in node_balances.items()}\n",
    "\n",
    "# Node sizes\n",
    "sizes = list(node_balances.values())\n",
    "\n",
    "# Prepare the figure\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# Draw the nodes\n",
    "pos = nx.spring_layout(G)\n",
    "nx.draw(G,\n",
    "        pos,\n",
    "        node_size=sizes)\n",
    "\n",
    "# Draw the balances\n",
    "nx.draw_networkx_labels(G, pos, labels=node_labels)\n",
    "\n",
    "# Show the visualization\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the agents network after 2 rounds\n",
    "G = df.agents_network.iloc[2]\n",
    "\n",
    "# Get the agents balance\n",
    "node_balances = nx.get_node_attributes(G,'balance')\n",
    "node_labels = {node: f\"{value :.0f}\"\n",
    "               for node, value\n",
    "               in node_balances.items()}\n",
    "\n",
    "# Node sizes\n",
    "sizes = list(node_balances.values())\n",
    "\n",
    "# Prepare the figure\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# Draw the nodes\n",
    "pos = nx.spring_layout(G)\n",
    "nx.draw(G,\n",
    "        pos,\n",
    "        node_size=sizes)\n",
    "\n",
    "# Draw the balances\n",
    "nx.draw_networkx_labels(G, pos, labels=node_labels)\n",
    "\n",
    "# Show the visualization\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the agents network at the end\n",
    "G = df.agents_network.iloc[-1]\n",
    "\n",
    "# Get the agents balance\n",
    "node_balances = nx.get_node_attributes(G,'balance')\n",
    "node_labels = {node: f\"{value :.0f}\"\n",
    "               for node, value\n",
    "               in node_balances.items()}\n",
    "\n",
    "# Node sizes\n",
    "sizes = list(node_balances.values())\n",
    "\n",
    "# Prepare the figure\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# Draw the nodes\n",
    "pos = nx.spring_layout(G)\n",
    "nx.draw(G,\n",
    "        pos,\n",
    "        node_size=sizes)\n",
    "\n",
    "# Draw the balances\n",
    "nx.draw_networkx_labels(G, pos, labels=node_labels)\n",
    "\n",
    "# Show the visualization\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (cadCAD Hacks)",
   "language": "python",
   "name": "python-cadcad-hacks"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}