season-1/cadcad-hack-12-history-erasing.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>\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 analytics\n",
    "import numpy as np\n",
    "# For visualization\n",
    "import plotly.express as px\n",
    "\n",
    "# Hack Specific\n",
    "import sys\n",
    "import psutil\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. State Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'things': array([0., 1.]), 'state_bytes': None, 'history_bytes': None, 'process_bytes': None, 'nothing': None}\n"
     ]
    }
   ],
   "source": [
    "initial_state = {\n",
    "    # List that is going to bust the memory\n",
    "    'things': np.array([0.0, 1.0]),\n",
    "    \n",
    "    # RAM used by the state variables\n",
    "    'state_bytes': None,\n",
    "    \n",
    "    # RAM used by the state history\n",
    "    'history_bytes': None,\n",
    "    \n",
    "    # RAM used by the process\n",
    "    'process_bytes': None,\n",
    "    \n",
    "    # Hack variable that is always nothing\n",
    "    'nothing': None\n",
    "}\n",
    "\n",
    "print(initial_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. System Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_params = {\n",
    "    # Number of random numbers to append to a list each timestep\n",
    "    \"amount_of_random_values\": [int(1e4)], \n",
    "    \"erase_history\": [True, False]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. State Update Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def s_add_things(params, substep, history, previous_state, policy_input):\n",
    "    \"\"\"\n",
    "    Add random numbers to the 'things' state variable\n",
    "    \"\"\"\n",
    "    things = previous_state['things']\n",
    "    N = params['amount_of_random_values']\n",
    "    values = np.random.rand(N)\n",
    "    things = np.append(things, values)\n",
    "    return ('things', things)\n",
    "\n",
    "\n",
    "def s_measure_history(params, substep, history, previous_state, policy_input):\n",
    "    \"\"\"\n",
    "    Measure how much memory the 'things' state variable history is using\n",
    "    \"\"\"\n",
    "    historic_bytes = [sys.getsizeof(substep_state.get('things', 0)) \n",
    "                      for timestep_state in history \n",
    "                      for substep_state in timestep_state]\n",
    "    used_bytes = sum(historic_bytes)\n",
    "    return ('history_bytes', used_bytes)\n",
    "\n",
    "\n",
    "def s_measure_state(params, substep, history, previous_state, policy_input):\n",
    "    \"\"\"\n",
    "    Measure how much memory the 'things' state variable is using\n",
    "    \"\"\"\n",
    "    used_bytes = sys.getsizeof(previous_state['things'])\n",
    "    return ('state_bytes', used_bytes)\n",
    "\n",
    "\n",
    "def s_measure_process(params, substep, history, previous_state, policy_input):\n",
    "    \"\"\"\n",
    "    Get the current process and see how much memory is on it\n",
    "    \"\"\"\n",
    "    process = psutil.Process(os.getpid())\n",
    "    used_bytes = process.memory_info().data\n",
    "    return ('process_bytes', used_bytes)\n",
    "\n",
    "\n",
    "def s_erase_history(params, substep, history, previous_state, policy_input):\n",
    "    \"\"\"\n",
    "    Iterate on the history and erase everything\n",
    "    \"\"\"\n",
    "    if params['erase_history'] is True:\n",
    "        for timestep_state in history:\n",
    "            for substep_state in timestep_state:\n",
    "                substep_state['things'] = None\n",
    "    return ('nothing', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Partial State Update Blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "partial_state_update_blocks = [\n",
    "  {\n",
    "      # Add lots of random numbers to the simulation\n",
    "      'policies': {},\n",
    "      'variables': {\n",
    "          'things': s_add_things\n",
    "      }\n",
    "  },\n",
    "  {\n",
    "      # Measure memory usage\n",
    "      'policies': {},\n",
    "      'variables': {\n",
    "          'state_bytes': s_measure_state,\n",
    "          'history_bytes': s_measure_history,\n",
    "          'process_bytes':s_measure_process\n",
    "      }\n",
    "  },\n",
    "  {\n",
    "      # Erase history of the variables\n",
    "      'policies': {},\n",
    "      'variables': {\n",
    "          'nothing': s_erase_history\n",
    "      }\n",
    "  },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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(200), # the number of timesteps the simulation will run for\n",
    "    \"M\": system_params # the parameters of the system\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "del configs[:] # Clear any prior configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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) = (200, 2, 2, 5)\n",
      "Execution Method: local_simulations\n",
      "SimIDs   : [0, 0]\n",
      "SubsetIDs: [0, 1]\n",
      "Ns       : [0, 1]\n",
      "ExpIDs   : [0, 0]\n",
      "Execution Mode: parallelized\n",
      "Total execution time: 13.60s\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": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>things</th>\n",
       "      <th>state_bytes</th>\n",
       "      <th>history_bytes</th>\n",
       "      <th>process_bytes</th>\n",
       "      <th>nothing</th>\n",
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       "      <th>substep</th>\n",
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       "      <th>amount_of_random_values</th>\n",
       "      <th>erase_history</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>10000</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>80112.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>727597056.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>80112.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>727597056.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>10000</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>80112.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>727597056.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>None</td>\n",
       "      <td>160112.0</td>\n",
       "      <td>240352.0</td>\n",
       "      <td>727994368.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>160112.0</td>\n",
       "      <td>240352.0</td>\n",
       "      <td>727994368.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>160112.0</td>\n",
       "      <td>240352.0</td>\n",
       "      <td>727994368.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>None</td>\n",
       "      <td>240112.0</td>\n",
       "      <td>480400.0</td>\n",
       "      <td>728715264.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10000</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>None</td>\n",
       "      <td>240112.0</td>\n",
       "      <td>480400.0</td>\n",
       "      <td>728715264.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10000</td>\n",
       "      <td>True</td>\n",
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      "text/plain": [
       "  things  state_bytes  history_bytes  process_bytes nothing  simulation  \\\n",
       "0   None          NaN            NaN            NaN    None           0   \n",
       "1   None          NaN            NaN            NaN    None           0   \n",
       "2   None      80112.0          112.0    727597056.0    None           0   \n",
       "3   None      80112.0          112.0    727597056.0    None           0   \n",
       "4   None      80112.0          112.0    727597056.0    None           0   \n",
       "5   None     160112.0       240352.0    727994368.0    None           0   \n",
       "6   None     160112.0       240352.0    727994368.0    None           0   \n",
       "7   None     160112.0       240352.0    727994368.0    None           0   \n",
       "8   None     240112.0       480400.0    728715264.0    None           0   \n",
       "9   None     240112.0       480400.0    728715264.0    None           0   \n",
       "\n",
       "   subset  run  substep  timestep  amount_of_random_values  erase_history  \n",
       "0       0    1        0         0                    10000           True  \n",
       "1       0    1        1         1                    10000           True  \n",
       "2       0    1        2         1                    10000           True  \n",
       "3       0    1        3         1                    10000           True  \n",
       "4       0    1        1         2                    10000           True  \n",
       "5       0    1        2         2                    10000           True  \n",
       "6       0    1        3         2                    10000           True  \n",
       "7       0    1        1         3                    10000           True  \n",
       "8       0    1        2         3                    10000           True  \n",
       "9       0    1        3         3                    10000           True  "
      ]
     },
     "execution_count": 11,
     "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(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.plotting.backend = \"plotly\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the used memory in bytes\n",
    "px.line(df,\n",
    "        x='timestep',\n",
    "        y=['state_bytes', 'history_bytes', 'process_bytes'],\n",
    "        facet_col='erase_history'\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "hovertemplate": "erase_history=True<br>timestep=%{x}<br>y=%{y}<extra></extra>",
         "legendgroup": "",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "mode": "lines",
         "name": "",
         "showlegend": false,
         "type": "scattergl",
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          "xaxis": {
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           "gridcolor": "white",
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           "ticks": "",
           "title": {
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           "zerolinewidth": 2
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          "yaxis": {
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           "gridcolor": "white",
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           "ticks": "",
           "title": {
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        "yaxis": {
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         "domain": [
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         "title": {
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Share of the process bytes being used for keeping memory\n",
    "px.line(df,\n",
    "        x='timestep',\n",
    "        y=df.history_bytes / df.process_bytes,\n",
    "        facet_col='erase_history'\n",
    "       )"
   ]
  }
 ],
 "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
}