Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5SXMcYAarz5"
   },
   "source": [
    "# Visualization of RICE-N and RL training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, glob, sys, numpy as np, scipy as sp, sklearn as skl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if \"_ROOT\" not in globals():\n",
    "    _ROOT = os.getcwd()\n",
    "    print(f\"Set _ROOT = {_ROOT}\")\n",
    "else: \n",
    "    print(f\"Already set: _ROOT = {_ROOT}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "OF4mXVOHlHS3"
   },
   "source": [
    "## Save or load from previous training results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "80g85HbZlHS4"
   },
   "source": [
    "This section is for saving and loading the results of training (not the trainer itself)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K9zxtDGzlHS4"
   },
   "outputs": [],
   "source": [
    "from opt_helper import save, load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "03fOZB9fpHAs"
   },
   "source": [
    "To save the output timeseries: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lYF6UDHKlHS4"
   },
   "outputs": [],
   "source": [
    "# [uncomment below to save]\n",
    "# save({\"nego_off\":gpu_nego_off_ts, \"nego_on\":gpu_nego_on_ts}, \"filename.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0vG1JZ75pIa7"
   },
   "source": [
    "To load the output timeseries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4TEE7CvHlHS4"
   },
   "outputs": [],
   "source": [
    "# [uncomment below to load]\n",
    "dict_ts = load(\"example_data/example.pkl\")\n",
    "nego_off_ts, nego_on_ts = dict_ts[\"nego_off\"], dict_ts[\"nego_on\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in nego_off_ts.items(): \n",
    "    print(f\"{key:40} {value.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training procedures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One may want to plot the some metrics such as `mean reward` which are logged during the training procedure.\n",
    "\n",
    "```python\n",
    "metrics = ['Iterations Completed',\n",
    " 'VF loss coefficient',\n",
    " 'Entropy coefficient',\n",
    " 'Total loss',\n",
    " 'Policy loss',\n",
    " 'Value function loss',\n",
    " 'Mean rewards',\n",
    " 'Max. rewards',\n",
    " 'Min. rewards',\n",
    " 'Mean value function',\n",
    " 'Mean advantages',\n",
    " 'Mean (norm.) advantages',\n",
    " 'Mean (discounted) returns',\n",
    " 'Mean normalized returns',\n",
    " 'Mean entropy',\n",
    " 'Variance explained by the value function',\n",
    " 'Gradient norm',\n",
    " 'Learning rate',\n",
    " 'Mean episodic reward',\n",
    " 'Mean policy eval time per iter (ms)',\n",
    " 'Mean action sample time per iter (ms)',\n",
    " 'Mean env. step time per iter (ms)',\n",
    " 'Mean training time per iter (ms)',\n",
    " 'Mean total time per iter (ms)',\n",
    " 'Mean steps per sec (policy eval)',\n",
    " 'Mean steps per sec (action sample)',\n",
    " 'Mean steps per sec (env. step)',\n",
    " 'Mean steps per sec (training time)',\n",
    " 'Mean steps per sec (total)'\n",
    " ]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check out the logged submissions, please run the following block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "submission_zip_files = glob(os.path.join(_ROOT,\"Submissions/*.zip\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If previous trainings are finished and logged properly, this should give a list of `*.zip` files where the logs are included. \n",
    "\n",
    "We picked one of the submissions and the metric `Mean episodic reward` as an example, please check the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from opt_helper import get_training_curve, plot_training_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WarpDrive version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TBC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RLLib (CPU) version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_zip = submission_zip_files[1]\n",
    "plot_training_curve(None, 'Mean episodic reward', log_zip)\n",
    "\n",
    "# to check the raw logging dictionary, uncomment below\n",
    "# logs = get_training_curve(log_zip)\n",
    "# logs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "0BCG5IYWlHS5"
   },
   "source": [
    "## Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oZW6-QJGlHS5"
   },
   "outputs": [],
   "source": [
    "from scripts.desired_outputs import desired_outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AdxL0JanlHS5"
   },
   "source": [
    "One may want to check the performance of the agents by plotting graphs. Below, we list all the logged variables. One may change the ``desired_outputs.py`` to add more variables of interest.\n",
    "\n",
    "```python\n",
    "desired_outputs = ['global_temperature', \n",
    "  'global_carbon_mass', \n",
    "  'capital_all_regions', \n",
    "  'labor_all_regions', \n",
    "  'production_factor_all_regions', \n",
    "  'intensity_all_regions', \n",
    "  'global_exogenous_emissions', \n",
    "  'global_land_emissions', \n",
    "  'timestep', \n",
    "  'activity_timestep', \n",
    "  'capital_depreciation_all_regions', \n",
    "  'savings_all_regions', \n",
    "  'mitigation_rate_all_regions', \n",
    "  'max_export_limit_all_regions', \n",
    "  'mitigation_cost_all_regions', \n",
    "  'damages_all_regions', \n",
    "  'abatement_cost_all_regions', \n",
    "  'utility_all_regions', \n",
    "  'social_welfare_all_regions', \n",
    "  'reward_all_regions', \n",
    "  'consumption_all_regions', \n",
    "  'current_balance_all_regions', \n",
    "  'gross_output_all_regions', \n",
    "  'investment_all_regions', \n",
    "  'production_all_regions', \n",
    "  'tariffs', \n",
    "  'future_tariffs', \n",
    "  'scaled_imports', \n",
    "  'desired_imports', \n",
    "  'tariffed_imports', \n",
    "  'stage', \n",
    "  'minimum_mitigation_rate_all_regions', \n",
    "  'promised_mitigation_rate', \n",
    "  'requested_mitigation_rate', \n",
    "  'proposal_decisions',\n",
    "  'global_consumption',\n",
    "  'global_production']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rYLsNHRjlHS5"
   },
   "outputs": [],
   "source": [
    "from opt_helper import plot_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Ab3Dd-zlHS5"
   },
   "source": [
    "`plot_result()` plots the time series of logged variables.\n",
    "\n",
    "```python\n",
    "plot_result(variables, nego_off, nego_on, k)\n",
    "```\n",
    "* ``variables`` can be either a single variable of interest or a list of variable names from the above list. \n",
    "* The ``nego_off_ts`` and ``nego_on_ts`` are the logged time series for these variables, with and without negotiation. \n",
    "* ``k`` represents the dimension of the variable of interest ( it should be ``0`` by default for most situations)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mrn13h2V2jBQ"
   },
   "source": [
    "Here's an example of plotting a single variable of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nDdreoz2lHS6"
   },
   "outputs": [],
   "source": [
    "plot_result(\"global_temperature\", \n",
    "  nego_off=nego_off_ts, # change it to cpu_nego_off_ts if using CPU\n",
    "  nego_on=nego_on_ts, \n",
    "  k=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VNXPz9kk2meL"
   },
   "source": [
    "Here's an example of plotting a list of variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z76yOBp3lHS5",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plot_result(desired_outputs[0:3], # truncated for demonstration purposes\n",
    "  nego_off=nego_off_ts, \n",
    "  nego_on=nego_on_ts, \n",
    "  k=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6sJSQ5gdxCni"
   },
   "source": [
    "If one only want to plot negotiation-off plots, feel free to set `nego_on=None`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FkuU8kV2xCnj"
   },
   "outputs": [],
   "source": [
    "plot_result(desired_outputs[0:3], # truncated for demonstration purposes\n",
    "  nego_off=nego_off_ts, \n",
    "  nego_on=None, \n",
    "  k=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot region data using a grid plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from opt_helper import make_grid_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = \"labor_all_regions\"\n",
    "feature_label = feature_name.replace(\"_\", \" \").title() + \" - atmosphere layer\"\n",
    "make_grid_plot(\n",
    "    nego_off_ts[feature_name], \n",
    "    xlabel=\"Year\", \n",
    "    ylabel=feature_label, \n",
    "    feature_label=feature_label, \n",
    "    cols=3, \n",
    "    fig_scale=3\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot multiple time series data with mean and spread around the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from opt_helper import plot_fig_with_bounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating some example perturbed data around the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy \n",
    "perturbed_nego_on_ts = [\n",
    "    deepcopy(nego_on_ts),\n",
    "    deepcopy(nego_on_ts),\n",
    "    deepcopy(nego_on_ts),\n",
    "]\n",
    "\n",
    "for key, ts in nego_on_ts.items():\n",
    "    perturbed_nego_on_ts[0][key] = ts\n",
    "    perturbed_nego_on_ts[1][key] = ts + 0.1 * ts\n",
    "    perturbed_nego_on_ts[2][key] = ts - 0.1 * ts\n",
    "\n",
    "\n",
    "perturbed_nego_off_ts = [\n",
    "    deepcopy(nego_off_ts),\n",
    "    deepcopy(nego_off_ts),\n",
    "    deepcopy(nego_off_ts),\n",
    "]\n",
    "\n",
    "for key, ts in nego_off_ts.items():\n",
    "    perturbed_nego_off_ts[0][key] = ts\n",
    "    perturbed_nego_off_ts[1][key] = ts + 0.1 * ts\n",
    "    perturbed_nego_off_ts[2][key] = ts - 0.1 * ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name =\"labor_all_regions\"\n",
    "\n",
    "plot_fig_with_bounds(\n",
    "    feature_name, # variable,\n",
    "    \"y_label\", # y_label,\n",
    "    list_of_dict_off=perturbed_nego_off_ts,\n",
    "    list_of_dict_on=perturbed_nego_on_ts,\n",
    "    title=None,\n",
    "    idx=0,\n",
    "    x_label=\"year\",\n",
    "    skips=3,\n",
    "    line_colors=[\"#0868ac\", \"#7e0018\"],\n",
    "    region_colors=[\"#7bccc4\", \"#ffac3b\"],\n",
    "    start=2020,\n",
    "    alpha=0.5,\n",
    "    is_grid=True,\n",
    "    is_save=True,\n",
    "    delta=5,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate simulation statistics to visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in nego_off_ts.items(): \n",
    "    print(f\"{k:40}{v.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = \"mitigation_rate_all_regions\"\n",
    "feature_label = feature_name.replace(\"_\", \" \").title()\n",
    "make_grid_plot(\n",
    "    nego_on_ts[feature_name],\n",
    "    feature_label=feature_label, \n",
    "    xlabel=\"Step\",\n",
    "    ylabel=feature_label,\n",
    "    cols=4,\n",
    "    fig_scale=3,\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = \"savings_all_regions\"\n",
    "feature_label = feature_name.replace(\"_\", \" \").title()\n",
    "make_grid_plot(\n",
    "    nego_on_ts[feature_name],\n",
    "    feature_label=feature_label, \n",
    "    xlabel=\"Step\",\n",
    "    ylabel=feature_label,\n",
    "    cols=4,\n",
    "    fig_scale=3,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = \"savings_all_regions\"\n",
    "lo_savings, med_savings, hi_savings = [], [], []\n",
    "steps, n_regions = nego_on_ts[feature_name].shape\n",
    "for region_j in range(n_regions):\n",
    "    mean_region_j = np.mean(nego_on_ts[feature_name][:, region_j])\n",
    "    print(f\"{region_j}: {mean_region_j:.2f}\")\n",
    "    if mean_region_j < 0.25: \n",
    "        lo_savings.append(region_j)\n",
    "    elif 0.25 < mean_region_j < 0.35:\n",
    "        med_savings.append(region_j)\n",
    "    else:\n",
    "        hi_savings.append(region_j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lo_savings, med_savings, hi_savings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from opt_helper import make_aggregate_data_across_three_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregate_nego_on_ts = make_aggregate_data_across_three_clusters(\n",
    "    nego_on_ts, \n",
    "    lo_savings, \n",
    "    med_savings, \n",
    "    hi_savings\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = \"savings_all_regions\"\n",
    "feature_label = feature_name.replace(\"_\", \" \").title() + \" - aggregate\"\n",
    "make_grid_plot(\n",
    "    aggregate_nego_on_ts[feature_name],\n",
    "    feature_label=feature_label, \n",
    "    xlabel=\"Step\",\n",
    "    ylabel=feature_label,\n",
    "    cols=4,\n",
    "    fig_scale=3,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation between clusters and feature of those cluster members?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from opt_helper import compute_correlation_across_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the groups of regions that are aggregated.\n",
    "# Note: these shoudl be the same groups that were used to generate the aggregate statistics.\n",
    "groups = (lo_savings, med_savings, hi_savings)\n",
    "\n",
    "# Defint the X variable\n",
    "x_feature_name = \"savings_all_regions\"\n",
    "aggregate_stats_across_groups = aggregate_nego_on_ts[x_feature_name]\n",
    "\n",
    "# Define the y data\n",
    "data_ts = nego_on_ts\n",
    "\n",
    "# Only compute correlations for the y variables that are region-specific time-series.\n",
    "y_features_names = [\n",
    "    i for i in data_ts.keys() if \"all_regions\" in i\n",
    "]\n",
    "\n",
    "for y_feature_name in y_features_names: \n",
    "    r2 = compute_correlation_across_groups(aggregate_stats_across_groups, \n",
    "        data_ts, \n",
    "        y_feature_name, \n",
    "        do_plot=False\n",
    "    )\n",
    "\n",
    "    print(f\"{x_feature_name:35} vs {y_feature_name:35} r2 = {r2:5.2f}\")"
   ]
  }
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