examples/06a_example_benchmark_performer_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "import json\n",
    "import datetime\n",
    "\n",
    "import h5py\n",
    "\n",
    "import numpy as np\n",
    "np.random.seed(17) # fix NumPy random number generator seed\n",
    "\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# add some relative paths so we can import sampling.py module.\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "sys.path.append(\"../src\")\n",
    "sys_path = sys.path\n",
    "\n",
    "import sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark Workflow\n",
    "\n",
    "The *benchmark performer* shall:\n",
    "1. Select an `instance.<uuid-x>.json` file from the `benchmark_instances` directory.\n",
    "2. Generate samples from the instance using their method and hardware of choice.  The `instance.<uuid-x>.json` file includes the appropriate time limit and number of samples $k$ to generate.\n",
    "3. Write the samples they generated to `solution.<uuid-y>.h5` file in the `benchmark_performer_submissions` folder.  Metadata fields inside of the `solution.<uuid-y>.h5` reference the original `instance.<uuid-x>.json` file and UUID.\n",
    "\n",
    "\n",
    "The *benchmark proctor* shall:\n",
    "1. Select a `solution.<uuid-y>.h5` file and perform verification that the $k$ samples were accurately drawn from the system of interest.\n",
    "2. Write out performance metrics to a `performance_metrics.<uuid-z>.json` file in the `benchmark_performance_metrics` directory.\n",
    "\n",
    "\n",
    "Optionally:  a script will parse `performance_metrics.*.json` files in the `benchmark_performance_metrics` directory and present summary statistics, plots, etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'average_node_connectivity': 4.0,\n",
      " 'beta': 0.3333333333333333,\n",
      " 'consistent_edge_weights': True,\n",
      " 'consistent_external_field': True,\n",
      " 'density': 0.5,\n",
      " 'edge_type': 'ferromagnetic',\n",
      " 'edge_weight': 1.0,\n",
      " 'effective_max_edge_weight_magnitude': 0.3333333333333333,\n",
      " 'effective_min_edge_weight_magnitude': 0.3333333333333333,\n",
      " 'external_field': 0.1,\n",
      " 'graph_name': '3x3x1-example',\n",
      " 'graph_uuid': '85f2e0b1-1812-49da-80c7-26a256511da2',\n",
      " 'has_nonzero_external_field': True,\n",
      " 'has_periodic_boundary_edges': True,\n",
      " 'is_planar': False,\n",
      " 'k_B': 1.0,\n",
      " 'max_degree': 4,\n",
      " 'max_edge_weight_magnitude': 1.0,\n",
      " 'min_edge_weight_magnitude': 1.0,\n",
      " 'number_of_edges': 18,\n",
      " 'number_of_nodes': 9,\n",
      " 'temperature_T': 3.0}\n"
     ]
    }
   ],
   "source": [
    "# Acting as the Benchmark Performer:  Fetch instance\n",
    "\n",
    "filename = \"../benchmark_instances/instance.a8be0d9a-3238-11ee-b3fb-9309595bcf35.EXAMPLE.json\"\n",
    "\n",
    "G = sampling.load_graph_instance_from_file(filename)\n",
    "\n",
    "G.plot()\n",
    "pprint.pprint(G.get_summary_details())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample: 0 / 1000 ...\n",
      "sample: 50 / 1000 ...\n",
      "sample: 100 / 1000 ...\n",
      "sample: 150 / 1000 ...\n",
      "sample: 200 / 1000 ...\n",
      "sample: 250 / 1000 ...\n",
      "sample: 300 / 1000 ...\n",
      "sample: 350 / 1000 ...\n",
      "sample: 400 / 1000 ...\n",
      "sample: 450 / 1000 ...\n",
      "sample: 500 / 1000 ...\n",
      "sample: 550 / 1000 ...\n",
      "sample: 600 / 1000 ...\n",
      "sample: 650 / 1000 ...\n",
      "sample: 700 / 1000 ...\n",
      "sample: 750 / 1000 ...\n",
      "sample: 800 / 1000 ...\n",
      "sample: 850 / 1000 ...\n",
      "sample: 900 / 1000 ...\n",
      "sample: 950 / 1000 ...\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "# Acting as the Benchmark Performer:  \n",
    "# \n",
    "# Generate Samples from selected instance using any method the benchmark performer chooses.  In this case, we use MCMC.  Any and all data structures and workflow can be adjusted by the performer.\n",
    "\n",
    "\n",
    "n = len(G.nodes())\n",
    "\n",
    "num_samples_k = 1000 # TODO: pull this from instance.<uuid-x>.json file.\n",
    "\n",
    "# Example:  we store the samples collected in a dictionary.  After building the dictionary, we will convert it to a NumPy array.\n",
    "samples_dict = {}\n",
    "\n",
    "\n",
    "\n",
    "overall_start_time = datetime.datetime.utcnow()\n",
    "\n",
    "\n",
    "\n",
    "preprocessing_start_time = datetime.datetime.utcnow()\n",
    "# preprocessing steps here.  (data loading, etc. if necessary.)\n",
    "preprocessing_stop_time = datetime.datetime.utcnow()\n",
    "preprocessing_time = preprocessing_stop_time - preprocessing_start_time\n",
    "preprocessing_seconds = preprocessing_time.total_seconds()\n",
    "\n",
    "\n",
    "algorithm_start_time = datetime.datetime.utcnow()\n",
    "for s in range(num_samples_k):\n",
    "    \n",
    "    # print status:\n",
    "    if s % 50 == 0:\n",
    "        print(\"sample:\",s,\"/\",num_samples_k,\"...\")  \n",
    "\n",
    "    # random starting spin_config:\n",
    "    spin_config = sampling.generate_random_spin_config(n)\n",
    "    G.set_spins(spin_config)\n",
    "\n",
    "    # run MCMC to generate one sample:\n",
    "    sample_energy, sample_config = G.generate_mcmc_sample_v1(num_steps=500)\n",
    "    \n",
    "    final_state_index = sampling.convert_spin_config_to_state_index(sample_config)\n",
    "    \n",
    "    try:\n",
    "        samples_dict[final_state_index] += 1 # increment counter of observances\n",
    "    except:\n",
    "        samples_dict[final_state_index] = 1\n",
    "\n",
    "print(\"done.\")    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "algorithm_stop_time = datetime.datetime.utcnow()\n",
    "algorithm_time = algorithm_stop_time - algorithm_start_time\n",
    "algorithm_seconds = algorithm_time.total_seconds()\n",
    "\n",
    "postprocessing_start_time = datetime.datetime.utcnow()\n",
    "# postprocessing steps here.  (data reading, measuring, etc. if necessary.)\n",
    "postprocessing_stop_time = datetime.datetime.utcnow()\n",
    "postprocessing_time = postprocessing_stop_time - postprocessing_start_time\n",
    "postprocessing_seconds = postprocessing_time.total_seconds()\n",
    "\n",
    "overall_stop_time = datetime.datetime.utcnow()\n",
    "overall_time = overall_stop_time - overall_start_time\n",
    "overall_seconds = overall_time.total_seconds()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "run_time = {}\n",
    "run_time[\"overall_time\"] = {}\n",
    "run_time[\"preprocessing_time\"] = {}\n",
    "run_time[\"algorithm_run_time\"] = {}\n",
    "run_time[\"postprocessing_time\"] = {}\n",
    "\n",
    "run_time[\"overall_time\"][\"wall_clock_start_time\"] = str(overall_start_time)\n",
    "run_time[\"overall_time\"][\"wall_clock_stop_time\"] = str(overall_stop_time)\n",
    "run_time[\"overall_time\"][\"seconds\"] = overall_seconds\n",
    "\n",
    "run_time[\"preprocessing_time\"][\"wall_clock_start_time\"] = str(preprocessing_start_time)\n",
    "run_time[\"preprocessing_time\"][\"wall_clock_stop_time\"] = str(preprocessing_stop_time)\n",
    "run_time[\"preprocessing_time\"][\"seconds\"] = preprocessing_seconds\n",
    "\n",
    "run_time[\"algorithm_run_time\"][\"wall_clock_start_time\"] = str(algorithm_start_time)\n",
    "run_time[\"algorithm_run_time\"][\"wall_clock_stop_time\"] = str(algorithm_stop_time)\n",
    "run_time[\"algorithm_run_time\"][\"seconds\"] = algorithm_seconds\n",
    "\n",
    "run_time[\"postprocessing_time\"][\"wall_clock_start_time\"] = str(postprocessing_start_time)\n",
    "run_time[\"postprocessing_time\"][\"wall_clock_stop_time\"] = str(postprocessing_stop_time)\n",
    "run_time[\"postprocessing_time\"][\"seconds\"] = postprocessing_seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Acting as the Benchmark Performer (Example workflow continued):  \n",
    "# \n",
    "# convert the samples_dict dictionary to a NumPy Array. \n",
    "\n",
    "\n",
    "\n",
    "num_unique_states_observed = len(samples_dict)\n",
    "# recall:  n = number of nodes in the graph.\n",
    "\n",
    "states_observed = np.zeros((num_unique_states_observed, n))\n",
    "states_observed_counts = np.zeros(num_unique_states_observed)\n",
    "\n",
    "i = 0\n",
    "for key, val in samples_dict.items():\n",
    "    spin_config = sampling.convert_state_index_to_spin_config(n, key)\n",
    "    states_observed[i,:] = spin_config\n",
    "    states_observed_counts[i] = val\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Acting as the Benchmark Performer:  \n",
    "# \n",
    "# aggregate results into a dictionary with other metadata and contact information\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# The UUID is fixed for the purposes of this example.  For future runs, the benchmark performer SHALL generate a new UUID.\n",
    "solution_uuid = \"ee69de1e-3620-11ee-be05-8f4ea6fc831a\" \n",
    "solution_filename = \"../benchmark_performer_submissions/solution.\" + solution_uuid + \".EXAMPLE.h5\"\n",
    "\n",
    "benchmark_performer_solution = {\n",
    "    \"solution\":{\n",
    "        \"solution_timestamp\": str(datetime.datetime.utcnow().isoformat()),\n",
    "        \"instance_uuid\":\"a8be0d9a-3238-11ee-b3fb-9309595bcf35\",\n",
    "        \"solution_uuid\":solution_uuid,\n",
    "        \"benchmark_performer_contact_info\":[\n",
    "            {\n",
    "                \"name\": \"Big Cat Computers, LLC\",\n",
    "                \"email\": \"test_team_distro@bigcat.com\",\n",
    "                \"institution\": \"Big Cat Computers, LLC\"\n",
    "            },\n",
    "            {\n",
    "                \"name\":\"the dude\",\n",
    "                \"email\": \"dude@bigcat.com\",\n",
    "                \"institution\": \"Big Cat Computers, LLC\"\n",
    "            },\n",
    "            {\n",
    "                \"name\": \"the other guy\",\n",
    "                \"email\": \"some.other.guy@gmail.com\",\n",
    "                \"institution\": \"University of Stuff\",\n",
    "                \"other_info\": {\n",
    "                    \"comment\": \"I like computers!\",\n",
    "                    \"website\": \"https://www.other-guy-testing.com\"\n",
    "                }\n",
    "            }\n",
    "        ],\n",
    "        \"compute_details\":{\n",
    "            \"hardware_type\":\"classical_computer\",\n",
    "            \"hardware_details\":{\n",
    "                \"name\": \"Panther Compute Cluster v1.2\",\n",
    "                \"compute_node_specifications\":[\n",
    "                    {\n",
    "                        \"name\":\"hostname_1\",\n",
    "                        \"total_memory_gb\": 16,\n",
    "                        \"memory_details\": None,\n",
    "                        \"total_cpus\": 4,\n",
    "                        \"cpu_details\":None,\n",
    "                        \"total_disk\": None,\n",
    "                        \"disk_details\":None,\n",
    "                        \"gpu_details\":None\n",
    "                    },\n",
    "                    {\n",
    "                        \"name\":\"hostname_2\",\n",
    "                        \"total_memory_gb\": 16,\n",
    "                        \"memory_details\": None,\n",
    "                        \"total_cpus\": 4,\n",
    "                        \"cpu_details\":None,\n",
    "                        \"total_disk\": None,\n",
    "                        \"disk_details\":None,\n",
    "                        \"gpu_details\":None\n",
    "                    }\n",
    "                ]\n",
    "            },\n",
    "            \"software_details\":{\n",
    "                \"name\": \"Panther MCMC-MH Sampler v1.0\",\n",
    "                \"version\": \"1.0\",\n",
    "                \"references\":{\n",
    "                    \"blaze2018\":\"https://www.bigcat.com/blazing_algorithm/\"\n",
    "                },\n",
    "                \"non_default_parameters\":None\n",
    "            }\n",
    "        },\n",
    "        \"run_time\":run_time, # as calculated above.\n",
    "        \"compute_resources_used\":None, # memory, CPU, etc.\n",
    "        \"solution_reported\":{\n",
    "            \"states_observed\":states_observed, # TODO: convert these to integers\n",
    "            \"states_observed_counts\":states_observed_counts, \n",
    "        }\n",
    "    },\n",
    "    \"digital_signature\":{\n",
    "        \"description\":\"calculated over the \\\"solution\\\" object.  Not implemented in this example.\"\n",
    "    }\n",
    "}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Acting as the Benchmark Performer:  \n",
    "# \n",
    "# write the results dictionary as a HDF5 file.\n",
    "\n",
    "def create_hdf5_structure(parent_group: h5py.Group, python_dict: dict):\n",
    "    \"\"\"Recursively create the HDF5 file structure from a Python dictionary.\n",
    "    \"\"\"\n",
    "    # TODO: implement compression\n",
    "    # compression_options = {\"compression\":\"gzip\", \"compression_opts\":9}\n",
    "\n",
    "    for key, val in python_dict.items():\n",
    "        if isinstance(val, dict):\n",
    "            subgroup = parent_group.create_group(key)\n",
    "            create_hdf5_structure(subgroup, val)\n",
    "        elif isinstance(val, list):\n",
    "            if isinstance(val[0], dict):\n",
    "                # handle a list of dictionaries\n",
    "                # TODO: fix this hacky way of consolidating dict/json/hdf5\n",
    "                nested_group = parent_group.create_group(key)\n",
    "                for j, nested_dict in enumerate(val):\n",
    "                    nested_subgroup = nested_group.create_group(f'_list_item_{j}_')\n",
    "                    create_hdf5_structure(nested_subgroup, nested_dict)\n",
    "            else:\n",
    "                dataset = parent_group.create_dataset(\n",
    "                        key,\n",
    "                        data=val\n",
    "                    )\n",
    "        else:\n",
    "            if val is None:\n",
    "                val = \"_null_\" #TODO: fix this hacky way of consolidating dict/json/hdf5\n",
    "            parent_group.create_dataset(\n",
    "                    key,\n",
    "                    data=val\n",
    "                )\n",
    "\n",
    "\n",
    "with h5py.File(solution_filename, \"w\") as f:\n",
    "    create_hdf5_structure(f, benchmark_performer_solution)\n",
    "    f.close()"
   ]
  }
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