notebooks/Example 1 - Running experiments.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "c76b8620",
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import glob\n",
        "import warnings\n",
        "import h5py as h5\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "from copy import deepcopy\n",
        "\n",
        "from gensit.experiments import *\n",
        "from gensit.config import Config\n",
        "from gensit.inputs import Inputs\n",
        "from gensit.outputs import Outputs\n",
        "from gensit.utils.misc_utils import *\n",
        "from gensit.utils.math_utils import *\n",
        "from gensit.utils.probability_utils import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "b718b815",
      "metadata": {},
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "# AUTO RELOAD EXTERNAL MODULES\n",
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "880cdfcb",
      "metadata": {},
      "source": [
        "## Load experiment config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "e21669cd",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Specify experiment id\n",
        "config_id = \"generic/joint_table_sim_inference.toml\"\n",
        "config_dir = f'../data/inputs/configs/{config_id}'\n",
        "relative_config_dir = os.path.relpath(config_dir,os.getcwd())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "9b1c638c",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create new logging object\n",
        "logger = setup_logger(\n",
        "    __name__,\n",
        "    console_level = 'INFO',\n",
        "    file_level = 'EMPTY'\n",
        ")\n",
        "\n",
        "# Read config\n",
        "config = Config(\n",
        "    path = relative_config_dir,\n",
        "    console_level = 'INFO',\n",
        "    logger = logger\n",
        ")\n",
        "\n",
        "# Update root\n",
        "config.path_sets_root()\n",
        "\n",
        "# Chane number of iterations to 100\n",
        "config['training']['N'] = 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "3bddab4f",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "16:04.024 config WARNING Excluding key path neural_network\n",
            "16:04.033 config WARNING Excluding key path experiments > 0 > grid_ranges\n",
            "16:04.858 experiments INFO Experiment JointTableSIM_MCMC has been set up.\n",
            "16:04.948 harris_wilson_model_mcmc INFO Building low TotallyConstrained Markov Chain Monte Carlo Engine\n",
            "16:04.964 contingency_table INFO margin constraints over axes (0, 1) provided\n"
          ]
        }
      ],
      "source": [
        "# Intialise experiment handler\n",
        "eh = ExperimentHandler(\n",
        "    config,\n",
        "    experiment_types = ['JointTableSIM_MCMC'],\n",
        "    logger = logger\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "7435b6fc",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "16:06.210 experiments INFO Running MCMC inference of low noise HarrisWilson.\n",
            "                                                                               \r"
          ]
        }
      ],
      "source": [
        "# Run experiment\n",
        "eh.experiments['JointTableSIM_MCMC'].run()"
      ]
    }
  ],
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    "kernelspec": {
      "display_name": "gensit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.0"
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