{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Guide to Sampling Your First Model With hopsy\n",
    "\n",
    "\n",
    "In this short example, we show a short code example for a general sampling problem.\n",
    "\n",
    "For a new sampling problem, we suggest trying the Coordinate-Hit-and-Run-Rounded (CHRR) algorithm first, because it will work in most cases.\n",
    "This is because it is comparatively cheap per iteration and its performance is not overly sensitive to the step size, meaning you won't have to worry \n",
    "in general, as it is simple to tune."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hopsy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Important note:\n",
    "# Functionality within multiprocessing requires that the __main__ module be importable by the children. See https://docs.python.org/3/library/multiprocessing.html#using-a-pool-of-workers.\n",
    "# Therefore, we have to define our custom model in a different file and import it\n",
    "\n",
    "from custom_models import CustomModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2025-03-22\n"
     ]
    }
   ],
   "source": [
    "# intializes model\n",
    "model = CustomModel()\n",
    "# Creates a problem object based on the inequality constraints Ax<b as determined by the model.\n",
    "problem = hopsy.add_box_constraints(hopsy.Problem(model.A, model.b, model), -1e3, 1e3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<custom_models.CustomModel object at 0x73625ad74830>\n"
     ]
    }
   ],
   "source": [
    "print(problem.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocess problem for more efficient sampling with Hit&Run type algorithms\n",
    "problem = hopsy.round(problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We suggest using the hopsy.GaussianCoordinateHitAndRunProposal, because it is computationally cheap and works well, when the influence of the polytope over the likelihood is strong\n",
    "proposal = hopsy.GaussianCoordinateHitAndRunProposal \n",
    "# If this proposal does not work well for your problem, you can try any of the other methods. Also feel free to contact us, as we are able to give more concrete advice for special cases.\n",
    "\n",
    "# Next, we need a starting point. This can be a point gained after optimization, a point from a previous MCMC run or the chebyshev centre, which hopsy can compute for us.\n",
    "\n",
    "starting_point = hopsy.compute_chebyshev_center(problem)\n",
    "\n",
    "# create markov chains. In this case 4, because we want to run 4 chains in parallel. Note that the chains could even start at different points.\n",
    "markov_chains = [hopsy.MarkovChain(problem, proposal, starting_point=starting_point) for i in range(4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In order to sample, we still need one rng for every chain. Take care to create a new seed for every chain!\n",
    "rng = [hopsy.RandomNumberGenerator(seed=i*42) for i in range(4)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tune the expected squared jump distance of the  markov chains:\n",
    "\n",
    "tuning_target = \"esjd\"  # alternative targets are acc_rate and esjd/s\n",
    "hopsy.tune(markov_chains, rng, target=tuning_target, n_tuning=5000)\n",
    "\n",
    "\n",
    "# sampling is called using the sample function. In this case we want to sample 4 chains using 4 parallel processes. Of course, you can vary the number of threads according to your CPU.\n",
    "acceptance_rates, samples = hopsy.sample(markov_chains, rng, n_samples=10000, thinning=50, n_procs=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4558040000000001, 0.458176, 0.45604800000000006, 0.45741400000000004]\n"
     ]
    }
   ],
   "source": [
    "# check acceptance rates are not too close to 0 or 1, rather they should be close to what they were tuned to:\n",
    "print(acceptance_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.9999551  1.00004789]]\n",
      "[[39386.55923423 39555.2000093 ]]\n"
     ]
    }
   ],
   "source": [
    "# checks convergence and ESS\n",
    "rhat = hopsy.rhat(samples)\n",
    "print(rhat)\n",
    "assert((rhat<=1.01).all()) # asserts that convergence has been reached. Here we use a strict limit of 1.01\n",
    "\n",
    "\n",
    "ess = hopsy.ess(samples)\n",
    "\n",
    "print(ess)\n",
    "assert((ess >= 400).all()) # asserts that we have reached the minimum number of effective samples we want, in this case 400."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 2)\n",
      "[0.4558040000000001, 0.458176, 0.45604800000000006, 0.45741400000000004]\n"
     ]
    }
   ],
   "source": [
    "print(samples[0].shape)\n",
    "print(acceptance_rates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=300)\n",
    "\n",
    "plt.title('samples')\n",
    "counts, bins, _ = plt.hist(samples[0, :,  0], bins=25, alpha=0.5, label='chain 1, parameter 1', density=True)\n",
    "plt.hist(samples[1, :,  0], bins=bins, alpha=0.5, label='chain 2, parameter 1', density=True)\n",
    "plt.hist(samples[2, :,  0], bins=bins, alpha=0.5, label='chain 3, parameter 1', density=True)\n",
    "plt.hist(samples[3, :,  0], bins=bins, alpha=0.5, label='chain 4, parameter 1', density=True)\n",
    "\n",
    "counts, bins, _ = plt.hist(samples[0, :,  1], bins=25, alpha=0.5, label='chain 1, parameter 2', density=True)\n",
    "plt.hist(samples[1, :,  1], bins=bins, alpha=0.5, label='chain 2, parameter 2', density=True)\n",
    "plt.hist(samples[2, :,  1], bins=bins, alpha=0.5, label='chain 3, parameter 2', density=True)\n",
    "plt.hist(samples[3, :,  1], bins=bins, alpha=0.5, label='chain 4, parameter 2', density=True)\n",
    "\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('parameter value [a. u.]')\n",
    "plt.ylabel('density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}