\n",
"Note: Due to unresolved problems, at the moment we recommend to use only the established DYN and STAT samplers, but not LA.\n",
"
\n",
"\n",
"In addition to various static (STAT) and dynamic (DYN) samplers implemented in particular using shared-memory multi-processing and distributed processing via a Redis server, pyABC provides a run-time minimizing so-called \"look-ahead\" (LA) sampler, which is an extension of DYN that uses free workers at the end of a generation in order to start sampling from the next generation already, based on a preliminary proposal distribution. It is particularly useful in the presence of heterogenous model runtimes, in which case STAT and also DYN may wait for single long-running simulations to finish before continuing with the next generation.\n",
"\n",
"In this notebook, we demonstrate the usage of this method in pyABC. It was run on a machine with 48 cores, using the same number of workers, for a population size of 50. If the notebook is run with a low number of workers (<< population size), advantages of LA do not get structurally apparent. See the corresponding publication for an in-depth analysis on high-performance infrastructure. For typical applications, we observed on average a reduction of the total wall-time of 10%-20% of LA over DYN (and a major reduction of 50% of DYN compared to STAT) when the number of workers roughly equals the population size, with reductions of up to nearly 50% when the number of workers far exceeds the population size."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# install if not done yet\n",
"!pip install pyabc --quiet"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import os\n",
"import tempfile\n",
"import time\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy as sp\n",
"\n",
"import pyabc\n",
"\n",
"# set to \"DEBUG\" to get full logging information from the sampler and workers\n",
"logging.getLogger(\"ABC.Sampler\").setLevel(\"WARNING\")\n",
"\n",
"pyabc.settings.set_figure_params('pyabc') # for beautified plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The LA sampler is implemented via Redis. In practice, server and workers need to be started as described in [the documentation](https://pyabc.readthedocs.io/en/latest/sampler.html), e.g. as follows: Start a redis server:\n",
"\n",
" redis-server --port 6379\n",
" \n",
"and connect workers to it via e.g.:\n",
"\n",
" abc-redis-worker --host=localhost --port=6379 --processes=4 --runtime=2h\n",
"\n",
"For convenience, in this notebook we use an integrated server starter that runs both server and workers locally."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we define a simple ODE based test model (similar to [this notebook](https://pyabc.readthedocs.io/en/latest/examples/conversion_reaction.html)) to perform the parameter inference on. To emulate runtime heterogeneity, which is often observed in practice when e.g. the number of reactions to be simulated is parameter-dependent, we randomly extend each model evaluation time."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"theta1_true, theta2_true = np.exp([-2.5, -2])\n",
"theta_true = {\"theta1\": theta1_true, \"theta2\": theta2_true}\n",
"measurement_times = np.arange(11)\n",
"init = np.array([1, 0])\n",
"sigma = 0.03\n",
"\n",
"\n",
"def f(y, t0, theta1, theta2):\n",
" x1, x2 = y\n",
" dx1 = -theta1 * x1 + theta2 * x2\n",
" dx2 = theta1 * x1 - theta2 * x2\n",
" return dx1, dx2\n",
"\n",
"\n",
"def model(pars):\n",
" # numerical integration\n",
" sol = sp.integrate.odeint(\n",
" f, init, measurement_times, args=(pars[\"theta1\"], pars[\"theta2\"])\n",
" )\n",
" # we only observe species 2\n",
" sol = sol[:, 1]\n",
"\n",
" # add multiplicative measurement noise to ODE solution\n",
" noise = np.random.normal(1, 0.03, size=len(sol))\n",
" noisysol = sol * np.random.normal(1, sigma, size=len(sol))\n",
"\n",
" # sleep a little to emulate heterogeneous run times\n",
" sleep_s = np.random.lognormal(mean=-2, sigma=1)\n",
" time.sleep(sleep_s)\n",
"\n",
" return {\"X_2\": noisysol}\n",
"\n",
"\n",
"def distance(simulation, data):\n",
" return np.absolute(data[\"X_2\"] - simulation[\"X_2\"]).sum()\n",
"\n",
"\n",
"measurement_data = model(theta_true)\n",
"\n",
"parameter_prior = pyabc.Distribution(\n",
" theta1=pyabc.RV(\"uniform\", 0, 1), theta2=pyabc.RV(\"uniform\", 0, 1)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sampling\n",
"\n",
"First, set some run parameters (epsilons, population size etc.)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"db_file = f\"sqlite:///{os.path.join(tempfile.gettempdir(), 'test.db')}\"\n",
"\n",
"# note: this population size is for demonstration purposes,\n",
"# it is far too low for applications\n",
"pop_size = 50\n",
"eps_list = np.logspace(start=np.log2(8), stop=np.log2(0.5), num=5, base=2)\n",
"eps = pyabc.ListEpsilon(eps_list)\n",
"\n",
"# run more often to get average statistics\n",
"iters = 3\n",
"iters_la = iters\n",
"iters_dyn = iters\n",
"iters_stat = iters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In practice, the below `RedisEvalParallelSamplerServerStarter` should be replaced by a `RedisEvalParallelSampler` with the correct host IP and port.\n",
"\n",
"Perform sampling using **DYN** scheduling:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 92 = 5.4348e-01, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 101 = 4.9505e-01, ESS: 4.4289e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 124 = 4.0323e-01, ESS: 4.7956e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 133 = 3.7594e-01, ESS: 4.9086e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Accepted: 50 / 386 = 1.2953e-01, ESS: 3.3828e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n",
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 77 = 6.4935e-01, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 94 = 5.3191e-01, ESS: 4.4829e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 135 = 3.7037e-01, ESS: 4.0740e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 205 = 2.4390e-01, ESS: 4.7993e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Accepted: 50 / 203 = 2.4631e-01, ESS: 4.8788e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n",
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 69 = 7.2464e-01, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 87 = 5.7471e-01, ESS: 4.3062e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 132 = 3.7879e-01, ESS: 4.1545e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 153 = 3.2680e-01, ESS: 4.7636e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Accepted: 50 / 408 = 1.2255e-01, ESS: 4.5771e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n"
]
}
],
"source": [
"redis_sampler = pyabc.sampler.RedisEvalParallelSamplerServerStarter(\n",
" look_ahead=False,\n",
" workers=pyabc.nr_cores_available(),\n",
" # wait_for_all_samples=True,\n",
")\n",
"hs_dyn = []\n",
"\n",
"for i in range(0, iters_dyn):\n",
" abc = pyabc.ABCSMC(\n",
" models=model,\n",
" parameter_priors=parameter_prior,\n",
" distance_function=distance,\n",
" population_size=pop_size,\n",
" sampler=redis_sampler,\n",
" eps=eps,\n",
" )\n",
"\n",
" abc.new(db_file, measurement_data)\n",
" h = abc.run(max_nr_populations=len(eps_list))\n",
" hs_dyn.append(h)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the dynamical Redis sampler also has an option `wait_for_all_particles=False`, which was newly introduced in version 0.10.15 and already speeds things up by keeping track of which simulations need to be waited for exactly. The previous implementation is equivalent to `False`, in which case all started samples are waited for, including ones that were started after the last accepted one and are thus disregarded anyway."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform sampling using **LA** scheduling:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 83 = 6.0241e-01, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 94 = 5.3191e-01, ESS: 4.5765e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 129 = 3.8760e-01, ESS: 4.5808e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 145 = 3.4483e-01, ESS: 4.9157e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC INFO: Accepted: 50 / 514 = 9.7276e-02, ESS: 4.6039e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n",
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 82 = 6.0976e-01, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 93 = 5.3763e-01, ESS: 2.8616e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 120 = 4.1667e-01, ESS: 4.3077e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 189 = 2.6455e-01, ESS: 4.3213e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC INFO: Accepted: 50 / 329 = 1.5198e-01, ESS: 3.0453e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n",
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 89 = 5.6180e-01, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 101 = 4.9505e-01, ESS: 4.4470e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 152 = 3.2895e-01, ESS: 4.5443e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 187 = 2.6738e-01, ESS: 4.7582e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC INFO: Accepted: 50 / 541 = 9.2421e-02, ESS: 4.5527e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n"
]
}
],
"source": [
"hs_la = []\n",
"sampler_logfiles = []\n",
"for i in range(0, iters_la):\n",
" logfile = tempfile.mkstemp(prefix=\"redis_log\", suffix=\".csv\")[1]\n",
" sampler_logfiles.append(logfile)\n",
" redis_sampler = pyabc.sampler.RedisEvalParallelSamplerServerStarter(\n",
" # main field: in generation t already preemptively sample for t+1 if cores\n",
" # are available\n",
" look_ahead=True,\n",
" # whether to delay evaluation until the next generation has really\n",
" # started, this is necessary if any component s.a. eps, distance is\n",
" # adaptive\n",
" look_ahead_delay_evaluation=True,\n",
" # determines how many samples to sample preemptively maximally without\n",
" # checking\n",
" max_n_eval_look_ahead_factor=2,\n",
" # a file for some sampler debugging output\n",
" log_file=logfile,\n",
" workers=pyabc.nr_cores_available(),\n",
" )\n",
"\n",
" abc = pyabc.ABCSMC(\n",
" models=model,\n",
" parameter_priors=parameter_prior,\n",
" distance_function=distance,\n",
" population_size=pop_size,\n",
" sampler=redis_sampler,\n",
" eps=eps,\n",
" )\n",
"\n",
" abc.new(db_file, measurement_data)\n",
" h = abc.run(max_nr_populations=len(eps_list))\n",
" hs_la.append(h)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The LA sampler uses a preliminary proposal distribution based on the last generation's preliminarily accepted particles, to start sampling for the next generation as soon as workers become idle. When the last generation is actually done, the proposal is updated and continued with. The accepted population can then consist of accepted particles from both preliminary and actual proposals, which are in pyABC weighted in order to maximize the overall effective sample size.\n",
"\n",
"If the problem possesses a highly skewed parameter-runtime structure (e.g. with fast runtimes in one regime, slow ones in another), then theoretically LA can lead to fast estimates that are however biased towards that fast regime, because the preliminary proposal distribution may be biased. In practical applications, we have observed similarly stable posterior approximations with all of STAT, DYN, LA, i.e. no such problem, but one may want to keep this in mind."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform sampling using **STAT** scheduling, which is known to be less efficient than DYN for large numbers of workers, but may be competitive for few (see e.g. [the publication](https://doi.org/10.1093/bioinformatics/bty361)):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 50 = 1.0000e+00, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 58 = 8.6207e-01, ESS: 4.1230e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 91 = 5.4945e-01, ESS: 4.7137e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 115 = 4.3478e-01, ESS: 4.9054e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Accepted: 50 / 224 = 2.2321e-01, ESS: 4.5663e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n",
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 50 = 1.0000e+00, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 63 = 7.9365e-01, ESS: 3.1889e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 118 = 4.2373e-01, ESS: 4.1969e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 184 = 2.7174e-01, ESS: 4.4354e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Accepted: 50 / 302 = 1.6556e-01, ESS: 4.1411e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n",
"ABC.History INFO: Start \n",
"ABC INFO: t: 0, eps: 8.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 50 = 1.0000e+00, ESS: 5.0000e+01.\n",
"ABC INFO: t: 1, eps: 4.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 73 = 6.8493e-01, ESS: 4.7525e+01.\n",
"ABC INFO: t: 2, eps: 2.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 97 = 5.1546e-01, ESS: 4.6615e+01.\n",
"ABC INFO: t: 3, eps: 1.00000000e+00.\n",
"ABC INFO: Accepted: 50 / 110 = 4.5455e-01, ESS: 4.3801e+01.\n",
"ABC INFO: t: 4, eps: 5.00000000e-01.\n",
"ABC INFO: Accepted: 50 / 335 = 1.4925e-01, ESS: 3.7353e+01.\n",
"ABC INFO: Stop: Maximum number of generations.\n",
"ABC.History INFO: Done \n"
]
}
],
"source": [
"redis_sampler = pyabc.sampler.RedisStaticSamplerServerStarter(\n",
" workers=pyabc.nr_cores_available(),\n",
")\n",
"\n",
"hs_stat = []\n",
"\n",
"for i in range(0, iters_stat):\n",
" abc = pyabc.ABCSMC(\n",
" models=model,\n",
" parameter_priors=parameter_prior,\n",
" distance_function=distance,\n",
" population_size=pop_size,\n",
" sampler=redis_sampler,\n",
" eps=eps,\n",
" )\n",
"\n",
" abc.new(db_file, measurement_data)\n",
" h = abc.run(max_nr_populations=len(eps_list))\n",
" hs_stat.append(h)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Results\n",
"\n",
"The posterior distributions of all samplers should look similar for large enough population sizes. Given the low population size used here, there is considerable stochasticity. For application runs, the population size should be considerably higher."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
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