{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Look-ahead sampling\n", "\n", "
\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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", "\n", "for i_h, h in enumerate(hs_dyn):\n", " df, w = h.get_distribution(m=0, t=h.max_t)\n", " for par, ax in zip([\"theta1\", \"theta2\"], axes):\n", " pyabc.visualization.plot_kde_1d(\n", " df,\n", " w,\n", " x=par,\n", " ax=ax,\n", " color=\"C0\",\n", " xmin=0,\n", " xmax=1,\n", " label=\"DYN\" if i_h == 0 else None,\n", " )\n", "\n", "for i_h, h in enumerate(hs_la):\n", " df, w = h.get_distribution(m=0, t=h.max_t)\n", " for par, ax in zip([\"theta1\", \"theta2\"], axes):\n", " pyabc.visualization.plot_kde_1d(\n", " df,\n", " w,\n", " x=par,\n", " ax=ax,\n", " color=\"C1\",\n", " xmin=0,\n", " xmax=1,\n", " label=\"LA\" if i_h == 0 else None,\n", " )\n", "\n", "for i_h, h in enumerate(hs_stat):\n", " df, w = h.get_distribution(m=0, t=h.max_t)\n", " for par, ax in zip([\"theta1\", \"theta2\"], axes):\n", " pyabc.visualization.plot_kde_1d(\n", " df,\n", " w,\n", " x=par,\n", " ax=ax,\n", " color=\"C2\",\n", " xmin=0,\n", " xmax=1,\n", " label=\"STAT\" if i_h == 0 else None,\n", " )\n", "\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LA generally minimizes the overall wall-time, as all cores are used at almost all times.\n", "The effect becomes more apparent when working on large-scale infrastructure with dozens or hundreds of workers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Epsilon over wall-time (for typical applications one would expect this to be lowest for LA and highest for STAT):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "hs = [*hs_dyn, *hs_la, *hs_stat]\n", "labels = [\n", " *[\"DYN\"] * len(hs_dyn),\n", " *[\"LA\"] * len(hs_la),\n", " *[\"STAT\"] * len(hs_stat),\n", "]\n", "\n", "pyabc.visualization.plot_eps_walltime(hs, labels, group_by_label=True)\n", "\n", "# for separate plotting\n", "# pyabc.visualization.plot_eps_walltime(hs[:iters], labels[:iters])\n", "# pyabc.visualization.plot_eps_walltime(hs[iters:2*iters], labels[iters:2*iters])\n", "# pyabc.visualization.plot_eps_walltime(hs[2*iters:], labels[2*iters:])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pyabc.visualization.plot_total_walltime(hs, labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Total number of samples (for typical applications one would expect this to be lowest for STAT and highest for LA):" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pyabc.visualization.plot_total_sample_numbers(hs, labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us investigate the behavior of the look-ahead sampler in a bit more detail, the number of samples generated in look-ahead mode, and the composition of the final accepted sample.\n", "\n", "Depending on the problem structure, this can look different. It can be that most or all samples of a generation are from look-ahead mode if the acceptance rate is high and there are some very long-running simulations, while it will typically be a lower percentage if the acceptance rate is lower (in later generations) or the simulation times are more homogeneous.\n", "\n", "Number of look-ahead and actual (=with the final proposal) samples:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KSrjhhht4+OGHK+S5a9cuTj/9dAYNGsSgQYP46aefXPsOHTrEFVdcQa9evbj22mspnaS4atUqhg0bxoknnsi5557L3r17AXjttdc46aSTiIuL4/LLL3et5b5z505OOeUU+vXrV2kZlFJ18+KLL7Jv3z4ee+wxTxelUk17RvPXD0LK+qozSPwNivMrpnu3gKgqFrBr3w/Om15tuVq2bMmhQ4fKpZU+Q2HYsGFMnjyZrKwsnnvuuSrThw8fzvTp03n++ec54YQTeOihhypcJycnBy8vL/z9/dm2bRtXX301K1euZNmyZVx88cX88ccfdOjQgaFDh/LUU08xePBghg0bxueff07btm354IMP+Pbbb3njjTdIT08nPDwcgIcffph27doxfvx4LrroIq644gquu+46XnzxRR544IEKnw10RrNStZGdnU1sbCzx8fF88803nixKlT3bzbv5qLKAUF36McrMzCQjI4Nhw4YBcP311zNmzJgq00vdeuutXHnllZUGBLAmvtx1112sWbMGb29vtm7d6tp38sknExUVBVhrquzatYuwsDA2bNjAyJEjrY9ZXOxaX2nDhg08/PDDZGRkcOjQIc4991wAfvzxRz755BMAxo4dywMPPODkV6NUszJjxgzS09OZOnWqp4tSpaYdFGr4Rc+zJ9hNR0cJ7QQ3fuWeMtXBqaeeytKlS7nvvvvw9/fns88+Y8qUKQDMmjWL+fPn065dO9auXUtJSQn+/v6uc8s+oMPb25uioiKMMfTt25eff/65wrVuuOEG5s2bR1xcHLNnz2bZsmWufQ1puJxSjVlQUBBjx47l5JNP9nRRqtS8+xRGTAbfgPJpvgFWuoNCQ0Np1aoV33//PQDvvPMOw4YNqzK91E033cTo0aO58sorKSoq4tJLL2XNmjWsWbOG+Ph4MjMziYyMxMvLi3feeYfi4uJqy9GzZ0/S0tJcQaGwsJA//vgDsKq1kZGRFBYWMmfOHNc5Q4cOdT1StGy6Uqru7r33Xt5++21PF6NazTso9L8SLpxh1QwQ6/XCGcc9+ignJ4eoqCjX3zPPPMNbb73FxIkT6d+/P2vWrGHyZCvwVJVeasKECQwcOJCxY8dSUlL+iXB33HEHb731FnFxcWzevJmgoKBqy+Xn58fHH3/MAw88QFxcHAMGDHB1Tj/22GMMHjyYoUOH0qtXL9c5zz//PC+++CL9+vUjKen4nl2tVHOVnp7Oxx9/XOHfcEPUtDuaVb3R712pqk2aNIknnniCjRs3lvvR5UG6dLZSSnlCamoqM2bM4KqrrmooAaFaGhSUUsqNnnjiCfLy8njkkUc8XZRaaZJBoTE3iTVG+n0rVbnk5GT++9//ct1119Gz57E/+70+Nbmg4O/vT3p6ut6o6okxhvT09HLDYZVSlt27d9O5c+cKA0gaMrd3NIuIN7ASSDLGXCAiscD7QDiwChhrjCkQkRbA28CJQDrwF2PMruryrqyjubCwkMTERPLy8pz/MKpS/v7+REVF4evr6+miKNXgGGMa4lwfj85ovgfYBITY758AnjXGvC8iLwM3AS/ZrweNMd1E5Cr7uL/U9WK+vr7ExsY6U3KllDpGCxcu5LTTTiMgIKDmg+tg3uoknvp2C8kZuXQIC2DiuT25ZGBHx/J3a/ORiEQB5wOz7PcCnAV8bB/yFnCJvX2x/R57/whpgOFVKaVqsmPHDkaPHu1agcAp81YnMenT9SRl5GKApIxcJn26nnmrnZtD5O4+heeA+4HSGRvhQIYxpsh+nwiUhriOwB4Ae3+mfbxSSjUqU6dOxcfHh3vuucfRfJ/6dgu5heVXLsgtLOapb7c4dg23BQURuQBINcascjjfcSKyUkRWpqWlOZm1Ukodty1btvDOO+9wxx13uBacdEpyRm6d0o+FO2sKQ4GLRGQXVsfyWcDzQJiIlPZlRAGl9Z4koBOAvT8Uq8O5HGPMq8aYeGNMfNu2bd1YfKWUqrspU6YQEBDglhWF2wa3qDS9Q5hz/RZuCwrGmEnGmChjTAxwFbDEGHMtsBS4wj7seuBze/sL+z32/iVGx5UqpRqRgoICEhMTGT9+PBEREY7nH1XJzT/A15uJ5zo3B8ITS2c/ALwvIv8CVgOv2+mvA++IyHbgAFYgUUqpRsPPz4/ly5dTWFjoeN5b92WzOjGDEb0j2Lw3222jj+olKBhjlgHL7O0dQIXFxI0xecCYo9OVUqox2LlzJwEBAbRv3x4/Pz/H839u0VaC/Hx4+oo4WgU5n3+pJjejWSmlPOGee+4hPj7eLbWEP5IzWbA+hb8NjXFrQAANCkopddx+/fVXvvzyS26//Xa3zOx/btE2gv19uOn0Lo7nfTQNCkopdZwmT55MeHg4d999t+N5r0vMYOHGfdxyehdCA9y/lEzTfkazUkq52Y8//si3337Lk08+SXBwsOP5P7NwK2GBvtw4NMbxvCujNQWllDoO33//PZGRkdxxxx2O570q4SDLtqRx6xldCfavnwUnNSgopdRxePDBB2v1jPRj8ezCrYQH+XH9qZ0dz7sqGhSUUuoYGGPYtm0bACEhITUcXXe/7Ejnh+37uX14VwL96q+lX4OCUkodg//973/07NmTb7/91vG8jTH8Z+FWIoJb8Nch9VdLAA0KSilVZ8YY/vnPfxIdHc3w4cMdz/+nP9P5decB7jyzG/6+3o7nXx0dfaSUUnU0f/58fvvtN2bNmkWLFpUvUnesjDH8539biAz15y8ndXI079rQmoJSStVBSUkJkydPpmvXrlx33XWO5798axq/787grrPqv5YAWlNQSqk62bRpE1u3buXll192fPayMYZnFm4lqlUAY06s/1oCaFBQSqk66du3Lzt27KBNmzaO571oUyrrEjN58vL++Pl4piFHm4+UUqqW9u3bhzGGdu3a4e3tbNNOSYlVS4gJD+SyQc4thV1XGhSUUqoWioqKOOOMM7j55pvdkv+3f6SwaW8W95zdHR9vz92aNSgopVQtzJkzh61bt3LBBRc4nndxieHZRVvp2jaIi+I8V0sADQpKKVWjwsJCpk6dyqBBg7jkkkscz/+r9XvZuu8Q957dA28vcTz/utCOZqWUqsHs2bPZsWMH8+fPR8TZm3ZRcQnPLdpKz3bBnN8v0tG8j4XWFJRSqgZvvPEGgwcPZvTo0Y7n/fmaZHakHebvI7vj5eFaAmhNQSmlarRkyRJSUlIcryUUFpcwY8k2+nYI4dy+7R3N+1hpTUEppapQUFBAQUEBAQEBxMbGOp7/p78nkpCew4SRPRwPOMeqVkFBRLqKSAt7e7iI3C0iYW4tmVJKediLL75Ir1692L9/v+N5FxSVMGPxduI6hXFWrwjH8z9Wta0pfAIUi0g34FWgE/Ce20qllFIedvjwYaZPn06XLl3cMnv5w5V7SMrIbVC1BKh9UCgxxhQBlwIvGGMmAp7vJldKKTeZOXMmqampPPbYY47nnVdYzMwl24nv3IozujsfcI5HbYNCoYhcDVwPzLfT6ueBoUopVc+ysrJ48sknOe+88zjllFMcz3/ur7tJycprcLUEqH1QuBE4BZhmjNkpIrHAO+4rllJKec67777LgQMHmDp1quN55xYU899lfzKkS2tO7dawaglQyyGpxpiNwN1l3u8EnnBXoZRSypNuu+024uLiiI+Pdzzvd1ckkJadz4vXDHI8byfUKiiIyFDgUaCzfY4AxhjTxX1FU0qp+ldUVISPjw9Dhw51PO/D+UW8tPxPTu/ehpNjWzuevxNq23z0OvAMcBpwEhBvvyqlVJOxf/9+YmNj+fTTT92S/1s/7+LA4QImjOzhlvydUNsZzZnGmK/dWhKllPKwJ598kqSkJHr16uV43tl5hbz63Q7O6hXBwOhWjufvlNoGhaUi8hTwKZBfmmiM+d0tpVJKqXqWkpLCzJkzueaaa+jTp4/j+b/xwy4ycgr5+9kNt5YAtQ8Kg+3Xsr0uBjjL2eIopZRnTJ8+nYKCAh555BHH887MKWTWDzs4p087+kWFOp6/k2o7+ujMumYsIv7Ad0AL+zofG2MesYezvg+EA6uAscaYAnsZjbeBE4F04C/GmF11va5SStVVeno6L7/8Mtdddx3du3d3PP9ZP+wgO6+IvzfgvoRStR19FAo8ApxhJy0HphpjMqs5LR84yxhzSER8gR9E5GtgAvCsMeZ9EXkZuAl4yX49aIzpJiJXYQ15/csxfSqllKqD8PBwli9fTmSk8ws1HDhcwBs/7OT8fpH0jgxxPH+n1Xb00RtANnCl/ZcFvFndCcZyyH7ra/+VNjl9bKe/BVxib19sv8feP0Ia2lQ/pVSTY4wBYPDgwURHRzue/6vf7SCnsJh7z3a+BuIOte1T6GqMubzM+ykisqamk0TEG6uJqBvwIvAnkGGvowSQCJQ+kLQjsAfAGFMkIplYTUz7j8pzHDAOcMt/QKVU83LHHXfg5eXFzJkzHV9yIi07n7d+2sXFcR3o3i7Y0bzdpbY1hVwROa30jT2ZLbemk4wxxcaYAUAUcDJw3OO8jDGvGmPijTHxbdu2Pd7slFLN2Pbt23nttdfw8fFxyxpELy//k/yiYu4e0ThqCVD7msLtwFt234IAB4AbansRY0yGiCzFWj8pTER87NpCFJBkH5aEtSR3ooj4AKFYHc5KKeUWU6dOxdfXlwcffNDxvPdl5fHuigQuGxRFl7YtHc/fXWpVUzDGrDHGxAH9gX7GmIHGmLXVnSMibUsfxCMiAcBIYBOwFLjCPux64HN7+wv7Pfb+Jaa0sU8ppRy2efNm5syZw5133umWDub/Lt1OcYnh7rMaTy0BaqgpiMhfjTHvisiEo9IBMMY8U83pkVi1C2+s4POhMWa+iGwE3heRfwGrsZbQwH59R0S2Y9VErjqWD6SUUrXx2GOPERAQwAMPPOB43kkZucz9dQ9j4qOIDg90PH93qqn5KMh+rayHpNpf8caYdcDAStJ3YPUvHJ2eB4ypoTxKKeWIxx9/nDFjxuCOvskXl24H4K5GVkuAGoKCMeYVe3ORMebHsvvszmallGqUoqOj3TKCcc+BHD78bQ/XDI6mY1iA4/m7W21HH71QyzSllGrQfv/9d8455xx27drllvxnLN6Gl5dwx/Bubsnf3WrqUzgFOBVoe1S/Qgjg7c6CKaWUO0yePJmVK1fSqpXzK5Xu3H+YT1cncf0pMbQP9Xc8//pQU5+CH9DSPq5sv0IWR0YQKaVUo7BixQq++uor/v3vfxMa6vzCdDMWb8PXW7h9eFfH864vNfUpLAeWi8hsY0xCPZVJKaXcYvLkybRt25bx48c7nvf21GzmrUli3OldaBvcwvH860ttJ6/l2M9T6Au46kTGGF06WynVKHz//fcsXLiQp59+mpYtnZ9M9tyibQT6enPrsMZbS4DadzTPATYDscAUYBfwm5vKpJRSjouLi+OJJ57g9ttvdzzvzSlZzF+3lxuHxtI6yM/x/OtTbYNCuDHmdaDQGLPcGPM39AE7SqlGJCQkhPvvv5/AQOcnkz27cCvBLXy4+fRYx/Oub7UNCoX2614ROV9EBgKt3VQmpZRyjDGGG2+8kQULFrgl/w1JmXz7xz5uOj2WsMDGXUuA2vcp/MteDO8+rPkJIcDf3VYqpZRyyDfffMPs2bMZMmSIW/J/duFWQgN8+dtpjb+WALV/HOd8ezMTqPOjOZVSyhOMMUyePJmYmBhuvPFGx/NfvfsgizenMvHcnoT4+zqevyfU9nGcb1LJWkd234JSSjVIX3zxBStXruSNN97Az8/5pp1nFm6ldZAfN5wa43jenlLb5qP5Zbb9gUuBZOeLo5RSzigpKWHy5Ml0796dsWPHOp7/b7sO8P22/fzf6F4EtajtrbThq23z0Sdl34vIXOAHt5RIKaUcYIxhwoQJtG7dGh8f52/az/xvK21atmDskBjH8/akY/2mugMRThZEKaWc5O3tzfXXX1/zgcfgpz/38/OOdCZf0IcAv6a1DFxt+xSysfoUxH5NAZx/MoVSSjngs88+Y+fOnYwfPx5fX2c7gI0xPLtwK+1CWnDNYOeX3va02jYfVfaQHaWUanCKiop44IEHCAgI4N5773U8/++37ee3XQd57OK++Ps2rVoC1Lx09qDq9htjfne2OEopdXzeeecdtm3bxrx58/Dyqu383NoxxvCfhVvpGBbAlSd1cjTvhqKmmsJ/qtln0KUulFINSEFBAVOmTCE+Pp6LLrrI8fyXbkll7Z4Mpl/WjxY+Ta+WADUvna0T1ZRSjcYbb7xBQkICL730EiLiaN7GGJ5ZuJXo1oFcfmKUo3k3JLUefSQiJwB9KL909tvuKJRSSh2Lfv36ceeddzJq1CjH8/72j31sSMri6TFx+Ho72yzVkNR29NEjwHCsoLAAOA9rnoIGBaVUgzF06FCGDh3qeL4lJYbnFm2lS5sgLhnQwfH8G5LahrsrgBFAijHmRiAOcP5ZdkopdQxycnKYNGkSKSkpbsl/wYa9bE7J5p6zu+PThGsJUPugkGuMKQGKRCQESAWaZte7UqrR+e9//8v06dPZtm2b43kXlxieW7SN7hEtuaB/064lQO37FFaKSBjwGrAKOAT87K5CKaVUbWVnZ/PEE08wcuRITj/9dMfz/3JtMttTD/Hfawfh7eVs53VDVNvJa3fYmy+LyDdAiDFmnfuKpZRStTNz5kz279/PY4895njeRcUlPL94G73aBzOqb3vH82+IatV8JCJfiMg1IhJkjNmlAUEp1RBkZmby1FNPcf755zN48GDH8/90dRI79x9mwsgeeDWDWgLUvk/hP8BpwEYR+VhErhAR/5pOUkopd8rNzWXUqFFMnTrV8bwLi0uYsXgb/TqGMrJPO8fzb6hq23y0HFguIt5Ys5hvAd7AeiynUkp5RPv27XnvvffckvdHKxNJPJjLYxef4PhEuIas1mOrRCQAuBy4DTgJeMtdhVJKqZrMnTuX9evXuyXv/KJiZi7ZxsDoMIb3bOuWazRUte1T+BDYhFVLmAl0NcaMd2fBlFKqKmlpadxyyy1MmzbNLfl/8NsekjPzuG9kz2ZVS4Da1xRexwoEtxljltpzFqolIp1EZKmIbBSRP0TkHju9tYgsFJFt9msrO11EZIaIbBeRdTWt0KqUar6eeOIJcnNzefTRRx3PO6+wmJlLtnNyTGuGdgt3PP+GrtqgICL3AxhjvgUuO2rfv2vIuwi4zxjTBxgC3CkifYAHgcXGmO7AYvs9WEtndLf/xgEv1e2jKKWag7179/Liiy9y7bXX0qtXL8fzf3dFAqnZ+Uw4p0ezqyVAzTWFq8psTzpqX7UrThlj9pY+b8EYk43V/NQRuJgj/RFvAZfY2xcDbxvLCiBMRCJr/ARKqWbl8ccfp7CwkMmTJzued05BES8v/5Oh3cIZ0qX51RKg5qAgVWxX9r7qTERigIHAL0A7Y8xee1cKUDrWqyOwp8xpiXba0XmNE5GVIrIyLS2ttkVQSjURYWFh3HnnnXTr1s3xvN/+OYH9hwqYMLKH43k3FjUNSTVVbFf2vlIi0hL4BLjXGJNVtjpmjDEiUqt8ypzzKvAqQHx8fJ3OVUo1fu6YkwBwKL+IV5b/ybAebTmxc2u3XKMxqKmmECciWSKSDfS3t0vf96spcxHxxQoIc4wxn9rJ+0qbhezXVDs9ifKL7EXZaUopRUJCAl9//TXGuOe34Owfd3Iwp7BZ1xKghqBgjPE2xoQYY4KNMT72dul73+rOFatK8DqwyRjzTJldXwDX29vXA5+XSb/OHoU0BMgs08yklGrmpkyZwqWXXkpqamrNB9dRZm4hr363g7N7RxDXKczx/BuTWj957RgMBcYC60VkjZ32f8B04EMRuQlIAK609y0ARgPbgRzgRjeWTSnViGzbto23336b8ePH066d80tOvP7DTrLyivh7M68lgBuDgjHmB6rujB5RyfEGuNNd5VFKNV5TpkyhRYsWPPjggzUfXEcZOQW88cNOzjuhPX076LPDmvYjhJRSjd7GjRt57733uOuuu9xSS3j1ux0cLiji3rO1lgAaFJRSDVxiYiI9e/Zk4sSJjuedfiif2T/t4oL+HejZPtjx/Bsjd/YpKKXUcTvnnHPYuHGjW2YXv/LdDvIKi7lnRHfH826stKaglGqwvv76awoKCtwSEFKz83j7511cMqAj3SJaOp5/Y6VBQSnVIK1cuZLRo0czc+ZMt+T/36V/UlhsuFtrCeVoUFBKNUiTJ0+mdevW3HzzzY7nvTczl/d+3c0Vg6KIaRPkeP6NmQYFpVSD8/PPP/P1119z//33ExLi/AMeX1y6HWMMd53l/PpJjZ0GBaVUg/PPf/6TiIgI7rrrLsfzTjyYwwe/7eHK+E50ah3oeP6NnQYFpVSDkpmZSXp6Og8++CBBQc437cxcsh1BtJZQBR2SqpRqUEJDQ1m1ahXFxcWO552QfpiPViUydkhnIkMDHM+/KdCaglKqwdiyZQsHDhzAy8sLX99q19w8Js8v3oaPl3DH8K6O591UaE1BKdUgGGO4/vrrycnJYe3atY7PTfgz7RDzVidx02mxRIT4O5p3U6JBQSnVICxYsIBffvmFV1991S2T1Z5ftA1/X29uHaa1hOpo85FSyqPmzJlD586dueCCC/Dx8aFFixaOX2NLSjZfrkvm+lNjaNPS+fybEq0pKKU8Zs6cOYwbN46cnBwAioqKuP322/H29ubaa6917DrPL95KkJ8P407v4lieTZXWFJRS9S4vL489e/Zw//33uwJCqZycHB566CHHrvVHciYL1qfwt6ExtArycyzfpkprCkopR6SlpZGcnExqaippaWmkpqbi7+/PbbfdBsDYsWP56aefSEtLIzs7u9q8du/e7Vi5nl24jRB/H27SWkKtaFBQSlVQXFzMgQMHOHDgAD179gSOdASnpqa6bvw+Pj4sWbIEsG763377bbl8evbs6QoKkZGRDBkyhLZt2xIREUFERAQPPfRQpc9cjo6OduRzrEvMYNGmfdw3sgehAc4PcW2KNCgo1UwcOnSowi/51NRUJk2ahI+PD8899xyzZs0iLS2N/fv3U1JSgo+Pj2vp6k8++YQ333yT8PBwIiIiaNu2LVFRUa78J06cyC233OLaFxERQVhYmGv/k08+WaFMAQEB5foUAAIDA5k2bZojn/mZhVsJC/TlhqExjuTXHGhQUKoRMsZw6NAh0tLS6NChA/7+/qxbt46vvvqq3E0/LS2Nr776ig4dOvDcc8/xz3/+s0Jet956KxEREYSEhNCjRw9OO+001029bdu2lJSU4O3tzQsvvMArr7yCj0/lt40RIyo8er1GpZ3JDz30ELt37yY6Oppp06Y50sm8KuEgy7ak8cCoXgT7ay2htsQY4+kyHLP4+HizcuVKTxdDKZc5c+Yc8w2usLCQvXv3Vvglf/nll9OlSxeWLl3KxIkTXfvy8vIAa0XRIUOG8Oabb/K3v/2Nli1blvu1PnPmTKKjo1m3bh3r168vt69Nmzb4+TXNzte/zvqFTXuz+P6BMwn009+/R6lyIoh+U0o55OjhlQkJCdxyyy0cPnyYcePGsX//ft59991ybfKpqak8/PDDnH/++fz0008MHz68Qr5dunShS5cuBAYG0rZtW/r27etqk2/bti2xsbEAXH311Vx11VUEBFS+pk///v3p37+/2z5/Q/LLjnR+2L6fh8/vrQGhjrSmoNRxMsYgInTq1InExMQK+8PCwjh48CDbt2+ne/fueHt7l/u1ft999zFq1CjS0tL4/PPPy3XERkRE0LJlS7fM8G2qjDH85dUV7Np/mO/uPxN/X29PF6kh0pqCUk7Yt28fa9asYe3ata7X0aNH89RTT5GUlFTpOZmZmQDExsaSnp5OWFgYXl4Vpwi1bdvWLU8Za25++jOdX3ceYMpFfTUgHAMNCkpVoqioiK1bt7J27VpKSkpc/QIDBgwgJSUFgE6dOhEXF0efPn0AaxhlQkJChbxKh1d6e3vTunXrevoEzZMxhv/8bwuRof785aROni5Oo6RBQTV7ubm5rnb4adOmMW/ePDZs2ODqyO3bt68rKLz44ou0atWKuLi4Cjf4adOmuXV4ZZO17kNYPBUyEyE0CkZMhv5XHlNWy7am8fvuDKZdeoLWEo6RBgXVrOzdu5dff/21XBPQgQMHOHjwICLCgQMHCA0N5Y477mDAgAHExcXRq1cv1/mXXXZZlXm7c3hlk7XuQ/jybijMtd5n7rHeQ50DgzGGZxduJapVAGNObMK1BAeDaGW0o1k1SXl5eWzcuNF18//Xv/5FcHAwDz/8MNOmTUNE6NGjB3FxccTFxXHfffe5ZXVOVYNnT7ACwdGC2sLlr4MIiBcg1WwD4sWKnQd5bP4m7j67B+f2jbT2i9jHlN2WKtIr2S53PamhHJVsOz1A4OggCuAbABfOqGtgqLJgGhRUo5eamkpQUBBBQUEsXLiQCRMmsGnTJtfjHIOCgvjxxx+Ji4vjzz//ZP/+/Zxwwgluef6vqoXiQkhcCTuWwfLpni5NPahDEKoqaJUGm8OpYEoqXiK0E/x9Qx0LVTltPlKNSmZmJgsWLGDt2rWu5p+UlBQ++ugjrrjiClq1akVMTAwXX3yxq/mna9eurtE+Xbt2pWtXfchKvTIGUjdaQWDHMkj4CQoOAQLevlaQOFpQBIyZbd8AjZWHa7sEDGW2Dat2pfPy8u3cdFoMQ2Jbu9LLHmOVpeSovGrappJrH53v0dum+mNqvEZV2yXw+9uVf8eZFYdCHysNCqpBysrKYt26da7mn3POOYcxY8aQnp7ONddcg6+vL3369OHcc88lLi6OgQMHAhAfH8+XX37p4dIrMvYcCQI7v7N+4QKEd4O4q6DLcIg5DbYtrLw55NxpEDO0VpcqLjFMWvAdxeEdOWnUMPBqwnM6/lxaeXNbaFTFtGPktqAgIm8AFwCpxpgT7LTWwAdADLALuNIYc1CsmTnPA6OBHOAGY8zv7iqbajiMMezevZucnBx69+5NYWEhffr0Yfv27a5jwsPD6dGjBwAxMTGsWbOG3r17N9nlGRqlnAOw63vYsdwKBAf+tNKDIqwA0GUYxA6DsKM6gEvbwY+j43T+umS27jvEC1cPxLspBwSwvpvKguiIyY5dwm19CiJyBnAIeLtMUHgSOGCMmS4iDwKtjDEPiMhoYDxWUBgMPG+MGVzTNbRPoXH64IMP+Omnn1xNQBkZGYwaNYqvv/4agLvvvpt27dq5mn86duyoM3obmsJc2L0CdtpBIHkNYMCvpVUDiB1mBYOI3s53tpZRVFzCOc9+h6+3F1/fczpeTT0ogFOjjzzT0SwiMcD8MkFhCzDcGLNXRCKBZcaYniLyir099+jjqstfg0LDlZqaWq7dPz8/n48++giA4cOH89tvv9G/f3/i4uIYMGAAJ598MoMGDfJwqVWVSoph75ojNYHdK6A4H7x8IOpkqybQZTh0PNHqJ6gnn6xK5L6P1vLyXwcx6oTIertuE9BgOprblbnRpwDt7O2OQNmGskQ7rdqgoNyvplU/i4uL2bZtG3/88QeXXXYZIsLtt9/Oyy+/7DqmY8eOnHzyya41gj755BPCwsLw9tbJRQ2WMXBgB+xYeqRfIM9aroOIvnDSzVYQ6HwqtGjpkSIWFpfw/OJt9O0Qwrl923ukDE2RxzqajTFGROpcTRGRccA4cO7pTKpyla36OW7cOLZt20ZycjJr165l/fr15OZa7ZvJyclERkZywQUX0L17d9ccgDZt2pTLNzw8vN4/i6qFQ6lHagI7lkGWPaIltBP0vhC6nAmxZ0DLCE+W0uXT3xPZfSCH16+P1+ZFB2nzUTOUn59PUlISmZmZ5f6GDRtG586d2bBhAzNmzGDOnDkVHqoO1k3dGOO66Ze2/Z9wwglVPoBFNUD52dbw0NIgkLrRSvcPs27+XYZbf627uLVf4FgUFJVw5tPLaBPcgnl3nKpBoe4aTPPRF8D1wHT79fMy6XeJyPtYHc2ZNQWE5qikpAQvLy8KCgpYt25duRt6VlYWQ4cOJT4+nj179jBhwoQK+59++mmuueYaVq1axdChFYf7ffDBB3Tu3Jn9+/fzxRdfVBoQAA4cOEBxcbH+Q2xsyk4a27kcEn+DkiLwbgGdT7E6K2OHQWQceDXspr0PVu4hKSOXf1/WT/8/dJg7h6TOBYYDbUQkEXgEKxh8KCI3AQlAaZf5AqyRR9uxhqTe6K5yTZz5IR9tycMEtkJyDjKmpz9P3eXcuiGVMcaQm5tLcXExwcHBGGNYtGhRhV/q8fHxXHjhheTm5nLhhRdW2P/AAw8wZcoU0tPTOemkkypcZ/r06cTHx2OMYcOGDYSGhhIWFkbnzp0JDQ11Nbf16NGD2bNnExoaSmhoKCEhIYSGhtKhQwfA6ghOSUkhJiamylU/9R9iI1Bu0thySPjxyKSxDgPh1LutDuJOg61hjY1EXmExLy7ZTnznVpzRvU3NJ6g6aVbLXEyc+SEf7vRGfP1daaYwjytji6sMDMYYDh8+TGZmJiUlJXTqZI2znj9/PsnJyeVu2j169GD8+PEAnHXWWSQkJLj2FRUV8de//pV33nkHsB5YXroKZ6nx48czY8YMSkpKOOOMM1w369Ib98iRIxk5ciSFhYV8/fXXrn1lj3Gy+eboPgWwVv189dVXdZG3hqp00tjO5VYgKDtprMtwqyYQcxoENt4lvN/8cSdTvtzIezcP5tRuGhSOka59BBAz/m0IqtjJWZyVxrCsRbRt25Znn30WgIsuuogffviBrKws1xo6Q4cO5YcffgCs5ZQ3brTaYL28vAgJCWH06NHMmTMHgJtuuom8vLxyN+24uDjOO+88AFasWEFgYKBrX3BwcIMcjXM8zxxW9SD3IOz8/ki/QLlJY8OOBIKjJ401UrkFxZz+5FK6RQTx/rhTPF2cxqzB9Cl4lAlsVek34RXchh8TWhJblMvh/CKCWvhw6qmnEh0dXe6m3rlzZ9c58+fPx9fXl9DQ0Eofl/j6669XW5YhQ4Y48ZHc7tprr9Ug0JAU5sGeFUeCQNlJY52HHhkq6uZJY57y7ooE9h/K57/X6pwWd9GaAlidbV5WfPQS6NU+hEGdwxgU3YpB0a3oHB6obejKM0qKYe/aI0Gg3KSxk46MEKrnSWOecDi/iNOfXErfDiG8c1ONCx6o6mlNAWBMT38+3JlXaZ/CQ387j9V7MlidcJDfd2cwb3Uy767YDUDrID8GRYcxMLoVA6PDiIsKI6hFs/rqVH2pMGnse8jLsPaVmzR2CrQI9mBB69/sn3Zx4HABE0b28HRRmrRmVVOA2o8+Ki4xbEvN5veEDH7ffZDfdx9kR9phQGsTymFlJ43tXH5kFczQTna/QMOaNOYJ2XmFnPbEUk7s3Io3bqg48k7VmXY0OyEjp4DVu48EibV7MjmUXwRAeJAfA+3axKDoVsR1CiXQT2sTqhLlJo0th9Q/rPRGMGnMU55ftI1nF23ly7tOo19UqKeL0xRo85ETwgL9OLNXBGf2sn6xVVabWLTJGgLo7SX0ah/MwGitTTQnv33xCp1+f4oIk0aqtGXPoImcdP7frEljpSuKVpg09mijmTTmCZk5hcz6YQfn9GmnAaEeaE3BYQcPF7Bmz5EgsWZ3BocLrCGtWpto2n774hVOWPUwAVLgSis2gvHywccU4po0Vvp8gUY2acxT/vO/LbywZDtf33M6vSNDPF2cpkJrCvWlVVDF2sTWfdlWkEjIYPWeirWJQdGtXP0T0a21NuFRJSWQn2V17uZlHvnLLfv+yHZJbgZFhw9i8jI5MSelwkO/vMVw2HhTfNksWnQ7o1FPGvOEA4cLeOOHnZzfL1IDQj3RoOBm3l5C78gQekeGcO1ga57D0bWJT39P5J0V1nISWps4TsZAweEKN++KN/ay+zMg107Lz8J+AHDl2SPkeLUkmyAyTAAHigPINCFkmUiu9E6p9PdXgMmn21xfYtqso3f7EHq1D6ZXpPUa1SpAfwRU45Xv/iSnsJh7z+7u6aI0G3q38YAaaxOV9E14rDbhzFOe6qYwr5qbd0YV+8r8lRRVn79fS/APxfiHUuwXQo5fO7JbdCWjJID0ogBSClqQlNeC3Tm+7M1vQZYJJNMEkUUQ+d4BtA8NomNYAB1bBRDVKoCOYQF0ahVAyjuDiWR/hculSDjjz+rO5pQsNiRn8tX6I2s9BrfwoWf7YHpFBtOrvfXjoWf7YFrqkGfSsvN5+6cELo7rQPd2zWv4rSdpn0IDdfBwAav3HGlyqtg3cSRI9I9yU21i3YeVPw/2whnVB4biokpu2Bm1+MVu/xXlVZ03gI8/+Ifaf2FltkMh4Mj7khZhZBLAvgJ/knL92J3rx65sb/ZkFpJ0MJfEgzmu77SUv68XUa0CXTf9jmFHbvxRrQJpG9yiyucAV9ankGv82HDivzjpoltdaYfyi9i6L5tNe7PYvDebzSnWa3b+kWAW3TrQVaPobb9Gtw5s+s8gLuOx+Rt588edLJowjC5tPfMgnyZMh6Q2dpXVJnbst+ZNuK028ewJR8bMl9UiBAZcU/XNveBQ9fmKd7mbd9U39qNf7T978mFRcQkpWXkkHcwlKSOXxIO5ru3Sv4KiknKXDvH3oaN9048q80u/NAC0DvI7ru/tyOij/aRKG2v0UZmAUBVjDEkZua4gsSklm817s9i5/zAl9j/RAF9verQPtoJE+2B6R4bQq30IoYFNbybzvqw8znhyKRfGdeDpMXGeLk5TpEGhKSpbm7DmTRypTbRp6ceATsdYmyguhD2/wuzRVR/TIhQCjr6ph1X6i73CPr+gWo2/zyssJrn0Bn/wyGui/ZqSlUdxSfn/f9u09KNjq0CiytzoO4YFENXaeg32b1w30LzCYrbuy2bz3mw22TWKTSlZZOQUuo7pEOrv6qMorVnEtgnCx9vLgyU/Po98voE5v+xmyX3DiQ4P9HRxmiINCs1BcYlhS4pVm1i9u2JtondksGvOxKDoVnRqXaaT82AC/LkYti+2JlQVZFd9odAo+Psfx13eQ/lF9s0+x/UrP7FMAEjLzi93vJdA+xD/Ms06gUdu/Parv2/TH+dvjCE1O99qfrJrFJtTstmeeogiO0j6+XjRPaKl3U9h9Vf0igymTcsWHi59zZIycjnzqWVcfmJHHr+sv6eL01RpUGiuqqpNtKCAcwK3cVHLzcQXraJVzi7rhNBO0O1s6DYCctIpWvAAPsVH2viLvP3xufiFGjubjTEczCksf9M/qoknM7ew3Dl+3l50CPMv8ws/sFxnbvtQf3wb8a9fdysoKmF76iGrjyIl2xU0ygbXNi1b0DuytOnJChZdI4Jo4dNwgumkT9fzyapElk4cTscwncfhJjpPoblqFeTHWb3acVbPCNi/jZJtq8jZ+C3+ySvwKcknP8uPFcW9WF4ylu9NHC28ezKopDWD8lqRnpPPpsKbuZf36SDpJJtwniu5itOKh3JRiSHtUD6Jdoft0U08SRm55BzViRvo5+36VT8wOqzcL/2oVgG0bdkCr2bUkeo0Px8v+nQIoU+H8uP59x/KZ0uZILE5JYvZP+1y9bf4eAld27Z0jYDqFRlM7/YhtAtpUe/DZfccyOGjlXu4ZnC0BgQP0ZpCU5aXZS2tsN1uFsq0Vn2lTY8jtYHOQzlQ4M1qu8np6L6Jynh7Cd4iFBSX78QNDfCt0HFrdeZaHbthgb46Jr+BKCouYVf6YTbuPdL8tHlvFsmZR2qFrQJ9ywWJXpHBdI8IJsDPfbWKiR+t5fO1yXw38Uzah/rXfII6Vtp81CyUlMC+9bB9kRUE9vxijdn3C7aWVeg2ArqOgFadq82mtG9i9Izvqzzm1mFdynTmWr/4dWx945eZU+hqftqcksWmvdlsSckmt9D6keAlENMmyC2T8HbuP8zZzyzn+lNimHxhHyc+jqqaNh81WYf3w59LrUDw52I4nGalt+9vPZi929nQ6eQ6PYDF20vo0yGEjmEBJGXkVtjfMSyASef1duoTqAYkNNCXwV3CGdzlyMOoSkoMuw/ksGnvkaGy65Ocn4Q3Y/E2fL2F24d3dfQzqbrRoNDYFBdB0iq7NrAIklcDBgJaH6kJdD0Lgtsd96UmntuTSZ+ud/1KBGus/MRzex533qrx8PISYtoEEdMmiPP6RbrSD+UXsSXlyOS7zSlZfL46mXfzd7uOqc0kvHmrk/j3gk2kZufTsoUPP27fzyUDO9brZ1RHaPNRY5CZZA8XXWQtvZyXCeJlPY6xtG8gcoBbll2etzqJp77dQnJGLh3CAph4bk/9B6uqZIwh8WBuuaGym1Ky2FXFJLyComLmr08pN8kwwNebxy/rp/+fuZf2KTQqRfnWQ1i2L4I/l0DqRis9uIMVALqNsJZfDmjl0WIqVVu5BcVsS61+El5ZHcMC+PHBs+q5lM2K9ik0eOl/2qOEFsGu76EwB7z9IPoUGPmYVSOI6K1P4lKNUoCfN/2jwugfFeZKM8bQZdKCStekTa6kL0vVDw0KnpJ/yLr5lwaCgzut9NZdYOBfrSAQc5q1JIRSTZCI0KGKwQwddI6Cx2hQqC/GWM1ApR3ECT9DSSH4BkHs6XDKnVYHcbiOvFDNhw5maHg0KLhTzgGrY3j7YqujONsewhfRF4bcbtUGooeAT8Nfj0YpdyjtTNbBDA2HBgUnlRRD8pojtYGklWBKrJVBu55lDRftNgJCOni6pEo1GJcM7KhBoAHRoHC8svcdGS765xLIPQgIdBwEZ0y0agMdBoG3ftVKqYZP71R1VVRgLR9RGghS1lvpQRHQ4zx7uOiZEBRefT5KKdUAaVCojYO7jiwqt3O59WQxLx/oNARGPGLVBtqdAF66rLNSqnHToFCZghxI+PHIwnLp26z0sGjrOQLdzoaY08E/pPp8lFKqkWlQQUFERgHPA97ALGPMdMcvsu5DWDwVMhOtJ4iNmAz9xkDaliNNQrt+hOJ86wHxMafDSTdbzULh3XTymFKqSWswy1yIiDewFRgJJAK/AVcbYzZWdU6dl7lY9yF8eTcUlpksI97Wg+jzDlrv2/Qs86yBU8FXJ9EopZqcRrHMxcnAdmPMDgAReR+4GKgyKNTZ4qnlAwKAKYaiPLjweWvIaFgnxy6nlFKNTUMKCh2BPWXeJwKDjz5IRMYB4+y3h0RkS20vcGKk14mV7znEqn/euKrWJW2e2gD7PV2IRkS/r7rT76xujuf7+sYYM6qyHQ0pKNSKMeZV4NXjzUdEVhpj4h0oUrOg31fd6PdVd/qd1Y27vq+GNIYyCSjbdhNlpymllKonDSko/AZ0F5FYEfEDrgK+8HCZlFKqWWkwzUfGmCIRuQv4FmtI6hvGmD/ceMnjboJqZvT7qhv9vupOv7O6ccv31WCGpCqllPK8htR8pJRSysM0KCillHJplkFBREaJyBYR2S4iD3q6PA2ZiLwhIqkissHTZWkMRKSTiCwVkY0i8oeI3OPpMjVkIuIvIr+KyFr7+5ri6TI1BiLiLSKrRWS+03k3u6BgL6fxInAe0Ae4WkT6eLZUDdpsoNJJLqpSRcB9xpg+wBDgTv3/q1r5wFnGmDhgADBKRIZ4tkiNwj3AJndk3OyCAmWW0zDGFACly2moShhjvgMOeLocjYUxZq8x5nd7OxvrH64+VqwKxnLIfutr/+nol2qISBRwPjDLHfk3x6BQ2XIa+o9WOU5EYoCBwC8eLkqDZjeFrAFSgYXGGP2+qvcccD9Q4o7Mm2NQUMrtRKQl8AlwrzEmy9PlaciMMcXGmAFYqxicLCIneLhIDZaIXACkGmPctlZbcwwKupyGcisR8cUKCHOMMZ96ujyNhTEmA1iK9mFVZyhwkYjswmr6PktE3nXyAs0xKOhyGsptRESA14FNxphnPF2ehk5E2opImL0dgPU8lc0eLVQDZoyZZIyJMsbEYN27lhhj/urkNZpdUDDGFAGly2lsAj5083IajZqIzAV+BnqKSKKI3OTpMjVwQ4GxWL/g1th/oz1dqAYsElgqIuuwfrAtNMY4PsxS1Z4uc6GUUsql2dUUlFJKVU2DglJKKRcNCkoppVw0KCillHLRoKCUUspFg4JqlESk2B7uuUFEPhKRQIfzXyYi1T4UXUTuLXtdEVlQOub+OK99m4hcV8dzaiyvUrWhQUE1VrnGmAHGmBOAAuA2D5ThXsAVFIwxo+1ZucfFGPOyMebt481HqWOhQUE1Bd8D3USktYjME5F1IrJCRPoDiMijIvKOiPwsIttE5BY7fXjZ9ehFZKaI3HB05iLykoisLLvev4jcDXTAmni11E7bJSJt7O0Jdi1mg4jca6fFiMgmEXnNzut/9izeo6/3qIj8w95eJiJP2M8c2Coip9vpASLyvp3fZ0BAmfPPsT/r73YtqqWIdLY/exsR8RKR70XkHCe+fNW0aFBQjZqI+GA9G2M9MAVYbYzpD/wfUPbXdn/gLOAUYLKIdKjDZR4yxsTbeQwTkf7GmBlAMnCmMebMo8p0InAjMBjrmQq3iMhAe3d34EVjTF8gA7i8Ftf3McacjFUzecROux3IMcb0ttNOtK/dBngYONsYMwhYCUwwxiQATwAvAfcBG40x/6vDd6CaCR9PF0CpYxRgL7cMVk3hdawlqi8HMMYsEZFwEQmxj/ncGJML5Nq/7E/GuinXxpUiMg7r30sk1sOZ1lVz/GnAZ8aYwwAi8ilwOtYaWzuNMaXlXgXE1OL6pYvqlT3+DGAGgDFmnb1MBFhBqA/wo7UME35Yy5RgjJklImOwmtoG1OK6qhnSoKAaq1x7uWUX+yZYlaPXczFYT0krW1v2P/okEYkF/gGcZIw5KCKzKzuuDvLLbBdTptmnFucUU/O/WcFaP+jqCjusTvEo+21LILsW11bNjDYfqabke+BasPoLgP1lnmVwsf084HBgONbiawlAHxFpYY8aGlFJniHAYSBTRNphNVWVygaCqyjHJSISKCJBwKV2mpO+A64BsJ8/0N9OXwEMFZFu9r4gEelh73sCmANMBl5zuDyqidCagmpKHgXesJtScoDry+xbh7VWfxvgMWNMMoCIfAhsAHYCq4/O0BizVkRWYy3nvAf4sczuV4FvRCS5bL+CMeZ3u0bxq500yxizWqwnsTnlJeBNEdmEtdrvKvvaaXZn+VwRaWEf+7CIRAInAUONMcUicrmI3GiMedPBMqkmQFdJVU2eiDwKHDLGPO3psijV0GnzkVJKKRetKSillHLRmoJSSikXDQpKKaVcNCgopZRy0aCglFLKRYOCUkopl/8HZHeG96m92w0AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pyabc.visualization.plot_lookahead_evaluations(\n", " sampler_df=sampler_logfiles[0],\n", " # relative=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Composition of final accepted population:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pyabc.visualization.plot_lookahead_final_acceptance_fractions(\n", " sampler_logfiles[0],\n", " hs_la[0],\n", " # relative=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Acceptance rates in look-ahead and actual mode:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pyabc.visualization.plot_lookahead_acceptance_rates(\n", " sampler_df=sampler_logfiles[0]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For details on the use and options of the various samplers, see the [API documentation](https://pyabc.readthedocs.io/en/latest/api_sampler.html). Note that unlike STAT and DYN, LA is currently only implemented via Redis, but e.g. not for multi-processing, which typically does not scale beyond a few dozen workers." ] } ], "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 }