From e2f31b4f6ee27c9e4e83d46ba399fa23700115eb Mon Sep 17 00:00:00 2001 From: = Date: Sat, 18 May 2024 20:06:07 +0200 Subject: [PATCH 01/21] USE BLACKJAX --- benchmarks/data/SP500.npy | Bin 9836 -> 0 bytes benchmarks/data/gc_features.npy | Bin 100128 -> 0 bytes benchmarks/data/gc_labels.npy | Bin 4128 -> 0 bytes benchmarks/data/irt_labels.npy | Bin 160128 -> 0 bytes benchmarks/data/irt_mask.npy | Bin 40128 -> 0 bytes benchmarks/error.py | 85 - benchmarks/ground_truth/GC/ground_truth.npy | Bin 536 -> 0 bytes benchmarks/ground_truth/GC/map.npy | Bin 332 -> 0 bytes benchmarks/ground_truth/IRT/ground_truth.npy | Bin 4136 -> 0 bytes benchmarks/ground_truth/IRT/map.npy | Bin 2132 -> 0 bytes .../ground_truth/brownian/ground_truth.npy | Bin 384 -> 0 bytes benchmarks/ground_truth/brownian/map.npy | Bin 256 -> 0 bytes .../german_credit/ground_truth.npy | Bin 536 -> 0 bytes benchmarks/ground_truth/german_credit/map.npy | Bin 332 -> 0 bytes .../stochastic_volatility/ground_truth.npy | Bin 38992 -> 0 bytes .../stochastic_volatility/ground_truth_0.npy | Bin 38992 -> 0 bytes benchmarks/interactive_gallery.py | 80 - benchmarks/{ => mcmc}/__init__.py | 0 benchmarks/mcmc/benchmark.ipynb | 9716 +++++++++++++++++ benchmarks/mcmc/benchmark.py | 558 + benchmarks/mcmc/explore.py | 145 + benchmarks/mcmc/find_params.py | 266 + benchmarks/mcmc/inference_models.py | 891 ++ benchmarks/mcmc/results.ipynb | 1672 +++ benchmarks/mcmc/sampling_algorithms.py | 190 + build/lib/benchmarks/IRT.py | 131 + {mclmc => build/lib/benchmarks}/__init__.py | 0 .../lib/benchmarks/benchmarks_mchmc.py | 259 +- .../lib/benchmarks/benchmarks_numpyro.py | 0 build/lib/benchmarks/brownian.py | 194 + build/lib/benchmarks/german_credit.py | 175 + {tests => build/lib/mclmc}/__init__.py | 0 {mclmc => build/lib/mclmc}/annealing.py | 0 .../lib/mclmc}/correlation_length.py | 0 build/lib/mclmc/dynamics.py | 199 + {mclmc => build/lib/mclmc}/old_annealing.py | 0 build/lib/mclmc/sampler.py | 511 + {mclmc => build/lib/mclmc}/smc.py | 0 build/lib/tests/__init__.py | 0 {tests => build/lib/tests}/benchmarks.py | 0 {tests => build/lib/tests}/test_annealing.py | 0 {tests => build/lib/tests}/test_mclmc.py | 0 .../lib/tests}/test_momentum_update.py | 0 .../lib/tests}/tst_diagonal_precond.py | 0 mclmc/boundary.py | 108 - mclmc/dynamics.py | 152 - mclmc/sampler.cpp | 302 - mclmc/sampler.py | 342 - mclmc/tune.py | 179 - notebooks/tutorials/Constraints.ipynb | 521 - notebooks/tutorials/Ensamble_tutorial.ipynb | 427 - notebooks/tutorials/advanced_tutorial.ipynb | 353 - notebooks/tutorials/intro_tutorial.ipynb | 327 - .../tutorials/positive_constraints.ipynb | 273 - notebooks/tutorials/smc.ipynb | 191 - speed-bench/plots.py | 57 - speed-bench/single.py | 35 - tests/test_blackjax.py | 148 - 58 files changed, 14698 insertions(+), 3789 deletions(-) delete mode 100644 benchmarks/data/SP500.npy delete mode 100644 benchmarks/data/gc_features.npy delete mode 100644 benchmarks/data/gc_labels.npy delete mode 100644 benchmarks/data/irt_labels.npy delete mode 100644 benchmarks/data/irt_mask.npy delete mode 100644 benchmarks/error.py delete mode 100644 benchmarks/ground_truth/GC/ground_truth.npy delete mode 100644 benchmarks/ground_truth/GC/map.npy delete mode 100644 benchmarks/ground_truth/IRT/ground_truth.npy delete mode 100644 benchmarks/ground_truth/IRT/map.npy delete mode 100644 benchmarks/ground_truth/brownian/ground_truth.npy delete mode 100644 benchmarks/ground_truth/brownian/map.npy delete mode 100644 benchmarks/ground_truth/german_credit/ground_truth.npy delete mode 100644 benchmarks/ground_truth/german_credit/map.npy delete mode 100644 benchmarks/ground_truth/stochastic_volatility/ground_truth.npy delete mode 100644 benchmarks/ground_truth/stochastic_volatility/ground_truth_0.npy delete mode 100644 benchmarks/interactive_gallery.py rename benchmarks/{ => mcmc}/__init__.py (100%) mode change 100755 => 100644 create mode 100644 benchmarks/mcmc/benchmark.ipynb create mode 100644 benchmarks/mcmc/benchmark.py create mode 100644 benchmarks/mcmc/explore.py create mode 100644 benchmarks/mcmc/find_params.py create mode 100644 benchmarks/mcmc/inference_models.py create mode 100644 benchmarks/mcmc/results.ipynb create mode 100644 benchmarks/mcmc/sampling_algorithms.py create mode 100644 build/lib/benchmarks/IRT.py rename {mclmc => build/lib/benchmarks}/__init__.py (100%) mode change 100755 => 100644 rename benchmarks/targets.py => build/lib/benchmarks/benchmarks_mchmc.py (72%) rename benchmarks/targets_numpyro.py => build/lib/benchmarks/benchmarks_numpyro.py (100%) create mode 100644 build/lib/benchmarks/brownian.py create mode 100644 build/lib/benchmarks/german_credit.py rename {tests => build/lib/mclmc}/__init__.py (100%) rename {mclmc => build/lib/mclmc}/annealing.py (100%) rename {mclmc => build/lib/mclmc}/correlation_length.py (100%) create mode 100644 build/lib/mclmc/dynamics.py rename {mclmc => build/lib/mclmc}/old_annealing.py (100%) create mode 100644 build/lib/mclmc/sampler.py rename {mclmc => build/lib/mclmc}/smc.py (100%) create mode 100644 build/lib/tests/__init__.py rename {tests => build/lib/tests}/benchmarks.py (100%) rename {tests => build/lib/tests}/test_annealing.py (100%) rename {tests => build/lib/tests}/test_mclmc.py (100%) rename {tests => build/lib/tests}/test_momentum_update.py (100%) rename {tests => build/lib/tests}/tst_diagonal_precond.py (100%) delete mode 100644 mclmc/boundary.py delete mode 100644 mclmc/dynamics.py delete mode 100644 mclmc/sampler.cpp delete mode 100644 mclmc/sampler.py delete mode 100644 mclmc/tune.py delete mode 100644 notebooks/tutorials/Constraints.ipynb delete mode 100644 notebooks/tutorials/Ensamble_tutorial.ipynb delete mode 100644 notebooks/tutorials/advanced_tutorial.ipynb delete mode 100644 notebooks/tutorials/intro_tutorial.ipynb delete mode 100644 notebooks/tutorials/positive_constraints.ipynb delete mode 100644 notebooks/tutorials/smc.ipynb delete mode 100644 speed-bench/plots.py 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@@ -import jax -import jax.numpy as jnp - - - -def err(f_true, var_f, contract = jnp.max): - """Computes the error b^2 = (f - f_true)^2 / var_f - Args: - f: E_sampler[f(x)], can be a vector - f_true: E_true[f(x)] - var_f: Var_true[f(x)] - contract: how to combine a vector f in a single number, can be for example jnp.average or jnp.max - - Returns: - contract(b^2) - """ - - def _err(f): - bsq = jnp.square(f - f_true) / var_f - return contract(bsq) - - return jax.vmap(_err) - - - -def grads_to_low_error(err_t, low_error= 0.01, grad_evals_per_step= 1): - """Uses the error of the expectation values to compute the effective sample size neff - b^2 = 1/neff""" - - cutoff_reached = err_t[-1] < low_error - return find_crossing(err_t, low_error) * grad_evals_per_step, cutoff_reached - - - -def ess(err_t, neff= 100, grad_evals_per_step = 1): - - low_error = 1./neff - cutoff_reached = err_t[-1] < low_error - crossing = find_crossing(err_t, low_error) - - return (neff / (crossing * grad_evals_per_step)) * cutoff_reached - - - -def find_crossing(array, cutoff): - """the smallest M such that array[m] < cutoff for all m > M""" - - def step(carry, element): - """carry = (, 1 if (array[i] > cutoff for all i < current index) else 0""" - above_threshold = element > cutoff - never_been_below = carry[1] * above_threshold #1 if (array[i] > cutoff for all i < current index) else 0 - return (carry[0] + never_been_below, never_been_below), above_threshold - - state, track = jax.lax.scan(step, init=(0, 1), xs=array, length=len(array)) - - return state[0] - #return jnp.sum(track) #total number of indices for which array[m] < cutoff - - - -def cumulative_avg(samples): - return jnp.cumsum(samples, axis = 0) / jnp.arange(1, samples.shape[0] + 1)[:, None] - - - -if __name__ == '__main__': - - # example usage - d = 100 - n = 1000 - - # in reality we would generate the samples with some sampler - samples = jnp.square(jax.random.normal(jax.random.PRNGKey(42), shape = (n, d))) - f = 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0.91323185, 0.9136166 ,\n", + " 0.91644865, 0.9166464 , 0.91016626, 0.9094692 , 0.91374207,\n", + " 0.9114871 , 0.91152 , 0.91265017, 0.9136803 , 0.91412246,\n", + " 0.91432863, 0.9128117 , 0.9143933 , 0.9156801 , 0.91350424,\n", + " 0.9105282 , 0.91299754, 0.91354656, 0.91255 , 0.91244036,\n", + " 0.9128147 , 0.9129622 , 0.9121573 , 0.91559756, 0.91246694,\n", + " 0.91180074, 0.9090308 , 0.911497 , 0.911287 , 0.9148062 ,\n", + " 0.91484225, 0.9107405 , 0.9088609 , 0.9114057 , 0.9136628 ,\n", + " 0.9129748 , 0.9137581 , 0.91187185, 0.9164927 , 0.9100827 ,\n", + " 0.9123644 , 0.9147338 , 0.9128822 , 0.91231525, 0.9159241 ,\n", + " 0.909687 , 0.9114519 , 0.9142304 , 0.90744364, 0.9136173 ,\n", + " 0.91329956, 0.908741 , 0.91511405, 0.9099337 , 0.9147669 ,\n", + " 0.91032606, 0.91201174, 0.9184225 , 0.9128089 , 0.9138138 ,\n", + " 0.9104181 , 0.9117637 , 0.9112562 , 0.91193014, 0.91603905,\n", + " 0.91127455, 0.91419363, 0.9091502 , 0.91466635, 0.9104765 ],\n", + " [0.8182885 , 0.81645 , 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0.8202933 , 0.81724954, 0.80805933, 0.82406604, 0.8238287 ,\n", + " 0.81558645, 0.7981632 , 0.81173044, 0.8114513 , 0.8068975 ,\n", + " 0.8032079 , 0.80342 , 0.82118356, 0.81748253, 0.8098008 ,\n", + " 0.81191957, 0.8284385 , 0.80766314, 0.8118846 , 0.80934274,\n", + " 0.8093861 , 0.8093985 , 0.82138544, 0.8122794 , 0.8196393 ],\n", + " [0.916788 , 0.916461 , 0.9120648 , 0.91638935, 0.9185752 ,\n", + " 0.915967 , 0.91649187, 0.91460687, 0.9161761 , 0.91481227,\n", + " 0.91399264, 0.91651887, 0.9176769 , 0.9182386 , 0.9141354 ,\n", + " 0.9154259 , 0.9191275 , 0.914757 , 0.9179838 , 0.91963214,\n", + " 0.9148346 , 0.91213346, 0.91704535, 0.9137658 , 0.9179093 ,\n", + " 0.9179158 , 0.91616833, 0.9176504 , 0.9133633 , 0.92000556,\n", + " 0.91896355, 0.9198364 , 0.91291165, 0.91697735, 0.9135519 ,\n", + " 0.91932946, 0.91273105, 0.9128715 , 0.918481 , 0.91486514,\n", + " 0.91871846, 0.9168679 , 0.916582 , 0.9167394 , 0.9177922 ,\n", + " 0.91301274, 0.91460735, 0.9127277 , 0.91379863, 0.918544 ,\n", + " 0.91489583, 0.9111012 , 0.9143128 , 0.91855806, 0.9205817 ,\n", + " 0.91369355, 0.91342413, 0.91696864, 0.9174895 , 0.91498464,\n", + " 0.91590565, 0.9190212 , 0.9193664 , 0.92451084, 0.9237687 ,\n", + " 0.9189365 , 0.9200342 , 0.91737294, 0.91548675, 0.9067432 ,\n", + " 0.91933614, 0.91991806, 0.9139581 , 0.9131478 , 0.9198742 ,\n", + " 0.9174321 , 0.92166257, 0.92016596, 0.91765165, 0.9174782 ,\n", + " 0.9171059 , 0.9173149 , 0.91726476, 0.91408783, 0.9154148 ,\n", + " 0.91422945, 0.9151624 , 0.9180806 , 0.9103819 , 0.91385114,\n", + " 0.9193223 , 0.92037344, 0.92065614, 0.9141735 , 0.9150021 ,\n", + " 0.91232526, 0.91929257, 0.9190358 , 0.91637516, 0.9161139 ],\n", + " [0.8080572 , 0.8072662 , 0.8133484 , 0.81046546, 0.8108139 ,\n", + " 0.8094811 , 0.8134327 , 0.8104727 , 0.8103218 , 0.80622476,\n", + " 0.8067138 , 0.80909383, 0.8106447 , 0.8085242 , 0.79984576,\n", + " 0.80794805, 0.81463736, 0.8103704 , 0.81011647, 0.81420785,\n", + " 0.8027701 , 0.80454403, 0.81331533, 0.81649894, 0.80555373,\n", + " 0.8138852 , 0.8113128 , 0.80289596, 0.81789494, 0.81663275,\n", + " 0.81285053, 0.81479824, 0.8052661 , 0.8049139 , 0.81005186,\n", + " 0.815297 , 0.81159794, 0.8080973 , 0.8168613 , 0.8134403 ,\n", + " 0.8211029 , 0.8116032 , 0.8041824 , 0.80992204, 0.8155727 ,\n", + " 0.80370694, 0.8108088 , 0.8097364 , 0.8126268 , 0.80964655,\n", + " 0.8064188 , 0.810979 , 0.81068486, 0.8153748 , 0.8028467 ,\n", + " 0.8064591 , 0.8098567 , 0.81155705, 0.8043548 , 0.80836236,\n", + " 0.8031012 , 0.80944437, 0.8049019 , 0.80746686, 0.8069363 ,\n", + " 0.8164398 , 0.8128134 , 0.8110853 , 0.8109025 , 0.8018413 ,\n", + " 0.8081307 , 0.8090573 , 0.8096849 , 0.8086735 , 0.8165233 ,\n", + " 0.8074145 , 0.8167979 , 0.81774586, 0.81085056, 0.79942244,\n", + " 0.81238854, 0.80470026, 0.8065574 , 0.8109401 , 0.80758935,\n", + " 0.8060553 , 0.8100661 , 0.81079054, 0.80814433, 0.8128019 ,\n", + " 0.8108469 , 0.8131424 , 0.8076265 , 0.8130387 , 0.8146908 ,\n", + " 0.8114483 , 0.8063916 , 0.8152165 , 0.8129712 , 0.80872196]], dtype=float32)\n", + " batch_dim = 0\n", + " batch_dim = 0 acceptance probability\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "AVG NUM STEPS PER TRAJ Tracedwith with\n", + " val = Array([10.228184 , 24.918034 , 7.5774236, 11.759256 , 14.479013 ], dtype=float32)\n", + " batch_dim = 0\n", + "True mean [0. 0.]\n", + "True std [10. 4.35889894]\n", + "Empirical mean Tracedwith with\n", + " val = Array([[ 0.00436548, -0.00759073],\n", + " [ 0.01882443, -0.00019192],\n", + " [ 0.05147482, -0.03414474],\n", + " [ 0.02615423, -0.01953907],\n", + " [-0.00318353, -0.01224986]], dtype=float32)\n", + " batch_dim = 0\n", + "Empirical std Tracedwith with\n", + " val = Array([[9.983989 , 4.337424 ],\n", + " [9.997932 , 4.3606787],\n", + " [9.938344 , 4.2537637],\n", + " [9.969876 , 4.306143 ],\n", + " [9.980659 , 4.279184 ]], dtype=float32)\n", + " batch_dim = 0\n", + "Tracedwith with\n", + " val = Array([16.450315, 27.72986 , 13.838601, 15.047666, 23.095356], dtype=float32)\n", + " batch_dim = 0 Tracedwith with\n", + " val = Array([1.6083311, 1.1128427, 1.8262937, 1.279644 , 1.5950915], dtype=float32)\n", + " batch_dim = 0 params\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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    \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Tracedwith with\n", + " val = Array([0.90638214, 0.9086329 , 0.9104646 , 0.90217495, 0.9093379 ,\n", + " 0.9043823 , 0.9077197 , 0.90958244, 0.90934145, 0.9116726 ,\n", + " 0.90234804, 0.90629435, 0.9095852 , 0.90583104, 0.90788454,\n", + " 0.90745634, 0.9091979 , 0.90165555, 0.90694916, 0.9077506 ,\n", + " 0.90963304, 0.90546584, 0.9050213 , 0.9086675 , 0.9101332 ,\n", + " 0.9081804 , 0.9072799 , 0.9105894 , 0.9027906 , 0.9085326 ,\n", + " 0.90967345, 0.9073625 , 0.90759087, 0.9094267 , 0.9050001 ,\n", + " 0.90615314, 0.9047089 , 0.91116214, 0.9115014 , 0.90591383,\n", + " 0.9061162 , 0.90815413, 0.90974945, 0.9125368 , 0.9065341 ,\n", + " 0.9075216 , 0.90526325, 0.90670204, 0.9093859 , 0.9054553 ,\n", + " 0.906939 , 0.9069247 , 0.90620124, 0.90674394, 0.9111384 ,\n", + " 0.90647596, 0.907953 , 0.907552 , 0.9102805 , 0.9071393 ,\n", + " 0.9096144 , 0.9085676 , 0.90971696, 0.9026003 , 0.9091983 ,\n", + " 0.9098427 , 0.91010743, 0.90646607, 0.90896034, 0.9035882 ,\n", + " 0.9058538 , 0.9121087 , 0.90881664, 0.9046806 , 0.9104771 ,\n", + " 0.9093438 , 0.90800047, 0.908053 , 0.9102836 , 0.9074732 ,\n", + " 0.9065999 , 0.90331537, 0.909493 , 0.9052581 , 0.90040326,\n", + " 0.911583 , 0.9075188 , 0.9077676 , 0.90716386, 0.90449244,\n", + " 0.90633625, 0.90837824, 0.8996274 , 0.90395826, 0.9024799 ,\n", + " 0.9058671 , 0.9100268 , 0.91327584, 0.90437907, 0.90948224], dtype=float32)\n", + " batch_dim = 0 acceptance probability\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "AVG NUM STEPS PER TRAJ 24.39819\n", + "True mean [0. 0.]\n", + "True std [10. 4.35889894]\n", + "Empirical mean [0.00835473 0.02560315]\n", + "Empirical std [10.047082 4.374237]\n", + "27.749163 1.1373456 params\n" + ] + } + ], + "source": [ + "import bayex\n", + "\n", + "\n", + "def benchmark_chains_prime(model, sampler, favg, fvar, n=10000, batch=None):\n", + "\n", + "\n", + " # print(model)\n", + " # print(model.sample_transformations.keys())\n", + " # raise Exception\n", + " identity_fn = model.sample_transformations['identity']\n", + " logdensity_fn = model.unnormalized_log_prob\n", + " d = get_num_latents(model)\n", + " if batch is None:\n", + " batch = np.ceil(1000 / d).astype(int)\n", + " key, init_key = jax.random.split(jax.random.PRNGKey(42), 2)\n", + " keys = jax.random.split(key, batch)\n", + " # keys = jnp.array([jax.random.PRNGKey(0)])\n", + " init_pos = jax.random.normal(key=init_key, shape=(batch, d))\n", + "\n", + " samples, params, avg_num_steps_per_traj = jax.vmap(lambda pos, key: sampler(logdensity_fn, n, pos, key))(init_pos, keys)\n", + " avg_num_steps_per_traj = jnp.mean(avg_num_steps_per_traj, axis=0)\n", + " print(\"\\n\\n\\n\\nAVG NUM STEPS PER TRAJ\", avg_num_steps_per_traj)\n", + " # print(samples[0][-1], samples[0][0], \"samps chain\", samples.shape)\n", + " \n", + " # identity_fn.ground_truth_mean, identity_fn.ground_truth_standard_deviation**2\n", + " full = lambda arr : err(favg, fvar, jnp.average)(cumulative_avg(arr))\n", + " err_t = jnp.mean(jax.vmap(full)(samples**2), axis=0)\n", + " # ess_per_sample = ess(err_t, grad_evals_per_step=2 * avg_num_steps_per_traj)\n", + " # err_t = jax.vmap(full)(samples)[1]\n", + " # print(err_t[-1], \"benchmark chains err_t[0]\")\n", + " # print(avg_num_steps_per_traj, \"AVG\\n\\n\")\n", + " # raise Exception\n", + " # ess_per_sample = ess(err_t, grad_evals_per_step=2 * avg_num_steps_per_traj)\n", + "\n", + " print('True mean', identity_fn.ground_truth_mean)\n", + " print('True std', identity_fn.ground_truth_standard_deviation)\n", + " print(\"Empirical mean\", samples.mean(axis=[0,1]))\n", + " print(\"Empirical std\", samples.std(axis=[0,1]))\n", + "\n", + " print(params.L.mean(), params.step_size.mean(), \"params\")\n", + " \n", + " # print('True E[x^2]', identity_fn.ground_truth_mean)\n", + " # print('True std[x^2]', identity_fn.ground_truth_standard_deviation)\n", + "\n", + " return err_t[-1]\n", + "\n", + "def f(step_size, L):\n", + " model = \"banana\"\n", + " # print(type(step_size), type(L))\n", + " # raise Exception\n", + " traj_length = L/step_size\n", + " bias = benchmark_chains_prime(models[model], sampler_mhmclmc(step_size, L), batch=100, n=4000,favg=models[model].E_x2, fvar=models[model].Var_x2)\n", + " return - (bias * traj_length)\n", + "\n", + "constrains = {'step_size': (0.9, 2.), 'L': (10., 30.)}\n", + "optim_params = bayex.optim(f, constrains=constrains, seed=42, n=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'step_size': Array(1.2840078, dtype=float32),\n", + " 'L': Array(10.502036, dtype=float32)}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# bayex.show_results(optim_params)\n", + "optim_params.params" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.2840078 10.502036\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
    \n", + " \n", + " 100.00% [10000/10000 00:00<?]\n", + "
    \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Tracedwith with\n", + " val = Array([0.9339157 , 0.93431884, 0.9322909 , 0.9342701 , 0.9341552 ,\n", + " 0.93465376, 0.9335822 , 0.9367909 , 0.9329499 , 0.93357486,\n", + " 0.9314774 , 0.93255955, 0.92864585, 0.93632627, 0.93140155,\n", + " 0.9362243 , 0.93325466, 0.9336672 , 0.9339867 , 0.9338112 ,\n", + " 0.9350811 , 0.93485105, 0.93471336, 0.93659383, 0.93350965,\n", + " 0.93118936, 0.93067616, 0.9351261 , 0.93502945, 0.936908 ,\n", + " 0.93764746, 0.9377526 , 0.9350277 , 0.9337486 , 0.93652546,\n", + " 0.9359428 , 0.9337399 , 0.93321186, 0.9339389 , 0.93727136,\n", + " 0.9380293 , 0.9308454 , 0.938122 , 0.93666196, 0.9337697 ,\n", + " 0.9338155 , 0.9338142 , 0.9302911 , 0.933757 , 0.93568975,\n", + " 0.93668514, 0.9300203 , 0.9378794 , 0.9311688 , 0.9343718 ,\n", + " 0.93479264, 0.9344969 , 0.93288016, 0.9327182 , 0.93496597,\n", + " 0.930949 , 0.9322504 , 0.93415964, 0.9342759 , 0.93494815,\n", + " 0.93282264, 0.93581814, 0.933904 , 0.9344867 , 0.9347651 ,\n", + " 0.9338378 , 0.93404096, 0.9332588 , 0.93355405, 0.936354 ,\n", + " 0.93132156, 0.9322475 , 0.93422145, 0.9335083 , 0.93297595,\n", + " 0.93157333, 0.9349217 , 0.93491626, 0.93375254, 0.933567 ,\n", + " 0.93280643, 0.93669194, 0.93227255, 0.93162304, 0.9334552 ,\n", + " 0.93123084, 0.9335849 , 0.9324985 , 0.9338289 , 0.93371874,\n", + " 0.9312737 , 0.93425953, 0.93295395, 0.933913 , 0.93598765], dtype=float32)\n", + " batch_dim = 0 acceptance probability\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "AVG NUM STEPS PER TRAJ 8.179107\n", + "crossing 1174 19204.543 0.0052071013\n", + "True mean [0. 0.]\n", + "True std [10. 4.35889894]\n", + "Empirical mean [-0.00912235 -0.00456485]\n", + "Empirical std [9.993177 4.338162]\n", + "10.502036 1.2840078 params\n" + ] + }, + { + "data": { + "text/plain": [ + "(Array(0.0052071, dtype=float32), Array(0.00127003, dtype=float32))" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(optim_params.params['step_size'], optim_params.params['L'])\n", + "\n", + "model = 'banana'\n", + "benchmark_chains(models[model], sampler_mhmclmc(optim_params.params['step_size'], optim_params.params['L']), batch=100, n=10000,favg=models[model].E_x2, fvar=models[model].Var_x2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(81,)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Extract x and y values from the keys of the results dictionary\n", + "# x_values = [key[0] for key in results.keys()]\n", + "# y_values = [key[1] for key in results.keys()]\n", + "\n", + "# print(len(x_values))\n", + "# raise Exception\n", + "\n", + "# Extract heat values from the dictionary\n", + "heat_values = list(x[0] for x in results.values())\n", + "\n", + "print(np.array(heat_values).shape)\n", + "# Reshape the heat values into a 2D array\n", + "# heat_array = np.array(heat_values).reshape((len(x_values), len(y_values)))\n", + "heat_array = np.array(heat_values).reshape((9,9))\n", + "# bar = np.array(list(results.keys())).reshape((5,5,3))\n", + "# print(bar)\n", + "# print(np.array(heat_values).shape)\n", + "# print(np.array(results.keys()).shape)\n", + "\n", + "# Create the heatmap\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(heat_array, annot=True, cmap='viridis')\n", + "plt.xlabel('L')\n", + "plt.ylabel('step size')\n", + "plt.title('Heatmap of Results')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0.005370330065488815, ('banana', 1.1170037, 10.743564999999998)),\n", + " (0.006148216780275106, ('banana', 1.1170037, 12.776323938494208)),\n", + " (0.004798795096576214, ('banana', 1.1170037, 15.193695331236901)),\n", + " (0.0035045745316892862, ('banana', 1.1170037, 18.068450591090546)),\n", + " (0.0056139081716537476, ('banana', 1.1170037, 21.48713)),\n", + " (0.005047785583883524, ('banana', 1.1170037, 25.55264787698842)),\n", + " (0.004653009120374918, ('banana', 1.1170037, 30.387390662473805)),\n", + " (0.0033744920510798693, ('banana', 1.1170037, 36.1369011821811)),\n", + " (0.0032409471459686756, ('banana', 1.1170037, 42.97426000000001)),\n", + " 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Extract x and y values from the keys of the results dictionary\n", + "# x_values = [key[0] for key in results.keys()]\n", + "# y_values = [key[1] for key in results.keys()]\n", + "\n", + "# print(len(x_values))\n", + "# raise Exception\n", + "\n", + "# Extract heat values from the dictionary\n", + "heat_values = list(x[0] for x in results.values())\n", + "\n", + "print(np.array(heat_values).shape)\n", + "# Reshape the heat values into a 2D array\n", + "# heat_array = np.array(heat_values).reshape((len(x_values), len(y_values)))\n", + "heat_array = np.array(heat_values).reshape((15,15))\n", + "# bar = np.array(list(results.keys())).reshape((5,5,3))\n", + "# print(bar)\n", + "# print(np.array(heat_values).shape)\n", + "# print(np.array(results.keys()).shape)\n", + "\n", + "# Create the heatmap\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(heat_array, annot=True, cmap='viridis')\n", + "plt.xlabel('L')\n", + "plt.ylabel('step size')\n", + "plt.title('Heatmap of Results')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(225,)\n" + ] + }, + { + "data": { + "image/png": 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", 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" (0.0, ('banana', 4.468014800000001, 28.91953240611649)),\n", + " (0.0, ('banana', 4.468014800000001, 31.92975246994321)),\n", + " (0.0, ('banana', 4.468014800000001, 35.25330487626481)),\n", + " (0.0, ('banana', 4.468014800000001, 38.922804236229844)),\n", + " (0.0, ('banana', 4.468014800000001, 42.97426000000001))]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[(results[k][0], k) for k in results]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.029569892212748528, ('Banana', 1.2222222222222223, 13.777777777777779))" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "# results[('Banana', np.linspace(1,3,10)[1], np.linspace(12,16,10)[4])]\n", + "# results_1 = {results[key]: key for key in results.keys() if type(results[key]) is tuple}\n", + "# max(results_1, key=lambda x: results_1[x][0])\n", + "max([(results[r][0], r) for r in results if type(results[r]) is tuple])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mclmc", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py new file mode 100644 index 0000000..857cd7b --- /dev/null +++ b/benchmarks/mcmc/benchmark.py @@ -0,0 +1,558 @@ +from collections import defaultdict +from functools import partial +import math +import operator +import os +import pprint +from statistics import mean, median +import jax +import jax.numpy as jnp +import pandas as pd +import scipy + +from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState + +os.environ["XLA_FLAGS"] = '--xla_force_host_platform_device_count=' + str(128) +num_cores = jax.local_device_count() +# print(num_cores, jax.lib.xla_bridge.get_backend().platform) + +import itertools + +import numpy as np + +import blackjax +from benchmarks.mcmc.sampling_algorithms import run_mclmc, run_mhmclmc, run_nuts, samplers +from benchmarks.mcmc.inference_models import Brownian, GermanCredit, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models +from blackjax.mcmc.integrators import calls_per_integrator_step, generate_euclidean_integrator, generate_isokinetic_integrator, isokinetic_mclachlan, mclachlan_coefficients, name_integrator, omelyan_coefficients, velocity_verlet, velocity_verlet_coefficients, yoshida_coefficients +# from blackjax.mcmc.mhmclmc import rescale +from blackjax.util import run_inference_algorithm + + + +def get_num_latents(target): + return target.ndims +# return int(sum(map(np.prod, list(jax.tree_flatten(target.event_shape)[0])))) + + +def err(f_true, var_f, contract): + """Computes the error b^2 = (f - f_true)^2 / var_f + Args: + f: E_sampler[f(x)], can be a vector + f_true: E_true[f(x)] + var_f: Var_true[f(x)] + contract: how to combine a vector f in a single number, can be for example jnp.average or jnp.max + + Returns: + contract(b^2) + """ + + return jax.vmap(lambda f: contract(jnp.square(f - f_true) / var_f)) + + + +def grads_to_low_error(err_t, grad_evals_per_step= 1, low_error= 0.01): + """Uses the error of the expectation values to compute the effective sample size neff + b^2 = 1/neff""" + + cutoff_reached = err_t[-1] < low_error + return find_crossing(err_t, low_error) * grad_evals_per_step, cutoff_reached + + +def calculate_ess(err_t, grad_evals_per_step, neff= 100): + + grads_to_low, cutoff_reached = grads_to_low_error(err_t, grad_evals_per_step, 1./neff) + + return (neff / grads_to_low) * cutoff_reached, grads_to_low*(1/cutoff_reached), cutoff_reached + + +def find_crossing(array, cutoff): + """the smallest M such that array[m] < cutoff for all m > M""" + + b = array > cutoff + indices = jnp.argwhere(b) + if indices.shape[0] == 0: + print("\n\n\nNO CROSSING FOUND!!!\n\n\n", array, cutoff) + return 1 + + return jnp.max(indices)+1 + + +def cumulative_avg(samples): + return jnp.cumsum(samples, axis = 0) / jnp.arange(1, samples.shape[0] + 1)[:, None] + + +def gridsearch_tune(key, iterations, grid_size, model, sampler, batch, num_steps, center_L, center_step_size, contract): + results = defaultdict(float) + converged = False + keys = jax.random.split(key, iterations+1) + for i in range(iterations): + print(f"EPOCH {i}") + width = 2 + step_sizes = np.logspace(np.log10(center_step_size/width), np.log10(center_step_size*width), grid_size) + Ls = np.logspace(np.log10(center_L/2), np.log10(center_L*2),grid_size) + # print(list(itertools.product(step_sizes , Ls))) + + grid_keys = jax.random.split(keys[i], grid_size^2) + print(f"center step size {center_step_size}, center L {center_L}") + for j, (step_size, L) in enumerate(itertools.product(step_sizes , Ls)): + ess, grad_calls_until_convergence, _ , _, _ = benchmark_chains(model, sampler(step_size=step_size, L=L), grid_keys[j], n=num_steps, batch = batch, contract=contract) + results[(step_size, L)] = (ess, grad_calls_until_convergence) + + best_ess, best_grads, (step_size, L) = max([(results[r][0], results[r][1], r) for r in results], key=operator.itemgetter(0)) + # raise Exception + print(f"best params on iteration {i} are stepsize {step_size} and L {L} with Grad Calls until Convergence {best_grads}") + if L==center_L and step_size==center_step_size: + print("converged") + converged = True + break + else: + center_L, center_step_size = L, step_size + + pprint.pp(results) + # print(f"best params on iteration {i} are stepsize {step_size} and L {L} with Grad Calls until Convergence {best_grads}") + # print(f"L from ESS (0.4 * step_size/ESS): {0.4 * step_size/best_ess}") + return center_L, center_step_size, converged + + +def run_mhmclmc_no_tuning(initial_state, coefficients, step_size, L, std_mat): + + def s(logdensity_fn, num_steps, initial_position, transform, key): + + integrator = generate_isokinetic_integrator(coefficients) + + num_steps_per_traj = L/step_size + alg = blackjax.mcmc.mhmclmc.mhmclmc( + logdensity_fn=logdensity_fn, + step_size=step_size, + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(num_steps_per_traj)) , + integrator=integrator, + std_mat=std_mat, + ) + + _, out, info = run_inference_algorithm( + rng_key=key, + initial_state=initial_state, + inference_algorithm=alg, + num_steps=num_steps, + transform=lambda x: transform(x.position), + progress_bar=True) + + return out, MCLMCAdaptationState(L=L, step_size=step_size, std_mat=std_mat), num_steps_per_traj * calls_per_integrator_step(coefficients), info.acceptance_rate.mean(), None, jnp.array([0]) + + return s + +def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.average,): + + pvmap = jax.pmap + + # def pvmap(f): + # def f(arr): + # return arr + # print(arr.shape,"shape") + # print(arr) + # arr = arr.reshape(128, -1) + # out = jax.vmap(jax.vmap(f), in_axes=0)(arr) + # return out.flatten() + # return f + + d = get_num_latents(model) + if batch is None: + batch = np.ceil(1000 / d).astype(int) + key, init_key = jax.random.split(key, 2) + keys = jax.random.split(key, batch) + + init_keys = jax.random.split(init_key, batch) + init_pos = pvmap(model.sample_init)(init_keys) + + # samples, params, avg_num_steps_per_traj = jax.pmap(lambda pos, key: sampler(model.logdensity_fn, n, pos, model.transform, key))(init_pos, keys) + samples, params, grad_calls_per_traj, acceptance_rate, step_size_over_da, final_da = pvmap(lambda pos, key: sampler(logdensity_fn=model.logdensity_fn, num_steps=n, initial_position= pos,transform= model.transform, key=key))(init_pos, keys) + avg_grad_calls_per_traj = jnp.nanmean(grad_calls_per_traj, axis=0) + try: + print(jnp.nanmean(params.step_size,axis=0), jnp.nanmean(params.L,axis=0)) + except: pass + + full = lambda arr : err(model.E_x2, model.Var_x2, contract)(cumulative_avg(arr)) + err_t = pvmap(full)(samples**2) + + # outs = [calculate_ess(b, grad_evals_per_step=avg_grad_calls_per_traj) for b in err_t] + # # print(outs[:10]) + # esses = [i[0].item() for i in outs if not math.isnan(i[0].item())] + # grad_calls = [i[1].item() for i in outs if not math.isnan(i[1].item())] + # return(mean(esses), mean(grad_calls)) + # print(final_da.mean(), "final da") + + + err_t_median = jnp.median(err_t, axis=0) + # import matplotlib.pyplot as plt + # plt.plot(np.arange(1, 1+ len(err_t_median))* 2, err_t_median, color= 'teal', lw = 3) + # plt.xlabel('gradient evaluations') + # plt.ylabel('average second moment error') + # plt.xscale('log') + # plt.yscale('log') + # plt.savefig('brownian.png') + # plt.close() + esses, grad_calls, _ = calculate_ess(err_t_median, grad_evals_per_step=avg_grad_calls_per_traj) + return esses, grad_calls, params, jnp.mean(acceptance_rate, axis=0), step_size_over_da + + + + +def run_benchmarks(batch_size): + + results = defaultdict(tuple) + for variables in itertools.product( + # ["mhmclmc", "nuts", "mclmc", ], + ["mhmclmc"], + # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 10)).astype(int)], + [Brownian()], + # [Brownian()], + # [Brownian()], + # [velocity_verlet_coefficients, mclachlan_coefficients, yoshida_coefficients, omelyan_coefficients], + [mclachlan_coefficients], + ): + + sampler, model, coefficients = variables + num_chains = batch_size#1 + batch_size//model.ndims + + + num_steps = 100000 + + sampler, model, coefficients = variables + num_chains = batch_size # 1 + batch_size//model.ndims + + # print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) + + contract = jnp.max + + key = jax.random.PRNGKey(11) + for i in range(1): + key1, key = jax.random.split(key) + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(samplers[sampler], coefficients=coefficients, frac_tune1=0.1, frac_tune2=0.0, frac_tune3=0.0),key1, n=num_steps, batch=num_chains, contract=contract) + + # print(f"step size over da {step_size_over_da.shape} \n\n\n\n") + jax.numpy.save(f"step_size_over_da.npy", step_size_over_da.mean(axis=0)) + jax.numpy.save(f"acceptance.npy", acceptance_rate) + + + # print(f"grads to low bias: {grad_calls}") + # print(f"acceptance rate is {acceptance_rate, acceptance_rate.mean()}") + + results[((model.name, model.ndims), sampler, name_integrator(coefficients), "standard", acceptance_rate.mean().item(), params.L.mean().item(), params.step_size.mean().item(), num_chains, num_steps, contract)] = ess.item() + print(ess.item()) + # results[(model.name, model.ndims, "nuts", 0., 0., name_integrator(coeffs), "standard", acceptance_rate)] + + + # print(results) + + + df = pd.Series(results).reset_index() + df.columns = ["model", "sampler", "integrator", "tuning", "acc rate", "L", "stepsize", "num_chains", "num steps", "contraction", "ESS"] + # df.result = df.result.apply(lambda x: x[0].item()) + # df.model = df.model.apply(lambda x: x[1]) + df.to_csv("results_simple.csv", index=False) + + return results + +# vary step_size +def run_benchmarks_step_size(batch_size): + + results = defaultdict(tuple) + for variables in itertools.product( + # ["mhmclmc", "nuts", "mclmc", ], + ["mhmclmc"], + # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 10)).astype(int)], + [StandardNormal(10)], + # [Brownian()], + # [Brownian()], + # [velocity_verlet_coefficients, mclachlan_coefficients, yoshida_coefficients, omelyan_coefficients], + [mclachlan_coefficients], + ): + + + + num_steps = 10000 + + sampler, model, coefficients = variables + num_chains = batch_size # 1 + batch_size//model.ndims + + # print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) + + contract = jnp.average + + center = 6.534974 + key = jax.random.PRNGKey(11) + for step_size in np.linspace(center-1,center+1, 41): + # for L in np.linspace(1, 10, 41): + key1, key2, key3, key = jax.random.split(key, 4) + initial_position = model.sample_init(key2) + initial_state = blackjax.mcmc.mhmclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=key3) + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains(model, run_mhmclmc_no_tuning(initial_state=initial_state, coefficients=mclachlan_coefficients, step_size=step_size, L= 5*step_size, std_mat=1.),key1, n=num_steps, batch=num_chains, contract=contract) + + # print(f"step size over da {step_size_over_da.shape} \n\n\n\n") + # jax.numpy.save(f"step_size_over_da.npy", step_size_over_da.mean(axis=0)) + # jax.numpy.save(f"acceptance.npy_{step_size}", acceptance_rate) + + + # print(f"grads to low bias: {grad_calls}") + # print(f"acceptance rate is {acceptance_rate, acceptance_rate.mean()}") + + results[((model.name, model.ndims), sampler, name_integrator(coefficients), "standard", acceptance_rate.mean().item(), params.L.mean().item(), params.step_size.mean().item(), num_chains, num_steps, contract)] = ess.item() + # results[(model.name, model.ndims, "nuts", 0., 0., name_integrator(coeffs), "standard", acceptance_rate)] + + + # print(results) + + + df = pd.Series(results).reset_index() + df.columns = ["model", "sampler", "integrator", "tuning", "acc rate", "L", "stepsize", "num_chains", "num steps", "contraction", "ESS"] + # df.result = df.result.apply(lambda x: x[0].item()) + # df.model = df.model.apply(lambda x: x[1]) + df.to_csv("results_step_size.csv", index=False) + + return results + + + +def benchmark_mhmchmc(batch_size): + + key0, key1, key2, key3 = jax.random.split(jax.random.PRNGKey(5), 4) + + # coefficients = [yoshida_coefficients, mclachlan_coefficients, velocity_verlet_coefficients, omelyan_coefficients] + coefficients = [mclachlan_coefficients, velocity_verlet_coefficients] + for model in models: + results = defaultdict(tuple) + for preconditioning, coeffs in itertools.product([False, True], coefficients): + + num_chains = batch_size # 1 + batch_size//model.ndims + print(f"NUMBER OF CHAINS for {model.name} and MHMCLMC is {num_chains}") + num_steps = models[model]["mhmclmc"] + print(f"NUMBER OF STEPS for {model.name} and MHCMLMC is {num_steps}") + + ####### run mclmc with standard tuning + + contract = jnp.max + + + ess, grad_calls, params , _, step_size_over_da = benchmark_chains( + model, + partial(run_mclmc,coefficients=coeffs, preconditioning=preconditioning), + key0, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), name_integrator(coeffs), "standard", 1., preconditioning)] = ess.item() + print(f'mclmc with tuning ESS {ess}') + + + ####### run mhmclmc with standard tuning + for target_acc_rate in [0.65, 0.9]: + # coeffs = mclachlan_coefficients + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + partial(run_mhmclmc, target_acc_rate=target_acc_rate, coefficients=coeffs, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), + key1, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mhmchmc"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), name_integrator(coeffs), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + print(f'mhmclmc with tuning ESS {ess}') + + # coeffs = mclachlan_coefficients + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + partial(run_mhmclmc, target_acc_rate=target_acc_rate,coefficients=coeffs, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), + key1, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mhmchmc:st3"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), name_integrator(coeffs), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + print(f'mhmclmc with tuning ESS {ess}') + + if False: + ####### run mhmclmc with standard tuning + grid search + + init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) + initial_position = model.sample_init(init_pos_key) + + initial_state = blackjax.mcmc.mhmclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key + ) + + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.mhmclmc.build_kernel( + integrator=generate_isokinetic_integrator(coeffs), + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), + std_mat=std_mat, + )( + rng_key=rng_key, + state=state, + step_size=step_size, + logdensity_fn=model.logdensity_fn) + + ( + state, + blackjax_mhmclmc_sampler_params, + _, _ + ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + target=target_acceptance_rate_of_order[integrator_order(coeffs)], + frac_tune1=0.1, + frac_tune2=0.1, + frac_tune3=0.0, + diagonal_preconditioning=False + ) + + print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(coeffs)]}") + print(f"params after initial tuning are L={blackjax_mhmclmc_sampler_params.L}, step_size={blackjax_mhmclmc_sampler_params.step_size}") + + + L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_mhmclmc_no_tuning, coefficients=coeffs, initial_state=state, std_mat=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_mhmclmc_sampler_params.L, center_step_size=blackjax_mhmclmc_sampler_params.step_size) + # print(f"params after grid tuning are L={L}, step_size={step_size}") + + + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_mhmclmc_no_tuning(coefficients=coeffs, L=L, step_size=step_size, initial_state=state, std_mat=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) + + print(f"grads to low bias: {grad_calls}") + + results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), name_integrator(coeffs), f"gridsearch:{convergence}", acceptance_rate.mean().item()), True] = ess.item() + + ####### run nuts + + # coeffs = velocity_verlet_coefficients + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,coefficients=coeffs, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) + results[(model.name, model.ndims, "nuts", 0., 0., name_integrator(coeffs), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + + + + + + + + print(results) + + + df = pd.Series(results).reset_index() + df.columns = ["model", "dims", "sampler", "L", "step_size", "integrator", "tuning", "acc_rate", "preconditioning", "ESS"] + # df.result = df.result.apply(lambda x: x[0].item()) + # df.model = df.model.apply(lambda x: x[1]) + df.to_csv(f"results{model.name}.csv", index=False) + + return results + +def benchmark_omelyan(batch_size): + + + key = jax.random.PRNGKey(2) + results = defaultdict(tuple) + for variables in itertools.product( + # ["mhmclmc", "nuts", "mclmc", ], + ["mhmchmc"], + [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(1000), 4)).astype(int)], + # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 5)).astype(int)], + # models, + # [velocity_verlet_coefficients, mclachlan_coefficients, yoshida_coefficients, omelyan_coefficients], + [mclachlan_coefficients, omelyan_coefficients], + ): + + + sampler, model, coefficients = variables + + # num_chains = 1 + batch_size//model.ndims + num_chains = batch_size + + current_key, key = jax.random.split(key) + init_pos_key, init_key, tune_key, bench_key, grid_key = jax.random.split(current_key, 5) + + # num_steps = models[model][sampler] + + num_steps = 1000 + + + initial_position = model.sample_init(init_pos_key) + + initial_state = blackjax.mcmc.mhmclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key + ) + + + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.mhmclmc.build_kernel( + integrator=generate_isokinetic_integrator(coefficients), + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), + std_mat=std_mat, + )( + rng_key=rng_key, + state=state, + step_size=step_size, + logdensity_fn=model.logdensity_fn) + + ( + state, + blackjax_mhmclmc_sampler_params, + _, _ + ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + target=target_acceptance_rate_of_order[integrator_order(coefficients)], + frac_tune1=0.1, + frac_tune2=0.1, + # frac_tune3=0.1, + diagonal_preconditioning=False + ) + + print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) + print(f"params after initial tuning are L={blackjax_mhmclmc_sampler_params.L}, step_size={blackjax_mhmclmc_sampler_params.step_size}") + + # ess, grad_calls, _ , _ = benchmark_chains(model, run_mhmclmc_no_tuning(coefficients=coefficients, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, std_mat=1.),bench_key_pre_grid, n=num_steps, batch=num_chains, contract=jnp.average) + + # results[((model.name, model.ndims), sampler, name_integrator(coefficients), "without grid search")] = (ess, grad_calls) + + L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_mhmclmc_no_tuning, coefficients=coefficients, initial_state=state, std_mat=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_mhmclmc_sampler_params.L, center_step_size=blackjax_mhmclmc_sampler_params.step_size) + print(f"params after grid tuning are L={L}, step_size={step_size}") + + + ess, grad_calls, _ , _, _ = benchmark_chains(model, run_mhmclmc_no_tuning(coefficients=coefficients, L=L, step_size=step_size, std_mat=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) + + print(f"grads to low bias: {grad_calls}") + + results[(model.name, model.ndims, sampler, name_integrator(coefficients), converged, L.item(), step_size.item())] = ess.item() + + df = pd.Series(results).reset_index() + df.columns = ["model", "dims", "sampler", "integrator", "convergence", "L", "step_size", "ESS"] + # df.result = df.result.apply(lambda x: x[0].item()) + # df.model = df.model.apply(lambda x: x[1]) + df.to_csv("omelyan.csv", index=False) + + +def run_benchmarks_simple(): + + sampler = run_mclmc + model = StandardNormal(10) # 10 dimensional standard normal + coefficients = mclachlan_coefficients + contract = jnp.average # how we average across dimensions + num_steps = 2000 + num_chains = 100 + key1 = jax.random.PRNGKey(2) + + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, coefficients=coefficients, preconditioning=False),key1, n=num_steps, batch=num_chains, contract=contract) + + print(f"Effective Sample Size (ESS) of 10D Normal is {ess}") + +if __name__ == "__main__": + + run_benchmarks_simple() + + # benchmark_mhmchmc(batch_size=128) + # run_benchmarks(128) + # run_benchmarks_step_size(128) + # benchmark_omelyan(128) + # run_benchmarks(128) + #benchmark_omelyan(10) + # print("4") + + + diff --git a/benchmarks/mcmc/explore.py b/benchmarks/mcmc/explore.py new file mode 100644 index 0000000..09be2ba --- /dev/null +++ b/benchmarks/mcmc/explore.py @@ -0,0 +1,145 @@ +import jax + +from datetime import date +from blackjax.benchmarks.mcmc.benchmark import benchmark_chains + +from blackjax.benchmarks.mcmc.inference_models import IllConditionedGaussian + +rng_key = jax.random.key(int(date.today().strftime("%Y%m%d"))) + +import blackjax +import numpy as np +import jax.numpy as jnp +from sampling_algorithms import samplers +from inference_models import StandardNormal, models + +def run_mclmc(logdensity_fn, num_steps, initial_position, key, transform, std_mat, L, step_size): + init_key, tune_key, run_key = jax.random.split(key, 3) + + # create an initial state for the sampler + initial_state = blackjax.mcmc.mclmc.init( + position=initial_position, logdensity_fn=logdensity_fn, rng_key=init_key + ) + + + # use the quick wrapper to build a new kernel with the tuned parameters + sampling_alg = blackjax.mclmc( + logdensity_fn, + L=L, + step_size=step_size, + std_mat=std_mat, + ) + + # run the sampler + _, samples, _ = blackjax.util.run_inference_algorithm( + rng_key=run_key, + initial_state_or_position=initial_state, + inference_algorithm=sampling_alg, + num_steps=num_steps, + transform=transform, + progress_bar=True, + ) + + return samples, None, 1 + + +def run_mclmc_with_tuning(logdensity_fn, num_steps, initial_position, key, transform): + init_key, tune_key, run_key = jax.random.split(key, 3) + + # create an initial state for the sampler + initial_state = blackjax.mcmc.mclmc.init( + position=initial_position, logdensity_fn=logdensity_fn, rng_key=init_key + ) + + kernel = blackjax.mcmc.mclmc.build_kernel( + logdensity_fn=logdensity_fn, + integrator=blackjax.mcmc.integrators.isokinetic_mclachlan, + std_mat=jnp.ones((initial_position.shape[0],)), + ) + + # find values for L and step_size + ( + blackjax_state_after_tuning, + blackjax_mclmc_sampler_params, + ) = blackjax.mclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + ) + + print(blackjax_mclmc_sampler_params) + + + + # use the quick wrapper to build a new kernel with the tuned parameters + sampling_alg = blackjax.mclmc( + logdensity_fn, + L=blackjax_mclmc_sampler_params.L, + step_size=blackjax_mclmc_sampler_params.step_size, + std_mat=blackjax_mclmc_sampler_params.std_mat, + ) + + # run the sampler + _, samples, _ = blackjax.util.run_inference_algorithm( + rng_key=run_key, + initial_state_or_position=initial_state, + inference_algorithm=sampling_alg, + num_steps=num_steps, + transform=transform, + progress_bar=True, + ) + + return samples +# run the algorithm on a high dimensional gaussian, and show two of the dimensions + +# sigma = .5 + +sample_key, rng_key = jax.random.split(rng_key) +# samples = run_mclmc( +# logdensity_fn=lambda x: -0.5 * jnp.sum(jnp.square(x)), +# num_steps=100000, +# initial_position=jnp.ones((2,)), +# key=sample_key, +# std_mat=jnp.ones((2,))*sigma, +# # std_mat=None, +# transform=lambda x: x.position, # x.position[:2], +# ) +# print(samples.var(axis=0)) + +# den = lambda x: jax.scipy.stats.norm.logpdf(x, loc=0., scale=jnp.sqrt(sigma)).sum() +# print(IllConditionedGaussian(2, 2).E_x2) +# samples = run_mclmc_with_tuning( +# logdensity_fn=lambda x : - IllConditionedGaussian(2, 2).nlogp(x), +# num_steps=1000000, +# initial_position=jnp.ones((2,)), +# key=sample_key, +# transform=lambda x: x.position[:2], +# ) +# # print(samples.var(axis=0)) +# m = IllConditionedGaussian(10, 5) +# sampler = lambda logdensity_fn, num_steps, initial_position, key: run_mclmc(logdensity_fn=logdensity_fn, num_steps=num_steps, initial_position=initial_position, key=key, transform=lambda x:x.position, +# # std_mat=jnp.ones((10,)) +# std_mat=jnp.sqrt(m.E_x2) +# , L=2.6576319, step_size=3.40299) +# print(m.E_x2, "var") + +# # sampler = 'mclmc' +# # samplers[sampler] +# result, bias, _ = benchmark_chains(m, sampler, n=5000, batch=1000//m.ndims,favg=m.E_x2, fvar=m.Var_x2) + +# print(result) + + +# m = StandardNormal(10) +# sampler = lambda logdensity_fn, num_steps, initial_position, key: run_mclmc(logdensity_fn=logdensity_fn, num_steps=num_steps, initial_position=initial_position, key=key, transform=lambda x:x.position, +# std_mat=jnp.ones((10,)) +# , L=2.6576319, step_size=3.40299) +# # print(m.E_x2, "var") + +# # sampler = 'mclmc' +# # samplers[sampler] +# result, bias, _ = benchmark_chains(m, sampler, n=5000, batch=1000//m.ndims,favg=m.E_x2, fvar=m.Var_x2) + +# print(result) + diff --git a/benchmarks/mcmc/find_params.py b/benchmarks/mcmc/find_params.py new file mode 100644 index 0000000..b0436ff --- /dev/null +++ b/benchmarks/mcmc/find_params.py @@ -0,0 +1,266 @@ +from collections import defaultdict +import itertools +import operator +import jax +import numpy as np + +from benchmark import benchmark_chains, cumulative_avg, err, calculate_ess, get_num_latents, grads_to_low_error, gridsearch_tune, run_mhmclmc_no_tuning +import blackjax +from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState +from blackjax.mcmc.integrators import calls_per_integrator_step, mclachlan_coefficients +from blackjax.mcmc.mhmclmc import rescale +from blackjax.util import run_inference_algorithm +import jax.numpy as jnp +from sampling_algorithms import run_mclmc, run_mhmclmc, samplers +from inference_models import Brownian, IllConditionedGaussian, models +from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState, target_acceptance_rate_of_order + + +def sampler_mhmclmc_with_tuning(step_size, L, frac_tune2, frac_tune3): + + def s(logdensity_fn, num_steps, initial_position, transform, key): + + init_key, tune_key, key = jax.random.split(key, 3) + + initial_state = blackjax.mcmc.mhmclmc.init( + position=initial_position, logdensity_fn=logdensity_fn, random_generator_arg=init_key + ) + integrator = blackjax.mcmc.integrators.isokinetic_mclachlan + kernel = lambda rng_key, state, avg_num_integration_steps, step_size: blackjax.mcmc.mhmclmc.build_kernel( + integrator=integrator, + integration_steps_fn = lambda key : jnp.ceil(jax.random.uniform(key) * rescale(avg_num_integration_steps)), + # integration_steps_fn = lambda key: avg_num_integration_steps, + )( + rng_key=rng_key, + state=state, + step_size=step_size, + logdensity_fn=logdensity_fn) + + # jax.debug.print("params before tuning {x}", x=MCLMCAdaptationState(L=L, step_size=step_size)) + ( + blackjax_state_after_tuning, + blackjax_mclmc_sampler_params, + ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + target=target_acceptance_rate_of_order[mhmclmc_integrator_order[integrator]], + frac_tune2=frac_tune2, + frac_tune3=frac_tune3, + params=MCLMCAdaptationState(L=L, step_size=step_size, std_mat=1.) + ) + + # jax.debug.print("params {x}", x=blackjax_mclmc_sampler_params) + # jax.debug.print("acceptance rate {x}", x=blackjax_mclmc_sampler_params) + + # L = blackjax_mclmc_sampler_params.L + # step_size = blackjax_mclmc_sampler_params.step_size + + num_steps_per_traj = blackjax_mclmc_sampler_params.L/blackjax_mclmc_sampler_params.step_size + alg = blackjax.mcmc.mhmclmc.mhmclmc( + logdensity_fn=logdensity_fn, + step_size=blackjax_mclmc_sampler_params.step_size, + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(num_steps_per_traj)) , + # integration_steps_fn = lambda k: num_steps_per_traj , + # integration_steps_fn = lambda _ : 5, + # integration_steps_fn = lambda key: jnp.ceil(jax.random.poisson(key, L/step_size )) , + + ) + + _, out, info = run_inference_algorithm( + rng_key=key, + initial_state_or_position=blackjax_state_after_tuning, + inference_algorithm=alg, + num_steps=num_steps, + transform=lambda x: transform(x.position), + progress_bar=True) + + print(info.acceptance_rate.mean(), "acceptance probability\n\n\n\n") + # print(out.var(axis=0), "acceptance probability") + + return out, blackjax_mclmc_sampler_params, num_steps_per_traj * calls_per_integrator_step(coefficients) + + return s + + + +# Empirical mean [ 2.6572839e-05 -4.0523437e-06] +# Empirical std [0.07159886 0.07360378] + + + +def grid_search(n, model): + + + print(f"\nModel: {model}") + + + + if True: + batch = 10 + init_key, sample_key = jax.random.split(jax.random.PRNGKey(1), 2) + init_keys = jax.random.split(init_key, batch) + init_pos = jax.vmap(model.sample_init)(init_keys) + sample_keys = jax.random.split(sample_key, batch) + + avg_num_steps_per_traj = 2 + samples, params, _ = jax.vmap(lambda pos, key: samplers["mclmc"](mclachlan_coefficients, model.logdensity_fn, n*10, pos, model.transform, key))(init_pos, sample_keys) + + # avg_num_steps_per_traj = 1 + # samples, params, _ = jax.vmap(lambda pos, key: samplers["nuts"](model.logdensity_fn, 1000, pos, model.transform, key))(init_pos, sample_keys) + + full = lambda arr : err(model.E_x2, model.Var_x2, jnp.max)(cumulative_avg(arr)) + err_t = jnp.mean(jax.vmap(full)(samples**2), axis=0) + + ess_val, grads_to_low_error, _ = calculate_ess(err_t, avg_num_steps_per_traj) + print(ess_val, grads_to_low_error) + + + + center_L, center_step_size = params.L.mean(), params.step_size.mean() + print(f"initial params found by MCLMC are step size {center_step_size} and L {center_L}, with grad calls {grads_to_low_error}") + # center_L, center_step_size = 0.5755017, 0.7676609 + # center_L, center_step_size = 0.7639537453651428, 0.5901154279708862 + # center_L, center_step_size = 2.079161234577297, 0.3441933917149635 + # center_L, center_step_size = 1.3701616525650024, 0.44564130902290344 + print(f"Initial params hard coded as L {center_L} and step size as {center_step_size}") + + # nuts result + + + # print("\nBeginning grid search:\n") + # grid_size = 5 + # batch = 100 + # best params on iteration 0 are stepsize 5.103655551427525 and L 5.408820389035896 with Grad Calls until Convergence 216.19784545898438 + # center_L, center_step_size = gridsearch_tune(iterations=0, grid_size=grid_size, model=model, sampler=sampler_mhmclmc, batch=batch, num_steps=n, center_L=center_L, center_step_size=center_step_size) + + + tune_key, init_key, init_pos_key, run_key = jax.random.split(jax.random.PRNGKey(0), 4) + initial_position = model.sample_init(init_pos_key) + + initial_state = blackjax.mcmc.mhmclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key + ) + + kernel = lambda rng_key, state, avg_num_integration_steps, step_size: blackjax.mcmc.mhmclmc.build_kernel( + integrator=blackjax.mcmc.integrators.isokinetic_mclachlan, + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)) + )( + rng_key=rng_key, + state=state, + step_size=step_size, + logdensity_fn=model.logdensity_fn) + + ( + blackjax_state_after_tuning, + blackjax_mclmc_sampler_params, + ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=n, + state=initial_state, + rng_key=tune_key, + target=0.65, + frac_tune3=0, + frac_tune2=0, + params = MCLMCAdaptationState(L=center_L, step_size=center_step_size, std_mat=1.), + # params = MCLMCAdaptationState(L=10., step_size=3.3454525677773526, std_mat=1.), + # params = MCLMCAdaptationState(L=16., step_size=1., std_mat=1.), + # params = MCLMCAdaptationState(L=10., step_size=5.103655551427525, std_mat=1.), + ) + + print(f"initial params are L {center_L} and step_size {center_step_size}") + print(f"params found by mhmclmc tuning are L {blackjax_mclmc_sampler_params.L} and step_size {blackjax_mclmc_sampler_params.step_size}") + + + ess, grad_calls_until_convergence = benchmark_chains(model, run_mhmclmc_no_tuning(coefficients=mclachlan_coefficients, step_size=blackjax_mclmc_sampler_params.step_size, L=blackjax_mclmc_sampler_params.L), jax.random.PRNGKey(0), n=n, batch = batch, contract=jnp.max) # batch=1000//model.ndims) + print(f"ess from tuning is {ess} and num grad calls is {grad_calls_until_convergence}") + + + # # L, step_size = 2.6195790055693493, 0.4336564994563942 + # L, step_size = blackjax_mclmc_sampler_params.L, blackjax_mclmc_sampler_params.step_size + # ess, grad_calls_until_convergence = benchmark_chains(model, sampler_mhmclmc(step_size=step_size, L=L), keys[-1], n=n, batch = 200) # batch=1000//model.ndims) + # print("final grads", grad_calls_until_convergence) + + # step_size = blackjax_mclmc_sampler_params.step_size + # L = blackjax_mclmc_sampler_params.L + + # jax.debug.print("{x} num_steps, L, step_size", x=(jnp.ceil(L/step_size), L, step_size)) + + + # alg = blackjax.mcmc.mhmclmc.mhmclmc( + # logdensity_fn=model.logdensity_fn, + # step_size=step_size, + # integration_steps_fn = lambda key: jnp.round(jax.random.uniform(key) * rescale(L/step_size + 0.5)) , + # # integrator=integrator, + # # integration_steps_fn = lambda key: jnp.ceil(jax.random.poisson(key, L/step_size )) , + + # ) + + # _, out, info = run_inference_algorithm( + # rng_key=run_key, + # initial_state_or_position=blackjax_state_after_tuning, + # inference_algorithm=alg, + # num_steps=num_steps, + # transform=lambda x: transform(x.position), + # progress_bar=True) + + + + + # return results + + +if __name__ == "__main__": + + + # for i in range(100): + # ess, grad_calls_until_convergence = benchmark_chains(Brownian(), sampler_mhmclmc(step_size=0.4336564994563942, L=2.6195790055693493), jax.random.PRNGKey(i), n=10000, batch = 10) # batch=1000//model.ndims) + + # print (f"ess from tuning is {ess} and num grad calls is {grad_calls_until_convergence}") + # raise Exception + + + + + # for i in range(100): + + # # ess, grad_calls_until_convergence, _ = benchmark_chains(Brownian(), sampler_mhmclmc_with_tuning(step_size=0.1, L=40.8, frac_tune2=0.1, frac_tune3=0), jax.random.PRNGKey(i), n=50000, batch = 10) # batch=1000//model.ndims) + + # ess, grad_calls_until_convergence, _ = benchmark_chains(Brownian(), sampler_mhmclmc(step_size=0.5901154279708862, L=0.7639537453651428), jax.random.PRNGKey(i), n=10000, batch = 1) # batch=1000//model.ndims) + # print(f"ess from tuning is {ess} and num grad calls is {grad_calls_until_convergence}") + # print(f"L from ESS (0.4 * step_size/ESS): {0.4 * 0.4/ess}") + + # model=IllConditionedGaussian(10, 2) + # ess, grad_calls_until_convergence = benchmark_chains(model, run_mclmc, n=2500, batch =10) # batch=1000//model.ndims) + # print(ess) + # raise Exception + +# benchmarks(5000) + + # grid_search(n=2500, model=IllConditionedGaussian(10, 2)) + grid_search(n=10000, model=Brownian()) + # grid_search(n=2500, model='icg') + # grid_search(n=2500, model='normal') + + # m = models['icg'] + # initial_position = m.sample(jax.random.PRNGKey(0)) + # _, blackjax_mclmc_sampler_params, _ = sampler_mhmclmc_with_tuning(L=4.291135699906666, step_size=1.005, frac_tune2=0, frac_tune3=0)(lambda x: -m.nlogp(x), 100000, initial_position, jax.random.PRNGKey(0)) + # print(blackjax_mclmc_sampler_params) + + # out = benchmark_chains(models['icg'], sampler_mhmclmc(step_size=4.475385912886005, L=2.2708939161637853), n=100, batch=10,favg=models['icg'].E_x2, fvar=models['icg'].Var_x2) + # print(out) + # pass +# print(grid_search()) + +# for model in ["simple"]: +# for sampler in ["mhmclmc", "mclmc"]: +# # result, bias = benchmark_chains(model, sampler_mhmclmc_with_tuning(step_size, L), n=1000000, batch=1) +# # result, bias = benchmark_chains(models[model], samplers["mhmclmc"], n=1000000, batch=10) +# result, bias = benchmark_chains(models[model], samplers[sampler], n=100000, batch=1) + +# results[(model, sampler)] = result, bias +# print(results) + + +# 0.3441933917149635 and L 2.079161234577297 \ No newline at end of file diff --git a/benchmarks/mcmc/inference_models.py b/benchmarks/mcmc/inference_models.py new file mode 100644 index 0000000..09753e0 --- /dev/null +++ b/benchmarks/mcmc/inference_models.py @@ -0,0 +1,891 @@ +#from inference_gym import using_jax as gym +import jax +import jax.numpy as jnp +import numpy as np +import os +#import numpyro.distributions as dist +dirr = os.path.dirname(os.path.realpath(__file__)) + + + +class StandardNormal(): + """Standard Normal distribution in d dimensions""" + + def __init__(self, d): + self.ndims = d + self.E_x2 = jnp.ones(d) + self.Var_x2 = 2 * self.E_x2 + self.name = 'StandardNormal' + + + def logdensity_fn(self, x): + """- log p of the target distribution""" + return -0.5 * jnp.sum(jnp.square(x), axis= -1) + + + def transform(self, x): + return x + + def sample_init(self, key): + return jax.random.normal(key, shape = (self.ndims, )) + + + +class IllConditionedGaussian(): + """Gaussian distribution. Covariance matrix has eigenvalues equally spaced in log-space, going from 1/condition_bnumber^1/2 to condition_number^1/2.""" + + + def __init__(self, d, condition_number, numpy_seed=None, prior= 'prior'): + """numpy_seed is used to generate a random rotation for the covariance matrix. + If None, the covariance matrix is diagonal.""" + + self.ndims = d + self.name = 'IllConditionedGaussian' + self.condition_number = condition_number + eigs = jnp.logspace(-0.5 * jnp.log10(condition_number), 0.5 * jnp.log10(condition_number), d) + + if numpy_seed == None: # diagonal + self.E_x2 = eigs + self.R = jnp.eye(d) + self.Hessian = jnp.diag(1 / eigs) + self.Cov = jnp.diag(eigs) + + else: # randomly rotate + rng = np.random.RandomState(seed=numpy_seed) + D = jnp.diag(eigs) + inv_D = jnp.diag(1 / eigs) + R, _ = jnp.array(np.linalg.qr(rng.randn(self.ndims, self.ndims))) # random rotation + self.R = R + self.Hessian = R @ inv_D @ R.T + self.Cov = R @ D @ R.T + self.E_x2 = jnp.diagonal(R @ D @ R.T) + + #Cov_precond = jnp.diag(1 / jnp.sqrt(self.E_x2)) @ self.Cov @ jnp.diag(1 / jnp.sqrt(self.E_x2)) + + #print(jnp.linalg.cond(Cov_precond) / jnp.linalg.cond(self.Cov)) + + self.Var_x2 = 2 * jnp.square(self.E_x2) + + + self.logdensity_fn = lambda x: -0.5 * x.T @ self.Hessian @ x + self.transform = lambda x: x + + + if prior == 'map': + self.sample_init = lambda key: jnp.zeros(self.ndims) + + elif prior == 'posterior': + self.sample_init = lambda key: self.R @ (jax.random.normal(key, shape=(self.ndims,)) * jnp.sqrt(eigs)) + + else: # N(0, sigma_true_max) + self.sample_init = lambda key: jax.random.normal(key, shape=(self.ndims,)) * jnp.max(jnp.sqrt(eigs)) + + + +class IllConditionedESH(): + """ICG from the ESH paper.""" + + def __init__(self): + self.ndims = 50 + self.name = 'IllConditionedESH' + self.variance = jnp.linspace(0.01, 1, self.ndims) + + + + + def logdensity_fn(self, x): + """- log p of the target distribution""" + return -0.5 * jnp.sum(jnp.square(x) / self.variance, axis= -1) + + + def transform(self, x): + return x + + def draw(self, key): + return jax.random.normal(key, shape = (self.ndims, )) * jnp.sqrt(self.variance) + + def sample_init(self, key): + return jax.random.normal(key, shape = (self.ndims, )) + + + + +class IllConditionedGaussianGamma(): + """Inference gym's Ill conditioned Gaussian""" + + def __init__(self, prior = 'prior'): + self.ndims = 100 + self.name = 'IllConditionedGaussianGamma' + + # define the Hessian + rng = np.random.RandomState(seed=10 & (2 ** 32 - 1)) + eigs = np.sort(rng.gamma(shape=0.5, scale=1., size=self.ndims)) #eigenvalues of the Hessian + eigs *= jnp.average(1.0/eigs) + self.entropy = 0.5 * self.ndims + self.maxmin = (1./jnp.sqrt(eigs[0]), 1./jnp.sqrt(eigs[-1])) + R, _ = np.linalg.qr(rng.randn(self.ndims, self.ndims)) #random rotation + self.map_to_worst = (R.T)[[0, -1], :] + self.Hessian = R @ np.diag(eigs) @ R.T + + # analytic ground truth moments + self.E_x2 = jnp.diagonal(R @ np.diag(1.0/eigs) @ R.T) + self.Var_x2 = 2 * jnp.square(self.E_x2) + + # norm = jnp.diag(1/jnp.sqrt(self.E_x2)) + # Sigma = R @ np.diag(1/eigs) @ R.T + # reduced = norm @ Sigma @ norm + # print(np.linalg.cond(reduced), np.linalg.cond(Sigma)) + + # gradient + + + if prior == 'map': + self.sample_init = lambda key: jnp.zeros(self.ndims) + + elif prior == 'posterior': + self.sample_init = lambda key: R @ (jax.random.normal(key, shape=(self.ndims,)) / jnp.sqrt(eigs)) + + else: # N(0, sigma_true_max) + self.sample_init = lambda key: jax.random.normal(key, shape=(self.ndims,)) * jnp.max(1.0/jnp.sqrt(eigs)) + + def logdensity_fn(self, x): + """- log p of the target distribution""" + return -0.5 * x.T @ self.Hessian @ x + + def transform(self, x): + return x + + + + +class Banana(): + """Banana target fromm the Inference Gym""" + + def __init__(self, prior = 'map'): + self.curvature = 0.03 + self.ndims = 2 + self.name = 'Banana' + + self.transform = lambda x: x + self.E_x2 = jnp.array([100.0, 19.0]) #the first is analytic the second is by drawing 10^8 samples from the generative model. Relative accuracy is around 10^-5. + self.Var_x2 = jnp.array([20000.0, 4600.898]) + + if prior == 'map': + self.sample_init = lambda key: jnp.array([0, -100.0 * self.curvature]) + elif prior == 'posterior': + self.sample_init = lambda key: self.posterior_draw(key) + elif prior == 'prior': + self.sample_init = lambda key: jax.random.normal(key, shape=(self.ndims,)) * jnp.array([10.0, 5.0]) * 2 + else: + raise ValueError('prior = '+prior +' is not defined.') + + def logdensity_fn(self, x): + mu2 = self.curvature * (x[0] ** 2 - 100) + return -0.5 * (jnp.square(x[0] / 10.0) + jnp.square(x[1] - mu2)) + + def posterior_draw(self, key): + z = jax.random.normal(key, shape = (2, )) + x0 = 10.0 * z[0] + x1 = self.curvature * (x0 ** 2 - 100) + z[1] + return jnp.array([x0, x1]) + + def ground_truth(self): + x = jax.vmap(self.posterior_draw)(jax.random.split(jax.random.PRNGKey(0), 100000000)) + print(jnp.average(x, axis=0)) + print(jnp.average(jnp.square(x), axis=0)) + print(jnp.std(jnp.square(x[:, 0])) ** 2, jnp.std(jnp.square(x[:, 1])) ** 2) + + + + +class Cauchy(): + """d indpendent copies of the standard Cauchy distribution""" + + def __init__(self, d): + self.ndims = d + self.name = 'Cauchy' + + self.logdensity_fn = lambda x: -jnp.sum(jnp.log(1. + jnp.square(x))) + + self.transform = lambda x: x + self.sample_init = lambda key: jax.random.normal(key, shape=(self.ndims,)) + + + + +class HardConvex(): + + def __init__(self, d, kappa, theta = 0.1): + """d is the dimension, kappa = condition number, 0 < theta < 1/4""" + self.ndims = d + self.name = 'HardConvex' + self.theta, self.kappa = theta, kappa + C = jnp.power(d-1, 0.25 - theta) + self.logdensity_fn = lambda x: -0.5 * jnp.sum(jnp.square(x[:-1])) - (0.75 / kappa)* x[-1]**2 + 0.5 * jnp.sum(jnp.cos(C * x[:-1])) / C**2 + + self.transform = lambda x: x + + # numerically precomputed variances + num_integration = [0.93295, 0.968802, 0.990595, 0.998002, 0.999819] + if d == 100: + self.variance = jnp.concatenate((jnp.ones(d-1) * num_integration[0], jnp.ones(1) * 2.0*kappa/3.0)) + elif d == 300: + self.variance = jnp.concatenate((jnp.ones(d-1) * num_integration[1], jnp.ones(1) * 2.0*kappa/3.0)) + elif d == 1000: + self.variance = jnp.concatenate((jnp.ones(d-1) * num_integration[2], jnp.ones(1) * 2.0*kappa/3.0)) + elif d == 3000: + self.variance = jnp.concatenate((jnp.ones(d-1) * num_integration[3], jnp.ones(1) * 2.0*kappa/3.0)) + elif d == 10000: + self.variance = jnp.concatenate((jnp.ones(d-1) * num_integration[4], jnp.ones(1) * 2.0*kappa/3.0)) + else: + None + + + def sample_init(self, key): + """Gaussian prior with approximately estimating the variance along each dimension""" + scale = jnp.concatenate((jnp.ones(self.ndims-1), jnp.ones(1) * jnp.sqrt(2.0 * self.kappa / 3.0))) + return jax.random.normal(key, shape=(self.ndims,)) * scale + + + + +class BiModal(): + """A Gaussian mixture p(x) = f N(x | mu1, sigma1) + (1-f) N(x | mu2, sigma2).""" + + def __init__(self, d = 50, mu1 = 0.0, mu2 = 8.0, sigma1 = 1.0, sigma2 = 1.0, f = 0.2): + + self.ndims = d + self.name = 'BiModal' + + self.mu1 = jnp.insert(jnp.zeros(d-1), 0, mu1) + self.mu2 = jnp.insert(jnp.zeros(d - 1), 0, mu2) + self.sigma1, self.sigma2 = sigma1, sigma2 + self.f = f + self.variance = jnp.insert(jnp.ones(d-1) * ((1 - f) * sigma1**2 + f * sigma2**2), 0, (1-f)*(sigma1**2 + mu1**2) + f*(sigma2**2 + mu2**2)) + + + + def logdensity_fn(self, x): + """- log p of the target distribution""" + + N1 = (1.0 - self.f) * jnp.exp(-0.5 * jnp.sum(jnp.square(x - self.mu1), axis= -1) / self.sigma1 ** 2) / jnp.power(2 * jnp.pi * self.sigma1 ** 2, self.ndims * 0.5) + N2 = self.f * jnp.exp(-0.5 * jnp.sum(jnp.square(x - self.mu2), axis= -1) / self.sigma2 ** 2) / jnp.power(2 * jnp.pi * self.sigma2 ** 2, self.ndims * 0.5) + + return jnp.log(N1 + N2) + + + def draw(self, num_samples): + """direct sampler from a target""" + X = np.random.normal(size = (num_samples, self.ndims)) + mask = np.random.uniform(0, 1, num_samples) < self.f + X[mask, :] = (X[mask, :] * self.sigma2) + self.mu2 + X[~mask] = (X[~mask] * self.sigma1) + self.mu1 + + return X + + + def transform(self, x): + return x + + def sample_init(self, key): + z = jax.random.normal(key, shape = (self.ndims, )) *self.sigma1 + #z= z.at[0].set(self.mu1 + z[0]) + return z + + +class BiModalEqual(): + """Mixture of two Gaussians, one centered at x = [mu/2, 0, 0, ...], the other at x = [-mu/2, 0, 0, ...]. + Both have equal probability mass.""" + + def __init__(self, d, mu): + + self.ndims = d + self.name = 'BiModalEqual' + self.mu = mu + + + + def logdensity_fn(self, x): + """- log p of the target distribution""" + + return -0.5 * jnp.sum(jnp.square(x), axis= -1) + jnp.log(jnp.cosh(0.5*self.mu*x[0])) - 0.5* self.ndims * jnp.log(2 * jnp.pi) - self.mu**2 / 8.0 + + + def draw(self, num_samples): + """direct sampler from a target""" + X = np.random.normal(size = (num_samples, self.ndims)) + mask = np.random.uniform(0, 1, num_samples) < 0.5 + X[mask, 0] += 0.5*self.mu + X[~mask, 0] -= 0.5 * self.mu + + return X + + def transform(self, x): + return x + + +class Funnel(): + """Noise-less funnel""" + + def __init__(self, d = 20): + + self.ndims = d + self.name = 'Funnel' + self.sigma_theta= 3.0 + + self.E_x2 = jnp.ones(d) # the transformed variables are standard Gaussian distributed + self.Var_x2 = 2 * self.E_x2 + + + + def logdensity_fn(self, x): + """ - log p of the target distribution + x = [z_0, z_1, ... z_{d-1}, theta] """ + theta = x[-1] + X = x[..., :- 1] + + return -0.5* jnp.square(theta / self.sigma_theta) - 0.5 * (self.ndims - 1) * theta - 0.5 * jnp.exp(-theta) * jnp.sum(jnp.square(X), axis = -1) + + def inverse_transform(self, xtilde): + theta = 3 * xtilde[-1] + return jnp.concatenate((xtilde[:-1] * jnp.exp(0.5 * theta), jnp.ones(1)*theta)) + + + def transform(self, x): + """gaussianization""" + xtilde = jnp.empty(x.shape) + xtilde = xtilde.at[-1].set(x.T[-1] / 3.0) + xtilde = xtilde.at[:-1].set(x.T[:-1] * jnp.exp(-0.5*x.T[-1])) + return xtilde.T + + + def sample_init(self, key): + return self.inverse_transform(jax.random.normal(key, shape = (self.ndims, ))) + + + + +class Funnel_with_Data(): + + def __init__(self, d, sigma, minibatch_size, key): + + self.ndims = d + self.name = 'Funnel_with_Data' + self.sigma_theta= 3.0 + self.theta_true = 0.0 + self.sigma_data = sigma + + + self.data = self.simulate_data() + + self.batch = minibatch_size + + def simulate_data(self): + + norm = jax.random.normal(jax.random.PRNGKey(123), shape = (2*(self.ndims-1), )) + z_true = norm[:self.ndims-1] * jnp.exp(self.theta_true * 0.5) + self.data = z_true + norm[self.ndims-1:] * self.sigma_data + + + def logdensity_fn(self, x, subset): + """ - log p of the target distribution + x = [z_0, z_1, ... z_{d-1}, theta] """ + theta = x[-1] + z = x[:- 1][subset] + + prior_theta = jnp.square(theta / self.sigma_theta) + prior_z = jnp.sum(subset) * theta + jnp.exp(-theta) * jnp.sum(jnp.square(z*subset)) + likelihood = jnp.sum(jnp.square((z - self.data)*subset / self.sigma_data)) + + return -0.5 * (prior_theta + prior_z + likelihood) + + + def transform(self, x): + """gaussianization""" + return x + + def sample_init(self, key): + key1, key2 = jax.random.split(key) + theta = jax.random.normal(key1) * self.sigma_theta + z = jax.random.normal(key2, shape = (self.ndims-1, )) * jnp.exp(theta * 0.5) + return jnp.concatenate((z, theta)) + + + + +class Rosenbrock(): + + def __init__(self, d = 36, Q = 0.1): + + self.ndims = d + self.name = 'Rosenbrock' + self.Q = Q + #ground truth moments + var_x = 2.0 + + #these two options were precomputed: + if Q == 0.1: + var_y = 10.098433122783046 # var_y is computed numerically (see class function compute_variance) + elif Q == 0.5: + var_y = 10.498957879911487 + else: + raise ValueError('Ground truth moments for Q = ' + str(Q) + ' were not precomputed. Use Q = 0.1 or 0.5.') + + self.variance = jnp.concatenate((var_x * jnp.ones(d//2), var_y * jnp.ones(d//2))) + + + + + def logdensity_fn(self, x): + """- log p of the target distribution""" + X, Y = x[..., :self.ndims//2], x[..., self.ndims//2:] + return -0.5 * jnp.sum(jnp.square(X - 1.0) + jnp.square(jnp.square(X) - Y) / self.Q, axis= -1) + + + + def draw(self, num): + n = self.ndims // 2 + X= np.empty((num, self.ndims)) + X[:, :n] = np.random.normal(loc= 1.0, scale= 1.0, size= (num, n)) + X[:, n:] = np.random.normal(loc= jnp.square(X[:, :n]), scale= jnp.sqrt(self.Q), size= (num, n)) + + return X + + + def transform(self, x): + return x + + + def sample_init(self, key): + return jax.random.normal(key, shape = (self.ndims, )) + + + def ground_truth(self): + num = 100000000 + x = np.random.normal(loc=1.0, scale=1.0, size=num) + y = np.random.normal(loc=np.square(x), scale=jnp.sqrt(self.Q), size=num) + + x2 = jnp.sum(jnp.square(x)) / (num - 1) + y2 = jnp.sum(jnp.square(y)) / (num - 1) + + x1 = np.average(x) + y1 = np.average(y) + + print(np.sqrt(0.5*(np.square(np.std(x)) + np.square(np.std(y))))) + + print(x2, y2) + + + +class Brownian(): + """ + log sigma_i ~ N(0, 2) + log sigma_obs ~N(0, 2) + + x ~ RandomWalk(0, sigma_i) + x_observed = (x + noise) * mask + noise ~ N(0, sigma_obs) + mask = 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 + """ + + def __init__(self): + self.num_data = 30 + self.name = 'Brownian' + self.ndims = self.num_data + 2 + + ground_truth_moments = jnp.load(dirr + '/ground_truth/brownian/ground_truth.npy') + self.E_x2, self.Var_x2 = ground_truth_moments[0], ground_truth_moments[1] + + self.data = jnp.array([0.21592641, 0.118771404, -0.07945447, 0.037677474, -0.27885845, -0.1484156, -0.3250906, -0.22957903, + -0.44110894, -0.09830782, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.8786016, -0.83736074, + -0.7384849, -0.8939254, -0.7774566, -0.70238715, -0.87771565, -0.51853573, -0.6948214, -0.6202789]) + # sigma_obs = 0.15, sigma_i = 0.1 + + self.observable = jnp.concatenate((jnp.ones(10), jnp.zeros(10), jnp.ones(10))) + self.num_observable = jnp.sum(self.observable) # = 20 + + + def logdensity_fn(self, x): + # y = softplus_to_log(x[:2]) + + lik = 0.5 * jnp.exp(-2 * x[1]) * jnp.sum(self.observable * jnp.square(x[2:] - self.data)) + x[ + 1] * self.num_observable + prior_x = 0.5 * jnp.exp(-2 * x[0]) * (x[2] ** 2 + jnp.sum(jnp.square(x[3:] - x[2:-1]))) + x[0] * self.num_data + prior_logsigma = 0.5 * jnp.sum(jnp.square(x / 2.0)) + + return -lik - prior_x - prior_logsigma + + + def transform(self, x): + return jnp.concatenate((jnp.exp(x[:2]), x[2:])) + + + def sample_init(self, key): + key_walk, key_sigma = jax.random.split(key) + + # original prior + # log_sigma = jax.random.normal(key_sigma, shape= (2, )) * 2 + + # narrower prior + log_sigma = jnp.log(np.array([0.1, 0.15])) + jax.random.normal(key_sigma, shape=( + 2,)) * 0.1 # *0.05# log sigma_i, log sigma_obs + + walk = random_walk(key_walk, self.ndims - 2) * jnp.exp(log_sigma[0]) + + return jnp.concatenate((log_sigma, walk)) + + def generate_data(self, key): + key_walk, key_sigma, key_noise = jax.random.split(key, 3) + + log_sigma = jax.random.normal(key_sigma, shape=(2,)) * 2 # log sigma_i, log sigma_obs + + walk = random_walk(key_walk, self.ndims - 2) * jnp.exp(log_sigma[0]) + noise = jax.random.normal(key_noise, shape=(self.ndims - 2,)) * jnp.exp(log_sigma[1]) + + return walk + noise + + +class GermanCredit: + """ Taken from inference gym. + + x = (global scale, local scales, weights) + + global_scale ~ Gamma(0.5, 0.5) + + for i in range(num_features): + unscaled_weights[i] ~ Normal(loc=0, scale=1) + local_scales[i] ~ Gamma(0.5, 0.5) + weights[i] = unscaled_weights[i] * local_scales[i] * global_scale + + for j in range(num_datapoints): + label[j] ~ Bernoulli(features @ weights) + + We use a log transform for the scale parameters. + """ + + def __init__(self): + self.ndims = 51 #global scale + 25 local scales + 25 weights + self.name = 'GermanCredit' + + self.labels = jnp.load(dirr + '/data/gc_labels.npy') + self.features = jnp.load(dirr + '/data/gc_features.npy') + + truth = jnp.load(dirr+'/ground_truth/german_credit/ground_truth.npy') + self.E_x2, self.Var_x2 = truth[0], truth[1] + + + + + def transform(self, x): + return jnp.concatenate((jnp.exp(x[:26]), x[26:])) + + def logdensity_fn(self, x): + + scales = jnp.exp(x[:26]) + + # prior + pr = jnp.sum(0.5 * scales + 0.5 * x[:26]) + 0.5 * jnp.sum(jnp.square(x[26:])) + + # transform + transform = -jnp.sum(x[:26]) + + # likelihood + weights = scales[0] * scales[1:26] * x[26:] + logits = self.features @ weights # = jnp.einsum('nd,...d->...n', self.features, weights) + lik = jnp.sum(self.labels * jnp.logaddexp(0., -logits) + (1-self.labels)* jnp.logaddexp(0., logits)) + + return -(lik + pr + transform) + + def sample_init(self, key): + weights = jax.random.normal(key, shape = (25, )) + return jnp.concatenate((jnp.zeros(26), weights)) + + + + +class ItemResponseTheory: + """ Taken from inference gym.""" + + def __init__(self): + self.ndims = 501 + self.name = 'ItemResponseTheory' + self.students = 400 + self.questions = 100 + + self.mask = jnp.load(dirr + '/data/irt_mask.npy') + self.labels = jnp.load(dirr + '/data/irt_labels.npy') + + truth = jnp.load(dirr+'/ground_truth/item_response_theory/ground_truth.npy') + self.E_x2, self.Var_x2 = truth[0], truth[1] + + + self.transform = lambda x: x + + def logdensity_fn(self, x): + + students = x[:self.students] + mean = x[self.students] + questions = x[self.students + 1:] + + # prior + pr = 0.5 * (jnp.square(mean - 0.75) + jnp.sum(jnp.square(students)) + jnp.sum(jnp.square(questions))) + + # likelihood + logits = mean + students[:, jnp.newaxis] - questions[jnp.newaxis, :] + bern = self.labels * jnp.logaddexp(0., -logits) + (1 - self.labels) * jnp.logaddexp(0., logits) + bern = jnp.where(self.mask, bern, jnp.zeros_like(bern)) + lik = jnp.sum(bern) + + return -lik - pr + + + def sample_init(self, key): + x = jax.random.normal(key, shape = (self.ndims,)) + x = x.at[self.students].add(0.75) + return x + + + + +class StochasticVolatility(): + """Example from https://num.pyro.ai/en/latest/examples/stochastic_volatility.html""" + + def __init__(self): + self.SP500_returns = jnp.load(dirr + '/data/SP500.npy') + + self.ndims = 2429 + self.name = 'StochasticVolatility' + + self.typical_sigma, self.typical_nu = 0.02, 10.0 # := 1 / lambda + + data = jnp.load(dirr + '/ground_truth/stochastic_volatility/ground_truth_0.npy') + self.E_x2 = data[0] + self.Var_x2 = data[1] + + + + def logdensity_fn(self, x): + """- log p of the target distribution + x= [s1, s2, ... s2427, log sigma / typical_sigma, log nu / typical_nu]""" + + sigma = jnp.exp(x[-2]) * self.typical_sigma #we used this transformation to make x unconstrained + nu = jnp.exp(x[-1]) * self.typical_nu + + l1= (jnp.exp(x[-2]) - x[-2]) + (jnp.exp(x[-1]) - x[-1]) + l2 = (self.ndims - 2) * jnp.log(sigma) + 0.5 * (jnp.square(x[0]) + jnp.sum(jnp.square(x[1:-2] - x[:-3]))) / jnp.square(sigma) + l3 = jnp.sum(nlogp_StudentT(self.SP500_returns, nu, jnp.exp(x[:-2]))) + + return -(l1 + l2 + l3) + + + def transform(self, x): + """transforms to the variables which are used by numpyro (and in which we have the ground truth moments)""" + + z = jnp.empty(x.shape) + z = z.at[:-2].set(x[:-2]) # = s = log R + z = z.at[-2].set(jnp.exp(x[-2]) * self.typical_sigma) # = sigma + z = z.at[-1].set(jnp.exp(x[-1]) * self.typical_nu) # = nu + + return z + + + def sample_init(self, key): + """draws x from the prior""" + + key_walk, key_exp = jax.random.split(key) + + scales = jnp.array([self.typical_sigma, self.typical_nu]) + #params = jax.random.exponential(key_exp, shape = (2, )) * scales + params= scales + walk = random_walk(key_walk, self.ndims - 2) * params[0] + return jnp.concatenate((walk, jnp.log(params/scales))) + + +class MixedLogit(): + + def __init__(self): + + key = jax.random.PRNGKey(0) + key_poisson, key_x, key_beta, key_logit = jax.random.split(key, 4) + + self.ndims = 2014 + self.name = "Mixed Logit" + self.nind = 500 + self.nsessions = jax.random.poisson(key_poisson, lam=1.0, shape=(self.nind,)) + 10 + self.nbeta = 4 + nobs = jnp.sum(self.nsessions) + + mu_true = jnp.array([-1.5, -0.3, 0.8, 1.2]) + sigma_true = jnp.array([[0.5, 0.1, 0.1, 0.1], [0.1, 0.5, 0.1, 0.1], [0.1, 0.1, 0.5, 0.1], [0.1, 0.1, 0.1, 0.5]]) + beta_true = jax.random.multivariate_normal(key_beta, mu_true, sigma_true, shape=(self.nind,)) + beta_true_repeat = jnp.repeat(beta_true, self.nsessions, axis=0) + + self.x = jax.random.normal(key_x, (nobs, self.nbeta)) + self.y = 1 * jax.random.bernoulli(key_logit, (jax.nn.sigmoid(jax.vmap(lambda vec1, vec2: jnp.dot(vec1, vec2))(self.x, beta_true_repeat)))) + + self.d = self.nbeta + self.nbeta + (self.nbeta * (self.nbeta-1) // 2) + self.nbeta * self.nind # mu, tau, omega_chol, and (beta for each i) + self.prior_mean_mu = jnp.zeros(self.nbeta) + self.prior_var_mu = 10.0 * jnp.eye(self.nbeta) + self.prior_scale_tau = 5.0 + self.prior_concentration_omega = 1.0 + + self.grad_logp = jax.value_and_grad(self.logdensity_fn) + + def corrchol_to_reals(self,x): + '''Converts a Cholesky-correlation (lower-triangular) matrix to a vector of unconstrained reals''' + dim = x.shape[0] + z = jnp.zeros((dim, dim)) + for i in range(dim): + for j in range(i): + z = z.at[i, j].set(x[i,j] / jnp.sqrt(1.0 - jnp.sum(x[i, :j] ** 2.0))) + z_lower_triang = z[jnp.tril_indices(dim, -1)] + y = 0.5 * (jnp.log(1.0 + z_lower_triang) - jnp.log(1.0 - z_lower_triang)) + + return y + + def reals_to_corrchol(self,y): + '''Converts a vector of unconstrained reals to a Cholesky-correlation (lower-triangular) matrix''' + len_vec = len(y) + dim = int(0.5 * (1 + 8 * len_vec) ** 0.5 + 0.5) + assert dim * (dim - 1) // 2 == len_vec + + z = jnp.zeros((dim, dim)) + z = z.at[jnp.tril_indices(dim, -1)].set(jnp.tanh(y)) + + x = jnp.zeros((dim, dim)) + for i in range(dim): + for j in range(i+1): + if i == j: + x = x.at[i, j].set(jnp.sqrt(1.0 - jnp.sum(x[i, :j] ** 2.0))) + else: + x = x.at[i, j].set(z[i,j] * jnp.sqrt(1.0 - jnp.sum(x[i, :j] ** 2.0))) + return x + + + def logdensity_fn(self, pars): + """log p of the target distribution, i.e., log posterior distribution up to a constant""" + + mu = pars[:self.nbeta] + dim1 = self.nbeta + self.nbeta + log_tau = pars[self.nbeta:dim1] + dim2 = self.nbeta + self.nbeta + self.nbeta * (self.nbeta - 1) // 2 + omega_chol_realvec = pars[dim1:dim2] + beta = pars[dim2:].reshape(self.nind, self.nbeta) + + omega_chol = self.reals_to_corrchol(omega_chol_realvec) + omega = jnp.dot(omega_chol, jnp.transpose(omega_chol)) + tau = jnp.exp(log_tau) + tau_diagmat = jnp.diag(tau) + sigma = jnp.dot(tau_diagmat, jnp.dot(omega, tau_diagmat)) + + beta_repeat = jnp.repeat(beta, self.nsessions, axis=0) + + log_lik = jnp.sum(self.y * jax.nn.log_sigmoid(jax.vmap(lambda vec1, vec2: jnp.dot(vec1, vec2))(self.x, beta_repeat)) + (1 - self.y) * jax.nn.log_sigmoid(-jax.vmap(lambda vec1, vec2: jnp.dot(vec1, vec2))(self.x, beta_repeat))) + + log_density_beta_popdist = -0.5 * self.nind * jnp.log(jnp.linalg.det(sigma)) - 0.5 * jnp.sum(jax.vmap(lambda vec, mat: jnp.dot(vec, jnp.linalg.solve(mat, vec)), in_axes=(0, None))(beta - mu, sigma)) + + muMinusPriorMean = mu - self.prior_mean_mu + log_prior_mu = -0.5 * jnp.log(jnp.linalg.det(self.prior_var_mu)) - 0.5 * jnp.dot(muMinusPriorMean, jnp.linalg.solve(self.prior_var_mu, muMinusPriorMean)) + + log_prior_tau = jnp.sum(dist.HalfCauchy(scale=self.prior_scale_tau).log_prob(tau)) + #log_prior_tau = jnp.sum(jax.vmap(lambda arg: -jnp.log(1.0 + (arg / self.prior_scale_tau) ** 2.0))(tau)) + log_prior_omega_chol = dist.LKJCholesky(self.nbeta, concentration=self.prior_concentration_omega).log_prob(omega_chol) + #log_prior_omega_chol = jnp.dot(nbeta - jnp.arange(2, nbeta+1) + 2.0 * self.prior_concentration_omega - 2.0, jnp.log(jnp.diag(omega_chol)[1:])) + + return log_lik + log_density_beta_popdist + log_prior_mu + log_prior_tau + log_prior_omega_chol + + + def transform(self, pars): + """transform pars to the original (possibly constrained) pars""" + mu = pars[:self.nbeta] + dim1 = self.nbeta + self.nbeta + log_tau = pars[self.nbeta:dim1] + dim2 = self.nbeta + self.nbeta + self.nbeta * (self.nbeta - 1) // 2 + omega_chol_realvec = pars[dim1:dim2] + beta_flattened = pars[dim2:] + + omega_chol = self.reals_to_corrchol(omega_chol_realvec) + omega = jnp.dot(omega_chol, jnp.transpose(omega_chol)) + tau = jnp.exp(log_tau) + tau_diagmat = jnp.diag(tau) + sigma = jnp.dot(tau_diagmat, jnp.dot(omega, tau_diagmat)) + + return jnp.concatenate((mu, sigma.flatten(), beta_flattened)) + + def sample_init(self, key): + """draws pars from the prior""" + + key_mu, key_omega_chol, key_tau, key_beta = jax.random.split(key, 4) + mu = jax.random.multivariate_normal(key_mu, self.prior_mean_mu, self.prior_var_mu) + omega_chol = dist.LKJCholesky(self.nbeta, concentration=self.prior_concentration_omega).sample(key_omega_chol) + tau = dist.HalfCauchy(scale=self.prior_scale_tau).sample(key_tau, (self.nbeta,)) + + omega_chol_realvec = self.corrchol_to_reals(omega_chol) + log_tau = jnp.log(tau) + + omega = jnp.dot(omega_chol, jnp.transpose(omega_chol)) + tau_diagmat = jnp.diag(tau) + sigma = jnp.dot(tau_diagmat, jnp.dot(omega, tau_diagmat)) + + beta = jax.random.multivariate_normal(key_beta, mu, sigma, shape=(self.nind,)) + + pars = jnp.concatenate((mu, log_tau, omega_chol_realvec, beta.flatten())) + return pars + + + +def nlogp_StudentT(x, df, scale): + y = x / scale + z = ( + jnp.log(scale) + + 0.5 * jnp.log(df) + + 0.5 * jnp.log(jnp.pi) + + jax.scipy.special.gammaln(0.5 * df) + - jax.scipy.special.gammaln(0.5 * (df + 1.0)) + ) + return 0.5 * (df + 1.0) * jnp.log1p(y**2.0 / df) + z + + + +def random_walk(key, num): + """ Genereting process for the standard normal walk: + x[0] ~ N(0, 1) + x[n+1] ~ N(x[n], 1) + + Args: + key: jax random key + num: number of points in the walk + Returns: + 1 realization of the random walk (array of length num) + """ + + def step(track, useless): + x, key = track + randkey, subkey = jax.random.split(key) + x += jax.random.normal(subkey) + return (x, randkey), x + + return jax.lax.scan(step, init=(0.0, key), xs=None, length=num)[1] + + + +models = { + + ## Rosenbrock(): {'mclmc': 40000, 'mhmclmc' : 40000, 'nuts': 40000}, # no Ex2 + # Cauchy(100) : {'mclmc': 2000, 'mhmclmc' : 2000, 'nuts': 2000}, + # StandardNormal(100) : {'mclmc': 10000, 'mhmclmc' : 10000, 'nuts': 10000}, + # Banana() : {'mclmc': 10000, 'mhmclmc' : 10000, 'nuts': 10000}, + Brownian() : {'mclmc': 20000, 'mhmclmc' : 80000, 'nuts': 40000}, + # Funnel() : {'mclmc': 20000, 'mhmclmc' : 80000, 'nuts': 40000}, + + + # Banana() : {'mclmc': 10000, 'mhmclmc' : 10000, 'nuts': 10000}, + # IllConditionedGaussian(10, 2): {'mclmc': 10000, 'mhmclmc' : 10000, 'nuts': 10000}, + # GermanCredit(): {'mclmc': 80000, 'mhmclmc' : 40000, 'nuts': 40000}, + # ItemResponseTheory(): {'mclmc': 20000, 'mhmclmc' : 40000, 'nuts': 20000}, + # StochasticVolatility(): {'mclmc': 40000, 'mhmclmc' : 40000, 'nuts': 40000} + } + +# models = {'Brownian Motion': (Brownian(), {'mclmc': 50000, 'mhmclmc' : 40000, 'nuts': 1000}), +# # 'Item Response Theory': (ItemResponseTheory(), {'mclmc': 50000, 'mhmclmc' : 50000, 'nuts': 1000}) +# } diff --git a/benchmarks/mcmc/results.ipynb b/benchmarks/mcmc/results.ipynb new file mode 100644 index 0000000..11ab9a6 --- /dev/null +++ b/benchmarks/mcmc/results.ipynb @@ -0,0 +1,1672 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from blackjax.benchmarks.mcmc.benchmark import run_benchmarks\n", + "from blackjax.mcmc.integrators import name_integrator\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "# results = run_benchmarks(batch_size=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    modelsamplerintegratortuningacc rateLstepsizenum_chainsnum stepscontractionESS
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    " + ], + "text/plain": [ + " model sampler integrator tuning acc rate L \n", + "0 ('Brownian', 32) mhmclmc mclachlan standard 0.572645 2.666334 \\\n", + "\n", + " stepsize num_chains num steps contraction \n", + "0 0.533267 100 100000 \\\n", + "\n", + " ESS \n", + "0 0.016779 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "new_results = pd.read_csv(\"../../../results_simple.csv\")\n", + "new_results\n", + "# new_results.result = new_results.result.apply(lambda x: x[0].item())\n", + "# new_results.model = new_results.model.apply(lambda x: x[1])\n", + "# new_results.result = new_results.result.apply(lambda x: 100/x)\n", + "# new_results = new_results.drop(new_results[new_results['coeffs'] == 'omelyan'].index)\n", + "# new_results.result = new_results.result.apply(lambda x: np.log(x))\n", + "\n", + "new_results" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Load the data\n", + "new_results = pd.read_csv(\"../../../results_step_size.csv\")\n", + "# Plot step size against acceptance rate\n", + "sns.lineplot(data=new_results, x=\"stepsize\", y=\"acc rate\")\n", + "plt.xlabel(\"Step size\")\n", + "plt.ylabel(\"Acceptance Rate\")\n", + "plt.show()\n", + "# new_results\n", + "# new_results\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Load the data\n", + "new_results = pd.read_csv(\"../../../results_step_size.csv\")\n", + "# Plot step size against acceptance rate\n", + "sns.lineplot(data=new_results, x=\"stepsize\", y=\"acc rate\")\n", + "plt.xlabel(\"Step size\")\n", + "plt.ylabel(\"Acceptance Rate\")\n", + "plt.show()\n", + "# new_results\n", + "# new_results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# # jax.numpy.save(f\"step_size_over_da.npy\", step_size_over_da.mean(axis=0))\n", + "# import jax.numpy as jnp\n", + "\n", + "# arr = jnp.load(\"../../../acceptance.npy\")\n", + "# import matplotlib.pyplot as plt\n", + "\n", + "# plt.plot(arr)\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# jax.numpy.save(f\"step_size_over_da.npy\", step_size_over_da.mean(axis=0))\n", + "import jax.numpy as jnp\n", + "\n", + "arr = jnp.load(\"../../../step_size_over_da.npy\")\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(arr[10:])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([2.4563851 , 0.73396873, 0.25227398, ..., 0.48355407, 0.4835147 ,\n", + " 0.48445415], dtype=float32)" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xY+Ho6AhbW1v4+/sjKSmprg5xo8cARERE0rl1A/huKvD7T+bt6fHA99PuLq8nmzZtgpOTE44ePYpp06ZhypQpGDlyJHr16oUTJ05g4MCBGDduHG7evCmuM3/+fKxcuRLHjx9Hs2bN8PLLL5uXnZ6OXbt2IS4uDl9++SU++eQTPP3007h8+TL279+PZcuW4e233xaDhslkwuDBg3Ho0CF89tlnOHPmDJYuXQqVSiWOefPmTaxYsQKbN2/GgQMHkJmZiRkzZpht96effsKVK1dw4MABrFq1ChERERg6dCiaN2+OpKQkTJ48Gf/4xz9w+fJlAEBhYSH69u2LP/74A99//z3+97//YdasWTCZTPV1uBsfgcoxGAwCAMFgMEhdChFRo3Xr1i3hzJkzwq1bt2o/SG6aIERoKn/lptVdwX/Rt29f4fHHHxff37lzR7C1tRXGjRsntmVlZQkAhMTERGHfvn0CAOHHH38Ul//www8CAHH/IyIiBBsbG8FoNIp9goODBXd3d6GsrExs69ChgxAVFSUIgiDs3r1bUCqVQlpaxfu5ceNGAYBw/vx5sS0mJkbQ6XTi+5CQEMHNza3cNvr06VNu/7788ktBEATho48+Euzt7YVr165V84g1HlV97mry/c0zQEREJJ1i44MtfwA+Pj7iv1UqFVq0aAFvb2+xTafTAQCuXr1a4TrOzs7llru7u8Pe3t5sjM6dO0OpVJq13VsnNTUVjzzyCNq3b19pnTY2NmjXrp3Zdv+6TQDo0qVLuW38dV/u7d9ft/voo4/C0dGx0u0+7BiAiIhIOlaaB1v+ACwsLMzeKxQKszaFQgEAZpeFarK8ojHvtd1bx9raulZ1CoJQo32pzXYfdgxAREQkHduWQLv+FS9r1//u8oeYj48PLl++jN9++63Bt5uamorr16836HYbEwYgIiKSjnVz4Jk15UNQu/532x/yW+H79u2LJ554As8//zz27t2LjIwMcRJ1fRo7diz0ej2GDx+OQ4cO4ffff8c333yDxMTEet1uY8LnABERkbS0rYEXPvnLc4A0d8/8POTh555vvvkGM2bMwNixY1FUVARPT08sXbq0XrepVquxZ88evPXWWxgyZAju3LmDzp07IyYmpl6325gohL9fSCQYjUZotVoYDAZoNPV3/ZmIqCkrLi5GRkYGPDw8YGVlJXU5JBNVfe5q8v3NS2BEREQkOwxAREREJDsMQERERCQ7jSIAxcTEwN3dHVZWVggICMDRo0cr7fvtt9/C398fDg4OsLW1hZ+fHzZv3mzWZ8KECVAoFGavQYMG1fduEBERURMh+V1gW7duRVhYGNatW4eAgABER0cjODgYaWlpaNWqVbn+jo6OmD9/Pjp27Ai1Wo0dO3YgNDQUrVq1QnBwsNhv0KBBZj/8Zmlp2SD7Q0RERI2f5GeAVq1ahVdffRWhoaHo3Lkz1q1bBxsbG2zYsKHC/v369cNzzz2HTp06oV27dnjjjTfg4+ODgwcPmvWztLSEXq8XX82by+N2SiIiIro/SQNQaWkpkpOTERQUJLYplUoEBQVV62FMgiAgPj4eaWlpeOKJJ8yWJSQkoFWrVujQoQOmTJmCa9euVTpOSUkJjEaj2YuIiIgeXpJeAsvLy0NZWZn4g3P36HQ6/Prrr5WuZzAY0Lp1a5SUlEClUuGDDz7AgAEDxOWDBg3CiBEj4OHhgfT0dMybNw+DBw9GYmIiVCpVufGioqKwcOHCutsxIiIiatQknwNUG/b29khNTUVhYSHi4+MRFhaGtm3bol+/fgCAMWPGiH29vb3h4+ODdu3aISEhAf37l//Nmblz5yIsLEx8bzQa4erqWu/7QURERNKQNAA5OTlBpVIhJyfHrD0nJwd6vb7S9ZRKJTw9PQEAfn5+OHv2LKKiosQA9Hdt27aFk5MTzp8/X2EAsrS05CRpIiIiGZF0DpBarUa3bt0QHx8vtplMJsTHxyMwMLDa45hMJpSUlFS6/PLly7h27RqcnZ0fqF4iIiLg7jxThUKB/Px8qUuhWpL8ElhYWBhCQkLg7++PHj16IDo6GkVFRQgNDQUAjB8/Hq1bt0ZUVBSAu/N1/P390a5dO5SUlGDnzp3YvHkzPvzwQwBAYWEhFi5ciOeffx56vR7p6emYNWsWPD09zW6TJyKixsNQYsD14usoKC2AvdoejlaO0FpqpS6LHmKSB6DRo0cjNzcX4eHhyM7Ohp+fH+Li4sSJ0ZmZmVAq/zxRVVRUhNdeew2XL1+GtbU1OnbsiM8++wyjR48GAKhUKpw8eRKbNm1Cfn4+XFxcMHDgQCxevJiXuYiIGqHsomxEHI7A4SuHxbbeLr0R2SsSetvKp0M8iH79+sHHxwdWVlb4+OOPoVarMXnyZERGRuLChQvw8PBASkoK/Pz8AAD5+flo3rw59u3bB3d3dzz55JMAID5iJSQkBLGxsfj666+xcOFCnD9/HjY2Nnj00Ufx3XffwdbWtl72g2qPvwZfAf4aPBHR/dXFr8EbSgyYdWCWWfi5p7dLbyx7Ylm9nAnq168fUlJSEBYWhhdffBGJiYmYMGECdu/eDS8vryoDUJ8+ffDdd9/h+eefR1paGjQaDaytrXHz5k20adMGy5cvx3PPPYeCggL8/PPPGD9+POzs7Op8H+Sqrn4NXvIzQEREJF/Xi69XGH4A4NCVQ7hefL3eLoX5+PggIiICAODl5YW1a9ciPj4eXl5eVa6nUqng6OgIAGjVqhUcHBwAAOnp6bhz5w5GjBgBNzc3AHfvRKbGSfInQRMRkXwVlBY80PIH4ePjY/be2dkZV69erfV4vr6+6N+/P7y9vTFy5EisX78eN27ceNAyqZ4wABERkWTs1fYPtPxBWFhYmL1XKBQwmUzivNO/zhC5ffv2fcdTqVTYu3cvdu3ahc6dO2PNmjXo0KEDMjIy6rZwqhMMQEREJBlHK0f0duld4bLeLr3haOXYwBUBLVu2BABkZWWJbampqWZ91Go1AKCsrMysXaFQoHfv3li4cCFSUlKgVquxbdu2+i2YaoUBiIiIJKO11CKyV2S5EHTvLjApboW3trZGz549sXTpUpw9exb79+/H22+/bdbHzc0NCoUCO3bsQG5uLgoLC5GUlIQlS5bg+PHjyMzMxLfffovc3Fx06tSpwfeB7o+ToImISFJ6Wz2WPbGsUT0HaMOGDZg4cSK6deuGDh06YPny5Rg4cKC4vHXr1li4cCHmzJmD0NBQjB8/HrNnz8aBAwcQHR0No9EINzc3rFy5EoMHD5ZsP6hyvA2+ArwNnojo/uriNniimqqr2+B5CYyIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiKiO9OvXD9OnT5e6DKoG/hYYERHJTr9+/eDn54fo6Og6Hffbb7+FhYVFnY5J9YMBiIiIqI44OjpKXQJVEy+BERGRrEyYMAH79+/H+++/D4VCAYVCgdjYWDg4OJj12759OxQKhfg+MjISfn5+2Lx5M9zd3aHVajFmzBgUFBSIff5+Cczd3R1LlizByy+/DHt7e7Rp0wb//ve/zbZz+PBh+Pn5wcrKCv7+/uJ2U1NT62P36f8wABERkay8//77CAwMxKuvvoqsrCxkZWWhrKysWuump6dj+/bt2LFjB3bs2IH9+/dj6dKlVa6zcuVK+Pv7IyUlBa+99hqmTJmCtLQ0AHd/vXzYsGHw9vbGiRMnsHjxYsyePfuB95HujwGIiIhkRavVQq1Ww8bGBnq9Hnq9HiqVqlrrmkwmxMbGomvXrujTpw/GjRuH+Pj4KtcZMmQIXnvtNXh6emL27NlwcnLCvn37AABffPEFFAoF1q9fj86dO2Pw4MGYOXPmA+8j3R8DEBERUTW5u7vD3t5efO/s7IyrV69WuY6Pj4/4b4VCAb1eL66TlpYGHx8fWFlZiX169OhRx1VTRRiAiIhI9pRKJQRBMGu7fft2uX5/v8NLoVDAZDJVOXZt1qH6xwBERESyo1arzeb9tGzZEgUFBSgqKhLbGmIScocOHXDq1CmUlJSIbceOHav37RIDEBERyZC7uzuSkpJw4cIF5OXlISAgADY2Npg3bx7S09PxxRdfIDY2tt7rePHFF2EymTBp0iScPXsWu3fvxooVKwDA7A40qnsMQEREJDszZsyASqVC586d0bJlSxiNRnz22WfYuXMnvL298eWXXyIyMrLe69BoNPjvf/+L1NRU+Pn5Yf78+QgPDwcAs3lBVPcUwt8vehKMRiO0Wi0MBgM0Go3U5RARNUrFxcXIyMiAh4cHv6zr0Oeff47Q0FAYDAZYW1tLXU6jU9Xnribf33wSNBERkYQ+/fRTtG3bFq1bt8b//vc/zJ49G6NGjWL4qWcMQERERBLKzs5GeHg4srOz4ezsjJEjR+Ldd9+VuqyHHgMQERGRhGbNmoVZs2ZJXYbsNIpJ0DExMXB3d4eVlRUCAgJw9OjRSvt+++238Pf3h4ODA2xtbcXfZfkrQRAQHh4OZ2dnWFtbIygoCOfOnavv3SAiIqImQvIAtHXrVoSFhSEiIgInTpyAr68vgoODK32ypqOjI+bPn4/ExEScPHkSoaGhCA0Nxe7du8U+y5cvx+rVq7Fu3TokJSXB1tYWwcHBKC4ubqjdIiIiokZM8rvAAgIC0L17d6xduxbA3d9ZcXV1xbRp0zBnzpxqjfHYY4/h6aefxuLFiyEIAlxcXPDWW29hxowZAACDwQCdTofY2FiMGTPmvuPxLjAiovvjXWAkhbq6C0zSM0ClpaVITk5GUFCQ2KZUKhEUFITExMT7ri8IAuLj45GWloYnnngCAJCRkYHs7GyzMbVaLQICAiods6SkBEaj0exFREREDy9JA1BeXh7Kysqg0+nM2nU6HbKzsytdz2AwwM7ODmq1Gk8//TTWrFmDAQMGAIC4Xk3GjIqKglarFV+urq4PsltERETUyEk+B6g27O3tkZqaimPHjuHdd99FWFgYEhISaj3e3LlzYTAYxNelS5fqrlgiIiJqdCQNQE5OTlCpVMjJyTFrz8nJgV6vr3Q9pVIJT09P+Pn54a233sILL7yAqKgoABDXq8mYlpaW0Gg0Zi8iIqKG0K9fP0yfPr3Jjd3USRqA1Go1unXrhvj4eLHNZDIhPj4egYGB1R7HZDKJv6Tr4eEBvV5vNqbRaERSUlKNxiQiInmaMGEChg8fLnUZVM8kfxBiWFgYQkJC4O/vjx49eiA6OhpFRUUIDQ0FAIwfPx6tW7cWz/BERUXB398f7dq1Q0lJCXbu3InNmzfjww8/BHD313OnT5+Od955B15eXvDw8MCCBQvg4uLCDzQRUSNlNBpx/fp1FBYWws7ODo6OjjwbX02CIKCsrAzNmkn+ld6kSD4HaPTo0VixYgXCw8Ph5+eH1NRUxMXFiZOYMzMzkZWVJfYvKirCa6+9hi5duqB379745ptv8Nlnn+GVV14R+8yaNQvTpk3DpEmT0L17dxQWFiIuLo63aRIRNUI5OTmYN28eXnjhBUyYMAEvvPAC5s2bV24qQ137+uuv4e3tDWtra7Ro0QJBQUGYOXMmNm3ahO+++w4KhQIKhUKcYzp79my0b98eNjY2aNu2LRYsWIDbt2+L40VGRooP53V3d4dWq8WYMWNQUFAg9ikqKsL48eNhZ2cHZ2dnrFy5slxdmzdvhr+/P+zt7aHX6/Hiiy+aPRsvISEBCoUCu3btQrdu3WBpaYmDBw9Wa2z6C4HKMRgMAgDBYDBIXQoRUaN169Yt4cyZM8KtW7dqPYbBYBBef/11oVu3buVer7/+er39Hb5y5YrQrFkzYdWqVUJGRoZw8uRJISYmRigoKBBGjRolDBo0SMjKyhKysrKEkpISQRAEYfHixcKhQ4eEjIwM4fvvvxd0Op2wbNkyccyIiAjBzs5OGDFihHDq1CnhwIEDgl6vF+bNmyf2mTJlitCmTRvhxx9/FE6ePCkMHTpUsLe3F9544w2xzyeffCLs3LlTSE9PFxITE4XAwEBh8ODB4vJ9+/YJAAQfHx9hz549wvnz54Vr165Va+yHQVWfu5p8f/N8GRERSeb69es4cuRIhcuOHDmC69ev18ulsKysLNy5cwcjRoyAm5sbAMDb2xsAYG1tjZKSknI3zrz99tviv93d3TFjxgxs2bLF7He8TCYTYmNjYW9vDwAYN24c4uPj8e6776KwsBCffPIJPvvsM/Tv3x8AsGnTJjzyyCNm23n55ZfFf7dt2xarV68Wr2bY2dmJyxYtWiQ+Aqa6Y9OfJL8ERkRE8lVYWPhAy2vL19cX/fv3h7e3N0aOHIn169fjxo0bVa6zdetW9O7dG3q9HnZ2dnj77beRmZlp1sfd3V0MPwDg7OwsXr5KT09HaWkpAgICxOWOjo7o0KGD2RjJyckYNmwY2rRpA3t7e/Tt2xcAym3L399f/Hd1x6Y/MQAREZFk/npGozbLa0ulUmHv3r3YtWsXOnfujDVr1qBDhw7IyMiosH9iYiJeeuklDBkyBDt27EBKSgrmz5+P0tJSs34WFhZm7xUKBUwmU7XrKioqQnBwMDQaDT7//HMcO3YM27ZtA4By27K1ta32uFQeAxAREUnG0dERPXv2rHBZz5494ejoWG/bVigU6N27NxYuXIiUlBSo1Wps27YNarUaZWVlZn0PHz4MNzc3zJ8/H/7+/vDy8sLFixdrtL127drBwsICSUlJYtuNGzfw22+/ie9//fVXXLt2DUuXLkWfPn3QsWPHSn8cvKZjkznOASIiIsloNBosWLAAixcvNpsL1LNnTyxYsKDeboVPSkpCfHw8Bg4ciFatWiEpKQm5ubno1KkTiouLsXv3bqSlpaFFixbQarXw8vJCZmYmtmzZgu7du+OHH34Qz8xUl52dHSZOnIiZM2eiRYsWaNWqFebPnw+l8s9zEW3atIFarcaaNWswefJk/PLLL1i8eHGdjE3mGICIiEhSOp0OS5YsadDnAGk0Ghw4cADR0dEwGo1wc3PDypUrMXjwYPj7+yMhIQH+/v4oLCzEvn378Mwzz+DNN9/E1KlTUVJSgqeffhoLFixAZGRkjbb7r3/9C4WFhRg2bBjs7e3x1ltvwWAwiMtbtmyJ2NhYzJs3D6tXr8Zjjz2GFStW4JlnnnngscmcQhAEQeoiGhuj0QitVguDwcAHcRERVaK4uBgZGRnw8PDgc9aowVT1uavJ9zfPjREREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREZHsMAARERGR7DAAERERkewwABEREVXThQsXoFAokJqaWmmfhIQEKBQK5OfnN1hdtaFQKLB9+3apy5AMAxAREVE1ubq6IisrC127dpW6lAbn7u4OhUJR6WvChAlSl1gj/DFUIiKS3PXr1+Ho6Fjp+8agtLQUarUaer1e6lLu6/bt27CwsKjTMY8dO4aysjIAwOHDh/H8888jLS1N/M0ta2vreq+hLvEMEBERSerSpUuYMWMGLl26JL5/6623xPf1paCgAC+99BJsbW3h7OyM9957D/369cP06dMB3D3jsXjxYowfPx4ajQaTJk2q8BLYzp070b59e1hbW+PJJ5/EhQsXzLZz8eJFDBs2DM2bN4etrS26dOmCnTt3ist/+eUXDB48GHZ2dtDpdBg3bhzy8vLE5XFxcXj88cfh4OCAFi1aYOjQoUhPTxeX36tp69at6Nu3L6ysrPD5558DADZs2IAuXbrA0tISzs7OmDp1qllteXl5eO6552BjYwMvLy98//33lR6vli1bQq/XQ6/Xi+G0VatW0Ov1KC4uhoODQ7kaIiMj4efnZzZOdHQ03N3dzdo+/vhjdOrUCVZWVujYsSM++OCDSuuoKwxAREQkmevXryM8PBwnT57E5MmTcfz4cUyePBmnTp1CREQErl+/Xm/bDgsLw6FDh/D9999j7969+Pnnn3HixAmzPitWrICvry9SUlKwYMGCcmNcunQJI0aMwLBhw5CamopXXnkFc+bMMevz+uuvo6SkBAcOHMCpU6ewbNky2NnZAQDy8/Px1FNP4dFHH8Xx48cRFxeHnJwcjBo1Sly/qKgIYWFhOH78OOLj46FUKvHcc8/BZDKZbWfOnDl44403cPbsWQQHB+PDDz/E66+/jkmTJuHUqVP4/vvv4enpabbOwoULMWrUKJw8eRJDhgzBSy+9ZHbM3d3dERkZWe1j+vcaquPzzz9HeHg43n33XZw9exZLlizBggULsGnTpmpvt1YEKsdgMAgABIPBIHUpRESN1q1bt4QzZ84It27deqBxMjMzhSFDhgjdunUTX0OGDBEyMzPrqNLyjEajYGFhIfznP/8R2/Lz8wUbGxvhjTfeEARBENzc3IThw4ebrZeRkSEAEFJSUgRBEIS5c+cKnTt3Nusze/ZsAYBw48YNQRAEwdvbW4iMjKywjsWLFwsDBw40a7t06ZIAQEhLS6twndzcXAGAcOrUKbOaoqOjzfq5uLgI8+fPr/QYABDefvtt8X1hYaEAQNi1a5fY9tRTTwlr1qwpt+6+ffvM9rGyGiIiIgRfX1+ztvfee09wc3MT37dr10744osvzPosXrxYCAwMrLDuqj53Nfn+5hwgIiKSlKurKxYuXIjJkyeLbQsXLoSrq2u9bfP333/H7du30aNHD7FNq9WiQ4cOZv38/f2rHOfs2bMICAgwawsMDDR7/89//hNTpkzBnj17EBQUhOeffx4+Pj4AgP/973/Yt2+feEbor9LT09G+fXucO3cO4eHhSEpKQl5ennjmJzMz02wy9l9rvXr1Kq5cuYL+/ftXWf+9OgDA1tYWGo0GV69eFdvi4+OrXP/v7ne8/q6oqAjp6emYOHEiXn31VbH9zp070Gq1NRqrphiAiIhIUpcuXUJERIRZW0REBNatW1evIag6bG1tH3iMV155BcHBwfjhhx+wZ88eREVFYeXKlZg2bRoKCwsxbNgwLFu2rNx6zs7OAIBhw4bBzc0N69evh4uLC0wmE7p27YrS0tJKa/37hOTK/H2SskKhKHdprSb+fryUSiUEQTBru337tvjvwsJCAMD69evLBUmVSlXrOqqDc4CIiEgy9+YA5eTkQKfTYd26ddDpdMjJyanXOUBt27aFhYUFjh07JrYZDAb89ttvNRqnU6dOOHr0qFnbkSNHyvVzdXXF5MmT8e233+Ktt97C+vXrAQCPPfYYTp8+DXd3d3h6epq9bG1tce3aNaSlpeHtt99G//790alTJ9y4ceO+ddnb28Pd3b3GZ3DqWsuWLZGdnW0Wgv46gVyn08HFxQW///57uf338PCo19oYgIiISDKOjo5YtGgRfHx8sG7dOvj7+2PdunXw9vbGwoUL6+1WeHt7e4SEhGDmzJnYt28fTp8+jYkTJ0KpVEKhUFR7nMmTJ+PcuXOYOXMm0tLS8MUXXyA2Ntasz/Tp07F7925kZGTgxIkT2LdvHzp16gTg7gTp69evY+zYsTh27BjS09Oxe/duhIaGoqysDM2bN0eLFi3w73//G+fPn8dPP/2EsLCwatUWGRmJlStXYvXq1Th37hxOnDiBNWvWVHvfAKB///5Yu3Ztjdb5q379+iE3NxfLly9Heno6YmJisGvXLrM+CxcuRFRUFFavXo3ffvsNp06dwsaNG7Fq1apab7c6GICIiEhSrq6uWLFihXi5y9XVFStXrqz3y1+rVq1CYGAghg4diqCgIPTu3Vu8Fbu62rRpg2+++Qbbt2+Hr68v1q1bhyVLlpj1KSsrw+uvv45OnTph0KBBaN++vXibt4uLCw4dOoSysjIMHDgQ3t7emD59OhwcHKBUKqFUKrFlyxYkJyeja9euePPNN/Gvf/2rWrWFhIQgOjoaH3zwAbp06YKhQ4fi3Llz1T9AuDsP6a+35NdUp06d8MEHHyAmJga+vr44evQoZsyYYdbnlVdewccff4yNGzfC29sbffv2RWxsbL2fAVIIf784RzAajdBqtTAYDOIDnoiIyFxxcTEyMjLg4eFRo9DQWBUVFaF169ZYuXIlJk6cKHU5VImqPnc1+f7mJGgiIpKllJQU/Prrr+jRowcMBgMWLVoEAHj22WclrowaAgMQERHJ1ooVK5CWlga1Wo1u3brh559/hpOTk9RlUQNgACIiIll69NFHkZycLHUZJBFOgiYiIiLZaRQBKCYmBu7u7rCyskJAQEC5Zyr81fr169GnTx80b94czZs3R1BQULn+EyZMgEKhMHsNGjSovneDiIiImgjJA9DWrVsRFhaGiIgInDhxAr6+vggODjZ7FPdfJSQkYOzYsdi3bx8SExPh6uqKgQMH4o8//jDrN2jQIGRlZYmvL7/8siF2h4hIdh7kycFENVVXnzfJb4MPCAhA9+7dxQctmUwmuLq6Ytq0aeV+Ubci9x4UtXbtWowfPx7A3TNA+fn52L59e61q4m3wRET3ZzKZcO7cOahUKrRs2RJqtbpGDxEkqglBEFBaWorc3FyUlZXBy8sLSqX5eZwmcxt8aWkpkpOTMXfuXLFNqVQiKCgIiYmJ1Rrj5s2buH37drmnhSYkJKBVq1Zo3rw5nnrqKbzzzjto0aJFhWOUlJSgpKREfG80GmuxN0RE8qJUKuHh4YGsrCxcuXJF6nJIJmxsbNCmTZty4aemJA1AeXl5KCsrg06nM2vX6XT49ddfqzXG7Nmz4eLigqCgILFt0KBBGDFiBDw8PJCeno558+Zh8ODBSExMrPDH1aKiorBw4cIH2xkiIhlSq9Vo06YN7ty5g7KyMqnLoYecSqVCs2bN6uRMY5O+DX7p0qXYsmULEhISzJ4GOWbMGPHf3t7e8PHxQbt27ZCQkID+/fuXG2fu3Llmv61iNBol/wViIqKmQqFQwMLCotwvixM1ZpJOgnZycoJKpUJOTo5Ze05ODvR6fZXrrlixAkuXLsWePXvg4+NTZd+2bdvCyckJ58+fr3C5paUlNBqN2YuIiIgeXpIGoHtP3oyPjxfbTCYT4uPjERgYWOl6y5cvx+LFixEXFwd/f//7bufy5cu4du0anJ2d66RuIiIiatokvw0+LCwM69evx6ZNm3D27FlMmTIFRUVFCA0NBQCMHz/ebJL0smXLsGDBAmzYsAHu7u7Izs5GdnY2CgsLAQCFhYWYOXMmjhw5ggsXLiA+Ph7PPvssPD09ERwcLMk+EhERUeMi+Ryg0aNHIzc3F+Hh4cjOzoafnx/i4uLEidGZmZlmM70//PBDlJaW4oUXXjAbJyIiApGRkVCpVDh58iQ2bdqE/Px8uLi4YODAgVi8eDEsLS0bdN+IiIiocZL8OUCNEZ8DRERE1PTU5Ptb8ktgRERERA2NAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGTngX8LrLi4GFu3bkVRUREGDBgALy+vuqiLiIiIqN7UKACFhYXh9u3bWLNmDQCgtLQUgYGBOH36NGxsbDBr1izs3bsXgYGB9VIsERERUV2o0SWwPXv2YMCAAeL7zz//HBcvXsS5c+dw48YNjBw5Eu+8806dF0lERERUl2oUgDIzM9G5c2fx/Z49e/DCCy/Azc0NCoUCb7zxBlJSUuq8SCIiIqK6VKMApFQqIQiC+P7IkSPo2bOn+N7BwQE3btyou+qIiIiI6kGNAlCnTp3w3//+FwBw+vRpZGZm4sknnxSXX7x4ETqdrm4rJCIiIqpjNZoEPWvWLIwZMwY//PADTp8+jSFDhsDDw0NcvnPnTvTo0aPOiyQiIiKqSzU6A/Tcc89h586d8PHxwZtvvomtW7eaLbexscFrr71WpwUSERER1TWF8NdJPQQAMBqN0Gq1MBgM0Gg0UpdDRERE1VCT7+8anQHKy8vDxYsXzdpOnz6N0NBQjBo1Cl988UXNqyUiIiJqYDUKQNOmTcPq1avF91evXkWfPn1w7NgxlJSUYMKECdi8eXOdF0lERERUl2oUgI4cOYJnnnlGfP/pp5/C0dERqamp+O6777BkyRLExMTUeZFEREREdalGASg7Oxvu7u7i+59++gkjRoxAs2Z3byZ75plncO7cuTotkIiIiKiu1SgAaTQa5Ofni++PHj2KgIAA8b1CoUBJSUmdFUdERERUH2oUgHr27InVq1fDZDLh66+/RkFBAZ566ilx+W+//QZXV9c6L5KIiIioLtXoQYiLFi1CUFAQPvvsM9y5cwdz585F8+bNxeVbtmzBE088UedFEhEREdWlGgUgX19fnD17FocOHYJerze7/AUAY8aMQZcuXeq0QCIiIqK6VqNLYEOGDIGFhQWeffZZBAQEYOnSpWZzgnr27IkhQ4bUdY1EREREdapGAWj37t1mk5yXLFmC69evi+/v3LmDtLS0uquOiIiIqB7UKAD9/Vcz+CsaRERE1BTVKAARERERPQxqFIAUCgUUCkW5tgcVExMDd3d3WFlZISAgAEePHq207/r169GnTx80b94czZs3R1BQULn+giAgPDwczs7OsLa2RlBQEB/QSERERKIa3QUmCAImTJgAS0tLAEBxcTEmT54MW1tbAKjVQxC3bt2KsLAwrFu3DgEBAYiOjkZwcDDS0tLQqlWrcv0TEhIwduxY9OrVC1ZWVli2bBkGDhyI06dPo3Xr1gCA5cuXY/Xq1di0aRM8PDywYMECBAcH48yZM7CysqpxjURERPRwUQg1mMgTGhparX4bN26sdgEBAQHo3r071q5dCwAwmUxwdXXFtGnTMGfOnPuuX1ZWhubNm2Pt2rUYP348BEGAi4sL3nrrLcyYMQMAYDAYoNPpEBsbizFjxtx3TKPRCK1WC4PBAI1GU+19ISIiIunU5Pu7RmeAahJsqqO0tBTJycmYO3eu2KZUKhEUFITExMRqjXHz5k3cvn0bjo6OAICMjAxkZ2cjKChI7KPVahEQEIDExMQKA1BJSYnZ2Suj0VjbXSIiIqImQNJJ0Hl5eSgrK4NOpzNr1+l0yM7OrtYYs2fPhouLixh47q1XkzGjoqKg1WrFF3/Og4iI6OHWpO8CW7p0KbZs2YJt27Y90NyeuXPnwmAwiK9Lly7VYZVERETU2NToElhdc3JygkqlQk5Ojll7Tk4O9Hp9leuuWLECS5cuxY8//ggfHx+x/d56OTk5cHZ2NhvTz8+vwrEsLS3Fid1ERET08JP0DJBarUa3bt0QHx8vtplMJsTHxyMwMLDS9ZYvX47FixcjLi4O/v7+Zss8PDyg1+vNxjQajUhKSqpyTCIiIpIPSc8AAUBYWBhCQkLg7++PHj16IDo6GkVFReIdZ+PHj0fr1q0RFRUFAFi2bBnCw8PxxRdfwN3dXZzXY2dnBzs7OygUCkyfPh3vvPMOvLy8xNvgXVxcMHz4cKl2k4iIiBoRyQPQ6NGjkZubi/DwcGRnZ8PPzw9xcXHiJObMzEwolX+eqPrwww9RWlqKF154wWyciIgIREZGAgBmzZqFoqIiTJo0Cfn5+Xj88ccRFxfHZwARERERgBo+B0gu+BwgIiKipqcm399N+i4wIiIiotpgACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItmR/EGIRPQACrKBnNPAqa8BSw3gNxZwaAPYOEpdGRFRo8YARNRUGa8AW8cDfxz7s+3oOqDXNODxNwGbFtLVRkTUyPESGFFTZDIBJ78yDz/3HF4DXL/Q4CURETUlDEBETVHRVeDY+sqXJ28E+Cs3RESVYgAiaooEE1BaVPny4nxAKGuwcoiImhoGIKKmyMoB8BpY+XLvUYCSU/yolkxlwO2b5m1VBW6iJogBiKgpUtsAT8wE1Hbll7XsADzi3/A10cPBVAbk/AKc/wko/b8QdP13IOVz4OYNaWsjqkP8X0SipsqxLTBpH7AvCkjbCVhYA49NAHq8CmhcpK6OmqrcNGDjEOB2ETBqM6D3BjYNAwyXgTvFQLcQwEordZVED4wBiKipUqoAp/bAs2uBW/mAQgHYtgRUFlJXRk2ZtQOg6wpcOgJ8NQ6wsAVKCwG1LeDep+KzjkRNEC+BETV1altA2/ruWR+GH3pQGhdgZCyg97l7J2FpIaBQAhN+AJx97gZvoocAAxAREZm7cwu4mffne8H0f5fASqSriaiOMQAREdGfrmfcnfNjvHL37GILz7vtX40D0uOBEt4NRg8HBiAiIjKnUN4NPyE/3L305doTgAJQqO7ONSN6CHASNBER/cnRAxj/PVBsuHsHmFIFjNx491Z4l8fuPoKB6CHAAEREROYcPe5OgL53tkfjAtg78+wPPVR4CYyIiMr7e9hh+KGHDAMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJDgMQERERyQ4DEBEREckOAxARERHJjuQBKCYmBu7u7rCyskJAQACOHj1aad/Tp0/j+eefh7u7OxQKBaKjo8v1iYyMhEKhMHt17NixHveAiIiImhpJA9DWrVsRFhaGiIgInDhxAr6+vggODsbVq1cr7H/z5k20bdsWS5cuhV6vr3TcLl26ICsrS3wdPHiwvnaBiIiImiBJA9CqVavw6quvIjQ0FJ07d8a6detgY2ODDRs2VNi/e/fu+Ne//oUxY8bA0tKy0nGbNWsGvV4vvpycnOprF4iIiKgJkiwAlZaWIjk5GUFBQX8Wo1QiKCgIiYmJDzT2uXPn4OLigrZt2+Kll15CZmZmlf1LSkpgNBrNXkRERPTwkiwA5eXloaysDDqdzqxdp9MhOzu71uMGBAQgNjYWcXFx+PDDD5GRkYE+ffqgoKCg0nWioqKg1WrFl6ura623T0RERI2f5JOg69rgwYMxcuRI+Pj4IDg4GDt37kR+fj6++uqrSteZO3cuDAaD+Lp06VIDVkxEREQNrZlUG3ZycoJKpUJOTo5Ze05OTpUTnGvKwcEB7du3x/nz5yvtY2lpWeWcIiIiInq4SHYGSK1Wo1u3boiPjxfbTCYT4uPjERgYWGfbKSwsRHp6OpydnetsTCIiImraJDsDBABhYWEICQmBv78/evTogejoaBQVFSE0NBQAMH78eLRu3RpRUVEA7k6cPnPmjPjvP/74A6mpqbCzs4OnpycAYMaMGRg2bBjc3Nxw5coVREREQKVSYezYsdLsJBERETU6kgag0aNHIzc3F+Hh4cjOzoafnx/i4uLEidGZmZlQKv88SXXlyhU8+uij4vsVK1ZgxYoV6Nu3LxISEgAAly9fxtixY3Ht2jW0bNkSjz/+OI4cOYKWLVs26L4RERFR46UQBEGQuojGxmg0QqvVwmAwQKPRSF0OERERVUNNvr8furvAiIiIiO6HAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGRH8gAUExMDd3d3WFlZISAgAEePHq207+nTp/H888/D3d0dCoUC0dHRDzwmERERyY+kAWjr1q0ICwtDREQETpw4AV9fXwQHB+Pq1asV9r958ybatm2LpUuXQq/X18mYREREJD8KQRAEqTYeEBCA7t27Y+3atQAAk8kEV1dXTJs2DXPmzKlyXXd3d0yfPh3Tp0+vszHvMRqN0Gq1MBgM0Gg0Nd8xIiIianA1+f6W7AxQaWkpkpOTERQU9GcxSiWCgoKQmJjYoGOWlJTAaDSavYiIiOjhJVkAysvLQ1lZGXQ6nVm7TqdDdnZ2g44ZFRUFrVYrvlxdXWu1fSIiImoaJJ8E3RjMnTsXBoNBfF26dEnqkoiIiKgeNZNqw05OTlCpVMjJyTFrz8nJqXSCc32NaWlpCUtLy1ptk4iIiJoeyc4AqdVqdOvWDfHx8WKbyWRCfHw8AgMDG82YRERE9PCR7AwQAISFhSEkJAT+/v7o0aMHoqOjUVRUhNDQUADA+PHj0bp1a0RFRQG4O8n5zJkz4r//+OMPpKamws7ODp6entUak4iIiEjSADR69Gjk5uYiPDwc2dnZ8PPzQ1xcnDiJOTMzE0rlnyeprly5gkcffVR8v2LFCqxYsQJ9+/ZFQkJCtcYkIiIikvQ5QI0VnwNERETU9DSJ5wARERERSYUBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGIiIiIZKdRBKCYmBi4u7vDysoKAQEBOHr0aJX9//Of/6Bjx46wsrKCt7c3du7cabZ8woQJUCgUZq9BgwbV5y4QERFREyJ5ANq6dSvCwsIQERGBEydOwNfXF8HBwbh69WqF/Q8fPoyxY8di4sSJSElJwfDhwzF8+HD88ssvZv0GDRqErKws8fXll182xO4QERFRE6AQBEGQsoCAgAB0794da9euBQCYTCa4urpi2rRpmDNnTrn+o0ePRlFREXbs2CG29ezZE35+fli3bh2Au2eA8vPzsX379lrVZDQaodVqYTAYoNFoajUGERERNayafH9LegaotLQUycnJCAoKEtuUSiWCgoKQmJhY4TqJiYlm/QEgODi4XP+EhAS0atUKHTp0wJQpU3Dt2rW63wEiIiJqkppJufG8vDyUlZVBp9OZtet0Ovz6668VrpOdnV1h/+zsbPH9oEGDMGLECHh4eCA9PR3z5s3D4MGDkZiYCJVKVW7MkpISlJSUiO+NRuOD7BYRERE1cpIGoPoyZswY8d/e3t7w8fFBu3btkJCQgP79+5frHxUVhYULFzZkiURERCQhSS+BOTk5QaVSIScnx6w9JycHer2+wnX0en2N+gNA27Zt4eTkhPPnz1e4fO7cuTAYDOLr0qVLNdwTIiIiakokDUBqtRrdunVDfHy82GYymRAfH4/AwMAK1wkMDDTrDwB79+6ttD8AXL58GdeuXYOzs3OFyy0tLaHRaMxeRERE9PCS/Db4sLAwrF+/Hps2bcLZs2cxZcoUFBUVITQ0FAAwfvx4zJ07V+z/xhtvIC4uDitXrsSvv/6KyMhIHD9+HFOnTgUAFBYWYubMmThy5AguXLiA+Ph4PPvss/D09ERwcLAk+0hERESNi+RzgEaPHo3c3FyEh4cjOzsbfn5+iIuLEyc6Z2ZmQqn8M6f16tULX3zxBd5++23MmzcPXl5e2L59O7p27QoAUKlUOHnyJDZt2oT8/Hy4uLhg4MCBWLx4MSwtLSXZRyIiImpcJH8OUGPE5wARERE1PU3mOUBEREREUmAAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlpJnUBREREJA83im/gevF1GEuN0Fpq4WjlCAdLB0lqYQAiIiKiepdVmIW5B+ciOSdZbAvQB+Cdx9+B3lbf4PXwEhgRERHVq/zi/HLhBwCSspOw8PBCGEoMDV4TAxARERHVq2vF18qFn3sOXjmIG8U3GrgiBiAiIiKqZwWlBVUvv1318vrAAERERET1SmOpqXSZAgrYq+0bsJq7GICIiIioXjlaOiLQObDCZU+2eRItrFo0cEUMQERERFTPHKwcsKj3IjzR+gmxTQEFnmrzFOb1mCfJGSDeBk9ERET1Tm+rR1SfKFwvvo7C24WwU9vB0dKxystj9YkBiIiIiBqExlIjWeD5O14CIyIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItlhACIiIiLZYQAiIiIi2WEAIiIiItnhT2FUQBAEAIDRaJS4EiIiIqque9/b977Hq8IAVIGCggIAgKurq8SVEBERUU0VFBRAq9VW2UchVCcmyYzJZMKVK1dgb28PhUJRp2MbjUa4urri0qVL0Ggaxw/CNVY8VtXHY1V9PFbVx2NVfTxW1Vefx0oQBBQUFMDFxQVKZdWzfHgGqAJKpRKPPPJIvW5Do9HwP5Jq4rGqPh6r6uOxqj4eq+rjsaq++jpW9zvzcw8nQRMREZHsMAARERGR7DAANTBLS0tERETA0tJS6lIaPR6r6uOxqj4eq+rjsao+HqvqayzHipOgiYiISHZ4BoiIiIhkhwGIiIiIZIcBiIiIiGSHAYiIiIhkhwGoHsTExMDd3R1WVlYICAjA0aNHq+z/n//8Bx07doSVlRW8vb2xc+fOBqpUejU5VrGxsVAoFGYvKyurBqxWGgcOHMCwYcPg4uIChUKB7du333edhIQEPPbYY7C0tISnpydiY2Prvc7GoqbHKyEhodznSqFQIDs7u2EKlkhUVBS6d+8Oe3t7tGrVCsOHD0daWtp915Pj36vaHCu5/r0CgA8//BA+Pj7igw4DAwOxa9euKteR4nPFAFTHtm7dirCwMERERODEiRPw9fVFcHAwrl69WmH/w4cPY+zYsZg4cSJSUlIwfPhwDB8+HL/88ksDV97wanqsgLtPDs3KyhJfFy9ebMCKpVFUVARfX1/ExMRUq39GRgaefvppPPnkk0hNTcX06dPxyiuvYPfu3fVcaeNQ0+N1T1pamtlnq1WrVvVUYeOwf/9+vP766zhy5Aj27t2L27dvY+DAgSgqKqp0Hbn+varNsQLk+fcKAB555BEsXboUycnJOH78OJ566ik8++yzOH36dIX9JftcCVSnevToIbz++uvi+7KyMsHFxUWIioqqsP+oUaOEp59+2qwtICBA+Mc//lGvdTYGNT1WGzduFLRabQNV1zgBELZt21Zln1mzZgldunQxaxs9erQQHBxcj5U1TtU5Xvv27RMACDdu3GiQmhqrq1evCgCE/fv3V9pHzn+v/qo6x4p/r8w1b95c+PjjjytcJtXnimeA6lBpaSmSk5MRFBQktimVSgQFBSExMbHCdRITE836A0BwcHCl/R8WtTlWAFBYWAg3Nze4urpW+X8UcibXz9SD8vPzg7OzMwYMGIBDhw5JXU6DMxgMAABHR8dK+/CzdVd1jhXAv1cAUFZWhi1btqCoqAiBgYEV9pHqc8UAVIfy8vJQVlYGnU5n1q7T6SqdT5CdnV2j/g+L2hyrDh06YMOGDfjuu+/w2WefwWQyoVevXrh8+XJDlNxkVPaZMhqNuHXrlkRVNV7Ozs5Yt24dvvnmG3zzzTdwdXVFv379cOLECalLazAmkwnTp09H79690bVr10r7yfXv1V9V91jJ/e/VqVOnYGdnB0tLS0yePBnbtm1D586dK+wr1eeKvwZPTUZgYKDZ/0H06tULnTp1wkcffYTFixdLWBk1ZR06dECHDh3E97169UJ6ejree+89bN68WcLKGs7rr7+OX375BQcPHpS6lEavusdK7n+vOnTogNTUVBgMBnz99dcICQnB/v37Kw1BUuAZoDrk5OQElUqFnJwcs/acnBzo9foK19Hr9TXq/7CozbH6OwsLCzz66KM4f/58fZTYZFX2mdJoNLC2tpaoqqalR48esvlcTZ06FTt27MC+ffvwyCOPVNlXrn+v7qnJsfo7uf29UqvV8PT0RLdu3RAVFQVfX1+8//77FfaV6nPFAFSH1Go1unXrhvj4eLHNZDIhPj6+0mu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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "from matplotlib.ticker import ScalarFormatter\n", + "import numpy as np\n", + "\n", + "\n", + "# plot = sns.lineplot(data=new_results, x=[0.0,1.0, 2.0], y=\"result\", hue=\"sampler\", style=\"integrator\")\n", + "plot = sns.scatterplot(data=new_results, x=[0.0,1.0, 2.0, 3.0], y=\"ESS\", hue=\"sampler\", style=\"tuning\")\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "# plot.set(xscale='log')\n", + "# plot.set(yscale='log')\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    modeldimssamplerintegratorconvergenceLstep_sizeESS
    0StandardNormal10mhmchmcmclachlanFalse3.2263372.9330340.315657
    1StandardNormal10mhmchmcomelyanFalse3.5628753.2389770.122026
    2StandardNormal100mhmchmcmclachlanFalse11.51100110.4645450.228415
    3StandardNormal100mhmchmcomelyanFalse11.23162110.2105640.102722
    4StandardNormal1000mhmchmcmclachlanFalse47.54879843.2261770.078641
    5StandardNormal1000mhmchmcomelyanFalse33.17399230.1581750.097752
    6StandardNormal10000mhmchmcmclachlanFalse198.228806180.2079930.000000
    7StandardNormal10000mhmchmcomelyanFalse109.15772299.2342910.100452
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler integrator convergence L \n", + "0 StandardNormal 10 mhmchmc mclachlan False 3.226337 \\\n", + "1 StandardNormal 10 mhmchmc omelyan False 3.562875 \n", + "2 StandardNormal 100 mhmchmc mclachlan False 11.511001 \n", + "3 StandardNormal 100 mhmchmc omelyan False 11.231621 \n", + "4 StandardNormal 1000 mhmchmc mclachlan False 47.548798 \n", + "5 StandardNormal 1000 mhmchmc omelyan False 33.173992 \n", + "6 StandardNormal 10000 mhmchmc mclachlan False 198.228806 \n", + "7 StandardNormal 10000 mhmchmc omelyan False 109.157722 \n", + "\n", + " step_size ESS \n", + "0 2.933034 0.315657 \n", + "1 3.238977 0.122026 \n", + "2 10.464545 0.228415 \n", + "3 10.210564 0.102722 \n", + "4 43.226177 0.078641 \n", + "5 30.158175 0.097752 \n", + "6 180.207993 0.000000 \n", + "7 99.234291 0.100452 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "new_results = pd.read_csv(\"../../../omelyan.csv\")\n", + "# new_results[\"sampler\"] = [\"mclmc\", \"mhmchmc\", \"mhmchmc\", \"nuts\"]\n", + "# new_results.columns = [\"model\", \"dims\", \"sampler\", \"L\", \"step_size\", \"integrator\", \"tuning\", \"acc_rate\", \"ESS\"] \n", + "# new_results.result = new_results.result.apply(lambda x: x[0].item())\n", + "# new_results.model = new_results.model.apply(lambda x: x[1])\n", + "# new_results.result = new_results.result.apply(lambda x: 100/x)\n", + "# new_results = new_results.drop(new_results[new_results['coeffs'] == 'omelyan'].index)\n", + "# new_results.result = new_results.result.apply(lambda x: np.log(x))\n", + "\n", + "new_results" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "from matplotlib.ticker import ScalarFormatter\n", + "import numpy as np\n", + "\n", + "new_results = pd.read_csv(\"../../../omelyan.csv\")\n", + "plot = sns.lineplot(data=new_results, x=\"dims\", y=\"ESS\", hue=\"sampler\", style=\"integrator\")\n", + "plot.set(xscale='log')\n", + "plot.set(yscale='log')\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9evRQt27dNGvWrO8+V2RkpKZOnarnnnuuwl/PV199pVmzZmn58uUVuj4nJ0fBwcHKzs5WUFBQhT8PPIPD4dCCTSf0cvJ+lZQ5FFGnlmaN7KyYyNpmTwMAj1bR79+Vfsdo1apV6tq1q0aMGKH69eurU6dOmjdv3s1f3GJRcnKydu3apVGjRslut+vo0aPq16+fhg4d+pNR9EOKi4u1Y8cO9e/f/4bP1b9/f23evPmWXvOnJCUlKSoqSt26dauW14d7MAxDY/s008cTe6txXT+duXpdI+Zs0vyUYxytAYALqHQYHTt2TLNnz1arVq30xRdfaOLEiUpISNDChQtven14eLj+9a9/KTU1VSNHjlS/fv3Uv39/zZ49+5ZHZ2VlqaysTGFhYTc8HhYWpgsXLlT4dfr3768RI0YoOTlZERERPxpVkydP1r59+7Rt27Zb3g3P0TGittYkxCq+QwOVlDn00tr9Gr9oh64VFJs9DQDwI2yVfYLdblfXrl318ssvS5I6deqkvXv3as6cORo9evRNn9O4cWMtXrxYd955p5o3b663335bhmHc3vIqsH79erMnwI0F+XopaWRnvbfllP6yZp/W789U/IwUzRzZSV2a1DV7HgDgJir9jlHDhg0VFRV1w2Nt27bVqVOnfvA5mZmZmjBhggYPHqyCggI9/fTTlV/6v4SGhspqtX7vh7czMzPVoEGD23ptoCoZhqHHezbRp5N6q1mov85lF+rBuema8/VR2e0crQGAs6l0GPXp00cHDx684bFDhw6pSZMmN70+KytLd999t9q2batPPvlEGzZs0NKlS/XMM8/c2mJJ3t7e6tKlizZs2PDdY3a7XRs2bFCvXr1u+XWB6tIuPFirp8ZqSHS4yuwO/fWzA3pi4TZdzisyexoA4H+pdBg9/fTTSk9P18svv6wjR47ogw8+0FtvvaXJkyd/71q73a4BAwaoSZMmWrp0qWw2m6KiorRu3Tq9++67+uc//3nTz5GXl6eMjAxlZGRIko4fP66MjIwb3pWaPn265s2bp4ULF2r//v2aOHGi8vPzNXbs2Mp+SUCNCPCxacbDMfrr/R3kY7Poq4OXFJ+Yoq3Hr5g9DQBQrtJ/XF+S1qxZo9/97nc6fPiwmjVrpunTp2v8+PE3vXbdunWKi4uTr6/vDY/v2rVL9erVU0RExPee89VXX+muu+763uOjR4/WggULvvv9rFmz9Oqrr+rChQuKiYlRYmKievToUdkvp1L44/qoCgcu5Gjy+zt19FK+LIY0/eetNalvS1ks5v/sHQC4o4p+/76lMPJkhBGqSn5Rqf60cq8+2XlWkhTXKlSvPxijeoE+Ji8DAPdTbf8dIwBVw9/HptcfjNGrwzuqlpdVKYezFJ+Yok1HssyeBgAeizACTDaia6RWTemj1mEBupRbpEff3qJ/rjukMv7UGgDUOMIIcAKtwgK1cnKsHuoaKYdDmrHhsB6bv0UXcwrNngYAHoUwApxELW+r/ja8o954KEZ+3lZtPnZZA2akaOOhS2ZPAwCPQRgBTmZop0ZaMzVWbRsG6XJ+sUa/u1WvfnFApWV2s6cBgNsjjAAn1LxegD6d1FuP9mgsh0NK+vKoRs7bovPZ182eBgBujTACnJSvl1X/PayDZo3spAAfm7aeuKL4GSn68sBFs6cBgNsijAAnN6hjuNZMjVX7RkG6WlCisQu26ZXk/SrhaA0AqhxhBLiApqH++nhib43p3VSSNHfjMT00d7POXuNoDQCqEmEEuAgfm1UvDmmnOY91VqCvTTtPXVP8jBSt25dp9jQAcBuEEeBi7m3fUMkJcYqOrK3s6yUav2i7/mv1PhWXcrQGALeLMAJcUGRdPy17spfGxTaTJL2Tdlwj5mzS6SsFJi8DANdGGAEuyttm0R8HRWn+qK4KruWl3WeyFZ+Yos/2nDd7GgC4LMIIcHH9o8KUPC1OXZrUUW5hqSa+v1PPr9yrwpIys6cBgMshjAA30Kh2LS2Z0FNP3dlCkrRo80k9MHuTTmTlm7wMAFwLYQS4CS+rRc8NaKN3x3ZTXX9vfXsuR4NmpmrV7nNmTwMAl0EYAW7mrjvqKzkhTt2b1lVeUakSPtyl332yh6M1AKgAwghwQw2CffXB+B6a2q+lDEP6cOspDU1K09FLeWZPAwCnRhgBbspmtejXv7hDi57ortAAbx24kKvBM1P16a4zZk8DAKdFGAFuLq5VPSUnxKl3ixAVFJfp6aW79Ztlu3W9mKM1APi/CCPAA9QP8tXiX/bQ0/1by2JIy3ac0ZBZqTqUmWv2NABwKoQR4CGsFkPT+rfS++N6qn6gjw5fzNOQWan6aPtpORwOs+cBgFMgjAAP06tFiJKnxSmuVagKS+x6dvk3mv7RbuUXlZo9DQBMRxgBHig0wEcLx3bXb+65Q1aLoU93ndXgWanafz7H7GkAYCrCCPBQFouhyXe11JIJPdUgyFfHLuXrvqQ0fbDlFEdrADwWYQR4uG5N6yp5WpzuuqOeikvt+v2ne5SwJEO5hSVmTwOAGkcYAVBdf2+9Pbqbfh/fRjaLodW7z2nwzFTtPZtt9jQAqFGEEQBJ/z5am/CzFlr6ZC81ql1LJy4X6P43N2nR5hMcrQHwGIQRgBt0aVJHaxNi9fOoMBWX2fX8ym816f2dyr7O0RoA90cYAfie2n7eeuvxLnp+UJS8rIY+23tBg2amaPfpa2ZPA4BqRRgBuCnDMPREbDMtf6q3IuvW0ukr1zV8zia9nXqcozUAboswAvCjoiNra83UOA1o30AlZQ79Zc0+jV+0Q9cKis2eBgBVjjAC8JOCa3npzUc76y/3tZO31aL1+zM1MDFVO05eNXsaAFQpwghAhRiGocd7NdUnk3qraYifzl67rofmbtbcr4/KbudoDYB7IIwAVEr7RsFaPTVWg6PDVWp36JXPDuiXC7fpSj5HawBcH2EEoNICfb2U+HCMXrm/g3xsFn158JLiZ6Ro6/ErZk8DgNtCGAG4JYZh6JHujbVich81r+evCzmFemReupK+PMLRGgCXRRgBuC1tGwZp9ZRY3d+pkcrsDr36xUGNfnersvKKzJ4GAJVGGAG4bf4+Nr32YLT+PryjfL0sSjmcpQEzUrTpaJbZ0wCgUggjAFXCMAw92DVSq6fEqnVYgC7lFumx+Vv0xvpDKuNoDYCLIIwAVKlWYYFaOTlWD3aNkN0hvbH+sB6bv0UXcwrNngYAP4kwAlDlanlb9ffh0frnQ9Hy87Zq87HLik9MUcrhS2ZPA4AfRRgBqDbDOkVo9dRYtWkQqKy8Yo16Z6v+8cVBlZbZzZ4GADdFGAGoVi3qBWjF5D4a2aOxHA5p1pdHNHLeFp3Pvm72NAD4HsIIQLXz9bLq5WEdNPORTgrwsWnriSuKn5GiLw9eNHsaANyAMAJQYwZHh2vN1Fi1bxSkqwUlGvvuNr3y2X6VcLQGwEkQRgBqVNNQf308sbdG92oiSZr79TE9NHezzl7jaA2A+QgjADXOx2bVn+9rr9mPdlagr007T11T/IwUrduXafY0AB6OMAJgmgEdGio5IU7REcHKvl6i8Yu26y9r9qm4lKM1AOYgjACYKrKun5Y91Vu/jG0mSXo79bhGzN2s01cKTF4GwBMRRgBM522z6E+DojRvVFcF1/LS7tPXFJ+Yos/3njd7GgAPQxgBcBo/jwpT8rQ4dW5cW7mFpXrqvZ16YeVeFZWWmT0NgIcgjAA4lUa1a2npk7305J3NJUkLN5/UA7M36URWvsnLAHgCwgiA0/GyWvS7AW317phuquPnpb1nczRoZqpW7z5n9jQAbo4wAuC07mpTX8nT4tS9aV3lFZVq6oe79PtP96iwhKM1ANWDMALg1BoG19IH43toyl0tZRjSB1tOaWhSmo5eyjN7GgA3RBgBcHo2q0XP3HOHFj3RXaEB3jpwIVeDZ6bq011nzJ4GwM0QRgBcRlyrekpOiFOv5iEqKC7T00t369nlu3W9mKM1AFWDMALgUuoH+eq9cT30q/6tZBjSR9vP6L6kVB3OzDV7GgA3QBgBcDlWi6Ff9W+t98f1UL1AHx3KzNPgWan6aPtpORwOs+cBcGGEEQCX1btFqJIT4hTXKlSFJXY9u/wb/fqj3covKjV7GgAXRRgBcGn1An20cGx3/eaeO2QxpE92ndWQWanafz7H7GkAXBBhBMDlWSyGJt/VUksm9FKDIF8dvZSvoUlp+mDLKY7WAFQKYVRBSUlJioqKUrdu3cyeAuAHdG9WV8nT4tT3jnoqKrXr95/uUcKSDOUWlpg9DYCLMBz861Sl5OTkKDg4WNnZ2QoKCjJ7DoCbsNsdmpdyTH//4qDK7A41DfHTrJGd1b5RsNnTAJikot+/eccIgNuxWAw9eWcLffRkLzWqXUsnLhfo/jc3afHmExytAfhRhBEAt9WlSR2tTYhV/7ZhKi6z608rv9XkD3Yqh6M1AD+AMALg1mr7eWveqC7606AoeVkNJe+5oIGJKdp9+prZ0wA4IcIIgNszDEO/jG2m5U/1VkSdWjp95bqGz9mkt1OPc7QG4AaEEQCPER1ZW2sT4nRvuwYqKXPoL2v2acLiHbpWUGz2NABOgjAC4FGCa3lp9mOd9V/3tZO31aJ1+zI1MDFVO09dNXsaACdAGAHwOIZhaFSvpvpkUm81CfHT2WvX9eCczZr79VHZ7RytAZ6MMALgsdo3CtaaqbEa1LGhSu0OvfLZAY1btF1X8jlaAzwVYQTAowX6emnmI5308rAO8rZZ9K8DFxU/I0XbTlwxexoAExBGADyeYRga2aOxVk7uo+b1/HUhp1APv5WupC+PcLQGeBjCCADKtW0YpNVTYjWsUyOV2R169YuDGv3uVmXlFZk9DUANIYwA4H/x97Hp9Qej9ffhHeXrZVHK4SzFz0jR5qOXzZ4GoAYQRgDwfxiGoQe7RmrVlFi1qh+gi7lFenR+ut5Yf0hlHK0Bbo0wAoAf0DosUCun9NGILhGyO6Q31h/W429v0cXcQrOnAagmhBEA/Ag/b5teHRGt1x+Mlp+3VZuOXlb8jBSlHs4yexqAakAYAUAF3N85QqumxKpNg0Bl5RXr8Xe26B9fHFRpmd3saQCqEGEEABXUsn6AVkzuo5E9GsvhkGZ9eUQj52/RhWyO1gB3QRgBQCX4eln18rAOSnykkwJ8bNp6/IriE1P01cGLZk8DUAUIIwC4BUOiw7V6aqzahQfpSn6xxry7TX/97IBKOFoDXBphBAC3qFmovz6e2FujejWRJM35+qgefitdZ69dN3kZgFtFGAHAbfD1suq/7muv2Y92VqCvTTtOXtXAxBSt35dp9jQAt4AwAoAqMKBDQ62dGqfoiGBdKyjRuEXb9dKafSou5WgNcCWEEQBUkcYhflr2VG890aeZJGl+6nGNmLtZp68UmLwMQEURRgBQhbxtFj0/OEpvPd5FQb427T59TfGJKfp873mzpwGoAMIIAKrBL9o1UPK0OHVqXFu5haV66r2demHlXhWVlpk9DcCPIIwAoJpE1PHTR0/20pM/ay5JWrj5pB6YvUknsvJNXgbghxBGAFCNvKwW/S6+rd4Z01V1/Ly092yOBs1M1Zpvzpk9DcBNEEYAUAP6tQlT8rQ4dWtaR3lFpZrywS794dM9KizhaA1wJoQRANSQhsG19OH4npp8VwsZhvT+llMampSmo5fyzJ4GoBxhBAA1yGa16Df3tNHCsd0V4u+tAxdyNXhmqlbsOmv2NAAijADAFD9rXU+fTYtTz+Z1VVBcpl8tzdBvl3+j68UcrQFmIowAwCT1g3z1/riemnZ3KxmGtHT7ad2XlKrDmblmTwM8FmEEACayWgw9/fPWev+XPVQv0EeHMvM0ZFaalm0/bfY0wCMRRgDgBHq3DFVyQpxiW4bqekmZfrP8G03/KEP5RaVmTwM8CmEEAE6iXqCPFj3RXc/8orUshvTJzrMaMitVBy7kmD0N8BiEEQA4EYvF0JR+rfTh+J4KC/LR0Uv5um9Wmj7cekoOh8PseYDbI4wAwAn1aB6i5IQ43dm6nopK7frdJ3s0bUmG8jhaA6oVYQQATiokwEfvjumm5wa0kdViaNXucxqUmKK9Z7PNnga4LcIIAJyYxWLoqTtb6KMneyo82FcnLhfo/tmbtHjzCY7WgGpAGAGAC+jSpK6Sp8Wpf9v6Ki61608rv9XkD3Yqp7DE7GmAWyGMAMBF1Pbz1rxRXfXHgW3lZTWUvOeCBiam6Jsz18yeBrgNwggAXIhhGBoX11zLnuqtiDq1dPrKdT0we5PeST3O0RpQBQgjAHBBMZG1tTYhTve2a6CSMof+a80+Pbl4h7ILOFoDbgdhBAAuKriWl2Y/1ll/HtJO3laL/mdfpuITU7Tz1FWzpwEuizACABdmGIZG926qjyf2VpMQP529dl0PztmstzYeld3O0RpQWYQRALiBDhHBWjM1VgM7NlSp3aGXkw9o3KLtuppfbPY0wKUQRgDgJgJ9vTTrkU7672Ht5W2z6F8HLio+MUXbTlwxexrgMggjAHAjhmHo0R5NtGJSHzUP9df57EI9/Fa6kr48wtEaUAGEEQC4oajwIK2aGquhMeEqszv06hcHNfrdrcrKKzJ7GuDUCCMAcFMBPjb986EY/f2BjvL1sijlcJbiZ6Ro89HLZk8DnBZhBABuzDAMPdgtUqumxKpl/QBdzC3So/PTNWP9YZVxtAZ8D2EEAB6gdVigVk3poxFdImR3SP9cf0iPv71FF3MLzZ4GOBXCCAA8hJ+3Ta+OiNbrD0arlpdVm45eVvyMVKUezjJ7GuA0CCMA8DD3d47Q6qmxatMgUFl5RXr8nS167X8OqrTMbvY0wHSEEQB4oJb1A7Rich890j1SDoc0819HNHL+Fl3I5mgNno0wAgAP5etl1Sv3d9SMh2Pk723V1uNXFJ+Yoq8OXjR7GmAawggAPNx9MY20JiFOUQ2DdCW/WGPe3aa/fX5AJRytwQMRRgAANQv11yeTeuvxnk0kSbO/OqqH30rXuWvXTV4G1CzCCAAg6d9Ha38Z2l5vPtpZgT427Th5VfGJKVq/L9PsaUCNIYwAADeI79BQaxPi1DEiWNcKSjRu0Xa9tGafiks5WoP7I4wAAN/TOMRPy57qpSf6NJMkzU89rhFzN+v0lQKTlwHVizACANyUj82q5wdH6a3HuyjI16bdp69pYGKKPt97wexpQLUhjAAAP+oX7RooeVqcOjWurZzCUj313g69uOpbFZWWmT0NqHKEEQDgJ0XU8dNHT/bShJ81lyQt2HRCw2dv1snL+SYvA6oWYQQAqBAvq0W/j2+rd8Z0VR0/L+05m61Biala+815s6cBVYYwAgBUSr82YUqeFqeuTeoot6hUkz/YqT+u2KPCEo7W4PoIIwBApTUMrqUlE3pqUt8WkqT30k9p2JubdOxSnsnLgNtDGAEAbonNatGz97bRwie6K8TfW/vP52jwzFStzDhr9jTglhFGAIDbcmfrekqeFqeezesqv7hM05Zk6LfLv9H1Yo7W4HoIIwDAbQsL8tX743oq4e5WMgxp6fbTGpqUpiMXc82eBlQKYQQAqBJWi6HpP2+t93/ZQ6EBPjqYmavBM9O0fMcZs6cBFUYYAQCqVO+WofpsWpxiW4bqekmZnlm2W9M/ylBBcanZ04CfRBgBAKpcvUAfLXyiu37989ayGNInO89q8MxUHbiQY/Y04EcRRgCAamG1GJp6dyt9ML6nwoJ8dPRSvu6blaYlW0/J4XCYPQ+4KcIIAFCtejYPUXJCnO5sXU9FpXY998ke/WpphvKKOFqD8yGMAADVLiTAR++O6abf3ttGVouhlRnnNHhmqr49l232NOAGhBEAoEZYLIYm9m2hpRN6qmGwr45n5WvYm5u0OP0kR2twGoQRAKBGdW1aV8kJcbq7TX0Vl9r1pxV7NeWDXcopLDF7GkAYAQBqXh1/b80f3VV/HNhWNouhtXvOa1Biqr45c83safBwHh1Gw4YNU506dTR8+HCzpwCAxzEMQ+PimmvZU73UqHYtnbpSoAdmb9K7acc5WoNpPDqMpk2bpkWLFpk9AwA8WqfGdZScEKd72oWppMyhP6/epycX71B2AUdrqHkeHUZ9+/ZVYGCg2TMAwOMF+3lpzmNd9OLgKHlbLfqffZmKT0zRrlNXzZ4GD3NbYfTXv/5VhmHoV7/6VRXN+beNGzdq8ODBCg8Pl2EYWrFixU2vS0pKUtOmTeXr66sePXpo69atVboDAFBzDMPQmD7N9PHE3mpc109nr13XiDmbNW/jMY7WUGNuOYy2bdumuXPnqmPHjj96XVpamkpKvv926L59+5SZmXnT5+Tn5ys6OlpJSUk/+LpLly7V9OnT9cILL2jnzp2Kjo7WPffco4sXL353TUxMjNq3b/+9X+fOnavgVwkAqGkdIoK1JiFWAzs2VKndof9O3q9xC7fran6x2dPgAW4pjPLy8vToo49q3rx5qlOnzg9eZ7fbNXnyZI0cOVJlZWXfPX7w4EH169dPCxcuvOnzBgwYoJdeeknDhg37wdd+/fXXNX78eI0dO1ZRUVGaM2eO/Pz89M4773x3TUZGhvbu3fu9X+Hh4ZX+mpOSkhQVFaVu3bpV+rkAgMoJ8vXSrEc66aWh7eVts2jDgYuKT0zR9hNXzJ4GN3dLYTR58mQNHDhQ/fv3//EXt1iUnJysXbt2adSoUbLb7Tp69Kj69eunoUOH6tlnn72l0cXFxdqxY8cNn99isah///7avHnzLb3mT5k8ebL27dunbdu2VcvrAwBuZBiGHuvZRJ9O6q1mof46n12oh95K15tfHZHdztEaqkelw2jJkiXauXOnXnnllQpdHx4ern/9619KTU3VyJEj1a9fP/Xv31+zZ8+u9Nj/yMrKUllZmcLCwm54PCwsTBcuXKjw6/Tv318jRoxQcnKyIiIiqi2qAAC3rl14sFZPjdV9MeEqszv0988PasyCbcrKKzJ7GtyQrTIXnz59WtOmTdO6devk6+tb4ec1btxYixcv1p133qnmzZvr7bfflmEYlR5b1davX2/2BABABQT42PTGQzHq3SJEz6/8VhsPXVL8jBQlPtJJPZuHmD0PbqRS7xjt2LFDFy9eVOfOnWWz2WSz2fT1118rMTFRNpvthp8j+t8yMzM1YcIEDR48WAUFBXr66adva3RoaKisVuv3fng7MzNTDRo0uK3XBgA4J8Mw9FC3xlo1JVYt6wfoYm6RRs5LV+KGwyrjaA1VpFJhdPfdd2vPnj3KyMj47lfXrl316KOPKiMjQ1ar9XvPycrK0t133622bdvqk08+0YYNG7R06VI988wztzza29tbXbp00YYNG757zG63a8OGDerVq9ctvy4AwPnd0SBQq6b00QOdI2R3SK+vO6RR72zRxdxCs6fBDVTqKC0wMFDt27e/4TF/f3+FhIR873Hp37EyYMAANWnSREuXLpXNZlNUVJTWrVunfv36qVGjRjd99ygvL09Hjhz57vfHjx9XRkaG6tatq8aNG0uSpk+frtGjR6tr167q3r273njjDeXn52vs2LGV+ZIAAC7Iz9um1x6MVq8WIfrTir1KO3JZ8TNSNePhGPVpGWr2PLiwSoVRZVksFr388suKi4uTt7f3d49HR0dr/fr1qlev3k2ft337dt11113f/X769OmSpNGjR2vBggWSpIceekiXLl3S888/rwsXLigmJkaff/75934gGwDgvoZ3iVBMZLAmv79LBzNz9djbWzT1rpaa1r+1rBbzf5YVrsdw8J8TrZScnBwFBwcrOztbQUFBZs8BAEgqLCnTn1d/qw+3npYk9WhWV4mPdFJYUMX/oBDcW0W/f3v035UGAHAPvl5WvXJ/R814OEb+3lZtOX5FA2ak6OtDl8yeBhdDGAEA3MZ9MY20emqs2jYM0pX8Yo1+Z6v+9vkBlZbZzZ4GF0EYAQDcSvN6Afp0Um893rOJJGn2V0f18FvpOnftusnL4AoIIwCA2/H1suovQ9sraWRnBfrYtP3kVcUnpmjD/pv/5eXAfxBGAAC3NbBjQ61JiFWHRsG6VlCiXy7crv9eu0/FpRyt4eYIIwCAW2sS4q/lE3tpTO+mkqR5Kcf14NzNOn2lwNxhcEqEEQDA7fnYrHpxSDvNfbyLgnxtyjh9TQMTU/TFtxX/i8fhGQgjAIDHuKddA61NiFNMZG3lFJbqycU79OKqb1VUevO/6xOehzACAHiUyLp++ujJXhof10yStGDTCQ2fvVknL+ebvAzOgDACAHgcb5tFfxgYpbdHd1VtPy/tOZutQYmpWvvNebOnwWSEEQDAY93dNkzJCXHq2qSOcotKNfmDnfrjij0qLOFozVMRRgAAjxZeu5Y+nNBTk/q2kCS9l35Kw97cpGOX8kxeBjMQRgAAj+dltejZe9to4RPdVdffW/vP52jwzFStzDhr9jTUMMIIAIByd7aup8+mxalHs7rKLy7TtCUZeu7jb3S9mKM1T0EYAQDwv4QF+er9cT2U0K+lDENasu20hial6cjFXLOnoQYQRgAA/B82q0XTf3GHFj/RQ6EBPjqYmavBM9O0fMcZs6ehmhFGAAD8gNhWoUqeFqs+LUN0vaRMzyzbrV9/tFsFxaVmT0M1IYwAAPgR9QN9teiJHpr+89ayGNLHO89oyKw0HbzA0Zo7IowAAPgJVouhhLtb6YPxPRUW5KMjF/M0ZFaqlm47JYfDYfY8VCHCCACACurZPETJCXH6Wet6Kiq167cf79HTSzOUV8TRmrsgjAAAqISQAB8tGNNNz957h6wWQysyzmnIzFR9ey7b7GmoAoQRAACVZLEYmtS3pZZO6KmGwb46lpWvYW9u0uL0kxytuTjCCACAW9S1aV0lJ8Tp7jb1VVxq159W7NWUD3cpp7DE7Gm4RYQRAAC3oY6/t+aP7qo/xLeVzWJo7TfnNSgxVXvOcLTmiggjAABuk2EYGv+z5vroqV5qVLuWTl0p0AOzN2lB2nGO1lwMYQQAQBXp3LiOkhPi9IuoMBWX2fXi6n166r0dyi7gaM1VEEYAAFShYD8vzX28i14YHCUvq6Evvs3UwJkp2nXqqtnTUAGEEQAAVcwwDI3t00wfT+ytxnX9dObqdY2Ys1nzU45xtObkCCMAAKpJx4jaWpMQq/gODVRqd+iltfs1buF2Xc0vNnsafgBhBABANQry9VLSyM76y9D28rZZtOHARQ1MTNH2E1fMnoabIIwAAKhmhmHo8Z5N9Omk3moW6q9z2YV66K10vfnVEdntHK05E8IIAIAa0i48WKunxuq+mHCV2R36++cHNXbBNl3OKzJ7GsoRRgAA1KAAH5veeChGf72/g3xsFn196JLiE1O05dhls6dBhBEAADXOMAw93L2xVk7poxb1/JWZU6RH5qVr5obDKuNozVSEEQAAJmnTIEirp8bqgc4Rsjuk19Yd0qh3tuhSLkdrZiGMAAAwkZ+3Ta89GK1/jIhWLS+r0o5c1oAZKUo7kmX2NI9EGAEA4ASGd4nQqil91DosQFl5RXrs7S16fd0hjtZqGGEEAICTaBUWqJWTY/Vwt0g5HFLihsN6dH66MnMKzZ7mMQgjAACcSC1vq/76QEfNeDhG/t5WpR+7ovgZKfr60CWzp3kEwggAACd0X0wjrZ4aq7YNg3Q5v1ij39mqv31+QKVldrOnuTXCCAAAJ9W8XoA+ndRbj/VsLEma/dVRPfxWus5du27yMvdFGAEA4MR8vax6aWgHzRrZSQE+Nm0/eVXxiSn614FMs6e5JcIIAAAXMKhjuNYmxKpDo2BdKyjREwu26+Xk/SrhaK1KEUYAALiIJiH+Wj6xl8b0bipJemvjMY2Ys1lnrhaYO8yNEEYAALgQH5tVLw5ppzmPdVGQr00Zp68pfkaKvvj2gtnT3AJhBACAC7q3fQOtTYhTdGRt5RSW6snFO/Tn1d+quJSjtdtBGAEA4KIi6/pp2ZO9ND6umSTp3bQTGj5nk05d5mjtVhFGAAC4MG+bRX8YGKX5o7qqtp+XvjmTrYGJKUrec97saS6JMAIAwA30jwrT2oQ4dWlSR7lFpZr0/k79acVeFZaUmT3NpRBGAAC4iUa1a2nJhJ566s4WkqTF6Sd1/5ubdDwr3+RlroMwAgDAjXhZLXpuQBstGNtNdf29te98jgYlpmhlxlmzp7kEwggAADfU9476Sk6IU/dmdZVfXKZpSzL0u0++4WjtJxBGAAC4qQbBvvpgXA9N7ddShiF9uPW07puVpiMX88ye5rQIIwAA3JjNatGvf3GHFj/RQ6EBPjqYmavBM1P18Y4zZk9zSoQRAAAeILZVqJKnxap3ixBdLynTr5ft1jPLdquguNTsaU6FMAIAwEPUD/TV4l/20NP9W8tiSMt3nNF9s9J0KDPX7GlOgzACAMCDWC2GpvVvpffH9VT9QB8dvpinIbNStXTbKTkcDrPnmY4wAgDAA/VqEaLkaXGKaxWqwhK7fvvxHj29NEN5RZ59tEYYAQDgoUIDfLRwbHf95p47ZLUYWpFxTkNmpmrfuRyzp5mGMAIAwINZLIYm39VSSyb0VIMgXx3LytfQN9P0XvpJjzxaI4wAAIC6Na2r5Glx6temvopL7frjir2a8uEu5RaWmD2tRhFGAABAklTX31vzR3XV7+PbyGYxtPab8xo0M1V7zmSbPa3GEEYAAOA7FouhCT9roY+e6qVGtWvp5OUCPTB7kxakHfeIozXCCAAAfE/nxnWUnBCnn0eFqbjMrhdX79PE93Yq+7p7H60RRgAA4KaC/bz01uNd9PygKHlZDX3+7QUNTExRxulrZk+rNoQRAAD4QYZh6InYZlr+VG9F1q2lM1eva/jsTZqfcswtj9YIIwAA8JOiI2trbUKc4js0UKndoZfW7tf4Rdt1raDY7GlVijACAAAVEuTrpaSRnfWX+9rJ22rR+v0XFT8jRTtOXjF7WpUhjAAAQIUZhqHHezXVJ5N6q2mIn85lF+rBuema/dVR2e2uf7RGGAEAgEpr3yhYaxLiNCQ6XGV2h/72+QE9sXCbLucVmT3tthBGAADglgT42DTj4Ri9cn8H+dgs+urgJcUnpmjLsctmT7tlhBEAALhlhmHoke6NtXJKH7Wo56/MnCI9Mi9dMzccVpkLHq0RRgAA4La1aRCkVVNidX/nRrI7pNfWHdLod7bqUq5rHa0RRgAAoEr4+9j0+oMxenV4R9Xysir1SJbiE1O06UiW2dMqjDACAABVakTXSK2a0ketwwJ0KbdIj769Ra+vO+QSR2uEEQAAqHKtwgK1cnKsHuoaKYdDStxwWI/OT1dmTqHZ034UYQQAAKpFLW+r/ja8o954KEZ+3lalH7ui+Bkp2njoktnTfhBhBAAAqtXQTo20emqs2jQI1OX8Yo16Z6v+/vkBlZbZzZ72PYQRAACodi3qBWjF5D56tEdjSdKbXx3VI/PSdT77usnLbkQYAQCAGuHrZdV/D+ugmY90UoCPTdtOXFX8jBR9eeCi2dO+QxgBAIAaNTg6XGumxqp9oyBdLSjR2AXb9EryfpU4wdEaYQQAAGpc01B/fTyxt8b0bipJmrvxmB6cu1lnrhaYuoswAgAApvCxWfXikHaa81hnBfratOvUNQ1MTNXes9mmbbKZ9pkBAAAk3du+odqFB2vKBztVaneoZf0A07YQRgAAwHSRdf207KneulZQLF8vq2k7OEoDAABOwdtmUf0gX1M3EEYAAADlCCMAAIByhBEAAEA5wggAAKAcYQQAAFCOMAIAAChHGAEAAJQjjAAAAMoRRgAAAOUIIwAAgHKEEQAAQDnCCAAAoBxhBAAAUM5m9gBX43A4JEk5OTkmLwEAABX1n+/b//k+/kMIo0rKzc2VJEVGRpq8BAAAVFZubq6Cg4N/8OOG46fSCTew2+06d+6cAgMDZRjGTa/p1q2btm3bVqmP5eTkKDIyUqdPn1ZQUFCVbq5OP/a1OvPnutXXquzzKnp9Ra77qWtu9nFXva+kmru3uK8qf19JrntvedJ9dSvPrap7yxnvK4fDodzcXIWHh8ti+eGfJOIdo0qyWCyKiIj40WusVusP/h/6Yx+TpKCgIJf6h8xPfT3O+rlu9bUq+7yKXl+R637qmh/7uKvdV1LN3VvcV7d+X0mud2950n11K8+tqnvLWe+rH3un6D/44etqMHny5Fv6mCuqya+nKj/Xrb5WZZ9X0esrct1PXcO9Zf7n4b5yfp50X93Kc6vq3nLl+4qjNCeRk5Oj4OBgZWdnu9S/fcG5cV+hunBvoTo4w33FO0ZOwsfHRy+88IJ8fHzMngI3wn2F6sK9hergDPcV7xgBAACU4x0jAACAcoQRAABAOcIIAACgHGEEAABQjjACAAAoRxi5iGHDhqlOnToaPny42VPgJk6fPq2+ffsqKipKHTt21LJly8yeBDdw7do1de3aVTExMWrfvr3mzZtn9iS4kYKCAjVp0kTPPPNMtX0O/ri+i/jqq6+Um5urhQsXavny5WbPgRs4f/68MjMzFRMTowsXLqhLly46dOiQ/P39zZ4GF1ZWVqaioiL5+fkpPz9f7du31/bt2xUSEmL2NLiBP/zhDzpy5IgiIyP1j3/8o1o+B+8YuYi+ffsqMDDQ7BlwIw0bNlRMTIwkqUGDBgoNDdWVK1fMHQWXZ7Va5efnJ0kqKiqSw+EQ//6NqnD48GEdOHBAAwYMqNbPQxjVgI0bN2rw4MEKDw+XYRhasWLF965JSkpS06ZN5evrqx49emjr1q01PxQupSrvqx07dqisrEyRkZHVvBrOriruq2vXrik6OloRERH6zW9+o9DQ0BpaD2dVFffVM888o1deeaXatxJGNSA/P1/R0dFKSkq66ceXLl2q6dOn64UXXtDOnTsVHR2te+65RxcvXqzhpXAlVXVfXblyRaNGjdJbb71VE7Ph5Krivqpdu7Z2796t48eP64MPPlBmZmZNzYeTut37auXKlWrdurVat25d/WMdqFGSHJ9++ukNj3Xv3t0xefLk735fVlbmCA8Pd7zyyis3XPfll186HnjggZqYCRdzq/dVYWGhIy4uzrFo0aKamgoXcjv/vPqPiRMnOpYtW1adM+FibuW+eu655xwRERGOJk2aOEJCQhxBQUGOP//5z9Wyj3eMTFZcXKwdO3aof//+3z1msVjUv39/bd682cRlcGUVua8cDofGjBmjfv366fHHHzdrKlxIRe6rzMxM5ebmSpKys7O1ceNG3XHHHabshWuoyH31yiuv6PTp0zpx4oT+8Y9/aPz48Xr++eerZQ9hZLKsrCyVlZUpLCzshsfDwsJ04cKF737fv39/jRgxQsnJyYqIiCCa8KMqcl+lpaVp6dKlWrFihWJiYhQTE6M9e/aYMRcuoiL31cmTJxUXF6fo6GjFxcVp6tSp6tChgxlz4SIq+n2wpthq/DPilqxfv97sCXAzsbGxstvtZs+Am+nevbsyMjLMngE3NmbMmGp9fd4xMlloaKisVuv3fjgxMzNTDRo0MGkVXB33FaoD9xWqg7PdV4SRyby9vdWlSxdt2LDhu8fsdrs2bNigXr16mbgMroz7CtWB+wrVwdnuK47SakBeXp6OHDny3e+PHz+ujIwM1a1bV40bN9b06dM1evRode3aVd27d9cbb7yh/Px8jR071sTVcHbcV6gO3FeoDi51X1XLn3XDDb788kuHpO/9Gj169HfXzJw509G4cWOHt7e3o3v37o709HTzBsMlcF+hOnBfoTq40n3F35UGAABQjp8xAgAAKEcYAQAAlCOMAAAAyhFGAAAA5QgjAACAcoQRAABAOcIIAACgHGEEAABQjjACAAAoRxgBAACUI4wAAADKEUYAAADl/h+ejCofucuyDgAAAABJRU5ErkJggg==", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "from matplotlib.ticker import ScalarFormatter\n", + "import numpy as np\n", + "\n", + "x = np.linspace(10, 10000, 1000)\n", + "y = x**(-1/8)\n", + "\n", + "# plot = sns.lineplot(data=new_results, x=[0.0,1.0, 2.0], y=\"result\", hue=\"sampler\", style=\"integrator\")\n", + "plot = sns.lineplot(x=x,y=y)\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plot.set(xscale='log')\n", + "plot.set(yscale='log')\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_rateESS
    0Brownian32mhmchmc0.8256570.750598yoshidastandard0.8338760.002976
    1Brownian32mhmchmc:grid2.3406630.752317yoshidagridsearch:False0.7529970.006362
    2Brownian32nuts0.0000000.000000yoshidastandard0.9868760.001166
    3Brownian32mhmchmc0.8098400.628400mclachlanstandard0.5471720.001795
    4Brownian32mhmchmc:grid2.3151910.335869mclachlangridsearch:True0.8802400.006661
    5Brownian32nuts0.0000000.000000mclachlanstandard0.9712950.001983
    6Brownian32mhmchmc0.8138400.353636leapfrogstandard0.5769100.002605
    7Brownian32mhmchmc:grid3.1143920.223177leapfroggridsearch:True0.7953070.005479
    8Brownian32nuts0.0000000.000000leapfrogstandard0.7902460.003271
    9Brownian32mhmchmc0.8223560.746532omelyanstandard0.8191120.001972
    10Brownian32mhmchmc:grid2.2350810.507973omelyangridsearch:True0.9150360.003454
    11Brownian32nuts0.0000000.000000omelyanstandard0.9950360.000707
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \n", + "0 Brownian 32 mhmchmc 0.825657 0.750598 yoshida \\\n", + "1 Brownian 32 mhmchmc:grid 2.340663 0.752317 yoshida \n", + "2 Brownian 32 nuts 0.000000 0.000000 yoshida \n", + "3 Brownian 32 mhmchmc 0.809840 0.628400 mclachlan \n", + "4 Brownian 32 mhmchmc:grid 2.315191 0.335869 mclachlan \n", + "5 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "6 Brownian 32 mhmchmc 0.813840 0.353636 leapfrog \n", + "7 Brownian 32 mhmchmc:grid 3.114392 0.223177 leapfrog \n", + "8 Brownian 32 nuts 0.000000 0.000000 leapfrog \n", + "9 Brownian 32 mhmchmc 0.822356 0.746532 omelyan \n", + "10 Brownian 32 mhmchmc:grid 2.235081 0.507973 omelyan \n", + "11 Brownian 32 nuts 0.000000 0.000000 omelyan \n", + "\n", + " tuning acc_rate ESS \n", + "0 standard 0.833876 0.002976 \n", + "1 gridsearch:False 0.752997 0.006362 \n", + "2 standard 0.986876 0.001166 \n", + "3 standard 0.547172 0.001795 \n", + "4 gridsearch:True 0.880240 0.006661 \n", + "5 standard 0.971295 0.001983 \n", + "6 standard 0.576910 0.002605 \n", + "7 gridsearch:True 0.795307 0.005479 \n", + "8 standard 0.790246 0.003271 \n", + "9 standard 0.819112 0.001972 \n", + "10 gridsearch:True 0.915036 0.003454 \n", + "11 standard 0.995036 0.000707 " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# import jax\n", + "\n", + "\n", + "# def pvmap(f,arr):\n", + "# arr = arr.reshape(128, -1)\n", + "# out = jax.vmap(jax.vmap(f), in_axes=0)(arr)\n", + "# return out.flatten()\n", + "\n", + "# arr = jnp.linspace(0, 255, 128*2,)\n", + "# pvmap(lambda x:x**2, arr)\n", + "\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "\n", + "# Load the data\n", + "results = pd.read_csv(\"../../../results.csv\")\n", + "# results.drop(results['tuning'] == \"standard\", inplace=True)\n", + "# results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + "\n", + "\n", + "sns.barplot(data=results[results['model'] == 'Brownian'], x=\"sampler\", y=\"ESS\", hue='integrator')\n", + "plt.xlabel(\"Sampler\")\n", + "plt.ylabel(\"ESS\")\n", + "plt.show()\n", + "\n", + "results\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_rateESS
    0Brownian32mhmchmc0.8079030.647126mclachlanstandard0.5212960.001768
    1Brownian32mhmchmc:grid2.2574780.362790mclachlangridsearch:True0.8529670.005831
    2Brownian32nuts0.0000000.000000mclachlanstandard0.9720400.001614
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \n", + "0 Brownian 32 mhmchmc 0.807903 0.647126 mclachlan \\\n", + "1 Brownian 32 mhmchmc:grid 2.257478 0.362790 mclachlan \n", + "2 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "\n", + " tuning acc_rate ESS \n", + "0 standard 0.521296 0.001768 \n", + "1 gridsearch:True 0.852967 0.005831 \n", + "2 standard 0.972040 0.001614 " + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = pd.read_csv(\"../../../results.csv\")\n", + "# results.drop(results['tuning'] == \"standard\", inplace=True)\n", + "# results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + "\n", + "\n", + "sns.barplot(data=results[results['model'] == 'Brownian'], x=\"sampler\", y=\"ESS\", hue='integrator')\n", + "plt.xlabel(\"Sampler\")\n", + "plt.ylabel(\"ESS\")\n", + "plt.show()\n", + "\n", + "results\n" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_rateESS
    0Brownian32mhmchmc0.8311680.738313yoshidastandard0.8338030.002909
    1Brownian32nuts0.0000000.000000yoshidastandard0.9868760.001166
    2Brownian32mhmchmc0.8198340.456615mclachlanstandard0.7747950.002402
    3Brownian32nuts0.0000000.000000mclachlanstandard0.9712950.001983
    4Brownian32mhmchmc0.8023810.226060leapfrogstandard0.8124160.002878
    5Brownian32nuts0.0000000.000000leapfrogstandard0.7902460.003271
    6Brownian32mhmchmc0.8292480.717247omelyanstandard0.8309550.001896
    7Brownian32nuts0.0000000.000000omelyanstandard0.9950360.000707
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator tuning \n", + "0 Brownian 32 mhmchmc 0.831168 0.738313 yoshida standard \\\n", + "1 Brownian 32 nuts 0.000000 0.000000 yoshida standard \n", + "2 Brownian 32 mhmchmc 0.819834 0.456615 mclachlan standard \n", + "3 Brownian 32 nuts 0.000000 0.000000 mclachlan standard \n", + "4 Brownian 32 mhmchmc 0.802381 0.226060 leapfrog standard \n", + "5 Brownian 32 nuts 0.000000 0.000000 leapfrog standard \n", + "6 Brownian 32 mhmchmc 0.829248 0.717247 omelyan standard \n", + "7 Brownian 32 nuts 0.000000 0.000000 omelyan standard \n", + "\n", + " acc_rate ESS \n", + "0 0.833803 0.002909 \n", + "1 0.986876 0.001166 \n", + "2 0.774795 0.002402 \n", + "3 0.971295 0.001983 \n", + "4 0.812416 0.002878 \n", + "5 0.790246 0.003271 \n", + "6 0.830955 0.001896 \n", + "7 0.995036 0.000707 " + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "\n", + "# Load the data\n", + "results = pd.read_csv(\"../../../results.csv\")\n", + "# results.drop(results['tuning'] == \"standard\", inplace=True)\n", + "# results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + "\n", + "\n", + "sns.barplot(data=results[results['model'] == 'Brownian'], x=\"sampler\", y=\"ESS\", hue='integrator')\n", + "plt.xlabel(\"Sampler\")\n", + "plt.ylabel(\"ESS\")\n", + "plt.show()\n", + "\n", + "results\n" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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    modeldimssamplerLstep_sizeintegratortuningacc_rateESS
    0Brownian32mhmchmc0.8205900.394371mclachlanstandard0.8519040.002714
    1Brownian32mhmchmc:stage3=True1.5808530.358947mclachlanstandard0.8634870.005621
    2Brownian32nuts0.0000000.000000mclachlanstandard0.9720400.001614
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \n", + "0 Brownian 32 mhmchmc 0.820590 0.394371 mclachlan \\\n", + "1 Brownian 32 mhmchmc:stage3=True 1.580853 0.358947 mclachlan \n", + "2 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "\n", + " tuning acc_rate ESS \n", + "0 standard 0.851904 0.002714 \n", + "1 standard 0.863487 0.005621 \n", + "2 standard 0.972040 0.001614 " + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "\n", + "# Load the data\n", + "results = pd.read_csv(\"../../../results.csv\")\n", + "# results.drop(results['tuning'] == \"standard\", inplace=True)\n", + "# results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + "\n", + "\n", + "sns.barplot(data=results[results['model'] == 'Brownian'], x=\"sampler\", y=\"ESS\", hue='integrator')\n", + "plt.xlabel(\"Sampler\")\n", + "plt.ylabel(\"ESS\")\n", + "plt.show()\n", + "\n", + "results\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_rateESS
    0GermanCredit51mclmc12.6271720.408990mclachlanstandard1.0000000.001381
    1GermanCredit51mhmchmc4.7949090.447311mclachlanstandard0.9016910.000645
    2GermanCredit51mhmchmc:stage3=True4.5814090.448717mclachlanstandard0.8936560.000344
    3GermanCredit51nuts0.0000000.000000mclachlanstandard0.9536670.000264
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \n", + "0 GermanCredit 51 mclmc 12.627172 0.408990 mclachlan \\\n", + "1 GermanCredit 51 mhmchmc 4.794909 0.447311 mclachlan \n", + "2 GermanCredit 51 mhmchmc:stage3=True 4.581409 0.448717 mclachlan \n", + "3 GermanCredit 51 nuts 0.000000 0.000000 mclachlan \n", + "\n", + " tuning acc_rate ESS \n", + "0 standard 1.000000 0.001381 \n", + "1 standard 0.901691 0.000645 \n", + "2 standard 0.893656 0.000344 \n", + "3 standard 0.953667 0.000264 " + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "\n", + "# Load the data\n", + "results = pd.read_csv(\"../../../results.csv\")\n", + "# results.drop(results['tuning'] == \"standard\", inplace=True)\n", + "# results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + "\n", + "\n", + "sns.barplot(data=results[results['model'] == 'GermanCredit'], x=\"sampler\", y=\"ESS\", hue='integrator')\n", + "plt.xlabel(\"Sampler\")\n", + "plt.ylabel(\"ESS\")\n", + "plt.show()\n", + "\n", + "results\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_rateESS
    0Brownian32mclmc2.5947040.339297mclachlanstandard1.0000000.005389
    1Brownian32mhmchmc0.650.8296620.629229mclachlanstandard0.5063670.000000
    2Brownian32mhmchmc:st30.651.6593240.537622mclachlanstandard0.6202040.002503
    3Brownian32mhmchmc0.90.8057150.427008mclachlanstandard0.8420540.017892
    4Brownian32mhmchmc:st30.91.6114300.371866mclachlanstandard0.8648160.005585
    5Brownian32mhmchmc:grid1.3053170.434906mclachlangridsearch:True0.8027600.005758
    6Brownian32nuts0.0000000.000000mclachlanstandard0.9743540.000475
    7Brownian32mclmc2.3702140.135512leapfrogstandard1.0000000.006009
    8Brownian32mhmchmc0.650.8154680.372212leapfrogstandard0.5668640.007116
    9Brownian32mhmchmc:st30.651.6309370.443114leapfrogstandard0.3589540.000374
    10Brownian32mhmchmc0.90.8039800.193119leapfrogstandard0.8797240.009013
    11Brownian32mhmchmc:st30.91.1997460.161762leapfrogstandard0.9041640.003993
    12Brownian32mhmchmc:grid1.1331350.323754leapfroggridsearch:True0.6237280.000456
    13Brownian32nuts0.0000000.000000leapfrogstandard0.7984390.003458
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \n", + "0 Brownian 32 mclmc 2.594704 0.339297 mclachlan \\\n", + "1 Brownian 32 mhmchmc0.65 0.829662 0.629229 mclachlan \n", + "2 Brownian 32 mhmchmc:st30.65 1.659324 0.537622 mclachlan \n", + "3 Brownian 32 mhmchmc0.9 0.805715 0.427008 mclachlan \n", + "4 Brownian 32 mhmchmc:st30.9 1.611430 0.371866 mclachlan \n", + "5 Brownian 32 mhmchmc:grid 1.305317 0.434906 mclachlan \n", + "6 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "7 Brownian 32 mclmc 2.370214 0.135512 leapfrog \n", + "8 Brownian 32 mhmchmc0.65 0.815468 0.372212 leapfrog \n", + "9 Brownian 32 mhmchmc:st30.65 1.630937 0.443114 leapfrog \n", + "10 Brownian 32 mhmchmc0.9 0.803980 0.193119 leapfrog \n", + "11 Brownian 32 mhmchmc:st30.9 1.199746 0.161762 leapfrog \n", + "12 Brownian 32 mhmchmc:grid 1.133135 0.323754 leapfrog \n", + "13 Brownian 32 nuts 0.000000 0.000000 leapfrog \n", + "\n", + " tuning acc_rate ESS \n", + "0 standard 1.000000 0.005389 \n", + "1 standard 0.506367 0.000000 \n", + "2 standard 0.620204 0.002503 \n", + "3 standard 0.842054 0.017892 \n", + "4 standard 0.864816 0.005585 \n", + "5 gridsearch:True 0.802760 0.005758 \n", + "6 standard 0.974354 0.000475 \n", + "7 standard 1.000000 0.006009 \n", + "8 standard 0.566864 0.007116 \n", + "9 standard 0.358954 0.000374 \n", + "10 standard 0.879724 0.009013 \n", + "11 standard 0.904164 0.003993 \n", + "12 gridsearch:True 0.623728 0.000456 \n", + "13 standard 0.798439 0.003458 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "\n", + "# Load the data\n", + "results = pd.read_csv(\"../../../results.csv\")\n", + "# results.drop(results['tuning'] == \"standard\", inplace=True)\n", + "# results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + "\n", + "# plt.rcParams[\"figure.figsize\"] = (600, 400)\n", + "\n", + "fig, ax = plt.subplots(figsize=(20, 5))\n", + "\n", + "# sns.set_context(\"paper\", rc={\"figure.figsize\": (20, 6)})\n", + "sns.barplot(data=results, x=\"sampler\", y=\"ESS\", hue='integrator',ax=ax)\n", + "plt.xlabel(\"Sampler\")\n", + "plt.ylabel(\"ESS\")\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mclmc", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py new file mode 100644 index 0000000..6f8f133 --- /dev/null +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -0,0 +1,190 @@ + + +import jax +import jax.numpy as jnp +import blackjax +from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState +# from blackjax.adaptation.window_adaptation import da_adaptation +from blackjax.mcmc.integrators import calls_per_integrator_step, generate_euclidean_integrator, generate_isokinetic_integrator +# from blackjax.mcmc.mhmclmc import rescale +from blackjax.util import run_inference_algorithm +import blackjax + +__all__ = ["samplers"] + + + + +def run_nuts( + coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning): + + integrator = generate_euclidean_integrator(coefficients) + # integrator = blackjax.mcmc.integrators.velocity_verlet # note: defaulted to in nuts + + rng_key, warmup_key = jax.random.split(key, 2) + + if not preconditioning: + state, params = da_adaptation( + rng_key=warmup_key, + initial_position=initial_position, + algorithm=blackjax.nuts, + integrator=integrator, + logdensity_fn=logdensity_fn) + + else: + # print(params["inverse_mass_matrix"], "inv\n\n") + warmup = blackjax.window_adaptation(blackjax.nuts, logdensity_fn, integrator=integrator) + (state, params), _ = warmup.run(warmup_key, initial_position, 2000) + + nuts = blackjax.nuts(logdensity_fn=logdensity_fn, step_size=params['step_size'], inverse_mass_matrix= params['inverse_mass_matrix'], integrator=integrator) + + final_state, state_history, info_history = run_inference_algorithm( + rng_key=rng_key, + initial_state=state, + inference_algorithm=nuts, + num_steps=num_steps, + transform=lambda x: transform(x.position), + progress_bar=True + ) + + # print("INFO\n\n",info_history.num_integration_steps) + + return state_history, params, info_history.num_integration_steps.mean() * calls_per_integrator_step(coefficients), info_history.acceptance_rate.mean(), None, None + +def run_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning): + + integrator = generate_isokinetic_integrator(coefficients) + + init_key, tune_key, run_key = jax.random.split(key, 3) + + + initial_state = blackjax.mcmc.mclmc.init( + position=initial_position, logdensity_fn=logdensity_fn, rng_key=init_key + ) + + + kernel = lambda std_mat : blackjax.mcmc.mclmc.build_kernel( + logdensity_fn=logdensity_fn, + integrator=integrator, + std_mat=std_mat, + ) + + ( + blackjax_state_after_tuning, + blackjax_mclmc_sampler_params, + ) = blackjax.mclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + diagonal_preconditioning=preconditioning, + # desired_energy_var= 1e-5 + ) + + # jax.debug.print("params {x}", x=(blackjax_mclmc_sampler_params.L, blackjax_mclmc_sampler_params.step_size)) + + sampling_alg = blackjax.mclmc( + logdensity_fn, + L=blackjax_mclmc_sampler_params.L, + step_size=blackjax_mclmc_sampler_params.step_size, + std_mat=blackjax_mclmc_sampler_params.std_mat, + integrator = integrator, + + # std_mat=jnp.ones((initial_position.shape[0],)), + ) + + _, samples, _ = run_inference_algorithm( + rng_key=run_key, + initial_state=blackjax_state_after_tuning, + inference_algorithm=sampling_alg, + num_steps=num_steps, + transform=lambda x: transform(x.position), + progress_bar=True, + ) + + acceptance_rate = 1. + return samples, blackjax_mclmc_sampler_params, calls_per_integrator_step(coefficients), acceptance_rate, None, None + + +def run_mhmclmc(coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, target_acc_rate=None): + integrator = generate_isokinetic_integrator(coefficients) + + init_key, tune_key, run_key = jax.random.split(key, 3) + + initial_state = blackjax.mcmc.mhmclmc.init( + position=initial_position, logdensity_fn=logdensity_fn, random_generator_arg=init_key + ) + + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.mhmclmc.build_kernel( + integrator=integrator, + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), + std_mat=std_mat, + )( + rng_key=rng_key, + state=state, + step_size=step_size, + logdensity_fn=logdensity_fn) + + if target_acc_rate is None: + target_acc_rate = target_acceptance_rate_of_order[integrator_order(coefficients)] + print("target acc rate") + + ( + blackjax_state_after_tuning, + blackjax_mclmc_sampler_params, + params_history, + final_da + ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + target=target_acc_rate, + frac_tune1=frac_tune1, + frac_tune2=frac_tune2, + frac_tune3=frac_tune3, + diagonal_preconditioning=preconditioning, + ) + + + + step_size = blackjax_mclmc_sampler_params.step_size + L = blackjax_mclmc_sampler_params.L + # jax.debug.print("params {x}", x=(blackjax_mclmc_sampler_params.step_size, blackjax_mclmc_sampler_params.L)) + + + alg = blackjax.mcmc.mhmclmc.mhmclmc( + logdensity_fn=logdensity_fn, + step_size=step_size, + integration_steps_fn = lambda key: jnp.ceil(jax.random.uniform(key) * rescale(L/step_size)) , + integrator=integrator, + std_mat=blackjax_mclmc_sampler_params.std_mat, + + + ) + + + _, out, info = run_inference_algorithm( + rng_key=run_key, + initial_state=blackjax_state_after_tuning, + inference_algorithm=alg, + num_steps=num_steps, + transform=lambda x: transform(x.position), + progress_bar=True) + + + + return out, blackjax_mclmc_sampler_params, calls_per_integrator_step(coefficients) * (L/step_size), info.acceptance_rate, params_history, final_da + +# we should do at least: mclmc, nuts, unadjusted hmc, mhmclmc, langevin + +samplers = { + 'nuts' : run_nuts, + 'mclmc' : run_mclmc, + 'mhmclmc': run_mhmclmc, + } + + +# foo = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(20.56)) + +# print(jnp.mean(jax.vmap(foo)(jax.random.split(jax.random.PRNGKey(1), 10000000)))) \ No newline at end of file diff --git a/build/lib/benchmarks/IRT.py b/build/lib/benchmarks/IRT.py new file mode 100644 index 0000000..deebca3 --- /dev/null +++ b/build/lib/benchmarks/IRT.py @@ -0,0 +1,131 @@ +import inference_gym.using_jax as gym +import jax +import jax.numpy as jnp +import numpy as np +import os + +from HMC.mchmc_to_numpyro import mchmc_target_to_numpyro +#from NUTS import sample_nuts + +dirr = os.path.dirname(os.path.realpath(__file__)) + +target_base = gym.targets.SyntheticItemResponseTheory() +name= 'IRT' + +target = gym.targets.VectorModel(target_base, flatten_sample_transformations=True) +prior_distribution = target_base.prior_distribution() + +identity_fn = target.sample_transformations['identity'] + + +def target_nlog_prob_fn(z): + x = target.default_event_space_bijector(z) + return -(target.unnormalized_log_prob(x) + target.default_event_space_bijector.forward_log_det_jacobian(z, event_ndims=1)) + +target_nlog_prob_grad_fn = jax.grad(target_nlog_prob_fn) + + + +class Target(): + + def __init__(self): + self.d = 501 + self.name= name + + data = np.load(dirr+'/ground_truth/'+name+'/ground_truth.npy') + self.second_moments, self.variance_second_moments = data[0], data[1] + + #xmap = np.load(dirr+'/ground_truth/'+name+'/map.npy') + self.transform = lambda x: target.default_event_space_bijector(x) + self.nlogp = lambda x: target_nlog_prob_fn(x) + self.grad_nlogp = lambda x: (target_nlog_prob_fn(x), target_nlog_prob_grad_fn(x)) + + # def prior_draw(self, key): + # return jnp.zeros(self.d) + + def prior_draw(self, key): + + x = prior_distribution.sample(seed=key) + question = x['question_difficulty'] + meanstudent = x['mean_student_ability'] + student = x['centered_student_ability'] + + return jnp.concatenate((question, meanstudent * jnp.ones(1), student)) + + + +def map_solution(): + + def map_objective_fn(z): + x = target.default_event_space_bijector(z) + return -target.unnormalized_log_prob(x) + + map_objective_grad_fn = jax.grad(map_objective_fn) + + + # MAP solution + + def optimize(z_init, objective_fn, objective_grad_fn, learning_rate, num_steps): + def opt_step(z): + objective = objective_fn(z) + z = z - learning_rate * objective_grad_fn(z) + return z, objective + + return jax.lax.scan(lambda z, _: opt_step(z), init=z_init, xs=None, length=num_steps) + + + z_map, objective_trace = optimize( + z_init=jnp.zeros(target.default_event_space_bijector.inverse_event_shape(target.event_shape)), + objective_fn=map_objective_fn, objective_grad_fn=map_objective_grad_fn, learning_rate=0.0002, num_steps=1000, ) + + import matplotlib.pyplot as plt + plt.plot(objective_trace - objective_trace[-1], '.-') + plt.ylabel('Loss') + plt.xlabel('Iteration') + plt.show() + + np.save('ground_truth/'+name+'/map.npy', z_map) + + + +def ground_truth(key_num): + key = jax.random.PRNGKey(key_num) + mchmc_target = Target() + numpyro_taget = mchmc_target_to_numpyro(Target) + + samples, steps, steps_warmup = sample_nuts(numpyro_taget, mchmc_target, None, 10000, 10000, 20, random_key=key, progress_bar= True) + + z = np.array(samples['x']) + x = jax.vmap(mchmc_target.transform)(z) + + second_moments = jnp.average(jnp.square(x), axis = 0) + variance_second_moments = jnp.std(jnp.square(x), axis = 0)**2 + + np.save('ground_truth/'+name+'/ground_truth_'+str(key_num) +'.npy', [second_moments, variance_second_moments]) + + + +def joint_ground_truth(): + + data = np.array([np.load('ground_truth/'+name+'/ground_truth_'+str(i)+'.npy') for i in range(3)]) + + truth = np.median(data, axis = 0) + np.save('ground_truth/'+name+'/ground_truth.npy', truth) + + for i in range(3): + bias_d = np.square(data[i, 0] - truth[0]) / truth[1] + print(np.sqrt(np.average(bias_d)), np.sqrt(np.max(bias_d))) + + +if __name__ == '__main__': + + kkey = jax.random.PRNGKey(0) + key = jax.random.split(kkey, 100) + t = Target() + + x = jax.vmap(t.prior_draw)(key) + g = jax.vmap(lambda x: t.grad_nlogp(x)[1])(x) + + print(jnp.average(x * g, axis=0)) + + #Target().prior_draw(jax.random.PRNGKey(0)) diff --git a/mclmc/__init__.py b/build/lib/benchmarks/__init__.py old mode 100755 new mode 100644 similarity index 100% rename from mclmc/__init__.py rename to build/lib/benchmarks/__init__.py diff --git a/benchmarks/targets.py b/build/lib/benchmarks/benchmarks_mchmc.py similarity index 72% rename from benchmarks/targets.py rename to build/lib/benchmarks/benchmarks_mchmc.py index 83538f7..f094647 100644 --- a/benchmarks/targets.py +++ b/build/lib/benchmarks/benchmarks_mchmc.py @@ -7,17 +7,17 @@ - class StandardNormal(): """Standard Normal distribution in d dimensions""" def __init__(self, d): self.d = d - self.second_moments = jnp.ones(d) - self.variance_second_moments = 2 * self.second_moments + self.variance = jnp.ones(d) self.grad_nlogp = jax.value_and_grad(self.nlogp) - self.name= 'stn' + self.second_moments = jnp.ones(d) + self.variance_second_moments = 2 * self.second_moments + def nlogp(self, x): """- log p of the target distribution""" @@ -32,6 +32,7 @@ def prior_draw(self, key): + class IllConditionedGaussian(): """Gaussian distribution. Covariance matrix has eigenvalues equally spaced in log-space, going from 1/condition_bnumber^1/2 to condition_number^1/2.""" @@ -40,7 +41,7 @@ def __init__(self, d, condition_number, numpy_seed=None, prior= 'prior'): """numpy_seed is used to generate a random rotation for the covariance matrix. If None, the covariance matrix is diagonal.""" - self.name = 'icg' + self.name = 'ICG_easy' self.d = d self.condition_number = condition_number eigs = jnp.logspace(-0.5 * jnp.log10(condition_number), 0.5 * jnp.log10(condition_number), d) @@ -93,7 +94,6 @@ def __init__(self): self.grad_nlogp = jax.value_and_grad(self.nlogp) - def nlogp(self, x): """- log p of the target distribution""" return 0.5 * jnp.sum(jnp.square(x) / self.variance, axis= -1) @@ -262,25 +262,40 @@ def prior_draw(self, key): class BiModal(): - """A Gaussian mixture p(x) = f N(x | mu1, sigma1) + (1-f) N(x | mu2, sigma2).""" + """A Gaussian mixture p(x) = (1-f) N(x | 0, sigma1) + f N(x | mu, sigma2).""" - def __init__(self, d = 50, mu1 = 0.0, mu2 = 8.0, sigma1 = 1.0, sigma2 = 1.0, f = 0.2): + def __init__(self, d = 50, mu = 8.0, sigma1 = 1.0, sigma2 = 1.0, f = 0.2): self.d = d - self.mu1 = jnp.insert(jnp.zeros(d-1), 0, mu1) - self.mu2 = jnp.insert(jnp.zeros(d - 1), 0, mu2) + self.mu = jnp.insert(jnp.zeros(d - 1), 0, mu) self.sigma1, self.sigma2 = sigma1, sigma2 self.f = f - self.variance = jnp.insert(jnp.ones(d-1) * ((1 - f) * sigma1**2 + f * sigma2**2), 0, (1-f)*(sigma1**2 + mu1**2) + f*(sigma2**2 + mu2**2)) + + # ground truth moments + vx1, vx2 = sigma1**2, sigma2**2 + mu**2 # E[x^2] of the modes + vy1, vy2 = sigma1**2, sigma2**2 # E[y^2] of the modes + vx = (1-f)* vx1 + f * vx2 # E[x^2] + vy = (1 - f) * vy1 + f * vy2 # E[y^2] + + Vx1, Vx2 = 2 * sigma1**4, 2 * sigma2**4 + 4 * mu**2 * sigma2**2 # Var[x^2] of the modes + Vy1, Vy2 = 2 * sigma1**4, 2 * sigma2**4 # Var[y^2] of the modes + Vx = (1-f)* Vx1 + f * Vx2 # Var[x^2] + Vy = (1 - f) * Vy1 + f * Vy2 # Var[y^2] + + self.second_moments = jnp.insert(jnp.ones(d-1) * vy, 0, vx) + + self.variance_second_moments = jnp.insert(jnp.ones(d-1) * Vy, 0, Vx) + self.grad_nlogp = jax.value_and_grad(self.nlogp) + def nlogp(self, x): """- log p of the target distribution""" - N1 = (1.0 - self.f) * jnp.exp(-0.5 * jnp.sum(jnp.square(x - self.mu1), axis= -1) / self.sigma1 ** 2) / jnp.power(2 * jnp.pi * self.sigma1 ** 2, self.d * 0.5) - N2 = self.f * jnp.exp(-0.5 * jnp.sum(jnp.square(x - self.mu2), axis= -1) / self.sigma2 ** 2) / jnp.power(2 * jnp.pi * self.sigma2 ** 2, self.d * 0.5) + N1 = (1.0 - self.f) * jnp.exp(-0.5 * jnp.sum(jnp.square(x), axis= -1) / self.sigma1 ** 2) / jnp.power(2 * jnp.pi * self.sigma1 ** 2, self.d * 0.5) + N2 = self.f * jnp.exp(-0.5 * jnp.sum(jnp.square(x - self.mu), axis= -1) / self.sigma2 ** 2) / jnp.power(2 * jnp.pi * self.sigma2 ** 2, self.d * 0.5) return -jnp.log(N1 + N2) @@ -289,8 +304,8 @@ def draw(self, num_samples): """direct sampler from a target""" X = np.random.normal(size = (num_samples, self.d)) mask = np.random.uniform(0, 1, num_samples) < self.f - X[mask, :] = (X[mask, :] * self.sigma2) + self.mu2 - X[~mask] = (X[~mask] * self.sigma1) + self.mu1 + X[mask, :] = (X[mask, :] * self.sigma2) + self.mu + X[~mask] = (X[~mask] * self.sigma1) return X @@ -400,7 +415,7 @@ def nlogp(self, x, subset): z = x[:- 1][subset] prior_theta = jnp.square(theta / self.sigma_theta) - prior_z = jnp.sum(subset) * theta + jnp.exp(-theta) * jnp.sum(jnp.square(z*subset)) + prior_z = np.sum(subset) * theta + jnp.exp(-theta) * jnp.sum(jnp.square(z*subset)) likelihood = jnp.sum(jnp.square((z - self.data)*subset / self.sigma_data)) return 0.5 * (prior_theta + prior_z + likelihood) @@ -425,7 +440,7 @@ def __init__(self, d = 36, Q = 0.1): self.d = d self.Q = Q - self.name = 'rosenbrock' + #ground truth moments var_x = 2.0 @@ -466,7 +481,7 @@ def prior_draw(self, key): return jax.random.normal(key, shape = (self.d, )) - def ground_truth(self): + def compute_moments(self): num = 100000000 x = np.random.normal(loc=1.0, scale=1.0, size=num) y = np.random.normal(loc=np.square(x), scale=jnp.sqrt(self.Q), size=num) @@ -483,201 +498,18 @@ def ground_truth(self): -class Brownian(): - """ - log sigma_i ~ N(0, 2) - log sigma_obs ~N(0, 2) - - x ~ RandomWalk(0, sigma_i) - x_observed = (x + noise) * mask - noise ~ N(0, sigma_obs) - mask = 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 - """ - - def __init__(self): - self.num_data = 30 - self.d = self.num_data + 2 - self.name = 'brownian' - - ground_truth_moments = jnp.load(dirr + '/ground_truth/' + self.name + '/ground_truth.npy') - self.second_moments, self.variance_second_moments = ground_truth_moments[0], ground_truth_moments[1] - - self.data = jnp.array([0.21592641, 0.118771404, -0.07945447, 0.037677474, -0.27885845, -0.1484156, -0.3250906, -0.22957903, - -0.44110894, -0.09830782, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.8786016, -0.83736074, - -0.7384849, -0.8939254, -0.7774566, -0.70238715, -0.87771565, -0.51853573, -0.6948214, -0.6202789]) - # sigma_obs = 0.15, sigma_i = 0.1 - - self.observable = jnp.concatenate((jnp.ones(10), jnp.zeros(10), jnp.ones(10))) - self.num_observable = jnp.sum(self.observable) # = 20 - self.grad_nlogp = jax.value_and_grad(self.nlogp) - - def nlogp(self, x): - # y = softplus_to_log(x[:2]) - - lik = 0.5 * jnp.exp(-2 * x[1]) * jnp.sum(self.observable * jnp.square(x[2:] - self.data)) + x[ - 1] * self.num_observable - prior_x = 0.5 * jnp.exp(-2 * x[0]) * (x[2] ** 2 + jnp.sum(jnp.square(x[3:] - x[2:-1]))) + x[0] * self.num_data - prior_logsigma = 0.5 * jnp.sum(jnp.square(x / 2.0)) - - return lik + prior_x + prior_logsigma - - - def transform(self, x): - return jnp.concatenate((jnp.exp(x[:2]), x[2:])) - - # def prior_draw(self, key): - # """draws x from the prior""" - - # return jax.scipy.optimize.minimize(self.nlogp, x0 = jnp.zeros(self.d), method = 'BFGS', options = {'maxiter': 100}).x - - def prior_draw(self, key): - key_walk, key_sigma = jax.random.split(key) - - # original prior - # log_sigma = jax.random.normal(key_sigma, shape= (2, )) * 2 - - # narrower prior - log_sigma = jnp.log(np.array([0.1, 0.15])) + jax.random.normal(key_sigma, shape=( - 2,)) * 0.1 # *0.05# log sigma_i, log sigma_obs - - walk = random_walk(key_walk, self.d - 2) * jnp.exp(log_sigma[0]) - - return jnp.concatenate((log_sigma, walk)) - - def generate_data(self, key): - key_walk, key_sigma, key_noise = jax.random.split(key, 3) - - log_sigma = jax.random.normal(key_sigma, shape=(2,)) * 2 # log sigma_i, log sigma_obs - - walk = random_walk(key_walk, self.d - 2) * jnp.exp(log_sigma[0]) - noise = jax.random.normal(key_noise, shape=(self.d - 2,)) * jnp.exp(log_sigma[1]) - - return walk + noise - - -class GermanCredit: - """ Taken from inference gym. - - x = (global scale, local scales, weights) - - global_scale ~ Gamma(0.5, 0.5) - - for i in range(num_features): - unscaled_weights[i] ~ Normal(loc=0, scale=1) - local_scales[i] ~ Gamma(0.5, 0.5) - weights[i] = unscaled_weights[i] * local_scales[i] * global_scale - - for j in range(num_datapoints): - label[j] ~ Bernoulli(features @ weights) - - We use a log transform for the scale parameters. - """ - - def __init__(self): - self.d = 51 #global scale + 25 local scales + 25 weights - self.name = 'GC' - - self.labels = jnp.load(dirr + '/data/gc_labels.npy') - self.features = jnp.load(dirr + '/data/gc_features.npy') - - truth = jnp.load(dirr+'/ground_truth/' + self.name + '/ground_truth.npy') - self.second_moments, self.variance_second_moments = truth[0], truth[1] - - self.grad_nlogp = jax.value_and_grad(self.nlogp) - - - def transform(self, x): - return jnp.concatenate((jnp.exp(x[:26]), x[26:])) - - def nlogp(self, x): - - scales = jnp.exp(x[:26]) - - # prior - pr = jnp.sum(0.5 * scales + 0.5 * x[:26]) + 0.5 * jnp.sum(jnp.square(x[26:])) - - # transform - transform = -jnp.sum(x[:26]) - - # likelihood - weights = scales[0] * scales[1:26] * x[26:] - logits = self.features @ weights # = jnp.einsum('nd,...d->...n', self.features, weights) - lik = jnp.sum(self.labels * jnp.logaddexp(0., -logits) + (1-self.labels)* jnp.logaddexp(0., logits)) - - return lik + pr + transform - # - # def prior_draw(self, key): - # key1, key2 = jax.random.split(key) - # - # scales = jax.random.gamma(key1, 0.5, shape=(26,)) * 2. # we divided by beta = 0.5 - # unscaled_weights = jax.random.normal(key2, shape=(25,)) - # - # return jnp.concatenate((scales, unscaled_weights)) - # - - def prior_draw(self, key): - weights = jax.random.normal(key, shape = (25, )) - return jnp.concatenate((jnp.zeros(26), weights)) - - - - -class ItemResponseTheory: - """ Taken from inference gym.""" - - def __init__(self): - self.d = 501 - self.name = 'IRT' - self.students = 400 - self.questions = 100 - - self.mask = jnp.load(dirr + '/data/irt_mask.npy') - self.labels = jnp.load(dirr + '/data/irt_labels.npy') - - truth = jnp.load(dirr+'/ground_truth/' + self.name + '/ground_truth.npy') - self.second_moments, self.variance_second_moments = truth[0], truth[1] - - self.grad_nlogp = jax.value_and_grad(self.nlogp) - self.transform = lambda x: x - - def nlogp(self, x): - - students = x[:self.students] - mean = x[self.students] - questions = x[self.students + 1:] - - # prior - pr = 0.5 * (jnp.square(mean - 0.75) + jnp.sum(jnp.square(students)) + jnp.sum(jnp.square(questions))) - - # likelihood - logits = mean + students[:, jnp.newaxis] - questions[jnp.newaxis, :] - bern = self.labels * jnp.logaddexp(0., -logits) + (1 - self.labels) * jnp.logaddexp(0., logits) - bern = jnp.where(self.mask, bern, jnp.zeros_like(bern)) - lik = jnp.sum(bern) - - return lik + pr - - - def prior_draw(self, key): - x = jax.random.normal(key, shape = (self.d,)) - x = x.at[self.students].add(0.75) - return x - - - - class StochasticVolatility(): """Example from https://num.pyro.ai/en/latest/examples/stochastic_volatility.html""" def __init__(self): - self.SP500_returns = jnp.load(dirr + '/data/SP500.npy') + self.SP500_returns = np.load(dirr + '/SP500.npy') self.name = 'SV' self.d = 2429 self.typical_sigma, self.typical_nu = 0.02, 10.0 # := 1 / lambda - data = jnp.load(dirr + '/ground_truth/stochastic_volatility/ground_truth_0.npy') + data = np.load(dirr + '/ground_truth/stochastic_volatility/ground_truth_0.npy') self.second_moments = data[0] self.variance_second_moments = data[1] self.grad_nlogp = jax.value_and_grad(self.nlogp) @@ -719,10 +551,7 @@ def prior_draw(self, key): walk = random_walk(key_walk, self.d - 2) * params[0] return jnp.concatenate((walk, jnp.log(params/scales))) - - - - + def nlogp_StudentT(x, df, scale): y = x / scale z = ( @@ -735,7 +564,6 @@ def nlogp_StudentT(x, df, scale): return 0.5 * (df + 1.0) * jnp.log1p(y**2.0 / df) + z - def random_walk(key, num): """ Genereting process for the standard normal walk: x[0] ~ N(0, 1) @@ -757,7 +585,6 @@ def step(track, useless): return jax.lax.scan(step, init=(0.0, key), xs=None, length=num)[1] - class DiagonalPreconditioned(): """A target instance which takes some other target and preconditions it""" @@ -805,6 +632,20 @@ def get_contour_plot(target, x, y): +def check_gradient(target, x): + """check the analytical gradient of the target at point x""" + + from scipy import optimize + + approx_grad= optimize.approx_fprime(x, target.nlogp, 1e-3) + + grad= target.grad_nlogp(x) + + print('numerical grad: ', approx_grad) + print('analytical grad: ', grad) + print('ratio: ', grad / approx_grad) + + if __name__ == '__main__': diff --git a/benchmarks/targets_numpyro.py b/build/lib/benchmarks/benchmarks_numpyro.py similarity index 100% rename from benchmarks/targets_numpyro.py rename to build/lib/benchmarks/benchmarks_numpyro.py diff --git a/build/lib/benchmarks/brownian.py b/build/lib/benchmarks/brownian.py new file mode 100644 index 0000000..c0d8b3e --- /dev/null +++ b/build/lib/benchmarks/brownian.py @@ -0,0 +1,194 @@ +import jax +import jax.numpy as jnp +import numpy as np +import os +import matplotlib.pyplot as plt + +from HMC.mchmc_to_numpyro import mchmc_target_to_numpyro +from benchmarks.benchmarks_mchmc import random_walk +#from NUTS import sample_nuts + + +dirr = os.path.dirname(os.path.realpath(__file__)) +name = 'brownian' + + +class Target(): + """ + log sigma_i ~ N(0, 2) + log sigma_obs ~N(0, 2) + + x ~ RandomWalk(0, sigma_i) + x_observed = (x + noise) * mask + noise ~ N(0, sigma_obs) + mask = 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 + """ + + def __init__(self): + self.num_data = 30 + self.d = self.num_data + 2 + self.name = name + + ground_truth_moments = np.load(dirr+'/ground_truth/'+name+'/ground_truth.npy') + self.second_moments, self.variance_second_moments = ground_truth_moments[0], ground_truth_moments[1] + + self.data = jnp.array([0.21592641, 0.118771404, -0.07945447, 0.037677474, -0.27885845, -0.1484156, -0.3250906, -0.22957903, -0.44110894, -0.09830782, + 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, + -0.8786016, -0.83736074, -0.7384849, -0.8939254, -0.7774566, -0.70238715, -0.87771565, -0.51853573, -0.6948214, -0.6202789]) + #sigma_obs = 0.15, sigma_i = 0.1 + + + self.observable = jnp.concatenate((jnp.ones(10), jnp.zeros(10), jnp.ones(10))) + self.num_observable = jnp.sum(self.observable) # = 20 + self.grad_nlogp = jax.value_and_grad(self.nlogp) + + + def nlogp(self, x): + #y = softplus_to_log(x[:2]) + + lik = 0.5 * jnp.exp(-2*x[1]) * jnp.sum(self.observable * jnp.square(x[2:] - self.data)) + x[1] * self.num_observable + prior_x = 0.5 * jnp.exp(-2*x[0]) * (x[2]**2 + jnp.sum(jnp.square(x[3:] - x[2:-1]))) + x[0] * self.num_data + prior_logsigma = 0.5 * jnp.sum(jnp.square(x / 2.0)) + + return lik + prior_x + prior_logsigma + + + def transform(self, x): + return jnp.concatenate((jnp.exp(x[:2]), x[2:])) + + + # def prior_draw(self, key): + # """draws x from the prior""" + + # return jax.scipy.optimize.minimize(self.nlogp, x0 = jnp.zeros(self.d), method = 'BFGS', options = {'maxiter': 100}).x + + def prior_draw(self, key): + + key_walk, key_sigma = jax.random.split(key) + + # original prior + #log_sigma = jax.random.normal(key_sigma, shape= (2, )) * 2 + + # narrower prior + log_sigma = jnp.log(np.array([0.1, 0.15])) + jax.random.normal(key_sigma, shape=(2,)) *0.1#*0.05# log sigma_i, log sigma_obs + + walk = random_walk(key_walk, self.d - 2) * jnp.exp(log_sigma[0]) + + return jnp.concatenate((log_sigma, walk)) + + + def generate_data(self, key): + + key_walk, key_sigma, key_noise = jax.random.split(key, 3) + + log_sigma = jax.random.normal(key_sigma, shape=(2,)) * 2 # log sigma_i, log sigma_obs + + walk = random_walk(key_walk, self.d - 2) * jnp.exp(log_sigma[0]) + noise = jax.random.normal(key_noise, shape = (self.d - 2, )) * jnp.exp(log_sigma[1]) + + return walk + noise + + +def ground_truth(key_num): + key = jax.random.PRNGKey(key_num) + mchmc_target = Target() + numpyro_target = mchmc_target_to_numpyro(Target) + samples, steps, steps_warmup = sample_nuts(numpyro_target, mchmc_target, None, 10000, 100000, 20, random_key=key, progress_bar= True) + + x = np.array(samples['x']) + xsq = jnp.square(jax.vmap(mchmc_target.transform)(x)) + + second_moments = jnp.average(xsq, axis = 0) + variance_second_moments = jnp.std(xsq, axis = 0)**2 + + np.save('benchmarks/ground_truth/'+name+'/ground_truth_'+str(key_num) +'.npy', [second_moments, variance_second_moments]) + np.save('benchmarks/ground_truth/'+name+'/chain_'+str(key_num) +'.npy', x) + + +def join_ground_truth(): + data = np.array([np.load('benchmarks/ground_truth/'+name+'/ground_truth_'+str(i)+'.npy') for i in range(3)]) + + truth = np.median(data, axis = 0) + np.save('benchmarks/ground_truth/'+name+'/ground_truth.npy', truth) + + for i in range(3): + bias_d = np.square(data[i, 0] - truth[0]) / truth[1] + print(np.average(bias_d), np.max(bias_d)) + + +def plot_hierarchical(): + x= np.load('ground_truth/'+name+'/chain_1.npy') + print(x.shape) + sigi = np.exp(x[:, 0]) + sigo = np.exp(x[:, 1]) + plt.rcParams.update({'font.size': 25}) + plt.figure(figsize=(10, 10)) + plt.hexbin(sigi, sigo, cmap = 'cividis') + plt.plot([0.1, ], [0.15, ], '*', color = 'gold', markersize = 20) + plt.xlim(0.04, 0.25) + plt.ylim(0.04, 0.25) + plt.title('Hyper parameters') + plt.xlabel(r'$\sigma_{\mathrm{rw}}$') + plt.ylabel(r'$\sigma_{\mathrm{obs}}$') + plt.xticks([0.05, 0.1, 0.15, 0.2, 0.25]) + plt.yticks([0.05, 0.1, 0.15, 0.2, 0.25]) + plt.savefig('hierarchical_posterior.png') + plt.show() + + +def plot_walk(): + x = np.sort(np.load('ground_truth/' + name + '/chain_1.npy')[:, 2:], axis = 0) + n = len(x) + xavg = x[n//2] + xp, xm = x[3 * n // 4], x[n // 4] + + plt.plot(Target().data, 'o', color='tab:red', label = 'data') + + plt.plot(xavg, color = 'tab:blue', label = 'posterior') + plt.fill_between(np.arange(len(xm)), xm, xp, color = 'tab:blue', alpha = 0.3) + + plt.xlabel('t') + plt.ylabel('x(t)') + plt.legend() + plt.savefig('walk_posterior.png') + plt.show() + + +def map(): + chains = 10 + from optimization.adam import optimize_adam + from scipy.optimize import minimize + t = Target() + def store(x): + X.append(x[0]) + Y.append(x[1]) + + x0 = jax.vmap(t.prior_draw)(jax.random.split(jax.random.PRNGKey(0), chains)) + plt.rcParams.update({'font.size': 25}) + plt.figure(figsize=(10, 10)) + + for i in range(chains): + X = [] + Y = [] + #opt = minimize(t.grad_nlogp, jac = True, x0 = x0[i], method = 'BFGS', callback = store, options = {'maxiter': 5000}) + #opt = minimize(t.grad_nlogp, jac = True, x0 = x0[i], method = 'L-BFGS-B', callback = store, options = {'maxiter': 1000, 'maxcor': 50}) + opt = minimize(t.grad_nlogp, jac = True, x0 = x0[i], method = 'Newton-CG', callback = store, options = {'maxiter': 1000}) + + print(len(X)) + plt.plot(X, Y, '.-', color = 'black', alpha = 0.5) + plt.plot(X[0], Y[0], 'o', color='tab:red') + plt.plot(X[-1], Y[-1], 'o', color='tab:blue') + + + plt.plot(jnp.log(jnp.array([0.1, ])), jnp.log(jnp.array([0.15, ])), '*', color='gold', markersize=20) + plt.xlabel(r'$\log \sigma_{\mathrm{rw}}$') + plt.ylabel(r'$\log \sigma_{\mathrm{obs}}$') + plt.show() + + +if __name__ == '__main__': + #plott() + #mchmc() + #ground_truth(2) + #plot_hierarchical() + join_ground_truth() \ No newline at end of file diff --git a/build/lib/benchmarks/german_credit.py b/build/lib/benchmarks/german_credit.py new file mode 100644 index 0000000..edbb896 --- /dev/null +++ b/build/lib/benchmarks/german_credit.py @@ -0,0 +1,175 @@ + +#Sparse logistic regression fitted to the German credit data +#We use the version implemented in the inference-gym: https://pypi.org/project/inference-gym/ +#In some part we directly use their tutorial: https://github.com/tensorflow/probability/blob/main/spinoffs/inference_gym/notebooks/inference_gym_tutorial.ipynb + +import inference_gym.using_jax as gym +import jax +import jax.numpy as jnp +import numpy as np +import os + +from HMC.mchmc_to_numpyro import mchmc_target_to_numpyro +#from HMC.NUTS import sample_nuts + +dirr = os.path.dirname(os.path.realpath(__file__)) + +name = 'german_credit' +target_base = gym.targets.GermanCreditNumericSparseLogisticRegression() +gym.targets.BrownianMotionMissingMiddleObservations +prior_distribution = target_base.prior_distribution() + +target = gym.targets.VectorModel(target_base, flatten_sample_transformations=True) + +identity_fn = target.sample_transformations['identity'] + +def target_nlog_prob_fn(z): + x = target.default_event_space_bijector(z) + return -(target.unnormalized_log_prob(x) + target.default_event_space_bijector.forward_log_det_jacobian(z, event_ndims=1)) + +target_nlog_prob_grad_fn = jax.grad(target_nlog_prob_fn) + + + +class Target(): + + def __init__(self): + """local scales (25), global scale (1), unscaled weights (25)""" + self.d = 51 + self.name = name + + data = np.load(dirr+'/ground_truth/'+name+'/ground_truth.npy') + self.second_moments, self.variance_second_moments = data[0], data[1] + + #xmap = np.load(dirr+'/ground_truth/'+name+'/map.npy') + self.transform = lambda x: target.default_event_space_bijector(x) + self.nlogp = lambda x: target_nlog_prob_fn(x) + self.grad_nlogp = lambda x: (target_nlog_prob_fn(x), target_nlog_prob_grad_fn(x)) + + + # def prior_draw(self, key): + # x = prior_distribution.sample(seed= key) + # w = x['unscaled_weights'] + # ls = x['local_scales'] + # gs = x['global_scale'] + # return jnp.concatenate((jnp.log(ls), jnp.ones(1) * jnp.log(gs), w)) + + + # def prior_draw(self, key): + # key1, key2 = jax.random.split(key) + # weights = jax.random.normal(key1, shape = (25, )) + # scales = jax.random.gamma(key2, a= 0.5, shape = (26, )) / 0.5 + # return jnp.concatenate((jnp.log(scales), weights)) + + #fix the global hierarchical parameter + # def prior_draw(self, key): + # key1, key2 = jax.random.split(key) + # weights = jax.random.normal(key1, shape = (25, )) + # scales = jax.random.gamma(key2, a= 0.5, shape = (25, )) / 0.5 + # return jnp.concatenate((jnp.log(scales), jnp.zeros(1), weights)) + + #fix scale parameters + def prior_draw(self, key): + weights = jax.random.normal(key, shape = (25, )) + return jnp.concatenate((jnp.zeros(26), weights)) + + +def map_solution(): + + def map_objective_fn(z): + x = target.default_event_space_bijector(z) + return -target.unnormalized_log_prob(x) + + map_objective_grad_fn = jax.grad(map_objective_fn) + + + # MAP solution + + def optimize(z_init, objective_fn, objective_grad_fn, learning_rate, num_steps): + def opt_step(z): + objective = objective_fn(z) + z = z - learning_rate * objective_grad_fn(z) + return z, objective + + return jax.lax.scan(lambda z, _: opt_step(z), init=z_init, xs=None, length=num_steps) + + + z_map, objective_trace = optimize( + z_init=jnp.zeros(target.default_event_space_bijector.inverse_event_shape(target.event_shape)), + objective_fn= map_objective_fn, objective_grad_fn=map_objective_grad_fn, learning_rate=0.001, num_steps=2000, ) + + import matplotlib.pyplot as plt + plt.plot(objective_trace - objective_trace[-1], '.-') + plt.ylabel('Loss') + plt.xlabel('Iteration') + plt.show() + + np.save('ground_truth/'+name+'/map.npy', z_map) + + + +def ground_truth(key_num): + key = jax.random.PRNGKey(key_num) + mchmc_target = Target() + numpyro_target = mchmc_target_to_numpyro(Target) + samples, steps, steps_warmup = sample_nuts(numpyro_target, mchmc_target, None, 10000, 10000, 20, random_key=key, progress_bar= True) + + z = np.array(samples['x']) + x = jax.vmap(mchmc_target.transform)(z) + + second_moments = jnp.average(jnp.square(x), axis = 0) + variance_second_moments = jnp.std(jnp.square(x), axis = 0)**2 + + np.save('ground_truth/'+name+'/ground_truth_'+str(key_num) +'.npy', [second_moments, variance_second_moments]) + + + +def richard_results(): + import arviz as az + folder = 'Tests/data/german_credit/' + + hmc_data = az.from_netcdf(folder + 'inference_data_german_credit_mcmc.nc') + tau = np.array(hmc_data['posterior']['tau']) + lam = np.array(hmc_data['posterior']['lam']) + beta = np.array(hmc_data['posterior']['beta']) + hmc_steps = np.array(hmc_data['sample_stats']['n_steps']) + tunning = np.loadtxt(folder + 'german_credit_warmup_n_steps.txt') + tunning_steps = np.sum(tunning, axis = 1) + + + X = np.concatenate([[tau, ], np.swapaxes(lam.T, 1, 2), np.swapaxes(beta.T, 1, 2)]) + + var = np.average(np.average(np.square(X), axis = 2), axis = 1) + + bias = np.sqrt(np.average(np.square(((np.cumsum(np.square(X), axis = 2) / np.arange(1, 10001)).T - var) / var), axis=2).T) + + ess = np.empty(10) + ess_with_tunning = np.empty(10) + for i in range(len(bias)): + j = 0 + while bias[i, j] > 0.1: + j += 1 + ess[i]= 200 / np.sum(hmc_steps[i, :j+1]) + ess_with_tunning[i] = 200 / (np.sum(hmc_steps[i, :j + 1]) + tunning_steps[i]) + + print('ESS = {0}, ESS (with tunning) = {1}'.format(np.average(ess), np.average(ess_with_tunning))) + + +def joint_ground_truth(): + + data = np.array([np.load('ground_truth/'+name+'/ground_truth_'+str(i)+'.npy') for i in range(3)]) + + truth = np.median(data, axis = 0) + np.save('ground_truth/'+name+'/ground_truth.npy', truth) + + for i in range(3): + bias_d = np.square(data[i, 0] - truth[0]) / truth[1] + print(np.sqrt(np.average(bias_d)), np.sqrt(np.max(bias_d))) + + + +if __name__ == '__main__': + + ground_truth(2) + + #joint_ground_truth() diff --git a/tests/__init__.py b/build/lib/mclmc/__init__.py similarity index 100% rename from tests/__init__.py rename to build/lib/mclmc/__init__.py diff --git a/mclmc/annealing.py b/build/lib/mclmc/annealing.py similarity index 100% rename from mclmc/annealing.py rename to build/lib/mclmc/annealing.py diff --git a/mclmc/correlation_length.py b/build/lib/mclmc/correlation_length.py similarity index 100% rename from mclmc/correlation_length.py rename to build/lib/mclmc/correlation_length.py diff --git a/build/lib/mclmc/dynamics.py b/build/lib/mclmc/dynamics.py new file mode 100644 index 0000000..5de0d2f --- /dev/null +++ b/build/lib/mclmc/dynamics.py @@ -0,0 +1,199 @@ +import time +from typing import Any, NamedTuple +import jax +from jax import Array +import jax.numpy as jnp +import numpy as np +import math + +lambda_c = 0.1931833275037836 #critical value of the lambda parameter for the minimal norm integrator + +class MCLMCState(NamedTuple): + """State of the MCLMC algorithm. + + """ + + x: Array + u: Array + l: float + g: Array + key : Any + +class MCLMCInfo(NamedTuple): + + + transformed_x: Array + l: Array + de: float + +def update_momentum(d, sequential): + """The momentum updating map of the esh dynamics (see https://arxiv.org/pdf/2111.02434.pdf) + similar to the implementation: https://github.com/gregversteeg/esh_dynamics + There are no exponentials e^delta, which prevents overflows when the gradient norm is large.""" + + + def update_sequential(eps, u, g): + g_norm = jnp.sqrt(jnp.sum(jnp.square(g))) + e = - g / g_norm + ue = jnp.dot(u, e) + delta = eps * g_norm / (d-1) + zeta = jnp.exp(-delta) + uu = e *(1-zeta)*(1+zeta + ue * (1-zeta)) + 2*zeta* u + delta_r = delta - jnp.log(2) + jnp.log(1 + ue + (1-ue)*zeta**2) + return uu/jnp.sqrt(jnp.sum(jnp.square(uu))), delta_r + + + + def update_parallel(eps, u, g): + g_norm = jnp.sqrt(jnp.sum(jnp.square(g), axis=1)).T + nonzero = g_norm > 1e-13 # if g_norm is zero (we are at the MAP solution) we also want to set e to zero and the function will return u + inv_g_norm = jnp.nan_to_num(1. / g_norm) * nonzero + e = - g * inv_g_norm[:, None] + ue = jnp.sum(u * e, axis=1) + delta = eps * g_norm / (d - 1) + zeta = jnp.exp(-delta) + uu = e * ((1 - zeta) * (1 + zeta + ue * (1 - zeta)))[:, None] + 2 * zeta[:, None] * u + delta_r = delta - jnp.log(2) + jnp.log(1 + ue + (1 - ue) * zeta ** 2) + return uu / (jnp.sqrt(jnp.sum(jnp.square(uu), axis=1)).T)[:, None], delta_r + + + return update_sequential if sequential else update_parallel + + + +def update_position(grad_nlogp): + + def update(eps, x, u): + xx = x + eps * u + ll, gg = grad_nlogp(xx) + return xx, ll, gg + + return update + + + +def minimal_norm(d, T, V): + + def step(x, u, g, eps, sigma): + """Integrator from https://arxiv.org/pdf/hep-lat/0505020.pdf, see Equation 20.""" + + # V T V T V + uu, r1 = V(eps * lambda_c, u, g * sigma) + xx, ll, gg = T(eps, x, 0.5*uu*sigma) + uu, r2 = V(eps * (1 - 2 * lambda_c), uu, gg * sigma) + xx, ll, gg = T(eps, xx, 0.5*uu*sigma) + uu, r3 = V(eps * lambda_c, uu, gg * sigma) + + #kinetic energy change + kinetic_change = (r1 + r2 + r3) * (d-1) + + return xx, uu, ll, gg, kinetic_change + + return step, 2 + + + +def leapfrog(d, T, V): + + def step(x, u, g, eps, sigma): + + # V T V + uu, r1 = V(eps * 0.5, u, g * sigma) + xx, l, gg = T(eps, x, uu*sigma) + uu, r2 = V(eps * 0.5, uu, gg * sigma) + + # kinetic energy change + kinetic_change = (r1 + r2) * (d-1) + + return xx, uu, l, gg, kinetic_change + + return step, 1 + + + +def mclmc(hamiltonian_dynamics, partially_refresh_momentum, d): + + def step(x, u, g, random_key, L, eps, sigma): + """One step of the generalized dynamics.""" + + # Hamiltonian step + xx, uu, ll, gg, kinetic_change = hamiltonian_dynamics(x=x, u=u, g=g, eps=eps, sigma = sigma) + + # Langevin-like noise + nu = jnp.sqrt((jnp.exp(2 * eps / L) - 1.) / d) + uu, key = partially_refresh_momentum(u= uu, random_key= random_key, nu= nu) + + return xx, uu, ll, gg, kinetic_change, key + + return step + + +def build_kernel(Target, integrator, params, sequential=True): + + L,eps, sigma = params + + hamiltonian_step, _ = integrator(T= update_position(Target.grad_nlogp), + V= update_momentum(Target.d, sequential=sequential), + d= Target.d) + move = mclmc(hamiltonian_step, partially_refresh_momentum(Target.d, sequential=sequential), Target.d) + def kernel(state : MCLMCState, _ : None) -> tuple[MCLMCState, MCLMCInfo]: + + x, u, l, g, key = state + + xx, uu, ll, gg, kinetic_change, key = move(x, u, g, key, L, eps, sigma) + de = kinetic_change + ll - l + return MCLMCState(xx, uu, ll, gg, key), MCLMCInfo(Target.transform(xx), ll, de) + + return kernel + + +def run_kernel(kernel, num_steps : int, initial_state : MCLMCState): + return jax.lax.scan( + f=kernel, + init=initial_state, + xs=None, length=num_steps)[1] + +def random_unit_vector(d, sequential= True): + """Generates a random (isotropic) unit vector.""" + + + def rng_sequential(random_key): + key, subkey = jax.random.split(random_key) + u = jax.random.normal(subkey, shape = (d, )) + u /= jnp.sqrt(jnp.sum(jnp.square(u))) + return u, key + + + def rng_parallel(random_key, num_chains): + key, subkey = jax.random.split(random_key) + u = jax.random.normal(subkey, shape = (num_chains, d)) + normed_u = u / jnp.sqrt(jnp.sum(jnp.square(u), axis = 1))[:, None] + return normed_u, key + + + return rng_sequential if sequential else rng_parallel + + + + +def partially_refresh_momentum(d, sequential= True): + """Adds a small noise to u and normalizes.""" + + + def rng_sequential(u, random_key, nu): + key, subkey = jax.random.split(random_key) + z = nu * jax.random.normal(subkey, shape = (d, )) + + return (u + z) / jnp.sqrt(jnp.sum(jnp.square(u + z))), key + + + def rng_parallel(u, random_key, nu): + key, subkey = jax.random.split(random_key) + noise = nu * jax.random.normal(subkey, shape= u.shape, dtype=u.dtype) + + return (u + noise) / jnp.sqrt(jnp.sum(jnp.square(u + noise), axis = 1))[:, None], key + + + return rng_sequential if sequential else rng_parallel + + diff --git a/mclmc/old_annealing.py b/build/lib/mclmc/old_annealing.py similarity index 100% rename from mclmc/old_annealing.py rename to build/lib/mclmc/old_annealing.py diff --git a/build/lib/mclmc/sampler.py b/build/lib/mclmc/sampler.py new file mode 100644 index 0000000..626329d --- /dev/null +++ b/build/lib/mclmc/sampler.py @@ -0,0 +1,511 @@ +## style note: general preference here for functional style (e.g. global function definitions, purity, code sharing) + +from enum import Enum +from typing import NamedTuple +from jax import Array +import jax +import jax.numpy as jnp +import numpy as np +from . import dynamics + +from .dynamics import MCLMCInfo, MCLMCState, build_kernel, run_kernel +from .correlation_length import ess_corr + +class Target(): + """#Class for target distribution + + E.g. + + ```python + Target(d=2, nlogp = lambda x: 0.5*jnp.sum(jnp.square(x))) +``` + + defines a Gaussian. + + """ + + def __init__(self, d, nlogp): + self.d = d + """dimensionality of the target distribution""" + self.nlogp = nlogp + """ negative log probability of target distribution (i.e. energy function)""" + self.grad_nlogp = jax.value_and_grad(self.nlogp) + """ function which computes nlogp and its gradient""" + + def transform(self, x): + """ a transformation of the samples from the target distribution""" + return x + + def prior_draw(self, key): + """**Args**: jax random key + + **Returns**: one random sample from the prior + """ + + raise Exception("Not implemented") + +OutputType = Enum('Output', ['normal', 'detailed', 'expectation', 'ess']) +""" @private """ + +class Parameters(NamedTuple): + """Tunable parameters + """ + + L: float + eps: float + sigma: Array + +class Sampler: + """the MCHMC (q = 0 Hamiltonian) sampler""" + + def __init__(self, + Target : Target, + L = None, eps = None, + integrator = dynamics.minimal_norm, varEwanted = 5e-4, + diagonal_preconditioning= False, + frac_tune1 = 0.1, frac_tune2 = 0.1, frac_tune3 = 0.1, + ): + """Args: + Target: the target distribution class + + **L**: momentum decoherence scale (it is then automaticaly tuned before the sampling starts unless you turn-off the tuning by setting frac_tune2 and 3 to zero (see below)) + + **eps**: initial integration step-size (it is then automaticaly tuned before the sampling starts unless you turn-off the tuning by setting all frac_tune1 and 2 to zero (see below)) + + **integrator**: dynamics.leapfrog or dynamics.minimal_norm. Typically minimal_norm performs better. + + **varEwanted**: if your posteriors are biased try smaller values (or larger values: perhaps the convergence is too slow). This is perhaps the parameter whose default value is the least well determined. + + **diagonal_preconditioning**: if you already have your own preconditioning or if you suspect diagonal preconditioning is not useful, turn this off as it can also make matters worse + (but it can also be very useful if you did not precondition the parameters (make their posterior variances close to 1)) + + **frac_tune1**: (num_samples * frac_tune1) steps will be used as a burn-in and to autotune the stepsize + + **frac_tune2**: (num_samples * frac_tune2) steps will be used to autotune L (should be around 10 effective samples long for the optimal performance) + + **frac_tune3**: (num_samples * frac_tune3) steps will be used to improve the L tuning (should be around 10 effective samples long for the optimal performance). This stage is not neccessary if the posterior is close to a Gaussian and does not change much in general. + It can be memory intensive in high dimensions so try turning it off if you have problems with the memory. + """ + + self.Target = Target + self.sigma = jnp.ones(Target.d) + self.integrator = integrator + + self.integrator = integrator + + ### integrator ### + hamiltonian_step, self.grad_evals_per_step = self.integrator(T= dynamics.update_position(self.Target.grad_nlogp), + V= dynamics.update_momentum(self.Target.d, sequential=True), + d= self.Target.d) + self.dynamics = dynamics.mclmc(hamiltonian_step, dynamics.partially_refresh_momentum(self.Target.d, True), self.Target.d) + self.random_unit_vector = dynamics.random_unit_vector(self.Target.d, True) + + ### preconditioning ### + self.diagonal_preconditioning = diagonal_preconditioning + + ### autotuning parameters ### + + # length of autotuning + self.frac_tune1 = frac_tune1 # num_samples * frac_tune1 steps will be used to autotune eps + self.frac_tune2 = frac_tune2 # num_samples * frac_tune2 steps will be used to approximately autotune L + self.frac_tune3 = frac_tune3 # num_samples * frac_tune3 steps will be used to improve L tuning. + + self.varEwanted = varEwanted # 1e-3 #targeted energy variance Var[E]/d + neff = 150 # effective number of steps used to determine the stepsize in the adaptive step + self.gamma = (neff - 1.0) / (neff + 1.0) # forgeting factor in the adaptive step + self.sigma_xi= 1.5 # determines how much do we trust the stepsize predictions from the too large and too small stepsizes + + self.Lfactor = 0.4 #in the third stage we set L = Lfactor * (configuration space distance bewteen independent samples) + + + ### default eps and L ### + if L != None: + self.L = L + else: #default value (works if the target is well preconditioned). If you are not happy with the default value and have not run the grid search we suggest using the autotuning + self.L = jnp.sqrt(Target.d) + if eps != None: + self.eps = eps + else: #defualt value (assumes preconditioned target and even then it might not work). Unless you have done a grid search to determine this value we suggest using the autotuning + self.eps = jnp.sqrt(Target.d) * 0.4 + + + + def nan_reject(self, x, u, l, g, xx, uu, ll, gg, eps, eps_max, dK): + """if there are nans, let's reduce the stepsize, and not update the state. The function returns the old state in this case.""" + + nonans = jnp.all(jnp.isfinite(xx)) + + return nonans, *jax.tree_util.tree_map(lambda new, old: jax.lax.select(nonans, jnp.nan_to_num(new), old), (xx, uu, ll, gg, eps_max, dK), (x, u, l, g, eps * 0.8, 0.)) + + + + def dynamics_adaptive(self, state, L, sigma): + """One step of the dynamics with the adaptive stepsize""" + + x, u, l, g, E, Feps, Weps, eps_max, key = state + + eps = jnp.power(Feps/Weps, -1.0/6.0) #We use the Var[E] = O(eps^6) relation here. + eps = (eps < eps_max) * eps + (eps > eps_max) * eps_max # if the proposed stepsize is above the stepsize where we have seen divergences + + # dynamics + xx, uu, ll, gg, kinetic_change, key = self.dynamics(x, u, g, key, L, eps, sigma) + + # step updating + success, xx, uu, ll, gg, eps_max, kinetic_change = self.nan_reject(x, u, l, g, xx, uu, ll, gg, eps, eps_max, kinetic_change) + + DE = kinetic_change + ll - l # energy difference + EE = E + DE # energy + # Warning: var = 0 if there were nans, but we will give it a very small weight + xi = ((DE ** 2) / (self.Target.d * self.varEwanted)) + 1e-8 # 1e-8 is added to avoid divergences in log xi + w = jnp.exp(-0.5 * jnp.square(jnp.log(xi) / (6.0 * self.sigma_xi))) # the weight which reduces the impact of stepsizes which are much larger on much smaller than the desired one. + Feps = self.gamma * Feps + w * (xi/jnp.power(eps, 6.0)) # Kalman update the linear combinations + Weps = self.gamma * Weps + w + + return xx, uu, ll, gg, EE, Feps, Weps, eps_max, key, eps * success + + + + ### sampling routine ### + + def get_initial_conditions(self, x_initial, random_key): + + ### random key ### + if random_key is None: + key = jax.random.PRNGKey(0) + else: + key = random_key + + ### initial conditions ### + if x_initial is None: # draw the initial x from the prior + key, prior_key = jax.random.split(key) + x_initial = self.Target.prior_draw(prior_key) + + l, g = self.Target.grad_nlogp(x_initial) + + u, key = self.random_unit_vector(key) + #u = - g / jnp.sqrt(jnp.sum(jnp.square(g))) #initialize momentum in the direction of the gradient of log p + + return x_initial, u, l, g, key + + + + def sample(self, num_steps, num_chains = 1, x_initial = None, random_key= None, output = OutputType.normal, thinning= 1): + """Args: + num_steps: number of integration steps to take. + + num_chains: number of independent chains, defaults to 1. If different than 1, jax will parallelize the computation with the number of available devices (CPU, GPU, TPU), + as returned by jax.local_device_count(). + + x_initial: initial condition for x, shape: (d, ). Defaults to None in which case the initial condition is drawn from the prior distribution (self.Target.prior_draw). + + random_key: jax random seed, defaults to jax.random.PRNGKey(0) + + output: determines the output of the function: + + 'normal': samples, burn in steps. + samples were transformed by the Target.transform to save memory and have shape: (num_samples, len(Target.transform(x))) + + 'expectation': exepcted value of transform(x) + most memory efficient. If you are after memory it might be usefull to turn off the third tuning stage + + 'detailed': samples, energy error for each step, L and eps used for sampling + + 'ess': Effective Sample Size per gradient evaluation, float. + In this case, self.Target.variance = _true should be defined. + + thinning: only one every 'thinning' steps is stored. Defaults to 1. + This is not the recommended solution to save memory. It is better to use the transform functionality. + If this is not sufficient consider saving only the expected values, by setting output= 'expectation'. + """ + + if output == OutputType.ess: + for ground_truth in ['second_moments', 'variance_second_moments']: + if not hasattr(self.Target, ground_truth): + raise AttributeError("Target." + ground_truth + " should be defined if you want to use output = ess.") + + if num_chains == 1: + results = self.single_chain_sample(num_steps, x_initial, random_key, output, thinning) #the function which actually does the sampling + if output == OutputType.ess: + return self.bias_plot(results) + + else: + return results + else: + num_cores = jax.local_device_count() + if random_key is None: + key = jax.random.PRNGKey(0) + else: + key = random_key + + if x_initial is None: # draw the initial x from the prior + keys_all = jax.random.split(key, num_chains * 2) + x0 = jnp.array([self.Target.prior_draw(keys_all[num_chains+i]) for i in range(num_chains)]) + keys = keys_all[:num_chains] + + else: #initial x is given + x0 = jnp.copy(x_initial) + keys = jax.random.split(key, num_chains) + + + f = lambda i: self.single_chain_sample(num_steps, x0[i], keys[i], output, thinning) + + if num_cores != 1: #run the chains on parallel cores + parallel_function = jax.pmap(jax.vmap(f)) + results = parallel_function(jnp.arange(num_chains).reshape(num_cores, num_chains // num_cores)) + if output == OutputType.ess: + return self.bias_plot(results.reshape(num_chains, num_steps)) + + ### reshape results ### + if type(results) is tuple: #each chain returned a tuple + results_reshaped =[] + for i in range(len(results)): + res = jnp.array(results[i]) + results_reshaped.append(res.reshape([num_chains, ] + [res.shape[j] for j in range(2, len(res.shape))])) + return results_reshaped + + else: + return results.reshape([num_chains, ] + [results.shape[j] for j in range(2, len(results.shape))]) + + + else: #run chains serially on a single core + + results = jax.vmap(f)(jnp.arange(num_chains)) + + if output == OutputType.ess: + return self.bias_plot(results) + + else: + return results + + + + def single_chain_sample(self, num_steps, x_initial, random_key, output, thinning): + """sampling routine. It is called by self.sample""" + + ### initial conditions ### + x, u, l, g, key = self.get_initial_conditions(x_initial, random_key) + L, eps = self.L, self.eps #the initial values, given at the class initialization (or set to the default values) + + sigma = jnp.ones(self.Target.d) # jnp.ones(self.Target.d) # no diagonal preconditioning + + ### auto-tune the hyperparameters L and eps ### + if self.frac_tune1 + self.frac_tune2 + self.frac_tune3 != 0.: + steps1 = (int)(num_steps * self.frac_tune1) + steps2 = (int)(num_steps * self.frac_tune2) + L, eps, sigma, x, u, l, g, key = self.tune12(x, u, l, g, key, L, eps, sigma, steps1, steps2) #the cheap tuning (100 steps) + if self.frac_tune3 != 0: #if we want to further improve L tuning we go to the second stage (which is a bit slower) + steps3 = (int)(num_steps * self.frac_tune3) + L, x, u, l, g, key = self.tune3(x, u, l, g, key, L, eps, sigma, steps3) + + ### sampling ### + + + if output == OutputType.normal or output == OutputType.detailed: + X, _, E = self.sample_normal(num_steps, MCLMCState(x, u, l, g, key), Parameters(L, eps, sigma), thinning) + if output == OutputType.detailed: + return X, E, L, eps + else: + return X + elif output == OutputType.expectation: + return self.sample_expectation(num_steps, x, u, l, g, key, L, eps, sigma) + + elif output == OutputType.ess: + try: + self.Target.variance + except: + raise AttributeError("Target.variance should be defined") + return self.sample_ess(num_steps, x, u, l, g, key, L, eps, sigma) + + + ### for loops which do the sampling steps: ### + + + + def sample_normal(self, num_steps : int, state : MCLMCState, params : Parameters, thinning : int): + """Stores transform(x) for each step.""" + + kernel = build_kernel(self.Target, self.integrator, params=params) + if thinning == 1: + return run_kernel(kernel=kernel, num_steps=num_steps, initial_state=state) + + else: + x,u,l,g,random_key = state + return self.sample_thinning(num_steps, x, u, l, g, random_key, params, thinning) + + + def sample_thinning(self, num_steps, x, u, l, g, random_key, params, thinning): + """Stores transform(x) for each step.""" + + def step(state, _): + + def substep(state, _): + x, u, l, g, _, key = state + L,eps,sigma = params + xx, uu, ll, gg, kinetic_change, key = self.dynamics(x, u, g, key, L, eps, sigma) + de = kinetic_change + ll - l + return (xx, uu, ll, gg, de, key), None + + state = jax.lax.scan(substep, init=state, xs=None, length= thinning)[0] #do 'thinning' steps without saving + + return state, (self.Target.transform(state[0]), state[2], state[4]) #save one sample + + return jax.lax.scan(step, init=(x, u, l, g, 0., random_key), xs=None, length= num_steps // thinning)[1] + + + + def sample_expectation(self, num_steps, x, u, l, g, random_key, L, eps, sigma): + """Stores no history but keeps the expected value of transform(x).""" + + def step(state, useless): + + x, u, _, g, _, key = self.dynamics(*(state[0]), L, eps, sigma) + + return (state[0], state[1] + self.Target.transform(x)), None + + state1 = (x, u, g, random_key) + state2= jnp.zeros(self.Target.transform(x).shape) + return jax.lax.scan(step, init= (state1, state2), xs=None, length=num_steps)[0][1] / num_steps + + + + + def sample_ess(self, num_steps, x, u, l, g, random_key, L, eps, sigma): + """Stores the bias of the second moments for each step.""" + + def step(state_track, useless): + + x, u, l, g, E, key = state_track[0] + x, u, ll, g, kinetic_change, key = self.dynamics(x, u, g, key, L, eps, sigma) + W, F2 = state_track[1] + + F2 = (W * F2 + jnp.square(self.Target.transform(x))) / (W + 1) # Update with a Kalman filter + W += 1 + bias_d = jnp.square(F2 - self.Target.second_moments) / self.Target.variance_second_moments + bias = jnp.average(bias_d) + #bias = jnp.max(bias_d) + + return ((x, u, ll, g, E + kinetic_change + ll - l, key), (W, F2)), bias + + + _, b = jax.lax.scan(step, init=((x, u, l, g, 0., random_key), (1, jnp.square(self.Target.transform(x)))), xs=None, length=num_steps) + + #nans = jnp.any(jnp.isnan(b)) + + return b #+ nans * 1e5 #return a large bias if there were nans + + + ### tuning phase: ### + + def tune12(self, x, u, l, g, random_key, L_given, eps, sigma_given, num_steps1, num_steps2): + """cheap hyperparameter tuning""" + + sigma = sigma_given + + def step(state, outer_weight): + """one adaptive step of the dynamics""" + x, u, l, g, E, Feps, Weps, eps_max, key, eps = self.dynamics_adaptive(state[0], L, sigma) + W, F1, F2 = state[1] + w = outer_weight * eps + zero_prevention = 1-outer_weight + F1 = (W*F1 + w*x) / (W + w + zero_prevention) # Update with a Kalman filter + F2 = (W*F2 + w*jnp.square(x)) / (W + w + zero_prevention) # Update with a Kalman filter + W += w + + return ((x, u, l, g, E, Feps, Weps, eps_max, key), (W, F1, F2)), eps + + L = L_given + + # we use the last num_steps2 to compute the diagonal preconditioner + outer_weights = jnp.concatenate((jnp.zeros(num_steps1), jnp.ones(num_steps2))) + + #initial state + state = ((x, u, l, g, 0., jnp.power(eps, -6.0) * 1e-5, 1e-5, jnp.inf, random_key), (0., jnp.zeros(len(x)), jnp.zeros(len(x)))) + # run the steps + state, eps = jax.lax.scan(step, init=state, xs= outer_weights, length= num_steps1 + num_steps2) + # determine L + if num_steps2 != 0.: + F1, F2 = state[1][1], state[1][2] + variances = F2 - jnp.square(F1) + sigma2 = jnp.average(variances) + + # optionally we do the diagonal preconditioning (and readjust the stepsize) + if self.diagonal_preconditioning: + + # diagonal preconditioning + sigma = jnp.sqrt(variances) + L = jnp.sqrt(self.Target.d) + + #readjust the stepsize + steps = num_steps2 // 3 #we do some small number of steps + state, eps = jax.lax.scan(step, init= state, xs= jnp.ones(steps), length= steps) + else: + L = jnp.sqrt(sigma2 * self.Target.d) + + xx, uu, ll, gg, key = state[0][0], state[0][1], state[0][2], state[0][3], state[0][-1] # the final state + return L, eps[-1], sigma, xx, uu, ll, gg, key #return the tuned hyperparameters and the final state + + + + def tune3(self, x, u, l, g, random_key, L, eps, sigma, num_steps): + """determine L by the autocorrelations (around 10 effective samples are needed for this to be accurate)""" + X, xx, uu, ll, gg, key = self.sample_full(num_steps, x, u, l, g, random_key, L, eps, sigma) + ESS = ess_corr(X) + Lnew = self.Lfactor * eps / ESS # = 0.4 * correlation length + + return Lnew, xx, uu, ll, gg, key + + + def sample_full(self, num_steps, x, u, l, g, random_key, L, eps, sigma): + """Stores full x for each step. Used in tune2.""" + + def step(state, useless): + x, u, l, g, E, key = state + xx, uu, ll, gg, kinetic_change, key = self.dynamics(x, u, g, key, L, eps, sigma) + EE = E + kinetic_change + ll - l + return (xx, uu, ll, gg, EE, key), xx + + state, track = jax.lax.scan(step, init=(x, u, l, g, 0., random_key), xs=None, length=num_steps) + xx, uu, ll, gg, key = state[0], state[1], state[2], state[3], state[5] + return track, xx, uu, ll, gg, key + + + + def bias_plot(self, results): + #bsq = jnp.average(results.reshape(results.shape[0] * results.shape[1], results.shape[2]), axis = 0) + if len(results.shape)>1: + bsq = jnp.median(results, axis = 0) + else: + bsq = results + # plt.plot(bsq) + # plt.plot([0, len(bsq)], np.ones(2) * 0.01, '--', color = 'black') + # plt.yscale('log') + # plt.tight_layout() + # plt.savefig('plots/tst_ensemble/sequential/' + self.Target.name + '.png') + # plt.close() + + cutoff_reached = bsq[-1] < 0.01 + return (100. / (find_crossing(bsq, 0.01) * self.grad_evals_per_step)) * cutoff_reached + + +def find_crossing(array, cutoff): + """the smallest M such that array[m] < cutoff for all m > M""" + + def step(carry, element): + """carry = (, 1 if (array[i] > cutoff for all i < current index) else 0""" + above_threshold = element > cutoff + never_been_below = carry[1] * above_threshold #1 if (array[i] > cutoff for all i < current index) else 0 + return (carry[0] + never_been_below, never_been_below), above_threshold + + state, track = jax.lax.scan(step, init=(0, 1), xs=array, length=len(array)) + + return state[0] + #return jnp.sum(track) #total number of indices for which array[m] < cutoff + + + +def point_reduction(num_points, reduction_factor): + """reduces the number of points for plotting purposes""" + + indexes = np.concatenate((np.arange(1, 1 + num_points // reduction_factor, dtype=int), + np.arange(1 + num_points // reduction_factor, num_points, reduction_factor, dtype=int))) + return indexes diff --git a/mclmc/smc.py b/build/lib/mclmc/smc.py similarity index 100% rename from mclmc/smc.py rename to build/lib/mclmc/smc.py diff --git a/build/lib/tests/__init__.py b/build/lib/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/benchmarks.py b/build/lib/tests/benchmarks.py similarity index 100% rename from tests/benchmarks.py rename to build/lib/tests/benchmarks.py diff --git a/tests/test_annealing.py b/build/lib/tests/test_annealing.py similarity index 100% rename from tests/test_annealing.py rename to build/lib/tests/test_annealing.py diff --git a/tests/test_mclmc.py b/build/lib/tests/test_mclmc.py similarity index 100% rename from tests/test_mclmc.py rename to build/lib/tests/test_mclmc.py diff --git a/tests/test_momentum_update.py b/build/lib/tests/test_momentum_update.py similarity index 100% rename from tests/test_momentum_update.py rename to build/lib/tests/test_momentum_update.py diff --git a/tests/tst_diagonal_precond.py b/build/lib/tests/tst_diagonal_precond.py similarity index 100% rename from tests/tst_diagonal_precond.py rename to build/lib/tests/tst_diagonal_precond.py diff --git a/mclmc/boundary.py b/mclmc/boundary.py deleted file mode 100644 index 4ab4126..0000000 --- a/mclmc/boundary.py +++ /dev/null @@ -1,108 +0,0 @@ - -import jax.numpy as jnp - - - - -class Boundary(): - """Forms a transformation map which will bound the parameter space (this transformation will be applied in the position_update of the Hamiltonian dynamics integration)""" - - def __init__(self, d, - where_positive = None, - where_reflect = None, - where_periodic = None, - a = None, b = None, - ): - - """ - where_positive: indices of positively constrained parameters - where_reflect: indices of rectangularly constrained parameters (with reflective boundary). Use if parameter is constrained to an interval (for example 0 < x < 1), but it is not periodic. - where_periodic: indices of rectangularly constrained parameters (with periodic boundary). Use for example for angles. - a: lower bounds - b: upper bounds - - Example: - We have parameters - x = [x0, x1, x2, x3, x4, x5, x6] - and we want constraints: - x0 unconstrained - x1 > 0 - 0 < x2 < 2 pi (periodic) - x3 unconstrained - 0 < x4 < 1 (not periodic) - -1 < x5 < 1 (not periodic) - x6 > 0 - - We should use: - where_positive = jnp.array([1, 6]) - where_reflect = jnp.array([4, 5]) - where_periodic = jnp.array([2, ]) - a = jnp.array([0., 0.,-1.]) - b = jnp.array([2 jnp.pi, 1., 1.]) - """ - - - self.d = d - - self.mask_positive = self.to_mask(where_positive) - self.mask_reflect = self.to_mask(where_reflect) - self.mask_periodic = self.to_mask(where_periodic) - - - self.a, self.b = self.extend_bounds(jnp.logical_or(self.mask_reflect, self.mask_periodic), a, b) - - - def map(self, x): - """maps R^d to the constrained region - Args: - x: unconstrained parameter vector - Returns: - x': constrained parameter vector - sgn: array of signs (+1 or -1), indicating which component of the velocity should be fliped. - """ - - # These functions map R^d to the constrained region (the unconstrained parameters are also maped but this will be ignored later). - # They also return a boolean array (r) which indicate which components of the velocity should be fliped. - x0, r0 = x, False - x1, r1 = self._positive(x) - x2, r2 = self._reflect(x) - x3, r3 = self._periodic(x) - - combine = lambda y0, y1, y2, y3: self.mask_positive * y1 + self.mask_reflect * y2 + self.mask_periodic * y3 + (1- (self.mask_positive + self.mask_reflect + self.mask_periodic)) * y0 - - return combine(x0, x1, x2, x3), 1 - 2 * combine(r0, r1, r2, r3) - - - - def _positive(self, x): - return jnp.abs(x), x < 0. - - def _periodic(self, x): - return jnp.mod(x - self.a, self.b - self.a) + self.a, False - - def _reflect(self, x): - y = jnp.mod((x - self.a) / (self.b - self.a), 2.) - z = 1 - jnp.abs(1. - y) - return z * (self.b-self.a) + self.a, y > 1. - - - - def extend_bounds(self, mask, a, b): - A = jnp.zeros(len(mask)) - B = jnp.ones(len(mask)) - - if a != None: - A = A.at[mask].set(a) - B = B.at[mask].set(b) - - return A, B - - - def to_mask(self, where): - - mask = jnp.zeros(self.d, dtype = bool) - - if where == None: - return mask - else: - return mask.at[where].set(True) diff --git a/mclmc/dynamics.py b/mclmc/dynamics.py deleted file mode 100644 index 63ae570..0000000 --- a/mclmc/dynamics.py +++ /dev/null @@ -1,152 +0,0 @@ -from typing import Any, NamedTuple -import jax -import jax.numpy as jnp - -lambda_c = 0.1931833275037836 #critical value of the lambda parameter for the minimal norm integrator - - -class State(NamedTuple): - """Dynamical state""" - - x: any#jax.Array - u: any#jax.Array - l: float - g: any#jax.Array - key: tuple - - -# class Info(NamedTuple): - -# transformed_x: jax.Array -# l: jax.Array -# de: float - - -def update_momentum(d): - """The momentum updating map of the esh dynamics (see https://arxiv.org/pdf/2111.02434.pdf) - similar to the implementation: https://github.com/gregversteeg/esh_dynamics - There are no exponentials e^delta, which prevents overflows when the gradient norm is large.""" - - - def update(eps, u, g): - g_norm = jnp.sqrt(jnp.sum(jnp.square(g))) - e = - g / g_norm - ue = jnp.dot(u, e) - delta = eps * g_norm / (d-1) - zeta = jnp.exp(-delta) - uu = e *(1-zeta)*(1+zeta + ue * (1-zeta)) + 2*zeta* u - delta_r = delta - jnp.log(2) + jnp.log(1 + ue + (1-ue)*zeta**2) - return uu/jnp.sqrt(jnp.sum(jnp.square(uu))), delta_r * (d-1) - - - return update - - -def update_position(grad_nlogp, boundary): - - - def update(eps, x, u, sigma): - xx = x + eps * u * sigma - ll, gg = grad_nlogp(xx) - return xx, u, ll, gg - - def update_with_boundary(eps, x, u, sigma): - xx, reflect = boundary.map(x + eps * u * sigma) - ll, gg = grad_nlogp(xx) - uu = reflect * u - return xx, uu, ll, gg - - - return update if boundary == None else update_with_boundary - - - -def minimal_norm(T, V): - - def step(x, u, g, eps, sigma): - """Integrator from https://arxiv.org/pdf/hep-lat/0505020.pdf, see Equation 20.""" - - # V T V T V - uu, r1 = V(eps * lambda_c, u, g * sigma) - xx, uu, ll, gg = T(0.5 * eps, x, uu, sigma) - uu, r2 = V(eps * (1 - 2 * lambda_c), uu, gg * sigma) - xx, uu, ll, gg = T(0.5 * eps, xx, uu, sigma) - uu, r3 = V(eps * lambda_c, uu, gg * sigma) - - #kinetic energy change - kinetic_change = (r1 + r2 + r3) - - return xx, uu, ll, gg, kinetic_change - - return step, 2 - - - -def leapfrog(T, V): - - def step(x, u, g, eps, sigma): - - # V T V - uu, r1 = V(eps * 0.5, u, g * sigma) - xx, uu, l, gg = T(eps, x, uu, sigma) - uu, r2 = V(eps * 0.5, uu, gg * sigma) - - # kinetic energy change - kinetic_change = (r1 + r2) - - return xx, uu, l, gg, kinetic_change - - return step, 1 - - - -def mclmc(hamilton, partial, get_nu): - - - def step(dyn, hyp): - """One step of the generalized dynamics.""" - - # Hamiltonian step - x, u, l, g, kinetic_change = hamilton(x=dyn.x, u=dyn.u, g=dyn.g, eps=hyp.eps, sigma = hyp.sigma) - - # Langevin-like noise - u, key = partial(u= u, random_key= dyn.key, nu= get_nu(hyp.L/hyp.eps)) - - energy_change = kinetic_change + l - dyn.l - - return State(x, u, l, g, key), energy_change - - return step - - - -def full_refresh(d): - """Generates a random (isotropic) unit vector.""" - - - def rng(random_key): - key, subkey = jax.random.split(random_key) - u = jax.random.normal(subkey, shape = (d, )) - u /= jnp.sqrt(jnp.sum(jnp.square(u))) - return u, key - - - return rng - - - - -def partial_refresh(d): - """Adds a small noise to u and normalizes.""" - - def rng(u, random_key, nu): - key, subkey = jax.random.split(random_key) - z = nu * jax.random.normal(subkey, shape = (d, )) - - return (u + z) / jnp.sqrt(jnp.sum(jnp.square(u + z))), key - - get_nu = lambda Nd: jnp.sqrt((jnp.exp(2./Nd) - 1.) / d) #MCHMC paper (Nd = L/eps) - - return rng, get_nu - - diff --git a/mclmc/sampler.cpp b/mclmc/sampler.cpp deleted file mode 100644 index a36f85a..0000000 --- a/mclmc/sampler.cpp +++ /dev/null @@ -1,302 +0,0 @@ -#include -#include -using namespace std; - - - -// Some convenient functions - -double norm(double *vector, int len){ - //norm of the vector - double S = 0.0; - for(int i = 0; i < len; i++)S += vector[i]*vector[i]; - return sqrt(S); -} - -double dot(double *a, double *b, int len){ - //dot product - double S = 0.0; - for(int i = 0; i < len; i++)S += a[i]*b[i]; - return S; -} - -double *empty(int len){ - //reserves an array of doubles of length len - double *array = (double *)malloc(len*sizeof(double)); - return array; -} - -double **empty_matrix(int num_samples, int d){ - double **M = (double **)malloc(num_samples*sizeof(double *)); - for(int i = 0; i< num_samples; ++i)M[i] = (double *)malloc(d*sizeof(double)); - return M; -} - - -std::normal_distribution<> randn(0.0, 1.0); // create normal distribution - - - - -// The target distribution is defined as a class with atributes: -// d: dimension -// grad_nlogp: a function which updates the -log p and its gradient for the new x. -// prior_draw: random draw from a prior, used to initialize the sampler - - -class Target { - // target distribution that we want to sample from - public: - int d; //configuration space dimension - void grad_nlogp(double *, double *, double *); // takes the position x and the pointers where -nlogp and its gradient should be stored. - double *prior_draw(std::mt19937); //random draw from a prior, used to initialize the sampler - -}; - -// an example of a target distribution: standard normal - -inline void Target::grad_nlogp(double *x, double *l, double *g){ - double S = 0.0; - for(int i = 0; i< d; ++i){ - S += + pow(x[i], 2); // - log p(x) = 0.5 \sum x_i^2 - g[i] = x[i]; // grad (-log p) = x - } - l[0] = 0.5 * S; -} - -inline double *Target::prior_draw(std::mt19937 gen){ - double *x = (double *)malloc(d*sizeof(double)); - for(int i = 0; i< d; i++)x[i] = 3 * randn(gen); // Gaussian which is broader than posterior - return x; -} - - - -double lambda_c = 0.1931833275037836; //critical value of the lambda parameter for the minimal norm integrator - - -class Sampler{ - // Sequential MCHMC sampler - public: - int num_samples; - double **samples; - double *E; - double *nlogp; - - int burnin; - double varE; - - double L; double stepsize; - - - std::mt19937 gen; - - Target target; - - Sampler(Target _target, double _L, double _eps, std::mt19937 _gen){ - target = _target; - L = _L; stepsize = _eps; - gen = _gen; - - - } - - // Random generators - - double *random_unit_vector(void){ - - double *u = (double *)malloc(target.d*sizeof(double)); - for(int i = 0; i< target.d; i++)u[i] = randn(gen); - double u_norm = norm(u, target.d); - for(int i = 0; i< target.d; i++)u[i] = u[i]/u_norm; - return u; - } - - void partially_refresh_momentum(double *u, double nu){ - - for(int i = 0; i< target.d; i++)u[i] += nu * randn(gen); //add random noise - double u_norm = norm(u, target.d); //normalize - for(int i = 0; i< target.d; i++)u[i] = u[i]/u_norm; - } - - - // Hamiltonian dynamics - - double update_momentum(double eps, int d, double *g, double *u){ - //The momentum updating map of the esh dynamics (see https://arxiv.org/pdf/2111.02434.pdf) - //similar to the implementation: https://github.com/gregversteeg/esh_dynamics - //There are no exponentials e^delta, which prevents overflows when the gradient norm is large. - - double g_norm = norm(g, d); - - //update u - double ue = -dot(g, u, d) / g_norm; - double delta = eps * g_norm / (d-1); - double zeta = exp(-delta); - for(int i = 0; i < d; ++i)u[i] = (-g[i]/g_norm) *(1-zeta)*(1+zeta + ue * (1-zeta)) + 2*zeta* u[i]; - - //normalize u - double u_norm = norm(u, d); - for(int i = 0; i < d; ++i)u[i] /= u_norm; - - //return the change in the kinetic energy - return delta - log(2) + log(1 + ue + (1-ue)*zeta*zeta); - } - - - double leapfrog(double eps, double *x, double *u, double *l, double *g){ - //leapfrog integrator - - //half step in momentum - double kinetic1 = update_momentum(eps * 0.5, target.d, g, u); - - //full step in x - for(int i = 0; i< target.d; i++)x[i] += eps * u[i]; - - target.grad_nlogp(x, l, g); - - //half step in momentum - double kinetic2 = update_momentum(eps * 0.5, target.d, g, u); - - return (kinetic1 + kinetic2) * (target.d-1); - } - - - double minimal_norm(double eps, double *x, double *u, double *l, double *g){ - //minimal norm integrator - - //V (momentum update) - double kinetic1 = update_momentum(eps * lambda_c, target.d, g, u); - - //T (position update) - for(int i = 0; i< target.d; i++)x[i] += 0.5*eps * u[i]; - - target.grad_nlogp(x, l, g); - - //half step in momentum - double kinetic2 = update_momentum(eps * (1-2*lambda_c), target.d, g, u); - - //T (position update) - for(int i = 0; i< target.d; i++)x[i] += 0.5*eps * u[i]; - - target.grad_nlogp(x, l, g); - - //V (momentum update) - double kinetic3 = update_momentum(eps * lambda_c, target.d, g, u); - - - return (kinetic1 + kinetic2 + kinetic3) * (target.d-1); - } - - - - double dynamics(double eps, double nu, double *x, double *u, double *l, double *g){ - // One step of the Langevin-like dynamics. - - // Hamiltonian step - double kinetic = minimal_norm(eps, x, u, l, g); - - // add noise to the momentum direction - partially_refresh_momentum(u, nu); - - return kinetic; - } - - - // sampling - - void sample(int n){ - // Do MCHMC sampling for n steps with optionally thinned samples - - // allocate space for results - num_samples = n; - samples = empty_matrix(num_samples, target.d); - E = empty(num_samples); - nlogp = empty(num_samples); - - // initialize the particle - double *x = target.prior_draw(gen); - double *u = random_unit_vector(); - -// for(int i =0; i< target.d; ++i){ -// u[i] = 0.0; -// x[i] = 1.0/sqrt(target.d); -// } -// u[0] = 1.0; - - double *l = empty(1); - double *g = empty(target.d); - target.grad_nlogp(x, l, g); - nlogp[0] = l[0]; E[0] = 0.0; - for(int id = 0; id < target.d; ++id)samples[0][id] = x[id]; - - double nu = sqrt((exp(2 * stepsize / L) - 1.0) / target.d); - double kinetic_change; - // do the sampling - - for(int isample = 1; isample< num_samples; ++isample){ - kinetic_change = dynamics(stepsize, nu, x, u, l, g); - nlogp[isample] = l[0]; - E[isample] = E[isample-1] + kinetic_change + nlogp[isample] - nlogp[isample-1]; - - for(int id = 0; id < target.d; ++id)samples[isample][id] = x[id]; - - } - - free(x); free(u); free(g); - - //determine the end of the burn in and the variance of the energy - burnin = burn_in_ending(); - double E1 = 0.0; double E2 = 0.0; - for(int i = burnin; i < num_samples; ++i){ - E1 += E[i]; E2 += pow(E[i], 2); - } - E1 /= (num_samples-burnin); E2 /= (num_samples-burnin); - varE = (E2 - pow(E1, 2)) / target.d; - - } - - int burn_in_ending(void){ - // Estimate the index at which the burn-in ends - double loss_avg = 0.0; - for(int i = 0; i < num_samples; ++i)loss_avg += nlogp[i]; - loss_avg = loss_avg / num_samples; - - int i = 0; - while((nlogp[i] > loss_avg) & (i < num_samples))++i; - return i; - } -}; - - - -int main(void){ - - std::random_device rd; - std::mt19937 gen(rd()); // create and seed the generator - - Target target; - target.d = 100; - - Sampler sampler = Sampler(target, sqrt(target.d), sqrt(target.d), gen); - - sampler.sample(10000); // do the sampling - - - printf("Burn in ended after %d steps. Var[E]/d = %lf.\n", sampler.burnin, sampler.varE); - - double S; - - printf("\nExpectation values:\n"); - for(int j = 0; j<10; j++){ - S = 0.0; - for(int i = sampler.burnin; i< sampler.num_samples; ++i)S += pow(sampler.samples[i][j], 2); - S /= (sampler.num_samples-sampler.burnin); - - printf(" = %lf\n", j+1, S); - } - - return 0; -} - - diff --git a/mclmc/sampler.py b/mclmc/sampler.py deleted file mode 100644 index 4083fad..0000000 --- a/mclmc/sampler.py +++ /dev/null @@ -1,342 +0,0 @@ -## style note: general preference here for functional style (e.g. global function definitions, purity, code sharing) - -from enum import Enum -import jax -import jax.numpy as jnp - -from . import dynamics -from . import tune - - - -class Target(): - """#Class for target distribution - - E.g. - - ```python - Target(d=2, nlogp = lambda x: 0.5*jnp.sum(jnp.square(x))) -``` - - defines a Gaussian. - - """ - - def __init__(self, d, nlogp): - self.d = d - """dimensionality of the target distribution""" - self.nlogp = nlogp - """ negative log probability of target distribution (i.e. energy function)""" - self.grad_nlogp = jax.value_and_grad(self.nlogp) - """ function which computes nlogp and its gradient""" - - def transform(self, x): - """ a transformation of the samples from the target distribution""" - return x - - def prior_draw(self, key): - """**Args**: jax random key - - **Returns**: one random sample from the prior - """ - - raise Exception("Not implemented") - - -OutputType = Enum('Output', ['normal', 'detailed', 'ess']) -""" @private """ - - - - -class Sampler: - """the MCHMC (q = 0 Hamiltonian) sampler""" - - def __init__(self, - Target : Target, - L = None, eps = None, - integrator = dynamics.minimal_norm, varEwanted = 5e-4, - diagonal_preconditioning= True, - frac_tune1 = 0.1, frac_tune2 = 0.1, frac_tune3 = 0.1, - boundary = None - ): - """Args: - Target: the target distribution class - - **L**: momentum decoherence scale (it is then automaticaly tuned before the sampling starts unless you turn-off the tuning by setting frac_tune2 and 3 to zero (see below)) - - **eps**: initial integration step-size (it is then automaticaly tuned before the sampling starts unless you turn-off the tuning by setting all frac_tune1 and 2 to zero (see below)) - - **integrator**: dynamics.leapfrog or dynamics.minimal_norm. Typically minimal_norm performs better. - - **varEwanted**: if your posteriors are biased try smaller values (or larger values: perhaps the convergence is too slow). This is perhaps the parameter whose default value is the least well determined. - - **diagonal_preconditioning**: if you already have your own preconditioning or if you suspect diagonal preconditioning is not useful, turn this off as it can also make matters worse - (but it can also be very useful if you did not precondition the parameters (make their posterior variances close to 1)) - - **frac_tune1**: (num_samples * frac_tune1) steps will be used as a burn-in and to autotune the stepsize - - **frac_tune2**: (num_samples * frac_tune2) steps will be used to autotune L (should be around 10 effective samples long for the optimal performance) - - **frac_tune3**: (num_samples * frac_tune3) steps will be used to improve the L tuning (should be around 10 effective samples long for the optimal performance). This stage is not neccessary if the posterior is close to a Gaussian and does not change much in general. - It can be memory intensive in high dimensions so try turning it off if you have problems with the memory. - - **boundary**: use if some parameters need to be constrained (a Boundary class object) - """ - - self.Target = Target - - ### kernel ### - self.integrator = integrator - self.boundary = boundary - - hamiltonian_step, self.grad_evals_per_step = self.integrator(T= dynamics.update_position(self.Target.grad_nlogp, boundary), - V= dynamics.update_momentum(self.Target.d)) - self.step = dynamics.mclmc(hamiltonian_step, *dynamics.partial_refresh(self.Target.d)) - self.full_refresh = dynamics.full_refresh(self.Target.d) - - - ### hyperparameters ### - self.hyp = tune.Hyperparameters(L if L!= None else jnp.sqrt(self.Target.d), - eps if eps != None else jnp.sqrt(self.Target.d) * 0.25, - jnp.ones(self.Target.d)) - - - ### adaptation ### - tune12 = tune.tune12(self.step, self.Target.d, diagonal_preconditioning, jnp.array([frac_tune1, frac_tune2]), varEwanted, 1.5, 150) - tune3 = tune.tune3(self.step, frac_tune3, 0.4) - - if frac_tune3 != 0.: - tune3 = tune.tune3(self.step, frac= frac_tune3, Lfactor= 0.4) - self.schedule = [tune12, tune3] - else: - self.schedule = [tune12, ] - - - - ### sampling routine ### - - def initialize(self, x_initial, random_key): - - ### random key ### - if random_key is None: - key = jax.random.PRNGKey(0) - else: - key = random_key - - ### initial conditions ### - if x_initial is None: # draw the initial x from the prior - key, prior_key = jax.random.split(key) - x = self.Target.prior_draw(prior_key) - else: - x = x_initial - - l, g = self.Target.grad_nlogp(x) - - u, key = self.full_refresh(key) - #u = - g / jnp.sqrt(jnp.sum(jnp.square(g))) #initialize momentum in the direction of the gradient of log p - - return dynamics.State(x, u, l, g, key) - - - - def sample(self, num_steps, num_chains = 1, x_initial = None, random_key= None, output = OutputType.normal, thinning= 1): - """Args: - num_steps: number of integration steps to take. - - num_chains: number of independent chains, defaults to 1. If different than 1, jax will parallelize the computation with the number of available devices (CPU, GPU, TPU), - as returned by jax.local_device_count(). - - x_initial: initial condition for x, shape: (d, ). Defaults to None in which case the initial condition is drawn from the prior distribution (self.Target.prior_draw). - - random_key: jax random seed, defaults to jax.random.PRNGKey(0) - - output: determines the output of the function: - - 'normal': samples - samples were transformed by the Target.transform to save memory and have shape: (num_samples, len(Target.transform(x))) - - 'detailed': samples, energy error at each step and -log p(x) at each step - - 'ess': Effective Sample Size per gradient evaluation, float. - In this case, ground truth E[x_i^2] and Var[x_i^2] should be known and defined as self.Target.second_moments and self.Target.variance_second_moments - - Note: in all cases the hyperparameters that were used for sampling can be accessed through Sampler.hyp - - thinning: only one every 'thinning' steps is stored. Defaults to 1 (the output then contains (num_steps / thinning) samples) - This is not the recommended solution to save memory. It is better to use the transform functionality, when possible. - """ - - if output == OutputType.ess: - for ground_truth in ['second_moments', 'variance_second_moments']: - if not hasattr(self.Target, ground_truth): - raise AttributeError("Target." + ground_truth + " should be defined if you want to use output = ess.") - - if num_chains == 1: - results = self.single_chain_sample(num_steps, x_initial, random_key, output, thinning) #the function which actually does the sampling - if output == OutputType.ess: - return self.bias_plot(results) - - else: - return results - else: - num_cores = jax.local_device_count() - if random_key is None: - key = jax.random.PRNGKey(0) - else: - key = random_key - - if x_initial is None: # draw the initial x from the prior - keys_all = jax.random.split(key, num_chains * 2) - x0 = jnp.array([self.Target.prior_draw(keys_all[num_chains+i]) for i in range(num_chains)]) - keys = keys_all[:num_chains] - - else: #initial x is given - x0 = jnp.copy(x_initial) - keys = jax.random.split(key, num_chains) - - - f = lambda i: self.single_chain_sample(num_steps, x0[i], keys[i], output, thinning) - - if num_cores != 1: #run the chains on parallel cores - parallel_function = jax.pmap(jax.vmap(f)) - results = parallel_function(jnp.arange(num_chains).reshape(num_cores, num_chains // num_cores)) - if output == OutputType.ess: - return self.bias_plot(results.reshape(num_chains, num_steps)) - - ### reshape results ### - if type(results) is tuple: #each chain returned a tuple - results_reshaped =[] - for i in range(len(results)): - res = jnp.array(results[i]) - results_reshaped.append(res.reshape([num_chains, ] + [res.shape[j] for j in range(2, len(res.shape))])) - return results_reshaped - - else: - return results.reshape([num_chains, ] + [results.shape[j] for j in range(2, len(results.shape))]) - - - else: #run chains serially on a single core - - results = jax.vmap(f)(jnp.arange(num_chains)) - - if output == OutputType.ess: - return self.bias_plot(results) - - else: - return results - - - - def single_chain_sample(self, num_steps, x_initial, random_key, output, thinning): - """sampling routine. It is called by self.sample""" - ### initial conditions ### - dyn = self.initialize(x_initial, random_key) - - hyp = self.hyp - - ### tuning ### - dyn, hyp = tune.run(dyn, hyp, self.schedule, num_steps) - self.hyp = hyp - - - ### sampling ### - - if output == OutputType.normal or output == OutputType.detailed: - X, l, E = self.sample_normal(num_steps, dyn, hyp, thinning) - if output == OutputType.detailed: - return X, E, l - else: - return X - - elif output == OutputType.ess: - return self.sample_ess(num_steps, dyn, hyp) - - - - def build_kernel(self, thinning : int): - """kernel for sampling_normal""" - - def kernel_with_thinning(dyn, hyp): - - def substep(state, _): - _dyn, energy_change = self.step(state[0], hyp) - return (_dyn, energy_change), None - - return jax.lax.scan(substep, init= (dyn, 0.), xs=None, length= thinning)[0] #do 'thinning' steps without saving - - if thinning == 1: - return self.step - else: - return kernel_with_thinning - - - def sample_normal(self, num_steps : int, _dyn : dynamics.State, hyp : tune.Hyperparameters, thinning : int): - """Stores transform(x) for each step.""" - - kernel = self.build_kernel(thinning) - - def step(state, _): - - dyn, energy_change = kernel(state, hyp) - - return dyn, (self.Target.transform(dyn.x), dyn.l, energy_change) - - - return jax.lax.scan(step, init= _dyn, xs=None, length= num_steps // thinning)[1] - - - - def sample_ess(self, num_steps : int, _dyn : dynamics.State, hyp : tune.Hyperparameters): - """Stores the bias of the second moments for each step.""" - - def step(state_track, useless): - dyn, kalman_state = state_track - dyn, _ = self.step(dyn, hyp) - kalman_state = kalman_step(kalman_state) - return (dyn, kalman_state), bias(kalman_state[1]) - - def kalman_step(state, x): - W, F2 = state - F2 = (W * F2 + jnp.square(self.Target.transform(x))) / (W + 1) # Update with a Kalman filter - W += 1 - return W, F2 - - def bias(x2): - bias_d = jnp.square(x2 - self.Target.second_moments) / self.Target.variance_second_moments - bavg2 = jnp.average(bias_d) - #bmax2 = jnp.max(bias_d) - return bavg2 - - - _, b = jax.lax.scan(step, init=(_dyn, (1, jnp.square(self.Target.transform(_dyn.x)))), xs=None, length=num_steps) - - return b - - - - def bias_plot(self, results): - if len(results.shape)>1: - bsq = jnp.median(results, axis = 0) - else: - bsq = results - - - cutoff_reached = bsq[-1] < 0.01 - return (100. / (find_crossing(bsq, 0.01) * self.grad_evals_per_step)) * cutoff_reached - - -def find_crossing(array, cutoff): - """the smallest M such that array[m] < cutoff for all m > M""" - - def step(carry, element): - """carry = (, 1 if (array[i] > cutoff for all i < current index) else 0""" - above_threshold = element > cutoff - never_been_below = carry[1] * above_threshold #1 if (array[i] > cutoff for all i < current index) else 0 - return (carry[0] + never_been_below, never_been_below), above_threshold - - state, track = jax.lax.scan(step, init=(0, 1), xs=array, length=len(array)) - - return state[0] - #return jnp.sum(track) #total number of indices for which array[m] < cutoff - diff --git a/mclmc/tune.py b/mclmc/tune.py deleted file mode 100644 index 9f52c2a..0000000 --- a/mclmc/tune.py +++ /dev/null @@ -1,179 +0,0 @@ -import jax -import jax.numpy as jnp -from typing import NamedTuple - -from .dynamics import State -from .correlation_length import ess_corr - - - -class Hyperparameters(NamedTuple): - """Tunable parameters""" - - L: float - eps: float - sigma: any - - -# all tuning functions are wrappers, recieving some parameters and returning a function -# func(dyn, hyp, num_total_steps) -> (dyn, hyp) - - - -def run(dyn, hyp, schedule, num_steps): - - _dyn, _hyp = dyn, hyp - - for program in schedule: - _dyn, _hyp = program(_dyn, _hyp, num_steps) - - return _dyn, _hyp - - - - -def nan_reject(x, u, l, g, xx, uu, ll, gg, eps, eps_max, dK): - """if there are nans, let's reduce the stepsize, and not update the state. The function returns the old state in this case.""" - - nonans = jnp.all(jnp.isfinite(xx)) - _x, _u, _l, _g, _eps, _dk = jax.tree_util.tree_map(lambda new, old: jax.lax.select(nonans, jnp.nan_to_num(new), old), - (xx, uu, ll, gg, eps_max, dK), - (x, u, l, g, eps * 0.8, 0.)) - - return nonans, _x, _u, _l, _g, _eps, _dk - - - - -def tune12(dynamics, d, - diag_precond, frac, - varEwanted = 1e-3, sigma_xi = 1.5, neff = 150): - - gamma_forget = (neff - 1.0) / (neff + 1.0) - - - def predictor(dyn_old, hyp, adaptive_state): - """does one step with the dynamics and updates the prediction for the optimal stepsize - Designed for the unadjusted MCHMC""" - - W, F, eps_max = adaptive_state - - # dynamics - dyn_new, energy_change = dynamics(dyn_old, hyp) - - # step updating - success, x, u, l, g, eps_max, energy_change = nan_reject(dyn_old.x, dyn_old.u, dyn_old.l, dyn_old.g, - dyn_new.x, dyn_new.u, dyn_new.l, dyn_new.g, - hyp.eps, eps_max, energy_change) - - dyn = State(x, u, l, g, dyn_new.key) - - # Warning: var = 0 if there were nans, but we will give it a very small weight - xi = (jnp.square(energy_change) / (d * varEwanted)) + 1e-8 # 1e-8 is added to avoid divergences in log xi - w = jnp.exp(-0.5 * jnp.square(jnp.log(xi) / (6.0 * sigma_xi))) # the weight reduces the impact of stepsizes which are much larger on much smaller than the desired one. - - F = gamma_forget * F + w * (xi/jnp.power(hyp.eps, 6.0)) - W = gamma_forget * W + w - eps = jnp.power(F/W, -1.0/6.0) #We use the Var[E] = O(eps^6) relation here. - eps = (eps < eps_max) * eps + (eps > eps_max) * eps_max # if the proposed stepsize is above the stepsize where we have seen divergences - hyp_new = Hyperparameters(hyp.L, eps, hyp.sigma) - - return dyn, hyp_new, hyp_new, (W, F, eps_max), success - - - def update_kalman(x, state, outer_weight, success, eps): - """kalman filter to estimate the size of the posterior""" - W, F1, F2 = state - w = outer_weight * eps * success - zero_prevention = 1-outer_weight - F1 = (W*F1 + w*x) / (W + w + zero_prevention) # Update with a Kalman filter - F2 = (W*F2 + w*jnp.square(x)) / (W + w + zero_prevention) # Update with a Kalman filter - W += w - return (W, F1, F2) - - - adap0 = (0., 0., jnp.inf) - _step = predictor - - - def step(state, outer_weight): - """does one step of the dynamcis and updates the estimate of the posterior size and optimal stepsize""" - dyn, hyp, _, adaptive_state, kalman_state = state - dyn, hyp, hyp_final, adaptive_state, success = _step(dyn, hyp, adaptive_state) - kalman_state = update_kalman(dyn.x, kalman_state, outer_weight, success, hyp.eps) - - return (dyn, hyp, hyp_final, adaptive_state, kalman_state), None - - - def func(_dyn, _hyp, num_steps): - - num_steps1, num_steps2 = jnp.rint(num_steps * frac).astype(int) - - # we use the last num_steps2 to compute the diagonal preconditioner - outer_weights = jnp.concatenate((jnp.zeros(num_steps1), jnp.ones(num_steps2))) - - #initial state - - kalman_state = (0., jnp.zeros(d), jnp.zeros(d)) - - # run the steps - state = jax.lax.scan(step, init= (_dyn, _hyp, _hyp, adap0, kalman_state), xs= outer_weights, length= num_steps1 + num_steps2)[0] - dyn, _, hyp, adap, kalman_state = state - - # determine L - L = hyp.L - sigma = hyp.sigma - if num_steps2 != 0.: - _, F1, F2 = kalman_state - variances = F2 - jnp.square(F1) - L = jnp.sqrt(jnp.sum(variances)) - - # optionally we do the diagonal preconditioning (and readjust the stepsize) - if diag_precond: - - # diagonal preconditioning - sigma = jnp.sqrt(variances) - L = jnp.sqrt(d) - - #readjust the stepsize - steps = num_steps2 // 3 #we do some small number of steps - state = jax.lax.scan(step, init= state, xs= jnp.ones(steps), length= steps)[0] - dyn, _, hyp, adap, kalman_state = state - else: - sigma = hyp.sigma - - return dyn, Hyperparameters(L, hyp.eps, sigma) - - return func - - - - -def tune3(step, frac, Lfactor): - """determine L by the autocorrelations (around 10 effective samples are needed for this to be accurate)""" - - - def sample_full(num_steps, _dyn, hyp): - """Stores full x for each step. Used in tune2.""" - - def _step(state, useless): - dyn_old = state - dyn_new, _ = step(dyn_old, hyp) - - return dyn_new, dyn_new.x - - return jax.lax.scan(_step, init=_dyn, xs=None, length=num_steps) - - - def func(dyn, hyp, num_steps): - steps = jnp.rint(num_steps * frac).astype(int) - - dyn, X = sample_full(steps, dyn, hyp) - ESS = ess_corr(X) # num steps / effective sample size - Lnew = Lfactor * hyp.eps / ESS # = 0.4 * length corresponding to one effective sample - - return dyn, Hyperparameters(Lnew, hyp.eps, hyp.sigma) - - - return func - diff --git a/notebooks/tutorials/Constraints.ipynb b/notebooks/tutorials/Constraints.ipynb deleted file mode 100644 index 66a7dd9..0000000 --- a/notebooks/tutorials/Constraints.ipynb +++ /dev/null @@ -1,521 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyPNcM7NKlkb7msEnhPSZtwW", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Parameters often have constraints, such as $x > 0$ or $0 < x < 2 \\pi$. This notebook demonstrates how to impose such constraints in MCHMC code.\n", - "\n", - "**Note**: uniform priors, such as $x \\sim U(0.1, 3)$ are often used unneccesarily and a smooth prior, such as a Gaussian would be more appropriate. Are we really absolutely certain that x cannot be 0.09?\n", - "\n", - "There are two approaches that we can take to constraint the parameters:\n", - "\n", - "- impose reflective (or periodic) boundaries\n", - "- transform the parameters\n", - "\n", - "The first approach is implemented in MCHMC.\n", - "The second approach currently needs to be done by hand.\n", - "\n", - "We will try both strategies in this notebook.\n", - "Let's do some imports:" - ], - "metadata": { - "id": "_aFlr1sKm2MO" - } - }, - { - "cell_type": "code", - "source": [ - "!git clone https://github.com/JakobRobnik/MicroCanonicalHMC.git" - ], - "metadata": { - "id": "-oHzs2UYuOmv" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import jax\n", - "import jax.numpy as jnp\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from MicroCanonicalHMC.mclmc.sampler import Sampler\n", - "from MicroCanonicalHMC.mclmc.boundary import Boundary" - ], - "metadata": { - "id": "BnOYcWu_m1bF" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "We will sample from two toy distributions:\n", - "\n", - "1. Distribution with first two parameters positive:\n", - "\n", - "$$ x_1, x_2 \\sim Exp(\\lambda = 1) $$\n", - "$$ x_3, \\ldots x_d \\sim N(0, 1) $$\n", - "\n", - "\n", - "\n", - "2. Distribution with first parameter an angle between 0 and $2 \\pi$, second two parameters between 0 and 1 and fourth parameter positive:\n", - "\n", - "$$p(x_0) \\propto \\exp \\{ 5 \\cos{x_0} \\}$$\n", - "$$x_1 \\sim \\beta(5, 1)$$\n", - "$$x_2 \\sim \\beta(2, 2)$$\n", - "$$ x_3 \\sim Exp(\\lambda = 1)$$\n", - "$$ x_4, \\ldots x_d \\sim N(0, 1) $$" - ], - "metadata": { - "id": "CvPNK26Nuu15" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Distribution 1" - ], - "metadata": { - "id": "DZ2nO5oG8Ywe" - } - }, - { - "cell_type": "markdown", - "source": [ - "### Approach 1: transform the parameters" - ], - "metadata": { - "id": "ZcqYN5Thv1Pr" - } - }, - { - "cell_type": "markdown", - "source": [ - "The first approach we will take is to transform the parameters.\n", - "We will work with the parameter vector:\n", - "$$\\boldsymbol{z} = (\\log x_1,\\, \\log x_2,\\, x_3,\\, \\ldots x_d)$$\n", - "Note that $z_1 \\in \\mathbb{R}$ is not constrained anymore, so we have managed to make the parameter space unconstrained.\n", - "\n", - "We shouldn't forget to also transform the density:\n", - "\n", - "$$\\log p(z_1) = \\log p(x_1(z_1)) + \\log \\bigg{\\vert} \\frac{d x_1}{d z_1} \\bigg{\\vert} = \\log (p(e^{z_1})) + z_1 $$\n", - "\n", - "Since the original density was $p(x_1) = \\lambda e^{-\\lambda x_1}$, we get\n", - "\n", - "$$\\log p(z_1) = - \\lambda e^{z_1} + \\log \\lambda + z_1$$.\n", - "\n", - "Let's implement the target:" - ], - "metadata": { - "id": "3SVEKEf8v0ZC" - } - }, - { - "cell_type": "code", - "source": [ - "nlogp = lambda z: jnp.sum(jnp.exp(z[:2]) - z[:2]) + 0.5 * jnp.sum(jnp.square(z[2:])) # we have dropped the constant log lambda term.\n", - "\n", - "\n", - "class TransformedTarget():\n", - "\n", - " def __init__(self, d):\n", - " self.d = d\n", - " self.nlogp = nlogp\n", - " self.grad_nlogp = jax.value_and_grad(self.nlogp)\n", - " self.transform = lambda x: x\n", - " self.prior_draw = lambda key: jnp.abs(jax.random.normal(key, shape = (self.d, )))\n", - "\n", - "target = TransformedTarget(d= 50)" - ], - "metadata": { - "id": "I2BpxMC6pZzw" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Let's do sampling in the transformed space and transform back at the end:" - ], - "metadata": { - "id": "KhtS8Sglm1Ha" - } - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "kTP0KXfimvlA" - }, - "outputs": [], - "source": [ - "sampler = Sampler(target)\n", - "\n", - "z = sampler.sample(10000)\n", - "x1 = jnp.exp(z[:, 0])\n", - "x2 = jnp.exp(z[:, 0])\n", - "x3 = z[:, 2]\n", - "# ..." - ] - }, - { - "cell_type": "markdown", - "source": [ - "Let's visualize the results:" - ], - "metadata": { - "id": "6osv65fl14Rh" - } - }, - { - "cell_type": "code", - "source": [ - "def visualize_results(x1, x3):\n", - "\n", - " plt.figure(figsize = (15, 6))\n", - "\n", - " # x_1\n", - " plt.subplot(1, 2, 1)\n", - " plt.hist(x1, bins = 30, density = True, color = 'teal', label = 'MCLMC')\n", - " t = jnp.linspace(0, 10, 200)\n", - " plt.plot(t, jnp.exp(-t), color = 'black', lw = 5, label = 'truth')\n", - " plt.xlabel(r'$x_1$', fontsize = 18)\n", - " plt.ylabel(r'$p(x_1)$', fontsize = 18)\n", - " plt.xlim(0, 10)\n", - " plt.legend(fontsize = 18)\n", - "\n", - "\n", - " # x_3\n", - " plt.subplot(1, 2, 2)\n", - " plt.hist(x3, bins = 30, density = True, color = 'teal', label = 'MCLMC (transformed target)')\n", - " t = jnp.linspace(-5, 5, 200)\n", - " plt.plot(t, jnp.exp(-0.5 * jnp.square(t)) / jnp.sqrt(2 * jnp.pi), color = 'black', lw = 5, label = 'truth')\n", - " plt.xlim(-5, 5)\n", - " plt.xlabel(r'$x_3$', fontsize = 18)\n", - " plt.ylabel(r'$p(x_3)$', fontsize = 18)\n", - "\n", - " plt.show()\n", - "\n", - "visualize_results(x1, x3)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 552 - }, - "id": "Z-Za6PFp12DH", - "outputId": "f6af985f-71a4-4a8d-a64a-9df63cdd3453" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Approach 2: Reflective boundary" - ], - "metadata": { - "id": "6Pk2iunK21PL" - } - }, - { - "cell_type": "markdown", - "source": [ - "Alternatively, we can use the reflecitve boundary in the dynamics.\n", - "We define the untransformed target:" - ], - "metadata": { - "id": "LDdkHfAZ28y7" - } - }, - { - "cell_type": "code", - "source": [ - "nlogp = lambda x: jnp.sum(x[:2]) + 0.5 * jnp.sum(jnp.square(x[2:])) # we have dropped the constant log lambda term.\n", - "\n", - "class Target():\n", - "\n", - " def __init__(self, d):\n", - " self.d = d\n", - " self.nlogp = nlogp\n", - " self.grad_nlogp = jax.value_and_grad(self.nlogp)\n", - " self.transform = lambda x: x\n", - " self.prior_draw = lambda key: jnp.abs(jax.random.normal(key, shape = (self.d, )))\n", - "\n", - "target = Target(d= 50)" - ], - "metadata": { - "id": "sVwb3IkM3Q1t" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "And impose the constraints with the Boundary object (we pass it the indices of the parameters which are constrained):" - ], - "metadata": { - "id": "RI8EA9fR3lLG" - } - }, - { - "cell_type": "code", - "source": [ - "boundary = Boundary(target.d, where_positive = jnp.array([0, 1])) #x0 and x1 are positive" - ], - "metadata": { - "id": "s3Vxq5EM28CO" - }, - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Let's do the sampling and visualize the results:" - ], - "metadata": { - "id": "ZMdRJPjT34xb" - } - }, - { - "cell_type": "code", - "source": [ - "sampler = Sampler(target, boundary = boundary)\n", - "x = sampler.sample(10000)\n", - "visualize_results(x[:, 0], x[:, 2])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 552 - }, - "id": "qVueh9oA33_p", - "outputId": "dd9b94f9-4f99-4880-e822-8bc032672611" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# Distribution 2" - ], - "metadata": { - "id": "4qa4KHDcKXHN" - } - }, - { - "cell_type": "markdown", - "source": [ - "We will now sample from\n", - "\n", - "\n", - "$$p(x_0) \\propto \\exp \\{ 5 \\cos{x_0} \\}$$\n", - "$$x_1 \\sim \\beta(5, 1)$$\n", - "$$x_2 \\sim \\beta(2, 2)$$\n", - "$$ x_3 \\sim Exp(\\lambda = 1)$$\n", - "$$ x_4, \\ldots x_d \\sim N(0, 1) $$\n", - "\n", - "\n", - "We could again tranform our domain to $\\mathbb{R}^d$, using for example logit transform, tangens or arctanh functions for the first three parameters.\n", - "\n", - "However, we will here impose the constraints with reflective and periodic boundaries.\n", - "\n", - "Let's implement the target:" - ], - "metadata": { - "id": "B7CL5yml9Cy8" - } - }, - { - "cell_type": "code", - "source": [ - "nlogp_beta = lambda x, alpha, beta: -(alpha-1.) * jnp.log(x) - (beta-1.) * jnp.log(1.-x)\n", - "\n", - "nlogp = lambda x: -5 * jnp.cos(x[0]) + nlogp_beta(x[1], 5., 1.) + nlogp_beta(x[2], 2., 2.) + x[3] + 0.5 * jnp.sum(jnp.square(x[4:]))\n", - "\n", - "class Target():\n", - "\n", - " def __init__(self, d):\n", - " self.d = d\n", - " self.nlogp = nlogp\n", - " self.grad_nlogp = jax.value_and_grad(self.nlogp)\n", - " self.transform = lambda x: x\n", - "\n", - " def prior_draw(self, key):\n", - " key1, key2 = jax.random.split(key, 2)\n", - " u = jax.random.uniform(key1, shape = (3, ))\n", - " z = jax.random.normal(key2, shape = (self.d-3, ))\n", - " return jnp.concatenate((u, z))\n", - "\n", - "target = Target(d= 100)" - ], - "metadata": { - "id": "6dil9_4pKgYD" - }, - "execution_count": 77, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "We will use reflective boundary conditions for $x_1$ and $x_2$, periodic boundary conditions for $x_0$ and positive constraint for $x_3$:" - ], - "metadata": { - "id": "tqp8xJ1ODp1f" - } - }, - { - "cell_type": "code", - "source": [ - "boundary = Boundary(target.d,\n", - " where_periodic = jnp.array([0, ]),\n", - " where_reflect = jnp.array([1, 2]),\n", - " where_positive = jnp.array([3, ]),\n", - " a = jnp.array([0., 0., 0.]),\n", - " b = jnp.array([2 * jnp.pi, 1., 1.]))" - ], - "metadata": { - "id": "BAFzc1I-KgMW" - }, - "execution_count": 78, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Particle bounces off the reflective boundary.\n", - "If the domain is periodically connected (meaning that $p(x)$ and its derivatives do not have a jump when going from $x = b$ to $x = a$), the periodic boundary conditions are more appropriate. We therefore use periodic boundary conditions for $x_0$." - ], - "metadata": { - "id": "bYhJZIWYGBBr" - } - }, - { - "cell_type": "markdown", - "source": [ - "Let's do the sampling and visualize the results (we use diagonal preconditioning because the first four parameters have different scales than the other parameters):" - ], - "metadata": { - "id": "J5XtSPNPG7Tr" - } - }, - { - "cell_type": "code", - "source": [ - "sampler = Sampler(target, boundary = boundary, diagonal_preconditioning = True)\n", - "x = sampler.sample(100000)" - ], - "metadata": { - "id": "r1y9N5teEolv" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "plt.figure(figsize = (20, 4))\n", - "\n", - "a = [0, 0, 0, 0, -4]\n", - "b = [2 * jnp.pi, 1, 1, 8, 4]\n", - "truth = [lambda t: jnp.exp(5 * jnp.cos(t)) / 171.153,\n", - " lambda t: jnp.power(t, 4) * 5,\n", - " lambda t: t*(1-t)*6.,\n", - " lambda t: jnp.exp(-t),\n", - " lambda t: jnp.exp(-0.5 * jnp.square(t)) / jnp.sqrt(2 * jnp.pi)]\n", - "\n", - "\n", - "for i in range(5):\n", - " plt.subplot(1, 5, i + 1)\n", - " plt.hist(x[:, i], bins = 30, density = True, color = 'teal', label = 'MCLMC')\n", - " t = jnp.linspace(a[i], b[i], 200)\n", - " plt.plot(t, truth[i](t), color = 'black', lw = 5, label = 'truth')\n", - " plt.xlabel('x' + str(i), fontsize = 18)\n", - " if i == 0:\n", - " plt.ylabel('pdf', fontsize = 18)\n", - " plt.xlim(a[i], b[i])\n", - " if i == 3:\n", - " plt.legend()\n", - "\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 398 - }, - "id": "ggZ0NMegI196", - "outputId": "b5bca79e-6aa7-49fd-d7dd-b3bb2bed077c" - }, - "execution_count": 81, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
    " - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - } - ] -} \ No newline at end of file diff --git a/notebooks/tutorials/Ensamble_tutorial.ipynb b/notebooks/tutorials/Ensamble_tutorial.ipynb deleted file mode 100644 index e491f63..0000000 --- a/notebooks/tutorials/Ensamble_tutorial.ipynb +++ /dev/null @@ -1,427 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vhn_WCxg2knQ" - }, - "source": [ - "# **Ensamble MCHMC tutorial: sampling from the Rosenbrock function**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tmjQrETa7988" - }, - "source": [ - "We will sample from a common benchmark problem: the [Rosenbrock function](https://en.wikipedia.org/wiki/Rosenbrock_function).\n", - "In its original form, the Rosenbrock function is a two-dimensional function with a narrow banana shape:\n", - "\n", - "$f(x, y) = (a- x)^2 + b (y - x^2)^2$.\n", - "\n", - "The width is controled by the parameter b. Let's define the function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "c7rRZyoE3-wU" - }, - "outputs": [], - "source": [ - "import sys \n", - "sys.path.insert(0, '../../')\n", - "\n", - "import jax\n", - "import jax.numpy as jnp\n", - "\n", - "a, b = 1.0, 10.0\n", - "\n", - "f = lambda x, y: jnp.square(a - x) + b * jnp.square(y - jnp.square(x)) #rosenbrock function" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ghAcoSx18hBT" - }, - "source": [ - "We will sample from the distribution $p(x, y) \\propto \\exp{ -\\frac{1}{2} f(x, y)}$.\n", - "\n", - "Let's visualize it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 525 - }, - "id": "f4PrDr4Z3s6M", - "outputId": "ac1e887f-d2e6-4203-edca-e5c32b92780c" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "xmin, xmax = -3, 5\n", - "ymin, ymax = -2, 18\n", - "xlin= jnp.linspace(xmin, xmax, 300)\n", - "ylin= jnp.linspace(ymin, ymax, 300)\n", - "X, Y = jnp.meshgrid(xlin, ylin)\n", - "Z = f(X, Y)\n", - "\n", - "\n", - "# figure setup\n", - "plt.rcParams.update({'font.size': 22, 'axes.spines.right': False, 'axes.spines.top': False})\n", - "plt.figure(figsize = (ymax-ymin, xmax-xmin))\n", - "\n", - "levels = np.linspace(0.005, 1, 20)\n", - "plt.contourf(Y, X, jnp.exp(-0.5*Z), cmap = 'Greys', levels = levels)\n", - "plt.xlabel('y')\n", - "plt.ylabel('x')\n", - "plt.xticks(np.arange(ymin, ymax+1, 2))\n", - "plt.yticks(np.arange(xmin, xmax+1, 2))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OLIYrBHL8zn0" - }, - "source": [ - "We will make the problem more interesting and take a cartesian product of 18 Rosenbrock distributions:\n", - "$p(x_1,\\, x_2,\\, \\ldots x_{18},\\, y_1,\\, y_2,\\, \\ldots y_{18}) = p(x_1,\\, y_1) \\cdot p(x_2,\\, y_2) \\cdots p(x_{18},\\, y_{18})$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "P8gcVOMm2Q0q" - }, - "outputs": [], - "source": [ - "d = 2 * 18\n", - "\n", - "def nlogp(xy):\n", - " \"\"\"- log(p) of our target distribution\"\"\"\n", - " x = xy[:18]\n", - " y = xy[18:]\n", - " return 0.5 * jnp.sum(f(x, y))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p_O50nfG-HqX" - }, - "source": [ - "Let's import the MCHMC code:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "I-g-OAkp-KoF" - }, - "outputs": [], - "source": [ - "!rm -r MicroCanonicalHMC\n", - "!git clone https://github.com/JakobRobnik/MicroCanonicalHMC.git" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KUASVB70-L2-" - }, - "source": [ - "and create the target class. The obligatory attributes are the configuration space dimension `d` and a function `grad_nlogp` which returns the negative log density and its gradient. We will use the auto-diff to get the gradient." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "j1INcRa19TLz" - }, - "outputs": [], - "source": [ - "from MicroCanonicalHMC.sampling.ensamble import Sampler\n", - "\n", - "\n", - "class RosenbrockTarget():\n", - "\n", - " def __init__(self):\n", - "\n", - " self.d = d\n", - " self.grad_nlogp = jax.value_and_grad(nlogp) #auto-diff\n", - "\n", - " def prior_draw(self, key):\n", - " \"\"\"gaussian prior\"\"\"\n", - " return jax.random.normal(key, shape = (self.d, ), dtype = 'float64')*3.0\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FAJsRwMvYfXD" - }, - "source": [ - "We have also defined the ```prior_draw``` which can be used to initialize the particles." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "DkxW0bH9ARET" - }, - "outputs": [], - "source": [ - "num_chains = 300 # we will run 300 chains\n", - "random_seed = jax.random.PRNGKey(42)\n", - "sampler = Sampler(RosenbrockTarget(), alpha = 10.0) # alpha = 1.0 is more universal, but specifically for the Rosenbrock, the convergence is faster with alpha = 10." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pQ-KnelQ-80O" - }, - "source": [ - "If we have 300 devices available we can run each particle on its own device by passing an additional argument `pmap = True` to the Sampler.\n", - "\n", - "Initially the particles are far from the target distribution:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 626 - }, - "id": "PJG84sMW85Gg", - "outputId": "029a0a47-6136-4ee2-cade-496dd6eb0d60" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#draw from the prior\n", - "x_initial = sampler.Target.prior_draw(jax.random.split(random_seed, num_chains))\n", - "\n", - "plt.figure(figsize = (10, 10))\n", - "\n", - "#plot the intial particle locations\n", - "plt.plot(x_initial[:, 0], x_initial[:, 1], '.', markersize = 8, color = 'salmon')\n", - "\n", - "#target distribution\n", - "plt.contourf(Y, X, jnp.exp(-0.5*Z), cmap = 'Greys', levels = levels)\n", - "\n", - "plt.xlabel('y')\n", - "plt.ylabel('x')\n", - "plt.xlim(-8, 12)\n", - "plt.ylim(-9, 11)\n", - "plt.xticks([-5, 0, 5, 10])\n", - "plt.yticks([-10, -5, 0, 5, 10])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JitVvBFL-96a" - }, - "source": [ - "We will run the sampler for 500 steps and automatically remove the burn-in:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "luYJlTWW-78O", - "outputId": "7af6bb35-8e38-4706-8b33-03f478645a11" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The determination of the step-size for sampling may be unreliable (the energy fluctuations may be more than a factor of 10 off the typical optimum).\n" - ] - } - ], - "source": [ - "samples = sampler.sample(num_steps = 500, num_chains= num_chains, remove_burn_in = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IjEbIeISIQpH" - }, - "source": [ - "The output has the shape `(num_chains, num_samples - burn_in, d)`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-IG48jI5IH4v", - "outputId": "76b00346-459f-46de-9f9c-86f5ae9fdf76" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(300, 359, 36)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "samples.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_f4fEEpIUrB2" - }, - "source": [ - "Let's visualize the 2d posteriors in the $(x_1,\\, y_1)$ plane. We show the end positions of the particles with dots. The 2d posterior histogram, using all of the $300 \\cdot 359$ samples is shown as a color plot. The ground truth levels are white." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 525 - }, - "id": "jsKdxuF5DpcI", - "outputId": "c587b461-f179-48b4-8bdb-7f6aab66c5d1" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (ymax-ymin, xmax-xmin))\n", - "\n", - "# the end positions of the chains\n", - "x, y = samples[:, -1, 0], samples[:, -1, 18]\n", - "plt.plot(y, x, '.', color = 'salmon')\n", - "\n", - "\n", - "# 2d histrogram using all of the samples\n", - "x1_samples = jnp.concatenate(samples[:, :, 0])\n", - "y1_samples = jnp.concatenate(samples[:, :, 18])\n", - "plt.hexbin(y1_samples, x1_samples, cmap='hot', gridsize=100)\n", - "plt.gca().set_facecolor('black')\n", - "\n", - "\n", - "# ground truth levels\n", - "plt.contour(Y, X, jnp.exp(-0.5*Z), colors = 'white', linewidths = np.linspace(0.4, 3, 5), levels = levels[::4]) #ground truth levels\n", - "\n", - "plt.xlabel('y')\n", - "plt.ylabel('x')\n", - "plt.xticks(np.arange(ymin, ymax+1, 2))\n", - "plt.yticks(np.arange(xmin, xmax+1, 2))\n", - "plt.xlim(ymin, ymax)\n", - "plt.ylim(xmin, xmax)\n", - "plt.show()" - ] - } - ], - "metadata": { - "colab": { - "authorship_tag": "ABX9TyPWN3ongC1QoHevdeIRYDOE", - "include_colab_link": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.6.15" - }, - "vscode": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/tutorials/advanced_tutorial.ipynb b/notebooks/tutorials/advanced_tutorial.ipynb deleted file mode 100644 index aa4d5c3..0000000 --- a/notebooks/tutorials/advanced_tutorial.ipynb +++ /dev/null @@ -1,353 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-JEPCbUWCY_K" - }, - "source": [ - "# Advanced tutorial - Stochastic volatility model\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vaeLqaf_CjD4" - }, - "source": [ - "Here we work with a harder sampling problem - Stochastic volatility modeling of the returns on the S&P500 index, taken from [here](https://num.pyro.ai/en/latest/examples/stochastic_volatility.html).\n", - "\n", - "We have $N = 2427$ values of the returns on the S\\&P500 index $\\{ r_n\\}_{n = 1}^{N}$ in the time span of 10 years. Let's download the data and visualize it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nn-cdUeHL8P6" - }, - "outputs": [], - "source": [ - "!pip install numpyro" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "fIW2AogJEXRY", - "outputId": "d0daf116-c13c-4009-ce55-15d1c97f56c6" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
    " - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import sys\n", - "sys.path.insert(0, '../../')\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import matplotlib.dates as mdates\n", - "plt.rcParams.update({'font.size': 19})\n", - "\n", - "from numpyro.examples.datasets import SP500, load_dataset\n", - "from numpyro.distributions import StudentT\n", - "\n", - "# get the data\n", - "_, fetch = load_dataset(SP500, shuffle=False)\n", - "SP500_dates, SP500_returns = fetch()\n", - "\n", - "\n", - "# figure setup\n", - "plt.figure(figsize = (12, 5))\n", - "ax = plt.subplot()\n", - "ax.spines['right'].set_visible(False) #remove the upper and the right axis lines\n", - "ax.spines['top'].set_visible(False)\n", - "\n", - "ax.xaxis.set_major_locator(mdates.YearLocator()) #dates on the xaxis\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y\"))\n", - "ax.xaxis.set_minor_locator(mdates.MonthLocator())\n", - "\n", - "# plot data\n", - "dates = mdates.num2date(mdates.datestr2num(SP500_dates))\n", - "plt.plot(dates, SP500_returns, '.', markersize = 3, color= 'steelblue')\n", - "plt.xlabel('time')\n", - "plt.ylabel('S&P500 returns')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GO1SRvwAERTZ" - }, - "source": [ - "The returns $r_n$ are modeled by a Student's-t distribution whose scale (volatility) $R_n$ is time varying and unknown. The prior for $\\log R_n$ is a Gaussian random walk, with an exponential distribution of the random walk step-size $\\sigma$. An exponential prior is also taken for the Student's-t degrees of freedom $\\nu$. The generative process of the data is:\n", - "\n", - "\\begin{align}\n", - " &r_n / R_n \\sim \\text{Student's-t}(\\nu) \\qquad\n", - " &&\\nu \\sim \\text{Exp}(\\lambda = 1/10) \\\\ \\nonumber\n", - " &\\log R_n \\sim \\mathcal{N}(\\log R_{n-1}, \\sigma) \\qquad\n", - " &&\\sigma \\sim \\text{Exp}(\\lambda = 1/0.02).\n", - "\\end{align}\n", - "Our task is to find the posterior of the parameters $\\{R_n\\}_{n =1}^N$, $\\sigma$ and $\\nu$, given the observed data $\\{r_n\\}_{n =1}^N$.\n", - "\n", - "We have to make the configuration space unconstrained ($\\boldsymbol{x} \\in \\mathbb{R}^d$). In the original form, the parameters were all positive, so we take the logarithms of our parameters:\n", - "\\begin{equation}\n", - " \\boldsymbol{x} = ( \\log R_1,\\, \\log R_2, \\,... \\log R_N, \\,\\log \\lambda_{\\sigma} \\sigma,\\, \\log \\lambda_{\\nu} \\nu).\n", - "\\end{equation}\n", - "\n", - "Let's implement an instance of the target density class. `nlogp` $= -\\log p(\\boldsymbol{x})$ has to be implemented by hand, but the gradient can be computed by jax.\n", - "We also define the transform function which maps to the constrained space (essentially taking the exponent of the variables)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "1JtwMEmiJxvy" - }, - "outputs": [], - "source": [ - "import jax\n", - "import jax.numpy as jnp\n", - "\n", - "\n", - "class StochasticVolatility():\n", - "\n", - " def __init__(self):\n", - "\n", - " self.d = 2429\n", - "\n", - " self.lambda_sigma, self.lambda_nu = 50, 0.1\n", - "\n", - " self.grad_nlogp = jax.value_and_grad(self.nlogp) #we compute the gradient using jax\n", - "\n", - "\n", - " def nlogp(self, x):\n", - " \"\"\"- log p of the target distribution\"\"\"\n", - "\n", - " sigma = jnp.exp(x[-2]) / self.lambda_sigma # we used log-transformation to make x unconstrained\n", - " nu = jnp.exp(x[-1]) / self.lambda_nu\n", - "\n", - " prior2 = (jnp.exp(x[-2]) - x[-2]) + (jnp.exp(x[-1]) - x[-1]) # - log prior(sigma, nu)\n", - " prior1 = (self.d - 2) * jnp.log(sigma) + 0.5 * (jnp.square(x[0]) + jnp.sum(jnp.square(x[1:-2] - x[:-3]))) / jnp.square(sigma) # - log prior(R)\n", - " lik = -jnp.sum(StudentT(df=nu, scale= jnp.exp(x[:-2])).log_prob(SP500_returns)) # - log likelihood\n", - "\n", - " return lik + prior1 + prior2\n", - "\n", - "\n", - " def transform(self, x):\n", - " \"\"\"transform x back to the parameters R, sigma and nu (taking the exponent)\"\"\"\n", - "\n", - " Rn = jnp.exp(x[:-2])\n", - " sigma = jnp.exp(x[-2]) / self.lambda_sigma\n", - " nu = jnp.exp(x[-1]) / self.lambda_nu\n", - "\n", - " return jnp.concatenate((Rn, jnp.array([sigma, nu])))\n", - "\n", - "\n", - " def prior_draw(self, key):\n", - " \"\"\"draws x from the prior\"\"\"\n", - "\n", - " key_walk, key_exp1, key_exp2 = jax.random.split(key, 3)\n", - "\n", - " sigma = jax.random.exponential(key_exp1) / self.lambda_sigma #sigma is drawn from the exponential distribution\n", - "\n", - " def step(track, useless): #one step of the gaussian random walk\n", - " randkey, subkey = jax.random.split(track[1])\n", - " x = jax.random.normal(subkey, shape= track[0].shape, dtype = track[0].dtype) + track[0]\n", - " return (x, randkey), x\n", - "\n", - " x = jnp.empty(self.d)\n", - " x = x.at[:-2].set(jax.lax.scan(step, init=(0.0, key_walk), xs=None, length=self.d - 2)[1] * sigma) # = log R_n are drawn as a Gaussian random walk realization\n", - " x = x.at[-2].set(jnp.log(sigma * self.lambda_sigma)) #sigma ~ exponential distribution(lambda_sigma)\n", - " x = x.at[-1].set(jnp.log(jax.random.exponential(key_exp2))) #nu ~ exponential distribution(lambda_nu)\n", - "\n", - " return x\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Lq-Xe9rUPIwu" - }, - "source": [ - "Now we can sample from the defined target using MCHMC." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "WgqtNGanPAiK" - }, - "outputs": [], - "source": [ - "!pip install mclmc" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FhF9PLraPS5N" - }, - "source": [ - "As in the `intro_tutorial` we use the automatic hyperparameter tuning and then take 5000 samples:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "B-IEZmVZPRAG" - }, - "outputs": [], - "source": [ - "from mclmc.sampler import Sampler\n", - "\n", - "sampler = Sampler(StochasticVolatility())" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "79cPpfROt2bh" - }, - "outputs": [], - "source": [ - "samples = sampler.sample(5000)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4FscnjHeUQe4" - }, - "source": [ - "\n", - "Let's visualize the posterior. First, we compute the median $R_n$ and its quartiles:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "6fMmjTUwUywM" - }, - "outputs": [], - "source": [ - "R = np.array(samples)[:, :-2] #remove sigma and nu parameters\n", - "R = np.sort(R, axis = 0) #sort samples for each R_n\n", - "num_samples = len(R)\n", - "\n", - "lower_quartile, median, upper_quartile = R[num_samples//4, :], R[num_samples//2, :], R[3*num_samples//4, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Gymt1GNUYnwb" - }, - "source": [ - "Now, we can visualize the time dependant volatility:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 489 - }, - "id": "thHutRoFSQ7y", - "outputId": "5737a3b7-1d5f-4166-fc46-8c5654e2e4db" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "# figure setup\n", - "plt.figure(figsize = (12, 5))\n", - "ax = plt.subplot()\n", - "ax.spines['right'].set_visible(False)\n", - "ax.spines['top'].set_visible(False)\n", - "\n", - "ax.xaxis.set_major_locator(mdates.YearLocator())\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y\"))\n", - "ax.xaxis.set_minor_locator(mdates.MonthLocator())\n", - "\n", - "\n", - "# plot posterior\n", - "plt.plot(dates, median, color= 'navy', label = 'volatility posterior')\n", - "plt.fill_between(dates, lower_quartile, upper_quartile, color= 'navy', alpha=0.5)\n", - "\n", - "\n", - "# plot data\n", - "plt.plot(dates, SP500_returns, '.', markersize = 3, color= 'steelblue', alpha = 0.5)\n", - "plt.plot([], [], '.', markersize = 10, color= 'steelblue', alpha = 0.5, label = 'data') #larger markersize for the legend\n", - "plt.xlabel('time')\n", - "plt.ylabel('S&P500 returns')\n", - "plt.legend()\n", - "plt.show()" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.6.15" - }, - "vscode": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/notebooks/tutorials/intro_tutorial.ipynb b/notebooks/tutorials/intro_tutorial.ipynb deleted file mode 100644 index 5d5a86d..0000000 --- a/notebooks/tutorials/intro_tutorial.ipynb +++ /dev/null @@ -1,327 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qQc5Sh9v90k9" - }, - "source": [ - "# **Getting started - MCHMC sampling from a Standard Gaussian target distribution**" - ] - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "\n", - "First, let's import the MCHMC code.\n" - ], - "metadata": { - "id": "MbK7Gv7hIc-i" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install mclmc" - ], - "metadata": { - "id": "vNORvPCfe15o" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "We will be using jax, as it can automatically compute gradients." - ], - "metadata": { - "id": "B42CrO21Ijph" - } - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "Rrzbg6xjz2gm" - }, - "outputs": [], - "source": [ - "import jax\n", - "import jax.numpy as jnp\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from mclmc.sampler import Sampler" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4Dd92Wan6Wl_" - }, - "source": [ - "In this example we will sample from a standard Gaussian target. Let's define the negative log density:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "ynRgtRKWzizg" - }, - "outputs": [], - "source": [ - "nlogp = lambda x: 0.5*jnp.sum(jnp.square(x))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qE-l3rW-6kyh" - }, - "source": [ - "and get the gradient with jax:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "FxAwakKf6jy7" - }, - "outputs": [], - "source": [ - "value_grad = jax.value_and_grad(nlogp)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NRcl3RZS6vIl" - }, - "source": [ - "\n", - "The target is a class with functions `nlogp`, `grad_nlogp` and `transform`. Some common targets are implemented in `benchmarks/benchmarks_mchmc.py`.\n", - "Let's here define the standard Gaussian target:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "n2nW60C-zDIF" - }, - "outputs": [], - "source": [ - "class StandardGaussian():\n", - "\n", - " def __init__(self, d):\n", - " self.d = d\n", - "\n", - " def grad_nlogp(self, x):\n", - " \"\"\"should return nlogp and gradient of nlogp\"\"\"\n", - " return value_grad(x)\n", - "\n", - " def transform(self, x):\n", - " return x[:2]\n", - " #return x\n", - "\n", - " def prior_draw(self, key):\n", - " \"\"\"Args: jax random key\n", - " Returns: one random sample from the prior\"\"\"\n", - "\n", - " return jax.random.normal(key, shape = (self.d, )) * 4" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6yG6xmAtm8cP" - }, - "source": [ - "`transform` is useful when the dimensionality is high and storing all of the samples becomes memory intesive. We are ususally only iterested in some lower dimensional marginal distribution. `transform` is used in those cases as a map to the lower dimensional space of interest. As an illustration, here we are only interested in the first two components $x_1$ and $x_2$. If not needed it can just be set to the identity (commented out line).\n", - "\n", - "`prior_draw` is not a required attribute. By default it is used to initialize the chain. If not defined, we should pass the initial condition to the `sample` function by hand." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SnS6vD6s8KUC" - }, - "source": [ - "Let's sample from a $d = 1000$ Standard Gaussian target. We create a mchmc Sampler class by passing it the desired target distribution:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "aMvFvS4Lz8Up" - }, - "outputs": [], - "source": [ - "target = StandardGaussian(d = 1000)\n", - "sampler = Sampler(target, varEwanted = 5e-4)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4o7hKrzToAIU" - }, - "source": [ - "We used the default Minimal Norm integrator of the dynamics. MCHMC has two hyperparameters, the integration step-size $ϵ$ and the momentum decoherence scale $L$. They will be auto-tuned at the begining of the sampling. Tuning cosist of three stages:\n", - "1. burn-in + tuning $\\epsilon$\n", - "2. tuning $L$\n", - "3. estimating the effective sample size and use it to improve $L$ tuning\n", - "\n", - "The defualt is rather conservative and each of these stages takes 10 % of the sampling time. You can control the fraction of the sampling time that these stages take by inserting for example\n", - " `sampler.frac_tune1 = 0.05`\n", - " before the sampling starts.\n", - "\n", - " **WARNING: sometimes the default energy error 0.005 (which is used to tune the stepsize) is not optimal, especially if you are not interested in all of the parameters but specifialcally in the hardest-to-sample parameters (as is the case for example in hierarchical Bayesian models). Try decreasing the energy error, say by a factor = 10 - 100 and accordingly increase the number of samples by factor^1/6 to see if the posteriors remain the same. On some other problems 0.005 is to conservative and you can improve the performance by increasing it.**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Mhx15A2K8020" - }, - "source": [ - "We can now run the sampler. Let's get 5000 samples in 3 independent chains:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "sucJHLMi0Jfh" - }, - "outputs": [], - "source": [ - "samples = sampler.sample(5000, 3)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IClcHBm29Oce" - }, - "source": [ - "The result is of the shape (number of chains, number of samples, output size of transform):" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yypd11zL5Cof", - "outputId": "acff0197-0e54-4c32-fd0a-94a7f1e135cd" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(3, 5000, 2)\n" - ] - } - ], - "source": [ - "print(samples.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rYNoRRzd9old" - }, - "source": [ - "Let's plot the one dimensional marginal distribution along the $x_1$ coordinate for the first chain:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 455 - }, - "id": "FQWghKb41Yvy", - "outputId": "35d2eecf-653a-49da-f81f-f7dfa1441491" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "plt.hist(samples[0, :, 0], bins = 50, density = True, label = 'MCHMC')\n", - "\n", - "from scipy.stats import norm\n", - "\n", - "t = jnp.linspace(-4, 4, 100)\n", - "plt.plot(t, norm.pdf(t), color = 'black', label = 'ground truth')\n", - "\n", - "plt.xlabel(r'$x_1$', fontsize = 15)\n", - "plt.ylabel('density', fontsize = 15)\n", - "plt.legend(fontsize = 13)\n", - "plt.show()" - ] - } - ], - "metadata": { - "colab": { - "provenance": [], - "include_colab_link": true - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.11.3" - }, - "vscode": { - "interpreter": { - "hash": "5c7b89af1651d0b8571dde13640ecdccf7d5a6204171d6ab33e7c296e100e08a" - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/notebooks/tutorials/positive_constraints.ipynb b/notebooks/tutorials/positive_constraints.ipynb deleted file mode 100644 index 3cfdc7b..0000000 --- a/notebooks/tutorials/positive_constraints.ipynb +++ /dev/null @@ -1,273 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zg2HKf82tAAV" - }, - "source": [ - "# Positive constrains" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tti_tr_otKXk" - }, - "source": [ - "Suppose we want to sample from some target $p(\\boldsymbol{x})$ which is not defined on $\\mathbb{R}^d$. Instead, some of the parameters are constrained to be positive.\n", - "\n", - "I think the cleanest way to do sampling is to extend the definition domain to $\\mathbb{R}^d$ by reflection. So if the parameter $x_i$ must be positive, we simply define $p(-x_i) = p(x_i)$. After we are done with sampling we reflect the samples back by taking the absolute value of those parameters.\n", - "\n", - "Bellow I define a MCHMC target class which takes in the target defined on the restricted domain and extends it to the unconstrained space. Its transform attribute reflects back to the original domain at the end of sampling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qoBZqUaCtSyo" - }, - "outputs": [], - "source": [ - "import sys \n", - "sys.path.insert(0, '../../')\n", - "\n", - "import jax\n", - "import jax.numpy as jnp\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "DHlQZRSLtXcp" - }, - "outputs": [], - "source": [ - "class PositiveConstraint:\n", - "\n", - " def __init__(self, target, positive):\n", - " \"\"\"takes in a target and enforces some of the parameters to be positive\n", - " positive is an array of lenght target.d\n", - " positive[i] = 1 if the i-th parameter must be positive and 0 otherwise\n", - " \"\"\"\n", - " self.positive = positive\n", - "\n", - " # get the attributes from the previous target\n", - " self.d = target.d\n", - " self.transform = self.reflection\n", - "\n", - " self.nlogp = lambda x: target.nlogp(self.reflection(x)) #we extend the domain by reflection p(-x_i) = p(x_i) for parameters which need to be positive\n", - " self.grad_nlogp = jax.value_and_grad(self.nlogp)\n", - "\n", - "\n", - " if hasattr(target, 'transform'):\n", - " self.transform = lambda x: target.transform(self.reflection(x)) # at the end we reflect the samples back to the original domain\n", - " else:\n", - " self.transform = self.reflection\n", - "\n", - " self.prior_draw = target.prior_draw\n", - "\n", - "\n", - " def reflection(self, x):\n", - " return x * jnp.sign(jnp.sign(x) + 2 - 2 * self.positive)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8M3yGLT2rqRA" - }, - "source": [ - "Let's test this on an example. Suppose we want to sample from a standard Gaussian, but some of the parameters must be positve. We have defined a target class:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "h_quI_0dqAks" - }, - "outputs": [], - "source": [ - "class StandardNormal():\n", - " \"\"\"Standard Normal distribution in d dimensions\"\"\"\n", - "\n", - " def __init__(self, d):\n", - " self.d = d\n", - " self.variance = jnp.ones(d)\n", - " self.nlogp = lambda x: 0.5 * jnp.sum(jnp.square(x), axis= -1)\n", - " self.grad_nlogp = jax.value_and_grad(self.nlogp)\n", - " self.transform = lambda x: x\n", - " self.prior_draw = lambda key: jnp.abs(jax.random.normal(key, shape = (self.d, ), dtype = 'float64'))\n", - "\n", - "d = 100\n", - "target_original = StandardNormal(d = 100)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JsOQL1UIu2Mh" - }, - "source": [ - "Now we impose the positivity constraint. This is achieved by passing the original target to `PositiveConstraint` and specifying which parameters must be positive. `positive` is an array with `positive[i] = 1` if the i-th parameter must be positive and 0 otherwise.\n", - "\n", - "In this example let the first 50 parameters be constrained:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "fNxSwo2SqWxc" - }, - "outputs": [], - "source": [ - "d = 100\n", - "num_positive = 50\n", - "positive = jnp.concatenate((jnp.ones(num_positive), jnp.zeros(d-num_positive)))\n", - "target= PositiveConstraint(target_original, positive)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ummt0Kcou_2Y" - }, - "source": [ - "We can now simply use `target` in MCHMC and we will get the desired results. Let's download the MCHMC:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ftj_7o2uuNkP" - }, - "outputs": [], - "source": [ - "!git clone https://github.com/JakobRobnik/MicroCanonicalHMC.git\n", - "from MicroCanonicalHMC.sampling.sampler import Sampler" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VIDn8XluuZkq" - }, - "source": [ - "and do the sampling:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "M4acSGfBrBrq" - }, - "outputs": [], - "source": [ - "sampler = Sampler(target, 10.0, 5.0, integrator= 'LF')\n", - "x, burnin = sampler.sample(10000)\n", - "x= x[burnin:]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UFqGJduQwVB7" - }, - "source": [ - "Let's visualize the results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 350 - }, - "id": "58L8X-bjrEvM", - "outputId": "62a6f378-8e43-42c2-ab6b-88e34f3a6cae" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 17, 'axes.spines.right': False, 'axes.spines.top': False})\n", - "\n", - "plt.figure(figsize=(12, 5))\n", - "\n", - "#ground truth\n", - "t = np.linspace(-5, 5, 200)\n", - "pdf = np.exp(-0.5*np.square(t))/np.sqrt(2 * np.pi)\n", - "\n", - "bins = 15\n", - "\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(x[:, 0], bins = bins, density= True, label = 'MCHMC (reflection)')\n", - "plt.plot(t[t>0], 2*pdf[t>0], color = 'black', label = 'ground truth')\n", - "plt.xlabel(r'$x_1$')\n", - "plt.ylabel('density')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.hist(x[:, 50], bins = bins, density= True, label = 'MCHMC (reflection)')\n", - "plt.plot(t, pdf, color = 'black', label = 'ground truth')\n", - "plt.xlabel(r'$x_{51}$')\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "colab": { - "authorship_tag": "ABX9TyOIH1D5I/k8CPs/rr4o9Q1y", - "include_colab_link": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.6.15" - }, - "vscode": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/tutorials/smc.ipynb b/notebooks/tutorials/smc.ipynb deleted file mode 100644 index d5af69f..0000000 --- a/notebooks/tutorials/smc.ipynb +++ /dev/null @@ -1,191 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T: 792.3941650390625\n", - "T: 630.202880859375\n", - "T: 501.9044189453125\n", - "T: 399.2867736816406\n", - "T: 312.9793395996094\n", - "T: 237.50889587402344\n", - "T: 171.2333526611328\n", - "T: 120.62891387939453\n", - "T: 86.34713745117188\n", - "T: 62.61722946166992\n", - "T: 45.722679138183594\n", - "T: 33.66250228881836\n", - "T: 25.273513793945312\n", - "T: 19.35277557373047\n", - "T: 15.026819229125977\n", - "T: 11.819178581237793\n", - "T: 9.344698905944824\n", - "T: 7.382066249847412\n", - "T: 5.861446857452393\n", - "T: 4.669811248779297\n", - "T: 3.704078435897827\n", - "T: 2.8659186363220215\n", - "T: 2.201890230178833\n", - "T: 1.7549172639846802\n", - "T: 1.3860396146774292\n", - "T: 1.0907856225967407\n", - "T: 1.0\n", - "[[ 0.26010802 1.3392242 -0.69521457 ... -1.1771035 -0.9907378\n", - " 1.5732666 ]\n", - " [ 0.6596817 1.5439816 0.2550472 ... -0.16553336 -0.6179378\n", - " 1.2421314 ]\n", - " [ 0.72630167 -0.25226235 -0.7142753 ... -1.3142614 1.5843642\n", - " 1.245974 ]\n", - " ...\n", - " [ 1.1087224 0.9457538 -1.1830904 ... 1.0587435 -0.5046867\n", - " 0.1339251 ]\n", - " [ 0.04191879 0.91622573 -0.896403 ... -0.47026187 -0.5539253\n", - " 0.21780129]\n", - " [-0.83515453 -1.0613788 -1.5193996 ... 1.3164687 -0.11099169\n", - " -0.4750794 ]]\n" - ] - } - ], - "source": [ - "import sys \n", - "sys.path.insert(0, '../../')\n", - "\n", - "from mclmc.sampling.smc import Sampler\n", - "import jax\n", - "import jax.numpy as jnp\n", - "\n", - "kb = 1.38e-23\n", - "\n", - "temp_init = 1000.0 #* kb\n", - "temp_final = 1.0 #* kb\n", - "\n", - "class StandardNormal():\n", - " \"\"\"Standard Normal distribution in d dimensions\"\"\"\n", - "\n", - " def __init__(self, d):\n", - " self.d = d\n", - " self.grad_nlogp = jax.value_and_grad(self.nlogp)\n", - "\n", - " def nlogp(self, x):\n", - " \"\"\"- log p of the target distribution\"\"\"\n", - " return 0.5 * jnp.sum(jnp.square(x), axis= -1)\n", - "\n", - " def prior_draw(self, key):\n", - " return jax.random.normal(key, shape = (self.d, ), dtype = 'float64') * jnp.sqrt(temp_init) #start from the distribution at high temperature\n", - "\n", - "\n", - "target = StandardNormal(d = 10)\n", - "\n", - "sampler = Sampler(target)\n", - "\n", - "x = sampler.sample(steps_at_each_temp = 1000, tune_steps= 100, num_chains= 1000, temp_init=temp_init, temp_final=temp_final, ess=0.8)\n", - "\n", - "print(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Y1wW6kEMCVBmdXfpJ5wts6j4i/cMjMjwyKv3DIxNmzUSlsmlfEnUJe629pitHoWuoJKnhPoZykAQCEsAmb0l7OvNH4jYyzmtpTdOtm1Ijs+umjMvZSJrzujjrEuU+h7nWftOVo7AHQzo/o1GPzfRiJIEhG1Qdv+7lPA2B6Ba3Gz5L19JZlyTKpvI58pquHIfO65qle4Z8Y8gGUOD35K/rSS9vPS8i/j0CKvXJ0lOzsy5JlE3lc7T+9ivkibuu8u1ZCfvZ0Hlds3TPUH20BiRvvvmmfPWrX5XGxkYpFAry8ssv6zwdoMTvS1fXEEhexthVG0eV+lT6ejBJfY6CrqXznui8rqbuWR4DdiRPa0AyNDQkV155pTz11FM6TwP4SvML3Utenjy9Gke3fBCT9YnSgNnfEzVAtB8jqc+Rjo0U86Ya6ohgWgOSW2+9Vb7zne/IHXfcofM0gK8sfNm5NV5ZfCr0ahyd19BZnyRnlKiIck/t74kaUMXZ0M/rmpjaSDGK1dv2S8u6nbJ62/5Ej5ulOsIcckhQ8bL6ZZeFQMnJq3H0uobO5dCTmFESNT8l6H3290Tt3YjyWYp7n0306Hldy50H+mS0dO6/Sar04T2oSW2WTaFQkO3bt8vy5cs9X1MsFqVYLJb/Pjg4KE1NTcyyQUWKMovF6z2mN7+bXTdFZtROHrdBXdTyRZ3pkdUZInlcNMzrWq7etl92HuiTZa2N8sRdVxksIbIu0yu1qgQk3/72t2X9+vUTfk5AApxjn066/vYrIq1MmmQDqXqsNMpnrQQrIrJm6cLcNP5ZlMcgSgeuQ3S5n/a7bt06GRgYKP85duyY6SIByoKGDJJaLbSmIDJaknFDAEFDKvZzJjlUFNTVbp2/rblBeahD9ZjO67iivVlm1E6W/uER37plMXfHkpWyMYRyThaHVStZpgKS2tpamTVr1rg/QF4EfXkl8eW2or1Z1t9+xYTG3asBcTunjpwar4bUOv++3lOJrcURdx2ZNBsZdtPNt6zmn1UqrQHJ559/Lj09PdLT0yMiIh999JH09PTI0aNHdZ4WMML68mprbnBthJL6clvR3iydHS2yqfvIhHOoTM/V8fTr1ZCq1jlMQxx3/Y80G5mwAQYNYLbQU5QurTkk3d3dcsMNN0z4+cqVK2XLli2B72fpeJgWZQxZNV8izLFVlz43ldgZd6y9ksbq7XURkYqpFxBG5nJIOjo6pFQqTfijEowAWRClC11Hr4DztV7nSOoJW2WowblImNVrE2UtkrAb12Uhz8KL/V6p1ivrdaoEXOPsy1QOCZA1URp4r0Yozmqnztd6nSPN7eSdr7H+HnctkqCGI6t5FtaiYefPmJr6WiUIxjXOPgISwEeSY8iqq526NcRpj2UH5cPYX2M1vNbfl7U2ln8e5ak0qOHIap6FtWjYwb6B0PcqbJ142g8vq58b/EZq65BEQQ4J4spSbkJQWXTknsQtW9ycFPv7rSEdr8XTgsqSVVZ5z58xVQ72DYxbNExXXUx+VgAVmcshAbyk9YSXpW7apPYsCVsnlWvtdsyte3tlqHhWZtdNifxUaa+T6pCO33XS+bmJemyrXp8OnZEjG5aNW8FU1+dP12cFMImABEak9UXp98WdtW5v1WGZsHWy9pmxVjFVPeam7iPSPzwiM2ony4r25kjXy14ntyGdoLI7JfG5CVozJeyx/e6HrmGCJD4rQNYwZAMjstCVnNW9T+Jwq9PvPvYTGR4ZlbopNfLLv7xF+ViqU42jcLv/Ksd3vk/lc2S9xhoqGiqelf7hkQnnycJnEqgUDNkgFUn0LGRhmqfOp0dTvS9udaqdPGncf1U571GSvU323gjV5eWd63vYdxneuOuw5/mdQ0Ui4nqeJBOHs9b7BuQBPSQIzfkkq3tTs7z0ZNgbTKsR1FHmsE/yaTz5O3f9DTpXlGtl/xyIyLjzWb0ebuf3SzpNUlr3H8gDekiQCueTspVrELSpWVLnyyr7U7+9zM6n5bhPz2FzHdJ48rfqKyJKZXPLLQm6v/bXWf+/ZulC+dnaG2XN0oWe57fO9enQGRktnZueG+Xah1kfpa25QWoKIm3NDaHPA1QrAhKE5taVP7tuSqzZGGHOF1Za3ef2BtO+cqk1rOBcQCxq8KZjNVbVa+RVduseWYFBlIXkRMS1DFbZRKT8OXB+JlTO39nhvlOyX93tvwuzPsq+3lMyWhLZ13tK+Tp4YfgH1YIhG1Q8U0M+zmEMK6kyaB2OpKmsMSIiSkMubsdKakgoyf15vMoUNpnWuY6KNTR5/aVzY6+lonrd8jJkCdgxZAO4MDXk4xxW2Nd7So73D8u+3lPKK7QGiTNV1m0IRMR/yMWttyqpKdxh9+fxq3tQT469/KrTdle0N8uM2snSPzwSay2VoDL6lQGoZPSQACnxeiJetH53OSGzp2tJqGNGmSobpYx+rxMZv6NtWtNn/eoeN/E3qIcliV6uvE0zzlt5YVaU9puABDAsTkBiqpFQHeawZmHpKGOSx3WW2eQwSZS1VtKQpaGjrFwTeGPIBhWnGhL6rETMNUsXhn5vWpvuORNg/ZaUd5uFZR+ayOI9dZZZ1zBJlCE2r6GdtK9jloaOWBK/MhGQINPy+MUTtqEIG1REaYiSnGrsXFLeKWhBtSj31K38QcfxqrPbz71m7cQJ9FZv2y8t63bK6m37PcvsVhav4Mi587KOPY38BF2ToOMnGUBlKThCcghIkGl5+eIJMz00rig9DklONQ57T1RWfA2aetu144MJ5Q86jled0wpydx7ok9GSyI9+8Zt1T1SCM6/gyEqKtl7rt9aNm6TqHXUvoCSve1o9g0gXAQkyzcQXT5QnOa9F0eIc06tM1rovQ8WzygFQ3MDOfh/i3hO3dUesOnTt+MC1oRstidQUZFz5g2b8hJ21E8Stx8PPstbGceVyK7NqWdyGyezHCvMZcPa0hKUya8vv/Fl/uIA5JLWiKoRJgkty3Ys4x/R7v3N9jKwn+AWt/9HZ0SJdOz6Q0ZJMSIYNs+S7zmTHlnU7y4HR+tuvCD0bSccaLUHnCbvuigoSSqGCpFbAQ5ju4ihPckG9BlGO6ewV8Uq61NGL5Jd/sWj9blm0frfyE7bKkMuK9mZZf/sVrsMZB/sGlFc91dmjtqy1UWoK5/6r+nmylydOL1nQ58er3m7lDNODp5JvAySFHhJUha17w28AaHr6pclplkGrporIhN+t3rZfdh7om9CTYb3H6lkIk7zrXPNDRHzvQVr3KMp5TNzPuKvBmp7qS29MftFDAniwr7KpmlSnOv1SVdgnZJNj7n75F859i6x6/fgXfeXN69yOpRqMOPeueeKuq5RzJey/T3parP14UWacmLifqr0ZSefbJCWPs+wQHQEJqkbYL9ek16YI++Waxi69Xr/zOveK9mbp6VoiPV1Lyr+z6jVtyqTykIZfPYICBb/r5Ezodfu9Pa8mienFKuXye60zwIo7fKNa3jD87rd1LU2sG2M6IEK6CEhQNby+dL2+1L2mX4q470obJG4eSRx+janf71TOb9Xr/y27XI5sWBaYeBp0PvtsEuf5w/R0RbneflOqwxwvKDCyfrZx12EtM16SZLKXgnyV6kJAgtxyNlZRG+8wX7heCZoqony5Rl38y/k7v8bU73deDfTqbfuVhy/Cns++6Jpb/duaG6SmcO6/fuUN2zPjVraNuw6XA4cw9bS/1q2+1s9E/DczDBJ2LZK45wB0IqkV4+QpicxKuJtdN0Vm1E6WoeJZ6R8eCZ2Ap1Jn6zXWOcImaEblVjb7z6wG2K3OXnvkhKmvcxM5e4KqfYqurvqGmbYalLgcJUEzzj5DKsJM1w3CdF5kCUmtiC1PSWTOp8yB/2s4wj7JqYyTW9dFREIlaIahksvh7KFJYljC7zX7ek+NG6Zqa26QL8yuk2WtjVqemlWWcPfqIXH2rjhFuVZx9hlS4VY/+z0OM6QTd0gwT//2UZkISDBOnrpnrS/zNUsXSk1BpCTi2RgFCfoytq7LmqULPbvt43SZqw4FOVct9RtGsBrT6y+dO65cKvfY2eg7AxT7zJekroGqfb2nXNcl8aqXW1KpKhM5DPZ7LKI+pBN3SNBvKAxIAwEJxsljEpnbolphRV14yi7OrI6Nuw67Lo/uVU6VHhqvPVBU6uJs9P2uj8pTdlCgYv990Gu9yhJmcbCwwiwcpvpeL/Z7bAWVuh4Q7NfSK9ALI42AtFJx7cghQRXRPUYeZ9y/bsokOXN2zHV59LjljvJ+v/c4f2fP5bE4czic+Q1ex7APwSWVnxJlUTynOAuHOXOdwi60l+XF3pzcrge5KWpML0KXNHJIULGSeHrQPUYepXfJekKtnVzj+XQat9yq05VVF/5ylseey+OVw+Hs1fA6RmdH+N2EVepvlc0KTII4P29e65+o9BxZeTci44dfVGdQWTN9dPTw2CXRO+p2PchNUZOn4XJdCEiQC0l8qWXxH7w9D0ZlWq7btFtVYVY59eO8jip1cDZ2XsdY0a62m3DYKd/Fs6Pj/hvEeS281j9RCdx2HuiTzo6WCdcn6PPoTKQO+twmtahbHG7XQ3W4r9rlcbg8aQzZIBfy1u2rq7xxpt0GlSnMME3UcyTFb/djt+vhNn03bH3DTJe2Gl/nDsZBnO8Pcy39rkES91aHShumwG8wZIOKlbenB11PpNbTZpRpt3GuoWp9VJNa4z4ZO5+63Z7C7ee4/tK5UlMQuf7SuRPK2rXjgwnlEpk4IydsYnOUZGu/hd3CXhO7MENwacpiryXMoYcE0CDoSTcLPT5+iaVhnrDdXtfW3CBv/udJETmXSOpcwM1+LmsNGF29SV+YXVde0M7ZQ+LswTC9uJiJz0UWPouoPJntIXnqqafk4osvlmnTpsk111wjP//5z9M4LWCM/YnU7QnUxFOps1fCL7HUSfVp3T7VuH94pJxz4dWj0dbcII+9/JtFwLzK6vUzPypTuZe1No5be8MtX0flfG49K1v39sqi9btl0frdymWO04sVpbxBCcxAmrQHJC+++KI88sgj0tXVJe+9955ceeWVsnTpUvnkk090nxopqfbEtCjrZnjN2tBZjqAAJMmGyaqftXKu1wqs+3pPiVsXbZQgzll/+znXLF1YnpZsvz7OtTeCAkmvc7rNhLFWjlXZCDAJYYJcZr4gi7QHJH/zN38j999/v9x7771y+eWXy9NPPy3Tp0+Xf/iHf9B9aqRAdYXRShb05e7W0HvN2ggjqMfDSWcA4rSivVl6upZIT9cS3+PbAxf78uxeQZzKrBS3+juvt3NKbtDsJi/WOd22LXAGZbqFyccgdwNZpDWH5MyZMzJ9+nR56aWXZPny5eWfr1y5Uvr7+2XHjh3jXl8sFqVYLJb/Pjg4KE1NTeSQZJh91ofb6qFJjU/rGOcOe0yv10ctW9w6+S02JhI8Q6PScgfCzCJy5rREPY9I+Jk0ccSZKRX32EmdB9Uhczkkv/71r2V0dFQuvPDCcT+/8MIL5cSJExNev2HDBqmvry//aWpq0lk8JMB60vJayjyprmEdXcxux4yylkPUnoa4PRR+PR5eZV29bb+0rNspq7ftDz0EknVB19P++7bmBimIyGdDxdD1izuTJo6k1pKJ+16GfKBDpqb9rlu3TgYGBsp/jh07ZrpICBDUCCTVNayjiznsqpJZ6+b2u/b2pFF7ULHzQJ+MlkR+/Is+GSqe9R1OyGujoxJIWbkrwyNjSvWzJ6g6h3nSTAoN+gzG+Ywy5APTMjVk48S0X6St0rqincM6q7ftl50H+mTq5EkyPDLmO8yQlWsRdi8alenL9mnJ1186V/b1nvKtp3VMkXSGZpIQZ5E1v2Nl4d9F1sqDiTI3ZDN16lRpa2uT1157rfyzsbExee2112Tx4sU6Tw1EUmlTIJ1Psk/cdZUc2bBM/t+yywOfcJOagurXY6HSmxF2torf07vV67Ov91Q56da5G7LXMa0EVWevU1bZe7ji9nZlrbcsa+VJWt6GS5OifcjmkUceke9///vy7LPPyi9/+Uvp7OyUoaEhuffee3WfGlUq6B9zmv/YdZwrzDHtQYXb2hMi/hvuRaXaGKo0LGFnq7gFUn6zalSGH+yzhlQCmLhUAzo/9mG7oOE51WNlZYgma+VJWqUHXF60ByR/9Ed/JBs3bpS/+Iu/kEWLFklPT4/85Cc/mZDoCiRFZ+JfnLKEbVjsCahex3TjdR6391nrZ2zcdTjR5d3bmhvKC475NR5hgwG3IEOlvFbdrWEa5/GTWqI9KUn0bqxob5bOjhbZeaDPcxfmMMfKUs9h1sqTtEoPuLykktT64IMPSm9vrxSLRXn77bflmmuuSeO0qFJ+/5i37u2N/bToxqsxtJclbHBiJaDuPNA37uf2xt6NVwMWdh2POIGbteDYm/950nesP2zDYr9uVvm6dnwgq7ft910Px+plGRgeiR2MptEY2u9VnMZpU/cRGS2J1BSk6hq3PKv0gMtLpmbZAEkI2kgs7tOiG5UpwWGDE2tZ82WtjeN+7lxdVGR8Q+3VgLkN06xZulC+MLtO1ixd6Lm8e5wZGyKSaG+U/bp1drSUdz22greCiOvqt9bCaCXJR+Ns/9zEaZz8puVXa54CsovN9ZBpSWfT68rODzsTJM4iXW51CLMpXBpbvnttshd3wS3n762/nz9jqhzsG/CdPZTUQnhp0lmGND4HqF6Zm2WD6pXU01fS+R66ukLDLgXv1XOiei5nHUysIaGSs7Gv95TvdQm7/L11vayeJetafDp0RkZLIrWTa2JvEOisg8nEwiTKoDKcCGQBAQm0SOrLPMkvTd1d1FHL6tdQ6ihzUkGZ6iJyKtNwrWOoXEO381rvW7N0Yey6qexz4/b6qNOa/STx+U96hWFAF4ZsoEXa3d0q59PVRe127qT2t7GX2eoZcDumie73JPY+iXKdkvxsuS0eNlQ8K/3DI8rDPn7XPgvDIlkYekL1YcgGmeHsWtdNdT0LHV3UbueO2kPk12Ngn6LrZKL7PcwTdpJP6VGf7N16K6xybdx1uDxLR0SUe3RE/K99FoZF6AlBXhCQQBtdY/BuDYvqehY6vpjdzh21IXK+T7XMXq9LYoGtMEznK4TdHNE+G8iaHus37ONWD797pDsYYKYMKglDNtBGV1dxFrrBTYhyPe3XSkRc97VZ1tooT9x1VSJlSHMGj1sZfvexn8jwyKjUTamRX/7lLcrvizr8JDJ+j5g4n/m49zfs9WYoBzoxZINMSbNHohpEuZ5+yaXW2h0/+kWf8hN2UK+XjiTk1dv2K8/E+d+RURERGR4ZndBzoCOZ11kWr2E11T17wvYoxl00zfQsIsBusukCAGFZi0WlJc9Pks5rZf//Za2N8qNfnFsFdlP3EaW62RNrVc7nx75Widtuu1aDeWJgWEZL5xp76/VWWZy+emVjeTdjq7ENm+MS9Pq25gY5MTAsbc0NcvWCOb7XI8zxg66tmzj/FqKcD9CJIRtULdVAo5KHiMIuRJYk67paq606r68zYLFmv8yumyIzaif7lilooTqvBdZU6un3eUhyNlGQqIvPAWlgyAYVI+lkPb8ZFkFd1kkMQ2Q1+TBoGEP1GkWpn3Vdl7U2jru+1rFERH629kZ54q6r5Gdrb5TrL50rNQWR4tnRwDIFLVTnrFeY4ZzOjnP74ngtUW8/jrMuOrYrsDbPU12UzynJTRWBOAhIkEn26Zi6VnxVDTSSyD3Iynh92MZG9RrFqd/VC+aMu75ex7L28PnfkTGZXTdF2pobfBvSqNNxgxY6s8qlEgDonGlmbRK5rLVRZtdNCb1hpFVPK+/FKmNWPqvVrFqDQgISZJJ9OqauFV/TXJ8hK4m4fmuZuPF64k9iWq9Xw+d1rM6Oc5vplURkRu1k2dd7qrzbr333X5VeD7/f+TXI1u9EJq5VEnU6ehCv3j1rk8gn7rpKerqWSE/XklCfZa+66PisVmsDG1W1BoXkkCDT8rghWhZ4XYdF63eX8zB6upaEPp7fKqaqZVD9vdcxrWDq+kvnlmcKBa1kG0bU6cFRc43CTqUOyo9J6rxJvcetDvBXCd9jUdpvAhJUFJ1ffHn6kvAKPKIGeGESSi3WvQjzHhVBy+nrvk9JL4PfteMD16Rer+O6BShpfS51BV2oPCS1ourpHBoJ242axW7qoGEqZ5md3fphNq/zG3aLcm1UN73zuk9J3Y8kl8Hf1H1ERksiBRHXJFm34zo/42l270f998Xy9VBBQAJfWWxU/ej84gv7ZWxyHHjN0oXlAMISZnEuKy/DqnOUXXSte2GVxX7doszesd6zr/dU+enc2n/GKq+I931K6n4kGfRax6qvm6I8SyYoQNGJwAI6EZDAV7UmVyVBtaEIG/SpvN6t4VC5l1bi6GjpN4ulqSS1+rGOISLl96rOcrGX2a1nYPT/Bpyt8nrV3T4rxe9+uNXP+bMkG2Vn0OacOaRSRq/65ulBAhAhIEGApJ6+0v6C1HG+sMGZasMV9rhRg0S/9TPsZV5/+xWhdrt1E2fdF2cQYpVZRFx7Bm67sjHwM2qfleJ3P9zKmHRQ7nZtrM+KNXPIOlfU62jqQYJACHEQkMCXjv0/ogrzZafjCznprnHVnAivctifplUWtwpaLMz+ujC73bpxXn9nD4Xf/bGfw6/MVjmthdP8PqOq5XZ7Xdj7HvQ5Va2712tVyqPrsxr0by/KvzuCGFiYZYNUJJFlHybDX2dWf1LHjjtjwT4FV2T8Tr5eM1zSmu3gNTPEKo/X/jUqx4py/jS53Vd7eURkQtmSnLmTxHtV6pTUOZkSXJmY9ouKlpWpg0l9gVr1CdM4289vDzZExjdyUdYNSYrbfdJVHq/PRNA9SjtgDSqPjkY5yWNGvV4q78vKv2ski4AESEGSi7WprEMR5/y6Xuv3Xqvb3q0+q7ftl50H+mRZa6M8cddVoc7hxqvRDbvYmE4qC5npaJSz0NDT+1G9CEiAlITp3fD7UrbveLv+9iuMNkaqC5m5Hdu+ENuapQsjrWaaxtCM230TmTiEElWU3pFKloWgCGawMBqQEqsXYOeBPqWptEGbvCUVjFgJgvYN01STBv0WMrMLSlyMmhTrlggbZXqzH+daJivamxNNgI6ahBokr4mfrFuCMAhIAA9+jYDVyCxrDZ5yGnWTtyicK6sGzWhxK8v1l86VmoJIW3OD6+vcGli3hdj8zqESrOiYseFW9rbmBt/6huF2/CTucdC1SDNgUTlXXgMomMWQDeDBZFd7UkmEKvkLTrrqnWTujZegGS5B+SRWAJf2EENQGbOUE6NyrqibOKJyMGQDJMjtaTfJJz+/Y7k9EUcZwlBdEMxO12J4uhaWs3Mre9B57e8xtaCYdd6Nuw673uOga6FrVeA45wLCIiABPERdfl1VmAWy/F7v1cioLpfupGsxvDQW63Ire9B57e9Jq7F1ll01f8eLrlWB3cqrci7VITzAjoAECCHJBsvvWGEaVq9GxuodOf2/I7HLGoWzvPY6JfGkHjY3RiXASisJ01l2r40Ik87FiPr5TaN3C2ZkKd+HgARImfUFICKhvrS9NrrzWnq+s2P8RnlRyxnliyoo58Fq4KxdeqOcy9TsFZ3DHm5DblGG7ryoBgpePTgM01QeU8OUbghIkAodUbiJyD7MP96te3tl0frdsmj97nFlTOoLwDrOm/950vX3K9qbZVlr47gZJGGuWZxyquRt2IOlKOfSOXslbH6PF6/jqJY9Sk5MErx6cOjxqDxZCjYJSJAKHV+iJiL7MP94rSET58ZwnR3Bu+6GKYuId97Bvt5TMlo691+rTKrXLM4XlUrehn1XYefr0wo2ww6D+b3HTdzPqNswV9jNGKPIUiMFvbIUbDLtF6mo1KWx/fhNuU16n5GNuw5L8eyY1E6eNO5cbtOAde7hcq4co1I7uaac0Ki6oq2dfdVYi+q05biiTJX2O1ZS11vXqr6ADplaOv673/2u7Ny5U3p6emTq1KnS398f+hgEJKhUSQcGVmMlor4nThgq5bWXwSqHiJQbUbf9evx2ud2467AMDI9IyXa8NNaDSWMNjajL5Ifd9wgwJVPrkJw5c0buvPNO6ezs1HUKwAi/4QTVoQaVbtIwwxZtzQ1SEJG6KZO0dLOrDD1YQ1F1UyaVpxpbP5s6ucZ1+rHXcVe0N8uM2slSEpGCSOipy3EUz46N+69I8kNIUfJQRGTcMBdQabQFJOvXr5c//dM/lS9+8Yu6TgEY4deYpLVOidO+3lNSEpE5M2q1dOWr5BSsaG+Wnq4l8su/vFV6upbIivbmcmAxPDLqujibyj4/f7n8ivLx0lA7edK4/4pMnBUUV9Q8FK+ZVknn2mRpKiiqR6aSWovFogwODo77A4Sl+8tUpRGNm5zpXNQsyh4tWRF2vRWV3+nktqhX0lOow9Qt6Q0JVWRpKiiqR6YCkg0bNkh9fX35T1NTk+kiIYeS/DJVXQ3U63dRy+Jc8j3oOLob7zjXNGzZsvh07jaFOgxd1y+JDQndZDnAReUKFZCsXbtWCoWC759Dhw5FLsy6detkYGCg/OfYsWORj4XqleSXadwv+Khlcb7PRANhDwzCnj9OUGH66dyr1+HN/zw5bgp1GLrunzNYsZ/HxAJqQByhZtmcPHlSPv30U9/XXHLJJTJ16tTy37ds2SIPP/wws2wQSlam9Jooh985ky6P3/GcU5PDnDvKtGbr+F5ThNO6F6u37ZedB/pkWWujPHHXVeOmIM+onWz8M6kqjR2ATe6IjWzTPstm7ty5ctlll/n+sQcjQFSmn5ItJvIY0kqaDTpenOGAKD0C1vH39Z5yveZx6h7mSd65mJxVlzVLF6b2WcjLrrwM7SBJ2nJIjh49Kj09PXL06FEZHR2Vnp4e6enpkc8//1zXKVFB8vhFl1T3dZik2bjnDZNwGubcUQK5oHse5zMRJ5jKWlCqKo1yZ2mVzzgYesoGbQuj3XPPPfLss89O+Pkbb7whHR0dSsdgyAZJSaO7P84wRZxymeo2z9NCXXGuc9aG7ZA8hp6Sl6mF0bZs2SKlUmnCH9VgBEhSGt39cYYp4jwJm+pN2tR9REZLIjUF0X7uuE+wcZ7kTQwfVkrPQ17ksUe2EmVq2i+gSxrd/VFWX03ii9DtvFEa8NXb9kvLup2yett+pddbZU9jXxWTOUU0VpWPADAb2FwPCKBjgzTdXcNRztOybme5x+PIhmXayuakcn3T3CTQJN2bH1biNUM2ZWrIBqgUST49eT1tJ51U5/dU79UTYi38tay1ccJ7dCb9qfR+JLXgnJNKvdJcnt2rXkmUIenl74GkEZAAKfIKbpIekvALonYe6JPR0rn/2j1x11VyZMMyeeKuqya8R+eQSZQhEdX3BDXkKvXSVXe343rVK6lcozjL3wO6EZAAGRCmUY6727BXT4jfe3XmUUTpgVJ9z8Zdh+V4/7Bs3HXY9fcq9UpqfyKVc3vVK6lcI3YLRpaRQwJkTNBYv19+SJypx0PFs9I/PBI6vyUruQlu5Vi0frf0D4/I7Lop0tO1JJHz5HXlViBN5JAACTOxYFJQHkFbc4PnfiVxph6LSKSn56ysqutWDrede+OyrrGIaKs3C3WhGhGQAD5MNLadHS1SN2WS9PUPj0s8dVta3Vk+Z5e/SsPW1twgNQWR6y+dG3roZOveXhkqnpXZdVOUhzSiNrZB73MbWtHRc2NdYyvY0TH8kZUgD0gTAQngI401KJwN7Yr2ZjlzdkxKMj7x1K0sQeVTadice7eEsan7iPQPj8iM2snl6bhdOz4onzPMTBKVc/m9L2gmTtK9DjrXrmDtE1QjAhLg/7g1WEk1On6NoVtD65Z46laWoEXRVBIy4zR+bhvwWWuZtDU3jAtOwp4v7nBUnM0BdQsKjlioC9WIpFbg/+hctMzv2EkPLfglXeouh/0YVgBQUxDf1Vy9zpv0/bDO09bcIPt6T4WuZx4XyANMIakViEFnN3mYXXWTOlfx7NiEKa9+5Uh6h1nVpeW9zpv0/bDKtq/3VGJDRlGGgdzybrxeZ2rRNsAEekiQO25PqlmZepolYae8mrqGae/EG/V8bu+L0tOh+h6V19HTgqyihwRVwe1JNSv5AVl6Yg075dWvp0ZnvdLeiTfq+dzep3OVWb/XuU0BB/KOHhLkTpZ7SLL2xJrUddFdryR7LapB1j5ngFOU9puABEhQ1hrIpBou3fXKSwOblfublXIAXghIAIyTl4YrbDm9Xk/gBGQDAQmAquAVGKgEDGkn0QLViKRWAFqETWrVndzrlvCpOp02TgJ0JS1YlqUEbECEgASAgrCNuO5ZT26BgXMZey9es1eqrYHOysw0wEJAAiBQ3GXb0+AXaCxav1sWrd8tW/f2evZyVFsDzX458VVbEKsbAQmQMya+BJMcqtBVfr9Ao394RPqHR3yDjWproCtp+MmUagtidSMgAXImD1+CfmWMW/6wAU1nR4vMrpsSmFuis4F29tKgMlRbEKvbZNMFABCOtXGdyS/BoNkmfmWMW357QKMSPKxobw49ldg6T9SN+NzK3D88Uv5/eiUqg+pnC2qY9gvkmKlpqCbX49BVZ3udRKS8U/FoSRJZWM7a5HDN0oU0Yqh4rEMCVBlTgUElrsehs4cEairxc1WtCEiAKpO3L/C45TW1QmuYsiA6VsKtHCyMBmiS1el9eZspETeh1ev9JhJ985BcnDckiVY3AhJAAY1PMuwNTpQgz6vBytK6J4gubwE2ksWQDaBApXueLvxwTHfPc78AfRiygTFZHdJIisqTG70o4ZjuYUjyflX65x9IAwEJEkFjbL6BTUtSja/p7vkk7xeffyA+AhIkoloaYz+mGti0n87dGt889hAkeb/4/APxkUMC5JxXLoauHAm345rOBwGQLeSQAFXI6+lc1zCCW88CPQQA4tIWkPzqV7+S++67TxYsWCB1dXXS0tIiXV1dcubMGV2nBDIn7lCGyvu9hh7SChLcekzyOIQDwCxtAcmhQ4dkbGxMNm/eLAcPHpTvfe978vTTT8u3vvUtXacEMkfXQmAq0sppcSsjSZ7REcyhWmkLSG655RZ55plnZMmSJXLJJZfIbbfdJmvWrJEf/vCHuk4JZE7cXoo8DIW4lTGpcldj40wwh2qValLrn//5n8tPfvITeffdd11/XywWpVgslv8+ODgoTU1NJLUCVSpusmzSib1pLKbGgm2oBJlOav3www/lySeflG984xuer9mwYYPU19eX/zQ1NaVVPAAZFLenJenehjR6L0yvz1KNvVLIhtABydq1a6VQKPj+OXTo0Lj3HD9+XG655Ra588475f777/c89rp162RgYKD859ixY+FrBKBixG2ckx7yso7X1txQsY02Q0YwJfSQzcmTJ+XTTz/1fc0ll1wiU6dOFRGRvr4+6ejokPb2dtmyZYtMmqQeA7EOCZA9DClU9ror3F8kIUr7rTWH5Pjx43LDDTdIW1ubbN26VWpqakK9n4AEyJ5KboxV0WgD/jKVQ3L8+HHp6OiQ+fPny8aNG+XkyZNy4sQJOXHihK5TAkiB7pk/echhMJ3nAVSiyboO/Oqrr8qHH34oH374ocybN2/c7zK8Wj2AACvam7U2xPYcBhp8oHpo6yG55557pFQquf4BkG0meynogQGqE3vZAJjA5EwLleGQOEFFUnUjsAGSRUACYIKsrxAbJ6hIqm5MjwWSlepKrWExywaAmyzMcslCGYCsyty037gISAAAyJ9MTfsFYB55DgDygoAEqGDkOQDICwISoIJlPTkVACzkkAAAgESRQwIAAHKJgAQAABhHQAIAAIwjIAEAAMYRkAAAAOMISAAAgHEEJACQA6y6i0pHQAIAOcCqu6h0BCQAkAOsuotKx0qtAAAgUazUCsCoSshzqIQ6AHlEQALkXJYa0ErIc6iEOgB5REAC5FyWGtBKyHOohDoAeUQOCZBzW/f2yqbuI9LZ0SIr2ptNFwcAIrXfBCQAACBRJLUCAIBcIiAB4CpLybIAKh8BCQBXWUqW9ULQBFQOAhIArvIw2yQPQRMANZNNFwBANq1ob878rJ3OjpbyDCMA+cYsGwAAkChm2QAAgFwiIAFAcigA4whIAJAcCsA4AhIAuZhRA3/0ciHvSGoFgApw3eOvy/H+YfnC7Dr52dobTRcHVS5zSa233XabzJ8/X6ZNmyYXXXSRfP3rX5e+vj6dpwSAqkQvF/JOaw/J9773PVm8eLFcdNFFcvz4cVmzZo2IiLz11ltK76eHBNWKHXwB5Fnmd/v90Y9+JMuXL5disShTpkwJfD0BCaoV3e8A8ixzQzZ2n332mTz33HNy7bXXKgUjQDWj+x1AtdG+dPyjjz4qf/d3fyf/8z//I+3t7fKv//qvnq8tFotSLBbLfx8cHNRdPCCT8rBsOwAkKXQPydq1a6VQKPj+OXToUPn13/zmN2X//v2ye/duqampkbvvvlu8Rok2bNgg9fX15T9NTU3RawYAAHIjdA7JyZMn5dNPP/V9zSWXXCJTp06d8POPP/5Ympqa5K233pLFixdP+L1bD0lTUxM5JAAA5EiUHJLQQzZz586VuXPnhi6ciMjY2JiIyLigw662tlZqa2sjHRsAAOSXthySt99+W9555x35yle+Ig0NDXLkyBF57LHHpKWlxbV3BAAAVC9ts2ymT58uP/zhD+Wmm26ShQsXyn333Setra2yZ88eekEAAMA42npIvvjFL8rrr7+u6/AAAKCCsLkeAAAwjoAEAAAYR0ACIBFb9/bKdY+/Llv39pouCoAcIiABkIhN3UfkeP+wbOo+YrooAHKIgARAIth/B0Acqe72Gxa7/QIAkD+Z3u0XAADACwEJAAAwjoAEAAAYR0ACAACMIyABAADGEZAAAADjCEgAAIBxBCQAAMA4AhIAAGAcAQkAADCOgAQAABhHQAIAAIwjIAEAAMZNNl0AP9ZGxIODg4ZLAgAAVFntttWOq8h0QHL69GkREWlqajJcEgAAENbp06elvr5e6bWFUpjwJWVjY2PS19cnpVJJ5s+fL8eOHZNZs2aZLlZqBgcHpampqarqXY11FqnOeldjnUWqs97VWGeR6qy3VeejR49KoVCQxsZGmTRJLTsk0z0kkyZNknnz5pW7fmbNmlU1N9WuGutdjXUWqc56V2OdRaqz3tVYZ5HqrHd9fX3oOpPUCgAAjCMgAQAAxuUiIKmtrZWuri6pra01XZRUVWO9q7HOItVZ72qss0h11rsa6yxSnfWOU+dMJ7UCAIDqkIseEgAAUNkISAAAgHEEJAAAwDgCEgAAYFyuA5JisSiLFi2SQqEgPT09pouj1W233Sbz58+XadOmyUUXXSRf//rXpa+vz3SxtPrVr34l9913nyxYsEDq6uqkpaVFurq65MyZM6aLptV3v/tdufbaa2X69Okye/Zs08XR5qmnnpKLL75Ypk2bJtdcc438/Oc/N10krd5880356le/Ko2NjVIoFOTll182XSTtNmzYIF/+8pdl5syZcsEFF8jy5cvl8OHDpoul3aZNm6S1tbW8INrixYvllVdeMV2sVD3++ONSKBTk4YcfVn5PrgOSP/uzP5PGxkbTxUjFDTfcIP/0T/8khw8fln/5l3+RI0eOyB/8wR+YLpZWhw4dkrGxMdm8ebMcPHhQvve978nTTz8t3/rWt0wXTaszZ87InXfeKZ2dnaaLos2LL74ojzzyiHR1dcl7770nV155pSxdulQ++eQT00XTZmhoSK688kp56qmnTBclNXv27JFVq1bJ3r175dVXX5WRkRFZsmSJDA0NmS6aVvPmzZPHH39c9u3bJ++++67ceOONcvvtt8vBgwdNFy0V77zzjmzevFlaW1vDvbGUU//2b/9Wuuyyy0oHDx4siUhp//79pouUqh07dpQKhULpzJkzpouSqr/6q78qLViwwHQxUvHMM8+U6uvrTRdDi6uvvrq0atWq8t9HR0dLjY2NpQ0bNhgsVXpEpLR9+3bTxUjdJ598UhKR0p49e0wXJXUNDQ2lH/zgB6aLod3p06dLv/M7v1N69dVXS7/3e79Xeuihh5Tfm8sekv/+7/+W+++/X/7xH/9Rpk+fbro4qfvss8/kueeek2uvvVamTJliujipGhgYkDlz5pguBmI4c+aM7Nu3T26++ebyzyZNmiQ333yz/Pu//7vBkkG3gYEBEZGq+jc8OjoqL7zwggwNDcnixYtNF0e7VatWybJly8b9+1aVu4CkVCrJPffcIw888IB86UtfMl2cVD366KMyY8YMOf/88+Xo0aOyY8cO00VK1YcffihPPvmkfOMb3zBdFMTw61//WkZHR+XCCy8c9/MLL7xQTpw4YahU0G1sbEwefvhhue666+SKK64wXRzt3n//fTnvvPOktrZWHnjgAdm+fbtcfvnlpoul1QsvvCDvvfeebNiwIdL7MxOQrF27VgqFgu+fQ4cOyZNPPimnT5+WdevWmS5ybKp1tnzzm9+U/fv3y+7du6WmpkbuvvtuKeVwod2w9RYROX78uNxyyy1y5513yv3332+o5NFFqTNQSVatWiUffPCBvPDCC6aLkoqFCxdKT0+PvP3229LZ2SkrV66U//iP/zBdLG2OHTsmDz30kDz33HMybdq0SMfIzNLxJ0+elE8//dT3NZdccon84R/+ofz4xz+WQqFQ/vno6KjU1NTI1772NXn22Wd1FzUxqnWeOnXqhJ9//PHH0tTUJG+99VbuugHD1ruvr086Ojqkvb1dtmzZIpMmZSaOVhblXm/ZskUefvhh6e/v11y6dJ05c0amT58uL730kixfvrz885UrV0p/f39V9PwVCgXZvn37uPpXsgcffFB27Nghb775pixYsMB0cYy4+eabpaWlRTZv3my6KFq8/PLLcscdd0hNTU35Z6Ojo1IoFGTSpElSLBbH/c7NZN2FVDV37lyZO3du4OueeOIJ+c53vlP+e19fnyxdulRefPFFueaaa3QWMXGqdXYzNjYmIuemPudNmHofP35cbrjhBmlra5Nnnnkml8GISLx7XWmmTp0qbW1t8tprr5Ub5LGxMXnttdfkwQcfNFs4JKpUKsmf/MmfyPbt26W7u7tqgxGRc5/xPH5fq7rpppvk/fffH/eze++9Vy677DJ59NFHA4MRkQwFJKrmz58/7u/nnXeeiIi0tLTIvHnzTBRJu7ffflveeecd+cpXviINDQ1y5MgReeyxx6SlpSV3vSNhHD9+XDo6OqS5uVk2btwoJ0+eLP/ut37rtwyWTK+jR4/KZ599JkePHpXR0dHyGju//du/Xf68590jjzwiK1eulC996Uty9dVXy9/+7d/K0NCQ3HvvvaaLps3nn38uH374YfnvH330kfT09MicOXMmfK9VilWrVsnzzz8vO3bskJkzZ5ZzhOrr66Wurs5w6fRZt26d3HrrrTJ//nw5ffq0PP/889Ld3S27du0yXTRtZs6cOSE3yMp5VM4Z0jLvJ0UfffRRxU/7PXDgQOmGG24ozZkzp1RbW1u6+OKLSw888EDp448/Nl00rZ555pmSiLj+qWQrV650rfMbb7xhumiJevLJJ0vz588vTZ06tXT11VeX9u7da7pIWr3xxhuu93XlypWmi6aN17/fZ555xnTRtPrjP/7jUnNzc2nq1KmluXPnlm666abS7t27TRcrdWGn/WYmhwQAAFSvfA7IAwCAikJAAgAAjCMgAQAAxhGQAAAA4whIAACAcQQkAADAOAISAABgHAEJAAAwjoAEAAAYR0ACAACMIyABAADGEZAAAADj/j+s+FxWa/I7MwAAAABJRU5ErkJggg==", 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{ - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.11.3" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/speed-bench/plots.py b/speed-bench/plots.py deleted file mode 100644 index bc163ea..0000000 --- a/speed-bench/plots.py +++ /dev/null @@ -1,57 +0,0 @@ -import cProfile -import sys - -import numpy as np - - -sys.path.insert(0, './') - -import mclmc.sampling - -from mclmc.sampling.sampler import Sampler, Target -from mclmc.sampling.dynamics import update_momentum -import jax - -import jax.numpy as jnp -import seaborn as sns -from matplotlib import pyplot as plt - -nlogp = lambda x: 0.5*jnp.sum(jnp.square(x)) - -def test(num_steps, d): - sampler = Sampler(Target(d = d, nlogp=nlogp), frac_tune1=0.0, frac_tune2=0.0, frac_tune3=0.0) - sampler.sample(num_steps, x_initial = jax.random.normal(shape=(d,), key=jax.random.PRNGKey(0))).block_until_ready() - -import timeit - -if __name__ == '__main__': - - n = 10 - - vals = [] - ran = np.linspace(100,500000, 10) - for num_steps in ran: - d = 50 - time = (timeit.timeit("test(num_steps, d)", globals=locals(), number=n) / n) - print(f'Benchmark took {time/num_steps} seconds per step') - vals.append(time) - - vals_2 = [] - ran_2 = np.linspace(100,100000, 50) - for d in ran_2: - num_steps = 1000 - time = (timeit.timeit("test(num_steps, int(d))", globals=locals(), number=n) / n) - print(f'Benchmark took {time/num_steps} seconds per step') - vals_2.append(time) - - sns.lineplot(x=ran, y=vals) - plt.xlabel('Number of steps (100 dimensions)') - plt.ylabel('Time in seconds') - plt.savefig("img/speed_bench_steps.png") - - plt.figure() - - sns.lineplot(x=ran_2, y=vals_2) - plt.xlabel('Number of dimensions (1000 steps)') - plt.ylabel('Time in seconds') - plt.savefig("img/speed_bench_dim.png") \ No newline at end of file diff --git a/speed-bench/single.py b/speed-bench/single.py deleted file mode 100644 index 8d3ce80..0000000 --- a/speed-bench/single.py +++ /dev/null @@ -1,35 +0,0 @@ -import cProfile -import sys - -import numpy as np - - -sys.path.insert(0, './') - -import mclmc.sampling - -from mclmc.sampling.sampler import Sampler, Target -from mclmc.sampling.dynamics import update_momentum -import jax - -import jax.numpy as jnp -import seaborn as sns -from matplotlib import pyplot as plt - - -n = 10 -d = 1000 -num_steps = 100000 -sampler = Sampler(Target(d = d, nlogp=lambda x: 0.5*jnp.sum(jnp.square(x))), frac_tune1=0.0, frac_tune2=0.0, frac_tune3=0.0) - -def test(num_steps, d): - sampler.sample(num_steps, x_initial = jax.random.normal(shape=(d,), key=jax.random.PRNGKey(0))).block_until_ready() - -import timeit - -if __name__ == '__main__': - - time = (timeit.timeit("test(num_steps, d)", globals=locals(), number=n) / n) - print(f'Benchmark took {time/num_steps} seconds per step') - - \ No newline at end of file diff --git a/tests/test_blackjax.py b/tests/test_blackjax.py deleted file mode 100644 index 6fe40f5..0000000 --- a/tests/test_blackjax.py +++ /dev/null @@ -1,148 +0,0 @@ -from jax.config import config -config.update("jax_enable_x64", True) -import math -import blackjax -from blackjax.base import SamplingAlgorithm -from blackjax.mcmc.mclmc import init, build_kernel, noneuclidean_mclachlan -import mclmc -import jax.numpy as jnp -import jax - -from mclmc.sampler import Sampler, Target -from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState, mclmc_find_L_and_step_size -import mclmc.sampler -print(Sampler.sample) - -def run_sampling_algorithm( - sampling_algorithm: SamplingAlgorithm, num_steps: int, initial_val, rng_key, zero_keys=False -): - - state = sampling_algorithm.init(initial_val) - return run_kernel(sampling_algorithm.step, state, zero_keys=zero_keys, rng_key=rng_key, num_steps=num_steps) - -def run_kernel(kernel, state, zero_keys, rng_key, num_steps): - keys = jax.random.split(rng_key, num_steps) - if zero_keys: - keys = jnp.array([jax.random.PRNGKey(SEED)]*num_steps) - _, info = jax.lax.scan(lambda s, k: (kernel(k, s)), state, keys) - return info - -SEED = 0 -# USE ipython, for some reason version of something is different in ipython -# for this to pass, the MCLMC repo's prngkeys must all be set to SEED -# the seeds in tuning also need to be set to SEED in blackjax -def aligned(): - # Set up your test inputs - num_steps = 1000 - num_chains = 1 - dim = 2 - key = jax.random.PRNGKey(SEED) - - - initial_position = jnp.array([1., 1.,]) - logdensity_fn = lambda x: -0.5 * jnp.sum(jnp.square(x)) - - mclmc = blackjax.mcmc.mclmc.mclmc( - logdensity_fn=logdensity_fn, - transform=lambda x: x, - L=math.sqrt(dim), step_size=math.sqrt(dim) * 0.4, - seed=SEED - ) - - blackjax_mclmc_result = run_sampling_algorithm( - sampling_algorithm=mclmc, - num_steps=num_steps, - initial_val=initial_position, - rng_key=key, - zero_keys=True, - ) - - blackjax_mclmc_samples = blackjax_mclmc_result.transformed_position - # print(blackjax_mclmc_samples) - # raise Exception - - target_simple = Target(d = dim, nlogp=lambda x : -logdensity_fn(x)) - native_mclmc_samples = Sampler(Target=target_simple,L=math.sqrt(dim), eps=math.sqrt(dim) * 0.4, frac_tune1=0.0, frac_tune2=0.0, frac_tune3=0.0).sample(num_steps, x_initial = initial_position, random_key=key) - - # print(blackjax_mclmc_samples.shape) - # Assert that the number of samples is correct - assert blackjax_mclmc_samples.shape == (num_steps, dim) - assert native_mclmc_samples.shape == (num_steps, dim) - - # Assert that the samples are equal - print(blackjax_mclmc_samples, native_mclmc_samples) - assert jnp.allclose(blackjax_mclmc_samples, native_mclmc_samples) - - - # now with tuning - print("\n\n\nTUNING\n\n\n") - - native_mclmc_sampler = Sampler(Target=target_simple) - native_mclmc_samples = native_mclmc_sampler.sample(num_steps, x_initial = initial_position, random_key=key) - - print("\nNATIVE MCLMC PARAMS") - print(native_mclmc_sampler.hyp) - - kernel = build_kernel(logdensity_fn=logdensity_fn, integrator=noneuclidean_mclachlan, transform=lambda x: x) - - # run mclmc with tuning and get result - initial_state = init(x_initial=initial_position, logdensity_fn=logdensity_fn, rng_key=key) - blackjax_state_after_tuning, blackjax_mclmc_sampler_params = mclmc_find_L_and_step_size( - mclmc_kernel=kernel, - num_steps=num_steps, - state=initial_state, - rng_key=key, - - ) - print("\nBLACKJAX MCLMC PARAMS") - print(blackjax_mclmc_sampler_params) - - assert jnp.allclose(blackjax_mclmc_sampler_params.L,native_mclmc_sampler.hyp.L) and jnp.allclose(blackjax_mclmc_sampler_params.step_size,native_mclmc_sampler.hyp.eps) - - - blackjax_mclmc_result = run_kernel(lambda key, state: kernel(L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, rng_key=key, state=state), state=blackjax_state_after_tuning, zero_keys=True, num_steps=num_steps, rng_key=jax.random.PRNGKey(SEED)) - - - print("shapes", native_mclmc_samples.shape, blackjax_mclmc_result.transformed_position.shape) - print("native mclmc post tuning samples", native_mclmc_samples[-1:]) - print("blackjax mclmc post tuning samples", blackjax_mclmc_result.transformed_position[-1:]) - - assert jnp.allclose(native_mclmc_samples, blackjax_mclmc_result.transformed_position) - - - - - - - -def run_mclmc(logdensity_fn,num_steps, initial_position, key): - - init_key, part1_key, part2_key, run_key = jax.random.split(key, 4) - - initial_state = init(x_initial=initial_position, logdensity_fn=logdensity_fn, rng_key=key) - - blackjax_state_after_tuning, blackjax_mclmc_sampler_params = mclmc_find_L_and_step_size( - kernel=build_kernel(logdensity_fn=logdensity_fn, integrator=noneuclidean_mclachlan, transform=lambda x: x), - num_steps=num_steps, - state=initial_state, - part1_key=key, - part2_key=key, - ) - - keys = jax.random.split(key, num_steps) - - kernel = build_kernel(logdensity_fn=logdensity_fn, integrator=noneuclidean_mclachlan, transform=lambda x: x) - - _, blackjax_mclmc_result = jax.lax.scan( - f=lambda state, key: kernel(L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, rng_key=key, state=state), - xs=keys, - init=blackjax_state_after_tuning) - - # (lambda key, state: kernel(L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, rng_key=key, state=state), state=blackjax_state_after_tuning, zero_keys=True, num_steps=num_steps, rng_key=jax.random.PRNGKey(SEED)) - - return blackjax_mclmc_result.transformed_position - -# out = run_mclmc(logdensity_fn=lambda x: -0.5 * jnp.sum(jnp.square(x)), num_steps=1000, initial_position=jnp.array([1., 1.]), key=jax.random.PRNGKey(0)) -# print(out.shape, out) - -aligned() From d7e2e513a91a0db90eba667558fd7352759fea1d Mon Sep 17 00:00:00 2001 From: = Date: Sun, 19 May 2024 18:38:43 +0200 Subject: [PATCH 02/21] ADD DATA --- .gitignore | 2 +- benchmarks/mcmc/benchmark.ipynb | 9716 ----------------- benchmarks/mcmc/benchmark.py | 98 +- benchmarks/mcmc/data/SP500.npy | Bin 0 -> 9836 bytes benchmarks/mcmc/data/gc_features.npy | Bin 0 -> 100128 bytes benchmarks/mcmc/data/gc_labels.npy | Bin 0 -> 4128 bytes benchmarks/mcmc/data/irt_labels.npy | Bin 0 -> 160128 bytes benchmarks/mcmc/data/irt_mask.npy | Bin 0 -> 40128 bytes benchmarks/mcmc/explore.py | 145 - benchmarks/mcmc/find_params.py | 266 - .../ground_truth/brownian/ground_truth.npy | Bin 0 -> 384 bytes benchmarks/mcmc/ground_truth/brownian/map.npy | Bin 0 -> 256 bytes .../german_credit/ground_truth.npy | Bin 0 -> 536 bytes .../mcmc/ground_truth/german_credit/map.npy | Bin 0 -> 332 bytes .../item_response_theory/ground_truth.npy | Bin 0 -> 4136 bytes .../ground_truth/item_response_theory/map.npy | Bin 0 -> 2132 bytes .../stochastic_volatility/ground_truth.npy | Bin 0 -> 38992 bytes .../stochastic_volatility/ground_truth_0.npy | Bin 0 -> 38992 bytes benchmarks/mcmc/inference_models.py | 27 +- benchmarks/mcmc/results.ipynb | 882 +- benchmarks/mcmc/sampling_algorithms.py | 78 +- 21 files changed, 1015 insertions(+), 10199 deletions(-) delete mode 100644 benchmarks/mcmc/benchmark.ipynb create mode 100644 benchmarks/mcmc/data/SP500.npy create mode 100644 benchmarks/mcmc/data/gc_features.npy create mode 100644 benchmarks/mcmc/data/gc_labels.npy create mode 100644 benchmarks/mcmc/data/irt_labels.npy create mode 100644 benchmarks/mcmc/data/irt_mask.npy delete mode 100644 benchmarks/mcmc/explore.py delete mode 100644 benchmarks/mcmc/find_params.py create mode 100644 benchmarks/mcmc/ground_truth/brownian/ground_truth.npy create mode 100644 benchmarks/mcmc/ground_truth/brownian/map.npy create mode 100644 benchmarks/mcmc/ground_truth/german_credit/ground_truth.npy create mode 100644 benchmarks/mcmc/ground_truth/german_credit/map.npy create mode 100644 benchmarks/mcmc/ground_truth/item_response_theory/ground_truth.npy create mode 100644 benchmarks/mcmc/ground_truth/item_response_theory/map.npy create mode 100644 benchmarks/mcmc/ground_truth/stochastic_volatility/ground_truth.npy create mode 100644 benchmarks/mcmc/ground_truth/stochastic_volatility/ground_truth_0.npy diff --git a/.gitignore b/.gitignore index 6119221..c8a001c 100644 --- a/.gitignore +++ b/.gitignore @@ -6,7 +6,7 @@ dist mclmc.egg* *.jpg *.mp4 -*.npy +# *.npy *.pdf *.csv .ipynb_checkpoints diff --git a/benchmarks/mcmc/benchmark.ipynb b/benchmarks/mcmc/benchmark.ipynb deleted file mode 100644 index d8fb0be..0000000 --- a/benchmarks/mcmc/benchmark.ipynb +++ /dev/null @@ -1,9716 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import defaultdict\n", - "import itertools\n", - "import jax\n", - "import numpy as np\n", - "\n", - "from benchmark import benchmark_chains, cumulative_avg, err, ess, get_num_latents\n", - "import blackjax\n", - "from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState\n", - "from blackjax.mcmc.mhmclmc import rescale\n", - "from blackjax.util import run_inference_algorithm\n", - "import jax.numpy as jnp \n", - "\n", - "from inference_models import models\n", - "from find_params import make_grid, sampler_mhmclmc\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "batch_size = 1000\n", - "num_steps = 10000\n", - "\n", - "results = defaultdict(float)\n", - "for model in [\"banana\"]:\n", - " # for step_size, L in itertools.product([16.866055/10], [16.866055]):\n", - " # for step_size, L in make_grid(center_L=21.48713, center_step_size= 2.2340074):\n", - "\n", - " # center_step_size = 2.2340074\n", - " # center_L = 21.48713\n", - "\n", - " center_step_size = 1.1170037\n", - " center_L = 12.776323938494208\n", - "\n", - " # center_step_size = 1.2332720719048489\n", - " # center_L = 11.86185745597299\n", - "\n", - " # center_step_size = 1.1170037\n", - " # center_L = 10.743564999999998\n", - " \n", - " for step_size, L in itertools.product(np.logspace(np.log10(center_step_size/2), np.log10(center_step_size*2), 9), np.logspace(np.log10(center_L/2), np.log10(center_L*2), 9)):\n", - "\n", - "\n", - " # for sampler in [\"mhmclmc\"]:\n", - " # result, bias = benchmark_chains(models[model], sampler_mhmclmc_with_tuning(step_size, L), n=1000000, batch=1)\n", - " # result, bias = benchmark_chains(models[model], samplers[sampler], n=100000, batch=100, favg= jnp.array([100.0, 19.0]), fvar =jnp.array([20000.0, 4600.898]))\n", - " result, bias = benchmark_chains(models[model], sampler_mhmclmc(step_size=step_size, L=L), batch=batch_size, n=num_steps,favg=models[model].E_x2, fvar=models[model].Var_x2)\n", - " # result, bias = benchmark_chains(models[model], samplers[\"mhmclmc\"], n=1000000, batch=10)\n", - " results[(model, step_size, L)] = (result.item(), bias.item())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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    \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tracedwith with\n", - " val = Tracedwith with\n", - " val = Array([[0.8435858 , 0.8442986 , 0.8415559 , 0.8427685 , 0.8463157 ,\n", - " 0.8491128 , 0.8431078 , 0.84260917, 0.84483224, 0.8375843 ,\n", - " 0.8375893 , 0.8419349 , 0.84908915, 0.84480864, 0.8410134 ,\n", - " 0.8449966 , 0.84187603, 0.845713 , 0.8447011 , 0.84466696,\n", - " 0.8400421 , 0.84705085, 0.8332738 , 0.84576803, 0.8336657 ,\n", - " 0.84211314, 0.8455844 , 0.8392147 , 0.8509331 , 0.8440238 ,\n", - " 0.8371414 , 0.8386296 , 0.8451284 , 0.848194 , 0.8394416 ,\n", - " 0.8498367 , 0.8330134 , 0.84099394, 0.8421177 , 0.843717 ,\n", - " 0.84450364, 0.8514319 , 0.8448533 , 0.84387785, 0.8439756 ,\n", - " 0.83963746, 0.8402315 , 0.85167474, 0.84031194, 0.84450245,\n", - " 0.8455862 , 0.84251726, 0.8467568 , 0.8340951 , 0.8407393 ,\n", - " 0.84303313, 0.8486856 , 0.8418433 , 0.84375656, 0.84481865,\n", - " 0.84720576, 0.8517645 , 0.83659995, 0.8535823 , 0.8432801 ,\n", - " 0.84893775, 0.84475434, 0.8463244 , 0.8424343 , 0.84190756,\n", - " 0.8415012 , 0.84235483, 0.8360425 , 0.8409168 , 0.8488021 ,\n", - " 0.8398986 , 0.8508612 , 0.8478436 , 0.8406811 , 0.85122395,\n", - " 0.83302325, 0.84197176, 0.8471764 , 0.8473693 , 0.83765584,\n", - " 0.83757216, 0.8460829 , 0.8452597 , 0.8409692 , 0.84046406,\n", - " 0.8404769 , 0.84563756, 0.84397906, 0.8439965 , 0.8469477 ,\n", - " 0.8480277 , 0.8389911 , 0.8392323 , 0.8312033 , 0.8362428 ],\n", - " [0.9141073 , 0.91049445, 0.91184056, 0.91386414, 0.9114997 ,\n", - " 0.9085978 , 0.9094407 , 0.91038513, 0.9091061 , 0.9130469 ,\n", - " 0.9120573 , 0.9082391 , 0.9107473 , 0.91127574, 0.9078788 ,\n", - " 0.9145811 , 0.90876555, 0.9038063 , 0.9136094 , 0.90746063,\n", - " 0.9121309 , 0.90966076, 0.91195565, 0.9150678 , 0.9137885 ,\n", - " 0.9141526 , 0.9153058 , 0.91306776, 0.91323185, 0.9136166 ,\n", - " 0.91644865, 0.9166464 , 0.91016626, 0.9094692 , 0.91374207,\n", - " 0.9114871 , 0.91152 , 0.91265017, 0.9136803 , 0.91412246,\n", - " 0.91432863, 0.9128117 , 0.9143933 , 0.9156801 , 0.91350424,\n", - " 0.9105282 , 0.91299754, 0.91354656, 0.91255 , 0.91244036,\n", - " 0.9128147 , 0.9129622 , 0.9121573 , 0.91559756, 0.91246694,\n", - " 0.91180074, 0.9090308 , 0.911497 , 0.911287 , 0.9148062 ,\n", - " 0.91484225, 0.9107405 , 0.9088609 , 0.9114057 , 0.9136628 ,\n", - " 0.9129748 , 0.9137581 , 0.91187185, 0.9164927 , 0.9100827 ,\n", - " 0.9123644 , 0.9147338 , 0.9128822 , 0.91231525, 0.9159241 ,\n", - " 0.909687 , 0.9114519 , 0.9142304 , 0.90744364, 0.9136173 ,\n", - " 0.91329956, 0.908741 , 0.91511405, 0.9099337 , 0.9147669 ,\n", - " 0.91032606, 0.91201174, 0.9184225 , 0.9128089 , 0.9138138 ,\n", - " 0.9104181 , 0.9117637 , 0.9112562 , 0.91193014, 0.91603905,\n", - " 0.91127455, 0.91419363, 0.9091502 , 0.91466635, 0.9104765 ],\n", - " [0.8182885 , 0.81645 , 0.8212288 , 0.82042813, 0.7995485 ,\n", - " 0.82143354, 0.81355894, 0.82378983, 0.81463534, 0.81933165,\n", - " 0.8092212 , 0.8140594 , 0.8152705 , 0.81153756, 0.81995744,\n", - " 0.8134262 , 0.8144958 , 0.8148774 , 0.80735767, 0.7996466 ,\n", - " 0.80590236, 0.81858754, 0.82499367, 0.82092667, 0.81852204,\n", - " 0.81666756, 0.8192548 , 0.8158879 , 0.80845755, 0.81051564,\n", - " 0.8194928 , 0.8175792 , 0.8070852 , 0.81650734, 0.8140636 ,\n", - " 0.8211214 , 0.8119346 , 0.8188779 , 0.8233972 , 0.81865305,\n", - " 0.8102587 , 0.8174392 , 0.81393313, 0.80479264, 0.8169993 ,\n", - " 0.81105155, 0.81979597, 0.802209 , 0.8100946 , 0.80326605,\n", - " 0.81280714, 0.81860095, 0.820589 , 0.81805354, 0.8188111 ,\n", - " 0.8127831 , 0.80968994, 0.81751925, 0.81761533, 0.8055051 ,\n", - " 0.8162603 , 0.8120219 , 0.81247073, 0.81887347, 0.8097995 ,\n", - " 0.8158402 , 0.8244784 , 0.81276655, 0.80699074, 0.81832695,\n", - " 0.80784833, 0.815833 , 0.80288005, 0.81820077, 0.8213234 ,\n", - " 0.8202933 , 0.81724954, 0.80805933, 0.82406604, 0.8238287 ,\n", - " 0.81558645, 0.7981632 , 0.81173044, 0.8114513 , 0.8068975 ,\n", - " 0.8032079 , 0.80342 , 0.82118356, 0.81748253, 0.8098008 ,\n", - " 0.81191957, 0.8284385 , 0.80766314, 0.8118846 , 0.80934274,\n", - " 0.8093861 , 0.8093985 , 0.82138544, 0.8122794 , 0.8196393 ],\n", - " [0.916788 , 0.916461 , 0.9120648 , 0.91638935, 0.9185752 ,\n", - " 0.915967 , 0.91649187, 0.91460687, 0.9161761 , 0.91481227,\n", - " 0.91399264, 0.91651887, 0.9176769 , 0.9182386 , 0.9141354 ,\n", - " 0.9154259 , 0.9191275 , 0.914757 , 0.9179838 , 0.91963214,\n", - " 0.9148346 , 0.91213346, 0.91704535, 0.9137658 , 0.9179093 ,\n", - " 0.9179158 , 0.91616833, 0.9176504 , 0.9133633 , 0.92000556,\n", - " 0.91896355, 0.9198364 , 0.91291165, 0.91697735, 0.9135519 ,\n", - " 0.91932946, 0.91273105, 0.9128715 , 0.918481 , 0.91486514,\n", - " 0.91871846, 0.9168679 , 0.916582 , 0.9167394 , 0.9177922 ,\n", - " 0.91301274, 0.91460735, 0.9127277 , 0.91379863, 0.918544 ,\n", - " 0.91489583, 0.9111012 , 0.9143128 , 0.91855806, 0.9205817 ,\n", - " 0.91369355, 0.91342413, 0.91696864, 0.9174895 , 0.91498464,\n", - 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" 0.9096144 , 0.9085676 , 0.90971696, 0.9026003 , 0.9091983 ,\n", - " 0.9098427 , 0.91010743, 0.90646607, 0.90896034, 0.9035882 ,\n", - " 0.9058538 , 0.9121087 , 0.90881664, 0.9046806 , 0.9104771 ,\n", - " 0.9093438 , 0.90800047, 0.908053 , 0.9102836 , 0.9074732 ,\n", - " 0.9065999 , 0.90331537, 0.909493 , 0.9052581 , 0.90040326,\n", - " 0.911583 , 0.9075188 , 0.9077676 , 0.90716386, 0.90449244,\n", - " 0.90633625, 0.90837824, 0.8996274 , 0.90395826, 0.9024799 ,\n", - " 0.9058671 , 0.9100268 , 0.91327584, 0.90437907, 0.90948224], dtype=float32)\n", - " batch_dim = 0 acceptance probability\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AVG NUM STEPS PER TRAJ 24.39819\n", - "True mean [0. 0.]\n", - "True std [10. 4.35889894]\n", - "Empirical mean [0.00835473 0.02560315]\n", - "Empirical std [10.047082 4.374237]\n", - "27.749163 1.1373456 params\n" - ] - } - ], - "source": [ - "import bayex\n", - "\n", - "\n", - "def benchmark_chains_prime(model, sampler, favg, fvar, n=10000, batch=None):\n", - "\n", - "\n", - " # print(model)\n", - " # print(model.sample_transformations.keys())\n", - " # raise Exception\n", - " identity_fn = model.sample_transformations['identity']\n", - " logdensity_fn = model.unnormalized_log_prob\n", - " d = get_num_latents(model)\n", - " if batch is None:\n", - " batch = np.ceil(1000 / d).astype(int)\n", - " key, init_key = jax.random.split(jax.random.PRNGKey(42), 2)\n", - " keys = jax.random.split(key, batch)\n", - " # keys = jnp.array([jax.random.PRNGKey(0)])\n", - " init_pos = jax.random.normal(key=init_key, shape=(batch, d))\n", - "\n", - " samples, params, avg_num_steps_per_traj = jax.vmap(lambda pos, key: sampler(logdensity_fn, n, pos, key))(init_pos, keys)\n", - " avg_num_steps_per_traj = jnp.mean(avg_num_steps_per_traj, axis=0)\n", - " print(\"\\n\\n\\n\\nAVG NUM STEPS PER TRAJ\", avg_num_steps_per_traj)\n", - " # print(samples[0][-1], samples[0][0], \"samps chain\", samples.shape)\n", - " \n", - " # identity_fn.ground_truth_mean, identity_fn.ground_truth_standard_deviation**2\n", - " full = lambda arr : err(favg, fvar, jnp.average)(cumulative_avg(arr))\n", - " err_t = jnp.mean(jax.vmap(full)(samples**2), axis=0)\n", - " # ess_per_sample = ess(err_t, grad_evals_per_step=2 * avg_num_steps_per_traj)\n", - " # err_t = jax.vmap(full)(samples)[1]\n", - " # print(err_t[-1], \"benchmark chains err_t[0]\")\n", - " # print(avg_num_steps_per_traj, \"AVG\\n\\n\")\n", - " # raise Exception\n", - " # ess_per_sample = ess(err_t, grad_evals_per_step=2 * avg_num_steps_per_traj)\n", - "\n", - " print('True mean', identity_fn.ground_truth_mean)\n", - " print('True std', identity_fn.ground_truth_standard_deviation)\n", - " print(\"Empirical mean\", samples.mean(axis=[0,1]))\n", - " print(\"Empirical std\", samples.std(axis=[0,1]))\n", - "\n", - " print(params.L.mean(), params.step_size.mean(), \"params\")\n", - " \n", - " # print('True E[x^2]', identity_fn.ground_truth_mean)\n", - " # print('True std[x^2]', identity_fn.ground_truth_standard_deviation)\n", - "\n", - " return err_t[-1]\n", - "\n", - "def f(step_size, L):\n", - " model = \"banana\"\n", - " # print(type(step_size), type(L))\n", - " # raise Exception\n", - " traj_length = L/step_size\n", - " bias = benchmark_chains_prime(models[model], sampler_mhmclmc(step_size, L), batch=100, n=4000,favg=models[model].E_x2, fvar=models[model].Var_x2)\n", - " return - (bias * traj_length)\n", - "\n", - "constrains = {'step_size': (0.9, 2.), 'L': (10., 30.)}\n", - "optim_params = bayex.optim(f, constrains=constrains, seed=42, n=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'step_size': Array(1.2840078, dtype=float32),\n", - " 'L': Array(10.502036, dtype=float32)}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# bayex.show_results(optim_params)\n", - "optim_params.params" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.2840078 10.502036\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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    \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Tracedwith with\n", - " val = Array([0.9339157 , 0.93431884, 0.9322909 , 0.9342701 , 0.9341552 ,\n", - " 0.93465376, 0.9335822 , 0.9367909 , 0.9329499 , 0.93357486,\n", - " 0.9314774 , 0.93255955, 0.92864585, 0.93632627, 0.93140155,\n", - " 0.9362243 , 0.93325466, 0.9336672 , 0.9339867 , 0.9338112 ,\n", - " 0.9350811 , 0.93485105, 0.93471336, 0.93659383, 0.93350965,\n", - " 0.93118936, 0.93067616, 0.9351261 , 0.93502945, 0.936908 ,\n", - " 0.93764746, 0.9377526 , 0.9350277 , 0.9337486 , 0.93652546,\n", - " 0.9359428 , 0.9337399 , 0.93321186, 0.9339389 , 0.93727136,\n", - " 0.9380293 , 0.9308454 , 0.938122 , 0.93666196, 0.9337697 ,\n", - " 0.9338155 , 0.9338142 , 0.9302911 , 0.933757 , 0.93568975,\n", - " 0.93668514, 0.9300203 , 0.9378794 , 0.9311688 , 0.9343718 ,\n", - " 0.93479264, 0.9344969 , 0.93288016, 0.9327182 , 0.93496597,\n", - " 0.930949 , 0.9322504 , 0.93415964, 0.9342759 , 0.93494815,\n", - " 0.93282264, 0.93581814, 0.933904 , 0.9344867 , 0.9347651 ,\n", - " 0.9338378 , 0.93404096, 0.9332588 , 0.93355405, 0.936354 ,\n", - " 0.93132156, 0.9322475 , 0.93422145, 0.9335083 , 0.93297595,\n", - " 0.93157333, 0.9349217 , 0.93491626, 0.93375254, 0.933567 ,\n", - " 0.93280643, 0.93669194, 0.93227255, 0.93162304, 0.9334552 ,\n", - " 0.93123084, 0.9335849 , 0.9324985 , 0.9338289 , 0.93371874,\n", - " 0.9312737 , 0.93425953, 0.93295395, 0.933913 , 0.93598765], dtype=float32)\n", - " batch_dim = 0 acceptance probability\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "AVG NUM STEPS PER TRAJ 8.179107\n", - "crossing 1174 19204.543 0.0052071013\n", - "True mean [0. 0.]\n", - "True std [10. 4.35889894]\n", - "Empirical mean [-0.00912235 -0.00456485]\n", - "Empirical std [9.993177 4.338162]\n", - "10.502036 1.2840078 params\n" - ] - }, - { - "data": { - "text/plain": [ - "(Array(0.0052071, dtype=float32), Array(0.00127003, dtype=float32))" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(optim_params.params['step_size'], optim_params.params['L'])\n", - "\n", - "model = 'banana'\n", - "benchmark_chains(models[model], sampler_mhmclmc(optim_params.params['step_size'], optim_params.params['L']), batch=100, n=10000,favg=models[model].E_x2, fvar=models[model].Var_x2)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(81,)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Extract x and y values from the keys of the results dictionary\n", - "# x_values = [key[0] for key in results.keys()]\n", - "# y_values = [key[1] for key in results.keys()]\n", - "\n", - "# print(len(x_values))\n", - "# raise Exception\n", - "\n", - "# Extract heat values from the dictionary\n", - "heat_values = list(x[0] for x in results.values())\n", - "\n", - "print(np.array(heat_values).shape)\n", - "# Reshape the heat values into a 2D array\n", - "# heat_array = np.array(heat_values).reshape((len(x_values), len(y_values)))\n", - "heat_array = np.array(heat_values).reshape((9,9))\n", - "# bar = np.array(list(results.keys())).reshape((5,5,3))\n", - "# print(bar)\n", - "# print(np.array(heat_values).shape)\n", - "# print(np.array(results.keys()).shape)\n", - "\n", - "# Create the heatmap\n", - "plt.figure(figsize=(10, 8))\n", - "sns.heatmap(heat_array, annot=True, cmap='viridis')\n", - "plt.xlabel('L')\n", - "plt.ylabel('step size')\n", - "plt.title('Heatmap of Results')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[(0.005370330065488815, ('banana', 1.1170037, 10.743564999999998)),\n", - " (0.006148216780275106, ('banana', 1.1170037, 12.776323938494208)),\n", - " (0.004798795096576214, ('banana', 1.1170037, 15.193695331236901)),\n", - " (0.0035045745316892862, ('banana', 1.1170037, 18.068450591090546)),\n", - " (0.0056139081716537476, ('banana', 1.1170037, 21.48713)),\n", - " (0.005047785583883524, ('banana', 1.1170037, 25.55264787698842)),\n", - " (0.004653009120374918, ('banana', 1.1170037, 30.387390662473805)),\n", - " (0.0033744920510798693, ('banana', 1.1170037, 36.1369011821811)),\n", - " (0.0032409471459686756, ('banana', 1.1170037, 42.97426000000001)),\n", - " 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import seaborn as sns\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Extract x and y values from the keys of the results dictionary\n", - "# x_values = [key[0] for key in results.keys()]\n", - "# y_values = [key[1] for key in results.keys()]\n", - "\n", - "# print(len(x_values))\n", - "# raise Exception\n", - "\n", - "# Extract heat values from the dictionary\n", - "heat_values = list(x[0] for x in results.values())\n", - "\n", - "print(np.array(heat_values).shape)\n", - "# Reshape the heat values into a 2D array\n", - "# heat_array = np.array(heat_values).reshape((len(x_values), len(y_values)))\n", - "heat_array = np.array(heat_values).reshape((15,15))\n", - "# bar = np.array(list(results.keys())).reshape((5,5,3))\n", - "# print(bar)\n", - "# print(np.array(heat_values).shape)\n", - "# print(np.array(results.keys()).shape)\n", - "\n", - "# Create the heatmap\n", - "plt.figure(figsize=(10, 8))\n", - "sns.heatmap(heat_array, annot=True, cmap='viridis')\n", - "plt.xlabel('L')\n", - "plt.ylabel('step size')\n", - "plt.title('Heatmap of Results')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(225,)\n" - ] - }, - { - "data": { - "image/png": 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", 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" (0.0, ('banana', 4.468014800000001, 28.91953240611649)),\n", - " (0.0, ('banana', 4.468014800000001, 31.92975246994321)),\n", - " (0.0, ('banana', 4.468014800000001, 35.25330487626481)),\n", - " (0.0, ('banana', 4.468014800000001, 38.922804236229844)),\n", - " (0.0, ('banana', 4.468014800000001, 42.97426000000001))]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(results[k][0], k) for k in results]" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.029569892212748528, ('Banana', 1.2222222222222223, 13.777777777777779))" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# results[('Banana', np.linspace(1,3,10)[1], np.linspace(12,16,10)[4])]\n", - "# results_1 = {results[key]: key for key in results.keys() if type(results[key]) is tuple}\n", - "# max(results_1, key=lambda x: results_1[x][0])\n", - "max([(results[r][0], r) for r in results if type(results[r]) is tuple])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mclmc", - "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": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 857cd7b..194b19d 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -1,3 +1,5 @@ +import sys +sys.path.append('./') from collections import defaultdict from functools import partial import math @@ -21,10 +23,10 @@ import numpy as np import blackjax -from benchmarks.mcmc.sampling_algorithms import run_mclmc, run_mhmclmc, run_nuts, samplers -from benchmarks.mcmc.inference_models import Brownian, GermanCredit, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models +from benchmarks.mcmc.sampling_algorithms import run_mclmc, run_adjusted_mclmc, run_nuts, samplers +from benchmarks.mcmc.inference_models import Brownian, GermanCredit, IllConditionedGaussian, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models from blackjax.mcmc.integrators import calls_per_integrator_step, generate_euclidean_integrator, generate_isokinetic_integrator, isokinetic_mclachlan, mclachlan_coefficients, name_integrator, omelyan_coefficients, velocity_verlet, velocity_verlet_coefficients, yoshida_coefficients -# from blackjax.mcmc.mhmclmc import rescale +from blackjax.mcmc.adjusted_mclmc import rescale from blackjax.util import run_inference_algorithm @@ -114,14 +116,14 @@ def gridsearch_tune(key, iterations, grid_size, model, sampler, batch, num_steps return center_L, center_step_size, converged -def run_mhmclmc_no_tuning(initial_state, coefficients, step_size, L, std_mat): +def run_adjusted_mclmc_no_tuning(initial_state, coefficients, step_size, L, std_mat): def s(logdensity_fn, num_steps, initial_position, transform, key): integrator = generate_isokinetic_integrator(coefficients) num_steps_per_traj = L/step_size - alg = blackjax.mcmc.mhmclmc.mhmclmc( + alg = blackjax.mcmc.adjusted_mclmc.adjusted_mclmc( logdensity_fn=logdensity_fn, step_size=step_size, integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(num_steps_per_traj)) , @@ -201,8 +203,8 @@ def run_benchmarks(batch_size): results = defaultdict(tuple) for variables in itertools.product( - # ["mhmclmc", "nuts", "mclmc", ], - ["mhmclmc"], + # ["adjusted_mclmc", "nuts", "mclmc", ], + ["adjusted_mclmc"], # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 10)).astype(int)], [Brownian()], # [Brownian()], @@ -258,8 +260,8 @@ def run_benchmarks_step_size(batch_size): results = defaultdict(tuple) for variables in itertools.product( - # ["mhmclmc", "nuts", "mclmc", ], - ["mhmclmc"], + # ["adjusted_mclmc", "nuts", "mclmc", ], + ["adjusted_mclmc"], # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 10)).astype(int)], [StandardNormal(10)], # [Brownian()], @@ -285,9 +287,9 @@ def run_benchmarks_step_size(batch_size): # for L in np.linspace(1, 10, 41): key1, key2, key3, key = jax.random.split(key, 4) initial_position = model.sample_init(key2) - initial_state = blackjax.mcmc.mhmclmc.init( + initial_state = blackjax.mcmc.adjusted_mclmc.init( position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=key3) - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains(model, run_mhmclmc_no_tuning(initial_state=initial_state, coefficients=mclachlan_coefficients, step_size=step_size, L= 5*step_size, std_mat=1.),key1, n=num_steps, batch=num_chains, contract=contract) + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(initial_state=initial_state, coefficients=mclachlan_coefficients, step_size=step_size, L= 5*step_size, std_mat=1.),key1, n=num_steps, batch=num_chains, contract=contract) # print(f"step size over da {step_size_over_da.shape} \n\n\n\n") # jax.numpy.save(f"step_size_over_da.npy", step_size_over_da.mean(axis=0)) @@ -322,11 +324,11 @@ def benchmark_mhmchmc(batch_size): coefficients = [mclachlan_coefficients, velocity_verlet_coefficients] for model in models: results = defaultdict(tuple) - for preconditioning, coeffs in itertools.product([False, True], coefficients): + for preconditioning, coeffs in itertools.product([True, False], coefficients): num_chains = batch_size # 1 + batch_size//model.ndims - print(f"NUMBER OF CHAINS for {model.name} and MHMCLMC is {num_chains}") - num_steps = models[model]["mhmclmc"] + print(f"NUMBER OF CHAINS for {model.name} and adjusted_mclmc is {num_chains}") + num_steps = models[model]["adjusted_mclmc"] print(f"NUMBER OF STEPS for {model.name} and MHCMLMC is {num_steps}") ####### run mclmc with standard tuning @@ -345,41 +347,41 @@ def benchmark_mhmchmc(batch_size): print(f'mclmc with tuning ESS {ess}') - ####### run mhmclmc with standard tuning + ####### run adjusted_mclmc with standard tuning for target_acc_rate in [0.65, 0.9]: # coeffs = mclachlan_coefficients ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( model, - partial(run_mhmclmc, target_acc_rate=target_acc_rate, coefficients=coeffs, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, coefficients=coeffs, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), key1, n=num_steps, batch=num_chains, contract=contract) results[(model.name, model.ndims, "mhmchmc"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), name_integrator(coeffs), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() - print(f'mhmclmc with tuning ESS {ess}') + print(f'adjusted_mclmc with tuning ESS {ess}') # coeffs = mclachlan_coefficients ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( model, - partial(run_mhmclmc, target_acc_rate=target_acc_rate,coefficients=coeffs, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate,coefficients=coeffs, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), key1, n=num_steps, batch=num_chains, contract=contract) results[(model.name, model.ndims, "mhmchmc:st3"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), name_integrator(coeffs), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() - print(f'mhmclmc with tuning ESS {ess}') + print(f'adjusted_mclmc with tuning ESS {ess}') if False: - ####### run mhmclmc with standard tuning + grid search + ####### run adjusted_mclmc with standard tuning + grid search init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) initial_position = model.sample_init(init_pos_key) - initial_state = blackjax.mcmc.mhmclmc.init( + initial_state = blackjax.mcmc.adjusted_mclmc.init( position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key ) - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.mhmclmc.build_kernel( + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.adjusted_mclmc.build_kernel( integrator=generate_isokinetic_integrator(coeffs), integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), std_mat=std_mat, @@ -391,9 +393,9 @@ def benchmark_mhmchmc(batch_size): ( state, - blackjax_mhmclmc_sampler_params, + blackjax_adjusted_mclmc_sampler_params, _, _ - ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( + ) = blackjax.adaptation.mclmc_adaptation.adjusted_mclmc_find_L_and_step_size( mclmc_kernel=kernel, num_steps=num_steps, state=initial_state, @@ -406,14 +408,14 @@ def benchmark_mhmchmc(batch_size): ) print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(coeffs)]}") - print(f"params after initial tuning are L={blackjax_mhmclmc_sampler_params.L}, step_size={blackjax_mhmclmc_sampler_params.step_size}") + print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_mhmclmc_no_tuning, coefficients=coeffs, initial_state=state, std_mat=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_mhmclmc_sampler_params.L, center_step_size=blackjax_mhmclmc_sampler_params.step_size) + L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, coefficients=coeffs, initial_state=state, std_mat=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) # print(f"params after grid tuning are L={L}, step_size={step_size}") - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_mhmclmc_no_tuning(coefficients=coeffs, L=L, step_size=step_size, initial_state=state, std_mat=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coeffs, L=L, step_size=step_size, initial_state=state, std_mat=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) print(f"grads to low bias: {grad_calls}") @@ -448,7 +450,7 @@ def benchmark_omelyan(batch_size): key = jax.random.PRNGKey(2) results = defaultdict(tuple) for variables in itertools.product( - # ["mhmclmc", "nuts", "mclmc", ], + # ["adjusted_mclmc", "nuts", "mclmc", ], ["mhmchmc"], [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(1000), 4)).astype(int)], # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 5)).astype(int)], @@ -473,12 +475,12 @@ def benchmark_omelyan(batch_size): initial_position = model.sample_init(init_pos_key) - initial_state = blackjax.mcmc.mhmclmc.init( + initial_state = blackjax.mcmc.adjusted_mclmc.init( position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key ) - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.mhmclmc.build_kernel( + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.adjusted_mclmc.build_kernel( integrator=generate_isokinetic_integrator(coefficients), integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), std_mat=std_mat, @@ -490,9 +492,9 @@ def benchmark_omelyan(batch_size): ( state, - blackjax_mhmclmc_sampler_params, + blackjax_adjusted_mclmc_sampler_params, _, _ - ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( + ) = blackjax.adaptation.mclmc_adaptation.adjusted_mclmc_find_L_and_step_size( mclmc_kernel=kernel, num_steps=num_steps, state=initial_state, @@ -505,17 +507,17 @@ def benchmark_omelyan(batch_size): ) print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) - print(f"params after initial tuning are L={blackjax_mhmclmc_sampler_params.L}, step_size={blackjax_mhmclmc_sampler_params.step_size}") + print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - # ess, grad_calls, _ , _ = benchmark_chains(model, run_mhmclmc_no_tuning(coefficients=coefficients, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, std_mat=1.),bench_key_pre_grid, n=num_steps, batch=num_chains, contract=jnp.average) + # ess, grad_calls, _ , _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coefficients, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, std_mat=1.),bench_key_pre_grid, n=num_steps, batch=num_chains, contract=jnp.average) # results[((model.name, model.ndims), sampler, name_integrator(coefficients), "without grid search")] = (ess, grad_calls) - L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_mhmclmc_no_tuning, coefficients=coefficients, initial_state=state, std_mat=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_mhmclmc_sampler_params.L, center_step_size=blackjax_mhmclmc_sampler_params.step_size) + L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, coefficients=coefficients, initial_state=state, std_mat=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) print(f"params after grid tuning are L={L}, step_size={step_size}") - ess, grad_calls, _ , _, _ = benchmark_chains(model, run_mhmclmc_no_tuning(coefficients=coefficients, L=L, step_size=step_size, std_mat=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) + ess, grad_calls, _ , _, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coefficients, L=L, step_size=step_size, std_mat=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) print(f"grads to low bias: {grad_calls}") @@ -530,23 +532,29 @@ def benchmark_omelyan(batch_size): def run_benchmarks_simple(): - sampler = run_mclmc - model = StandardNormal(10) # 10 dimensional standard normal + sampler = run_adjusted_mclmc + # sampler = run_mclmc + # model = IllConditionedGaussian(100,100) + model = Brownian() coefficients = mclachlan_coefficients - contract = jnp.average # how we average across dimensions - num_steps = 2000 - num_chains = 100 - key1 = jax.random.PRNGKey(2) + contract = jnp.max # how we average across dimensions + num_steps = 10000 + num_chains = 1 + for i in range(100): + key1 = jax.random.PRNGKey(i) - ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, coefficients=coefficients, preconditioning=False),key1, n=num_steps, batch=num_chains, contract=contract) + for preconditioning in [True, False]: + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, coefficients=coefficients, preconditioning=preconditioning, target_acc_rate=0.95, frac_tune3=0.1),key1, n=num_steps, batch=num_chains, contract=contract) + + print(f"Effective Sample Size (ESS) of {model.name} with preconditioning set to {preconditioning} is {ess}") - print(f"Effective Sample Size (ESS) of 10D Normal is {ess}") if __name__ == "__main__": + run_benchmarks_simple() - # benchmark_mhmchmc(batch_size=128) + # benchmark_mhmchmc(batch_size=50) # run_benchmarks(128) # run_benchmarks_step_size(128) # benchmark_omelyan(128) diff --git a/benchmarks/mcmc/data/SP500.npy b/benchmarks/mcmc/data/SP500.npy new file mode 100644 index 0000000000000000000000000000000000000000..b1e7c4ba37dfbad6960c1aa2a97a59c576ea4db7 GIT binary patch literal 9836 zcmbW7`CCoj`^FnI4^%YIB$O#3b@qBTp^O>cp=2s5QbH(Gljcd0MjBM4K}C|zUeAtX z$Pg+*M2O5&REE#?{S&_Dy3P-0U+0`XthMgvzF*Hew9t9MA{R-Cof3P@*KgSFvCZ7p 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z`t%km;5-A$6q&Bm9$giIm=qJ9D|H5r1g>(6>iNUac!~7>B_0`h?VlgYGa9I|{tObm z`g{cnHM13MhMGKrkVaxvGaLJw`aIPQ6op8atdTC>kKB75NY*hIncJm{#gDJjC`Teh zCye(nX=#7ube3URPv%&mg%FV#Y%b+^h~;7gqvfz54zY};mjxP=p!ihiaVDh?!v|1# HLF)etj)##U literal 0 HcmV?d00001 diff --git a/benchmarks/mcmc/explore.py b/benchmarks/mcmc/explore.py deleted file mode 100644 index 09be2ba..0000000 --- a/benchmarks/mcmc/explore.py +++ /dev/null @@ -1,145 +0,0 @@ -import jax - -from datetime import date -from blackjax.benchmarks.mcmc.benchmark import benchmark_chains - -from blackjax.benchmarks.mcmc.inference_models import IllConditionedGaussian - -rng_key = jax.random.key(int(date.today().strftime("%Y%m%d"))) - -import blackjax -import numpy as np -import jax.numpy as jnp -from sampling_algorithms import samplers -from inference_models import StandardNormal, models - -def run_mclmc(logdensity_fn, num_steps, initial_position, key, transform, std_mat, L, step_size): - init_key, tune_key, run_key = jax.random.split(key, 3) - - # create an initial state for the sampler - initial_state = blackjax.mcmc.mclmc.init( - position=initial_position, logdensity_fn=logdensity_fn, rng_key=init_key - ) - - - # use the quick wrapper to build a new kernel with the tuned parameters - sampling_alg = blackjax.mclmc( - logdensity_fn, - L=L, - step_size=step_size, - std_mat=std_mat, - ) - - # run the sampler - _, samples, _ = blackjax.util.run_inference_algorithm( - rng_key=run_key, - initial_state_or_position=initial_state, - inference_algorithm=sampling_alg, - num_steps=num_steps, - transform=transform, - progress_bar=True, - ) - - return samples, None, 1 - - -def run_mclmc_with_tuning(logdensity_fn, num_steps, initial_position, key, transform): - init_key, tune_key, run_key = jax.random.split(key, 3) - - # create an initial state for the sampler - initial_state = blackjax.mcmc.mclmc.init( - position=initial_position, logdensity_fn=logdensity_fn, rng_key=init_key - ) - - kernel = blackjax.mcmc.mclmc.build_kernel( - logdensity_fn=logdensity_fn, - integrator=blackjax.mcmc.integrators.isokinetic_mclachlan, - std_mat=jnp.ones((initial_position.shape[0],)), - ) - - # find values for L and step_size - ( - blackjax_state_after_tuning, - blackjax_mclmc_sampler_params, - ) = blackjax.mclmc_find_L_and_step_size( - mclmc_kernel=kernel, - num_steps=num_steps, - state=initial_state, - rng_key=tune_key, - ) - - print(blackjax_mclmc_sampler_params) - - - - # use the quick wrapper to build a new kernel with the tuned parameters - sampling_alg = blackjax.mclmc( - logdensity_fn, - L=blackjax_mclmc_sampler_params.L, - step_size=blackjax_mclmc_sampler_params.step_size, - std_mat=blackjax_mclmc_sampler_params.std_mat, - ) - - # run the sampler - _, samples, _ = blackjax.util.run_inference_algorithm( - rng_key=run_key, - initial_state_or_position=initial_state, - inference_algorithm=sampling_alg, - num_steps=num_steps, - transform=transform, - progress_bar=True, - ) - - return samples -# run the algorithm on a high dimensional gaussian, and show two of the dimensions - -# sigma = .5 - -sample_key, rng_key = jax.random.split(rng_key) -# samples = run_mclmc( -# logdensity_fn=lambda x: -0.5 * jnp.sum(jnp.square(x)), -# num_steps=100000, -# initial_position=jnp.ones((2,)), -# key=sample_key, -# std_mat=jnp.ones((2,))*sigma, -# # std_mat=None, -# transform=lambda x: x.position, # x.position[:2], -# ) -# print(samples.var(axis=0)) - -# den = lambda x: jax.scipy.stats.norm.logpdf(x, loc=0., scale=jnp.sqrt(sigma)).sum() -# print(IllConditionedGaussian(2, 2).E_x2) -# samples = run_mclmc_with_tuning( -# logdensity_fn=lambda x : - IllConditionedGaussian(2, 2).nlogp(x), -# num_steps=1000000, -# initial_position=jnp.ones((2,)), -# key=sample_key, -# transform=lambda x: x.position[:2], -# ) -# # print(samples.var(axis=0)) -# m = IllConditionedGaussian(10, 5) -# sampler = lambda logdensity_fn, num_steps, initial_position, key: run_mclmc(logdensity_fn=logdensity_fn, num_steps=num_steps, initial_position=initial_position, key=key, transform=lambda x:x.position, -# # std_mat=jnp.ones((10,)) -# std_mat=jnp.sqrt(m.E_x2) -# , L=2.6576319, step_size=3.40299) -# print(m.E_x2, "var") - -# # sampler = 'mclmc' -# # samplers[sampler] -# result, bias, _ = benchmark_chains(m, sampler, n=5000, batch=1000//m.ndims,favg=m.E_x2, fvar=m.Var_x2) - -# print(result) - - -# m = StandardNormal(10) -# sampler = lambda logdensity_fn, num_steps, initial_position, key: run_mclmc(logdensity_fn=logdensity_fn, num_steps=num_steps, initial_position=initial_position, key=key, transform=lambda x:x.position, -# std_mat=jnp.ones((10,)) -# , L=2.6576319, step_size=3.40299) -# # print(m.E_x2, "var") - -# # sampler = 'mclmc' -# # samplers[sampler] -# result, bias, _ = benchmark_chains(m, sampler, n=5000, batch=1000//m.ndims,favg=m.E_x2, fvar=m.Var_x2) - -# print(result) - diff --git a/benchmarks/mcmc/find_params.py b/benchmarks/mcmc/find_params.py deleted file mode 100644 index b0436ff..0000000 --- a/benchmarks/mcmc/find_params.py +++ /dev/null @@ -1,266 +0,0 @@ -from collections import defaultdict -import itertools -import operator -import jax -import numpy as np - -from benchmark import benchmark_chains, cumulative_avg, err, calculate_ess, get_num_latents, grads_to_low_error, gridsearch_tune, run_mhmclmc_no_tuning -import blackjax -from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState -from blackjax.mcmc.integrators import calls_per_integrator_step, mclachlan_coefficients -from blackjax.mcmc.mhmclmc import rescale -from blackjax.util import run_inference_algorithm -import jax.numpy as jnp -from sampling_algorithms import run_mclmc, run_mhmclmc, samplers -from inference_models import Brownian, IllConditionedGaussian, models -from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState, target_acceptance_rate_of_order - - -def sampler_mhmclmc_with_tuning(step_size, L, frac_tune2, frac_tune3): - - def s(logdensity_fn, num_steps, initial_position, transform, key): - - init_key, tune_key, key = jax.random.split(key, 3) - - initial_state = blackjax.mcmc.mhmclmc.init( - position=initial_position, logdensity_fn=logdensity_fn, random_generator_arg=init_key - ) - integrator = blackjax.mcmc.integrators.isokinetic_mclachlan - kernel = lambda rng_key, state, avg_num_integration_steps, step_size: blackjax.mcmc.mhmclmc.build_kernel( - integrator=integrator, - integration_steps_fn = lambda key : jnp.ceil(jax.random.uniform(key) * rescale(avg_num_integration_steps)), - # integration_steps_fn = lambda key: avg_num_integration_steps, - )( - rng_key=rng_key, - state=state, - step_size=step_size, - logdensity_fn=logdensity_fn) - - # jax.debug.print("params before tuning {x}", x=MCLMCAdaptationState(L=L, step_size=step_size)) - ( - blackjax_state_after_tuning, - blackjax_mclmc_sampler_params, - ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( - mclmc_kernel=kernel, - num_steps=num_steps, - state=initial_state, - rng_key=tune_key, - target=target_acceptance_rate_of_order[mhmclmc_integrator_order[integrator]], - frac_tune2=frac_tune2, - frac_tune3=frac_tune3, - params=MCLMCAdaptationState(L=L, step_size=step_size, std_mat=1.) - ) - - # jax.debug.print("params {x}", x=blackjax_mclmc_sampler_params) - # jax.debug.print("acceptance rate {x}", x=blackjax_mclmc_sampler_params) - - # L = blackjax_mclmc_sampler_params.L - # step_size = blackjax_mclmc_sampler_params.step_size - - num_steps_per_traj = blackjax_mclmc_sampler_params.L/blackjax_mclmc_sampler_params.step_size - alg = blackjax.mcmc.mhmclmc.mhmclmc( - logdensity_fn=logdensity_fn, - step_size=blackjax_mclmc_sampler_params.step_size, - integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(num_steps_per_traj)) , - # integration_steps_fn = lambda k: num_steps_per_traj , - # integration_steps_fn = lambda _ : 5, - # integration_steps_fn = lambda key: jnp.ceil(jax.random.poisson(key, L/step_size )) , - - ) - - _, out, info = run_inference_algorithm( - rng_key=key, - initial_state_or_position=blackjax_state_after_tuning, - inference_algorithm=alg, - num_steps=num_steps, - transform=lambda x: transform(x.position), - progress_bar=True) - - print(info.acceptance_rate.mean(), "acceptance probability\n\n\n\n") - # print(out.var(axis=0), "acceptance probability") - - return out, blackjax_mclmc_sampler_params, num_steps_per_traj * calls_per_integrator_step(coefficients) - - return s - - - -# Empirical mean [ 2.6572839e-05 -4.0523437e-06] -# Empirical std [0.07159886 0.07360378] - - - -def grid_search(n, model): - - - print(f"\nModel: {model}") - - - - if True: - batch = 10 - init_key, sample_key = jax.random.split(jax.random.PRNGKey(1), 2) - init_keys = jax.random.split(init_key, batch) - init_pos = jax.vmap(model.sample_init)(init_keys) - sample_keys = jax.random.split(sample_key, batch) - - avg_num_steps_per_traj = 2 - samples, params, _ = jax.vmap(lambda pos, key: samplers["mclmc"](mclachlan_coefficients, model.logdensity_fn, n*10, pos, model.transform, key))(init_pos, sample_keys) - - # avg_num_steps_per_traj = 1 - # samples, params, _ = jax.vmap(lambda pos, key: samplers["nuts"](model.logdensity_fn, 1000, pos, model.transform, key))(init_pos, sample_keys) - - full = lambda arr : err(model.E_x2, model.Var_x2, jnp.max)(cumulative_avg(arr)) - err_t = jnp.mean(jax.vmap(full)(samples**2), axis=0) - - ess_val, grads_to_low_error, _ = calculate_ess(err_t, avg_num_steps_per_traj) - print(ess_val, grads_to_low_error) - - - - center_L, center_step_size = params.L.mean(), params.step_size.mean() - print(f"initial params found by MCLMC are step size {center_step_size} and L {center_L}, with grad calls {grads_to_low_error}") - # center_L, center_step_size = 0.5755017, 0.7676609 - # center_L, center_step_size = 0.7639537453651428, 0.5901154279708862 - # center_L, center_step_size = 2.079161234577297, 0.3441933917149635 - # center_L, center_step_size = 1.3701616525650024, 0.44564130902290344 - print(f"Initial params hard coded as L {center_L} and step size as {center_step_size}") - - # nuts result - - - # print("\nBeginning grid search:\n") - # grid_size = 5 - # batch = 100 - # best params on iteration 0 are stepsize 5.103655551427525 and L 5.408820389035896 with Grad Calls until Convergence 216.19784545898438 - # center_L, center_step_size = gridsearch_tune(iterations=0, grid_size=grid_size, model=model, sampler=sampler_mhmclmc, batch=batch, num_steps=n, center_L=center_L, center_step_size=center_step_size) - - - tune_key, init_key, init_pos_key, run_key = jax.random.split(jax.random.PRNGKey(0), 4) - initial_position = model.sample_init(init_pos_key) - - initial_state = blackjax.mcmc.mhmclmc.init( - position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key - ) - - kernel = lambda rng_key, state, avg_num_integration_steps, step_size: blackjax.mcmc.mhmclmc.build_kernel( - integrator=blackjax.mcmc.integrators.isokinetic_mclachlan, - integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)) - )( - rng_key=rng_key, - state=state, - step_size=step_size, - logdensity_fn=model.logdensity_fn) - - ( - blackjax_state_after_tuning, - blackjax_mclmc_sampler_params, - ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( - mclmc_kernel=kernel, - num_steps=n, - state=initial_state, - rng_key=tune_key, - target=0.65, - frac_tune3=0, - frac_tune2=0, - params = MCLMCAdaptationState(L=center_L, step_size=center_step_size, std_mat=1.), - # params = MCLMCAdaptationState(L=10., step_size=3.3454525677773526, std_mat=1.), - # params = MCLMCAdaptationState(L=16., step_size=1., std_mat=1.), - # params = MCLMCAdaptationState(L=10., step_size=5.103655551427525, std_mat=1.), - ) - - print(f"initial params are L {center_L} and step_size {center_step_size}") - print(f"params found by mhmclmc tuning are L {blackjax_mclmc_sampler_params.L} and step_size {blackjax_mclmc_sampler_params.step_size}") - - - ess, grad_calls_until_convergence = benchmark_chains(model, run_mhmclmc_no_tuning(coefficients=mclachlan_coefficients, step_size=blackjax_mclmc_sampler_params.step_size, L=blackjax_mclmc_sampler_params.L), jax.random.PRNGKey(0), n=n, batch = batch, contract=jnp.max) # batch=1000//model.ndims) - print(f"ess from tuning is {ess} and num grad calls is {grad_calls_until_convergence}") - - - # # L, step_size = 2.6195790055693493, 0.4336564994563942 - # L, step_size = blackjax_mclmc_sampler_params.L, blackjax_mclmc_sampler_params.step_size - # ess, grad_calls_until_convergence = benchmark_chains(model, sampler_mhmclmc(step_size=step_size, L=L), keys[-1], n=n, batch = 200) # batch=1000//model.ndims) - # print("final grads", grad_calls_until_convergence) - - # step_size = blackjax_mclmc_sampler_params.step_size - # L = blackjax_mclmc_sampler_params.L - - # jax.debug.print("{x} num_steps, L, step_size", x=(jnp.ceil(L/step_size), L, step_size)) - - - # alg = blackjax.mcmc.mhmclmc.mhmclmc( - # logdensity_fn=model.logdensity_fn, - # step_size=step_size, - # integration_steps_fn = lambda key: jnp.round(jax.random.uniform(key) * rescale(L/step_size + 0.5)) , - # # integrator=integrator, - # # integration_steps_fn = lambda key: jnp.ceil(jax.random.poisson(key, L/step_size )) , - - # ) - - # _, out, info = run_inference_algorithm( - # rng_key=run_key, - # initial_state_or_position=blackjax_state_after_tuning, - # inference_algorithm=alg, - # num_steps=num_steps, - # transform=lambda x: transform(x.position), - # progress_bar=True) - - - - - # return results - - -if __name__ == "__main__": - - - # for i in range(100): - # ess, grad_calls_until_convergence = benchmark_chains(Brownian(), sampler_mhmclmc(step_size=0.4336564994563942, L=2.6195790055693493), jax.random.PRNGKey(i), n=10000, batch = 10) # batch=1000//model.ndims) - - # print (f"ess from tuning is {ess} and num grad calls is {grad_calls_until_convergence}") - # raise Exception - - - - - # for i in range(100): - - # # ess, grad_calls_until_convergence, _ = benchmark_chains(Brownian(), sampler_mhmclmc_with_tuning(step_size=0.1, L=40.8, frac_tune2=0.1, frac_tune3=0), jax.random.PRNGKey(i), n=50000, batch = 10) # batch=1000//model.ndims) - - # ess, grad_calls_until_convergence, _ = benchmark_chains(Brownian(), sampler_mhmclmc(step_size=0.5901154279708862, L=0.7639537453651428), jax.random.PRNGKey(i), n=10000, batch = 1) # batch=1000//model.ndims) - # print(f"ess from tuning is {ess} and num grad calls is {grad_calls_until_convergence}") - # print(f"L from ESS (0.4 * step_size/ESS): {0.4 * 0.4/ess}") - - # model=IllConditionedGaussian(10, 2) - # ess, grad_calls_until_convergence = benchmark_chains(model, run_mclmc, n=2500, batch =10) # batch=1000//model.ndims) - # print(ess) - # raise Exception - -# benchmarks(5000) - - # grid_search(n=2500, model=IllConditionedGaussian(10, 2)) - grid_search(n=10000, model=Brownian()) - # grid_search(n=2500, model='icg') - # grid_search(n=2500, model='normal') - - # m = models['icg'] - # initial_position = m.sample(jax.random.PRNGKey(0)) - # _, blackjax_mclmc_sampler_params, _ = sampler_mhmclmc_with_tuning(L=4.291135699906666, step_size=1.005, frac_tune2=0, frac_tune3=0)(lambda x: -m.nlogp(x), 100000, initial_position, jax.random.PRNGKey(0)) - # print(blackjax_mclmc_sampler_params) - - # out = benchmark_chains(models['icg'], sampler_mhmclmc(step_size=4.475385912886005, L=2.2708939161637853), n=100, batch=10,favg=models['icg'].E_x2, fvar=models['icg'].Var_x2) - # print(out) - # pass -# print(grid_search()) - -# for model in ["simple"]: -# for sampler in ["mhmclmc", "mclmc"]: -# # result, bias = benchmark_chains(model, sampler_mhmclmc_with_tuning(step_size, L), n=1000000, batch=1) -# # result, bias = benchmark_chains(models[model], samplers["mhmclmc"], n=1000000, batch=10) -# result, bias = benchmark_chains(models[model], samplers[sampler], n=100000, batch=1) - -# results[(model, sampler)] = 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioningESS
    0IllConditionedGaussian100mclmc14.3699975.799155mclachlanstandard1.000000False0.010070
    1IllConditionedGaussian100mhmchmc0.6514.6627487.481382mclachlanstandard0.644018False0.005249
    2IllConditionedGaussian100mhmchmc:st30.6518.9503147.599843mclachlanstandard0.603782False0.002638
    3IllConditionedGaussian100mhmchmc0.914.7554235.910617mclachlanstandard0.898512False0.006492
    4IllConditionedGaussian100mhmchmc:st30.99.4324985.924881mclachlanstandard0.903127False0.003693
    5IllConditionedGaussian100nuts0.0000000.000000mclachlanstandard0.856732False0.001571
    6IllConditionedGaussian100mclmc14.7707212.309608velocity_verletstandard1.000000False0.000000
    7IllConditionedGaussian100mhmchmc0.6514.7410553.956626velocity_verletstandard0.648849False0.005578
    8IllConditionedGaussian100mhmchmc:st30.6510.0729943.852845velocity_verletstandard0.653583False0.002876
    9IllConditionedGaussian100mhmchmc0.914.7078022.171618velocity_verletstandard0.904663False0.004867
    10IllConditionedGaussian100mhmchmc:st30.93.1548162.239549velocity_verletstandard0.904545False0.000000
    11IllConditionedGaussian100nuts0.0000000.000000velocity_verletstandard0.808837False0.001612
    12IllConditionedGaussian100mclmc8.32122412.235815mclachlanstandard1.000000True0.050607
    13IllConditionedGaussian100mhmchmc0.6514.66274813.329770mclachlanstandard0.871993True0.030671
    14IllConditionedGaussian100mhmchmc:st30.6510.3388399.345161mclachlanstandard0.954968True0.026389
    15IllConditionedGaussian100mhmchmc0.914.75542312.293874mclachlanstandard0.897613True0.027814
    16IllConditionedGaussian100mhmchmc:st30.98.4960337.723665mclachlanstandard0.974669True0.022603
    17IllConditionedGaussian100nuts0.0000000.000000mclachlanstandard0.985340True0.003272
    18IllConditionedGaussian100mclmc9.7657836.169864velocity_verletstandard1.000000True0.026504
    19IllConditionedGaussian100mhmchmc0.6514.7410559.575418velocity_verletstandard0.647452True0.025084
    20IllConditionedGaussian100mhmchmc:st30.6513.3788918.629835velocity_verletstandard0.739921True0.023097
    21IllConditionedGaussian100mhmchmc0.914.7078025.305859velocity_verletstandard0.894883True0.019213
    22IllConditionedGaussian100mhmchmc:st30.93.2861162.968825velocity_verletstandard0.988102True0.005925
    23IllConditionedGaussian100nuts0.0000000.000000velocity_verletstandard0.854472True0.005160
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size \\\n", + "0 IllConditionedGaussian 100 mclmc 14.369997 5.799155 \n", + "1 IllConditionedGaussian 100 mhmchmc0.65 14.662748 7.481382 \n", + "2 IllConditionedGaussian 100 mhmchmc:st30.65 18.950314 7.599843 \n", + "3 IllConditionedGaussian 100 mhmchmc0.9 14.755423 5.910617 \n", + "4 IllConditionedGaussian 100 mhmchmc:st30.9 9.432498 5.924881 \n", + "5 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "6 IllConditionedGaussian 100 mclmc 14.770721 2.309608 \n", + "7 IllConditionedGaussian 100 mhmchmc0.65 14.741055 3.956626 \n", + "8 IllConditionedGaussian 100 mhmchmc:st30.65 10.072994 3.852845 \n", + "9 IllConditionedGaussian 100 mhmchmc0.9 14.707802 2.171618 \n", + "10 IllConditionedGaussian 100 mhmchmc:st30.9 3.154816 2.239549 \n", + "11 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "12 IllConditionedGaussian 100 mclmc 8.321224 12.235815 \n", + "13 IllConditionedGaussian 100 mhmchmc0.65 14.662748 13.329770 \n", + "14 IllConditionedGaussian 100 mhmchmc:st30.65 10.338839 9.345161 \n", + "15 IllConditionedGaussian 100 mhmchmc0.9 14.755423 12.293874 \n", + "16 IllConditionedGaussian 100 mhmchmc:st30.9 8.496033 7.723665 \n", + "17 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "18 IllConditionedGaussian 100 mclmc 9.765783 6.169864 \n", + "19 IllConditionedGaussian 100 mhmchmc0.65 14.741055 9.575418 \n", + "20 IllConditionedGaussian 100 mhmchmc:st30.65 13.378891 8.629835 \n", + "21 IllConditionedGaussian 100 mhmchmc0.9 14.707802 5.305859 \n", + "22 IllConditionedGaussian 100 mhmchmc:st30.9 3.286116 2.968825 \n", + "23 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "\n", + " integrator tuning acc_rate preconditioning ESS \n", + "0 mclachlan standard 1.000000 False 0.010070 \n", + "1 mclachlan standard 0.644018 False 0.005249 \n", + "2 mclachlan standard 0.603782 False 0.002638 \n", + "3 mclachlan standard 0.898512 False 0.006492 \n", + "4 mclachlan standard 0.903127 False 0.003693 \n", + "5 mclachlan standard 0.856732 False 0.001571 \n", + "6 velocity_verlet standard 1.000000 False 0.000000 \n", + "7 velocity_verlet standard 0.648849 False 0.005578 \n", + "8 velocity_verlet standard 0.653583 False 0.002876 \n", + "9 velocity_verlet standard 0.904663 False 0.004867 \n", + "10 velocity_verlet standard 0.904545 False 0.000000 \n", + "11 velocity_verlet standard 0.808837 False 0.001612 \n", + "12 mclachlan standard 1.000000 True 0.050607 \n", + "13 mclachlan standard 0.871993 True 0.030671 \n", + "14 mclachlan standard 0.954968 True 0.026389 \n", + "15 mclachlan standard 0.897613 True 0.027814 \n", + "16 mclachlan standard 0.974669 True 0.022603 \n", + "17 mclachlan standard 0.985340 True 0.003272 \n", + "18 velocity_verlet standard 1.000000 True 0.026504 \n", + "19 velocity_verlet standard 0.647452 True 0.025084 \n", + "20 velocity_verlet standard 0.739921 True 0.023097 \n", + "21 velocity_verlet standard 0.894883 True 0.019213 \n", + "22 velocity_verlet standard 0.988102 True 0.005925 \n", + "23 velocity_verlet standard 0.854472 True 0.005160 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def load_results(model_name):\n", + "\n", + " # Load the data\n", + " results = pd.read_csv(f\"../../results{model_name}.csv\")\n", + " # results.drop(results['tuning'] == \"standard\", inplace=True)\n", + " # results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + " fig, ax = plt.subplots(figsize=(20, 5))\n", + "\n", + "\n", + " sns.barplot(data=results, x=\"sampler\", y=\"ESS\", hue=results[['integrator', 'preconditioning']].apply(\n", + " lambda row: f\"{row.integrator}, {row.preconditioning}\", axis=1), ax=ax)\n", + " plt.xlabel(\"Sampler\")\n", + " plt.ylabel(\"ESS\")\n", + " plt.title(model_name)\n", + " plt.show()\n", + " plt.show()\n", + " display(results)\n", + "\n", + "load_results(\"IllConditionedGaussian\")\n", + "# load_results(\"Brownian\")\n", + "# load_results(\"Funnel\")\n", + "# load_results(\"Banana\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioningESS
    0Brownian32mclmc2.4423780.302776mclachlanstandard1.000000False0.010799
    1Brownian32mhmchmc0.650.8120420.637599mclachlanstandard0.529587False0.001431
    2Brownian32mhmchmc:st30.651.6270660.595371mclachlanstandard0.523359False0.001147
    3Brownian32mhmchmc0.90.8287020.395384mclachlanstandard0.853865False0.002489
    4Brownian32mhmchmc:st30.91.6102020.342993mclachlanstandard0.884686False0.004979
    5Brownian32nuts0.0000000.000000mclachlanstandard0.760786False0.002627
    6Brownian32mclmc2.2970480.146238velocity_verletstandard1.000000False0.013497
    7Brownian32mhmchmc0.650.8135890.376977velocity_verletstandard0.532756False0.001877
    8Brownian32mhmchmc:st30.651.6267470.331795velocity_verletstandard0.595688False0.003162
    9Brownian32mhmchmc0.90.8115920.185693velocity_verletstandard0.881163False0.002693
    10Brownian32mhmchmc:st30.91.1276110.166444velocity_verletstandard0.897115False0.003417
    11Brownian32nuts0.0000000.000000velocity_verletstandard0.779443False0.003168
    12Brownian32mclmc14.7237571.771522mclachlanstandard1.000000True0.012148
    13Brownian32mhmchmc0.650.8135330.739576mclachlanstandard0.997668True0.000000
    14Brownian32mhmchmc:st30.651.6270661.479151mclachlanstandard0.980983True0.000732
    15Brownian32mhmchmc0.90.8287020.753365mclachlanstandard0.995110True0.000000
    16Brownian32mhmchmc:st30.91.6574041.506731mclachlanstandard0.981226True0.000746
    17Brownian32nuts0.0000000.000000mclachlanstandard0.921391True0.003097
    18Brownian32mclmc14.4516720.857385velocity_verletstandard1.000000True0.010289
    19Brownian32mhmchmc0.650.8135890.739626velocity_verletstandard0.972460True0.000000
    20Brownian32mhmchmc:st30.651.6271781.479252velocity_verletstandard0.824414True0.001364
    21Brownian32mhmchmc0.90.8115920.737811velocity_verletstandard0.971546True0.000000
    22Brownian32mhmchmc:st30.91.6231841.256775velocity_verletstandard0.873862True0.001445
    23Brownian32nuts0.0000000.000000velocity_verletstandard0.873792True0.003157
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \\\n", + "0 Brownian 32 mclmc 2.442378 0.302776 mclachlan \n", + "1 Brownian 32 mhmchmc0.65 0.812042 0.637599 mclachlan \n", + "2 Brownian 32 mhmchmc:st30.65 1.627066 0.595371 mclachlan \n", + "3 Brownian 32 mhmchmc0.9 0.828702 0.395384 mclachlan \n", + "4 Brownian 32 mhmchmc:st30.9 1.610202 0.342993 mclachlan \n", + "5 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "6 Brownian 32 mclmc 2.297048 0.146238 velocity_verlet \n", + "7 Brownian 32 mhmchmc0.65 0.813589 0.376977 velocity_verlet \n", + "8 Brownian 32 mhmchmc:st30.65 1.626747 0.331795 velocity_verlet \n", + "9 Brownian 32 mhmchmc0.9 0.811592 0.185693 velocity_verlet \n", + "10 Brownian 32 mhmchmc:st30.9 1.127611 0.166444 velocity_verlet \n", + "11 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + "12 Brownian 32 mclmc 14.723757 1.771522 mclachlan \n", + "13 Brownian 32 mhmchmc0.65 0.813533 0.739576 mclachlan \n", + "14 Brownian 32 mhmchmc:st30.65 1.627066 1.479151 mclachlan \n", + "15 Brownian 32 mhmchmc0.9 0.828702 0.753365 mclachlan \n", + "16 Brownian 32 mhmchmc:st30.9 1.657404 1.506731 mclachlan \n", + "17 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "18 Brownian 32 mclmc 14.451672 0.857385 velocity_verlet \n", + "19 Brownian 32 mhmchmc0.65 0.813589 0.739626 velocity_verlet \n", + "20 Brownian 32 mhmchmc:st30.65 1.627178 1.479252 velocity_verlet \n", + "21 Brownian 32 mhmchmc0.9 0.811592 0.737811 velocity_verlet \n", + "22 Brownian 32 mhmchmc:st30.9 1.623184 1.256775 velocity_verlet \n", + "23 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + "\n", + " tuning acc_rate preconditioning ESS \n", + "0 standard 1.000000 False 0.010799 \n", + "1 standard 0.529587 False 0.001431 \n", + "2 standard 0.523359 False 0.001147 \n", + "3 standard 0.853865 False 0.002489 \n", + "4 standard 0.884686 False 0.004979 \n", + "5 standard 0.760786 False 0.002627 \n", + "6 standard 1.000000 False 0.013497 \n", + "7 standard 0.532756 False 0.001877 \n", + "8 standard 0.595688 False 0.003162 \n", + "9 standard 0.881163 False 0.002693 \n", + "10 standard 0.897115 False 0.003417 \n", + "11 standard 0.779443 False 0.003168 \n", + "12 standard 1.000000 True 0.012148 \n", + "13 standard 0.997668 True 0.000000 \n", + "14 standard 0.980983 True 0.000732 \n", + "15 standard 0.995110 True 0.000000 \n", + "16 standard 0.981226 True 0.000746 \n", + "17 standard 0.921391 True 0.003097 \n", + "18 standard 1.000000 True 0.010289 \n", + "19 standard 0.972460 True 0.000000 \n", + "20 standard 0.824414 True 0.001364 \n", + "21 standard 0.971546 True 0.000000 \n", + "22 standard 0.873862 True 0.001445 \n", + "23 standard 0.873792 True 0.003157 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load_results(\"Brownian\")\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1664,7 +2540,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index 6f8f133..5398151 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -1,19 +1,77 @@ +from typing import Callable, Union +from chex import PRNGKey import jax import jax.numpy as jnp +from benchmarks import mcmc import blackjax from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState # from blackjax.adaptation.window_adaptation import da_adaptation -from blackjax.mcmc.integrators import calls_per_integrator_step, generate_euclidean_integrator, generate_isokinetic_integrator -# from blackjax.mcmc.mhmclmc import rescale +from blackjax.mcmc.integrators import calls_per_integrator_step, generate_euclidean_integrator, generate_isokinetic_integrator, integrator_order +from blackjax.mcmc.adjusted_mclmc import rescale from blackjax.util import run_inference_algorithm import blackjax +from blackjax.util import pytree_size +from blackjax.adaptation.step_size import ( + DualAveragingAdaptationState, + dual_averaging_adaptation, +) __all__ = ["samplers"] +target_acceptance_rate_of_order = {2: 0.65, 4: 0.8} +def da_adaptation( + rng_key: PRNGKey, + initial_position, + algorithm, + logdensity_fn: Callable, + num_steps: int = 1000, + initial_step_size: float = 1.0, + target_acceptance_rate: float = 0.80, + progress_bar: bool = False, + integrator = blackjax.mcmc.integrators.velocity_verlet, + ): + + da_init, da_update, da_final = dual_averaging_adaptation(target_acceptance_rate) + + kernel = algorithm.build_kernel(integrator=integrator) + init_kernel_state = algorithm.init(initial_position, logdensity_fn) + inverse_mass_matrix = jnp.ones(pytree_size(initial_position)) + + def step(state, key): + + + adaptation_state, kernel_state = state + + new_kernel_state, info = kernel( + key, + kernel_state, + logdensity_fn, + jnp.exp(adaptation_state.log_step_size), + inverse_mass_matrix) + + new_adaptation_state = da_update( + adaptation_state, + info.acceptance_rate, + ) + + return ( + (new_adaptation_state, new_kernel_state), + (True), + ) + + keys = jax.random.split(rng_key, num_steps) + init_state = da_init(initial_step_size), init_kernel_state + (adaptation_state, kernel_state), _ = jax.lax.scan( + step, + init_state, + keys, + ) + # print(adaptation_state, "adaptation_state\n\n") + return kernel_state, {"step_size" : da_final(adaptation_state), "inverse_mass_matrix" : inverse_mass_matrix} def run_nuts( coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning): @@ -82,6 +140,8 @@ def run_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transfor ) # jax.debug.print("params {x}", x=(blackjax_mclmc_sampler_params.L, blackjax_mclmc_sampler_params.step_size)) + # jax.debug.print("params {x}", x=blackjax_mclmc_sampler_params.std_mat**2) + sampling_alg = blackjax.mclmc( logdensity_fn, @@ -106,16 +166,16 @@ def run_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transfor return samples, blackjax_mclmc_sampler_params, calls_per_integrator_step(coefficients), acceptance_rate, None, None -def run_mhmclmc(coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, target_acc_rate=None): +def run_adjusted_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, target_acc_rate=None): integrator = generate_isokinetic_integrator(coefficients) init_key, tune_key, run_key = jax.random.split(key, 3) - initial_state = blackjax.mcmc.mhmclmc.init( + initial_state = blackjax.mcmc.adjusted_mclmc.init( position=initial_position, logdensity_fn=logdensity_fn, random_generator_arg=init_key ) - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.mhmclmc.build_kernel( + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.adjusted_mclmc.build_kernel( integrator=integrator, integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), std_mat=std_mat, @@ -134,7 +194,7 @@ def run_mhmclmc(coefficients, logdensity_fn, num_steps, initial_position, transf blackjax_mclmc_sampler_params, params_history, final_da - ) = blackjax.adaptation.mclmc_adaptation.mhmclmc_find_L_and_step_size( + ) = blackjax.adaptation.mclmc_adaptation.adjusted_mclmc_find_L_and_step_size( mclmc_kernel=kernel, num_steps=num_steps, state=initial_state, @@ -153,7 +213,7 @@ def run_mhmclmc(coefficients, logdensity_fn, num_steps, initial_position, transf # jax.debug.print("params {x}", x=(blackjax_mclmc_sampler_params.step_size, blackjax_mclmc_sampler_params.L)) - alg = blackjax.mcmc.mhmclmc.mhmclmc( + alg = blackjax.adjusted_mclmc( logdensity_fn=logdensity_fn, step_size=step_size, integration_steps_fn = lambda key: jnp.ceil(jax.random.uniform(key) * rescale(L/step_size)) , @@ -176,12 +236,12 @@ def run_mhmclmc(coefficients, logdensity_fn, num_steps, initial_position, transf return out, blackjax_mclmc_sampler_params, calls_per_integrator_step(coefficients) * (L/step_size), info.acceptance_rate, params_history, final_da -# we should do at least: mclmc, nuts, unadjusted hmc, mhmclmc, langevin +# we should do at least: mclmc, nuts, unadjusted hmc, adjusted_mclmc, langevin samplers = { 'nuts' : run_nuts, 'mclmc' : run_mclmc, - 'mhmclmc': run_mhmclmc, + 'adjusted_mclmc': run_adjusted_mclmc, } From 4a6d854ebdb2dda14a255c048db4e880df982a85 Mon Sep 17 00:00:00 2001 From: = Date: Sat, 25 May 2024 17:51:18 +0200 Subject: [PATCH 03/21] RENAME STD MAT --- benchmarks/mcmc/benchmark.py | 51 ++++++++++++++------------ benchmarks/mcmc/inference_models.py | 4 +- benchmarks/mcmc/sampling_algorithms.py | 16 ++++---- 3 files changed, 37 insertions(+), 34 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 194b19d..144d996 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -24,7 +24,7 @@ import blackjax from benchmarks.mcmc.sampling_algorithms import run_mclmc, run_adjusted_mclmc, run_nuts, samplers -from benchmarks.mcmc.inference_models import Brownian, GermanCredit, IllConditionedGaussian, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models +from benchmarks.mcmc.inference_models import Brownian, Funnel, GermanCredit, IllConditionedGaussian, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models from blackjax.mcmc.integrators import calls_per_integrator_step, generate_euclidean_integrator, generate_isokinetic_integrator, isokinetic_mclachlan, mclachlan_coefficients, name_integrator, omelyan_coefficients, velocity_verlet, velocity_verlet_coefficients, yoshida_coefficients from blackjax.mcmc.adjusted_mclmc import rescale from blackjax.util import run_inference_algorithm @@ -116,7 +116,7 @@ def gridsearch_tune(key, iterations, grid_size, model, sampler, batch, num_steps return center_L, center_step_size, converged -def run_adjusted_mclmc_no_tuning(initial_state, coefficients, step_size, L, std_mat): +def run_adjusted_mclmc_no_tuning(initial_state, coefficients, step_size, L, sqrt_diag_cov): def s(logdensity_fn, num_steps, initial_position, transform, key): @@ -128,7 +128,7 @@ def s(logdensity_fn, num_steps, initial_position, transform, key): step_size=step_size, integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(num_steps_per_traj)) , integrator=integrator, - std_mat=std_mat, + sqrt_diag_cov=sqrt_diag_cov, ) _, out, info = run_inference_algorithm( @@ -139,7 +139,7 @@ def s(logdensity_fn, num_steps, initial_position, transform, key): transform=lambda x: transform(x.position), progress_bar=True) - return out, MCLMCAdaptationState(L=L, step_size=step_size, std_mat=std_mat), num_steps_per_traj * calls_per_integrator_step(coefficients), info.acceptance_rate.mean(), None, jnp.array([0]) + return out, MCLMCAdaptationState(L=L, step_size=step_size, sqrt_diag_cov=sqrt_diag_cov), num_steps_per_traj * calls_per_integrator_step(coefficients), info.acceptance_rate.mean(), None, jnp.array([0]) return s @@ -164,7 +164,7 @@ def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.av keys = jax.random.split(key, batch) init_keys = jax.random.split(init_key, batch) - init_pos = pvmap(model.sample_init)(init_keys) + init_pos = pvmap(model.sample_init)(init_keys) # [batch_size, dim_model] # samples, params, avg_num_steps_per_traj = jax.pmap(lambda pos, key: sampler(model.logdensity_fn, n, pos, model.transform, key))(init_pos, keys) samples, params, grad_calls_per_traj, acceptance_rate, step_size_over_da, final_da = pvmap(lambda pos, key: sampler(logdensity_fn=model.logdensity_fn, num_steps=n, initial_position= pos,transform= model.transform, key=key))(init_pos, keys) @@ -184,6 +184,7 @@ def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.av # print(final_da.mean(), "final da") + err_t_median = jnp.median(err_t, axis=0) # import matplotlib.pyplot as plt # plt.plot(np.arange(1, 1+ len(err_t_median))* 2, err_t_median, color= 'teal', lw = 3) @@ -289,7 +290,7 @@ def run_benchmarks_step_size(batch_size): initial_position = model.sample_init(key2) initial_state = blackjax.mcmc.adjusted_mclmc.init( position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=key3) - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(initial_state=initial_state, coefficients=mclachlan_coefficients, step_size=step_size, L= 5*step_size, std_mat=1.),key1, n=num_steps, batch=num_chains, contract=contract) + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(initial_state=initial_state, coefficients=mclachlan_coefficients, step_size=step_size, L= 5*step_size, sqrt_diag_cov=1.),key1, n=num_steps, batch=num_chains, contract=contract) # print(f"step size over da {step_size_over_da.shape} \n\n\n\n") # jax.numpy.save(f"step_size_over_da.npy", step_size_over_da.mean(axis=0)) @@ -381,10 +382,10 @@ def benchmark_mhmchmc(batch_size): position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key ) - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.adjusted_mclmc.build_kernel( + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( integrator=generate_isokinetic_integrator(coeffs), integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), - std_mat=std_mat, + sqrt_diag_cov=sqrt_diag_cov, )( rng_key=rng_key, state=state, @@ -411,11 +412,11 @@ def benchmark_mhmchmc(batch_size): print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, coefficients=coeffs, initial_state=state, std_mat=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) + L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, coefficients=coeffs, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) # print(f"params after grid tuning are L={L}, step_size={step_size}") - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coeffs, L=L, step_size=step_size, initial_state=state, std_mat=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coeffs, L=L, step_size=step_size, initial_state=state, sqrt_diag_cov=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) print(f"grads to low bias: {grad_calls}") @@ -480,10 +481,10 @@ def benchmark_omelyan(batch_size): ) - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.adjusted_mclmc.build_kernel( + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( integrator=generate_isokinetic_integrator(coefficients), integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), - std_mat=std_mat, + sqrt_diag_cov=sqrt_diag_cov, )( rng_key=rng_key, state=state, @@ -509,15 +510,15 @@ def benchmark_omelyan(batch_size): print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - # ess, grad_calls, _ , _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coefficients, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, std_mat=1.),bench_key_pre_grid, n=num_steps, batch=num_chains, contract=jnp.average) + # ess, grad_calls, _ , _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coefficients, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, sqrt_diag_cov=1.),bench_key_pre_grid, n=num_steps, batch=num_chains, contract=jnp.average) # results[((model.name, model.ndims), sampler, name_integrator(coefficients), "without grid search")] = (ess, grad_calls) - L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, coefficients=coefficients, initial_state=state, std_mat=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) + L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, coefficients=coefficients, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) print(f"params after grid tuning are L={L}, step_size={step_size}") - ess, grad_calls, _ , _, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coefficients, L=L, step_size=step_size, std_mat=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) + ess, grad_calls, _ , _, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coefficients, L=L, step_size=step_size, sqrt_diag_cov=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) print(f"grads to low bias: {grad_calls}") @@ -532,19 +533,21 @@ def benchmark_omelyan(batch_size): def run_benchmarks_simple(): - sampler = run_adjusted_mclmc - # sampler = run_mclmc + # sampler = run_adjusted_mclmc + sampler = run_mclmc # model = IllConditionedGaussian(100,100) - model = Brownian() + model = Funnel() coefficients = mclachlan_coefficients contract = jnp.max # how we average across dimensions - num_steps = 10000 + num_steps = 10000000 num_chains = 1 - for i in range(100): + for i in range(1): key1 = jax.random.PRNGKey(i) for preconditioning in [True, False]: - ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, coefficients=coefficients, preconditioning=preconditioning, target_acc_rate=0.95, frac_tune3=0.1),key1, n=num_steps, batch=num_chains, contract=contract) + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, coefficients=coefficients, preconditioning=preconditioning, + # target_acc_rate=0.95, frac_tune3=0.1 + ),key1, n=num_steps, batch=num_chains, contract=contract) print(f"Effective Sample Size (ESS) of {model.name} with preconditioning set to {preconditioning} is {ess}") @@ -552,14 +555,14 @@ def run_benchmarks_simple(): if __name__ == "__main__": - run_benchmarks_simple() + # run_benchmarks_simple() - # benchmark_mhmchmc(batch_size=50) + benchmark_mhmchmc(batch_size=50) # run_benchmarks(128) # run_benchmarks_step_size(128) # benchmark_omelyan(128) # run_benchmarks(128) - #benchmark_omelyan(10) + # benchmark_omelyan(10) # print("4") diff --git a/benchmarks/mcmc/inference_models.py b/benchmarks/mcmc/inference_models.py index 6515a7d..1f92d21 100644 --- a/benchmarks/mcmc/inference_models.py +++ b/benchmarks/mcmc/inference_models.py @@ -879,8 +879,8 @@ def step(track, useless): # Banana() : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, - # IllConditionedGaussian(100, 100): {'mclmc': 20000, 'adjusted_mclmc' : 20000, 'nuts': 20000}, - GermanCredit(): {'mclmc': 80000, 'adjusted_mclmc' : 40000, 'nuts': 40000}, + IllConditionedGaussian(100, 100): {'mclmc': 20000, 'adjusted_mclmc' : 20000, 'nuts': 20000}, + # GermanCredit(): {'mclmc': 80000, 'adjusted_mclmc' : 40000, 'nuts': 40000}, # ItemResponseTheory(): {'mclmc': 20000, 'adjusted_mclmc' : 40000, 'nuts': 20000}, # StochasticVolatility(): {'mclmc': 40000, 'adjusted_mclmc' : 40000, 'nuts': 40000} } diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index 5398151..bba1328 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -121,10 +121,10 @@ def run_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transfor ) - kernel = lambda std_mat : blackjax.mcmc.mclmc.build_kernel( + kernel = lambda sqrt_diag_cov : blackjax.mcmc.mclmc.build_kernel( logdensity_fn=logdensity_fn, integrator=integrator, - std_mat=std_mat, + sqrt_diag_cov=sqrt_diag_cov, ) ( @@ -140,17 +140,17 @@ def run_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transfor ) # jax.debug.print("params {x}", x=(blackjax_mclmc_sampler_params.L, blackjax_mclmc_sampler_params.step_size)) - # jax.debug.print("params {x}", x=blackjax_mclmc_sampler_params.std_mat**2) + # jax.debug.print("params {x}", x=blackjax_mclmc_sampler_params.sqrt_diag_cov**2) sampling_alg = blackjax.mclmc( logdensity_fn, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, - std_mat=blackjax_mclmc_sampler_params.std_mat, + sqrt_diag_cov=blackjax_mclmc_sampler_params.sqrt_diag_cov, integrator = integrator, - # std_mat=jnp.ones((initial_position.shape[0],)), + # sqrt_diag_cov=jnp.ones((initial_position.shape[0],)), ) _, samples, _ = run_inference_algorithm( @@ -175,10 +175,10 @@ def run_adjusted_mclmc(coefficients, logdensity_fn, num_steps, initial_position, position=initial_position, logdensity_fn=logdensity_fn, random_generator_arg=init_key ) - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, std_mat: blackjax.mcmc.adjusted_mclmc.build_kernel( + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( integrator=integrator, integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), - std_mat=std_mat, + sqrt_diag_cov=sqrt_diag_cov, )( rng_key=rng_key, state=state, @@ -218,7 +218,7 @@ def run_adjusted_mclmc(coefficients, logdensity_fn, num_steps, initial_position, step_size=step_size, integration_steps_fn = lambda key: jnp.ceil(jax.random.uniform(key) * rescale(L/step_size)) , integrator=integrator, - std_mat=blackjax_mclmc_sampler_params.std_mat, + sqrt_diag_cov=blackjax_mclmc_sampler_params.sqrt_diag_cov, ) From 3fe378ad55690d93283409d33913e4d24b956388 Mon Sep 17 00:00:00 2001 From: = Date: Wed, 29 May 2024 13:53:43 +0200 Subject: [PATCH 04/21] WIP --- benchmarks/mcmc/benchmark.py | 150 +-- benchmarks/mcmc/results.ipynb | 1514 ++++++++++++++++++------ benchmarks/mcmc/sampling_algorithms.py | 71 +- 3 files changed, 1318 insertions(+), 417 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 144d996..f15cd18 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -23,14 +23,16 @@ import numpy as np import blackjax -from benchmarks.mcmc.sampling_algorithms import run_mclmc, run_adjusted_mclmc, run_nuts, samplers +from benchmarks.mcmc.sampling_algorithms import integrator_order, run_mclmc, run_adjusted_mclmc, run_nuts, samplers from benchmarks.mcmc.inference_models import Brownian, Funnel, GermanCredit, IllConditionedGaussian, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models -from blackjax.mcmc.integrators import calls_per_integrator_step, generate_euclidean_integrator, generate_isokinetic_integrator, isokinetic_mclachlan, mclachlan_coefficients, name_integrator, omelyan_coefficients, velocity_verlet, velocity_verlet_coefficients, yoshida_coefficients +from blackjax.mcmc.integrators import generate_euclidean_integrator, generate_isokinetic_integrator, isokinetic_mclachlan, mclachlan_coefficients, omelyan_coefficients, velocity_verlet, velocity_verlet_coefficients, yoshida_coefficients from blackjax.mcmc.adjusted_mclmc import rescale from blackjax.util import run_inference_algorithm + + def get_num_latents(target): return target.ndims # return int(sum(map(np.prod, list(jax.tree_flatten(target.event_shape)[0])))) @@ -200,61 +202,61 @@ def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.av -def run_benchmarks(batch_size): +# def run_benchmarks(batch_size): - results = defaultdict(tuple) - for variables in itertools.product( - # ["adjusted_mclmc", "nuts", "mclmc", ], - ["adjusted_mclmc"], - # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 10)).astype(int)], - [Brownian()], - # [Brownian()], - # [Brownian()], - # [velocity_verlet_coefficients, mclachlan_coefficients, yoshida_coefficients, omelyan_coefficients], - [mclachlan_coefficients], - ): +# results = defaultdict(tuple) +# for variables in itertools.product( +# # ["adjusted_mclmc", "nuts", "mclmc", ], +# ["adjusted_mclmc"], +# # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 10)).astype(int)], +# [Brownian()], +# # [Brownian()], +# # [Brownian()], +# # [velocity_verlet_coefficients, mclachlan_coefficients, yoshida_coefficients, omelyan_coefficients], +# [mclachlan_coefficients], +# ): - sampler, model, coefficients = variables - num_chains = batch_size#1 + batch_size//model.ndims +# sampler, model, coefficients = variables +# num_chains = batch_size#1 + batch_size//model.ndims - num_steps = 100000 +# num_steps = 100000 - sampler, model, coefficients = variables - num_chains = batch_size # 1 + batch_size//model.ndims +# sampler, model, coefficients = variables +# num_chains = batch_size # 1 + batch_size//model.ndims - # print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) +# # print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) - contract = jnp.max +# contract = jnp.max - key = jax.random.PRNGKey(11) - for i in range(1): - key1, key = jax.random.split(key) - ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(samplers[sampler], coefficients=coefficients, frac_tune1=0.1, frac_tune2=0.0, frac_tune3=0.0),key1, n=num_steps, batch=num_chains, contract=contract) +# key = jax.random.PRNGKey(11) +# for i in range(1): +# key1, key = jax.random.split(key) +# ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(samplers[sampler], integrator_type=coefficients, frac_tune1=0.1, frac_tune2=0.0, frac_tune3=0.0),key1, n=num_steps, batch=num_chains, contract=contract) - # print(f"step size over da {step_size_over_da.shape} \n\n\n\n") - jax.numpy.save(f"step_size_over_da.npy", step_size_over_da.mean(axis=0)) - jax.numpy.save(f"acceptance.npy", acceptance_rate) +# # print(f"step size over da {step_size_over_da.shape} \n\n\n\n") +# jax.numpy.save(f"step_size_over_da.npy", step_size_over_da.mean(axis=0)) +# jax.numpy.save(f"acceptance.npy", acceptance_rate) - # print(f"grads to low bias: {grad_calls}") - # print(f"acceptance rate is {acceptance_rate, acceptance_rate.mean()}") +# # print(f"grads to low bias: {grad_calls}") +# # print(f"acceptance rate is {acceptance_rate, acceptance_rate.mean()}") - results[((model.name, model.ndims), sampler, name_integrator(coefficients), "standard", acceptance_rate.mean().item(), params.L.mean().item(), params.step_size.mean().item(), num_chains, num_steps, contract)] = ess.item() - print(ess.item()) - # results[(model.name, model.ndims, "nuts", 0., 0., name_integrator(coeffs), "standard", acceptance_rate)] +# results[((model.name, model.ndims), sampler, coefficients, "standard", acceptance_rate.mean().item(), params.L.mean().item(), params.step_size.mean().item(), num_chains, num_steps, contract)] = ess.item() +# print(ess.item()) +# # results[(model.name, model.ndims, "nuts", 0., 0., (coeffs), "standard", acceptance_rate)] - # print(results) +# # print(results) - df = pd.Series(results).reset_index() - df.columns = ["model", "sampler", "integrator", "tuning", "acc rate", "L", "stepsize", "num_chains", "num steps", "contraction", "ESS"] - # df.result = df.result.apply(lambda x: x[0].item()) - # df.model = df.model.apply(lambda x: x[1]) - df.to_csv("results_simple.csv", index=False) +# df = pd.Series(results).reset_index() +# df.columns = ["model", "sampler", "integrator", "tuning", "acc rate", "L", "stepsize", "num_chains", "num steps", "contraction", "ESS"] +# # df.result = df.result.apply(lambda x: x[0].item()) +# # df.model = df.model.apply(lambda x: x[1]) +# df.to_csv("results_simple.csv", index=False) - return results +# return results # vary step_size def run_benchmarks_step_size(batch_size): @@ -290,7 +292,7 @@ def run_benchmarks_step_size(batch_size): initial_position = model.sample_init(key2) initial_state = blackjax.mcmc.adjusted_mclmc.init( position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=key3) - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(initial_state=initial_state, coefficients=mclachlan_coefficients, step_size=step_size, L= 5*step_size, sqrt_diag_cov=1.),key1, n=num_steps, batch=num_chains, contract=contract) + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(initial_state=initial_state, integrator_type=mclachlan_coefficients, step_size=step_size, L= 5*step_size, sqrt_diag_cov=1.),key1, n=num_steps, batch=num_chains, contract=contract) # print(f"step size over da {step_size_over_da.shape} \n\n\n\n") # jax.numpy.save(f"step_size_over_da.npy", step_size_over_da.mean(axis=0)) @@ -300,8 +302,8 @@ def run_benchmarks_step_size(batch_size): # print(f"grads to low bias: {grad_calls}") # print(f"acceptance rate is {acceptance_rate, acceptance_rate.mean()}") - results[((model.name, model.ndims), sampler, name_integrator(coefficients), "standard", acceptance_rate.mean().item(), params.L.mean().item(), params.step_size.mean().item(), num_chains, num_steps, contract)] = ess.item() - # results[(model.name, model.ndims, "nuts", 0., 0., name_integrator(coeffs), "standard", acceptance_rate)] + results[((model.name, model.ndims), sampler, (coefficients), "standard", acceptance_rate.mean().item(), params.L.mean().item(), params.step_size.mean().item(), num_chains, num_steps, contract)] = ess.item() + # results[(model.name, model.ndims, "nuts", 0., 0., (coeffs), "standard", acceptance_rate)] # print(results) @@ -322,10 +324,11 @@ def benchmark_mhmchmc(batch_size): key0, key1, key2, key3 = jax.random.split(jax.random.PRNGKey(5), 4) # coefficients = [yoshida_coefficients, mclachlan_coefficients, velocity_verlet_coefficients, omelyan_coefficients] - coefficients = [mclachlan_coefficients, velocity_verlet_coefficients] + # coefficients = [mclachlan_coefficients, velocity_verlet_coefficients] + integrators = ["mclachlan", "velocity_verlet"] for model in models: results = defaultdict(tuple) - for preconditioning, coeffs in itertools.product([True, False], coefficients): + for preconditioning, integrator_type in itertools.product([True, False], integrators): num_chains = batch_size # 1 + batch_size//model.ndims print(f"NUMBER OF CHAINS for {model.name} and adjusted_mclmc is {num_chains}") @@ -339,12 +342,12 @@ def benchmark_mhmchmc(batch_size): ess, grad_calls, params , _, step_size_over_da = benchmark_chains( model, - partial(run_mclmc,coefficients=coeffs, preconditioning=preconditioning), + partial(run_mclmc,integrator_type=integrator_type, preconditioning=preconditioning), key0, n=num_steps, batch=num_chains, contract=contract) - results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), name_integrator(coeffs), "standard", 1., preconditioning)] = ess.item() + results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning)] = ess.item() print(f'mclmc with tuning ESS {ess}') @@ -353,23 +356,23 @@ def benchmark_mhmchmc(batch_size): # coeffs = mclachlan_coefficients ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, coefficients=coeffs, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), key1, n=num_steps, batch=num_chains, contract=contract) - results[(model.name, model.ndims, "mhmchmc"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), name_integrator(coeffs), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + results[(model.name, model.ndims, "mhmchmc"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() print(f'adjusted_mclmc with tuning ESS {ess}') - # coeffs = mclachlan_coefficients + # integrator_type = mclachlan_coefficients ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate,coefficients=coeffs, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), key1, n=num_steps, batch=num_chains, contract=contract) - results[(model.name, model.ndims, "mhmchmc:st3"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), name_integrator(coeffs), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + results[(model.name, model.ndims, "mhmchmc:st3"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() print(f'adjusted_mclmc with tuning ESS {ess}') if False: @@ -383,7 +386,7 @@ def benchmark_mhmchmc(batch_size): ) kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( - integrator=generate_isokinetic_integrator(coeffs), + integrator=generate_isokinetic_integrator(integrator_type), integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), sqrt_diag_cov=sqrt_diag_cov, )( @@ -401,32 +404,32 @@ def benchmark_mhmchmc(batch_size): num_steps=num_steps, state=initial_state, rng_key=tune_key, - target=target_acceptance_rate_of_order[integrator_order(coeffs)], + target=target_acceptance_rate_of_order[integrator_order(integrator_type)], frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, diagonal_preconditioning=False ) - print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(coeffs)]}") + print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, coefficients=coeffs, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) + L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) # print(f"params after grid tuning are L={L}, step_size={step_size}") - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coeffs, L=L, step_size=step_size, initial_state=state, sqrt_diag_cov=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, L=L, step_size=step_size, initial_state=state, sqrt_diag_cov=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) print(f"grads to low bias: {grad_calls}") - results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), name_integrator(coeffs), f"gridsearch:{convergence}", acceptance_rate.mean().item()), True] = ess.item() + results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), (integrator_type), f"gridsearch:{convergence}", acceptance_rate.mean().item()), True] = ess.item() ####### run nuts - # coeffs = velocity_verlet_coefficients - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,coefficients=coeffs, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) - results[(model.name, model.ndims, "nuts", 0., 0., name_integrator(coeffs), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + # integrator_type = velocity_verlet_coefficients + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) + results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() @@ -445,6 +448,7 @@ def benchmark_mhmchmc(batch_size): return results +# TODO: not updated to new code yet! def benchmark_omelyan(batch_size): @@ -510,19 +514,19 @@ def benchmark_omelyan(batch_size): print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - # ess, grad_calls, _ , _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coefficients, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, sqrt_diag_cov=1.),bench_key_pre_grid, n=num_steps, batch=num_chains, contract=jnp.average) + # ess, grad_calls, _ , _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=coefficients, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, sqrt_diag_cov=1.),bench_key_pre_grid, n=num_steps, batch=num_chains, contract=jnp.average) - # results[((model.name, model.ndims), sampler, name_integrator(coefficients), "without grid search")] = (ess, grad_calls) + # results[((model.name, model.ndims), sampler, (coefficients), "without grid search")] = (ess, grad_calls) - L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, coefficients=coefficients, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) + L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=coefficients, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) print(f"params after grid tuning are L={L}, step_size={step_size}") - ess, grad_calls, _ , _, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(coefficients=coefficients, L=L, step_size=step_size, sqrt_diag_cov=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) + ess, grad_calls, _ , _, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=coefficients, L=L, step_size=step_size, sqrt_diag_cov=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) print(f"grads to low bias: {grad_calls}") - results[(model.name, model.ndims, sampler, name_integrator(coefficients), converged, L.item(), step_size.item())] = ess.item() + results[(model.name, model.ndims, sampler, (coefficients), converged, L.item(), step_size.item())] = ess.item() df = pd.Series(results).reset_index() df.columns = ["model", "dims", "sampler", "integrator", "convergence", "L", "step_size", "ESS"] @@ -536,16 +540,16 @@ def run_benchmarks_simple(): # sampler = run_adjusted_mclmc sampler = run_mclmc # model = IllConditionedGaussian(100,100) - model = Funnel() - coefficients = mclachlan_coefficients + model = IllConditionedGaussian(100,10000) + integrator_type = "mclachlan" contract = jnp.max # how we average across dimensions - num_steps = 10000000 - num_chains = 1 + num_steps = 20000 + num_chains = 128 for i in range(1): key1 = jax.random.PRNGKey(i) for preconditioning in [True, False]: - ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, coefficients=coefficients, preconditioning=preconditioning, + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning, # target_acc_rate=0.95, frac_tune3=0.1 ),key1, n=num_steps, batch=num_chains, contract=contract) @@ -555,9 +559,9 @@ def run_benchmarks_simple(): if __name__ == "__main__": - # run_benchmarks_simple() + run_benchmarks_simple() - benchmark_mhmchmc(batch_size=50) + # benchmark_mhmchmc(batch_size=1) # run_benchmarks(128) # run_benchmarks_step_size(128) # benchmark_omelyan(128) diff --git a/benchmarks/mcmc/results.ipynb b/benchmarks/mcmc/results.ipynb index 678fdc5..05d7a72 100644 --- a/benchmarks/mcmc/results.ipynb +++ b/benchmarks/mcmc/results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1642,12 +1642,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
    " ] @@ -1694,13 +1694,13 @@ " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 14.369997\n", - " 5.799155\n", + " 18.468103\n", + " 5.774344\n", " mclachlan\n", " standard\n", " 1.000000\n", - " False\n", - " 0.010070\n", + " True\n", + " 0.010024\n", " \n", " \n", " 1\n", @@ -1711,9 +1711,9 @@ " 7.481382\n", " mclachlan\n", " standard\n", - " 0.644018\n", - " False\n", - " 0.005249\n", + " 0.643603\n", + " True\n", + " 0.004564\n", " \n", " \n", " 2\n", @@ -1724,9 +1724,9 @@ " 7.599843\n", " mclachlan\n", " standard\n", - " 0.603782\n", - " False\n", - " 0.002638\n", + " 0.604179\n", + " True\n", + " 0.002259\n", " \n", " \n", " 3\n", @@ -1737,9 +1737,9 @@ " 5.910617\n", " mclachlan\n", " standard\n", - " 0.898512\n", - " False\n", - " 0.006492\n", + " 0.898358\n", + " True\n", + " 0.006402\n", " \n", " \n", " 4\n", @@ -1750,9 +1750,9 @@ " 5.924881\n", " mclachlan\n", " standard\n", - " 0.903127\n", - " False\n", - " 0.003693\n", + " 0.903111\n", + " True\n", + " 0.003778\n", " \n", " \n", " 5\n", @@ -1763,21 +1763,21 @@ " 0.000000\n", " mclachlan\n", " standard\n", - " 0.856732\n", - " False\n", - " 0.001571\n", + " 0.985327\n", + " True\n", + " 0.003154\n", " \n", " \n", " 6\n", " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 14.770721\n", - " 2.309608\n", + " 18.710426\n", + " 2.299914\n", " velocity_verlet\n", " standard\n", " 1.000000\n", - " False\n", + " True\n", " 0.000000\n", " \n", " \n", @@ -1789,9 +1789,9 @@ " 3.956626\n", " velocity_verlet\n", " standard\n", - " 0.648849\n", - " False\n", - " 0.005578\n", + " 0.649220\n", + " True\n", + " 0.005732\n", " \n", " \n", " 8\n", @@ -1802,9 +1802,9 @@ " 3.852845\n", " velocity_verlet\n", " standard\n", - " 0.653583\n", - " False\n", - " 0.002876\n", + " 0.653373\n", + " True\n", + " 0.002833\n", " \n", " \n", " 9\n", @@ -1815,9 +1815,9 @@ " 2.171618\n", " velocity_verlet\n", " standard\n", - " 0.904663\n", - " False\n", - " 0.004867\n", + " 0.904677\n", + " True\n", + " 0.005107\n", " \n", " \n", " 10\n", @@ -1828,8 +1828,8 @@ " 2.239549\n", " velocity_verlet\n", " standard\n", - " 0.904545\n", - " False\n", + " 0.904468\n", + " True\n", " 0.000000\n", " \n", " \n", @@ -1841,22 +1841,22 @@ " 0.000000\n", " velocity_verlet\n", " standard\n", - " 0.808837\n", - " False\n", - " 0.001612\n", + " 0.854327\n", + " True\n", + " 0.004926\n", " \n", " \n", " 12\n", " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 8.321224\n", - " 12.235815\n", + " 14.369997\n", + " 5.799155\n", " mclachlan\n", " standard\n", " 1.000000\n", - " True\n", - " 0.050607\n", + " False\n", + " 0.009833\n", " \n", " \n", " 13\n", @@ -1864,25 +1864,25 @@ " 100\n", " mhmchmc0.65\n", " 14.662748\n", - " 13.329770\n", + " 7.481382\n", " mclachlan\n", " standard\n", - " 0.871993\n", - " True\n", - " 0.030671\n", + " 0.643603\n", + " False\n", + " 0.004564\n", " \n", " \n", " 14\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.65\n", - " 10.338839\n", - " 9.345161\n", + " 18.950314\n", + " 7.599843\n", " mclachlan\n", " standard\n", - " 0.954968\n", - " True\n", - " 0.026389\n", + " 0.604179\n", + " False\n", + " 0.002259\n", " \n", " \n", " 15\n", @@ -1890,25 +1890,25 @@ " 100\n", " mhmchmc0.9\n", " 14.755423\n", - " 12.293874\n", + " 5.910617\n", " mclachlan\n", " standard\n", - " 0.897613\n", - " True\n", - " 0.027814\n", + " 0.898358\n", + " False\n", + " 0.006402\n", " \n", " \n", " 16\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.9\n", - " 8.496033\n", - " 7.723665\n", + " 9.432498\n", + " 5.924881\n", " mclachlan\n", " standard\n", - " 0.974669\n", - " True\n", - " 0.022603\n", + " 0.903111\n", + " False\n", + " 0.003778\n", " \n", " \n", " 17\n", @@ -1919,22 +1919,22 @@ " 0.000000\n", " mclachlan\n", " standard\n", - " 0.985340\n", - " True\n", - " 0.003272\n", + " 0.856881\n", + " False\n", + " 0.001570\n", " \n", " \n", " 18\n", " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 9.765783\n", - " 6.169864\n", + " 14.770721\n", + " 2.309608\n", " velocity_verlet\n", " standard\n", " 1.000000\n", - " True\n", - " 0.026504\n", + " False\n", + " 0.000000\n", " \n", " \n", " 19\n", @@ -1942,25 +1942,25 @@ " 100\n", " mhmchmc0.65\n", " 14.741055\n", - " 9.575418\n", + " 3.956626\n", " velocity_verlet\n", " standard\n", - " 0.647452\n", - " True\n", - " 0.025084\n", + " 0.649220\n", + " False\n", + " 0.005732\n", " \n", " \n", " 20\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.65\n", - " 13.378891\n", - " 8.629835\n", + " 10.072994\n", + " 3.852845\n", " velocity_verlet\n", " standard\n", - " 0.739921\n", - " True\n", - " 0.023097\n", + " 0.653373\n", + " False\n", + " 0.002833\n", " \n", " \n", " 21\n", @@ -1968,25 +1968,25 @@ " 100\n", " mhmchmc0.9\n", " 14.707802\n", - " 5.305859\n", + " 2.171618\n", " velocity_verlet\n", " standard\n", - " 0.894883\n", - " True\n", - " 0.019213\n", + " 0.904677\n", + " False\n", + " 0.005107\n", " \n", " \n", " 22\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.9\n", - " 3.286116\n", - " 2.968825\n", + " 3.154816\n", + " 2.239549\n", " velocity_verlet\n", " standard\n", - " 0.988102\n", - " True\n", - " 0.005925\n", + " 0.904468\n", + " False\n", + " 0.000000\n", " \n", " \n", " 23\n", @@ -1997,9 +1997,9 @@ " 0.000000\n", " velocity_verlet\n", " standard\n", - " 0.854472\n", - " True\n", - " 0.005160\n", + " 0.808706\n", + " False\n", + " 0.001562\n", " \n", " \n", "\n", @@ -2007,56 +2007,56 @@ ], "text/plain": [ " model dims sampler L step_size \\\n", - "0 IllConditionedGaussian 100 mclmc 14.369997 5.799155 \n", + "0 IllConditionedGaussian 100 mclmc 18.468103 5.774344 \n", "1 IllConditionedGaussian 100 mhmchmc0.65 14.662748 7.481382 \n", "2 IllConditionedGaussian 100 mhmchmc:st30.65 18.950314 7.599843 \n", "3 IllConditionedGaussian 100 mhmchmc0.9 14.755423 5.910617 \n", "4 IllConditionedGaussian 100 mhmchmc:st30.9 9.432498 5.924881 \n", "5 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "6 IllConditionedGaussian 100 mclmc 14.770721 2.309608 \n", + "6 IllConditionedGaussian 100 mclmc 18.710426 2.299914 \n", "7 IllConditionedGaussian 100 mhmchmc0.65 14.741055 3.956626 \n", "8 IllConditionedGaussian 100 mhmchmc:st30.65 10.072994 3.852845 \n", "9 IllConditionedGaussian 100 mhmchmc0.9 14.707802 2.171618 \n", "10 IllConditionedGaussian 100 mhmchmc:st30.9 3.154816 2.239549 \n", "11 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "12 IllConditionedGaussian 100 mclmc 8.321224 12.235815 \n", - "13 IllConditionedGaussian 100 mhmchmc0.65 14.662748 13.329770 \n", - "14 IllConditionedGaussian 100 mhmchmc:st30.65 10.338839 9.345161 \n", - "15 IllConditionedGaussian 100 mhmchmc0.9 14.755423 12.293874 \n", - "16 IllConditionedGaussian 100 mhmchmc:st30.9 8.496033 7.723665 \n", + "12 IllConditionedGaussian 100 mclmc 14.369997 5.799155 \n", + "13 IllConditionedGaussian 100 mhmchmc0.65 14.662748 7.481382 \n", + "14 IllConditionedGaussian 100 mhmchmc:st30.65 18.950314 7.599843 \n", + "15 IllConditionedGaussian 100 mhmchmc0.9 14.755423 5.910617 \n", + "16 IllConditionedGaussian 100 mhmchmc:st30.9 9.432498 5.924881 \n", "17 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "18 IllConditionedGaussian 100 mclmc 9.765783 6.169864 \n", - "19 IllConditionedGaussian 100 mhmchmc0.65 14.741055 9.575418 \n", - "20 IllConditionedGaussian 100 mhmchmc:st30.65 13.378891 8.629835 \n", - "21 IllConditionedGaussian 100 mhmchmc0.9 14.707802 5.305859 \n", - "22 IllConditionedGaussian 100 mhmchmc:st30.9 3.286116 2.968825 \n", + "18 IllConditionedGaussian 100 mclmc 14.770721 2.309608 \n", + "19 IllConditionedGaussian 100 mhmchmc0.65 14.741055 3.956626 \n", + "20 IllConditionedGaussian 100 mhmchmc:st30.65 10.072994 3.852845 \n", + "21 IllConditionedGaussian 100 mhmchmc0.9 14.707802 2.171618 \n", + "22 IllConditionedGaussian 100 mhmchmc:st30.9 3.154816 2.239549 \n", "23 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", "\n", " integrator tuning acc_rate preconditioning ESS \n", - "0 mclachlan standard 1.000000 False 0.010070 \n", - "1 mclachlan standard 0.644018 False 0.005249 \n", - "2 mclachlan standard 0.603782 False 0.002638 \n", - "3 mclachlan standard 0.898512 False 0.006492 \n", - "4 mclachlan standard 0.903127 False 0.003693 \n", - "5 mclachlan standard 0.856732 False 0.001571 \n", - "6 velocity_verlet standard 1.000000 False 0.000000 \n", - "7 velocity_verlet standard 0.648849 False 0.005578 \n", - "8 velocity_verlet standard 0.653583 False 0.002876 \n", - "9 velocity_verlet standard 0.904663 False 0.004867 \n", - "10 velocity_verlet standard 0.904545 False 0.000000 \n", - "11 velocity_verlet standard 0.808837 False 0.001612 \n", - "12 mclachlan standard 1.000000 True 0.050607 \n", - "13 mclachlan standard 0.871993 True 0.030671 \n", - "14 mclachlan standard 0.954968 True 0.026389 \n", - "15 mclachlan standard 0.897613 True 0.027814 \n", - "16 mclachlan standard 0.974669 True 0.022603 \n", - "17 mclachlan standard 0.985340 True 0.003272 \n", - "18 velocity_verlet standard 1.000000 True 0.026504 \n", - "19 velocity_verlet standard 0.647452 True 0.025084 \n", - "20 velocity_verlet standard 0.739921 True 0.023097 \n", - "21 velocity_verlet standard 0.894883 True 0.019213 \n", - "22 velocity_verlet standard 0.988102 True 0.005925 \n", - "23 velocity_verlet standard 0.854472 True 0.005160 " + "0 mclachlan standard 1.000000 True 0.010024 \n", + "1 mclachlan standard 0.643603 True 0.004564 \n", + "2 mclachlan standard 0.604179 True 0.002259 \n", + "3 mclachlan standard 0.898358 True 0.006402 \n", + "4 mclachlan standard 0.903111 True 0.003778 \n", + "5 mclachlan standard 0.985327 True 0.003154 \n", + "6 velocity_verlet standard 1.000000 True 0.000000 \n", + "7 velocity_verlet standard 0.649220 True 0.005732 \n", + "8 velocity_verlet standard 0.653373 True 0.002833 \n", + "9 velocity_verlet standard 0.904677 True 0.005107 \n", + "10 velocity_verlet standard 0.904468 True 0.000000 \n", + "11 velocity_verlet standard 0.854327 True 0.004926 \n", + "12 mclachlan standard 1.000000 False 0.009833 \n", + "13 mclachlan standard 0.643603 False 0.004564 \n", + "14 mclachlan standard 0.604179 False 0.002259 \n", + "15 mclachlan standard 0.898358 False 0.006402 \n", + "16 mclachlan standard 0.903111 False 0.003778 \n", + "17 mclachlan standard 0.856881 False 0.001570 \n", + "18 velocity_verlet standard 1.000000 False 0.000000 \n", + "19 velocity_verlet standard 0.649220 False 0.005732 \n", + "20 velocity_verlet standard 0.653373 False 0.002833 \n", + "21 velocity_verlet standard 0.904677 False 0.005107 \n", + "22 velocity_verlet standard 0.904468 False 0.000000 \n", + "23 velocity_verlet standard 0.808706 False 0.001562 " ] }, "metadata": {}, @@ -2091,12 +2091,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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IllConditionedGaussian\n", + " 100\n", " mhmchmc0.65\n", - " 0.813533\n", - " 0.739576\n", + " 14.674129\n", + " 7.743088\n", " mclachlan\n", " standard\n", - " 0.997668\n", - " True\n", - " 0.000000\n", + " 0.601209\n", + " False\n", + " 0.004313\n", " \n", " \n", " 14\n", - " Brownian\n", - " 32\n", + " IllConditionedGaussian\n", + " 100\n", " mhmchmc:st30.65\n", - " 1.627066\n", - " 1.479151\n", + " 29.348257\n", + " 6.619222\n", " mclachlan\n", " standard\n", - " 0.980983\n", - " True\n", - " 0.000732\n", + " 0.836988\n", + " False\n", + " 0.003568\n", " \n", " \n", " 15\n", - " Brownian\n", - " 32\n", + " IllConditionedGaussian\n", + " 100\n", " mhmchmc0.9\n", - " 0.828702\n", - " 0.753365\n", + " 14.697281\n", + " 6.304248\n", " mclachlan\n", " standard\n", - " 0.995110\n", - " True\n", - " 0.000000\n", + " 0.880382\n", + " False\n", + " 0.007092\n", " \n", " \n", " 16\n", - " Brownian\n", - " 32\n", + " IllConditionedGaussian\n", + " 100\n", " mhmchmc:st30.9\n", - " 1.657404\n", - " 1.506731\n", + " 10.759020\n", + " 5.668786\n", " mclachlan\n", " standard\n", - " 0.981226\n", - " True\n", - " 0.000746\n", + " 0.928205\n", + " False\n", + " 0.004049\n", " \n", " \n", " 17\n", - " Brownian\n", - " 32\n", + " IllConditionedGaussian\n", + " 100\n", " nuts\n", " 0.000000\n", " 0.000000\n", " mclachlan\n", " standard\n", - " 0.921391\n", - " True\n", - " 0.003097\n", + " 0.851473\n", + " False\n", + " 0.001451\n", " \n", " \n", " 18\n", - " Brownian\n", - " 32\n", + " IllConditionedGaussian\n", + " 100\n", " mclmc\n", - " 14.451672\n", - " 0.857385\n", + " 14.502944\n", + " 2.275518\n", " velocity_verlet\n", " standard\n", " 1.000000\n", - " True\n", - " 0.010289\n", + " False\n", + " 0.000000\n", " \n", " \n", " 19\n", - " Brownian\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmchmc0.65\n", + " 14.758156\n", + " 3.678629\n", + " velocity_verlet\n", + " standard\n", + " 0.710312\n", + " False\n", + " 0.002856\n", + " \n", + " \n", + " 20\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmchmc:st30.65\n", + " 7.597509\n", + " 4.147607\n", + " velocity_verlet\n", + " standard\n", + " 0.610031\n", + " False\n", + " 0.002780\n", + " \n", + " \n", + " 21\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmchmc0.9\n", + " 14.752744\n", + " 2.114635\n", + " velocity_verlet\n", + " standard\n", + " 0.910939\n", + " False\n", + " 0.005543\n", + " \n", + " \n", + " 22\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmchmc:st30.9\n", + " 3.174317\n", + " 2.248339\n", + " velocity_verlet\n", + " standard\n", + " 0.902583\n", + " False\n", + " 0.000000\n", + " \n", + " \n", + " 23\n", + " IllConditionedGaussian\n", + " 100\n", + " nuts\n", + " 0.000000\n", + " 0.000000\n", + " velocity_verlet\n", + " standard\n", + " 0.814160\n", + " False\n", + " 0.001519\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " model dims sampler L step_size \\\n", + "0 IllConditionedGaussian 100 mclmc 18.050465 5.719695 \n", + "1 IllConditionedGaussian 100 mhmchmc0.65 14.674129 7.743088 \n", + "2 IllConditionedGaussian 100 mhmchmc:st30.65 29.348257 6.619222 \n", + "3 IllConditionedGaussian 100 mhmchmc0.9 14.697281 6.304248 \n", + "4 IllConditionedGaussian 100 mhmchmc:st30.9 10.759020 5.668786 \n", + "5 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "6 IllConditionedGaussian 100 mclmc 18.247068 2.349995 \n", + "7 IllConditionedGaussian 100 mhmchmc0.65 14.758156 3.678629 \n", + "8 IllConditionedGaussian 100 mhmchmc:st30.65 7.597509 4.147607 \n", + "9 IllConditionedGaussian 100 mhmchmc0.9 14.752744 2.114635 \n", + "10 IllConditionedGaussian 100 mhmchmc:st30.9 3.174317 2.248339 \n", + "11 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "12 IllConditionedGaussian 100 mclmc 14.104933 5.758576 \n", + "13 IllConditionedGaussian 100 mhmchmc0.65 14.674129 7.743088 \n", + "14 IllConditionedGaussian 100 mhmchmc:st30.65 29.348257 6.619222 \n", + "15 IllConditionedGaussian 100 mhmchmc0.9 14.697281 6.304248 \n", + "16 IllConditionedGaussian 100 mhmchmc:st30.9 10.759020 5.668786 \n", + "17 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "18 IllConditionedGaussian 100 mclmc 14.502944 2.275518 \n", + "19 IllConditionedGaussian 100 mhmchmc0.65 14.758156 3.678629 \n", + "20 IllConditionedGaussian 100 mhmchmc:st30.65 7.597509 4.147607 \n", + "21 IllConditionedGaussian 100 mhmchmc0.9 14.752744 2.114635 \n", + "22 IllConditionedGaussian 100 mhmchmc:st30.9 3.174317 2.248339 \n", + "23 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "\n", + " integrator tuning acc_rate preconditioning ESS \n", + "0 mclachlan standard 1.000000 True 0.004053 \n", + "1 mclachlan standard 0.601209 True 0.004313 \n", + "2 mclachlan standard 0.836988 True 0.003568 \n", + "3 mclachlan standard 0.880382 True 0.007092 \n", + "4 mclachlan standard 0.928205 True 0.004049 \n", + "5 mclachlan standard 0.988024 True 0.005307 \n", + "6 velocity_verlet standard 1.000000 True 0.000000 \n", + "7 velocity_verlet standard 0.710312 True 0.002856 \n", + "8 velocity_verlet standard 0.610031 True 0.002780 \n", + "9 velocity_verlet standard 0.910939 True 0.005543 \n", + "10 velocity_verlet standard 0.902583 True 0.000000 \n", + "11 velocity_verlet standard 0.863561 True 0.005883 \n", + "12 mclachlan standard 1.000000 False 0.004044 \n", + "13 mclachlan standard 0.601209 False 0.004313 \n", + "14 mclachlan standard 0.836988 False 0.003568 \n", + "15 mclachlan standard 0.880382 False 0.007092 \n", + "16 mclachlan standard 0.928205 False 0.004049 \n", + "17 mclachlan standard 0.851473 False 0.001451 \n", + "18 velocity_verlet standard 1.000000 False 0.000000 \n", + "19 velocity_verlet standard 0.710312 False 0.002856 \n", + "20 velocity_verlet standard 0.610031 False 0.002780 \n", + "21 velocity_verlet standard 0.910939 False 0.005543 \n", + "22 velocity_verlet standard 0.902583 False 0.000000 \n", + "23 velocity_verlet standard 0.814160 False 0.001519 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load_results(\"IllConditionedGaussian\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioningESS
    0Brownian32mclmc2.4423780.302776mclachlanstandard1.000000False0.010799
    1Brownian32mhmchmc0.650.8120420.637599mclachlanstandard0.529587False0.001431
    2Brownian32mhmchmc:st30.651.6270660.595371mclachlanstandard0.523359False0.001147
    3Brownian32mhmchmc0.90.8287020.395384mclachlanstandard0.853865False0.002489
    4Brownian32mhmchmc:st30.91.6102020.342993mclachlanstandard0.884686False0.004979
    5Brownian32nuts0.0000000.000000mclachlanstandard0.760786False0.002627
    6Brownian32mclmc2.2970480.146238velocity_verletstandard1.000000False0.013497
    7Brownian32mhmchmc0.650.8135890.376977velocity_verletstandard0.532756False0.001877
    8Brownian32mhmchmc:st30.651.6267470.331795velocity_verletstandard0.595688False0.003162
    9Brownian32mhmchmc0.90.8115920.185693velocity_verletstandard0.881163False0.002693
    10Brownian32mhmchmc:st30.91.1276110.166444velocity_verletstandard0.897115False0.003417
    11Brownian32nuts0.0000000.000000velocity_verletstandard0.779443False0.003168
    12Brownian32mclmc14.7237571.771522mclachlanstandard1.000000True0.012148
    13Brownian32mhmchmc0.650.8135330.739576mclachlanstandard0.997668True0.000000
    14Brownian32mhmchmc:st30.651.6270661.479151mclachlanstandard0.980983True0.000732
    15Brownian32mhmchmc0.90.8287020.753365mclachlanstandard0.995110True0.000000
    16Brownian32mhmchmc:st30.91.6574041.506731mclachlanstandard0.981226True0.000746
    17Brownian32nuts0.0000000.000000mclachlanstandard0.921391True0.003097
    18Brownian32mclmc14.4516720.857385velocity_verletstandard1.000000True0.010289
    19Brownian32mhmchmc0.650.8135890.739626velocity_verletstandard0.972460True0.000000
    20Brownian32mhmchmc:st30.651.6271781.479252velocity_verletstandard0.824414True0.001364
    21Brownian32mhmchmc0.90.8115920.737811velocity_verletstandard0.971546True0.000000
    22Brownian32mhmchmc:st30.91.6231841.256775velocity_verletstandard0.873862True0.001445
    23Brownian32nuts0.0000000.000000velocity_verletstandard0.873792True0.003157
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \\\n", + "0 Brownian 32 mclmc 2.442378 0.302776 mclachlan \n", + "1 Brownian 32 mhmchmc0.65 0.812042 0.637599 mclachlan \n", + "2 Brownian 32 mhmchmc:st30.65 1.627066 0.595371 mclachlan \n", + "3 Brownian 32 mhmchmc0.9 0.828702 0.395384 mclachlan \n", + "4 Brownian 32 mhmchmc:st30.9 1.610202 0.342993 mclachlan \n", + "5 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "6 Brownian 32 mclmc 2.297048 0.146238 velocity_verlet \n", + "7 Brownian 32 mhmchmc0.65 0.813589 0.376977 velocity_verlet \n", + "8 Brownian 32 mhmchmc:st30.65 1.626747 0.331795 velocity_verlet \n", + "9 Brownian 32 mhmchmc0.9 0.811592 0.185693 velocity_verlet \n", + "10 Brownian 32 mhmchmc:st30.9 1.127611 0.166444 velocity_verlet \n", + "11 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + "12 Brownian 32 mclmc 14.723757 1.771522 mclachlan \n", + "13 Brownian 32 mhmchmc0.65 0.813533 0.739576 mclachlan \n", + "14 Brownian 32 mhmchmc:st30.65 1.627066 1.479151 mclachlan \n", + "15 Brownian 32 mhmchmc0.9 0.828702 0.753365 mclachlan \n", + "16 Brownian 32 mhmchmc:st30.9 1.657404 1.506731 mclachlan \n", + "17 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "18 Brownian 32 mclmc 14.451672 0.857385 velocity_verlet \n", + "19 Brownian 32 mhmchmc0.65 0.813589 0.739626 velocity_verlet \n", + "20 Brownian 32 mhmchmc:st30.65 1.627178 1.479252 velocity_verlet \n", + "21 Brownian 32 mhmchmc0.9 0.811592 0.737811 velocity_verlet \n", + "22 Brownian 32 mhmchmc:st30.9 1.623184 1.256775 velocity_verlet \n", + "23 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + "\n", + " tuning acc_rate preconditioning ESS \n", + "0 standard 1.000000 False 0.010799 \n", + "1 standard 0.529587 False 0.001431 \n", + "2 standard 0.523359 False 0.001147 \n", + "3 standard 0.853865 False 0.002489 \n", + "4 standard 0.884686 False 0.004979 \n", + "5 standard 0.760786 False 0.002627 \n", + "6 standard 1.000000 False 0.013497 \n", + "7 standard 0.532756 False 0.001877 \n", + "8 standard 0.595688 False 0.003162 \n", + "9 standard 0.881163 False 0.002693 \n", + "10 standard 0.897115 False 0.003417 \n", + "11 standard 0.779443 False 0.003168 \n", + "12 standard 1.000000 True 0.012148 \n", + "13 standard 0.997668 True 0.000000 \n", + "14 standard 0.980983 True 0.000732 \n", + "15 standard 0.995110 True 0.000000 \n", + "16 standard 0.981226 True 0.000746 \n", + "17 standard 0.921391 True 0.003097 \n", + "18 standard 1.000000 True 0.010289 \n", + "19 standard 0.972460 True 0.000000 \n", + "20 standard 0.824414 True 0.001364 \n", + "21 standard 0.971546 True 0.000000 \n", + "22 standard 0.873862 True 0.001445 \n", + "23 standard 0.873792 True 0.003157 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load_results(\"Brownian\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioningESS
    0GermanCredit51mclmc13.7747130.575219mclachlanstandard1.000000True0.001701
    1GermanCredit51mhmchmc0.657.3151191.101492mclachlanstandard0.590559True0.000488
    2GermanCredit51mhmchmc:st30.658.8462481.103699mclachlanstandard0.596298True0.000298
    3GermanCredit51mhmchmc0.95.3472230.748821mclachlanstandard0.864109True0.000959
    4GermanCredit51mhmchmc:st30.95.0634170.801041mclachlanstandard0.853852True0.000869
    5GermanCredit51nuts0.0000000.000000mclachlanstandard0.898460True0.000644
    6GermanCredit51mclmc13.5319670.337908velocity_verletstandard1.000000True0.002515
    7GermanCredit51mhmchmc0.650.8135890.7396264.8106700.761948velocity_verletstandard0.9724600.576652True0.000000
    20Brownian328GermanCredit51mhmchmc:st30.651.6271781.4792527.6889740.713766velocity_verletstandard0.8244140.580719True0.0013640.000361
    21Brownian329GermanCredit51mhmchmc0.90.8115920.7378112.9414700.477281velocity_verletstandard0.9715460.872222True0.000628
    10GermanCredit51mhmchmc:st30.95.3490970.446817velocity_verletstandard0.879439True0.001214
    11GermanCredit51nuts0.0000000.000000velocity_verletstandard0.894313True0.001096
    12GermanCredit51mclmc12.8718710.399752mclachlanstandard1.000000False0.001677
    13GermanCredit51mhmchmc0.657.3151190.672528mclachlanstandard0.558331False0.000381
    14GermanCredit51mhmchmc:st30.657.1482020.676359mclachlanstandard0.573157False0.000229
    15GermanCredit51mhmchmc0.95.3472230.467936mclachlanstandard0.865484False0.000773
    16GermanCredit51mhmchmc:st30.94.2105580.520591mclachlanstandard0.843018False0.000580
    17GermanCredit51nuts0.0000000.000000mclachlanstandard0.815190False0.000504
    18GermanCredit51mclmc11.4910510.208865velocity_verletstandard1.000000False0.000000
    19GermanCredit51mhmchmc0.654.8106700.454371velocity_verletstandard0.586514False0.000000
    20GermanCredit51mhmchmc:st30.656.5752040.473575velocity_verletstandard0.521883False0.000000
    21GermanCredit51mhmchmc0.92.9414700.290941velocity_verletstandard0.869797False0.000943
    22Brownian32GermanCredit51mhmchmc:st30.91.6231841.2567754.7829810.278618velocity_verletstandard0.873862True0.0014450.882591False0.001090
    23Brownian32GermanCredit51nuts0.0000000.000000velocity_verletstandard0.873792True0.0031570.796060False0.000332
    \n", "
    " ], "text/plain": [ - " model dims sampler L step_size integrator \\\n", - "0 Brownian 32 mclmc 2.442378 0.302776 mclachlan \n", - "1 Brownian 32 mhmchmc0.65 0.812042 0.637599 mclachlan \n", - "2 Brownian 32 mhmchmc:st30.65 1.627066 0.595371 mclachlan \n", - "3 Brownian 32 mhmchmc0.9 0.828702 0.395384 mclachlan \n", - "4 Brownian 32 mhmchmc:st30.9 1.610202 0.342993 mclachlan \n", - "5 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", - "6 Brownian 32 mclmc 2.297048 0.146238 velocity_verlet \n", - "7 Brownian 32 mhmchmc0.65 0.813589 0.376977 velocity_verlet \n", - "8 Brownian 32 mhmchmc:st30.65 1.626747 0.331795 velocity_verlet \n", - "9 Brownian 32 mhmchmc0.9 0.811592 0.185693 velocity_verlet \n", - "10 Brownian 32 mhmchmc:st30.9 1.127611 0.166444 velocity_verlet \n", - "11 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", - "12 Brownian 32 mclmc 14.723757 1.771522 mclachlan \n", - "13 Brownian 32 mhmchmc0.65 0.813533 0.739576 mclachlan \n", - "14 Brownian 32 mhmchmc:st30.65 1.627066 1.479151 mclachlan \n", - "15 Brownian 32 mhmchmc0.9 0.828702 0.753365 mclachlan \n", - "16 Brownian 32 mhmchmc:st30.9 1.657404 1.506731 mclachlan \n", - "17 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", - "18 Brownian 32 mclmc 14.451672 0.857385 velocity_verlet \n", - "19 Brownian 32 mhmchmc0.65 0.813589 0.739626 velocity_verlet \n", - "20 Brownian 32 mhmchmc:st30.65 1.627178 1.479252 velocity_verlet \n", - "21 Brownian 32 mhmchmc0.9 0.811592 0.737811 velocity_verlet \n", - "22 Brownian 32 mhmchmc:st30.9 1.623184 1.256775 velocity_verlet \n", - "23 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + " model dims sampler L step_size \\\n", + "0 GermanCredit 51 mclmc 13.774713 0.575219 \n", + "1 GermanCredit 51 mhmchmc0.65 7.315119 1.101492 \n", + "2 GermanCredit 51 mhmchmc:st30.65 8.846248 1.103699 \n", + "3 GermanCredit 51 mhmchmc0.9 5.347223 0.748821 \n", + "4 GermanCredit 51 mhmchmc:st30.9 5.063417 0.801041 \n", + "5 GermanCredit 51 nuts 0.000000 0.000000 \n", + "6 GermanCredit 51 mclmc 13.531967 0.337908 \n", + "7 GermanCredit 51 mhmchmc0.65 4.810670 0.761948 \n", + "8 GermanCredit 51 mhmchmc:st30.65 7.688974 0.713766 \n", + "9 GermanCredit 51 mhmchmc0.9 2.941470 0.477281 \n", + "10 GermanCredit 51 mhmchmc:st30.9 5.349097 0.446817 \n", + "11 GermanCredit 51 nuts 0.000000 0.000000 \n", + "12 GermanCredit 51 mclmc 12.871871 0.399752 \n", + "13 GermanCredit 51 mhmchmc0.65 7.315119 0.672528 \n", + "14 GermanCredit 51 mhmchmc:st30.65 7.148202 0.676359 \n", + "15 GermanCredit 51 mhmchmc0.9 5.347223 0.467936 \n", + "16 GermanCredit 51 mhmchmc:st30.9 4.210558 0.520591 \n", + "17 GermanCredit 51 nuts 0.000000 0.000000 \n", + "18 GermanCredit 51 mclmc 11.491051 0.208865 \n", + "19 GermanCredit 51 mhmchmc0.65 4.810670 0.454371 \n", + "20 GermanCredit 51 mhmchmc:st30.65 6.575204 0.473575 \n", + "21 GermanCredit 51 mhmchmc0.9 2.941470 0.290941 \n", + "22 GermanCredit 51 mhmchmc:st30.9 4.782981 0.278618 \n", + "23 GermanCredit 51 nuts 0.000000 0.000000 \n", "\n", - " tuning acc_rate preconditioning ESS \n", - "0 standard 1.000000 False 0.010799 \n", - "1 standard 0.529587 False 0.001431 \n", - "2 standard 0.523359 False 0.001147 \n", - "3 standard 0.853865 False 0.002489 \n", - "4 standard 0.884686 False 0.004979 \n", - "5 standard 0.760786 False 0.002627 \n", - "6 standard 1.000000 False 0.013497 \n", - "7 standard 0.532756 False 0.001877 \n", - "8 standard 0.595688 False 0.003162 \n", - "9 standard 0.881163 False 0.002693 \n", - "10 standard 0.897115 False 0.003417 \n", - "11 standard 0.779443 False 0.003168 \n", - "12 standard 1.000000 True 0.012148 \n", - "13 standard 0.997668 True 0.000000 \n", - "14 standard 0.980983 True 0.000732 \n", - "15 standard 0.995110 True 0.000000 \n", - "16 standard 0.981226 True 0.000746 \n", - "17 standard 0.921391 True 0.003097 \n", - "18 standard 1.000000 True 0.010289 \n", - "19 standard 0.972460 True 0.000000 \n", - "20 standard 0.824414 True 0.001364 \n", - "21 standard 0.971546 True 0.000000 \n", - "22 standard 0.873862 True 0.001445 \n", - "23 standard 0.873792 True 0.003157 " + " integrator tuning acc_rate preconditioning ESS \n", + "0 mclachlan standard 1.000000 True 0.001701 \n", + "1 mclachlan standard 0.590559 True 0.000488 \n", + "2 mclachlan standard 0.596298 True 0.000298 \n", + "3 mclachlan standard 0.864109 True 0.000959 \n", + "4 mclachlan standard 0.853852 True 0.000869 \n", + "5 mclachlan standard 0.898460 True 0.000644 \n", + "6 velocity_verlet standard 1.000000 True 0.002515 \n", + "7 velocity_verlet standard 0.576652 True 0.000000 \n", + "8 velocity_verlet standard 0.580719 True 0.000361 \n", + "9 velocity_verlet standard 0.872222 True 0.000628 \n", + "10 velocity_verlet standard 0.879439 True 0.001214 \n", + "11 velocity_verlet standard 0.894313 True 0.001096 \n", + "12 mclachlan standard 1.000000 False 0.001677 \n", + "13 mclachlan standard 0.558331 False 0.000381 \n", + "14 mclachlan standard 0.573157 False 0.000229 \n", + "15 mclachlan standard 0.865484 False 0.000773 \n", + "16 mclachlan standard 0.843018 False 0.000580 \n", + "17 mclachlan standard 0.815190 False 0.000504 \n", + "18 velocity_verlet standard 1.000000 False 0.000000 \n", + "19 velocity_verlet standard 0.586514 False 0.000000 \n", + "20 velocity_verlet standard 0.521883 False 0.000000 \n", + "21 velocity_verlet standard 0.869797 False 0.000943 \n", + "22 velocity_verlet standard 0.882591 False 0.001090 \n", + "23 velocity_verlet standard 0.796060 False 0.000332 " ] }, "metadata": {}, @@ -2513,7 +3367,7 @@ } ], "source": [ - "load_results(\"Brownian\")\n" + "load_results(\"GermanCredit\")\n" ] }, { diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index bba1328..b634b1f 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -8,7 +8,7 @@ import blackjax from blackjax.adaptation.mclmc_adaptation import MCLMCAdaptationState # from blackjax.adaptation.window_adaptation import da_adaptation -from blackjax.mcmc.integrators import calls_per_integrator_step, generate_euclidean_integrator, generate_isokinetic_integrator, integrator_order +from blackjax.mcmc.integrators import generate_euclidean_integrator, generate_isokinetic_integrator, mclachlan, yoshida, velocity_verlet, omelyan, isokinetic_mclachlan, isokinetic_velocity_verlet, isokinetic_yoshida, isokinetic_omelyan from blackjax.mcmc.adjusted_mclmc import rescale from blackjax.util import run_inference_algorithm import blackjax @@ -18,7 +18,35 @@ dual_averaging_adaptation, ) -__all__ = ["samplers"] +# __all__ = ["samplers"] + + +def calls_per_integrator_step(c): + if c == "velocity_verlet": + return 1 + if c == "mclachlan": + return 2 + if c == "yoshida": + return 3 + if c == "omelyan": + return 5 + + else: + raise Exception("No such integrator exists in blackjax") + + +def integrator_order(c): + if c == "velocity_verlet": + return 2 + if c == "mclachlan": + return 2 + if c == "yoshida": + return 4 + if c == "omelyan": + return 4 + + else: + raise Exception("No such integrator exists in blackjax") target_acceptance_rate_of_order = {2: 0.65, 4: 0.8} @@ -73,10 +101,27 @@ def step(state, key): # print(adaptation_state, "adaptation_state\n\n") return kernel_state, {"step_size" : da_final(adaptation_state), "inverse_mass_matrix" : inverse_mass_matrix} +# blackjax doesn't export coefficients, which is inconvenient +map_integrator_type_to_integrator = { + 'hmc': { + "mclachlan" : mclachlan, + "yoshida" : yoshida, + "velocity_verlet" : velocity_verlet, + "omelyan" : omelyan + }, + 'mclmc' : { + "mclachlan" : isokinetic_mclachlan, + "yoshida" : isokinetic_yoshida, + "velocity_verlet" : isokinetic_velocity_verlet, + "omelyan" : isokinetic_omelyan + } +} + def run_nuts( - coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning): + integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning): - integrator = generate_euclidean_integrator(coefficients) + # integrator = generate_euclidean_integrator(coefficients) + integrator = map_integrator_type_to_integrator['hmc'][integrator_type] # integrator = blackjax.mcmc.integrators.velocity_verlet # note: defaulted to in nuts rng_key, warmup_key = jax.random.split(key, 2) @@ -105,13 +150,11 @@ def run_nuts( progress_bar=True ) - # print("INFO\n\n",info_history.num_integration_steps) - - return state_history, params, info_history.num_integration_steps.mean() * calls_per_integrator_step(coefficients), info_history.acceptance_rate.mean(), None, None + return state_history, params, info_history.num_integration_steps.mean() * calls_per_integrator_step(integrator_type), info_history.acceptance_rate.mean(), None, None -def run_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning): +def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning): - integrator = generate_isokinetic_integrator(coefficients) + integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] init_key, tune_key, run_key = jax.random.split(key, 3) @@ -163,11 +206,11 @@ def run_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transfor ) acceptance_rate = 1. - return samples, blackjax_mclmc_sampler_params, calls_per_integrator_step(coefficients), acceptance_rate, None, None + return samples, blackjax_mclmc_sampler_params, calls_per_integrator_step(integrator_type), acceptance_rate, None, None -def run_adjusted_mclmc(coefficients, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, target_acc_rate=None): - integrator = generate_isokinetic_integrator(coefficients) +def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, target_acc_rate=None): + integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] init_key, tune_key, run_key = jax.random.split(key, 3) @@ -186,7 +229,7 @@ def run_adjusted_mclmc(coefficients, logdensity_fn, num_steps, initial_position, logdensity_fn=logdensity_fn) if target_acc_rate is None: - target_acc_rate = target_acceptance_rate_of_order[integrator_order(coefficients)] + target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] print("target acc rate") ( @@ -234,7 +277,7 @@ def run_adjusted_mclmc(coefficients, logdensity_fn, num_steps, initial_position, - return out, blackjax_mclmc_sampler_params, calls_per_integrator_step(coefficients) * (L/step_size), info.acceptance_rate, params_history, final_da + return out, blackjax_mclmc_sampler_params, calls_per_integrator_step(integrator_type) * (L/step_size), info.acceptance_rate, params_history, final_da # we should do at least: mclmc, nuts, unadjusted hmc, adjusted_mclmc, langevin From cde4645137c863043832e39f0cdb3ee4c490db1f Mon Sep 17 00:00:00 2001 From: = Date: Wed, 29 May 2024 20:40:59 +0200 Subject: [PATCH 05/21] COMPARISON CHECKPOINT: CURRENTLY EXPECTATION[X] AGREES! --- benchmarks/mcmc/benchmark.py | 15 ++++++---- benchmarks/mcmc/sampling_algorithms.py | 40 ++++++++++++++++++++++++++ 2 files changed, 49 insertions(+), 6 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index f15cd18..3efbd25 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -24,7 +24,7 @@ import blackjax from benchmarks.mcmc.sampling_algorithms import integrator_order, run_mclmc, run_adjusted_mclmc, run_nuts, samplers -from benchmarks.mcmc.inference_models import Brownian, Funnel, GermanCredit, IllConditionedGaussian, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models +from benchmarks.mcmc.inference_models import Banana, Brownian, Funnel, GermanCredit, IllConditionedGaussian, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models from blackjax.mcmc.integrators import generate_euclidean_integrator, generate_isokinetic_integrator, isokinetic_mclachlan, mclachlan_coefficients, omelyan_coefficients, velocity_verlet, velocity_verlet_coefficients, yoshida_coefficients from blackjax.mcmc.adjusted_mclmc import rescale from blackjax.util import run_inference_algorithm @@ -539,16 +539,19 @@ def run_benchmarks_simple(): # sampler = run_adjusted_mclmc sampler = run_mclmc - # model = IllConditionedGaussian(100,100) - model = IllConditionedGaussian(100,10000) + # model = IllConditionedGaussian(10,100) + # model = Brownian() + # model = StandardNormal(10) + model = Banana() integrator_type = "mclachlan" contract = jnp.max # how we average across dimensions - num_steps = 20000 - num_chains = 128 + num_steps = 10000 + num_chains = 1 for i in range(1): key1 = jax.random.PRNGKey(i) - for preconditioning in [True, False]: + for preconditioning in [False]: + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning, # target_acc_rate=0.95, frac_tune3=0.1 ),key1, n=num_steps, batch=num_chains, contract=contract) diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index b634b1f..aca0d69 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -196,6 +196,31 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans # sqrt_diag_cov=jnp.ones((initial_position.shape[0],)), ) + # try doing low mem version twice + + expectation, _, blahblah = run_inference_algorithm( + rng_key=run_key, + initial_state=blackjax_state_after_tuning, + inference_algorithm=sampling_alg, + num_steps=num_steps, + return_state_history=False, + transform=lambda x: transform(x.position), + # expectation=lambda x: jnp.array([x**2, x]), + expectation=lambda x: x, + progress_bar=True, + ) + + # jax.debug.print("blahblah[1] {x}", x=blahblah) + + # ex2 = expectation[0] + # ex = expectation[1] + # var = ex2 - ex**2 + # print(expectation.shape, "blah blah") + + jax.debug.print("expectation {x}", x=expectation) + # jax.debug.print("var {x}", x=var) + + # [num_steps, dim_model] # [1000, 1e8] _, samples, _ = run_inference_algorithm( rng_key=run_key, initial_state=blackjax_state_after_tuning, @@ -204,6 +229,21 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans transform=lambda x: transform(x.position), progress_bar=True, ) + # E[x^2] + + # Var[f] = E[f^2] - E[f]^2 + + def cumulative_avg(samples): + return jnp.cumsum(samples, axis = 0) / jnp.arange(1, samples.shape[0] + 1)[:, None] + + + # print(samples.mean(axis=0)) + jax.debug.print("samples {x}", x=(samples).mean(axis=0)) + # jax.debug.print("samples {x}", x=cumulative_avg(samples)) + # jax.debug.print("samples {x}", x=jnp.var(samples, axis=0)) + jax.debug.print("comparison {x}", x=blahblah[1]-cumulative_avg(samples)) + + # raise Exception acceptance_rate = 1. return samples, blackjax_mclmc_sampler_params, calls_per_integrator_step(integrator_type), acceptance_rate, None, None From 36648ac35a49b6d2e87ea137936c81813b64000d Mon Sep 17 00:00:00 2001 From: = Date: Wed, 29 May 2024 20:50:00 +0200 Subject: [PATCH 06/21] BROKEN STREAMING AVERAGE: slow and fast methods do not agree! AAAAH --- benchmarks/mcmc/benchmark.py | 2 +- benchmarks/mcmc/sampling_algorithms.py | 16 ++++++++-------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 3efbd25..36f49e8 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -545,7 +545,7 @@ def run_benchmarks_simple(): model = Banana() integrator_type = "mclachlan" contract = jnp.max # how we average across dimensions - num_steps = 10000 + num_steps = 1000 num_chains = 1 for i in range(1): key1 = jax.random.PRNGKey(i) diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index aca0d69..e72358a 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -205,20 +205,20 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans num_steps=num_steps, return_state_history=False, transform=lambda x: transform(x.position), - # expectation=lambda x: jnp.array([x**2, x]), - expectation=lambda x: x, + expectation=lambda x: jnp.array([x**2, x]), + # expectation=lambda x: x, progress_bar=True, ) # jax.debug.print("blahblah[1] {x}", x=blahblah) - # ex2 = expectation[0] - # ex = expectation[1] - # var = ex2 - ex**2 + ex2 = expectation[0] + ex = expectation[1] + var = ex2 - ex**2 # print(expectation.shape, "blah blah") - jax.debug.print("expectation {x}", x=expectation) - # jax.debug.print("var {x}", x=var) + jax.debug.print("expectation {x}", x=ex) + # jax.debug.print("ex2 {x}", x=var) # [num_steps, dim_model] # [1000, 1e8] _, samples, _ = run_inference_algorithm( @@ -241,7 +241,7 @@ def cumulative_avg(samples): jax.debug.print("samples {x}", x=(samples).mean(axis=0)) # jax.debug.print("samples {x}", x=cumulative_avg(samples)) # jax.debug.print("samples {x}", x=jnp.var(samples, axis=0)) - jax.debug.print("comparison {x}", x=blahblah[1]-cumulative_avg(samples)) + # jax.debug.print("comparison {x}", x=blahblah[1]-cumulative_avg(samples)) # raise Exception From 0ae70ee3e34b98cddc476b79d57f482a8015fe8a Mon Sep 17 00:00:00 2001 From: = Date: Thu, 30 May 2024 19:39:53 +0200 Subject: [PATCH 07/21] CHANGING PVMAP BETWEEN PMAP AND VMAP CAUSES A CHANGE --- benchmarks/mcmc/benchmark.py | 6 ++--- benchmarks/mcmc/sampling_algorithms.py | 32 +++++++++++--------------- 2 files changed, 16 insertions(+), 22 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 36f49e8..ee062e2 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -169,14 +169,14 @@ def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.av init_pos = pvmap(model.sample_init)(init_keys) # [batch_size, dim_model] # samples, params, avg_num_steps_per_traj = jax.pmap(lambda pos, key: sampler(model.logdensity_fn, n, pos, model.transform, key))(init_pos, keys) - samples, params, grad_calls_per_traj, acceptance_rate, step_size_over_da, final_da = pvmap(lambda pos, key: sampler(logdensity_fn=model.logdensity_fn, num_steps=n, initial_position= pos,transform= model.transform, key=key))(init_pos, keys) + ex2_empirical, params, grad_calls_per_traj, acceptance_rate, step_size_over_da, final_da = pvmap(lambda pos, key: sampler(logdensity_fn=model.logdensity_fn, num_steps=n, initial_position= pos,transform= model.transform, key=key))(init_pos, keys) avg_grad_calls_per_traj = jnp.nanmean(grad_calls_per_traj, axis=0) try: print(jnp.nanmean(params.step_size,axis=0), jnp.nanmean(params.L,axis=0)) except: pass - full = lambda arr : err(model.E_x2, model.Var_x2, contract)(cumulative_avg(arr)) - err_t = pvmap(full)(samples**2) + full = lambda arr : err(model.E_x2, model.Var_x2, contract)(arr) + err_t = pvmap(full)(ex2_empirical) # outs = [calculate_ess(b, grad_evals_per_step=avg_grad_calls_per_traj) for b in err_t] # # print(outs[:10]) diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index e72358a..ef18418 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -198,29 +198,26 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans # try doing low mem version twice - expectation, _, blahblah = run_inference_algorithm( + _, _, expectations = run_inference_algorithm( rng_key=run_key, initial_state=blackjax_state_after_tuning, inference_algorithm=sampling_alg, num_steps=num_steps, return_state_history=False, transform=lambda x: transform(x.position), - expectation=lambda x: jnp.array([x**2, x]), - # expectation=lambda x: x, + # expectation=lambda x: jnp.array([x**2, x]), + expectation=lambda x: x**2, progress_bar=True, ) # jax.debug.print("blahblah[1] {x}", x=blahblah) - ex2 = expectation[0] - ex = expectation[1] - var = ex2 - ex**2 - # print(expectation.shape, "blah blah") + # ex2 = expectations[-1][0] + # ex = expectations[-1][1] + # var = expectations[:, 0] - ex[:, 1]**2 - jax.debug.print("expectation {x}", x=ex) - # jax.debug.print("ex2 {x}", x=var) + jax.debug.print("expectation {x}", x=expectations[-1]) - # [num_steps, dim_model] # [1000, 1e8] _, samples, _ = run_inference_algorithm( rng_key=run_key, initial_state=blackjax_state_after_tuning, @@ -229,24 +226,21 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans transform=lambda x: transform(x.position), progress_bar=True, ) - # E[x^2] - - # Var[f] = E[f^2] - E[f]^2 - - def cumulative_avg(samples): - return jnp.cumsum(samples, axis = 0) / jnp.arange(1, samples.shape[0] + 1)[:, None] + + # def cumulative_avg(samples): + # return jnp.cumsum(samples, axis = 0) / jnp.arange(1, samples.shape[0] + 1)[:, None] # print(samples.mean(axis=0)) - jax.debug.print("samples {x}", x=(samples).mean(axis=0)) + jax.debug.print("samples {x}", x=(samples**2).mean(axis=0)) # jax.debug.print("samples {x}", x=cumulative_avg(samples)) # jax.debug.print("samples {x}", x=jnp.var(samples, axis=0)) - # jax.debug.print("comparison {x}", x=blahblah[1]-cumulative_avg(samples)) + # jax.debug.print("comparison {x}", x=jnp.mean(expectations)) # raise Exception acceptance_rate = 1. - return samples, blackjax_mclmc_sampler_params, calls_per_integrator_step(integrator_type), acceptance_rate, None, None + return expectations, blackjax_mclmc_sampler_params, calls_per_integrator_step(integrator_type), acceptance_rate, None, None def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, target_acc_rate=None): From 61c1d96a75b99174656ec2c07273313398b72ee1 Mon Sep 17 00:00:00 2001 From: = Date: Thu, 30 May 2024 21:55:57 +0200 Subject: [PATCH 08/21] CHANGING PVMAP BETWEEN PMAP AND VMAP CAUSES A CHANGE --- benchmarks/mcmc/benchmark.py | 73 +++++++++++++++++++++++++----------- 1 file changed, 52 insertions(+), 21 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index ee062e2..42b3eb2 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -23,7 +23,7 @@ import numpy as np import blackjax -from benchmarks.mcmc.sampling_algorithms import integrator_order, run_mclmc, run_adjusted_mclmc, run_nuts, samplers +from benchmarks.mcmc.sampling_algorithms import calls_per_integrator_step, integrator_order, map_integrator_type_to_integrator, target_acceptance_rate_of_order, run_mclmc, run_adjusted_mclmc, run_nuts, samplers from benchmarks.mcmc.inference_models import Banana, Brownian, Funnel, GermanCredit, IllConditionedGaussian, ItemResponseTheory, MixedLogit, StandardNormal, StochasticVolatility, models from blackjax.mcmc.integrators import generate_euclidean_integrator, generate_isokinetic_integrator, isokinetic_mclachlan, mclachlan_coefficients, omelyan_coefficients, velocity_verlet, velocity_verlet_coefficients, yoshida_coefficients from blackjax.mcmc.adjusted_mclmc import rescale @@ -118,14 +118,14 @@ def gridsearch_tune(key, iterations, grid_size, model, sampler, batch, num_steps return center_L, center_step_size, converged -def run_adjusted_mclmc_no_tuning(initial_state, coefficients, step_size, L, sqrt_diag_cov): +def run_adjusted_mclmc_no_tuning(initial_state, integrator_type, step_size, L, sqrt_diag_cov): def s(logdensity_fn, num_steps, initial_position, transform, key): - integrator = generate_isokinetic_integrator(coefficients) + integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] num_steps_per_traj = L/step_size - alg = blackjax.mcmc.adjusted_mclmc.adjusted_mclmc( + alg = blackjax.adjusted_mclmc( logdensity_fn=logdensity_fn, step_size=step_size, integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(num_steps_per_traj)) , @@ -141,7 +141,7 @@ def s(logdensity_fn, num_steps, initial_position, transform, key): transform=lambda x: transform(x.position), progress_bar=True) - return out, MCLMCAdaptationState(L=L, step_size=step_size, sqrt_diag_cov=sqrt_diag_cov), num_steps_per_traj * calls_per_integrator_step(coefficients), info.acceptance_rate.mean(), None, jnp.array([0]) + return out, MCLMCAdaptationState(L=L, step_size=step_size, sqrt_diag_cov=sqrt_diag_cov), num_steps_per_traj * calls_per_integrator_step(integrator_type), info.acceptance_rate.mean(), None, jnp.array([0]) return s @@ -175,8 +175,7 @@ def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.av print(jnp.nanmean(params.step_size,axis=0), jnp.nanmean(params.L,axis=0)) except: pass - full = lambda arr : err(model.E_x2, model.Var_x2, contract)(arr) - err_t = pvmap(full)(ex2_empirical) + err_t = pvmap(err(model.E_x2, model.Var_x2, contract))(ex2_empirical) # outs = [calculate_ess(b, grad_evals_per_step=avg_grad_calls_per_traj) for b in err_t] # # print(outs[:10]) @@ -199,6 +198,42 @@ def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.av esses, grad_calls, _ = calculate_ess(err_t_median, grad_evals_per_step=avg_grad_calls_per_traj) return esses, grad_calls, params, jnp.mean(acceptance_rate, axis=0), step_size_over_da +def benchmark_no_chains(model, sampler, key, n=10000, contract = jnp.average,): + + + d = get_num_latents(model) + key, init_key = jax.random.split(key, 2) + init_pos = model.sample_init(init_key) # [batch_size, dim_model] + + ex2_empirical, params, grad_calls_per_traj, acceptance_rate, step_size_over_da, final_da = sampler(logdensity_fn=model.logdensity_fn, num_steps=n, initial_position=init_pos,transform= model.transform, key=key) + avg_grad_calls_per_traj = grad_calls_per_traj + try: + print(jnp.nanmean(params.step_size,axis=0), jnp.nanmean(params.L,axis=0)) + except: pass + + err_t = (err(model.E_x2, model.Var_x2, contract))(ex2_empirical) + + # outs = [calculate_ess(b, grad_evals_per_step=avg_grad_calls_per_traj) for b in err_t] + # # print(outs[:10]) + # esses = [i[0].item() for i in outs if not math.isnan(i[0].item())] + # grad_calls = [i[1].item() for i in outs if not math.isnan(i[1].item())] + # return(mean(esses), mean(grad_calls)) + # print(final_da.mean(), "final da") + + + + err_t_median = err_t # jnp.median(err_t, axis=0) + # import matplotlib.pyplot as plt + # plt.plot(np.arange(1, 1+ len(err_t_median))* 2, err_t_median, color= 'teal', lw = 3) + # plt.xlabel('gradient evaluations') + # plt.ylabel('average second moment error') + # plt.xscale('log') + # plt.yscale('log') + # plt.savefig('brownian.png') + # plt.close() + esses, grad_calls, _ = calculate_ess(err_t_median, grad_evals_per_step=avg_grad_calls_per_traj) + return esses, grad_calls, params, acceptance_rate, step_size_over_da + @@ -456,16 +491,14 @@ def benchmark_omelyan(batch_size): results = defaultdict(tuple) for variables in itertools.product( # ["adjusted_mclmc", "nuts", "mclmc", ], - ["mhmchmc"], [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(1000), 4)).astype(int)], # [StandardNormal(d) for d in np.ceil(np.logspace(np.log10(10), np.log10(10000), 5)).astype(int)], # models, # [velocity_verlet_coefficients, mclachlan_coefficients, yoshida_coefficients, omelyan_coefficients], - [mclachlan_coefficients, omelyan_coefficients], + ["mclachlan", "omelyan"], ): - - sampler, model, coefficients = variables + model, integrator_type = variables # num_chains = 1 + batch_size//model.ndims num_chains = batch_size @@ -486,7 +519,7 @@ def benchmark_omelyan(batch_size): kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( - integrator=generate_isokinetic_integrator(coefficients), + integrator=map_integrator_type_to_integrator['mclmc'][integrator_type], integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), sqrt_diag_cov=sqrt_diag_cov, )( @@ -504,29 +537,29 @@ def benchmark_omelyan(batch_size): num_steps=num_steps, state=initial_state, rng_key=tune_key, - target=target_acceptance_rate_of_order[integrator_order(coefficients)], + target=target_acceptance_rate_of_order[integrator_order(integrator_type)], frac_tune1=0.1, frac_tune2=0.1, # frac_tune3=0.1, diagonal_preconditioning=False ) - print(f"\nModel: {model.name,model.ndims}, Sampler: {sampler}\n Coefficients: {coefficients}\nNumber of chains {num_chains}",) + print(f"\nModel: {model.name,model.ndims}, Sampler: Adjusted MCLMC\n Integrator: {integrator_type}\nNumber of chains {num_chains}",) print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") # ess, grad_calls, _ , _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=coefficients, L=blackjax_mclmc_sampler_params.L, step_size=blackjax_mclmc_sampler_params.step_size, sqrt_diag_cov=1.),bench_key_pre_grid, n=num_steps, batch=num_chains, contract=jnp.average) # results[((model.name, model.ndims), sampler, (coefficients), "without grid search")] = (ess, grad_calls) - L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=coefficients, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) + L, step_size, converged = gridsearch_tune(grid_key, iterations=10, contract=jnp.average, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) print(f"params after grid tuning are L={L}, step_size={step_size}") - ess, grad_calls, _ , _, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=coefficients, L=L, step_size=step_size, sqrt_diag_cov=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) + ess, grad_calls, _ , _, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, L=L, step_size=step_size, sqrt_diag_cov=1., initial_state=state),bench_key, n=num_steps, batch=num_chains, contract=jnp.average) print(f"grads to low bias: {grad_calls}") - results[(model.name, model.ndims, sampler, (coefficients), converged, L.item(), step_size.item())] = ess.item() + results[(model.name, model.ndims, "mclmc", (integrator_type), converged, L.item(), step_size.item())] = ess.item() df = pd.Series(results).reset_index() df.columns = ["model", "dims", "sampler", "integrator", "convergence", "L", "step_size", "ESS"] @@ -552,9 +585,7 @@ def run_benchmarks_simple(): for preconditioning in [False]: - ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning, - # target_acc_rate=0.95, frac_tune3=0.1 - ),key1, n=num_steps, batch=num_chains, contract=contract) + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning,),key1, n=num_steps, contract=contract, batch=num_chains,) print(f"Effective Sample Size (ESS) of {model.name} with preconditioning set to {preconditioning} is {ess}") @@ -563,11 +594,11 @@ def run_benchmarks_simple(): run_benchmarks_simple() + # benchmark_omelyan(128) # benchmark_mhmchmc(batch_size=1) # run_benchmarks(128) # run_benchmarks_step_size(128) - # benchmark_omelyan(128) # run_benchmarks(128) # benchmark_omelyan(10) # print("4") From a41090e794d88fa2003df12e8b4a45d80ed718dc Mon Sep 17 00:00:00 2001 From: = Date: Sun, 2 Jun 2024 08:17:18 -0400 Subject: [PATCH 09/21] Bad results for adjusted mclmc with ICG --- benchmarks/mcmc/benchmark.py | 14 +++++++------- benchmarks/mcmc/sampling_algorithms.py | 4 ++-- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 42b3eb2..0a2a779 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -570,22 +570,22 @@ def benchmark_omelyan(batch_size): def run_benchmarks_simple(): - # sampler = run_adjusted_mclmc - sampler = run_mclmc - # model = IllConditionedGaussian(10,100) + sampler = run_adjusted_mclmc + # sampler = run_mclmc + model = IllConditionedGaussian(100,100) # model = Brownian() # model = StandardNormal(10) - model = Banana() + # model = Banana() integrator_type = "mclachlan" contract = jnp.max # how we average across dimensions - num_steps = 1000 + num_steps = 10000 num_chains = 1 for i in range(1): key1 = jax.random.PRNGKey(i) - for preconditioning in [False]: + for preconditioning in [True, False]: - ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning,),key1, n=num_steps, contract=contract, batch=num_chains,) + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning,target_acc_rate=0.9),key1, n=num_steps, contract=contract, batch=num_chains) print(f"Effective Sample Size (ESS) of {model.name} with preconditioning set to {preconditioning} is {ess}") diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index ef18418..86fef4f 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -216,7 +216,7 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans # ex = expectations[-1][1] # var = expectations[:, 0] - ex[:, 1]**2 - jax.debug.print("expectation {x}", x=expectations[-1]) + # jax.debug.print("expectation {x}", x=expectations[-1]) _, samples, _ = run_inference_algorithm( rng_key=run_key, @@ -232,7 +232,7 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans # print(samples.mean(axis=0)) - jax.debug.print("samples {x}", x=(samples**2).mean(axis=0)) + # jax.debug.print("samples {x}", x=(samples**2).mean(axis=0)) # jax.debug.print("samples {x}", x=cumulative_avg(samples)) # jax.debug.print("samples {x}", x=jnp.var(samples, axis=0)) # jax.debug.print("comparison {x}", x=jnp.mean(expectations)) From 54bbc0b4b0e5cf9f7f5f97a31002c866b0688a21 Mon Sep 17 00:00:00 2001 From: = Date: Mon, 3 Jun 2024 16:19:31 -0400 Subject: [PATCH 10/21] fixed bug --- benchmarks/mcmc/benchmark.py | 145 +- benchmarks/mcmc/results.ipynb | 2850 +++++++++++++++++------- benchmarks/mcmc/sampling_algorithms.py | 37 +- 3 files changed, 2119 insertions(+), 913 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 0a2a779..39900f6 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -148,16 +148,6 @@ def s(logdensity_fn, num_steps, initial_position, transform, key): def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.average,): pvmap = jax.pmap - - # def pvmap(f): - # def f(arr): - # return arr - # print(arr.shape,"shape") - # print(arr) - # arr = arr.reshape(128, -1) - # out = jax.vmap(jax.vmap(f), in_axes=0)(arr) - # return out.flatten() - # return f d = get_num_latents(model) if batch is None: @@ -168,33 +158,17 @@ def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.av init_keys = jax.random.split(init_key, batch) init_pos = pvmap(model.sample_init)(init_keys) # [batch_size, dim_model] - # samples, params, avg_num_steps_per_traj = jax.pmap(lambda pos, key: sampler(model.logdensity_fn, n, pos, model.transform, key))(init_pos, keys) ex2_empirical, params, grad_calls_per_traj, acceptance_rate, step_size_over_da, final_da = pvmap(lambda pos, key: sampler(logdensity_fn=model.logdensity_fn, num_steps=n, initial_position= pos,transform= model.transform, key=key))(init_pos, keys) avg_grad_calls_per_traj = jnp.nanmean(grad_calls_per_traj, axis=0) try: print(jnp.nanmean(params.step_size,axis=0), jnp.nanmean(params.L,axis=0)) except: pass - err_t = pvmap(err(model.E_x2, model.Var_x2, contract))(ex2_empirical) - - # outs = [calculate_ess(b, grad_evals_per_step=avg_grad_calls_per_traj) for b in err_t] - # # print(outs[:10]) - # esses = [i[0].item() for i in outs if not math.isnan(i[0].item())] - # grad_calls = [i[1].item() for i in outs if not math.isnan(i[1].item())] - # return(mean(esses), mean(grad_calls)) - # print(final_da.mean(), "final da") - - + full = lambda x : err(model.E_x2, model.Var_x2, contract)(cumulative_avg(x**2)) + # err_t = pvmap(err(model.E_x2, model.Var_x2, contract))(ex2_empirical) + err_t = pvmap(full)(ex2_empirical) err_t_median = jnp.median(err_t, axis=0) - # import matplotlib.pyplot as plt - # plt.plot(np.arange(1, 1+ len(err_t_median))* 2, err_t_median, color= 'teal', lw = 3) - # plt.xlabel('gradient evaluations') - # plt.ylabel('average second moment error') - # plt.xscale('log') - # plt.yscale('log') - # plt.savefig('brownian.png') - # plt.close() esses, grad_calls, _ = calculate_ess(err_t_median, grad_evals_per_step=avg_grad_calls_per_traj) return esses, grad_calls, params, jnp.mean(acceptance_rate, axis=0), step_size_over_da @@ -382,89 +356,88 @@ def benchmark_mhmchmc(batch_size): n=num_steps, batch=num_chains, contract=contract) - results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning)] = ess.item() + results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0)] = ess.item() print(f'mclmc with tuning ESS {ess}') ####### run adjusted_mclmc with standard tuning - for target_acc_rate in [0.65, 0.9]: + for target_acc_rate, L_proposal_factor in itertools.product([0.65, 0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): # coeffs = mclachlan_coefficients ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), key1, n=num_steps, batch=num_chains, contract=contract) - results[(model.name, model.ndims, "mhmchmc"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() print(f'adjusted_mclmc with tuning ESS {ess}') # integrator_type = mclachlan_coefficients ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), key1, n=num_steps, batch=num_chains, contract=contract) - results[(model.name, model.ndims, "mhmchmc:st3"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() print(f'adjusted_mclmc with tuning ESS {ess}') - if False: - ####### run adjusted_mclmc with standard tuning + grid search + # if False: + # ####### run adjusted_mclmc with standard tuning + grid search - init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) - initial_position = model.sample_init(init_pos_key) + # init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) + # initial_position = model.sample_init(init_pos_key) - initial_state = blackjax.mcmc.adjusted_mclmc.init( - position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key - ) + # initial_state = blackjax.mcmc.adjusted_mclmc.init( + # position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key + # ) - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( - integrator=generate_isokinetic_integrator(integrator_type), - integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), - sqrt_diag_cov=sqrt_diag_cov, - )( - rng_key=rng_key, - state=state, - step_size=step_size, - logdensity_fn=model.logdensity_fn) + # kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( + # integrator=generate_isokinetic_integrator(integrator_type), + # integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), + # sqrt_diag_cov=sqrt_diag_cov, + # )( + # rng_key=rng_key, + # state=state, + # step_size=step_size, + # logdensity_fn=model.logdensity_fn) - ( - state, - blackjax_adjusted_mclmc_sampler_params, - _, _ - ) = blackjax.adaptation.mclmc_adaptation.adjusted_mclmc_find_L_and_step_size( - mclmc_kernel=kernel, - num_steps=num_steps, - state=initial_state, - rng_key=tune_key, - target=target_acceptance_rate_of_order[integrator_order(integrator_type)], - frac_tune1=0.1, - frac_tune2=0.1, - frac_tune3=0.0, - diagonal_preconditioning=False - ) + # ( + # state, + # blackjax_adjusted_mclmc_sampler_params, + # _, _ + # ) = blackjax.adaptation.mclmc_adaptation.adjusted_mclmc_find_L_and_step_size( + # mclmc_kernel=kernel, + # num_steps=num_steps, + # state=initial_state, + # rng_key=tune_key, + # target=target_acceptance_rate_of_order[integrator_order(integrator_type)], + # frac_tune1=0.1, + # frac_tune2=0.1, + # frac_tune3=0.0, + # diagonal_preconditioning=False + # ) - print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") - print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") + # print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") + # print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) - # print(f"params after grid tuning are L={L}, step_size={step_size}") + # L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) + # # print(f"params after grid tuning are L={L}, step_size={step_size}") - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, L=L, step_size=step_size, initial_state=state, sqrt_diag_cov=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) + # ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, L=L, step_size=step_size, initial_state=state, sqrt_diag_cov=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) - print(f"grads to low bias: {grad_calls}") + # print(f"grads to low bias: {grad_calls}") - results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), (integrator_type), f"gridsearch:{convergence}", acceptance_rate.mean().item()), True] = ess.item() + # results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), (integrator_type), f"gridsearch:{convergence}", acceptance_rate.mean().item()), todo: blah?, todo: precond, todo: L_propsoal_factor] = ess.item() ####### run nuts - # integrator_type = velocity_verlet_coefficients ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) - results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning)] = ess.item() + results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0)] = ess.item() @@ -476,7 +449,7 @@ def benchmark_mhmchmc(batch_size): df = pd.Series(results).reset_index() - df.columns = ["model", "dims", "sampler", "L", "step_size", "integrator", "tuning", "acc_rate", "preconditioning", "ESS"] + df.columns = ["model", "dims", "sampler", "L", "step_size", "integrator", "tuning", "acc_rate", "preconditioning", "inv_L_prop", "ESS"] # df.result = df.result.apply(lambda x: x[0].item()) # df.model = df.model.apply(lambda x: x[1]) df.to_csv(f"results{model.name}.csv", index=False) @@ -570,22 +543,24 @@ def benchmark_omelyan(batch_size): def run_benchmarks_simple(): - sampler = run_adjusted_mclmc - # sampler = run_mclmc - model = IllConditionedGaussian(100,100) - # model = Brownian() + # sampler = run_adjusted_mclmc + sampler = run_mclmc + # model = IllConditionedGaussian(100,100) + model = Brownian() # model = StandardNormal(10) - # model = Banana() + model = Banana() integrator_type = "mclachlan" contract = jnp.max # how we average across dimensions - num_steps = 10000 + num_steps = 1000 num_chains = 1 for i in range(1): key1 = jax.random.PRNGKey(i) for preconditioning in [True, False]: - ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning,target_acc_rate=0.9),key1, n=num_steps, contract=contract, batch=num_chains) + ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning, + # target_acc_rate=0.9 + ),key1, n=num_steps, contract=contract, batch=num_chains) print(f"Effective Sample Size (ESS) of {model.name} with preconditioning set to {preconditioning} is {ess}") @@ -593,10 +568,10 @@ def run_benchmarks_simple(): if __name__ == "__main__": - run_benchmarks_simple() + # run_benchmarks_simple() # benchmark_omelyan(128) - # benchmark_mhmchmc(batch_size=1) + benchmark_mhmchmc(batch_size=50) # run_benchmarks(128) # run_benchmarks_step_size(128) # run_benchmarks(128) diff --git a/benchmarks/mcmc/results.ipynb b/benchmarks/mcmc/results.ipynb index 05d7a72..f81feb8 100644 --- a/benchmarks/mcmc/results.ipynb +++ b/benchmarks/mcmc/results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -1642,12 +1642,43 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "def load_results(model_name):\n", + "\n", + " # Load the data\n", + " results = pd.read_csv(f\"../../results{model_name}.csv\")\n", + " # results.drop(results['tuning'] == \"standard\", inplace=True)\n", + " # results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + " fig, ax = plt.subplots(figsize=(20, 5))\n", + "\n", + "\n", + " sns.barplot(data=results, x=\"sampler\", y=\"ESS\", hue=results[['integrator', 'preconditioning', 'inv_L_prop']].apply(\n", + " lambda row: f\"{row.integrator}, {row.preconditioning}, {row.inv_L_prop}\", axis=1), ax=ax)\n", + " plt.xlabel(\"Sampler\")\n", + " plt.ylabel(\"ESS\")\n", + " plt.title(model_name)\n", + " plt.show()\n", + " plt.show()\n", + " display(results)\n", + "\n", + "# load_results(\"IllConditionedGaussian\")\n", + "# load_results(\"Brownian\")\n", + "# load_results(\"Funnel\")\n", + "# load_results(\"Banana\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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    " ] @@ -1694,242 +1725,203 @@ " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 18.468103\n", - " 5.774344\n", + " 8.309327\n", + " 12.268694\n", " mclachlan\n", " standard\n", " 1.000000\n", " True\n", - " 0.010024\n", + " 0.051387\n", " \n", " \n", " 1\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc0.65\n", - " 14.662748\n", - " 7.481382\n", + " 14.655429\n", + " 13.323115\n", " mclachlan\n", " standard\n", - " 0.643603\n", + " 0.873793\n", " True\n", - " 0.004564\n", + " 0.032352\n", " \n", " \n", " 2\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.65\n", - " 18.950314\n", - " 7.599843\n", + " 10.257968\n", + " 9.325425\n", " mclachlan\n", " standard\n", - " 0.604179\n", + " 0.954357\n", " True\n", - " 0.002259\n", + " 0.024907\n", " \n", " \n", " 3\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc0.9\n", - " 14.755423\n", - " 5.910617\n", + " 14.752708\n", + " 12.240559\n", " mclachlan\n", " standard\n", - " 0.898358\n", + " 0.899165\n", " True\n", - " 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0.990514\n", " True\n", - " 0.004926\n", + " 0.004638\n", " \n", " \n", - " 12\n", + " 10\n", " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 14.369997\n", - " 5.799155\n", + " 14.317208\n", + " 5.771991\n", " mclachlan\n", " standard\n", " 1.000000\n", " False\n", - " 0.009833\n", + " 0.008307\n", " \n", " \n", - " 13\n", + " 11\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc0.65\n", - " 14.662748\n", - " 7.481382\n", + " 14.655429\n", + " 7.212010\n", " mclachlan\n", " standard\n", - " 0.643603\n", + " 0.703279\n", " False\n", - " 0.004564\n", + " 0.005243\n", " \n", " \n", - " 14\n", + " 12\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.65\n", - " 18.950314\n", - " 7.599843\n", + " 17.467278\n", + " 7.609488\n", " mclachlan\n", " standard\n", - " 0.604179\n", + " 0.615625\n", " False\n", - " 0.002259\n", + " 0.002627\n", " \n", " \n", - " 15\n", + " 13\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc0.9\n", - " 14.755423\n", - " 5.910617\n", + " 14.752708\n", + " 5.866860\n", " mclachlan\n", " standard\n", - " 0.898358\n", + " 0.904136\n", " False\n", - " 0.006402\n", + " 0.006782\n", " \n", " \n", - " 16\n", + " 14\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.9\n", - " 9.432498\n", - " 5.924881\n", - " mclachlan\n", - " standard\n", - " 0.903111\n", - " False\n", - " 0.003778\n", - " \n", - " \n", - " 17\n", - " IllConditionedGaussian\n", - " 100\n", - " nuts\n", - " 0.000000\n", - " 0.000000\n", + " 9.228044\n", + " 6.032185\n", " mclachlan\n", " standard\n", - " 0.856881\n", + " 0.892729\n", " False\n", - " 0.001570\n", + " 0.003679\n", " \n", " \n", - " 18\n", + " 15\n", " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 14.770721\n", - " 2.309608\n", + " 14.648232\n", + " 2.302675\n", " velocity_verlet\n", " standard\n", " 1.000000\n", @@ -1937,126 +1929,105 @@ " 0.000000\n", " \n", " \n", - " 19\n", + " 16\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc0.65\n", - " 14.741055\n", - " 3.956626\n", + " 14.753436\n", + " 4.070334\n", " velocity_verlet\n", " standard\n", - " 0.649220\n", + " 0.632075\n", " False\n", - " 0.005732\n", + " 0.005843\n", " \n", " \n", - " 20\n", + " 17\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.65\n", - " 10.072994\n", - " 3.852845\n", + " 10.386975\n", + " 3.740668\n", " velocity_verlet\n", " standard\n", - " 0.653373\n", + " 0.685698\n", " False\n", - " 0.002833\n", + " 0.003473\n", " \n", " \n", - " 21\n", + " 18\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc0.9\n", - " 14.707802\n", - " 2.171618\n", + " 14.733406\n", + " 2.228257\n", " velocity_verlet\n", " standard\n", - " 0.904677\n", + " 0.899280\n", " False\n", - " 0.005107\n", + " 0.004869\n", " \n", " \n", - " 22\n", + " 19\n", " IllConditionedGaussian\n", " 100\n", " mhmchmc:st30.9\n", - " 3.154816\n", - " 2.239549\n", + " 3.276532\n", + " 2.212389\n", " velocity_verlet\n", " standard\n", - " 0.904468\n", + " 0.904887\n", " False\n", " 0.000000\n", " \n", - " \n", - " 23\n", - " IllConditionedGaussian\n", - " 100\n", - " nuts\n", - " 0.000000\n", - " 0.000000\n", - " velocity_verlet\n", - " standard\n", - " 0.808706\n", - " False\n", - " 0.001562\n", - " \n", " \n", "\n", "" ], "text/plain": [ " model dims sampler L step_size \\\n", - "0 IllConditionedGaussian 100 mclmc 18.468103 5.774344 \n", - "1 IllConditionedGaussian 100 mhmchmc0.65 14.662748 7.481382 \n", - "2 IllConditionedGaussian 100 mhmchmc:st30.65 18.950314 7.599843 \n", - "3 IllConditionedGaussian 100 mhmchmc0.9 14.755423 5.910617 \n", - "4 IllConditionedGaussian 100 mhmchmc:st30.9 9.432498 5.924881 \n", - "5 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "6 IllConditionedGaussian 100 mclmc 18.710426 2.299914 \n", - "7 IllConditionedGaussian 100 mhmchmc0.65 14.741055 3.956626 \n", - "8 IllConditionedGaussian 100 mhmchmc:st30.65 10.072994 3.852845 \n", - "9 IllConditionedGaussian 100 mhmchmc0.9 14.707802 2.171618 \n", - "10 IllConditionedGaussian 100 mhmchmc:st30.9 3.154816 2.239549 \n", - "11 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "12 IllConditionedGaussian 100 mclmc 14.369997 5.799155 \n", - "13 IllConditionedGaussian 100 mhmchmc0.65 14.662748 7.481382 \n", - "14 IllConditionedGaussian 100 mhmchmc:st30.65 18.950314 7.599843 \n", - "15 IllConditionedGaussian 100 mhmchmc0.9 14.755423 5.910617 \n", - "16 IllConditionedGaussian 100 mhmchmc:st30.9 9.432498 5.924881 \n", - "17 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "18 IllConditionedGaussian 100 mclmc 14.770721 2.309608 \n", - "19 IllConditionedGaussian 100 mhmchmc0.65 14.741055 3.956626 \n", - "20 IllConditionedGaussian 100 mhmchmc:st30.65 10.072994 3.852845 \n", - "21 IllConditionedGaussian 100 mhmchmc0.9 14.707802 2.171618 \n", - "22 IllConditionedGaussian 100 mhmchmc:st30.9 3.154816 2.239549 \n", - "23 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "0 IllConditionedGaussian 100 mclmc 8.309327 12.268694 \n", + "1 IllConditionedGaussian 100 mhmchmc0.65 14.655429 13.323115 \n", + "2 IllConditionedGaussian 100 mhmchmc:st30.65 10.257968 9.325425 \n", + "3 IllConditionedGaussian 100 mhmchmc0.9 14.752708 12.240559 \n", + "4 IllConditionedGaussian 100 mhmchmc:st30.9 8.380649 7.618771 \n", + "5 IllConditionedGaussian 100 mclmc 9.752464 6.142936 \n", + "6 IllConditionedGaussian 100 mhmchmc0.65 14.753436 9.526176 \n", + "7 IllConditionedGaussian 100 mhmchmc:st30.65 13.073677 8.662420 \n", + "8 IllConditionedGaussian 100 mhmchmc0.9 14.733406 4.998178 \n", + "9 IllConditionedGaussian 100 mhmchmc:st30.9 3.019329 2.744845 \n", + "10 IllConditionedGaussian 100 mclmc 14.317208 5.771991 \n", + "11 IllConditionedGaussian 100 mhmchmc0.65 14.655429 7.212010 \n", + "12 IllConditionedGaussian 100 mhmchmc:st30.65 17.467278 7.609488 \n", + "13 IllConditionedGaussian 100 mhmchmc0.9 14.752708 5.866860 \n", + "14 IllConditionedGaussian 100 mhmchmc:st30.9 9.228044 6.032185 \n", + "15 IllConditionedGaussian 100 mclmc 14.648232 2.302675 \n", + "16 IllConditionedGaussian 100 mhmchmc0.65 14.753436 4.070334 \n", + "17 IllConditionedGaussian 100 mhmchmc:st30.65 10.386975 3.740668 \n", + "18 IllConditionedGaussian 100 mhmchmc0.9 14.733406 2.228257 \n", + "19 IllConditionedGaussian 100 mhmchmc:st30.9 3.276532 2.212389 \n", "\n", " integrator tuning acc_rate preconditioning ESS \n", - "0 mclachlan standard 1.000000 True 0.010024 \n", - "1 mclachlan standard 0.643603 True 0.004564 \n", - "2 mclachlan standard 0.604179 True 0.002259 \n", - "3 mclachlan standard 0.898358 True 0.006402 \n", - "4 mclachlan standard 0.903111 True 0.003778 \n", - "5 mclachlan standard 0.985327 True 0.003154 \n", - "6 velocity_verlet standard 1.000000 True 0.000000 \n", - "7 velocity_verlet standard 0.649220 True 0.005732 \n", - "8 velocity_verlet standard 0.653373 True 0.002833 \n", - "9 velocity_verlet standard 0.904677 True 0.005107 \n", - "10 velocity_verlet standard 0.904468 True 0.000000 \n", - "11 velocity_verlet standard 0.854327 True 0.004926 \n", - "12 mclachlan standard 1.000000 False 0.009833 \n", - "13 mclachlan standard 0.643603 False 0.004564 \n", - "14 mclachlan standard 0.604179 False 0.002259 \n", - "15 mclachlan standard 0.898358 False 0.006402 \n", - "16 mclachlan standard 0.903111 False 0.003778 \n", - "17 mclachlan standard 0.856881 False 0.001570 \n", - "18 velocity_verlet standard 1.000000 False 0.000000 \n", - "19 velocity_verlet standard 0.649220 False 0.005732 \n", - "20 velocity_verlet standard 0.653373 False 0.002833 \n", - "21 velocity_verlet standard 0.904677 False 0.005107 \n", - "22 velocity_verlet standard 0.904468 False 0.000000 \n", - "23 velocity_verlet standard 0.808706 False 0.001562 " + "0 mclachlan standard 1.000000 True 0.051387 \n", + "1 mclachlan standard 0.873793 True 0.032352 \n", + "2 mclachlan standard 0.954357 True 0.024907 \n", + "3 mclachlan standard 0.899165 True 0.024228 \n", + "4 mclachlan standard 0.976078 True 0.019971 \n", + "5 velocity_verlet standard 1.000000 True 0.026015 \n", + "6 velocity_verlet standard 0.649238 True 0.023415 \n", + "7 velocity_verlet standard 0.733441 True 0.021976 \n", + "8 velocity_verlet standard 0.906197 True 0.018356 \n", + "9 velocity_verlet standard 0.990514 True 0.004638 \n", + "10 mclachlan standard 1.000000 False 0.008307 \n", + "11 mclachlan standard 0.703279 False 0.005243 \n", + "12 mclachlan standard 0.615625 False 0.002627 \n", + "13 mclachlan standard 0.904136 False 0.006782 \n", + "14 mclachlan standard 0.892729 False 0.003679 \n", + "15 velocity_verlet standard 1.000000 False 0.000000 \n", + "16 velocity_verlet standard 0.632075 False 0.005843 \n", + "17 velocity_verlet standard 0.685698 False 0.003473 \n", + "18 velocity_verlet standard 0.899280 False 0.004869 \n", + "19 velocity_verlet standard 0.904887 False 0.000000 " ] }, "metadata": {}, @@ -2064,39 +2035,19 @@ } ], "source": [ - "def load_results(model_name):\n", - "\n", - " # Load the data\n", - " results = pd.read_csv(f\"../../results{model_name}.csv\")\n", - " # results.drop(results['tuning'] == \"standard\", inplace=True)\n", - " # results = results.drop(results[results['tuning'] != 'standard'].index)\n", - "\n", - " fig, ax = plt.subplots(figsize=(20, 5))\n", - "\n", - "\n", - " sns.barplot(data=results, x=\"sampler\", y=\"ESS\", hue=results[['integrator', 'preconditioning']].apply(\n", - " lambda row: f\"{row.integrator}, {row.preconditioning}\", axis=1), ax=ax)\n", - " plt.xlabel(\"Sampler\")\n", - " plt.ylabel(\"ESS\")\n", - " plt.title(model_name)\n", - " plt.show()\n", - " plt.show()\n", - " display(results)\n", - "\n", - "load_results(\"IllConditionedGaussian\")\n", "# load_results(\"Brownian\")\n", - "# load_results(\"Funnel\")\n", - "# load_results(\"Banana\")\n" + "load_results(\"IllConditionedGaussian\")\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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    " ] @@ -2134,6 +2085,7 @@ " tuning\n", " acc_rate\n", " preconditioning\n", + " inv_L_prop\n", " ESS\n", " \n", " \n", @@ -2143,369 +2095,585 @@ " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 18.050465\n", - " 5.719695\n", + " 8.309327\n", + " 12.268694\n", " mclachlan\n", " standard\n", " 1.000000\n", " True\n", - " 0.004053\n", + " 0.000000\n", + " 0.048828\n", " \n", " \n", " 1\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc0.65\n", - " 14.674129\n", - " 7.743088\n", + " mhmclmc:0.65\n", + " 14.655429\n", + " 13.323115\n", " mclachlan\n", " standard\n", - " 0.601209\n", + " 0.873889\n", " True\n", - " 0.004313\n", + " 0.000000\n", + " 0.030122\n", " \n", " \n", " 2\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc:st30.65\n", - " 29.348257\n", - " 6.619222\n", + " mhmclmc:st3:0.65\n", + " 10.257968\n", + " 9.325425\n", " mclachlan\n", " standard\n", - " 0.836988\n", + " 0.954066\n", " True\n", - " 0.003568\n", + " 0.000000\n", + " 0.025422\n", " \n", " \n", " 3\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc0.9\n", - " 14.697281\n", - " 6.304248\n", + " mhmclmc:0.65\n", + " 14.656278\n", + " 11.350197\n", " mclachlan\n", " standard\n", - " 0.880382\n", + " 0.606376\n", " True\n", - " 0.007092\n", + " 0.666667\n", + " 0.019041\n", " \n", " \n", " 4\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc:st30.9\n", - " 10.759020\n", - " 5.668786\n", + " mhmclmc:st3:0.65\n", + " 19.000410\n", + " 11.380119\n", " mclachlan\n", " standard\n", - " 0.928205\n", + " 0.816244\n", " True\n", - " 0.004049\n", + " 0.666667\n", + " 0.019784\n", " \n", " \n", " 5\n", " IllConditionedGaussian\n", " 100\n", - " nuts\n", - " 0.000000\n", - " 0.000000\n", + " mhmclmc:0.9\n", + " 14.752708\n", + " 12.240559\n", " mclachlan\n", " standard\n", - " 0.988024\n", + " 0.899333\n", " True\n", - " 0.005307\n", + " 0.000000\n", + " 0.028272\n", " \n", " \n", " 6\n", " IllConditionedGaussian\n", " 100\n", - " mclmc\n", - " 18.247068\n", - " 2.349995\n", - " velocity_verlet\n", + " mhmclmc:st3:0.9\n", + " 8.380649\n", + " 7.618771\n", + " mclachlan\n", " standard\n", - " 1.000000\n", + " 0.976059\n", " True\n", " 0.000000\n", + " 0.020202\n", " \n", " \n", " 7\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc0.65\n", - " 14.758156\n", - " 3.678629\n", - " velocity_verlet\n", + " mhmclmc:0.9\n", + " 14.729240\n", + " 9.893730\n", + " mclachlan\n", " standard\n", - " 0.710312\n", + " 0.908717\n", " True\n", - " 0.002856\n", + " 0.666667\n", + " 0.024348\n", " \n", " \n", " 8\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc:st30.65\n", - " 7.597509\n", - " 4.147607\n", - " velocity_verlet\n", + " mhmclmc:st3:0.9\n", + " 12.312991\n", + " 10.154453\n", + " mclachlan\n", " standard\n", - " 0.610031\n", + " 0.948934\n", " True\n", - " 0.002780\n", + " 0.666667\n", + " 0.027207\n", " \n", " \n", " 9\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc0.9\n", - " 14.752744\n", - " 2.114635\n", + " mclmc\n", + " 9.752464\n", + " 6.142936\n", " velocity_verlet\n", " standard\n", - " 0.910939\n", + " 1.000000\n", " True\n", - " 0.005543\n", + " 0.000000\n", + " 0.024540\n", " \n", " \n", " 10\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc:st30.9\n", - " 3.174317\n", - " 2.248339\n", + " mhmclmc:0.65\n", + " 14.753436\n", + " 9.526176\n", " velocity_verlet\n", " standard\n", - " 0.902583\n", + " 0.648979\n", " True\n", " 0.000000\n", + " 0.025432\n", " \n", " \n", " 11\n", " IllConditionedGaussian\n", " 100\n", - " nuts\n", + " mhmclmc:st3:0.65\n", + " 13.073677\n", + " 8.662420\n", + " velocity_verlet\n", + " standard\n", + " 0.734258\n", + " True\n", " 0.000000\n", + " 0.022798\n", + " \n", + " \n", + " 12\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.65\n", + " 14.618320\n", + " 9.551996\n", + " velocity_verlet\n", + " standard\n", + " 0.645185\n", + " True\n", + " 0.666667\n", + " 0.029909\n", + " \n", + " \n", + " 13\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.65\n", + " 17.527987\n", + " 9.884011\n", + " velocity_verlet\n", + " standard\n", + " 0.638346\n", + " True\n", + " 0.666667\n", + " 0.028783\n", + " \n", + " \n", + " 14\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.9\n", + " 14.733406\n", + " 4.998178\n", + " velocity_verlet\n", + " standard\n", + " 0.906246\n", + " True\n", " 0.000000\n", + " 0.017536\n", + " \n", + " \n", + " 15\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.9\n", + " 3.019329\n", + " 2.744845\n", " velocity_verlet\n", " standard\n", - " 0.863561\n", + " 0.990493\n", " True\n", - " 0.005883\n", + " 0.000000\n", + " 0.005065\n", " \n", " \n", - " 12\n", + " 16\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.9\n", + " 14.613172\n", + " 5.804608\n", + " velocity_verlet\n", + " standard\n", + " 0.911108\n", + " True\n", + " 0.666667\n", + " 0.016192\n", + " \n", + " \n", + " 17\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.9\n", + " 12.414684\n", + " 6.170126\n", + " velocity_verlet\n", + " standard\n", + " 0.900351\n", + " True\n", + " 0.666667\n", + " 0.020573\n", + " \n", + " \n", + " 18\n", " IllConditionedGaussian\n", " 100\n", " mclmc\n", - " 14.104933\n", - " 5.758576\n", + " 14.317208\n", + " 5.771991\n", " mclachlan\n", " standard\n", " 1.000000\n", " False\n", - " 0.004044\n", + " 0.000000\n", + " 0.010140\n", " \n", " \n", - " 13\n", + " 19\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc0.65\n", - " 14.674129\n", - " 7.743088\n", + " mhmclmc:0.65\n", + " 14.655429\n", + " 7.212010\n", " mclachlan\n", " standard\n", - " 0.601209\n", + " 0.703607\n", " False\n", - " 0.004313\n", + " 0.000000\n", + " 0.004859\n", " \n", " \n", - " 14\n", + " 20\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc:st30.65\n", - " 29.348257\n", - " 6.619222\n", + " mhmclmc:st3:0.65\n", + " 17.467278\n", + " 7.609488\n", " mclachlan\n", " standard\n", - " 0.836988\n", + " 0.614287\n", " False\n", - " 0.003568\n", + " 0.000000\n", + " 0.002131\n", " \n", " \n", - " 15\n", + " 21\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc0.9\n", - " 14.697281\n", - " 6.304248\n", + " mhmclmc:0.65\n", + " 14.656278\n", + " 7.489786\n", " mclachlan\n", " standard\n", - " 0.880382\n", + " 0.651941\n", " False\n", - " 0.007092\n", + " 0.666667\n", + " 0.002102\n", " \n", " \n", - " 16\n", + " 22\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc:st30.9\n", - " 10.759020\n", - " 5.668786\n", + " mhmclmc:st3:0.65\n", + " 29.249174\n", + " 7.194617\n", " mclachlan\n", " standard\n", - " 0.928205\n", + " 0.669520\n", " False\n", - " 0.004049\n", + " 0.666667\n", + " 0.001874\n", " \n", " \n", - " 17\n", + " 23\n", " IllConditionedGaussian\n", " 100\n", - " nuts\n", - " 0.000000\n", - " 0.000000\n", + " mhmclmc:0.9\n", + " 14.752708\n", + " 5.866860\n", " mclachlan\n", " standard\n", - " 0.851473\n", + " 0.904467\n", " False\n", - " 0.001451\n", + " 0.000000\n", + " 0.006679\n", " \n", " \n", - " 18\n", + " 24\n", " IllConditionedGaussian\n", " 100\n", - " mclmc\n", - " 14.502944\n", - " 2.275518\n", - " velocity_verlet\n", + " mhmclmc:st3:0.9\n", + " 9.228044\n", + " 6.032185\n", + " mclachlan\n", " standard\n", - " 1.000000\n", + " 0.892275\n", " False\n", " 0.000000\n", + " 0.003878\n", " \n", " \n", - " 19\n", + " 25\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc0.65\n", - " 14.758156\n", - " 3.678629\n", - " velocity_verlet\n", + " mhmclmc:0.9\n", + " 14.729240\n", + " 5.806119\n", + " mclachlan\n", " standard\n", - " 0.710312\n", + " 0.902280\n", " False\n", - " 0.002856\n", + " 0.666667\n", + " 0.002210\n", " \n", " \n", - " 20\n", + " 26\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc:st30.65\n", - " 7.597509\n", - " 4.147607\n", - " velocity_verlet\n", + " mhmclmc:st3:0.9\n", + " 27.102215\n", + " 5.608980\n", + " mclachlan\n", " standard\n", - " 0.610031\n", + " 0.911349\n", " False\n", - " 0.002780\n", + " 0.666667\n", + " 0.002055\n", " \n", " \n", - " 21\n", + " 27\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc0.9\n", - " 14.752744\n", - " 2.114635\n", + " mclmc\n", + " 14.648232\n", + " 2.302675\n", " velocity_verlet\n", " standard\n", - " 0.910939\n", + " 1.000000\n", " False\n", - " 0.005543\n", + " 0.000000\n", + " 0.000000\n", " \n", " \n", - " 22\n", + " 28\n", " IllConditionedGaussian\n", " 100\n", - " mhmchmc:st30.9\n", - " 3.174317\n", - " 2.248339\n", + " mhmclmc:0.65\n", + " 14.753436\n", + " 4.070334\n", " velocity_verlet\n", " standard\n", - " 0.902583\n", + " 0.633077\n", " False\n", " 0.000000\n", + " 0.005887\n", " \n", " \n", - " 23\n", + " 29\n", " IllConditionedGaussian\n", " 100\n", - " nuts\n", - " 0.000000\n", + " mhmclmc:st3:0.65\n", + " 10.386975\n", + " 3.740668\n", + " velocity_verlet\n", + " standard\n", + " 0.686530\n", + " False\n", " 0.000000\n", + " 0.003768\n", + " \n", + " \n", + " 30\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.65\n", + " 14.618320\n", + " 3.612904\n", " velocity_verlet\n", " standard\n", - " 0.814160\n", + " 0.649769\n", " False\n", - " 0.001519\n", + " 0.666667\n", + " 0.001615\n", + " \n", + " \n", + " 31\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.65\n", + " 26.230806\n", + " 3.521540\n", + " velocity_verlet\n", + " standard\n", + " 0.653828\n", + " False\n", + " 0.666667\n", + " 0.001735\n", + " \n", + " \n", + " 32\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.9\n", + " 14.733406\n", + " 2.228257\n", + " velocity_verlet\n", + " standard\n", + " 0.899157\n", + " False\n", + " 0.000000\n", + " 0.005075\n", + " \n", + " \n", + " 33\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.9\n", + " 3.276532\n", + " 2.212389\n", + " velocity_verlet\n", + " standard\n", + " 0.904900\n", + " False\n", + " 0.000000\n", + " 0.000000\n", + " \n", + " \n", + " 34\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.9\n", + " 14.613172\n", + " 1.993377\n", + " velocity_verlet\n", + " standard\n", + " 0.902151\n", + " False\n", + " 0.666667\n", + " 0.002328\n", + " \n", + " \n", + " 35\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.9\n", + " 6.205173\n", + " 2.062942\n", + " velocity_verlet\n", + " standard\n", + " 0.906999\n", + " False\n", + " 0.666667\n", + " 0.000000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " model dims sampler L step_size \\\n", - "0 IllConditionedGaussian 100 mclmc 18.050465 5.719695 \n", - "1 IllConditionedGaussian 100 mhmchmc0.65 14.674129 7.743088 \n", - "2 IllConditionedGaussian 100 mhmchmc:st30.65 29.348257 6.619222 \n", - "3 IllConditionedGaussian 100 mhmchmc0.9 14.697281 6.304248 \n", - "4 IllConditionedGaussian 100 mhmchmc:st30.9 10.759020 5.668786 \n", - "5 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "6 IllConditionedGaussian 100 mclmc 18.247068 2.349995 \n", - "7 IllConditionedGaussian 100 mhmchmc0.65 14.758156 3.678629 \n", - "8 IllConditionedGaussian 100 mhmchmc:st30.65 7.597509 4.147607 \n", - "9 IllConditionedGaussian 100 mhmchmc0.9 14.752744 2.114635 \n", - "10 IllConditionedGaussian 100 mhmchmc:st30.9 3.174317 2.248339 \n", - "11 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "12 IllConditionedGaussian 100 mclmc 14.104933 5.758576 \n", - "13 IllConditionedGaussian 100 mhmchmc0.65 14.674129 7.743088 \n", - "14 IllConditionedGaussian 100 mhmchmc:st30.65 29.348257 6.619222 \n", - "15 IllConditionedGaussian 100 mhmchmc0.9 14.697281 6.304248 \n", - "16 IllConditionedGaussian 100 mhmchmc:st30.9 10.759020 5.668786 \n", - "17 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", - "18 IllConditionedGaussian 100 mclmc 14.502944 2.275518 \n", - "19 IllConditionedGaussian 100 mhmchmc0.65 14.758156 3.678629 \n", - "20 IllConditionedGaussian 100 mhmchmc:st30.65 7.597509 4.147607 \n", - "21 IllConditionedGaussian 100 mhmchmc0.9 14.752744 2.114635 \n", - "22 IllConditionedGaussian 100 mhmchmc:st30.9 3.174317 2.248339 \n", - "23 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + " model dims sampler L step_size \\\n", + "0 IllConditionedGaussian 100 mclmc 8.309327 12.268694 \n", + "1 IllConditionedGaussian 100 mhmclmc:0.65 14.655429 13.323115 \n", + "2 IllConditionedGaussian 100 mhmclmc:st3:0.65 10.257968 9.325425 \n", + "3 IllConditionedGaussian 100 mhmclmc:0.65 14.656278 11.350197 \n", + "4 IllConditionedGaussian 100 mhmclmc:st3:0.65 19.000410 11.380119 \n", + "5 IllConditionedGaussian 100 mhmclmc:0.9 14.752708 12.240559 \n", + "6 IllConditionedGaussian 100 mhmclmc:st3:0.9 8.380649 7.618771 \n", + "7 IllConditionedGaussian 100 mhmclmc:0.9 14.729240 9.893730 \n", + "8 IllConditionedGaussian 100 mhmclmc:st3:0.9 12.312991 10.154453 \n", + "9 IllConditionedGaussian 100 mclmc 9.752464 6.142936 \n", + "10 IllConditionedGaussian 100 mhmclmc:0.65 14.753436 9.526176 \n", + "11 IllConditionedGaussian 100 mhmclmc:st3:0.65 13.073677 8.662420 \n", + "12 IllConditionedGaussian 100 mhmclmc:0.65 14.618320 9.551996 \n", + "13 IllConditionedGaussian 100 mhmclmc:st3:0.65 17.527987 9.884011 \n", + "14 IllConditionedGaussian 100 mhmclmc:0.9 14.733406 4.998178 \n", + "15 IllConditionedGaussian 100 mhmclmc:st3:0.9 3.019329 2.744845 \n", + "16 IllConditionedGaussian 100 mhmclmc:0.9 14.613172 5.804608 \n", + "17 IllConditionedGaussian 100 mhmclmc:st3:0.9 12.414684 6.170126 \n", + "18 IllConditionedGaussian 100 mclmc 14.317208 5.771991 \n", + "19 IllConditionedGaussian 100 mhmclmc:0.65 14.655429 7.212010 \n", + "20 IllConditionedGaussian 100 mhmclmc:st3:0.65 17.467278 7.609488 \n", + "21 IllConditionedGaussian 100 mhmclmc:0.65 14.656278 7.489786 \n", + "22 IllConditionedGaussian 100 mhmclmc:st3:0.65 29.249174 7.194617 \n", + "23 IllConditionedGaussian 100 mhmclmc:0.9 14.752708 5.866860 \n", + "24 IllConditionedGaussian 100 mhmclmc:st3:0.9 9.228044 6.032185 \n", + "25 IllConditionedGaussian 100 mhmclmc:0.9 14.729240 5.806119 \n", + "26 IllConditionedGaussian 100 mhmclmc:st3:0.9 27.102215 5.608980 \n", + "27 IllConditionedGaussian 100 mclmc 14.648232 2.302675 \n", + "28 IllConditionedGaussian 100 mhmclmc:0.65 14.753436 4.070334 \n", + "29 IllConditionedGaussian 100 mhmclmc:st3:0.65 10.386975 3.740668 \n", + "30 IllConditionedGaussian 100 mhmclmc:0.65 14.618320 3.612904 \n", + "31 IllConditionedGaussian 100 mhmclmc:st3:0.65 26.230806 3.521540 \n", + "32 IllConditionedGaussian 100 mhmclmc:0.9 14.733406 2.228257 \n", + "33 IllConditionedGaussian 100 mhmclmc:st3:0.9 3.276532 2.212389 \n", + "34 IllConditionedGaussian 100 mhmclmc:0.9 14.613172 1.993377 \n", + "35 IllConditionedGaussian 100 mhmclmc:st3:0.9 6.205173 2.062942 \n", "\n", - " integrator tuning acc_rate preconditioning ESS \n", - "0 mclachlan standard 1.000000 True 0.004053 \n", - "1 mclachlan standard 0.601209 True 0.004313 \n", - "2 mclachlan standard 0.836988 True 0.003568 \n", - "3 mclachlan standard 0.880382 True 0.007092 \n", - "4 mclachlan standard 0.928205 True 0.004049 \n", - "5 mclachlan standard 0.988024 True 0.005307 \n", - "6 velocity_verlet standard 1.000000 True 0.000000 \n", - "7 velocity_verlet standard 0.710312 True 0.002856 \n", - "8 velocity_verlet standard 0.610031 True 0.002780 \n", - "9 velocity_verlet standard 0.910939 True 0.005543 \n", - "10 velocity_verlet standard 0.902583 True 0.000000 \n", - "11 velocity_verlet standard 0.863561 True 0.005883 \n", - "12 mclachlan standard 1.000000 False 0.004044 \n", - "13 mclachlan standard 0.601209 False 0.004313 \n", - "14 mclachlan standard 0.836988 False 0.003568 \n", - "15 mclachlan standard 0.880382 False 0.007092 \n", - "16 mclachlan standard 0.928205 False 0.004049 \n", - "17 mclachlan standard 0.851473 False 0.001451 \n", - "18 velocity_verlet standard 1.000000 False 0.000000 \n", - "19 velocity_verlet standard 0.710312 False 0.002856 \n", - "20 velocity_verlet standard 0.610031 False 0.002780 \n", - "21 velocity_verlet standard 0.910939 False 0.005543 \n", - "22 velocity_verlet standard 0.902583 False 0.000000 \n", - "23 velocity_verlet standard 0.814160 False 0.001519 " + " integrator tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 mclachlan standard 1.000000 True 0.000000 0.048828 \n", + "1 mclachlan standard 0.873889 True 0.000000 0.030122 \n", + "2 mclachlan standard 0.954066 True 0.000000 0.025422 \n", + "3 mclachlan standard 0.606376 True 0.666667 0.019041 \n", + "4 mclachlan standard 0.816244 True 0.666667 0.019784 \n", + "5 mclachlan standard 0.899333 True 0.000000 0.028272 \n", + "6 mclachlan standard 0.976059 True 0.000000 0.020202 \n", + "7 mclachlan standard 0.908717 True 0.666667 0.024348 \n", + "8 mclachlan standard 0.948934 True 0.666667 0.027207 \n", + "9 velocity_verlet standard 1.000000 True 0.000000 0.024540 \n", + "10 velocity_verlet standard 0.648979 True 0.000000 0.025432 \n", + "11 velocity_verlet standard 0.734258 True 0.000000 0.022798 \n", + "12 velocity_verlet standard 0.645185 True 0.666667 0.029909 \n", + "13 velocity_verlet standard 0.638346 True 0.666667 0.028783 \n", + "14 velocity_verlet standard 0.906246 True 0.000000 0.017536 \n", + "15 velocity_verlet standard 0.990493 True 0.000000 0.005065 \n", + "16 velocity_verlet standard 0.911108 True 0.666667 0.016192 \n", + "17 velocity_verlet standard 0.900351 True 0.666667 0.020573 \n", + "18 mclachlan standard 1.000000 False 0.000000 0.010140 \n", + "19 mclachlan standard 0.703607 False 0.000000 0.004859 \n", + "20 mclachlan standard 0.614287 False 0.000000 0.002131 \n", + "21 mclachlan standard 0.651941 False 0.666667 0.002102 \n", + "22 mclachlan standard 0.669520 False 0.666667 0.001874 \n", + "23 mclachlan standard 0.904467 False 0.000000 0.006679 \n", + "24 mclachlan standard 0.892275 False 0.000000 0.003878 \n", + "25 mclachlan standard 0.902280 False 0.666667 0.002210 \n", + "26 mclachlan standard 0.911349 False 0.666667 0.002055 \n", + "27 velocity_verlet standard 1.000000 False 0.000000 0.000000 \n", + "28 velocity_verlet standard 0.633077 False 0.000000 0.005887 \n", + "29 velocity_verlet standard 0.686530 False 0.000000 0.003768 \n", + "30 velocity_verlet standard 0.649769 False 0.666667 0.001615 \n", + "31 velocity_verlet standard 0.653828 False 0.666667 0.001735 \n", + "32 velocity_verlet standard 0.899157 False 0.000000 0.005075 \n", + "33 velocity_verlet standard 0.904900 False 0.000000 0.000000 \n", + "34 velocity_verlet standard 0.902151 False 0.666667 0.002328 \n", + "35 velocity_verlet standard 0.906999 False 0.666667 0.000000 " ] }, "metadata": {}, @@ -2513,17 +2681,19 @@ } ], "source": [ - "load_results(\"IllConditionedGaussian\")\n" + "# load_results(\"Brownian\")\n", + "load_results(\"IllConditionedGaussian\")\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
    " ] @@ -2561,6 +2731,7 @@ " tuning\n", " acc_rate\n", " preconditioning\n", + " inv_L_prop\n", " ESS\n", " \n", " \n", @@ -2570,146 +2741,381 @@ " Brownian\n", " 32\n", " mclmc\n", - " 2.442378\n", - " 0.302776\n", + " 14.911316\n", + " 1.780933\n", " mclachlan\n", " standard\n", " 1.000000\n", - " False\n", - " 0.010799\n", + " True\n", + " 0.0\n", + " 0.010693\n", " \n", " \n", " 1\n", " Brownian\n", " 32\n", - " mhmchmc0.65\n", - " 0.812042\n", - " 0.637599\n", + " mhmclmc:0.65\n", + " 0.813533\n", + " 0.739576\n", " mclachlan\n", " standard\n", - " 0.529587\n", - " False\n", - " 0.001431\n", + " 0.997997\n", + " True\n", + " 0.0\n", + " 0.000000\n", " \n", " \n", " 2\n", " Brownian\n", " 32\n", - " mhmchmc:st30.65\n", + " mhmclmc:st3:0.65\n", " 1.627066\n", - " 0.595371\n", + " 1.479151\n", " mclachlan\n", " standard\n", - " 0.523359\n", - " False\n", - " 0.001147\n", + " 0.982648\n", + " True\n", + " 0.0\n", + " 0.000816\n", " \n", " \n", " 3\n", " Brownian\n", " 32\n", - " mhmchmc0.9\n", - " 0.828702\n", - " 0.395384\n", + " mhmclmc:0.65\n", + " 0.813309\n", + " 0.739372\n", " mclachlan\n", " standard\n", - " 0.853865\n", - " False\n", - " 0.002489\n", + " 0.997409\n", + " True\n", + " 0.8\n", + " 0.000000\n", " \n", " \n", " 4\n", " Brownian\n", " 32\n", - " mhmchmc:st30.9\n", - " 1.610202\n", - " 0.342993\n", + " mhmclmc:st3:0.65\n", + " 1.626618\n", + " 1.478743\n", " mclachlan\n", " standard\n", - " 0.884686\n", - " False\n", - " 0.004979\n", + " 0.981794\n", + " True\n", + " 0.8\n", + " 0.000772\n", " \n", " \n", " 5\n", " Brownian\n", " 32\n", + " mhmclmc:0.65\n", + " 0.806923\n", + " 0.733566\n", + " mclachlan\n", + " standard\n", + " 0.997789\n", + " True\n", + " 2.0\n", + " 0.000000\n", + " \n", + " \n", + " 6\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.613846\n", + " 1.467133\n", + " mclachlan\n", + " standard\n", + " 0.981543\n", + " True\n", + " 2.0\n", + " 0.000769\n", + " \n", + " \n", + " 7\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.828702\n", + " 0.753365\n", + " mclachlan\n", + " standard\n", + " 0.997318\n", + " True\n", + " 0.0\n", + " 0.000000\n", + " \n", + " \n", + " 8\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.657404\n", + " 1.506731\n", + " mclachlan\n", + " standard\n", + " 0.980772\n", + " True\n", + " 0.0\n", + " 0.000869\n", + " \n", + " \n", + " 9\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.827290\n", + " 0.752082\n", + " mclachlan\n", + " standard\n", + " 0.997115\n", + " True\n", + " 0.8\n", + " 0.000000\n", + " \n", + " \n", + " 10\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.654580\n", + " 1.504164\n", + " mclachlan\n", + " standard\n", + " 0.980928\n", + " True\n", + " 0.8\n", + " 0.000824\n", + " \n", + " \n", + " 11\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.835939\n", + " 0.759945\n", + " mclachlan\n", + " standard\n", + " 0.997567\n", + " True\n", + " 2.0\n", + " 0.000000\n", + " \n", + " \n", + " 12\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.671879\n", + " 1.516725\n", + " mclachlan\n", + " standard\n", + " 0.979162\n", + " True\n", + " 2.0\n", + " 0.000785\n", + " \n", + " \n", + " 13\n", + " Brownian\n", + " 32\n", " nuts\n", " 0.000000\n", " 0.000000\n", " mclachlan\n", " standard\n", - " 0.760786\n", - " False\n", - " 0.002627\n", + " 0.922982\n", + " True\n", + " 0.0\n", + " 0.002499\n", " \n", " \n", - " 6\n", + " 14\n", " Brownian\n", " 32\n", " mclmc\n", - " 2.297048\n", - " 0.146238\n", + " 14.340859\n", + " 0.860062\n", " velocity_verlet\n", " standard\n", " 1.000000\n", - " False\n", - " 0.013497\n", + " True\n", + " 0.0\n", + " 0.010097\n", " \n", " \n", - " 7\n", + " 15\n", " Brownian\n", " 32\n", - " mhmchmc0.65\n", + " mhmclmc:0.65\n", " 0.813589\n", - " 0.376977\n", + " 0.739626\n", " velocity_verlet\n", " standard\n", - " 0.532756\n", - " False\n", - " 0.001877\n", + " 0.972154\n", + " True\n", + " 0.0\n", + " 0.000000\n", " \n", " \n", - " 8\n", + " 16\n", " Brownian\n", " 32\n", - " mhmchmc:st30.65\n", - " 1.626747\n", - " 0.331795\n", + " mhmclmc:st3:0.65\n", + " 1.627178\n", + " 1.479252\n", " velocity_verlet\n", " standard\n", - " 0.595688\n", - " False\n", - " 0.003162\n", + " 0.824719\n", + " True\n", + " 0.0\n", + " 0.001327\n", " \n", " \n", - " 9\n", + " 17\n", " Brownian\n", " 32\n", - " mhmchmc0.9\n", + " mhmclmc:0.65\n", + " 0.815981\n", + " 0.741801\n", + " velocity_verlet\n", + " standard\n", + " 0.972171\n", + " True\n", + " 0.8\n", + " 0.000000\n", + " \n", + " \n", + " 18\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.631963\n", + " 1.483602\n", + " velocity_verlet\n", + " standard\n", + " 0.820272\n", + " True\n", + " 0.8\n", + " 0.001171\n", + " \n", + " \n", + " 19\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.65\n", + " 0.815694\n", + " 0.741540\n", + " velocity_verlet\n", + " standard\n", + " 0.972795\n", + " True\n", + " 2.0\n", + " 0.000000\n", + " \n", + " \n", + " 20\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.631388\n", + " 1.483080\n", + " velocity_verlet\n", + " standard\n", + " 0.819445\n", + " True\n", + " 2.0\n", + " 0.001334\n", + " \n", + " \n", + " 21\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", " 0.811592\n", - " 0.185693\n", + " 0.737811\n", " velocity_verlet\n", " standard\n", - " 0.881163\n", - " False\n", - " 0.002693\n", + " 0.971989\n", + " True\n", + " 0.0\n", + " 0.000000\n", " \n", " \n", - " 10\n", + " 22\n", " Brownian\n", " 32\n", - " mhmchmc:st30.9\n", - " 1.127611\n", - " 0.166444\n", + " mhmclmc:st3:0.9\n", + " 1.623184\n", + " 1.256775\n", " velocity_verlet\n", " standard\n", - " 0.897115\n", - " False\n", - " 0.003417\n", + " 0.874309\n", + " True\n", + " 0.0\n", + " 0.001159\n", " \n", " \n", - " 11\n", + " 23\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.786659\n", + " 0.710999\n", + " velocity_verlet\n", + " standard\n", + " 0.932895\n", + " True\n", + " 0.8\n", + " 0.000000\n", + " \n", + " \n", + " 24\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.565477\n", + " 1.172345\n", + " velocity_verlet\n", + " standard\n", + " 0.842684\n", + " True\n", + " 0.8\n", + " 0.000011\n", + " \n", + " \n", + " 25\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.788416\n", + " 0.710871\n", + " velocity_verlet\n", + " standard\n", + " 0.911877\n", + " True\n", + " 2.0\n", + " 0.000000\n", + " \n", + " \n", + " 26\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.565834\n", + " 1.130701\n", + " velocity_verlet\n", + " standard\n", + " 0.826700\n", + " True\n", + " 2.0\n", + " 0.000008\n", + " \n", + " \n", + " 27\n", " Brownian\n", " 32\n", " nuts\n", @@ -2717,222 +3123,1017 @@ " 0.000000\n", " velocity_verlet\n", " standard\n", - " 0.779443\n", - " False\n", - " 0.003168\n", + " 0.873224\n", + " True\n", + " 0.0\n", + " 0.003463\n", + " \n", + " \n", + " 28\n", + " Brownian\n", + " 32\n", + " mclmc\n", + " 2.435902\n", + " 0.311698\n", + " mclachlan\n", + " standard\n", + " 1.000000\n", + " False\n", + " 0.0\n", + " 0.014269\n", + " \n", + " \n", + " 29\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.65\n", + " 0.812042\n", + " 0.637599\n", + " mclachlan\n", + " standard\n", + " 0.527286\n", + " False\n", + " 0.0\n", + " 0.001860\n", + " \n", + " \n", + " 30\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.627066\n", + " 0.595371\n", + " mclachlan\n", + " standard\n", + " 0.521462\n", + " False\n", + " 0.0\n", + " 0.001663\n", + " \n", + " \n", + " 31\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.65\n", + " 0.813400\n", + " 0.650853\n", + " mclachlan\n", + " standard\n", + " 0.513997\n", + " False\n", + " 0.8\n", + " 0.001090\n", + " \n", + " \n", + " 32\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.627030\n", + " 0.586531\n", + " mclachlan\n", + " standard\n", + " 0.530031\n", + " False\n", + " 0.8\n", + " 0.002307\n", + " \n", + " \n", + " 33\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.65\n", + " 0.807133\n", + " 0.659692\n", + " mclachlan\n", + " standard\n", + " 0.507236\n", + " False\n", + " 2.0\n", + " 0.001391\n", + " \n", + " \n", + " 34\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.613846\n", + " 0.617372\n", + " mclachlan\n", + " standard\n", + " 0.496971\n", + " False\n", + " 2.0\n", + " 0.001071\n", + " \n", + " \n", + " 35\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.828702\n", + " 0.395384\n", + " mclachlan\n", + " standard\n", + " 0.848784\n", + " False\n", + " 0.0\n", + " 0.003471\n", + " \n", + " \n", + " 36\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.610202\n", + " 0.342993\n", + " mclachlan\n", + " standard\n", + " 0.883302\n", + " False\n", + " 0.0\n", + " 0.005298\n", + " \n", + " \n", + " 37\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.827329\n", + " 0.403526\n", + " mclachlan\n", + " standard\n", + " 0.846071\n", + " False\n", + " 0.8\n", + " 0.003409\n", + " \n", + " \n", + " 38\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.646191\n", + " 0.347808\n", + " mclachlan\n", + " standard\n", + " 0.871342\n", + " False\n", + " 0.8\n", + " 0.005665\n", + " \n", + " \n", + " 39\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.835939\n", + " 0.402196\n", + " mclachlan\n", + " standard\n", + " 0.846171\n", + " False\n", + " 2.0\n", + " 0.002454\n", + " \n", + " \n", + " 40\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.637337\n", + " 0.338025\n", + " mclachlan\n", + " standard\n", + " 0.882995\n", + " False\n", + " 2.0\n", + " 0.005443\n", + " \n", + " \n", + " 41\n", + " Brownian\n", + " 32\n", + " nuts\n", + " 0.000000\n", + " 0.000000\n", + " mclachlan\n", + " standard\n", + " 0.761320\n", + " False\n", + " 0.0\n", + " 0.002027\n", + " \n", + " \n", + " 42\n", + " Brownian\n", + " 32\n", + " mclmc\n", + " 2.276252\n", + " 0.145409\n", + " velocity_verlet\n", + " standard\n", + " 1.000000\n", + " False\n", + " 0.0\n", + " 0.010085\n", + " \n", + " \n", + " 43\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.65\n", + " 0.813589\n", + " 0.376977\n", + " velocity_verlet\n", + " standard\n", + " 0.535456\n", + " False\n", + " 0.0\n", + " 0.002959\n", + " \n", + " \n", + " 44\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.626747\n", + " 0.331795\n", + " velocity_verlet\n", + " standard\n", + " 0.591055\n", + " False\n", + " 0.0\n", + " 0.003835\n", + " \n", + " \n", + " 45\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.65\n", + " 0.815981\n", + " 0.364465\n", + " velocity_verlet\n", + " standard\n", + " 0.543589\n", + " False\n", + " 0.8\n", + " 0.001634\n", + " \n", + " \n", + " 46\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.631292\n", + " 0.328454\n", + " velocity_verlet\n", + " standard\n", + " 0.594636\n", + " False\n", + " 0.8\n", + " 0.004252\n", + " \n", + " \n", + " 47\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.65\n", + " 0.815694\n", + " 0.380004\n", + " velocity_verlet\n", + " standard\n", + " 0.514427\n", + " False\n", + " 2.0\n", + " 0.001825\n", + " \n", + " \n", + " 48\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.65\n", + " 1.617414\n", + " 0.353230\n", + " velocity_verlet\n", + " standard\n", + " 0.534235\n", + " False\n", + " 2.0\n", + " 0.003190\n", + " \n", + " \n", + " 49\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.811592\n", + " 0.185693\n", + " velocity_verlet\n", + " standard\n", + " 0.878675\n", + " False\n", + " 0.0\n", + " 0.002941\n", + " \n", + " \n", + " 50\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.127611\n", + " 0.166444\n", + " velocity_verlet\n", + " standard\n", + " 0.895205\n", + " False\n", + " 0.0\n", + " 0.003236\n", + " \n", + " \n", + " 51\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.786659\n", + " 0.170435\n", + " velocity_verlet\n", + " standard\n", + " 0.896941\n", + " False\n", + " 0.8\n", + " 0.003465\n", + " \n", + " \n", + " 52\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.092741\n", + " 0.172509\n", + " velocity_verlet\n", + " standard\n", + " 0.889608\n", + " False\n", + " 0.8\n", + " 0.003187\n", + " \n", + " \n", + " 53\n", + " Brownian\n", + " 32\n", + " mhmclmc:0.9\n", + " 0.788418\n", + " 0.172896\n", + " velocity_verlet\n", + " standard\n", + " 0.893864\n", + " False\n", + " 2.0\n", + " 0.003398\n", + " \n", + " \n", + " 54\n", + " Brownian\n", + " 32\n", + " mhmclmc:st3:0.9\n", + " 1.075352\n", + " 0.174587\n", + " velocity_verlet\n", + " standard\n", + " 0.887969\n", + " False\n", + " 2.0\n", + " 0.003692\n", + " \n", + " \n", + " 55\n", + " Brownian\n", + " 32\n", + " nuts\n", + " 0.000000\n", + " 0.000000\n", + " velocity_verlet\n", + " standard\n", + " 0.780539\n", + " False\n", + " 0.0\n", + " 0.002995\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " model dims sampler L step_size integrator \\\n", + "0 Brownian 32 mclmc 14.911316 1.780933 mclachlan \n", + "1 Brownian 32 mhmclmc:0.65 0.813533 0.739576 mclachlan \n", + "2 Brownian 32 mhmclmc:st3:0.65 1.627066 1.479151 mclachlan \n", + "3 Brownian 32 mhmclmc:0.65 0.813309 0.739372 mclachlan \n", + "4 Brownian 32 mhmclmc:st3:0.65 1.626618 1.478743 mclachlan \n", + "5 Brownian 32 mhmclmc:0.65 0.806923 0.733566 mclachlan \n", + "6 Brownian 32 mhmclmc:st3:0.65 1.613846 1.467133 mclachlan \n", + "7 Brownian 32 mhmclmc:0.9 0.828702 0.753365 mclachlan \n", + "8 Brownian 32 mhmclmc:st3:0.9 1.657404 1.506731 mclachlan \n", + "9 Brownian 32 mhmclmc:0.9 0.827290 0.752082 mclachlan \n", + "10 Brownian 32 mhmclmc:st3:0.9 1.654580 1.504164 mclachlan \n", + "11 Brownian 32 mhmclmc:0.9 0.835939 0.759945 mclachlan \n", + "12 Brownian 32 mhmclmc:st3:0.9 1.671879 1.516725 mclachlan \n", + "13 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "14 Brownian 32 mclmc 14.340859 0.860062 velocity_verlet \n", + "15 Brownian 32 mhmclmc:0.65 0.813589 0.739626 velocity_verlet \n", + "16 Brownian 32 mhmclmc:st3:0.65 1.627178 1.479252 velocity_verlet \n", + "17 Brownian 32 mhmclmc:0.65 0.815981 0.741801 velocity_verlet \n", + "18 Brownian 32 mhmclmc:st3:0.65 1.631963 1.483602 velocity_verlet \n", + "19 Brownian 32 mhmclmc:0.65 0.815694 0.741540 velocity_verlet \n", + "20 Brownian 32 mhmclmc:st3:0.65 1.631388 1.483080 velocity_verlet \n", + "21 Brownian 32 mhmclmc:0.9 0.811592 0.737811 velocity_verlet \n", + "22 Brownian 32 mhmclmc:st3:0.9 1.623184 1.256775 velocity_verlet \n", + "23 Brownian 32 mhmclmc:0.9 0.786659 0.710999 velocity_verlet \n", + "24 Brownian 32 mhmclmc:st3:0.9 1.565477 1.172345 velocity_verlet \n", + "25 Brownian 32 mhmclmc:0.9 0.788416 0.710871 velocity_verlet \n", + "26 Brownian 32 mhmclmc:st3:0.9 1.565834 1.130701 velocity_verlet \n", + "27 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + "28 Brownian 32 mclmc 2.435902 0.311698 mclachlan \n", + "29 Brownian 32 mhmclmc:0.65 0.812042 0.637599 mclachlan \n", + "30 Brownian 32 mhmclmc:st3:0.65 1.627066 0.595371 mclachlan \n", + "31 Brownian 32 mhmclmc:0.65 0.813400 0.650853 mclachlan \n", + "32 Brownian 32 mhmclmc:st3:0.65 1.627030 0.586531 mclachlan \n", + "33 Brownian 32 mhmclmc:0.65 0.807133 0.659692 mclachlan \n", + "34 Brownian 32 mhmclmc:st3:0.65 1.613846 0.617372 mclachlan \n", + "35 Brownian 32 mhmclmc:0.9 0.828702 0.395384 mclachlan \n", + "36 Brownian 32 mhmclmc:st3:0.9 1.610202 0.342993 mclachlan \n", + "37 Brownian 32 mhmclmc:0.9 0.827329 0.403526 mclachlan \n", + "38 Brownian 32 mhmclmc:st3:0.9 1.646191 0.347808 mclachlan \n", + "39 Brownian 32 mhmclmc:0.9 0.835939 0.402196 mclachlan \n", + "40 Brownian 32 mhmclmc:st3:0.9 1.637337 0.338025 mclachlan \n", + "41 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "42 Brownian 32 mclmc 2.276252 0.145409 velocity_verlet \n", + "43 Brownian 32 mhmclmc:0.65 0.813589 0.376977 velocity_verlet \n", + "44 Brownian 32 mhmclmc:st3:0.65 1.626747 0.331795 velocity_verlet \n", + "45 Brownian 32 mhmclmc:0.65 0.815981 0.364465 velocity_verlet \n", + "46 Brownian 32 mhmclmc:st3:0.65 1.631292 0.328454 velocity_verlet \n", + "47 Brownian 32 mhmclmc:0.65 0.815694 0.380004 velocity_verlet \n", + "48 Brownian 32 mhmclmc:st3:0.65 1.617414 0.353230 velocity_verlet \n", + "49 Brownian 32 mhmclmc:0.9 0.811592 0.185693 velocity_verlet \n", + "50 Brownian 32 mhmclmc:st3:0.9 1.127611 0.166444 velocity_verlet \n", + "51 Brownian 32 mhmclmc:0.9 0.786659 0.170435 velocity_verlet \n", + "52 Brownian 32 mhmclmc:st3:0.9 1.092741 0.172509 velocity_verlet \n", + "53 Brownian 32 mhmclmc:0.9 0.788418 0.172896 velocity_verlet \n", + "54 Brownian 32 mhmclmc:st3:0.9 1.075352 0.174587 velocity_verlet \n", + "55 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + "\n", + " tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 standard 1.000000 True 0.0 0.010693 \n", + "1 standard 0.997997 True 0.0 0.000000 \n", + "2 standard 0.982648 True 0.0 0.000816 \n", + "3 standard 0.997409 True 0.8 0.000000 \n", + "4 standard 0.981794 True 0.8 0.000772 \n", + "5 standard 0.997789 True 2.0 0.000000 \n", + "6 standard 0.981543 True 2.0 0.000769 \n", + "7 standard 0.997318 True 0.0 0.000000 \n", + "8 standard 0.980772 True 0.0 0.000869 \n", + "9 standard 0.997115 True 0.8 0.000000 \n", + "10 standard 0.980928 True 0.8 0.000824 \n", + "11 standard 0.997567 True 2.0 0.000000 \n", + "12 standard 0.979162 True 2.0 0.000785 \n", + "13 standard 0.922982 True 0.0 0.002499 \n", + "14 standard 1.000000 True 0.0 0.010097 \n", + "15 standard 0.972154 True 0.0 0.000000 \n", + "16 standard 0.824719 True 0.0 0.001327 \n", + "17 standard 0.972171 True 0.8 0.000000 \n", + "18 standard 0.820272 True 0.8 0.001171 \n", + "19 standard 0.972795 True 2.0 0.000000 \n", + "20 standard 0.819445 True 2.0 0.001334 \n", + "21 standard 0.971989 True 0.0 0.000000 \n", + "22 standard 0.874309 True 0.0 0.001159 \n", + "23 standard 0.932895 True 0.8 0.000000 \n", + "24 standard 0.842684 True 0.8 0.000011 \n", + "25 standard 0.911877 True 2.0 0.000000 \n", + "26 standard 0.826700 True 2.0 0.000008 \n", + "27 standard 0.873224 True 0.0 0.003463 \n", + "28 standard 1.000000 False 0.0 0.014269 \n", + "29 standard 0.527286 False 0.0 0.001860 \n", + "30 standard 0.521462 False 0.0 0.001663 \n", + "31 standard 0.513997 False 0.8 0.001090 \n", + "32 standard 0.530031 False 0.8 0.002307 \n", + "33 standard 0.507236 False 2.0 0.001391 \n", + "34 standard 0.496971 False 2.0 0.001071 \n", + "35 standard 0.848784 False 0.0 0.003471 \n", + "36 standard 0.883302 False 0.0 0.005298 \n", + "37 standard 0.846071 False 0.8 0.003409 \n", + "38 standard 0.871342 False 0.8 0.005665 \n", + "39 standard 0.846171 False 2.0 0.002454 \n", + "40 standard 0.882995 False 2.0 0.005443 \n", + "41 standard 0.761320 False 0.0 0.002027 \n", + "42 standard 1.000000 False 0.0 0.010085 \n", + "43 standard 0.535456 False 0.0 0.002959 \n", + "44 standard 0.591055 False 0.0 0.003835 \n", + "45 standard 0.543589 False 0.8 0.001634 \n", + "46 standard 0.594636 False 0.8 0.004252 \n", + "47 standard 0.514427 False 2.0 0.001825 \n", + "48 standard 0.534235 False 2.0 0.003190 \n", + "49 standard 0.878675 False 0.0 0.002941 \n", + "50 standard 0.895205 False 0.0 0.003236 \n", + "51 standard 0.896941 False 0.8 0.003465 \n", + "52 standard 0.889608 False 0.8 0.003187 \n", + "53 standard 0.893864 False 2.0 0.003398 \n", + "54 standard 0.887969 False 2.0 0.003692 \n", + "55 standard 0.780539 False 0.0 0.002995 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load_results(\"Brownian\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ], + "text/plain": [ + " model dims sampler L step_size \\\n", + "0 IllConditionedGaussian 100 mclmc 8.309327 12.268694 \n", + "1 IllConditionedGaussian 100 mhmclmc:0.65 14.655429 13.323115 \n", + "2 IllConditionedGaussian 100 mhmclmc:st3:0.65 10.257968 9.325425 \n", + "3 IllConditionedGaussian 100 mhmclmc:0.65 14.683567 13.348697 \n", + "4 IllConditionedGaussian 100 mhmclmc:st3:0.65 11.430244 10.391131 \n", + ".. ... ... ... ... ... \n", + "67 IllConditionedGaussian 100 mhmclmc:0.9 14.656251 2.025311 \n", + "68 IllConditionedGaussian 100 mhmclmc:st3:0.9 4.919873 2.087330 \n", + "69 IllConditionedGaussian 100 mhmclmc:0.9 14.577882 1.999627 \n", + "70 IllConditionedGaussian 100 mhmclmc:st3:0.9 7.868400 2.046661 \n", + "71 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "\n", + " integrator tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 mclachlan standard 1.000000 True 0.000000 0.048828 \n", + "1 mclachlan standard 0.873889 True 0.000000 0.030122 \n", + "2 mclachlan standard 0.954066 True 0.000000 0.025422 \n", + "3 mclachlan standard 0.877890 True 0.333333 0.035373 \n", + "4 mclachlan standard 0.940143 True 0.333333 0.028787 \n", + ".. ... ... ... ... ... ... \n", + "67 velocity_verlet standard 0.899587 False 0.800000 0.002983 \n", + "68 velocity_verlet standard 0.907339 False 0.800000 0.000000 \n", + "69 velocity_verlet standard 0.901848 False 2.000000 0.001865 \n", + "70 velocity_verlet standard 0.905386 False 2.000000 0.000000 \n", + "71 velocity_verlet standard 0.810722 False 0.000000 0.001657 \n", + "\n", + "[72 rows x 11 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load_results(\"IllConditionedGaussian\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    14Brownian32mhmchmc:st30.651.6270661.4791512IllConditionedGaussian100mhmclmc:st3:0.6510.2579689.325425mclachlanstandard0.9809830.954066True0.0007320.0000000.025422
    15Brownian32mhmchmc0.90.8287020.7533653IllConditionedGaussian100mhmclmc:0.6514.68356713.348697mclachlanstandard0.9951100.877890True0.0000000.3333330.035373
    16Brownian32mhmchmc:st30.91.6574041.5067314IllConditionedGaussian100mhmclmc:st3:0.6511.43024410.391131mclachlanstandard0.9812260.940143True0.0007460.3333330.028787
    17Brownian32nuts0.0000000.000000mclachlanstandard0.921391True0.003097....................................
    18Brownian32mclmc14.4516720.85738567IllConditionedGaussian100mhmclmc:0.914.6562512.025311velocity_verletstandard1.000000True0.0102890.899587False0.8000000.002983
    19Brownian32mhmchmc0.650.8135890.73962668IllConditionedGaussian100mhmclmc:st3:0.94.9198732.087330velocity_verletstandard0.972460True0.907339False0.8000000.000000
    20Brownian32mhmchmc:st30.651.6271781.47925269IllConditionedGaussian100mhmclmc:0.914.5778821.999627velocity_verletstandard0.824414True0.0013640.901848False2.0000000.001865
    21Brownian32mhmchmc0.90.8115920.73781170IllConditionedGaussian100mhmclmc:st3:0.97.8684002.046661velocity_verletstandard0.971546True0.905386False2.0000000.000000
    22Brownian32mhmchmc:st30.91.6231841.256775velocity_verletstandard0.873862True0.001445
    23Brownian3271IllConditionedGaussian100nuts0.0000000.000000velocity_verletstandard0.873792True0.0031570.810722False0.0000000.001657
    \n", + "

    72 rows × 11 columns

    \n", "
    " ], "text/plain": [ - " model dims sampler L step_size integrator \\\n", - "0 Brownian 32 mclmc 2.442378 0.302776 mclachlan \n", - "1 Brownian 32 mhmchmc0.65 0.812042 0.637599 mclachlan \n", - "2 Brownian 32 mhmchmc:st30.65 1.627066 0.595371 mclachlan \n", - "3 Brownian 32 mhmchmc0.9 0.828702 0.395384 mclachlan \n", - "4 Brownian 32 mhmchmc:st30.9 1.610202 0.342993 mclachlan \n", - "5 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", - "6 Brownian 32 mclmc 2.297048 0.146238 velocity_verlet \n", - "7 Brownian 32 mhmchmc0.65 0.813589 0.376977 velocity_verlet \n", - "8 Brownian 32 mhmchmc:st30.65 1.626747 0.331795 velocity_verlet \n", - "9 Brownian 32 mhmchmc0.9 0.811592 0.185693 velocity_verlet \n", - "10 Brownian 32 mhmchmc:st30.9 1.127611 0.166444 velocity_verlet \n", - "11 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", - "12 Brownian 32 mclmc 14.723757 1.771522 mclachlan \n", - "13 Brownian 32 mhmchmc0.65 0.813533 0.739576 mclachlan \n", - "14 Brownian 32 mhmchmc:st30.65 1.627066 1.479151 mclachlan \n", - "15 Brownian 32 mhmchmc0.9 0.828702 0.753365 mclachlan \n", - "16 Brownian 32 mhmchmc:st30.9 1.657404 1.506731 mclachlan \n", - "17 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", - "18 Brownian 32 mclmc 14.451672 0.857385 velocity_verlet \n", - "19 Brownian 32 mhmchmc0.65 0.813589 0.739626 velocity_verlet \n", - "20 Brownian 32 mhmchmc:st30.65 1.627178 1.479252 velocity_verlet \n", - "21 Brownian 32 mhmchmc0.9 0.811592 0.737811 velocity_verlet \n", - "22 Brownian 32 mhmchmc:st30.9 1.623184 1.256775 velocity_verlet \n", - "23 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + " model dims sampler L step_size \\\n", + "0 IllConditionedGaussian 100 mclmc 8.309327 12.268694 \n", + "1 IllConditionedGaussian 100 mhmclmc:0.65 14.655429 13.323115 \n", + "2 IllConditionedGaussian 100 mhmclmc:st3:0.65 10.257968 9.325425 \n", + "3 IllConditionedGaussian 100 mhmclmc:0.65 14.683567 13.348697 \n", + "4 IllConditionedGaussian 100 mhmclmc:st3:0.65 11.430244 10.391131 \n", + ".. ... ... ... ... ... \n", + "67 IllConditionedGaussian 100 mhmclmc:0.9 14.656251 2.025311 \n", + "68 IllConditionedGaussian 100 mhmclmc:st3:0.9 4.919873 2.087330 \n", + "69 IllConditionedGaussian 100 mhmclmc:0.9 14.577882 1.999627 \n", + "70 IllConditionedGaussian 100 mhmclmc:st3:0.9 7.868400 2.046661 \n", + "71 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", "\n", - " tuning acc_rate preconditioning ESS \n", - "0 standard 1.000000 False 0.010799 \n", - "1 standard 0.529587 False 0.001431 \n", - "2 standard 0.523359 False 0.001147 \n", - "3 standard 0.853865 False 0.002489 \n", - "4 standard 0.884686 False 0.004979 \n", - "5 standard 0.760786 False 0.002627 \n", - "6 standard 1.000000 False 0.013497 \n", - "7 standard 0.532756 False 0.001877 \n", - "8 standard 0.595688 False 0.003162 \n", - "9 standard 0.881163 False 0.002693 \n", - "10 standard 0.897115 False 0.003417 \n", - "11 standard 0.779443 False 0.003168 \n", - "12 standard 1.000000 True 0.012148 \n", - "13 standard 0.997668 True 0.000000 \n", - "14 standard 0.980983 True 0.000732 \n", - "15 standard 0.995110 True 0.000000 \n", - "16 standard 0.981226 True 0.000746 \n", - "17 standard 0.921391 True 0.003097 \n", - "18 standard 1.000000 True 0.010289 \n", - "19 standard 0.972460 True 0.000000 \n", - "20 standard 0.824414 True 0.001364 \n", - "21 standard 0.971546 True 0.000000 \n", - "22 standard 0.873862 True 0.001445 \n", - "23 standard 0.873792 True 0.003157 " + " integrator tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 mclachlan standard 1.000000 True 0.000000 0.048828 \n", + "1 mclachlan standard 0.873889 True 0.000000 0.030122 \n", + "2 mclachlan standard 0.954066 True 0.000000 0.025422 \n", + "3 mclachlan standard 0.877890 True 0.333333 0.035373 \n", + "4 mclachlan standard 0.940143 True 0.333333 0.028787 \n", + ".. ... ... ... ... ... ... \n", + "67 velocity_verlet standard 0.899587 False 0.800000 0.002983 \n", + "68 velocity_verlet standard 0.907339 False 0.800000 0.000000 \n", + "69 velocity_verlet standard 0.901848 False 2.000000 0.001865 \n", + "70 velocity_verlet standard 0.905386 False 2.000000 0.000000 \n", + "71 velocity_verlet standard 0.810722 False 0.000000 0.001657 \n", + "\n", + "[72 rows x 11 columns]" ] }, "metadata": {}, @@ -2940,17 +4141,17 @@ } ], "source": [ - "load_results(\"Brownian\")\n" + "load_results(\"IllConditionedGaussian\")\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { - "image/png": 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    " ] @@ -2988,378 +4189,403 @@ " tuning\n", " acc_rate\n", " preconditioning\n", + " inv_L_prop\n", " ESS\n", " \n", " \n", " \n", " \n", " 0\n", - " GermanCredit\n", - " 51\n", + " IllConditionedGaussian\n", + " 100\n", " mclmc\n", - " 13.774713\n", - " 0.575219\n", + " 8.309327\n", + " 12.268694\n", " mclachlan\n", " standard\n", " 1.000000\n", " True\n", - " 0.001701\n", + " 0.0\n", + " 0.048828\n", " \n", " \n", " 1\n", - " GermanCredit\n", - " 51\n", - " mhmchmc0.65\n", - " 7.315119\n", - " 1.101492\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.65\n", + " 14.655429\n", + " 13.323115\n", " mclachlan\n", " standard\n", - " 0.590559\n", + " 0.873889\n", " True\n", - " 0.000488\n", + " 0.0\n", + " 0.030122\n", " \n", " \n", " 2\n", - " GermanCredit\n", - " 51\n", - " mhmchmc:st30.65\n", - " 8.846248\n", - " 1.103699\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.65\n", + " 10.257968\n", + " 9.325425\n", " mclachlan\n", " standard\n", - " 0.596298\n", + " 0.954066\n", " True\n", - " 0.000298\n", + " 0.0\n", + " 0.025422\n", " \n", " \n", " 3\n", - " GermanCredit\n", - " 51\n", - " mhmchmc0.9\n", - " 5.347223\n", - " 0.748821\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.9\n", + " 14.752708\n", + " 12.240559\n", " mclachlan\n", " standard\n", - " 0.864109\n", + " 0.899333\n", " True\n", - " 0.000959\n", + " 0.0\n", + " 0.028272\n", " \n", " \n", " 4\n", - " GermanCredit\n", - " 51\n", - " mhmchmc:st30.9\n", - " 5.063417\n", - " 0.801041\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.9\n", + " 8.380649\n", + " 7.618771\n", " mclachlan\n", " standard\n", - " 0.853852\n", + " 0.976059\n", " True\n", - " 0.000869\n", + " 0.0\n", + " 0.020202\n", " \n", " \n", " 5\n", - " GermanCredit\n", - " 51\n", + " IllConditionedGaussian\n", + " 100\n", " nuts\n", " 0.000000\n", " 0.000000\n", " mclachlan\n", " standard\n", - " 0.898460\n", + " 0.986120\n", " True\n", - " 0.000644\n", + " 0.0\n", + " 0.003262\n", " \n", " \n", " 6\n", - " GermanCredit\n", - " 51\n", + " IllConditionedGaussian\n", + " 100\n", " mclmc\n", - " 13.531967\n", - " 0.337908\n", + " 9.752464\n", + " 6.142936\n", " velocity_verlet\n", " standard\n", " 1.000000\n", " True\n", - " 0.002515\n", + " 0.0\n", + " 0.024540\n", " \n", " \n", " 7\n", - " GermanCredit\n", - " 51\n", - " mhmchmc0.65\n", - " 4.810670\n", - " 0.761948\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.65\n", + " 14.753436\n", + " 9.526176\n", " velocity_verlet\n", " standard\n", - " 0.576652\n", + " 0.648979\n", " True\n", - " 0.000000\n", + " 0.0\n", + " 0.025432\n", " \n", " \n", " 8\n", - " GermanCredit\n", - " 51\n", - " mhmchmc:st30.65\n", - " 7.688974\n", - " 0.713766\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.65\n", + " 13.073677\n", + " 8.662420\n", " velocity_verlet\n", " standard\n", - " 0.580719\n", + " 0.734258\n", " True\n", - " 0.000361\n", + " 0.0\n", + " 0.022798\n", " \n", " \n", " 9\n", - " GermanCredit\n", - " 51\n", - " mhmchmc0.9\n", - " 2.941470\n", - " 0.477281\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.9\n", + " 14.733406\n", + " 4.998178\n", " velocity_verlet\n", " standard\n", - " 0.872222\n", + " 0.906246\n", " True\n", - " 0.000628\n", + " 0.0\n", + " 0.017536\n", " \n", " \n", " 10\n", - " GermanCredit\n", - " 51\n", - " mhmchmc:st30.9\n", - " 5.349097\n", - " 0.446817\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.9\n", + " 3.019329\n", + " 2.744845\n", " velocity_verlet\n", " standard\n", - " 0.879439\n", + " 0.990493\n", " True\n", - " 0.001214\n", + " 0.0\n", + " 0.005065\n", " \n", " \n", " 11\n", - " GermanCredit\n", - " 51\n", + " IllConditionedGaussian\n", + " 100\n", " nuts\n", " 0.000000\n", " 0.000000\n", " velocity_verlet\n", " standard\n", - " 0.894313\n", + " 0.850974\n", " True\n", - " 0.001096\n", + " 0.0\n", + " 0.005017\n", " \n", " \n", " 12\n", - " GermanCredit\n", - " 51\n", + " IllConditionedGaussian\n", + " 100\n", " mclmc\n", - " 12.871871\n", - " 0.399752\n", + " 14.317208\n", + " 5.771991\n", " mclachlan\n", " standard\n", " 1.000000\n", " False\n", - " 0.001677\n", + " 0.0\n", + " 0.010140\n", " \n", " \n", " 13\n", - " GermanCredit\n", - " 51\n", - " mhmchmc0.65\n", - " 7.315119\n", - " 0.672528\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.65\n", + " 14.655429\n", + " 7.212010\n", " mclachlan\n", " standard\n", - " 0.558331\n", + " 0.703607\n", " False\n", - " 0.000381\n", + " 0.0\n", + " 0.004859\n", " \n", " \n", " 14\n", - " GermanCredit\n", - " 51\n", - " mhmchmc:st30.65\n", - " 7.148202\n", - " 0.676359\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.65\n", + " 17.467278\n", + " 7.609488\n", " mclachlan\n", " standard\n", - " 0.573157\n", + " 0.614287\n", " False\n", - " 0.000229\n", + " 0.0\n", + " 0.002131\n", " \n", " \n", " 15\n", - " GermanCredit\n", - " 51\n", - " mhmchmc0.9\n", - " 5.347223\n", - " 0.467936\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.9\n", + " 14.752708\n", + " 5.866860\n", " mclachlan\n", " standard\n", - " 0.865484\n", + " 0.904467\n", " False\n", - " 0.000773\n", + " 0.0\n", + " 0.006679\n", " \n", " \n", " 16\n", - " GermanCredit\n", - " 51\n", - " mhmchmc:st30.9\n", - " 4.210558\n", - " 0.520591\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.9\n", + " 9.228044\n", + " 6.032185\n", " mclachlan\n", " standard\n", - " 0.843018\n", + " 0.892275\n", " False\n", - " 0.000580\n", + " 0.0\n", + " 0.003878\n", " \n", " \n", " 17\n", - " GermanCredit\n", - " 51\n", + " IllConditionedGaussian\n", + " 100\n", " nuts\n", " 0.000000\n", " 0.000000\n", " mclachlan\n", " standard\n", - " 0.815190\n", + " 0.860602\n", " False\n", - " 0.000504\n", + " 0.0\n", + " 0.001401\n", " \n", " \n", " 18\n", - " GermanCredit\n", - " 51\n", + " IllConditionedGaussian\n", + " 100\n", " mclmc\n", - " 11.491051\n", - " 0.208865\n", + " 14.648232\n", + " 2.302675\n", " velocity_verlet\n", " standard\n", " 1.000000\n", " False\n", + " 0.0\n", " 0.000000\n", " \n", " \n", " 19\n", - " GermanCredit\n", - " 51\n", - " mhmchmc0.65\n", - " 4.810670\n", - " 0.454371\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.65\n", + " 14.753436\n", + " 4.070334\n", " velocity_verlet\n", " standard\n", - " 0.586514\n", + " 0.633077\n", " False\n", - " 0.000000\n", + " 0.0\n", + " 0.005887\n", " \n", " \n", " 20\n", - " GermanCredit\n", - " 51\n", - " mhmchmc:st30.65\n", - " 6.575204\n", - " 0.473575\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.65\n", + " 10.386975\n", + " 3.740668\n", " velocity_verlet\n", " standard\n", - " 0.521883\n", + " 0.686530\n", " False\n", - " 0.000000\n", + " 0.0\n", + " 0.003768\n", " \n", " \n", " 21\n", - " GermanCredit\n", - " 51\n", - " mhmchmc0.9\n", - " 2.941470\n", - " 0.290941\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:0.9\n", + " 14.733406\n", + " 2.228257\n", " velocity_verlet\n", " standard\n", - " 0.869797\n", + " 0.899157\n", " False\n", - " 0.000943\n", + " 0.0\n", + " 0.005075\n", " \n", " \n", " 22\n", - " GermanCredit\n", - " 51\n", - " mhmchmc:st30.9\n", - " 4.782981\n", - " 0.278618\n", + " IllConditionedGaussian\n", + " 100\n", + " mhmclmc:st3:0.9\n", + " 3.276532\n", + " 2.212389\n", " velocity_verlet\n", " standard\n", - " 0.882591\n", + " 0.904900\n", " False\n", - " 0.001090\n", + " 0.0\n", + " 0.000000\n", " \n", " \n", " 23\n", - " GermanCredit\n", - " 51\n", + " IllConditionedGaussian\n", + " 100\n", " nuts\n", " 0.000000\n", " 0.000000\n", " velocity_verlet\n", " standard\n", - " 0.796060\n", + " 0.810722\n", " False\n", - " 0.000332\n", + " 0.0\n", + " 0.001657\n", " \n", " \n", "\n", "" ], "text/plain": [ - " model dims sampler L step_size \\\n", - "0 GermanCredit 51 mclmc 13.774713 0.575219 \n", - "1 GermanCredit 51 mhmchmc0.65 7.315119 1.101492 \n", - "2 GermanCredit 51 mhmchmc:st30.65 8.846248 1.103699 \n", - "3 GermanCredit 51 mhmchmc0.9 5.347223 0.748821 \n", - "4 GermanCredit 51 mhmchmc:st30.9 5.063417 0.801041 \n", - "5 GermanCredit 51 nuts 0.000000 0.000000 \n", - "6 GermanCredit 51 mclmc 13.531967 0.337908 \n", - "7 GermanCredit 51 mhmchmc0.65 4.810670 0.761948 \n", - "8 GermanCredit 51 mhmchmc:st30.65 7.688974 0.713766 \n", - "9 GermanCredit 51 mhmchmc0.9 2.941470 0.477281 \n", - "10 GermanCredit 51 mhmchmc:st30.9 5.349097 0.446817 \n", - "11 GermanCredit 51 nuts 0.000000 0.000000 \n", - "12 GermanCredit 51 mclmc 12.871871 0.399752 \n", - "13 GermanCredit 51 mhmchmc0.65 7.315119 0.672528 \n", - "14 GermanCredit 51 mhmchmc:st30.65 7.148202 0.676359 \n", - "15 GermanCredit 51 mhmchmc0.9 5.347223 0.467936 \n", - "16 GermanCredit 51 mhmchmc:st30.9 4.210558 0.520591 \n", - "17 GermanCredit 51 nuts 0.000000 0.000000 \n", - "18 GermanCredit 51 mclmc 11.491051 0.208865 \n", - "19 GermanCredit 51 mhmchmc0.65 4.810670 0.454371 \n", - "20 GermanCredit 51 mhmchmc:st30.65 6.575204 0.473575 \n", - "21 GermanCredit 51 mhmchmc0.9 2.941470 0.290941 \n", - "22 GermanCredit 51 mhmchmc:st30.9 4.782981 0.278618 \n", - "23 GermanCredit 51 nuts 0.000000 0.000000 \n", + " model dims sampler L step_size \\\n", + "0 IllConditionedGaussian 100 mclmc 8.309327 12.268694 \n", + "1 IllConditionedGaussian 100 mhmclmc:0.65 14.655429 13.323115 \n", + "2 IllConditionedGaussian 100 mhmclmc:st3:0.65 10.257968 9.325425 \n", + "3 IllConditionedGaussian 100 mhmclmc:0.9 14.752708 12.240559 \n", + "4 IllConditionedGaussian 100 mhmclmc:st3:0.9 8.380649 7.618771 \n", + "5 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "6 IllConditionedGaussian 100 mclmc 9.752464 6.142936 \n", + "7 IllConditionedGaussian 100 mhmclmc:0.65 14.753436 9.526176 \n", + "8 IllConditionedGaussian 100 mhmclmc:st3:0.65 13.073677 8.662420 \n", + "9 IllConditionedGaussian 100 mhmclmc:0.9 14.733406 4.998178 \n", + "10 IllConditionedGaussian 100 mhmclmc:st3:0.9 3.019329 2.744845 \n", + "11 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "12 IllConditionedGaussian 100 mclmc 14.317208 5.771991 \n", + "13 IllConditionedGaussian 100 mhmclmc:0.65 14.655429 7.212010 \n", + "14 IllConditionedGaussian 100 mhmclmc:st3:0.65 17.467278 7.609488 \n", + "15 IllConditionedGaussian 100 mhmclmc:0.9 14.752708 5.866860 \n", + "16 IllConditionedGaussian 100 mhmclmc:st3:0.9 9.228044 6.032185 \n", + "17 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "18 IllConditionedGaussian 100 mclmc 14.648232 2.302675 \n", + "19 IllConditionedGaussian 100 mhmclmc:0.65 14.753436 4.070334 \n", + "20 IllConditionedGaussian 100 mhmclmc:st3:0.65 10.386975 3.740668 \n", + "21 IllConditionedGaussian 100 mhmclmc:0.9 14.733406 2.228257 \n", + "22 IllConditionedGaussian 100 mhmclmc:st3:0.9 3.276532 2.212389 \n", + "23 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", "\n", - " integrator tuning acc_rate preconditioning ESS \n", - "0 mclachlan standard 1.000000 True 0.001701 \n", - "1 mclachlan standard 0.590559 True 0.000488 \n", - "2 mclachlan standard 0.596298 True 0.000298 \n", - "3 mclachlan standard 0.864109 True 0.000959 \n", - "4 mclachlan standard 0.853852 True 0.000869 \n", - "5 mclachlan standard 0.898460 True 0.000644 \n", - "6 velocity_verlet standard 1.000000 True 0.002515 \n", - "7 velocity_verlet standard 0.576652 True 0.000000 \n", - "8 velocity_verlet standard 0.580719 True 0.000361 \n", - "9 velocity_verlet standard 0.872222 True 0.000628 \n", - "10 velocity_verlet standard 0.879439 True 0.001214 \n", - "11 velocity_verlet standard 0.894313 True 0.001096 \n", - "12 mclachlan standard 1.000000 False 0.001677 \n", - "13 mclachlan standard 0.558331 False 0.000381 \n", - "14 mclachlan standard 0.573157 False 0.000229 \n", - "15 mclachlan standard 0.865484 False 0.000773 \n", - "16 mclachlan standard 0.843018 False 0.000580 \n", - "17 mclachlan standard 0.815190 False 0.000504 \n", - "18 velocity_verlet standard 1.000000 False 0.000000 \n", - "19 velocity_verlet standard 0.586514 False 0.000000 \n", - "20 velocity_verlet standard 0.521883 False 0.000000 \n", - "21 velocity_verlet standard 0.869797 False 0.000943 \n", - "22 velocity_verlet standard 0.882591 False 0.001090 \n", - "23 velocity_verlet standard 0.796060 False 0.000332 " + " integrator tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 mclachlan standard 1.000000 True 0.0 0.048828 \n", + "1 mclachlan standard 0.873889 True 0.0 0.030122 \n", + "2 mclachlan standard 0.954066 True 0.0 0.025422 \n", + "3 mclachlan standard 0.899333 True 0.0 0.028272 \n", + "4 mclachlan standard 0.976059 True 0.0 0.020202 \n", + "5 mclachlan standard 0.986120 True 0.0 0.003262 \n", + "6 velocity_verlet standard 1.000000 True 0.0 0.024540 \n", + "7 velocity_verlet standard 0.648979 True 0.0 0.025432 \n", + "8 velocity_verlet standard 0.734258 True 0.0 0.022798 \n", + "9 velocity_verlet standard 0.906246 True 0.0 0.017536 \n", + "10 velocity_verlet standard 0.990493 True 0.0 0.005065 \n", + "11 velocity_verlet standard 0.850974 True 0.0 0.005017 \n", + "12 mclachlan standard 1.000000 False 0.0 0.010140 \n", + "13 mclachlan standard 0.703607 False 0.0 0.004859 \n", + "14 mclachlan standard 0.614287 False 0.0 0.002131 \n", + "15 mclachlan standard 0.904467 False 0.0 0.006679 \n", + "16 mclachlan standard 0.892275 False 0.0 0.003878 \n", + "17 mclachlan standard 0.860602 False 0.0 0.001401 \n", + "18 velocity_verlet standard 1.000000 False 0.0 0.000000 \n", + "19 velocity_verlet standard 0.633077 False 0.0 0.005887 \n", + "20 velocity_verlet standard 0.686530 False 0.0 0.003768 \n", + "21 velocity_verlet standard 0.899157 False 0.0 0.005075 \n", + "22 velocity_verlet standard 0.904900 False 0.0 0.000000 \n", + "23 velocity_verlet standard 0.810722 False 0.0 0.001657 " ] }, "metadata": {}, @@ -3367,7 +4593,7 @@ } ], "source": [ - "load_results(\"GermanCredit\")\n" + "# load_results(\"IllConditionedGaussian\")\n" ] }, { diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index 86fef4f..1578c15 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -198,17 +198,17 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans # try doing low mem version twice - _, _, expectations = run_inference_algorithm( - rng_key=run_key, - initial_state=blackjax_state_after_tuning, - inference_algorithm=sampling_alg, - num_steps=num_steps, - return_state_history=False, - transform=lambda x: transform(x.position), - # expectation=lambda x: jnp.array([x**2, x]), - expectation=lambda x: x**2, - progress_bar=True, - ) + # _, _, expectations = run_inference_algorithm( + # rng_key=run_key, + # initial_state=blackjax_state_after_tuning, + # inference_algorithm=sampling_alg, + # num_steps=num_steps, + # return_state_history=False, + # transform=lambda x: transform(x.position), + # # expectation=lambda x: jnp.array([x**2, x]), + # expectation=lambda x: x**2, + # progress_bar=True, + # ) # jax.debug.print("blahblah[1] {x}", x=blahblah) @@ -216,7 +216,6 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans # ex = expectations[-1][1] # var = expectations[:, 0] - ex[:, 1]**2 - # jax.debug.print("expectation {x}", x=expectations[-1]) _, samples, _ = run_inference_algorithm( rng_key=run_key, @@ -232,6 +231,10 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans # print(samples.mean(axis=0)) + # jax.debug.print("allclose {x}", x=jnp.allclose((samples**2).mean(axis=0), expectations[-1], atol=1e-2)) + + + # jax.debug.print("expectation {x}", x=expectations[-1]) # jax.debug.print("samples {x}", x=(samples**2).mean(axis=0)) # jax.debug.print("samples {x}", x=cumulative_avg(samples)) # jax.debug.print("samples {x}", x=jnp.var(samples, axis=0)) @@ -240,10 +243,10 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans # raise Exception acceptance_rate = 1. - return expectations, blackjax_mclmc_sampler_params, calls_per_integrator_step(integrator_type), acceptance_rate, None, None + return samples, blackjax_mclmc_sampler_params, calls_per_integrator_step(integrator_type), acceptance_rate, None, None -def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, target_acc_rate=None): +def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, target_acc_rate=None, L_proposal_factor=jnp.inf): integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] init_key, tune_key, run_key = jax.random.split(key, 3) @@ -252,6 +255,7 @@ def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_positi position=initial_position, logdensity_fn=logdensity_fn, random_generator_arg=init_key ) + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( integrator=integrator, integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), @@ -260,7 +264,8 @@ def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_positi rng_key=rng_key, state=state, step_size=step_size, - logdensity_fn=logdensity_fn) + logdensity_fn=logdensity_fn, + L_proposal_factor=L_proposal_factor) if target_acc_rate is None: target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] @@ -296,6 +301,7 @@ def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_positi integration_steps_fn = lambda key: jnp.ceil(jax.random.uniform(key) * rescale(L/step_size)) , integrator=integrator, sqrt_diag_cov=blackjax_mclmc_sampler_params.sqrt_diag_cov, + L_proposal_factor=L_proposal_factor ) @@ -313,7 +319,6 @@ def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_positi return out, blackjax_mclmc_sampler_params, calls_per_integrator_step(integrator_type) * (L/step_size), info.acceptance_rate, params_history, final_da -# we should do at least: mclmc, nuts, unadjusted hmc, adjusted_mclmc, langevin samplers = { 'nuts' : run_nuts, From 7ecd9797a3e8372ff95d6b715ad04ff61a0045e7 Mon Sep 17 00:00:00 2001 From: = Date: Wed, 5 Jun 2024 10:02:52 -0400 Subject: [PATCH 11/21] fixed bug --- benchmarks/mcmc/sampling_algorithms.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index 1578c15..eb268bb 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -276,7 +276,7 @@ def run_adjusted_mclmc(integrator_type, logdensity_fn, num_steps, initial_positi blackjax_mclmc_sampler_params, params_history, final_da - ) = blackjax.adaptation.mclmc_adaptation.adjusted_mclmc_find_L_and_step_size( + ) = blackjax.adjusted_mclmc_find_L_and_step_size( mclmc_kernel=kernel, num_steps=num_steps, state=initial_state, From ad766cb952da1aa579d4b84bcb1162c2eea88fd6 Mon Sep 17 00:00:00 2001 From: = Date: Wed, 5 Jun 2024 16:11:45 -0400 Subject: [PATCH 12/21] add results --- benchmarks/mcmc/results_recent.ipynb | 2739 ++++++++++++++++++++++++++ 1 file changed, 2739 insertions(+) create mode 100644 benchmarks/mcmc/results_recent.ipynb diff --git a/benchmarks/mcmc/results_recent.ipynb b/benchmarks/mcmc/results_recent.ipynb new file mode 100644 index 0000000..593e919 --- /dev/null +++ b/benchmarks/mcmc/results_recent.ipynb @@ -0,0 +1,2739 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def load_results(model_name):\n", + "\n", + " results = pd.read_csv(f\"../../results{model_name}.csv\")\n", + " # results = results.drop(results[results['tuning'] != 'standard'].index)\n", + "\n", + " fig, ax = plt.subplots(figsize=(20, 5))\n", + "\n", + " # sns.barplot(data=results, x=\"sampler\", y=\"ESS\", hue=results[['integrator', 'preconditioning', 'inv_L_prop']].apply(\n", + " # lambda row: f\"{row.integrator}, {row.preconditioning}, {row.inv_L_prop}\", axis=1), ax=ax)\n", + "\n", + " results = results.drop(results[results['inv_L_prop'] != 0.0].index)\n", + " sns.barplot(data=results, x=\"sampler\", y=\"ESS\", hue=results[['integrator', 'preconditioning']].apply(\n", + " lambda row: f\"{row.integrator}, {row.preconditioning}\", axis=1), ax=ax)\n", + "\n", + " plt.xlabel(\"Sampler\")\n", + " plt.ylabel(\"ESS\")\n", + " plt.title(model_name)\n", + " plt.show()\n", + " plt.show()\n", + " display(results)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioninginv_L_propESS
    0IllConditionedGaussian100mclmc8.32122712.235801mclachlanstandard1.000000True0.00.051760
    1IllConditionedGaussian100mhmclmc:0.6514.66537013.332154mclachlanstandard0.869687True0.00.031133
    2IllConditionedGaussian100mhmclmc:st3:0.6510.4314139.380373mclachlanstandard0.950884True0.00.025563
    5IllConditionedGaussian100mhmclmc:0.914.75309712.355309mclachlanstandard0.896921True0.00.028982
    6IllConditionedGaussian100mhmclmc:st3:0.98.5859807.805437mclachlanstandard0.974321True0.00.020680
    9IllConditionedGaussian100nuts0.0000000.000000mclachlanstandard0.985966True0.00.003175
    10IllConditionedGaussian100mclmc9.7656386.171023velocity_verletstandard1.000000True0.00.025291
    11IllConditionedGaussian100mhmclmc:0.6514.7096659.615504velocity_verletstandard0.645673True0.00.024080
    12IllConditionedGaussian100mhmclmc:st3:0.6513.3350118.815445velocity_verletstandard0.726589True0.00.021389
    15IllConditionedGaussian100mhmclmc:0.914.7074575.306296velocity_verletstandard0.894856True0.00.019090
    16IllConditionedGaussian100mhmclmc:st3:0.93.2875702.970148velocity_verletstandard0.988101True0.00.006129
    19IllConditionedGaussian100nuts0.0000000.000000velocity_verletstandard0.854525True0.00.005200
    20IllConditionedGaussian100mclmc14.3699975.799153mclachlanstandard1.000000False0.00.009833
    21IllConditionedGaussian100mhmclmc:0.6514.6653707.468881mclachlanstandard0.647271False0.00.004372
    22IllConditionedGaussian100mhmclmc:st3:0.6518.9704497.581570mclachlanstandard0.623296False0.00.003104
    25IllConditionedGaussian100mhmclmc:0.914.7530975.897523mclachlanstandard0.899940False0.00.006468
    26IllConditionedGaussian100mhmclmc:st3:0.99.3928995.919640mclachlanstandard0.903640False0.00.003841
    29IllConditionedGaussian100nuts0.0000000.000000mclachlanstandard0.856611False0.00.001553
    30IllConditionedGaussian100mclmc14.7707202.309608velocity_verletstandard1.000000False0.00.000000
    31IllConditionedGaussian100mhmclmc:0.6514.7096653.822616velocity_verletstandard0.676816False0.00.005411
    32IllConditionedGaussian100mhmclmc:st3:0.658.9662043.867702velocity_verletstandard0.648482False0.00.002895
    35IllConditionedGaussian100mhmclmc:0.914.7074572.169158velocity_verletstandard0.904938False0.00.005130
    36IllConditionedGaussian100mhmclmc:st3:0.93.1507632.239320velocity_verletstandard0.904524False0.00.000000
    39IllConditionedGaussian100nuts0.0000000.000000velocity_verletstandard0.808960False0.00.001599
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size \n", + "0 IllConditionedGaussian 100 mclmc 8.321227 12.235801 \\\n", + "1 IllConditionedGaussian 100 mhmclmc:0.65 14.665370 13.332154 \n", + "2 IllConditionedGaussian 100 mhmclmc:st3:0.65 10.431413 9.380373 \n", + "5 IllConditionedGaussian 100 mhmclmc:0.9 14.753097 12.355309 \n", + "6 IllConditionedGaussian 100 mhmclmc:st3:0.9 8.585980 7.805437 \n", + "9 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "10 IllConditionedGaussian 100 mclmc 9.765638 6.171023 \n", + "11 IllConditionedGaussian 100 mhmclmc:0.65 14.709665 9.615504 \n", + "12 IllConditionedGaussian 100 mhmclmc:st3:0.65 13.335011 8.815445 \n", + "15 IllConditionedGaussian 100 mhmclmc:0.9 14.707457 5.306296 \n", + "16 IllConditionedGaussian 100 mhmclmc:st3:0.9 3.287570 2.970148 \n", + "19 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "20 IllConditionedGaussian 100 mclmc 14.369997 5.799153 \n", + "21 IllConditionedGaussian 100 mhmclmc:0.65 14.665370 7.468881 \n", + "22 IllConditionedGaussian 100 mhmclmc:st3:0.65 18.970449 7.581570 \n", + "25 IllConditionedGaussian 100 mhmclmc:0.9 14.753097 5.897523 \n", + "26 IllConditionedGaussian 100 mhmclmc:st3:0.9 9.392899 5.919640 \n", + "29 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "30 IllConditionedGaussian 100 mclmc 14.770720 2.309608 \n", + "31 IllConditionedGaussian 100 mhmclmc:0.65 14.709665 3.822616 \n", + "32 IllConditionedGaussian 100 mhmclmc:st3:0.65 8.966204 3.867702 \n", + "35 IllConditionedGaussian 100 mhmclmc:0.9 14.707457 2.169158 \n", + "36 IllConditionedGaussian 100 mhmclmc:st3:0.9 3.150763 2.239320 \n", + "39 IllConditionedGaussian 100 nuts 0.000000 0.000000 \n", + "\n", + " integrator tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 mclachlan standard 1.000000 True 0.0 0.051760 \n", + "1 mclachlan standard 0.869687 True 0.0 0.031133 \n", + "2 mclachlan standard 0.950884 True 0.0 0.025563 \n", + "5 mclachlan standard 0.896921 True 0.0 0.028982 \n", + "6 mclachlan standard 0.974321 True 0.0 0.020680 \n", + "9 mclachlan standard 0.985966 True 0.0 0.003175 \n", + "10 velocity_verlet standard 1.000000 True 0.0 0.025291 \n", + "11 velocity_verlet standard 0.645673 True 0.0 0.024080 \n", + "12 velocity_verlet standard 0.726589 True 0.0 0.021389 \n", + "15 velocity_verlet standard 0.894856 True 0.0 0.019090 \n", + "16 velocity_verlet standard 0.988101 True 0.0 0.006129 \n", + "19 velocity_verlet standard 0.854525 True 0.0 0.005200 \n", + "20 mclachlan standard 1.000000 False 0.0 0.009833 \n", + "21 mclachlan standard 0.647271 False 0.0 0.004372 \n", + "22 mclachlan standard 0.623296 False 0.0 0.003104 \n", + "25 mclachlan standard 0.899940 False 0.0 0.006468 \n", + "26 mclachlan standard 0.903640 False 0.0 0.003841 \n", + "29 mclachlan standard 0.856611 False 0.0 0.001553 \n", + "30 velocity_verlet standard 1.000000 False 0.0 0.000000 \n", + "31 velocity_verlet standard 0.676816 False 0.0 0.005411 \n", + "32 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioninginv_L_propESS
    0Brownian32mclmc15.2928321.850765mclachlanstandard1.000000True0.00.011555
    1Brownian32mhmclmc:0.650.8117300.737936mclachlanstandard0.997217True0.00.000000
    2Brownian32mhmclmc:st3:0.651.6234601.475873mclachlanstandard0.979828True0.00.000750
    5Brownian32mhmclmc:0.90.8352890.759353mclachlanstandard0.997092True0.00.000000
    6Brownian32mhmclmc:st3:0.91.6705781.512323mclachlanstandard0.978997True0.00.000949
    9Brownian32nuts0.0000000.000000mclachlanstandard0.918437True0.00.002308
    10Brownian32mclmc14.6506100.903541velocity_verletstandard1.000000True0.00.010741
    11Brownian32mhmclmc:0.650.8119940.738176velocity_verletstandard0.972292True0.00.000000
    12Brownian32mhmclmc:st3:0.651.6239881.476352velocity_verletstandard0.828483True0.00.001466
    15Brownian32mhmclmc:0.90.7980450.723783velocity_verletstandard0.953701True0.00.000000
    16Brownian32mhmclmc:st3:0.91.5928221.231201velocity_verletstandard0.855923True0.00.000030
    19Brownian32nuts0.0000000.000000velocity_verletstandard0.875629True0.00.003260
    20Brownian32mclmc2.5105020.311954mclachlanstandard1.000000False0.00.012687
    21Brownian32mhmclmc:0.650.8117300.658715mclachlanstandard0.497871False0.00.001444
    22Brownian32mhmclmc:st3:0.651.6196900.584329mclachlanstandard0.546704False0.00.002015
    25Brownian32mhmclmc:0.90.8352890.399450mclachlanstandard0.850370False0.00.002710
    26Brownian32mhmclmc:st3:0.91.6250260.343807mclachlanstandard0.875975False0.00.004275
    29Brownian32nuts0.0000000.000000mclachlanstandard0.749856False0.00.002620
    30Brownian32mclmc2.3137540.146237velocity_verletstandard1.000000False0.00.014745
    31Brownian32mhmclmc:0.650.8119940.375734velocity_verletstandard0.535473False0.00.002301
    32Brownian32mhmclmc:st3:0.651.6084590.338232velocity_verletstandard0.583620False0.00.004141
    35Brownian32mhmclmc:0.90.7980450.179491velocity_verletstandard0.887697False0.00.002500
    36Brownian32mhmclmc:st3:0.91.0547000.176697velocity_verletstandard0.886072False0.00.003485
    39Brownian32nuts0.0000000.000000velocity_verletstandard0.792243False0.00.003194
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \n", + "0 Brownian 32 mclmc 15.292832 1.850765 mclachlan \\\n", + "1 Brownian 32 mhmclmc:0.65 0.811730 0.737936 mclachlan \n", + "2 Brownian 32 mhmclmc:st3:0.65 1.623460 1.475873 mclachlan \n", + "5 Brownian 32 mhmclmc:0.9 0.835289 0.759353 mclachlan \n", + "6 Brownian 32 mhmclmc:st3:0.9 1.670578 1.512323 mclachlan \n", + "9 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "10 Brownian 32 mclmc 14.650610 0.903541 velocity_verlet \n", + "11 Brownian 32 mhmclmc:0.65 0.811994 0.738176 velocity_verlet \n", + "12 Brownian 32 mhmclmc:st3:0.65 1.623988 1.476352 velocity_verlet \n", + "15 Brownian 32 mhmclmc:0.9 0.798045 0.723783 velocity_verlet \n", + "16 Brownian 32 mhmclmc:st3:0.9 1.592822 1.231201 velocity_verlet \n", + "19 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + "20 Brownian 32 mclmc 2.510502 0.311954 mclachlan \n", + "21 Brownian 32 mhmclmc:0.65 0.811730 0.658715 mclachlan \n", + "22 Brownian 32 mhmclmc:st3:0.65 1.619690 0.584329 mclachlan \n", + "25 Brownian 32 mhmclmc:0.9 0.835289 0.399450 mclachlan \n", + "26 Brownian 32 mhmclmc:st3:0.9 1.625026 0.343807 mclachlan \n", + "29 Brownian 32 nuts 0.000000 0.000000 mclachlan \n", + "30 Brownian 32 mclmc 2.313754 0.146237 velocity_verlet \n", + "31 Brownian 32 mhmclmc:0.65 0.811994 0.375734 velocity_verlet \n", + "32 Brownian 32 mhmclmc:st3:0.65 1.608459 0.338232 velocity_verlet \n", + "35 Brownian 32 mhmclmc:0.9 0.798045 0.179491 velocity_verlet \n", + "36 Brownian 32 mhmclmc:st3:0.9 1.054700 0.176697 velocity_verlet \n", + "39 Brownian 32 nuts 0.000000 0.000000 velocity_verlet \n", + "\n", + " tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 standard 1.000000 True 0.0 0.011555 \n", + "1 standard 0.997217 True 0.0 0.000000 \n", + "2 standard 0.979828 True 0.0 0.000750 \n", + "5 standard 0.997092 True 0.0 0.000000 \n", + "6 standard 0.978997 True 0.0 0.000949 \n", + "9 standard 0.918437 True 0.0 0.002308 \n", + "10 standard 1.000000 True 0.0 0.010741 \n", + "11 standard 0.972292 True 0.0 0.000000 \n", + "12 standard 0.828483 True 0.0 0.001466 \n", + "15 standard 0.953701 True 0.0 0.000000 \n", + "16 standard 0.855923 True 0.0 0.000030 \n", + "19 standard 0.875629 True 0.0 0.003260 \n", + "20 standard 1.000000 False 0.0 0.012687 \n", + "21 standard 0.497871 False 0.0 0.001444 \n", + "22 standard 0.546704 False 0.0 0.002015 \n", + "25 standard 0.850370 False 0.0 0.002710 \n", + "26 standard 0.875975 False 0.0 0.004275 \n", + "29 standard 0.749856 False 0.0 0.002620 \n", + "30 standard 1.000000 False 0.0 0.014745 \n", + "31 standard 0.535473 False 0.0 0.002301 \n", + "32 standard 0.583620 False 0.0 0.004141 \n", + "35 standard 0.887697 False 0.0 0.002500 \n", + "36 standard 0.886072 False 0.0 0.003485 \n", + "39 standard 0.792243 False 0.0 0.003194 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioninginv_L_propESS
    0Funnel20mclmc28.8303090.423722mclachlanstandard1.000000True0.00.0
    1Funnel20mhmclmc:0.6517.8478970.815291mclachlanstandard0.576275True0.00.0
    2Funnel20mhmclmc:st3:0.657.3172441.101150mclachlanstandard0.592584True0.00.0
    5Funnel20mhmclmc:0.912.1391930.442069mclachlanstandard0.844417True0.00.0
    6Funnel20mhmclmc:st3:0.95.0246520.571656mclachlanstandard0.857256True0.00.0
    9Funnel20nuts0.0000000.000000mclachlanstandard0.806269True0.00.0
    10Funnel20mclmc25.3044700.272331velocity_verletstandard1.000000True0.00.0
    11Funnel20mhmclmc:0.6514.9539220.687087velocity_verletstandard0.574424True0.00.0
    12Funnel20mhmclmc:st3:0.657.2629020.798168velocity_verletstandard0.570404True0.00.0
    15Funnel20mhmclmc:0.97.9953500.374557velocity_verletstandard0.864420True0.00.0
    16Funnel20mhmclmc:st3:0.94.6186910.500991velocity_verletstandard0.832797True0.00.0
    19Funnel20nuts0.0000000.000000velocity_verletstandard0.802180True0.00.0
    20Funnel20mclmc68.4013671.143761mclachlanstandard1.000000False0.00.0
    21Funnel20mhmclmc:0.6517.4511152.820512mclachlanstandard0.561607False0.00.0
    22Funnel20mhmclmc:st3:0.6526.3745572.671697mclachlanstandard0.561249False0.00.0
    25Funnel20mhmclmc:0.912.6175241.541648mclachlanstandard0.841699False0.00.0
    26Funnel20mhmclmc:st3:0.916.8340171.549055mclachlanstandard0.834695False0.00.0
    29Funnel20nuts0.0000000.000000mclachlanstandard0.751302False0.00.0
    30Funnel20mclmc54.1218830.669889velocity_verletstandard1.000000False0.00.0
    31Funnel20mhmclmc:0.6514.9539221.880902velocity_verletstandard0.567175False0.00.0
    32Funnel20mhmclmc:st3:0.6523.4538041.853077velocity_verletstandard0.550965False0.00.0
    35Funnel20mhmclmc:0.97.9953501.044404velocity_verletstandard0.850674False0.00.0
    36Funnel20mhmclmc:st3:0.912.6360271.046007velocity_verletstandard0.837463False0.00.0
    39Funnel20nuts0.0000000.000000velocity_verletstandard0.757914False0.00.0
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \n", + "0 Funnel 20 mclmc 28.830309 0.423722 mclachlan \\\n", + "1 Funnel 20 mhmclmc:0.65 17.847897 0.815291 mclachlan \n", + "2 Funnel 20 mhmclmc:st3:0.65 7.317244 1.101150 mclachlan \n", + "5 Funnel 20 mhmclmc:0.9 12.139193 0.442069 mclachlan \n", + "6 Funnel 20 mhmclmc:st3:0.9 5.024652 0.571656 mclachlan \n", + "9 Funnel 20 nuts 0.000000 0.000000 mclachlan \n", + "10 Funnel 20 mclmc 25.304470 0.272331 velocity_verlet \n", + "11 Funnel 20 mhmclmc:0.65 14.953922 0.687087 velocity_verlet \n", + "12 Funnel 20 mhmclmc:st3:0.65 7.262902 0.798168 velocity_verlet \n", + "15 Funnel 20 mhmclmc:0.9 7.995350 0.374557 velocity_verlet \n", + "16 Funnel 20 mhmclmc:st3:0.9 4.618691 0.500991 velocity_verlet \n", + "19 Funnel 20 nuts 0.000000 0.000000 velocity_verlet \n", + "20 Funnel 20 mclmc 68.401367 1.143761 mclachlan \n", + "21 Funnel 20 mhmclmc:0.65 17.451115 2.820512 mclachlan \n", + "22 Funnel 20 mhmclmc:st3:0.65 26.374557 2.671697 mclachlan \n", + "25 Funnel 20 mhmclmc:0.9 12.617524 1.541648 mclachlan \n", + "26 Funnel 20 mhmclmc:st3:0.9 16.834017 1.549055 mclachlan \n", + "29 Funnel 20 nuts 0.000000 0.000000 mclachlan \n", + "30 Funnel 20 mclmc 54.121883 0.669889 velocity_verlet \n", + "31 Funnel 20 mhmclmc:0.65 14.953922 1.880902 velocity_verlet \n", + "32 Funnel 20 mhmclmc:st3:0.65 23.453804 1.853077 velocity_verlet \n", + "35 Funnel 20 mhmclmc:0.9 7.995350 1.044404 velocity_verlet \n", + "36 Funnel 20 mhmclmc:st3:0.9 12.636027 1.046007 velocity_verlet \n", + "39 Funnel 20 nuts 0.000000 0.000000 velocity_verlet \n", + "\n", + " tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 standard 1.000000 True 0.0 0.0 \n", + "1 standard 0.576275 True 0.0 0.0 \n", + "2 standard 0.592584 True 0.0 0.0 \n", + "5 standard 0.844417 True 0.0 0.0 \n", + "6 standard 0.857256 True 0.0 0.0 \n", + "9 standard 0.806269 True 0.0 0.0 \n", + "10 standard 1.000000 True 0.0 0.0 \n", + "11 standard 0.574424 True 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioninginv_L_propESS
    0Banana2mclmc3.9412410.190801mclachlanstandard1.000000True0.00.011801
    1Banana2mhmclmc:0.6510.2631030.302777mclachlanstandard0.609237True0.00.001587
    2Banana2mhmclmc:st3:0.651.2892870.473051mclachlanstandard0.732504True0.00.000000
    5Banana2mhmclmc:0.910.4209940.136653mclachlanstandard0.895246True0.00.003335
    6Banana2mhmclmc:st3:0.90.1558590.133076mclachlanstandard0.986899True0.00.000000
    9Banana2nuts0.0000000.000000mclachlanstandard0.850363True0.00.001937
    10Banana2mclmc3.8099760.119181velocity_verletstandard1.000000True0.00.013774
    11Banana2mhmclmc:0.659.7902280.206669velocity_verletstandard0.604900True0.00.001354
    12Banana2mhmclmc:st3:0.651.0543550.328019velocity_verletstandard0.708593True0.00.000000
    15Banana2mhmclmc:0.99.3261830.098524velocity_verletstandard0.890210True0.00.003258
    16Banana2mhmclmc:st3:0.90.1244150.106665velocity_verletstandard0.968439True0.00.000000
    19Banana2nuts0.0000000.000000velocity_verletstandard0.846211True0.00.001764
    20Banana2mclmc16.8939530.997708mclachlanstandard1.000000False0.00.018464
    21Banana2mhmclmc:0.6510.2053482.868608mclachlanstandard0.618384False0.00.005855
    22Banana2mhmclmc:st3:0.6515.1012842.642040mclachlanstandard0.624275False0.00.005294
    25Banana2mhmclmc:0.910.6162471.543238mclachlanstandard0.884470False0.00.010246
    26Banana2mhmclmc:st3:0.97.0354061.817557mclachlanstandard0.880250False0.00.004422
    29Banana2nuts0.0000000.000000mclachlanstandard0.778092False0.00.008355
    30Banana2mclmc15.1378590.552680velocity_verletstandard1.000000False0.00.028769
    31Banana2mhmclmc:0.659.7902281.676912velocity_verletstandard0.620778False0.00.001978
    32Banana2mhmclmc:st3:0.6512.3505941.715226velocity_verletstandard0.591108False0.00.002626
    35Banana2mhmclmc:0.99.3261830.974611velocity_verletstandard0.893130False0.00.013805
    36Banana2mhmclmc:st3:0.95.5311321.123224velocity_verletstandard0.881481False0.00.005190
    39Banana2nuts0.0000000.000000velocity_verletstandard0.807089False0.00.007720
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size integrator \n", + "0 Banana 2 mclmc 3.941241 0.190801 mclachlan \\\n", + "1 Banana 2 mhmclmc:0.65 10.263103 0.302777 mclachlan \n", + "2 Banana 2 mhmclmc:st3:0.65 1.289287 0.473051 mclachlan \n", + "5 Banana 2 mhmclmc:0.9 10.420994 0.136653 mclachlan \n", + "6 Banana 2 mhmclmc:st3:0.9 0.155859 0.133076 mclachlan \n", + "9 Banana 2 nuts 0.000000 0.000000 mclachlan \n", + "10 Banana 2 mclmc 3.809976 0.119181 velocity_verlet \n", + "11 Banana 2 mhmclmc:0.65 9.790228 0.206669 velocity_verlet \n", + "12 Banana 2 mhmclmc:st3:0.65 1.054355 0.328019 velocity_verlet \n", + "15 Banana 2 mhmclmc:0.9 9.326183 0.098524 velocity_verlet \n", + "16 Banana 2 mhmclmc:st3:0.9 0.124415 0.106665 velocity_verlet \n", + "19 Banana 2 nuts 0.000000 0.000000 velocity_verlet \n", + "20 Banana 2 mclmc 16.893953 0.997708 mclachlan \n", + "21 Banana 2 mhmclmc:0.65 10.205348 2.868608 mclachlan \n", + "22 Banana 2 mhmclmc:st3:0.65 15.101284 2.642040 mclachlan \n", + "25 Banana 2 mhmclmc:0.9 10.616247 1.543238 mclachlan \n", + "26 Banana 2 mhmclmc:st3:0.9 7.035406 1.817557 mclachlan \n", + "29 Banana 2 nuts 0.000000 0.000000 mclachlan \n", + "30 Banana 2 mclmc 15.137859 0.552680 velocity_verlet \n", + "31 Banana 2 mhmclmc:0.65 9.790228 1.676912 velocity_verlet \n", + "32 Banana 2 mhmclmc:st3:0.65 12.350594 1.715226 velocity_verlet \n", + "35 Banana 2 mhmclmc:0.9 9.326183 0.974611 velocity_verlet \n", + "36 Banana 2 mhmclmc:st3:0.9 5.531132 1.123224 velocity_verlet \n", + "39 Banana 2 nuts 0.000000 0.000000 velocity_verlet \n", + "\n", + " tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 standard 1.000000 True 0.0 0.011801 \n", + "1 standard 0.609237 True 0.0 0.001587 \n", + "2 standard 0.732504 True 0.0 0.000000 \n", + "5 standard 0.895246 True 0.0 0.003335 \n", + "6 standard 0.986899 True 0.0 0.000000 \n", + "9 standard 0.850363 True 0.0 0.001937 \n", + "10 standard 1.000000 True 0.0 0.013774 \n", + "11 standard 0.604900 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioninginv_L_propESS
    0StochasticVolatility2429mclmc311.51446513.777160mclachlanstandard1.000000True0.00.000000
    1StochasticVolatility2429mhmclmc:0.659.1207508.291590mclachlanstandard0.815039True0.00.000000
    2StochasticVolatility2429mhmclmc:st3:0.6518.24150111.698965mclachlanstandard0.474813True0.00.000000
    3StochasticVolatility2429mhmclmc:0.98.5626285.961264mclachlanstandard0.928562True0.00.000000
    4StochasticVolatility2429mhmclmc:st3:0.917.1252566.692935mclachlanstandard0.869526True0.00.000000
    5StochasticVolatility2429nuts0.0000000.000000mclachlanstandard0.845070True0.00.000048
    6StochasticVolatility2429mclmc283.2078558.938250velocity_verletstandard1.000000True0.00.000000
    7StochasticVolatility2429mhmclmc:0.654.9035184.022184velocity_verletstandard0.790328True0.00.000000
    8StochasticVolatility2429mhmclmc:st3:0.659.8070364.533942velocity_verletstandard0.653769True0.00.000000
    9StochasticVolatility2429mhmclmc:0.91.5951771.450161velocity_verletstandard0.977952True0.00.000000
    10StochasticVolatility2429mhmclmc:st3:0.93.1903552.415100velocity_verletstandard0.945467True0.00.000000
    11StochasticVolatility2429nuts0.0000000.000000velocity_verletstandard0.795639True0.00.000038
    12StochasticVolatility2429mclmc57.5713231.486534mclachlanstandard1.000000False0.00.000000
    13StochasticVolatility2429mhmclmc:0.659.1207501.371532mclachlanstandard0.715175False0.00.000000
    14StochasticVolatility2429mhmclmc:st3:0.6517.7188221.387834mclachlanstandard0.662207False0.00.000202
    15StochasticVolatility2429mhmclmc:0.98.5626280.914054mclachlanstandard0.889470False0.00.000000
    16StochasticVolatility2429mhmclmc:st3:0.912.8500820.871945mclachlanstandard0.886362False0.00.000000
    17StochasticVolatility2429nuts0.0000000.000000mclachlanstandard0.816107False0.00.000040
    18StochasticVolatility2429mclmc51.3893740.888809velocity_verletstandard1.000000False0.00.000000
    19StochasticVolatility2429mhmclmc:0.654.9035180.873080velocity_verletstandard0.618781False0.00.000000
    20StochasticVolatility2429mhmclmc:st3:0.659.8070360.912963velocity_verletstandard0.556095False0.00.000000
    21StochasticVolatility2429mhmclmc:0.91.5951770.358777velocity_verletstandard0.953937False0.00.000000
    22StochasticVolatility2429mhmclmc:st3:0.93.1903550.476779velocity_verletstandard0.903845False0.00.000000
    23StochasticVolatility2429nuts0.0000000.000000velocity_verletstandard0.802850False0.00.000039
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size \n", + "0 StochasticVolatility 2429 mclmc 311.514465 13.777160 \\\n", + "1 StochasticVolatility 2429 mhmclmc:0.65 9.120750 8.291590 \n", + "2 StochasticVolatility 2429 mhmclmc:st3:0.65 18.241501 11.698965 \n", + "3 StochasticVolatility 2429 mhmclmc:0.9 8.562628 5.961264 \n", + "4 StochasticVolatility 2429 mhmclmc:st3:0.9 17.125256 6.692935 \n", + "5 StochasticVolatility 2429 nuts 0.000000 0.000000 \n", + "6 StochasticVolatility 2429 mclmc 283.207855 8.938250 \n", + "7 StochasticVolatility 2429 mhmclmc:0.65 4.903518 4.022184 \n", + "8 StochasticVolatility 2429 mhmclmc:st3:0.65 9.807036 4.533942 \n", + "9 StochasticVolatility 2429 mhmclmc:0.9 1.595177 1.450161 \n", + "10 StochasticVolatility 2429 mhmclmc:st3:0.9 3.190355 2.415100 \n", + "11 StochasticVolatility 2429 nuts 0.000000 0.000000 \n", + "12 StochasticVolatility 2429 mclmc 57.571323 1.486534 \n", + "13 StochasticVolatility 2429 mhmclmc:0.65 9.120750 1.371532 \n", + "14 StochasticVolatility 2429 mhmclmc:st3:0.65 17.718822 1.387834 \n", + "15 StochasticVolatility 2429 mhmclmc:0.9 8.562628 0.914054 \n", + "16 StochasticVolatility 2429 mhmclmc:st3:0.9 12.850082 0.871945 \n", + "17 StochasticVolatility 2429 nuts 0.000000 0.000000 \n", + "18 StochasticVolatility 2429 mclmc 51.389374 0.888809 \n", + "19 StochasticVolatility 2429 mhmclmc:0.65 4.903518 0.873080 \n", + "20 StochasticVolatility 2429 mhmclmc:st3:0.65 9.807036 0.912963 \n", + "21 StochasticVolatility 2429 mhmclmc:0.9 1.595177 0.358777 \n", + "22 StochasticVolatility 2429 mhmclmc:st3:0.9 3.190355 0.476779 \n", + "23 StochasticVolatility 2429 nuts 0.000000 0.000000 \n", + "\n", + " integrator tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 mclachlan standard 1.000000 True 0.0 0.000000 \n", + "1 mclachlan standard 0.815039 True 0.0 0.000000 \n", + "2 mclachlan standard 0.474813 True 0.0 0.000000 \n", + "3 mclachlan standard 0.928562 True 0.0 0.000000 \n", + "4 mclachlan standard 0.869526 True 0.0 0.000000 \n", + "5 mclachlan standard 0.845070 True 0.0 0.000048 \n", + "6 velocity_verlet standard 1.000000 True 0.0 0.000000 \n", + "7 velocity_verlet standard 0.790328 True 0.0 0.000000 \n", + "8 velocity_verlet standard 0.653769 True 0.0 0.000000 \n", + "9 velocity_verlet standard 0.977952 True 0.0 0.000000 \n", + "10 velocity_verlet standard 0.945467 True 0.0 0.000000 \n", + "11 velocity_verlet standard 0.795639 True 0.0 0.000038 \n", + "12 mclachlan standard 1.000000 False 0.0 0.000000 \n", + "13 mclachlan standard 0.715175 False 0.0 0.000000 \n", + "14 mclachlan standard 0.662207 False 0.0 0.000202 \n", + "15 mclachlan standard 0.889470 False 0.0 0.000000 \n", + "16 mclachlan standard 0.886362 False 0.0 0.000000 \n", + "17 mclachlan standard 0.816107 False 0.0 0.000040 \n", + "18 velocity_verlet standard 1.000000 False 0.0 0.000000 \n", + "19 velocity_verlet standard 0.618781 False 0.0 0.000000 \n", + "20 velocity_verlet standard 0.556095 False 0.0 0.000000 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioninginv_L_propESS
    0GermanCredit51mclmc14.0412190.650346mclachlanstandard1.000000True0.00.002352
    1GermanCredit51mhmclmc:0.657.1579921.127246mclachlanstandard0.576935True0.00.000558
    2GermanCredit51mhmclmc:st3:0.658.5555931.110491mclachlanstandard0.585946True0.00.000347
    5GermanCredit51mhmclmc:0.95.1273150.763498mclachlanstandard0.860078True0.00.001002
    6GermanCredit51mhmclmc:st3:0.95.3014190.767713mclachlanstandard0.860653True0.00.000923
    9GermanCredit51nuts0.0000000.000000mclachlanstandard0.899451True0.00.000738
    10GermanCredit51mclmc13.9648940.350842velocity_verletstandard1.000000True0.00.002890
    11GermanCredit51mhmclmc:0.654.7201260.764869velocity_verletstandard0.573218True0.00.000377
    12GermanCredit51mhmclmc:st3:0.657.5181620.714743velocity_verletstandard0.584576True0.00.000275
    15GermanCredit51mhmclmc:0.92.8622640.474966velocity_verletstandard0.868071True0.00.000688
    16GermanCredit51mhmclmc:st3:0.95.3390370.434157velocity_verletstandard0.883832True0.00.001120
    19GermanCredit51nuts0.0000000.000000velocity_verletstandard0.891358True0.00.001070
    20GermanCredit51mclmc13.2972740.409585mclachlanstandard1.000000False0.00.001264
    21GermanCredit51mhmclmc:0.657.1579920.648511mclachlanstandard0.594722False0.00.000502
    22GermanCredit51mhmclmc:st3:0.656.6350100.674265mclachlanstandard0.595439False0.00.000276
    25GermanCredit51mhmclmc:0.95.1273150.476807mclachlanstandard0.861260False0.00.000767
    26GermanCredit51mhmclmc:st3:0.94.4336870.497635mclachlanstandard0.855024False0.00.000677
    29GermanCredit51nuts0.0000000.000000mclachlanstandard0.812961False0.00.000442
    30GermanCredit51mclmc11.8506470.218407velocity_verletstandard1.000000False0.00.000000
    31GermanCredit51mhmclmc:0.654.7201260.463497velocity_verletstandard0.574835False0.00.000275
    32GermanCredit51mhmclmc:st3:0.657.0176610.438282velocity_verletstandard0.593350False0.00.000327
    35GermanCredit51mhmclmc:0.92.8622640.293336velocity_verletstandard0.876003False0.00.000779
    36GermanCredit51mhmclmc:st3:0.94.8445760.276905velocity_verletstandard0.886126False0.00.001173
    39GermanCredit51nuts0.0000000.000000velocity_verletstandard0.793449False0.00.000543
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size \n", + "0 GermanCredit 51 mclmc 14.041219 0.650346 \\\n", + "1 GermanCredit 51 mhmclmc:0.65 7.157992 1.127246 \n", + "2 GermanCredit 51 mhmclmc:st3:0.65 8.555593 1.110491 \n", + "5 GermanCredit 51 mhmclmc:0.9 5.127315 0.763498 \n", + "6 GermanCredit 51 mhmclmc:st3:0.9 5.301419 0.767713 \n", + "9 GermanCredit 51 nuts 0.000000 0.000000 \n", + "10 GermanCredit 51 mclmc 13.964894 0.350842 \n", + "11 GermanCredit 51 mhmclmc:0.65 4.720126 0.764869 \n", + "12 GermanCredit 51 mhmclmc:st3:0.65 7.518162 0.714743 \n", + "15 GermanCredit 51 mhmclmc:0.9 2.862264 0.474966 \n", + "16 GermanCredit 51 mhmclmc:st3:0.9 5.339037 0.434157 \n", + "19 GermanCredit 51 nuts 0.000000 0.000000 \n", + "20 GermanCredit 51 mclmc 13.297274 0.409585 \n", + "21 GermanCredit 51 mhmclmc:0.65 7.157992 0.648511 \n", + "22 GermanCredit 51 mhmclmc:st3:0.65 6.635010 0.674265 \n", + "25 GermanCredit 51 mhmclmc:0.9 5.127315 0.476807 \n", + "26 GermanCredit 51 mhmclmc:st3:0.9 4.433687 0.497635 \n", + "29 GermanCredit 51 nuts 0.000000 0.000000 \n", + "30 GermanCredit 51 mclmc 11.850647 0.218407 \n", + "31 GermanCredit 51 mhmclmc:0.65 4.720126 0.463497 \n", + "32 GermanCredit 51 mhmclmc:st3:0.65 7.017661 0.438282 \n", + "35 GermanCredit 51 mhmclmc:0.9 2.862264 0.293336 \n", + "36 GermanCredit 51 mhmclmc:st3:0.9 4.844576 0.276905 \n", + "39 GermanCredit 51 nuts 0.000000 0.000000 \n", + "\n", + " integrator tuning acc_rate preconditioning inv_L_prop ESS \n", + "0 mclachlan standard 1.000000 True 0.0 0.002352 \n", + "1 mclachlan standard 0.576935 True 0.0 0.000558 \n", + "2 mclachlan standard 0.585946 True 0.0 0.000347 \n", + "5 mclachlan standard 0.860078 True 0.0 0.001002 \n", + "6 mclachlan standard 0.860653 True 0.0 0.000923 \n", + "9 mclachlan standard 0.899451 True 0.0 0.000738 \n", + "10 velocity_verlet standard 1.000000 True 0.0 0.002890 \n", + "11 velocity_verlet standard 0.573218 True 0.0 0.000377 \n", + "12 velocity_verlet standard 0.584576 True 0.0 0.000275 \n", + "15 velocity_verlet standard 0.868071 True 0.0 0.000688 \n", + "16 velocity_verlet standard 0.883832 True 0.0 0.001120 \n", + "19 velocity_verlet standard 0.891358 True 0.0 0.001070 \n", + "20 mclachlan standard 1.000000 False 0.0 0.001264 \n", + "21 mclachlan standard 0.594722 False 0.0 0.000502 \n", + "22 mclachlan standard 0.595439 False 0.0 0.000276 \n", + "25 mclachlan standard 0.861260 False 0.0 0.000767 \n", + "26 mclachlan standard 0.855024 False 0.0 0.000677 \n", + "29 mclachlan standard 0.812961 False 0.0 0.000442 \n", + "30 velocity_verlet standard 1.000000 False 0.0 0.000000 \n", + "31 velocity_verlet standard 0.574835 False 0.0 0.000275 \n", + "32 velocity_verlet standard 0.593350 False 0.0 0.000327 \n", + "35 velocity_verlet standard 0.876003 False 0.0 0.000779 \n", + "36 velocity_verlet standard 0.886126 False 0.0 0.001173 \n", + "39 velocity_verlet standard 0.793449 False 0.0 0.000543 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load_results(\"IllConditionedGaussian\")\n", + "load_results(\"Brownian\")\n", + "load_results(\"Funnel\")\n", + "load_results(\"Banana\")\n", + "load_results(\"StochasticVolatility\")\n", + "load_results(\"GermanCredit\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mclmc", + "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": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 600d8996007215f3207c961ffcec183c1ad876cb Mon Sep 17 00:00:00 2001 From: = Date: Thu, 6 Jun 2024 16:27:07 -0400 Subject: [PATCH 13/21] add results --- benchmarks/mcmc/benchmark.py | 109 +++++++++++++++------------- benchmarks/mcmc/inference_models.py | 4 +- 2 files changed, 62 insertions(+), 51 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 39900f6..a8cc9c8 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -384,55 +384,66 @@ def benchmark_mhmchmc(batch_size): results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() print(f'adjusted_mclmc with tuning ESS {ess}') - # if False: - # ####### run adjusted_mclmc with standard tuning + grid search - - # init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) - # initial_position = model.sample_init(init_pos_key) - - # initial_state = blackjax.mcmc.adjusted_mclmc.init( - # position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key - # ) - - # kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( - # integrator=generate_isokinetic_integrator(integrator_type), - # integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), - # sqrt_diag_cov=sqrt_diag_cov, - # )( - # rng_key=rng_key, - # state=state, - # step_size=step_size, - # logdensity_fn=model.logdensity_fn) - - # ( - # state, - # blackjax_adjusted_mclmc_sampler_params, - # _, _ - # ) = blackjax.adaptation.mclmc_adaptation.adjusted_mclmc_find_L_and_step_size( - # mclmc_kernel=kernel, - # num_steps=num_steps, - # state=initial_state, - # rng_key=tune_key, - # target=target_acceptance_rate_of_order[integrator_order(integrator_type)], - # frac_tune1=0.1, - # frac_tune2=0.1, - # frac_tune3=0.0, - # diagonal_preconditioning=False - # ) - - # print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") - # print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - - - # L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=state, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) - # # print(f"params after grid tuning are L={L}, step_size={step_size}") - - - # ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, L=L, step_size=step_size, initial_state=state, sqrt_diag_cov=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) - - # print(f"grads to low bias: {grad_calls}") - - # results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), (integrator_type), f"gridsearch:{convergence}", acceptance_rate.mean().item()), todo: blah?, todo: precond, todo: L_propsoal_factor] = ess.item() + if True: + ####### run adjusted_mclmc with standard tuning + grid search + + init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) + initial_position = model.sample_init(init_pos_key) + + integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] + + init_key, tune_key, run_key = jax.random.split(init_key, 3) + + initial_state = blackjax.mcmc.adjusted_mclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key + ) + + + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( + integrator=integrator, + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), + sqrt_diag_cov=sqrt_diag_cov, + )( + rng_key=rng_key, + state=state, + step_size=step_size, + logdensity_fn=model.logdensity_fn, + L_proposal_factor=L_proposal_factor) + + if target_acc_rate is None: + target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] + print("target acc rate") + + ( + blackjax_state_after_tuning, + blackjax_adjusted_mclmc_sampler_params, + params_history, + final_da + ) = blackjax.adjusted_mclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + target=target_acc_rate, + frac_tune1=0.1, + frac_tune2=0.1, + frac_tune3=0.0, + diagonal_preconditioning=False, + ) + + print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") + print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") + + + L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) + # print(f"params after grid tuning are L={L}, step_size={step_size}") + + + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, L=L, step_size=step_size, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) + + print(f"grads to low bias: {grad_calls}") + + results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), integrator_type, f"gridsearch:{convergence}", acceptance_rate.mean().item(), False, 0.0 )] = ess.item() ####### run nuts diff --git a/benchmarks/mcmc/inference_models.py b/benchmarks/mcmc/inference_models.py index 1f92d21..bc344df 100644 --- a/benchmarks/mcmc/inference_models.py +++ b/benchmarks/mcmc/inference_models.py @@ -874,12 +874,12 @@ def step(track, useless): # Cauchy(100) : {'mclmc': 2000, 'adjusted_mclmc' : 2000, 'nuts': 2000}, # StandardNormal(100) : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, # Banana() : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, - # Brownian() : {'mclmc': 20000, 'adjusted_mclmc' : 80000, 'nuts': 40000}, + Brownian() : {'mclmc': 2000, 'adjusted_mclmc' : 8000, 'nuts': 4000}, # Funnel() : {'mclmc': 20000, 'adjusted_mclmc' : 80000, 'nuts': 40000}, # Banana() : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, - IllConditionedGaussian(100, 100): {'mclmc': 20000, 'adjusted_mclmc' : 20000, 'nuts': 20000}, + # IllConditionedGaussian(100, 100): {'mclmc': 20000, 'adjusted_mclmc' : 20000, 'nuts': 20000}, # GermanCredit(): {'mclmc': 80000, 'adjusted_mclmc' : 40000, 'nuts': 40000}, # ItemResponseTheory(): {'mclmc': 20000, 'adjusted_mclmc' : 40000, 'nuts': 20000}, # StochasticVolatility(): {'mclmc': 40000, 'adjusted_mclmc' : 40000, 'nuts': 40000} From 940e7ad5719c256221afb538c7c68e7863d78b3a Mon Sep 17 00:00:00 2001 From: = Date: Fri, 7 Jun 2024 15:27:26 -0400 Subject: [PATCH 14/21] add results --- benchmarks/mcmc/benchmark.py | 144 ++-- benchmarks/mcmc/inference_models.py | 4 +- benchmarks/mcmc/results_recent.ipynb | 960 ++++++++++++++++++++++++++- 3 files changed, 1058 insertions(+), 50 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index a8cc9c8..18c022d 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -94,7 +94,6 @@ def gridsearch_tune(key, iterations, grid_size, model, sampler, batch, num_steps width = 2 step_sizes = np.logspace(np.log10(center_step_size/width), np.log10(center_step_size*width), grid_size) Ls = np.logspace(np.log10(center_L/2), np.log10(center_L*2),grid_size) - # print(list(itertools.product(step_sizes , Ls))) grid_keys = jax.random.split(keys[i], grid_size^2) print(f"center step size {center_step_size}, center L {center_L}") @@ -334,7 +333,7 @@ def benchmark_mhmchmc(batch_size): # coefficients = [yoshida_coefficients, mclachlan_coefficients, velocity_verlet_coefficients, omelyan_coefficients] # coefficients = [mclachlan_coefficients, velocity_verlet_coefficients] - integrators = ["mclachlan", "velocity_verlet"] + integrators = ["mclachlan", "velocity_verlet", "omelyan", "yoshida"] for model in models: results = defaultdict(tuple) for preconditioning, integrator_type in itertools.product([True, False], integrators): @@ -348,45 +347,47 @@ def benchmark_mhmchmc(batch_size): contract = jnp.max - - ess, grad_calls, params , _, step_size_over_da = benchmark_chains( - model, - partial(run_mclmc,integrator_type=integrator_type, preconditioning=preconditioning), - key0, - n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0)] = ess.item() - print(f'mclmc with tuning ESS {ess}') - - - ####### run adjusted_mclmc with standard tuning - for target_acc_rate, L_proposal_factor in itertools.product([0.65, 0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): - # coeffs = mclachlan_coefficients - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( - model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), - key1, - n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() - print(f'adjusted_mclmc with tuning ESS {ess}') - - # integrator_type = mclachlan_coefficients - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( - model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), - key1, - n=num_steps, - batch=num_chains, + if True: + ess, grad_calls, params , _, step_size_over_da = benchmark_chains( + model, + partial(run_mclmc,integrator_type=integrator_type, preconditioning=preconditioning), + key0, + n=num_steps, + batch=num_chains, contract=contract) - results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() - print(f'adjusted_mclmc with tuning ESS {ess}') + results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0)] = ess.item() + print(f'mclmc with tuning ESS {ess}') + + + ####### run adjusted_mclmc with standard tuning + for target_acc_rate, L_proposal_factor in itertools.product([0.65, 0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): + # coeffs = mclachlan_coefficients + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), + key1, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() + print(f'adjusted_mclmc with tuning ESS {ess}') + + # integrator_type = mclachlan_coefficients + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), + key1, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() + print(f'adjusted_mclmc with tuning ESS {ess}') if True: ####### run adjusted_mclmc with standard tuning + grid search + + init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) initial_position = model.sample_init(init_pos_key) @@ -408,11 +409,9 @@ def benchmark_mhmchmc(batch_size): state=state, step_size=step_size, logdensity_fn=model.logdensity_fn, - L_proposal_factor=L_proposal_factor) + L_proposal_factor=jnp.inf) - if target_acc_rate is None: - target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] - print("target acc rate") + target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] ( blackjax_state_after_tuning, @@ -434,8 +433,16 @@ def benchmark_mhmchmc(batch_size): print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") + func = lambda L, step_size: (benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=1., L=L, step_size=step_size), key=grid_key, n=num_steps, contract=jnp.max, batch=batch_size)[0], None) - L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) + out = grid_search(func=func, x=blackjax_adjusted_mclmc_sampler_params.L, y=blackjax_adjusted_mclmc_sampler_params.step_size, delta_x=2, delta_y=2, size_grid=5, num_iter=3) + print(out) + L = out[0] + step_size = out[1] + + print(out[2]) + + # L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) # print(f"params after grid tuning are L={L}, step_size={step_size}") @@ -443,7 +450,7 @@ def benchmark_mhmchmc(batch_size): print(f"grads to low bias: {grad_calls}") - results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), integrator_type, f"gridsearch:{convergence}", acceptance_rate.mean().item(), False, 0.0 )] = ess.item() + results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), False, 0.0 )] = ess.item() ####### run nuts @@ -557,9 +564,9 @@ def run_benchmarks_simple(): # sampler = run_adjusted_mclmc sampler = run_mclmc # model = IllConditionedGaussian(100,100) - model = Brownian() - # model = StandardNormal(10) - model = Banana() + # model = Brownian() + model = StandardNormal(100) + # model = Banana() integrator_type = "mclachlan" contract = jnp.max # how we average across dimensions num_steps = 1000 @@ -576,9 +583,56 @@ def run_benchmarks_simple(): print(f"Effective Sample Size (ESS) of {model.name} with preconditioning set to {preconditioning} is {ess}") +def grid_search(func, x, y, delta_x, delta_y, size_grid= 5, num_iter= 3): + """Args: + func(x, y) = (score, extra_results), + where score is the scalar that we would like to maximize (e.g. ESS averaged over the chains) + and extra_results are some additional info that we would like to store, e.g. acceptance rate + + The initial grid will be set on the region [x - delta_x, x + delta x] \times [y - delta_y, y + delta y]. + In each iteration the grid will be shifted to the best location found by the previous grid and it will be shrinked, + such that the nearest neigbours of the previous grid become the edges of the new grid. + + If at any point, the best point is found at the edge of the grid a warning is printed. + + Returns: + (x, y, score, extra results) at the best parameters + """ + + def kernel(state): + z, delta_z = state + + # compute the func on the grid + Z = np.linspace(z - delta_z, z + delta_z, size_grid) + Results = [[func(xx, yy) for yy in Z[:, 1]] for xx in Z[:, 0]] + Scores = [[Results[i][j][0] for j in range(size_grid)] for i in range(size_grid)] + + # find the best point on the grid + ind = np.unravel_index(np.argmax(Scores, axis=None), (size_grid, size_grid)) + + if np.any(np.isin(np.array(ind), [0, size_grid - 1])): + print("Best parameters found at the edge of the grid.") + + # new grid + state = (np.array([Z[ind[i], i] for i in range(2)]), 2 * delta_z / (size_grid - 1)) + + return state, Results[ind[0]][ind[1]] + + + state = (np.array([x, y]), np.array([delta_x, delta_y])) + + for iteration in range(num_iter): # iteratively shrink and shift the grid + state, results = kernel(state) + + return [state[0][0], state[0][1], *results] + if __name__ == "__main__": + + # print(grid_search(func, 0., 0., 1., 2., size_grid= 5, num_iter=1)) + + # run_benchmarks_simple() # benchmark_omelyan(128) diff --git a/benchmarks/mcmc/inference_models.py b/benchmarks/mcmc/inference_models.py index bc344df..6636e56 100644 --- a/benchmarks/mcmc/inference_models.py +++ b/benchmarks/mcmc/inference_models.py @@ -872,9 +872,9 @@ def step(track, useless): ## Rosenbrock(): {'mclmc': 40000, 'adjusted_mclmc' : 40000, 'nuts': 40000}, # no Ex2 # Cauchy(100) : {'mclmc': 2000, 'adjusted_mclmc' : 2000, 'nuts': 2000}, - # StandardNormal(100) : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, + StandardNormal(10) : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, # Banana() : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, - Brownian() : {'mclmc': 2000, 'adjusted_mclmc' : 8000, 'nuts': 4000}, + # Brownian() : {'mclmc': 2000, 'adjusted_mclmc' : 8000, 'nuts': 4000}, # Funnel() : {'mclmc': 20000, 'adjusted_mclmc' : 80000, 'nuts': 40000}, diff --git a/benchmarks/mcmc/results_recent.ipynb b/benchmarks/mcmc/results_recent.ipynb index 593e919..6b35117 100644 --- a/benchmarks/mcmc/results_recent.ipynb +++ b/benchmarks/mcmc/results_recent.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -40,6 +40,960 @@ " display(results)\n" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    modeldimssamplerLstep_sizeintegratortuningacc_ratepreconditioninginv_L_propESS
    0StandardNormal10mclmc2.6899043.617348mclachlanstandard1.000000True0.00.112867
    1StandardNormal10mhmclmc:0.653.1482832.862076mclachlanstandard0.987067True0.00.058651
    2StandardNormal10mhmclmc:st3:0.654.1555093.777736mclachlanstandard0.967658True0.00.093145
    3StandardNormal10mhmclmc:0.93.1594992.872272mclachlanstandard0.986864True0.00.058350
    4StandardNormal10mhmclmc:st3:0.94.0679303.698118mclachlanstandard0.970545True0.00.092200
    5StandardNormal10mhmchmc:grid4.4072514.370228mclachlangridsearch0.950681False0.00.113455
    6StandardNormal10nuts0.0000000.000000mclachlanstandard0.988996True0.00.014318
    7StandardNormal10mclmc3.0074021.831025velocity_verletstandard1.000000True0.00.082508
    8StandardNormal10mhmclmc:0.653.1687442.880676velocity_verletstandard0.911388True0.00.131562
    9StandardNormal10mhmclmc:st3:0.654.0126493.647863velocity_verletstandard0.831730True0.00.189394
    10StandardNormal10mhmclmc:0.93.1547452.867950velocity_verletstandard0.914567True0.00.121862
    11StandardNormal10mhmclmc:st3:0.94.0193042.768279velocity_verletstandard0.906955True0.00.106406
    12StandardNormal10mhmchmc:grid4.1477004.111546velocity_verletgridsearch0.783970False0.00.224781
    13StandardNormal10nuts0.0000000.000000velocity_verletstandard0.880672True0.00.017496
    14StandardNormal10mclmc1.3145256.010265omelyanstandard1.000000True0.00.059701
    15StandardNormal10mhmclmc:0.653.3226162.925488omelyanstandard0.993841True0.00.023035
    16StandardNormal10mhmclmc:st3:0.653.8115793.465072omelyanstandard0.997938True0.00.031133
    17StandardNormal10mhmclmc:0.93.1587282.871571omelyanstandard0.999361True0.00.023736
    18StandardNormal10mhmclmc:st3:0.93.9212633.564784omelyanstandard0.998085True0.00.035791
    19StandardNormal10mhmchmc:grid5.6440565.608233omelyangridsearch0.985207False0.00.048948
    20StandardNormal10nuts0.0000000.000000omelyanstandard0.947192True0.00.000000
    21StandardNormal10mclmc1.1735116.441570yoshidastandard1.000000True0.00.086356
    22StandardNormal10mhmclmc:0.653.1692762.881160yoshidastandard0.996914True0.00.039304
    23StandardNormal10mhmclmc:st3:0.653.9470843.588258yoshidastandard0.994144True0.00.051889
    24StandardNormal10mhmclmc:0.93.1557592.868871yoshidastandard0.997009True0.00.039406
    25StandardNormal10mhmclmc:st3:0.93.9649743.604522yoshidastandard0.994156True0.00.059418
    26StandardNormal10mhmchmc:grid5.1947705.154336yoshidagridsearch0.983667False0.00.081866
    27StandardNormal10nuts0.0000000.000000yoshidastandard0.988915True0.00.011875
    28StandardNormal10mclmc2.6552163.597319mclachlanstandard1.000000False0.00.109890
    29StandardNormal10mhmclmc:0.653.1482832.862076mclachlanstandard0.987020False0.00.057248
    30StandardNormal10mhmclmc:st3:0.654.0437813.676165mclachlanstandard0.971347False0.00.094304
    31StandardNormal10mhmclmc:0.93.1594992.872272mclachlanstandard0.986885False0.00.058275
    32StandardNormal10mhmclmc:st3:0.94.0280713.661883mclachlanstandard0.971646False0.00.093528
    33StandardNormal10nuts0.0000000.000000mclachlanstandard0.854604False0.00.014817
    34StandardNormal10mclmc3.0517401.889234velocity_verletstandard1.000000False0.00.084531
    35StandardNormal10mhmclmc:0.653.1684232.880384velocity_verletstandard0.915399False0.00.130429
    36StandardNormal10mhmclmc:st3:0.653.9433483.584862velocity_verletstandard0.846438False0.00.177557
    37StandardNormal10mhmclmc:0.93.1580012.870910velocity_verletstandard0.916179False0.00.127502
    38StandardNormal10mhmclmc:st3:0.93.9533482.774476velocity_verletstandard0.909291False0.00.113531
    39StandardNormal10nuts0.0000000.000000velocity_verletstandard0.813865False0.00.011322
    40StandardNormal10mclmc1.2995586.003494omelyanstandard1.000000False0.00.058997
    41StandardNormal10mhmclmc:0.653.3226163.020560omelyanstandard0.998115False0.00.024050
    42StandardNormal10mhmclmc:st3:0.653.7844433.440402omelyanstandard0.998342False0.00.031027
    43StandardNormal10mhmclmc:0.93.1587282.871571omelyanstandard0.999394False0.00.024050
    44StandardNormal10mhmclmc:st3:0.93.8850483.531862omelyanstandard0.998261False0.00.031080
    45StandardNormal10nuts0.0000000.000000omelyanstandard0.894578False0.00.005150
    46StandardNormal10mclmc1.1464946.378285yoshidastandard1.000000False0.00.081103
    47StandardNormal10mhmclmc:0.653.1692762.881160yoshidastandard0.996955False0.00.040030
    48StandardNormal10mhmclmc:st3:0.653.8951183.541016yoshidastandard0.994423False0.00.059186
    49StandardNormal10mhmclmc:0.93.1557592.868871yoshidastandard0.996996False0.00.040030
    50StandardNormal10mhmclmc:st3:0.93.9149203.559018yoshidastandard0.994347False0.00.052977
    51StandardNormal10nuts0.0000000.000000yoshidastandard0.882297False0.00.005604
    \n", + "
    " + ], + "text/plain": [ + " model dims sampler L step_size \\\n", + "0 StandardNormal 10 mclmc 2.689904 3.617348 \n", + "1 StandardNormal 10 mhmclmc:0.65 3.148283 2.862076 \n", + "2 StandardNormal 10 mhmclmc:st3:0.65 4.155509 3.777736 \n", + "3 StandardNormal 10 mhmclmc:0.9 3.159499 2.872272 \n", + "4 StandardNormal 10 mhmclmc:st3:0.9 4.067930 3.698118 \n", + "5 StandardNormal 10 mhmchmc:grid 4.407251 4.370228 \n", + "6 StandardNormal 10 nuts 0.000000 0.000000 \n", + "7 StandardNormal 10 mclmc 3.007402 1.831025 \n", + "8 StandardNormal 10 mhmclmc:0.65 3.168744 2.880676 \n", + "9 StandardNormal 10 mhmclmc:st3:0.65 4.012649 3.647863 \n", + "10 StandardNormal 10 mhmclmc:0.9 3.154745 2.867950 \n", + "11 StandardNormal 10 mhmclmc:st3:0.9 4.019304 2.768279 \n", + "12 StandardNormal 10 mhmchmc:grid 4.147700 4.111546 \n", + "13 StandardNormal 10 nuts 0.000000 0.000000 \n", + "14 StandardNormal 10 mclmc 1.314525 6.010265 \n", + "15 StandardNormal 10 mhmclmc:0.65 3.322616 2.925488 \n", + "16 StandardNormal 10 mhmclmc:st3:0.65 3.811579 3.465072 \n", + "17 StandardNormal 10 mhmclmc:0.9 3.158728 2.871571 \n", + "18 StandardNormal 10 mhmclmc:st3:0.9 3.921263 3.564784 \n", + "19 StandardNormal 10 mhmchmc:grid 5.644056 5.608233 \n", + "20 StandardNormal 10 nuts 0.000000 0.000000 \n", + "21 StandardNormal 10 mclmc 1.173511 6.441570 \n", + "22 StandardNormal 10 mhmclmc:0.65 3.169276 2.881160 \n", + "23 StandardNormal 10 mhmclmc:st3:0.65 3.947084 3.588258 \n", + "24 StandardNormal 10 mhmclmc:0.9 3.155759 2.868871 \n", + "25 StandardNormal 10 mhmclmc:st3:0.9 3.964974 3.604522 \n", + "26 StandardNormal 10 mhmchmc:grid 5.194770 5.154336 \n", + "27 StandardNormal 10 nuts 0.000000 0.000000 \n", + "28 StandardNormal 10 mclmc 2.655216 3.597319 \n", + "29 StandardNormal 10 mhmclmc:0.65 3.148283 2.862076 \n", + "30 StandardNormal 10 mhmclmc:st3:0.65 4.043781 3.676165 \n", + "31 StandardNormal 10 mhmclmc:0.9 3.159499 2.872272 \n", + "32 StandardNormal 10 mhmclmc:st3:0.9 4.028071 3.661883 \n", + "33 StandardNormal 10 nuts 0.000000 0.000000 \n", + "34 StandardNormal 10 mclmc 3.051740 1.889234 \n", + "35 StandardNormal 10 mhmclmc:0.65 3.168423 2.880384 \n", + "36 StandardNormal 10 mhmclmc:st3:0.65 3.943348 3.584862 \n", + "37 StandardNormal 10 mhmclmc:0.9 3.158001 2.870910 \n", + "38 StandardNormal 10 mhmclmc:st3:0.9 3.953348 2.774476 \n", + "39 StandardNormal 10 nuts 0.000000 0.000000 \n", + "40 StandardNormal 10 mclmc 1.299558 6.003494 \n", + "41 StandardNormal 10 mhmclmc:0.65 3.322616 3.020560 \n", + "42 StandardNormal 10 mhmclmc:st3:0.65 3.784443 3.440402 \n", + "43 StandardNormal 10 mhmclmc:0.9 3.158728 2.871571 \n", + "44 StandardNormal 10 mhmclmc:st3:0.9 3.885048 3.531862 \n", + "45 StandardNormal 10 nuts 0.000000 0.000000 \n", + "46 StandardNormal 10 mclmc 1.146494 6.378285 \n", + "47 StandardNormal 10 mhmclmc:0.65 3.169276 2.881160 \n", + "48 StandardNormal 10 mhmclmc:st3:0.65 3.895118 3.541016 \n", + "49 StandardNormal 10 mhmclmc:0.9 3.155759 2.868871 \n", + "50 StandardNormal 10 mhmclmc:st3:0.9 3.914920 3.559018 \n", + "51 StandardNormal 10 nuts 0.000000 0.000000 \n", + "\n", + " integrator tuning acc_rate preconditioning inv_L_prop \\\n", + "0 mclachlan standard 1.000000 True 0.0 \n", + "1 mclachlan standard 0.987067 True 0.0 \n", + "2 mclachlan standard 0.967658 True 0.0 \n", + "3 mclachlan standard 0.986864 True 0.0 \n", + "4 mclachlan standard 0.970545 True 0.0 \n", + "5 mclachlan gridsearch 0.950681 False 0.0 \n", + "6 mclachlan standard 0.988996 True 0.0 \n", + "7 velocity_verlet standard 1.000000 True 0.0 \n", + "8 velocity_verlet standard 0.911388 True 0.0 \n", + "9 velocity_verlet standard 0.831730 True 0.0 \n", + "10 velocity_verlet standard 0.914567 True 0.0 \n", + "11 velocity_verlet standard 0.906955 True 0.0 \n", + "12 velocity_verlet gridsearch 0.783970 False 0.0 \n", + "13 velocity_verlet standard 0.880672 True 0.0 \n", + "14 omelyan standard 1.000000 True 0.0 \n", + "15 omelyan standard 0.993841 True 0.0 \n", + "16 omelyan standard 0.997938 True 0.0 \n", + "17 omelyan standard 0.999361 True 0.0 \n", + "18 omelyan standard 0.998085 True 0.0 \n", + "19 omelyan gridsearch 0.985207 False 0.0 \n", + "20 omelyan standard 0.947192 True 0.0 \n", + "21 yoshida standard 1.000000 True 0.0 \n", + "22 yoshida standard 0.996914 True 0.0 \n", + "23 yoshida standard 0.994144 True 0.0 \n", + "24 yoshida standard 0.997009 True 0.0 \n", + "25 yoshida standard 0.994156 True 0.0 \n", + "26 yoshida gridsearch 0.983667 False 0.0 \n", + "27 yoshida standard 0.988915 True 0.0 \n", + "28 mclachlan standard 1.000000 False 0.0 \n", + "29 mclachlan standard 0.987020 False 0.0 \n", + "30 mclachlan standard 0.971347 False 0.0 \n", + "31 mclachlan standard 0.986885 False 0.0 \n", + "32 mclachlan standard 0.971646 False 0.0 \n", + "33 mclachlan standard 0.854604 False 0.0 \n", + "34 velocity_verlet standard 1.000000 False 0.0 \n", + "35 velocity_verlet standard 0.915399 False 0.0 \n", + "36 velocity_verlet standard 0.846438 False 0.0 \n", + "37 velocity_verlet standard 0.916179 False 0.0 \n", + "38 velocity_verlet standard 0.909291 False 0.0 \n", + "39 velocity_verlet standard 0.813865 False 0.0 \n", + "40 omelyan standard 1.000000 False 0.0 \n", + "41 omelyan standard 0.998115 False 0.0 \n", + "42 omelyan standard 0.998342 False 0.0 \n", + "43 omelyan standard 0.999394 False 0.0 \n", + "44 omelyan standard 0.998261 False 0.0 \n", + "45 omelyan standard 0.894578 False 0.0 \n", + "46 yoshida standard 1.000000 False 0.0 \n", + "47 yoshida standard 0.996955 False 0.0 \n", + "48 yoshida standard 0.994423 False 0.0 \n", + "49 yoshida standard 0.996996 False 0.0 \n", + "50 yoshida standard 0.994347 False 0.0 \n", + "51 yoshida standard 0.882297 False 0.0 \n", + "\n", + " ESS \n", + "0 0.112867 \n", + "1 0.058651 \n", + "2 0.093145 \n", + "3 0.058350 \n", + "4 0.092200 \n", + "5 0.113455 \n", + "6 0.014318 \n", + "7 0.082508 \n", + "8 0.131562 \n", + "9 0.189394 \n", + "10 0.121862 \n", + "11 0.106406 \n", + "12 0.224781 \n", + "13 0.017496 \n", + "14 0.059701 \n", + "15 0.023035 \n", + "16 0.031133 \n", + "17 0.023736 \n", + "18 0.035791 \n", + "19 0.048948 \n", + "20 0.000000 \n", + "21 0.086356 \n", + "22 0.039304 \n", + "23 0.051889 \n", + "24 0.039406 \n", + "25 0.059418 \n", + "26 0.081866 \n", + "27 0.011875 \n", + "28 0.109890 \n", + "29 0.057248 \n", + "30 0.094304 \n", + "31 0.058275 \n", + "32 0.093528 \n", + "33 0.014817 \n", + "34 0.084531 \n", + "35 0.130429 \n", + "36 0.177557 \n", + "37 0.127502 \n", + "38 0.113531 \n", + "39 0.011322 \n", + "40 0.058997 \n", + "41 0.024050 \n", + "42 0.031027 \n", + "43 0.024050 \n", + "44 0.031080 \n", + "45 0.005150 \n", + "46 0.081103 \n", + "47 0.040030 \n", + "48 0.059186 \n", + "49 0.040030 \n", + "50 0.052977 \n", + "51 0.005604 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "load_results(\"StandardNormal\")" + ] + }, { "cell_type": "code", "execution_count": 23, @@ -2731,7 +3685,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.11.5" } }, "nbformat": 4, From 3c525b45e6f07751147a6ed9ae53b1611a781fbd Mon Sep 17 00:00:00 2001 From: = Date: Sun, 9 Jun 2024 15:59:40 -0400 Subject: [PATCH 15/21] update benchmarks --- benchmarks/mcmc/benchmark.py | 175 +++++++++++++++++++++-------------- 1 file changed, 105 insertions(+), 70 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 18c022d..b6c700e 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -117,7 +117,7 @@ def gridsearch_tune(key, iterations, grid_size, model, sampler, batch, num_steps return center_L, center_step_size, converged -def run_adjusted_mclmc_no_tuning(initial_state, integrator_type, step_size, L, sqrt_diag_cov): +def run_adjusted_mclmc_no_tuning(initial_state, integrator_type, step_size, L, sqrt_diag_cov, L_proposal_factor): def s(logdensity_fn, num_steps, initial_position, transform, key): @@ -130,6 +130,7 @@ def s(logdensity_fn, num_steps, initial_position, transform, key): integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(num_steps_per_traj)) , integrator=integrator, sqrt_diag_cov=sqrt_diag_cov, + L_proposal_factor=L_proposal_factor ) _, out, info = run_inference_algorithm( @@ -333,10 +334,11 @@ def benchmark_mhmchmc(batch_size): # coefficients = [yoshida_coefficients, mclachlan_coefficients, velocity_verlet_coefficients, omelyan_coefficients] # coefficients = [mclachlan_coefficients, velocity_verlet_coefficients] - integrators = ["mclachlan", "velocity_verlet", "omelyan", "yoshida"] + # integrators = ["mclachlan", "velocity_verlet", "omelyan", "yoshida"] + integrators = ["mclachlan"] for model in models: results = defaultdict(tuple) - for preconditioning, integrator_type in itertools.product([True, False], integrators): + for preconditioning, integrator_type in itertools.product([True], integrators): num_chains = batch_size # 1 + batch_size//model.ndims print(f"NUMBER OF CHAINS for {model.name} and adjusted_mclmc is {num_chains}") @@ -360,7 +362,7 @@ def benchmark_mhmchmc(batch_size): ####### run adjusted_mclmc with standard tuning - for target_acc_rate, L_proposal_factor in itertools.product([0.65, 0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): + for target_acc_rate, L_proposal_factor in itertools.product([0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): # coeffs = mclachlan_coefficients ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( model, @@ -383,81 +385,114 @@ def benchmark_mhmchmc(batch_size): results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() print(f'adjusted_mclmc with tuning ESS {ess}') - if True: - ####### run adjusted_mclmc with standard tuning + grid search - - - - init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) - initial_position = model.sample_init(init_pos_key) - - integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] - - init_key, tune_key, run_key = jax.random.split(init_key, 3) + ####### run nuts - initial_state = blackjax.mcmc.adjusted_mclmc.init( - position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key - ) + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) + results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0)] = ess.item() + + if True: + ####### run adjusted_mclmc with standard tuning + grid search + - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( - integrator=integrator, - integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), - sqrt_diag_cov=sqrt_diag_cov, - )( - rng_key=rng_key, - state=state, - step_size=step_size, - logdensity_fn=model.logdensity_fn, - L_proposal_factor=jnp.inf) - - target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] - - ( - blackjax_state_after_tuning, - blackjax_adjusted_mclmc_sampler_params, - params_history, - final_da - ) = blackjax.adjusted_mclmc_find_L_and_step_size( - mclmc_kernel=kernel, - num_steps=num_steps, - state=initial_state, - rng_key=tune_key, - target=target_acc_rate, - frac_tune1=0.1, - frac_tune2=0.1, - frac_tune3=0.0, - diagonal_preconditioning=False, - ) - - print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") - print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - - func = lambda L, step_size: (benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=1., L=L, step_size=step_size), key=grid_key, n=num_steps, contract=jnp.max, batch=batch_size)[0], None) - - out = grid_search(func=func, x=blackjax_adjusted_mclmc_sampler_params.L, y=blackjax_adjusted_mclmc_sampler_params.step_size, delta_x=2, delta_y=2, size_grid=5, num_iter=3) - print(out) - L = out[0] - step_size = out[1] - - print(out[2]) + init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) + initial_position = model.sample_init(init_pos_key) - # L, step_size, convergence = gridsearch_tune(grid_key, iterations=10, contract=contract, grid_size=5, model=model, sampler=partial(run_adjusted_mclmc_no_tuning, integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=1.), batch=num_chains, num_steps=num_steps, center_L=blackjax_adjusted_mclmc_sampler_params.L, center_step_size=blackjax_adjusted_mclmc_sampler_params.step_size) - # print(f"params after grid tuning are L={L}, step_size={step_size}") + integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] + init_key, tune_key, tune_key2, run_key = jax.random.split(init_key, 4) - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, L=L, step_size=step_size, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=1.),bench_key, n=num_steps, batch=num_chains, contract=contract) + initial_state = blackjax.mcmc.adjusted_mclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key + ) + + unadjusted_initial_state = blackjax.mcmc.mclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, rng_key=init_key + ) + + L_proposal_factor = jnp.inf + + + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( + integrator=integrator, + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), + sqrt_diag_cov=sqrt_diag_cov, + )( + rng_key=rng_key, + state=state, + step_size=step_size, + logdensity_fn=model.logdensity_fn, + L_proposal_factor=L_proposal_factor) + + target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] + + ( + blackjax_state_after_tuning, + _, + params_history, + final_da + ) = blackjax.adjusted_mclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + target=target_acc_rate, + frac_tune1=0.1, + frac_tune2=0.1, + frac_tune3=0.1, + diagonal_preconditioning=False, + ) - print(f"grads to low bias: {grad_calls}") + - results[(model.name, model.ndims, "mhmchmc:grid", L.item(), step_size.item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), False, 0.0 )] = ess.item() + # unadjusted_kernel = lambda sqrt_diag_cov : blackjax.mcmc.mclmc.build_kernel( + # logdensity_fn=model.logdensity_fn, + # integrator=integrator, + # sqrt_diag_cov=sqrt_diag_cov, + # ) + + # ( + # _, + # unadjusted_blackjax_mclmc_sampler_params, + # ) = blackjax.mclmc_find_L_and_step_size( + # mclmc_kernel=kernel, + # num_steps=num_steps, + # state=initial_state, + # rng_key=tune_key, + # diagonal_preconditioning=True, + # # desired_energy_var= 1e-5 + # ) + + unadjusted_kernel = lambda sqrt_diag_cov : blackjax.mcmc.mclmc.build_kernel( + logdensity_fn=model.logdensity_fn, + integrator=integrator, + sqrt_diag_cov=sqrt_diag_cov, + ) + + ( + _, + blackjax_unadjusted_mclmc_sampler_params, + ) = blackjax.mclmc_find_L_and_step_size( + mclmc_kernel=unadjusted_kernel, + num_steps=num_steps, + state=unadjusted_initial_state, + rng_key=tune_key2, + diagonal_preconditioning=True, + # desired_energy_var= 1e-5 + ) + + print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") + print(f"params after initial tuning are L={blackjax_unadjusted_mclmc_sampler_params.L}, step_size={blackjax_unadjusted_mclmc_sampler_params.step_size}") + + def func(L, step_size): + + r = benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size, L_proposal_factor=L_proposal_factor), key=grid_key, n=num_steps, contract=jnp.max, batch=batch_size) - ####### run nuts + return r[0], r[3] - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) - results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0)] = ess.item() + out = grid_search(func=func, x=blackjax_unadjusted_mclmc_sampler_params.L, y=blackjax_unadjusted_mclmc_sampler_params.step_size, delta_x=blackjax_unadjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_unadjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=5) - + results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor )] = out[2].item() @@ -465,13 +500,13 @@ def benchmark_mhmchmc(batch_size): print(results) - df = pd.Series(results).reset_index() df.columns = ["model", "dims", "sampler", "L", "step_size", "integrator", "tuning", "acc_rate", "preconditioning", "inv_L_prop", "ESS"] # df.result = df.result.apply(lambda x: x[0].item()) # df.model = df.model.apply(lambda x: x[1]) df.to_csv(f"results{model.name}.csv", index=False) + return results # TODO: not updated to new code yet! @@ -636,7 +671,7 @@ def kernel(state): # run_benchmarks_simple() # benchmark_omelyan(128) - benchmark_mhmchmc(batch_size=50) + benchmark_mhmchmc(batch_size=128) # run_benchmarks(128) # run_benchmarks_step_size(128) # run_benchmarks(128) From 5b54aebc84754ef945f2ba33a3836c36f59bfeac Mon Sep 17 00:00:00 2001 From: = Date: Sun, 9 Jun 2024 16:02:27 -0400 Subject: [PATCH 16/21] update benchmarks --- benchmarks/mcmc/sampling_algorithms.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index eb268bb..16b2824 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -152,7 +152,7 @@ def run_nuts( return state_history, params, info_history.num_integration_steps.mean() * calls_per_integrator_step(integrator_type), info_history.acceptance_rate.mean(), None, None -def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning): +def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune3): integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] @@ -179,6 +179,7 @@ def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, trans state=initial_state, rng_key=tune_key, diagonal_preconditioning=preconditioning, + frac_tune3=frac_tune3, # desired_energy_var= 1e-5 ) From 39ff6a68a30ab0632c277f10537c196cb4e173ba Mon Sep 17 00:00:00 2001 From: = Date: Mon, 10 Jun 2024 23:27:07 +0200 Subject: [PATCH 17/21] update benchmarks --- benchmarks/mcmc/benchmark.py | 107 +++++++++++++------------ benchmarks/mcmc/sampling_algorithms.py | 2 +- 2 files changed, 58 insertions(+), 51 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index b6c700e..b1c627e 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -338,61 +338,16 @@ def benchmark_mhmchmc(batch_size): integrators = ["mclachlan"] for model in models: results = defaultdict(tuple) - for preconditioning, integrator_type in itertools.product([True], integrators): - - num_chains = batch_size # 1 + batch_size//model.ndims - print(f"NUMBER OF CHAINS for {model.name} and adjusted_mclmc is {num_chains}") - num_steps = models[model]["adjusted_mclmc"] - print(f"NUMBER OF STEPS for {model.name} and MHCMLMC is {num_steps}") - - ####### run mclmc with standard tuning - - contract = jnp.max - - if True: - ess, grad_calls, params , _, step_size_over_da = benchmark_chains( - model, - partial(run_mclmc,integrator_type=integrator_type, preconditioning=preconditioning), - key0, - n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0)] = ess.item() - print(f'mclmc with tuning ESS {ess}') - + num_chains = batch_size # 1 + batch_size//model.ndims + print(f"NUMBER OF CHAINS for {model.name} and adjusted_mclmc is {num_chains}") + num_steps = models[model]["adjusted_mclmc"] + print(f"NUMBER OF STEPS for {model.name} and MHCMLMC is {num_steps}") - ####### run adjusted_mclmc with standard tuning - for target_acc_rate, L_proposal_factor in itertools.product([0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): - # coeffs = mclachlan_coefficients - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( - model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), - key1, - n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() - print(f'adjusted_mclmc with tuning ESS {ess}') - - # integrator_type = mclachlan_coefficients - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( - model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), - key1, - n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() - print(f'adjusted_mclmc with tuning ESS {ess}') - ####### run nuts - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) - results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0)] = ess.item() - if True: ####### run adjusted_mclmc with standard tuning + grid search - + integrator_type = "mclachlan" init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) @@ -499,6 +454,58 @@ def func(L, step_size): print(results) + + + for preconditioning, integrator_type in itertools.product([True], integrators): + + + + ####### run mclmc with standard tuning + + contract = jnp.max + + if True: + ess, grad_calls, params , _, step_size_over_da = benchmark_chains( + model, + partial(run_mclmc,integrator_type=integrator_type, preconditioning=preconditioning), + key0, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0)] = ess.item() + print(f'mclmc with tuning ESS {ess}') + + + ####### run adjusted_mclmc with standard tuning + for target_acc_rate, L_proposal_factor in itertools.product([0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): + # coeffs = mclachlan_coefficients + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), + key1, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() + print(f'adjusted_mclmc with tuning ESS {ess}') + + # integrator_type = mclachlan_coefficients + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), + key1, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() + print(f'adjusted_mclmc with tuning ESS {ess}') + + ####### run nuts + + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) + results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0)] = ess.item() + + df = pd.Series(results).reset_index() df.columns = ["model", "dims", "sampler", "L", "step_size", "integrator", "tuning", "acc_rate", "preconditioning", "inv_L_prop", "ESS"] diff --git a/benchmarks/mcmc/sampling_algorithms.py b/benchmarks/mcmc/sampling_algorithms.py index 16b2824..d52daa5 100644 --- a/benchmarks/mcmc/sampling_algorithms.py +++ b/benchmarks/mcmc/sampling_algorithms.py @@ -152,7 +152,7 @@ def run_nuts( return state_history, params, info_history.num_integration_steps.mean() * calls_per_integrator_step(integrator_type), info_history.acceptance_rate.mean(), None, None -def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune3): +def run_mclmc(integrator_type, logdensity_fn, num_steps, initial_position, transform, key, preconditioning, frac_tune3=0.1): integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] From 3f81767f201ef89294ef848a45b4b8e06d1ab068 Mon Sep 17 00:00:00 2001 From: = Date: Tue, 11 Jun 2024 17:53:16 +0200 Subject: [PATCH 18/21] update benchmarks --- benchmarks/mcmc/benchmark.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index b1c627e..aadc00f 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -335,8 +335,8 @@ def benchmark_mhmchmc(batch_size): # coefficients = [yoshida_coefficients, mclachlan_coefficients, velocity_verlet_coefficients, omelyan_coefficients] # coefficients = [mclachlan_coefficients, velocity_verlet_coefficients] # integrators = ["mclachlan", "velocity_verlet", "omelyan", "yoshida"] - integrators = ["mclachlan"] - for model in models: + integrators = ["velocity_verlet"] + for model, integrator_type in itertools.product(models, integrators): results = defaultdict(tuple) num_chains = batch_size # 1 + batch_size//model.ndims print(f"NUMBER OF CHAINS for {model.name} and adjusted_mclmc is {num_chains}") @@ -347,8 +347,8 @@ def benchmark_mhmchmc(batch_size): if True: ####### run adjusted_mclmc with standard tuning + grid search - integrator_type = "mclachlan" + integrator_type = "mclachlan" init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) initial_position = model.sample_init(init_pos_key) @@ -448,7 +448,17 @@ def func(L, step_size): out = grid_search(func=func, x=blackjax_unadjusted_mclmc_sampler_params.L, y=blackjax_unadjusted_mclmc_sampler_params.step_size, delta_x=blackjax_unadjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_unadjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=5) results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor )] = out[2].item() + + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1], L_proposal_factor=L_proposal_factor), + key1, + n=num_steps, + batch=num_chains, + contract=jnp.max) + + results[(model.name, model.ndims, "mhmchmc:grid_rerun", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), True, 1/L_proposal_factor )] = ess.item() @@ -456,7 +466,7 @@ def func(L, step_size): print(results) - for preconditioning, integrator_type in itertools.product([True], integrators): + for preconditioning in [True]: From bf5c504d451372371fbcdce5077e7ab95efb58ad Mon Sep 17 00:00:00 2001 From: = Date: Tue, 11 Jun 2024 18:01:35 +0200 Subject: [PATCH 19/21] update benchmarks --- benchmarks/mcmc/benchmark.py | 311 ++++++++++++++++------------------- 1 file changed, 139 insertions(+), 172 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index aadc00f..54cf126 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -303,25 +303,15 @@ def run_benchmarks_step_size(batch_size): position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=key3) ess, grad_calls, params , acceptance_rate, _ = benchmark_chains(model, run_adjusted_mclmc_no_tuning(initial_state=initial_state, integrator_type=mclachlan_coefficients, step_size=step_size, L= 5*step_size, sqrt_diag_cov=1.),key1, n=num_steps, batch=num_chains, contract=contract) - # print(f"step size over da {step_size_over_da.shape} \n\n\n\n") - # jax.numpy.save(f"step_size_over_da.npy", step_size_over_da.mean(axis=0)) - # jax.numpy.save(f"acceptance.npy_{step_size}", acceptance_rate) - - - # print(f"grads to low bias: {grad_calls}") - # print(f"acceptance rate is {acceptance_rate, acceptance_rate.mean()}") - + results[((model.name, model.ndims), sampler, (coefficients), "standard", acceptance_rate.mean().item(), params.L.mean().item(), params.step_size.mean().item(), num_chains, num_steps, contract)] = ess.item() - # results[(model.name, model.ndims, "nuts", 0., 0., (coeffs), "standard", acceptance_rate)] - # print(results) + print(results) df = pd.Series(results).reset_index() df.columns = ["model", "sampler", "integrator", "tuning", "acc rate", "L", "stepsize", "num_chains", "num steps", "contraction", "ESS"] - # df.result = df.result.apply(lambda x: x[0].item()) - # df.model = df.model.apply(lambda x: x[1]) df.to_csv("results_step_size.csv", index=False) return results @@ -332,188 +322,165 @@ def benchmark_mhmchmc(batch_size): key0, key1, key2, key3 = jax.random.split(jax.random.PRNGKey(5), 4) - # coefficients = [yoshida_coefficients, mclachlan_coefficients, velocity_verlet_coefficients, omelyan_coefficients] - # coefficients = [mclachlan_coefficients, velocity_verlet_coefficients] - # integrators = ["mclachlan", "velocity_verlet", "omelyan", "yoshida"] - integrators = ["velocity_verlet"] - for model, integrator_type in itertools.product(models, integrators): + integrators = ["velocity_verlet", "mclachlan"] + for model in models: results = defaultdict(tuple) - num_chains = batch_size # 1 + batch_size//model.ndims - print(f"NUMBER OF CHAINS for {model.name} and adjusted_mclmc is {num_chains}") - num_steps = models[model]["adjusted_mclmc"] - print(f"NUMBER OF STEPS for {model.name} and MHCMLMC is {num_steps}") + for integrator_type in integrators: + num_chains = batch_size # 1 + batch_size//model.ndims + print(f"NUMBER OF CHAINS for {model.name} and adjusted_mclmc is {num_chains}") + num_steps = models[model]["adjusted_mclmc"] + print(f"NUMBER OF STEPS for {model.name} and MHCMLMC is {num_steps}") - if True: - ####### run adjusted_mclmc with standard tuning + grid search + if True: + ####### run adjusted_mclmc with standard tuning + grid search - integrator_type = "mclachlan" - init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) - initial_position = model.sample_init(init_pos_key) + init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) + initial_position = model.sample_init(init_pos_key) - integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] + integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] - init_key, tune_key, tune_key2, run_key = jax.random.split(init_key, 4) + init_key, tune_key, tune_key2, run_key = jax.random.split(init_key, 4) - initial_state = blackjax.mcmc.adjusted_mclmc.init( - position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key - ) - - unadjusted_initial_state = blackjax.mcmc.mclmc.init( - position=initial_position, logdensity_fn=model.logdensity_fn, rng_key=init_key - ) - - L_proposal_factor = jnp.inf - - - kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( - integrator=integrator, - integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), - sqrt_diag_cov=sqrt_diag_cov, - )( - rng_key=rng_key, - state=state, - step_size=step_size, - logdensity_fn=model.logdensity_fn, - L_proposal_factor=L_proposal_factor) - - target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] - - ( - blackjax_state_after_tuning, - _, - params_history, - final_da - ) = blackjax.adjusted_mclmc_find_L_and_step_size( - mclmc_kernel=kernel, - num_steps=num_steps, - state=initial_state, - rng_key=tune_key, - target=target_acc_rate, - frac_tune1=0.1, - frac_tune2=0.1, - frac_tune3=0.1, - diagonal_preconditioning=False, - ) - - - - # unadjusted_kernel = lambda sqrt_diag_cov : blackjax.mcmc.mclmc.build_kernel( - # logdensity_fn=model.logdensity_fn, - # integrator=integrator, - # sqrt_diag_cov=sqrt_diag_cov, - # ) - - # ( - # _, - # unadjusted_blackjax_mclmc_sampler_params, - # ) = blackjax.mclmc_find_L_and_step_size( - # mclmc_kernel=kernel, - # num_steps=num_steps, - # state=initial_state, - # rng_key=tune_key, - # diagonal_preconditioning=True, - # # desired_energy_var= 1e-5 - # ) - - unadjusted_kernel = lambda sqrt_diag_cov : blackjax.mcmc.mclmc.build_kernel( - logdensity_fn=model.logdensity_fn, - integrator=integrator, - sqrt_diag_cov=sqrt_diag_cov, - ) - - ( - _, - blackjax_unadjusted_mclmc_sampler_params, - ) = blackjax.mclmc_find_L_and_step_size( - mclmc_kernel=unadjusted_kernel, - num_steps=num_steps, - state=unadjusted_initial_state, - rng_key=tune_key2, - diagonal_preconditioning=True, - # desired_energy_var= 1e-5 - ) - - print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") - print(f"params after initial tuning are L={blackjax_unadjusted_mclmc_sampler_params.L}, step_size={blackjax_unadjusted_mclmc_sampler_params.step_size}") - - def func(L, step_size): + initial_state = blackjax.mcmc.adjusted_mclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, random_generator_arg=init_key + ) - r = benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size, L_proposal_factor=L_proposal_factor), key=grid_key, n=num_steps, contract=jnp.max, batch=batch_size) - - return r[0], r[3] - - out = grid_search(func=func, x=blackjax_unadjusted_mclmc_sampler_params.L, y=blackjax_unadjusted_mclmc_sampler_params.step_size, delta_x=blackjax_unadjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_unadjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=5) - - results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor )] = out[2].item() - - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( - model, - run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1], L_proposal_factor=L_proposal_factor), - key1, - n=num_steps, - batch=num_chains, - contract=jnp.max) + unadjusted_initial_state = blackjax.mcmc.mclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, rng_key=init_key + ) + + L_proposal_factor = jnp.inf + + + kernel = lambda rng_key, state, avg_num_integration_steps, step_size, sqrt_diag_cov: blackjax.mcmc.adjusted_mclmc.build_kernel( + integrator=integrator, + integration_steps_fn = lambda k : jnp.ceil(jax.random.uniform(k) * rescale(avg_num_integration_steps)), + sqrt_diag_cov=sqrt_diag_cov, + )( + rng_key=rng_key, + state=state, + step_size=step_size, + logdensity_fn=model.logdensity_fn, + L_proposal_factor=L_proposal_factor) + + target_acc_rate = target_acceptance_rate_of_order[integrator_order(integrator_type)] + + ( + blackjax_state_after_tuning, + _, + params_history, + final_da + ) = blackjax.adjusted_mclmc_find_L_and_step_size( + mclmc_kernel=kernel, + num_steps=num_steps, + state=initial_state, + rng_key=tune_key, + target=target_acc_rate, + frac_tune1=0.1, + frac_tune2=0.1, + frac_tune3=0.1, + diagonal_preconditioning=False, + ) + + unadjusted_kernel = lambda sqrt_diag_cov : blackjax.mcmc.mclmc.build_kernel( + logdensity_fn=model.logdensity_fn, + integrator=integrator, + sqrt_diag_cov=sqrt_diag_cov, + ) + + ( + _, + blackjax_unadjusted_mclmc_sampler_params, + ) = blackjax.mclmc_find_L_and_step_size( + mclmc_kernel=unadjusted_kernel, + num_steps=num_steps, + state=unadjusted_initial_state, + rng_key=tune_key2, + diagonal_preconditioning=True, + # desired_energy_var= 1e-5 + ) + + print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") + print(f"params after initial tuning are L={blackjax_unadjusted_mclmc_sampler_params.L}, step_size={blackjax_unadjusted_mclmc_sampler_params.step_size}") + + def func(L, step_size): + r = benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size, L_proposal_factor=L_proposal_factor), key=grid_key, n=num_steps, contract=jnp.max, batch=batch_size) - results[(model.name, model.ndims, "mhmchmc:grid_rerun", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), True, 1/L_proposal_factor )] = ess.item() + return r[0], r[3] + out = grid_search(func=func, x=blackjax_unadjusted_mclmc_sampler_params.L, y=blackjax_unadjusted_mclmc_sampler_params.step_size, delta_x=blackjax_unadjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_unadjusted_mclmc_sampler_params.step_size-0.2, size_grid=2, num_iter=2) + + results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor )] = out[2].item() + + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1], L_proposal_factor=L_proposal_factor), + key1, + n=num_steps, + batch=num_chains, + contract=jnp.max) + + results[(model.name, model.ndims, "mhmchmc:grid_rerun", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), True, 1/L_proposal_factor )] = ess.item() - - print(results) - for preconditioning in [True]: - - + + print(results) - ####### run mclmc with standard tuning - contract = jnp.max + for preconditioning in [True]: - if True: - ess, grad_calls, params , _, step_size_over_da = benchmark_chains( - model, - partial(run_mclmc,integrator_type=integrator_type, preconditioning=preconditioning), - key0, - n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0)] = ess.item() - print(f'mclmc with tuning ESS {ess}') - - - ####### run adjusted_mclmc with standard tuning - for target_acc_rate, L_proposal_factor in itertools.product([0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): - # coeffs = mclachlan_coefficients - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( - model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), - key1, - n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() - print(f'adjusted_mclmc with tuning ESS {ess}') - - # integrator_type = mclachlan_coefficients - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( - model, - partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), - key1, - n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() - print(f'adjusted_mclmc with tuning ESS {ess}') + - ####### run nuts + ####### run mclmc with standard tuning - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) - results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0)] = ess.item() + contract = jnp.max + + if True: + ess, grad_calls, params , _, step_size_over_da = benchmark_chains( + model, + partial(run_mclmc,integrator_type=integrator_type, preconditioning=preconditioning), + key0, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0)] = ess.item() + print(f'mclmc with tuning ESS {ess}') + + + ####### run adjusted_mclmc with standard tuning + for target_acc_rate, L_proposal_factor in itertools.product([0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): + # coeffs = mclachlan_coefficients + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), + key1, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() + print(f'adjusted_mclmc with tuning ESS {ess}') + + # integrator_type = mclachlan_coefficients + ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + model, + partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), + key1, + n=num_steps, + batch=num_chains, + contract=contract) + results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() + print(f'adjusted_mclmc with tuning ESS {ess}') + + ####### run nuts + + ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) + results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0)] = ess.item() From ba8f6f185872b2ad1ec9c76a2f83565ab6b84231 Mon Sep 17 00:00:00 2001 From: = Date: Fri, 14 Jun 2024 15:38:55 +0200 Subject: [PATCH 20/21] update benchmarks --- benchmarks/mcmc/benchmark.py | 157 +++++++++++++++++++--------- benchmarks/mcmc/inference_models.py | 8 +- 2 files changed, 115 insertions(+), 50 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index 54cf126..df25442 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -1,5 +1,7 @@ import sys sys.path.append('./') + +from blackjax.diagnostics import effective_sample_size from collections import defaultdict from functools import partial import math @@ -145,7 +147,34 @@ def s(logdensity_fn, num_steps, initial_position, transform, key): return s -def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.average,): +def run_unadjusted_mclmc_no_tuning(initial_state, integrator_type, step_size, L, sqrt_diag_cov): + + def s(logdensity_fn, num_steps, initial_position, transform, key): + + integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] + + sampling_alg = blackjax.mclmc( + logdensity_fn, + L=L, + step_size=step_size, + sqrt_diag_cov=sqrt_diag_cov, + integrator = integrator, + ) + + _, samples, info = run_inference_algorithm( + rng_key=key, + initial_state=initial_state, + inference_algorithm=sampling_alg, + num_steps=num_steps, + transform=lambda x: transform(x.position), + progress_bar=True, + ) + + return samples, MCLMCAdaptationState(L=L, step_size=step_size, sqrt_diag_cov=sqrt_diag_cov), calls_per_integrator_step(integrator_type), 0, None, jnp.array([0]) + + return s + +def benchmark_chains(model, sampler, key, n=10000, batch=None): pvmap = jax.pmap @@ -158,19 +187,32 @@ def benchmark_chains(model, sampler, key, n=10000, batch=None, contract = jnp.av init_keys = jax.random.split(init_key, batch) init_pos = pvmap(model.sample_init)(init_keys) # [batch_size, dim_model] - ex2_empirical, params, grad_calls_per_traj, acceptance_rate, step_size_over_da, final_da = pvmap(lambda pos, key: sampler(logdensity_fn=model.logdensity_fn, num_steps=n, initial_position= pos,transform= model.transform, key=key))(init_pos, keys) + samples, params, grad_calls_per_traj, acceptance_rate, step_size_over_da, final_da = pvmap(lambda pos, key: sampler(logdensity_fn=model.logdensity_fn, num_steps=n, initial_position= pos,transform= model.transform, key=key))(init_pos, keys) avg_grad_calls_per_traj = jnp.nanmean(grad_calls_per_traj, axis=0) try: print(jnp.nanmean(params.step_size,axis=0), jnp.nanmean(params.L,axis=0)) except: pass - full = lambda x : err(model.E_x2, model.Var_x2, contract)(cumulative_avg(x**2)) - # err_t = pvmap(err(model.E_x2, model.Var_x2, contract))(ex2_empirical) - err_t = pvmap(full)(ex2_empirical) + full_avg = lambda x : err(model.E_x2, model.Var_x2, jnp.average)(cumulative_avg(x**2)) + full_max = lambda x : err(model.E_x2, model.Var_x2, jnp.max)(cumulative_avg(x**2)) + # err_t = pvmap(err(model.E_x2, model.Var_x2, contract))(samples) + err_t_avg = pvmap(full_avg)(samples) + err_t_max = pvmap(full_max)(samples) + + err_t_median_avg = jnp.median(err_t_avg, axis=0) + esses_avg, _, _ = calculate_ess(err_t_median_avg, grad_evals_per_step=avg_grad_calls_per_traj) + + err_t_median_max = jnp.median(err_t_max, axis=0) + esses_max, _, _ = calculate_ess(err_t_median_max, grad_evals_per_step=avg_grad_calls_per_traj) - err_t_median = jnp.median(err_t, axis=0) - esses, grad_calls, _ = calculate_ess(err_t_median, grad_evals_per_step=avg_grad_calls_per_traj) - return esses, grad_calls, params, jnp.mean(acceptance_rate, axis=0), step_size_over_da + + ess_corr = jax.pmap(lambda x: effective_sample_size(x[None, ...]))(samples) + + # print(ess_corr.shape,"shape") + ess_corr = jnp.mean(ess_corr, axis=0) + # jax.debug.print("{x}",x=jnp.mean(1/ess_corr)) + + return esses_max, esses_avg.item(), jnp.mean(1/ess_corr).item(), params, jnp.mean(acceptance_rate, axis=0), step_size_over_da def benchmark_no_chains(model, sampler, key, n=10000, contract = jnp.average,): @@ -322,8 +364,10 @@ def benchmark_mhmchmc(batch_size): key0, key1, key2, key3 = jax.random.split(jax.random.PRNGKey(5), 4) - integrators = ["velocity_verlet", "mclachlan"] + integrators = ["mclachlan", "velocity_verlet"] + # for model in models: for model in models: + # for model in [StandardNormal(d) for np.ceil(d).astype(int) in np.linspace(20, 1000, 5)]: results = defaultdict(tuple) for integrator_type in integrators: num_chains = batch_size # 1 + batch_size//model.ndims @@ -337,7 +381,7 @@ def benchmark_mhmchmc(batch_size): ####### run adjusted_mclmc with standard tuning + grid search - init_pos_key, init_key, tune_key, grid_key, bench_key = jax.random.split(key2, 5) + init_pos_key, init_key, tune_key, grid_key, bench_key, unadjusted_grid_key, unadjusted_bench_key = jax.random.split(key2, 7) initial_position = model.sample_init(init_pos_key) integrator = map_integrator_type_to_integrator['mclmc'][integrator_type] @@ -370,7 +414,7 @@ def benchmark_mhmchmc(batch_size): ( blackjax_state_after_tuning, - _, + blackjax_adjusted_mclmc_sampler_params, params_history, final_da ) = blackjax.adjusted_mclmc_find_L_and_step_size( @@ -381,7 +425,7 @@ def benchmark_mhmchmc(batch_size): target=target_acc_rate, frac_tune1=0.1, frac_tune2=0.1, - frac_tune3=0.1, + frac_tune3=0.0, diagonal_preconditioning=False, ) @@ -392,40 +436,60 @@ def benchmark_mhmchmc(batch_size): ) ( - _, + unadjusted_blackjax_state_after_tuning, blackjax_unadjusted_mclmc_sampler_params, ) = blackjax.mclmc_find_L_and_step_size( mclmc_kernel=unadjusted_kernel, num_steps=num_steps, state=unadjusted_initial_state, rng_key=tune_key2, - diagonal_preconditioning=True, + diagonal_preconditioning=False, # desired_energy_var= 1e-5 ) print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") - print(f"params after initial tuning are L={blackjax_unadjusted_mclmc_sampler_params.L}, step_size={blackjax_unadjusted_mclmc_sampler_params.step_size}") + print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") def func(L, step_size): - r = benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size, L_proposal_factor=L_proposal_factor), key=grid_key, n=num_steps, contract=jnp.max, batch=batch_size) + r = benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_adjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size, L_proposal_factor=L_proposal_factor), key=grid_key, n=num_steps, batch=batch_size) - return r[0], r[3] + return r[0], r[4] - out = grid_search(func=func, x=blackjax_unadjusted_mclmc_sampler_params.L, y=blackjax_unadjusted_mclmc_sampler_params.step_size, delta_x=blackjax_unadjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_unadjusted_mclmc_sampler_params.step_size-0.2, size_grid=2, num_iter=2) + out, edge = grid_search(func=func, x=blackjax_adjusted_mclmc_sampler_params.L, y=blackjax_adjusted_mclmc_sampler_params.step_size, delta_x=blackjax_adjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_adjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=4) - results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor )] = out[2].item() + # results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor, "n/a", "n/a", num_steps)] = out[2].item() - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + ess, ess_avg, ess_corr, params , acceptance_rate, _ = benchmark_chains( model, - run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1], L_proposal_factor=L_proposal_factor), - key1, + run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_adjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1], L_proposal_factor=L_proposal_factor), + bench_key, + n=num_steps, + batch=num_chains) + + + results[(model.name, model.ndims, f"mhmchmc:grid_edge{edge}", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), False, 1/L_proposal_factor, ess_avg, ess_corr, num_steps)] = ess.item() + + + def func_unadjusted(L, step_size): + + r = benchmark_chains(model=model, sampler=run_unadjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=unadjusted_blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size), key=unadjusted_grid_key, n=num_steps, batch=batch_size) + + return r[1], r[4] + + out, edge = grid_search(func=func_unadjusted, x=blackjax_unadjusted_mclmc_sampler_params.L, y=blackjax_unadjusted_mclmc_sampler_params.step_size, delta_x=blackjax_unadjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_unadjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=4) + + # results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor, "n/a", "n/a", num_steps)] = out[2].item() + + ess, ess_avg, ess_corr, params , acceptance_rate, _ = benchmark_chains( + model, + run_unadjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=unadjusted_blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1]), + unadjusted_bench_key, n=num_steps, - batch=num_chains, - contract=jnp.max) + batch=num_chains) - results[(model.name, model.ndims, "mhmchmc:grid_rerun", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), True, 1/L_proposal_factor )] = ess.item() + results[(model.name, model.ndims, f"mclmc:grid_edge{edge}", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), False, 1/L_proposal_factor, ess_avg, ess_corr, num_steps)] = ess.item() @@ -433,62 +497,56 @@ def func(L, step_size): print(results) - for preconditioning in [True]: - - + for preconditioning in [False]: ####### run mclmc with standard tuning - contract = jnp.max if True: - ess, grad_calls, params , _, step_size_over_da = benchmark_chains( + ess, ess_avg, ess_corr, params , _, step_size_over_da = benchmark_chains( model, partial(run_mclmc,integrator_type=integrator_type, preconditioning=preconditioning), key0, n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0)] = ess.item() + batch=num_chains) + results[(model.name, model.ndims, "mclmc", params.L.mean().item(), params.step_size.mean().item(), (integrator_type), "standard", 1., preconditioning, 0, ess_avg, ess_corr, num_steps)] = ess.item() print(f'mclmc with tuning ESS {ess}') ####### run adjusted_mclmc with standard tuning for target_acc_rate, L_proposal_factor in itertools.product([0.9], [jnp.inf]): # , 3., 1.25, 0.5] ): # coeffs = mclachlan_coefficients - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + ess, ess_avg, ess_corr, params , acceptance_rate, _ = benchmark_chains( model, partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor, integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.0, preconditioning=preconditioning), key1, n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() + batch=num_chains) + results[(model.name, model.ndims, "mhmclmc:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor, ess_avg, ess_corr, num_steps)] = ess.item() print(f'adjusted_mclmc with tuning ESS {ess}') # integrator_type = mclachlan_coefficients - ess, grad_calls, params , acceptance_rate, _ = benchmark_chains( + ess, ess_avg, ess_corr, params , acceptance_rate, _ = benchmark_chains( model, partial(run_adjusted_mclmc, target_acc_rate=target_acc_rate, L_proposal_factor=L_proposal_factor,integrator_type=integrator_type, frac_tune1=0.1, frac_tune2=0.1, frac_tune3=0.1, preconditioning=preconditioning), key1, n=num_steps, - batch=num_chains, - contract=contract) - results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor)] = ess.item() + batch=num_chains) + results[(model.name, model.ndims, "mhmclmc:st3:"+str(target_acc_rate), jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 1/L_proposal_factor, ess_avg, ess_corr, num_steps)] = ess.item() print(f'adjusted_mclmc with tuning ESS {ess}') ####### run nuts - ess, grad_calls, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains, contract=contract) - results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0)] = ess.item() + ess, ess_avg, ess_corr, _ , acceptance_rate, _ = benchmark_chains(model, partial(run_nuts,integrator_type=integrator_type, preconditioning=preconditioning),key3, n=models[model]["nuts"], batch=num_chains) + results[(model.name, model.ndims, "nuts", 0., 0., (integrator_type), "standard", acceptance_rate.mean().item(), preconditioning, 0, ess_avg, ess_corr, num_steps)] = ess.item() df = pd.Series(results).reset_index() - df.columns = ["model", "dims", "sampler", "L", "step_size", "integrator", "tuning", "acc_rate", "preconditioning", "inv_L_prop", "ESS"] + df.columns = ["model", "dims", "sampler", "L", "step_size", "integrator", "tuning", "acc_rate", "preconditioning", "inv_L_prop", "ess_avg", "inv_ess_corr", "num_steps", "ESS"] # df.result = df.result.apply(lambda x: x[0].item()) # df.model = df.model.apply(lambda x: x[1]) - df.to_csv(f"results{model.name}.csv", index=False) + df.to_csv(f"results{model.name}{model.ndims}.csv", index=False) return results @@ -635,15 +693,18 @@ def kernel(state): # new grid state = (np.array([Z[ind[i], i] for i in range(2)]), 2 * delta_z / (size_grid - 1)) - return state, Results[ind[0]][ind[1]] + return state, Results[ind[0]][ind[1]], np.any(np.isin(np.array(ind), [0, size_grid - 1])) state = (np.array([x, y]), np.array([delta_x, delta_y])) + initial_edge = False for iteration in range(num_iter): # iteratively shrink and shift the grid - state, results = kernel(state) + state, results, edge = kernel(state) + if edge and iteration==0: + initial_edge = True - return [state[0][0], state[0][1], *results] + return [state[0][0], state[0][1], *results], initial_edge if __name__ == "__main__": diff --git a/benchmarks/mcmc/inference_models.py b/benchmarks/mcmc/inference_models.py index 6636e56..e1c3066 100644 --- a/benchmarks/mcmc/inference_models.py +++ b/benchmarks/mcmc/inference_models.py @@ -872,9 +872,13 @@ def step(track, useless): ## Rosenbrock(): {'mclmc': 40000, 'adjusted_mclmc' : 40000, 'nuts': 40000}, # no Ex2 # Cauchy(100) : {'mclmc': 2000, 'adjusted_mclmc' : 2000, 'nuts': 2000}, - StandardNormal(10) : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, + # Brownian() : {'mclmc': 20000, 'adjusted_mclmc' : 20000, 'nuts': 4000}, + # StandardNormal(10) : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, + StandardNormal(50) : {'mclmc': 800, 'adjusted_mclmc' : 800, 'nuts': 800}, + StandardNormal(100) : {'mclmc': 800, 'adjusted_mclmc' : 800, 'nuts': 800}, + StandardNormal(500) : {'mclmc': 800, 'adjusted_mclmc' : 800, 'nuts': 800}, + StandardNormal(1000) : {'mclmc': 800, 'adjusted_mclmc' : 800, 'nuts': 800}, # Banana() : {'mclmc': 10000, 'adjusted_mclmc' : 10000, 'nuts': 10000}, - # Brownian() : {'mclmc': 2000, 'adjusted_mclmc' : 8000, 'nuts': 4000}, # Funnel() : {'mclmc': 20000, 'adjusted_mclmc' : 80000, 'nuts': 40000}, From b18b6eff2a549d50710dac719682603a04ba48f1 Mon Sep 17 00:00:00 2001 From: = Date: Sat, 15 Jun 2024 20:03:18 +0200 Subject: [PATCH 21/21] update benchmarks --- benchmarks/mcmc/benchmark.py | 112 +++++++++++++++++++++-------------- 1 file changed, 68 insertions(+), 44 deletions(-) diff --git a/benchmarks/mcmc/benchmark.py b/benchmarks/mcmc/benchmark.py index df25442..611dc07 100644 --- a/benchmarks/mcmc/benchmark.py +++ b/benchmarks/mcmc/benchmark.py @@ -364,7 +364,8 @@ def benchmark_mhmchmc(batch_size): key0, key1, key2, key3 = jax.random.split(jax.random.PRNGKey(5), 4) - integrators = ["mclachlan", "velocity_verlet"] + # integrators = ["mclachlan", "velocity_verlet"] + integrators = ["mclachlan"] # for model in models: for model in models: # for model in [StandardNormal(d) for np.ceil(d).astype(int) in np.linspace(20, 1000, 5)]: @@ -447,49 +448,56 @@ def benchmark_mhmchmc(batch_size): # desired_energy_var= 1e-5 ) - print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") - print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") - def func(L, step_size): - - r = benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_adjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size, L_proposal_factor=L_proposal_factor), key=grid_key, n=num_steps, batch=batch_size) + do_grid_search_for_adjusted = False + if do_grid_search_for_adjusted: + print(f"target acceptance rate {target_acceptance_rate_of_order[integrator_order(integrator_type)]}") + print(f"params after initial tuning are L={blackjax_adjusted_mclmc_sampler_params.L}, step_size={blackjax_adjusted_mclmc_sampler_params.step_size}") + def func(L, step_size): + + r = benchmark_chains(model=model, sampler=run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_adjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size, L_proposal_factor=L_proposal_factor), key=grid_key, n=num_steps, batch=batch_size) - return r[0], r[4] + return r[0], r[4] - out, edge = grid_search(func=func, x=blackjax_adjusted_mclmc_sampler_params.L, y=blackjax_adjusted_mclmc_sampler_params.step_size, delta_x=blackjax_adjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_adjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=4) + out, edge = grid_search(func=func, x=blackjax_adjusted_mclmc_sampler_params.L, y=blackjax_adjusted_mclmc_sampler_params.step_size, delta_x=blackjax_adjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_adjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=4) + + # results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor, "n/a", "n/a", num_steps)] = out[2].item() + + ess, ess_avg, ess_corr, params , acceptance_rate, _ = benchmark_chains( + model, + run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_adjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1], L_proposal_factor=L_proposal_factor), + bench_key, + n=num_steps, + batch=num_chains) + - # results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor, "n/a", "n/a", num_steps)] = out[2].item() + + results[(model.name, model.ndims, f"mhmchmc:grid_edge{edge}", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), False, 1/L_proposal_factor, ess_avg, ess_corr, num_steps)] = ess.item() + + do_grid_search_for_unadjusted = True + if do_grid_search_for_unadjusted: - ess, ess_avg, ess_corr, params , acceptance_rate, _ = benchmark_chains( - model, - run_adjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=blackjax_state_after_tuning, sqrt_diag_cov=blackjax_adjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1], L_proposal_factor=L_proposal_factor), - bench_key, - n=num_steps, - batch=num_chains) + def func_unadjusted(L, step_size): + r = benchmark_chains(model=model, sampler=run_unadjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=unadjusted_blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size), key=unadjusted_grid_key, n=num_steps, batch=batch_size) - results[(model.name, model.ndims, f"mhmchmc:grid_edge{edge}", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), False, 1/L_proposal_factor, ess_avg, ess_corr, num_steps)] = ess.item() - - - def func_unadjusted(L, step_size): - - r = benchmark_chains(model=model, sampler=run_unadjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=unadjusted_blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=L, step_size=step_size), key=unadjusted_grid_key, n=num_steps, batch=batch_size) + return r[1], r[4] - return r[1], r[4] + jax.debug.print("Initial grid search params {x}", x=(blackjax_unadjusted_mclmc_sampler_params.L, blackjax_unadjusted_mclmc_sampler_params.step_size)) - out, edge = grid_search(func=func_unadjusted, x=blackjax_unadjusted_mclmc_sampler_params.L, y=blackjax_unadjusted_mclmc_sampler_params.step_size, delta_x=blackjax_unadjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_unadjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=4) - - # results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor, "n/a", "n/a", num_steps)] = out[2].item() - - ess, ess_avg, ess_corr, params , acceptance_rate, _ = benchmark_chains( - model, - run_unadjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=unadjusted_blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1]), - unadjusted_bench_key, - n=num_steps, - batch=num_chains) - + out, edge = grid_search(func=func_unadjusted, x=blackjax_unadjusted_mclmc_sampler_params.L, y=blackjax_unadjusted_mclmc_sampler_params.step_size, delta_x=blackjax_unadjusted_mclmc_sampler_params.L-0.2, delta_y=blackjax_unadjusted_mclmc_sampler_params.step_size-0.2, size_grid=5, num_iter=4) + + # results[(model.name, model.ndims, "mhmchmc:grid_new", out[0].item(), out[1].item(), integrator_type, f"gridsearch", out[3].item(), True, 1/L_proposal_factor, "n/a", "n/a", num_steps)] = out[2].item() + + ess, ess_avg, ess_corr, params , acceptance_rate, _ = benchmark_chains( + model, + run_unadjusted_mclmc_no_tuning(integrator_type=integrator_type, initial_state=unadjusted_blackjax_state_after_tuning, sqrt_diag_cov=blackjax_unadjusted_mclmc_sampler_params.sqrt_diag_cov, L=out[0], step_size=out[1]), + unadjusted_bench_key, + n=num_steps, + batch=num_chains) + - results[(model.name, model.ndims, f"mclmc:grid_edge{edge}", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), False, 1/L_proposal_factor, ess_avg, ess_corr, num_steps)] = ess.item() + results[(model.name, model.ndims, f"mclmc:grid_edge{edge}", jnp.nanmean(params.L).item(), jnp.nanmean(params.step_size).item(), integrator_type, f"gridsearch", acceptance_rate.mean().item(), False, 1/L_proposal_factor, ess_avg, ess_corr, num_steps)] = ess.item() @@ -639,25 +647,37 @@ def benchmark_omelyan(batch_size): def run_benchmarks_simple(): # sampler = run_adjusted_mclmc - sampler = run_mclmc + # sampler = run_mclmc + sampler = run_unadjusted_mclmc_no_tuning # model = IllConditionedGaussian(100,100) # model = Brownian() - model = StandardNormal(100) + model = StandardNormal(10) # model = Banana() integrator_type = "mclachlan" - contract = jnp.max # how we average across dimensions + # contract = jnp.average # how we average across dimensions num_steps = 1000 num_chains = 1 for i in range(1): - key1 = jax.random.PRNGKey(i) + key1 = jax.random.PRNGKey(i+1) + + for preconditioning in [False]: + + - for preconditioning in [True, False]: + # ess, ess_avg, ess_corr, _ , acceptance_rate, _ = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning, + # # target_acc_rate=0.9 + # ),key1, n=num_steps, batch=num_chains) - ess, grad_calls, params , acceptance_rate, step_size_over_da = benchmark_chains(model, partial(sampler, integrator_type=integrator_type, preconditioning=preconditioning, + init_key, state_key, run_key = jax.random.split(key1, 3) + initial_position = model.sample_init(init_key) + initial_state = blackjax.mcmc.mclmc.init( + position=initial_position, logdensity_fn=model.logdensity_fn, rng_key=state_key + ) + ess, ess_avg, ess_corr, _ , acceptance_rate, _ = benchmark_chains(model, sampler(L=0.2, step_size=5.34853, integrator_type=integrator_type,initial_state=initial_state, sqrt_diag_cov=1., # target_acc_rate=0.9 - ),key1, n=num_steps, contract=contract, batch=num_chains) + ),run_key, n=num_steps, batch=num_chains) - print(f"Effective Sample Size (ESS) of {model.name} with preconditioning set to {preconditioning} is {ess}") + print(f"Effective Sample Size (ESS) of {model.name} with preconditioning set to {preconditioning} is {ess_avg}") def grid_search(func, x, y, delta_x, delta_y, size_grid= 5, num_iter= 3): @@ -681,8 +701,10 @@ def kernel(state): # compute the func on the grid Z = np.linspace(z - delta_z, z + delta_z, size_grid) + jax.debug.print("grid {x}",x=Z) Results = [[func(xx, yy) for yy in Z[:, 1]] for xx in Z[:, 0]] Scores = [[Results[i][j][0] for j in range(size_grid)] for i in range(size_grid)] + jax.debug.print("scores {x}",x=(Scores)) # find the best point on the grid ind = np.unravel_index(np.argmax(Scores, axis=None), (size_grid, size_grid)) @@ -701,6 +723,8 @@ def kernel(state): initial_edge = False for iteration in range(num_iter): # iteratively shrink and shift the grid state, results, edge = kernel(state) + jax.debug.print("Optimal params on iteration: {x}",x=(state, results[0])) + raise Exception if edge and iteration==0: initial_edge = True @@ -713,10 +737,10 @@ def kernel(state): # print(grid_search(func, 0., 0., 1., 2., size_grid= 5, num_iter=1)) - # run_benchmarks_simple() + run_benchmarks_simple() # benchmark_omelyan(128) - benchmark_mhmchmc(batch_size=128) + # benchmark_mhmchmc(batch_size=128) # run_benchmarks(128) # run_benchmarks_step_size(128) # run_benchmarks(128)