From 3de08a3356dc011e03de73eb72e91071543f0d64 Mon Sep 17 00:00:00 2001 From: teddygroves Date: Fri, 8 Nov 2024 14:52:13 +0100 Subject: [PATCH] improve benchmarks --- benchmarks/analyse_results.py | 48 ++++ benchmarks/figure.png | Bin 0 -> 34615 bytes benchmarks/linear_pathway | Bin 0 -> 3001600 bytes benchmarks/linear_pathway.csv | 31 +++ benchmarks/linear_pathway.py | 104 +++++++- benchmarks/linear_pathway.stan | 65 +++++ benchmarks/simple_example | Bin 0 -> 2856768 bytes benchmarks/simple_example.csv | 31 +++ benchmarks/simple_example.png | Bin 0 -> 23084 bytes benchmarks/simple_example.py | 122 +++++++-- benchmarks/simple_example.stan | 33 +++ pdm.lock | 459 ++++++++++++++++++++++++++++++++- pyproject.toml | 6 + src/grapevine/__init__.py | 9 +- src/grapevine/util.py | 23 ++ 15 files changed, 885 insertions(+), 46 deletions(-) create mode 100644 benchmarks/analyse_results.py create mode 100644 benchmarks/figure.png create mode 100755 benchmarks/linear_pathway create mode 100644 benchmarks/linear_pathway.csv create mode 100644 benchmarks/linear_pathway.stan create mode 100755 benchmarks/simple_example create mode 100644 benchmarks/simple_example.csv create mode 100644 benchmarks/simple_example.png create mode 100644 benchmarks/simple_example.stan diff --git a/benchmarks/analyse_results.py b/benchmarks/analyse_results.py new file mode 100644 index 0000000..0f99c93 --- /dev/null +++ b/benchmarks/analyse_results.py @@ -0,0 +1,48 @@ +from pathlib import Path +from matplotlib import pyplot as plt +import numpy as np +import polars as pl + +HERE = Path(__file__).parent +CSV_FILE_SIMPLE = HERE / "simple_example.csv" +CSV_FILE_LINEAR = HERE / "linear_pathway.csv" + + +def plot_comparison(name: str, df: pl.DataFrame, ax: plt.Axes): + xlow, xhigh = ax.get_xlim() + groups = df.group_by(["algorithm"]) + n_group = df["algorithm"].n_unique() + x = np.linspace(xlow, xhigh, n_group) + algs = [] + for xi, ((algorithm_name,), subdf) in zip(x, groups): + xs = np.linspace(xi - 0.01, xi + 0.01, len(subdf)) + ax.scatter(xs, subdf["neff_per_s"]) + algs.append(algorithm_name) + ax.set( + title=name, + ylabel="Effective samples per second\n(higher is better)", + xlabel="Algorithm", + ) + ax.set_xticks(x, algs) + ax.set_ylim(ymin=0) + return ax + + +def comparison_figure(df_dict): + f, axes = plt.subplots(1, len(df_dict.keys()), figsize=[12, 5]) + f.suptitle("Benchmark comparison") + for (name, df), ax in zip(df_dict.items(), axes): + ax = plot_comparison(name, df.filter(pl.col("algorithm") != "Stan"), ax) + return f, axes + + +def main(): + df_simple = pl.read_csv(CSV_FILE_SIMPLE) + df_linear = pl.read_csv(CSV_FILE_LINEAR) + df_dict = {"Simple model": df_simple, "Michaelis Menten model": df_linear} + f, _ = comparison_figure(df_dict) + f.savefig(HERE / "figure.png", bbox_inches="tight") + + +if __name__ == "__main__": + main() diff --git a/benchmarks/figure.png b/benchmarks/figure.png new file mode 100644 index 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+NUTS,1.4445977910072543,418.88989401017136,289.9699117759954 +Stan,5.421461499994621,545.005411550966,100.52739681938287 +grapeNUTS,0.41057587500836235,412.7281800223967,1005.2421614250728 +NUTS,1.4517032089934219,418.88989401017136,288.5506427313198 +Stan,5.45155062500271,545.005411550966,99.97254892055506 +grapeNUTS,0.4171639160049381,412.7281800223967,989.3669231389398 +NUTS,1.556118750013411,418.88989401017136,269.1888996302893 +Stan,5.465081374990405,545.005411550966,99.72503136825165 +grapeNUTS,0.4117212919954909,412.7281800223967,1002.4455573381346 +NUTS,1.4517152920016088,418.88989401017136,288.5482410484295 +Stan,5.425389500000165,545.005411550966,100.45461465042268 +grapeNUTS,0.4133557080058381,412.7281800223967,998.4818693166987 +NUTS,1.4468039579951437,418.88989401017136,289.52774955815914 +Stan,5.518105625000317,545.005411550966,98.76675957085085 +grapeNUTS,0.4129722499928903,412.7281800223967,999.4089918378347 +NUTS,1.597287917000358,418.88989401017136,262.250712317933 +Stan,5.551175000000512,545.005411550966,98.17838773789614 +grapeNUTS,0.4166600830067182,412.7281800223967,990.5632837301141 +NUTS,1.500209250007174,418.88989401017136,279.2209780123461 +Stan,5.495392291006283,545.005411550966,99.17497836194836 +grapeNUTS,0.4184131669899216,412.7281800223967,986.4129826304874 +NUTS,1.5448935000022175,418.88989401017136,271.14483555634746 +Stan,5.51635812499444,545.005411550966,98.79804740768445 +grapeNUTS,0.4137904169911053,412.7281800223967,997.43291065938 +NUTS,1.4955780839954969,418.88989401017136,280.0856060227161 +Stan,5.522869459004141,545.005411550966,98.6815668189338 diff --git a/benchmarks/linear_pathway.py b/benchmarks/linear_pathway.py index c9a014c..a01edc5 100644 --- a/benchmarks/linear_pathway.py +++ b/benchmarks/linear_pathway.py @@ -14,27 +14,35 @@ """ +import logging +import timeit from collections import OrderedDict from functools import partial -import timeit +from pathlib import Path +import arviz as az import equinox as eqx import jax import jax.numpy as jnp import optimistix as optx - +import polars as pl from blackjax import nuts from blackjax import window_adaptation as nuts_window_adaptation from blackjax.util import run_inference_algorithm +from cmdstanpy import CmdStanModel from jax.scipy.stats import norm -from grapevine import run_grapenuts +from grapevine import run_grapenuts, get_idata # Use 64 bit floats jax.config.update("jax_enable_x64", True) + SEED = 1234 SD = 0.05 +HERE = Path(__file__).parent +STAN_FILE = HERE / "linear_pathway.stan" +CSV_OUTPUT_FILE = HERE / "linear_pathway.csv" TRUE_PARAMS = OrderedDict( log_km=jnp.array([2.0, 3.0]), log_vmax=jnp.array(0.0), @@ -43,6 +51,24 @@ log_conc_ext=jnp.array([1.0, 0.0]), ) DEFAULT_GUESS = jnp.array([0.01, 0.01]) +N_WARMUP = 1000 +N_SAMPLE = 1000 +INIT_STEPSIZE = 0.0001 +MAX_TREEDEPTH = 10 +TARGET_ACCEPT = 0.95 + + +# override timeit template: +# see https://stackoverflow.com/questions/24812253/how-can-i-capture-return-value-with-python-timeit-module +timeit.template = """ +def inner(_it, _timer{init}): + {setup} + _t0 = _timer() + for _i in _it: + retval = {stmt} + _t1 = _timer() + return _t1 - _t0, retval +""" @eqx.filter_jit @@ -112,7 +138,39 @@ def simulate(key, params, guess): ) +def fit_stan(sim): + data = { + "y": sim, + "sd": SD, + "prior_log_km": [TRUE_PARAMS["log_km"].tolist(), [0.1, 0.1]], + "prior_log_vmax": [TRUE_PARAMS["log_vmax"].tolist(), 0.1], + "prior_log_keq": [TRUE_PARAMS["log_keq"].tolist(), [0.1, 0.1, 0.1]], + "prior_log_kf": [TRUE_PARAMS["log_kf"].tolist(), [0.1, 0.1]], + "prior_log_cext": [TRUE_PARAMS["log_conc_ext"].tolist(), [0.1, 0.1]], + "y_guess": DEFAULT_GUESS.tolist(), + "scaling_step": 1e-9, + "ftol": 1e-9, + "max_steps": 1000, + } + model = CmdStanModel(stan_file=STAN_FILE) + mcmc = model.sample( + data=data, + chains=1, + inits={k: v.tolist() for k, v in TRUE_PARAMS.items()}, + iter_warmup=N_WARMUP, + iter_sampling=N_SAMPLE, + adapt_delta=TARGET_ACCEPT, + step_size=INIT_STEPSIZE, + max_treedepth=MAX_TREEDEPTH, + show_progress=False, + seed=SEED, + ) + return mcmc + + def main(): + cmdstanpy_logger = logging.getLogger("cmdstanpy") + cmdstanpy_logger.disabled = True key = jax.random.key(SEED) key, sim_key = jax.random.split(key) _, sim = simulate(sim_key, TRUE_PARAMS, DEFAULT_GUESS) @@ -160,17 +218,35 @@ def run_nuts_example(): initial_state, ) - # run once for jitting - _ = run_grapenuts_example() - _ = run_nuts_example() - - # timers - time_grapenuts = timeit.timeit(run_grapenuts_example, number=5) #  type: ignore - time_nuts = timeit.timeit(run_nuts_example, number=5) #  type: ignore - - # print results - print(f"Runtime for grapenuts: {round(time_grapenuts, 4)}") - print(f"Runtime for nuts: {round(time_nuts, 4)}") + def run_stan_example(): + return fit_stan(sim.tolist()) + + results = [] + + _ = run_stan_example() # run once for jitting + _ = run_grapenuts_example() # run once for jitting + _ = run_nuts_example() # run once for jitting + for _ in range(10): + _ = run_nuts_example() # one more time to be safe! + time_nuts, (_, out_nuts) = timeit.timeit(run_nuts_example, number=1) + idata_nuts = get_idata(*out_nuts) + neff_nuts = az.ess(idata_nuts.sample_stats)["energy"].item() + time_gn, out_gn = timeit.timeit(run_grapenuts_example, number=1) + idata_gn = get_idata(*out_gn) + neff_gn = az.ess(idata_gn.sample_stats)["energy"].item() + results += [{"algorithm": "grapeNUTS", "time": time_gn, "neff": neff_gn}] + results += [{"algorithm": "NUTS", "time": time_nuts, "neff": neff_nuts}] + time_stan, mcmc = timeit.timeit(run_stan_example, number=1) + idata_stan = az.from_cmdstanpy(mcmc) + neff_stan = az.ess(idata_stan.sample_stats)["lp"].item() + results += [{"algorithm": "Stan", "time": time_stan, "neff": neff_stan}] + results_df = pl.from_records(results).with_columns( + neff_per_s=pl.col("neff") / pl.col("time") + ) + print(f"Benchmark results saved to {CSV_OUTPUT_FILE}") + print("Mean results:") + results_df.write_csv(CSV_OUTPUT_FILE) + print(results_df.group_by("algorithm").mean()) if __name__ == "__main__": diff --git a/benchmarks/linear_pathway.stan b/benchmarks/linear_pathway.stan new file mode 100644 index 0000000..b38a513 --- /dev/null +++ b/benchmarks/linear_pathway.stan @@ -0,0 +1,65 @@ +functions { + real rmm(real s, real p, real km_s, real km_p, real vmax, real keq){ + real num = vmax * (s - p / keq) / km_s; + real denom = 1 + s / km_s + p / km_p; + return num / denom; + } + real ma(real s, real p, real kf, real keq){ + return kf * (s - p / keq); + } + vector dcdt(vector c, vector km, real vmax, vector keq, vector kf, vector cext){ + matrix[4, 3] S = [[-1, 1, 0, 0], [0, -1, 1, 0], [0, 0, -1, 1]]'; + vector[3] v = [ + ma(cext[1], c[1], kf[1], keq[1]), + rmm(c[1], c[2], km[1], km[2], vmax, keq[2]), + ma(c[1], cext[2], kf[2], keq[3]) + ]'; + vector[2] out = (S * v)[2:3]; + return out; + } +} +data { + vector[2] y; + real sd; + array[2] vector[2] prior_log_km; + array[2] real prior_log_vmax; + array[2] vector[3] prior_log_keq; + array[2] vector[2] prior_log_kf; + array[2] vector[2] prior_log_cext; + vector[2] y_guess; + real scaling_step; + real ftol; + int max_steps; +} +parameters { + vector[2] log_km; + real log_vmax; + vector[3] log_keq; + vector[2] log_kf; + vector[2] log_cext; +} +transformed parameters { + vector[2] km = exp(log_km); + real vmax = exp(log_vmax); + vector[3] keq = exp(log_keq); + vector[2] kf = exp(log_kf); + vector[2] cext = exp(log_cext); + vector[2] yhat = solve_newton_tol(dcdt, + y_guess, + scaling_step, + ftol, + max_steps, + km, + vmax, + keq, + kf, + cext); +} +model { + log_km ~ normal(prior_log_km[1], prior_log_km[2]); + log_vmax ~ normal(prior_log_vmax[1], prior_log_vmax[2]); + log_keq ~ normal(prior_log_keq[1], prior_log_keq[2]); + log_kf ~ normal(prior_log_kf[1], prior_log_kf[2]); + log_cext ~ normal(prior_log_cext[1], prior_log_cext[2]); + y ~ lognormal(log(yhat), sd); +} diff --git a/benchmarks/simple_example b/benchmarks/simple_example new file mode 100755 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license = {text = "MIT"} demos = [ "optimistix>=0.0.8", ] +benchmarks = [ + "cmdstanpy>=1.2.4", + "polars>=1.12.0", + "matplotlib>=3.9.2", +] [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/src/grapevine/__init__.py b/src/grapevine/__init__.py index 2294c65..be37405 100644 --- a/src/grapevine/__init__.py +++ b/src/grapevine/__init__.py @@ -1,5 +1,10 @@ from grapevine.grapenuts import grapenuts_sampler from grapevine.integrator import grapevine_velocity_verlet -from grapevine.util import run_grapenuts +from grapevine.util import run_grapenuts, get_idata -__all__ = ["grapenuts_sampler", "grapevine_velocity_verlet", "run_grapenuts"] +__all__ = [ + "grapenuts_sampler", + "grapevine_velocity_verlet", + "run_grapenuts", + "get_idata", +] diff --git a/src/grapevine/util.py b/src/grapevine/util.py index 479119f..8aae120 100644 --- a/src/grapevine/util.py +++ b/src/grapevine/util.py @@ -2,12 +2,14 @@ from typing import Callable, TypedDict, Unpack +import arviz as az import equinox as eqx import jax from blackjax.types import ArrayTree from blackjax.util import run_inference_algorithm from jax._src.random import KeyArray +from jax import numpy as jnp from grapevine import grapenuts_sampler, grapevine_velocity_verlet from grapevine.adaptation import grapenuts_window_adaptation @@ -62,3 +64,24 @@ def run_grapenuts( progress_bar=progress_bar, ) return states, info + + +def get_idata(samples, info, coords=None, dims=None) -> az.InferenceData: + """Get an arviz InferenceData from a grapeNUTS output.""" + sample_dict = {k: jnp.expand_dims(v, 0) for k, v in samples.position.items()} + posterior = az.convert_to_inference_data( + sample_dict, + group="posterior", + coords=coords, + dims=dims, + ) + sample_stats = az.convert_to_inference_data( + { + "diverging": info.is_divergent, + "energy": info.energy, + }, + group="sample_stats", + ) + idata = az.concat(posterior, sample_stats) + assert idata is not None, "idata should not be None!" + return idata