Pathfinder Method for Approximate Bayesian Inference
The CmdStan method pathfinder uses the Pathfinder algorithm of Zhang et al. (2022), which is further described in the Stan Reference Manual.
A single run of the Pathfinder algorithm generates a set of approximate draws. Inference is improved by running multiple Pathfinder instances and using Pareto-smoothed importance resampling (PSIS) of the resulting sets of draws. This better matches non-normal target densities and also eliminates minor modes.
The pathfinder method runs multi-path Pathfinder by default, which returns a PSIS sample over the draws from several individual (“single-path”) Pathfinder runs. Argument num_paths specifies the number of single-path Pathfinders, the default is \(4\). If num_paths is set to 1, then only one individual Pathfinder is run without the PSIS reweighting of the sample.
The full set of configuration options available for the pathfinder method is available by using the pathfinder help-all subcommand. The arguments with their requested values or defaults are also reported at the beginning of the algorithm’s console output and in the output CSV file’s comments.
The following is a minimal call the Pathfinder algorithm using defaults for everything but the location of the data file.
> ./bernoulli pathfinder data file=bernoulli.data.RExecuting this command prints both output to the console and csv files.
The first part of the console output reports on the configuration used.
method = pathfinder
pathfinder
init_alpha = 0.001 (Default)
tol_obj = 1e-12 (Default)
tol_rel_obj = 10000 (Default)
tol_grad = 1e-08 (Default)
tol_rel_grad = 1e+07 (Default)
tol_param = 1e-08 (Default)
history_size = 5 (Default)
num_psis_draws = 1000 (Default)
num_paths = 4 (Default)
save_single_paths = false (Default)
psis_resample = true (Default)
calculate_lp = true (Default)
max_lbfgs_iters = 1000 (Default)
num_draws = 1000 (Default)
num_elbo_draws = 25 (Default)
id = 1 (Default)
data
file = bernoulli.data.json
init = 2 (Default)
random
seed = 2790476610 (Default)
output
file = output.csv (Default)
diagnostic_file = (Default)
refresh = 100 (Default)
sig_figs = -1 (Default)
profile_file = profile.csv (Default)
save_cmdstan_config = false (Default)
num_threads = 1 (Default)The rest of the output describes the progression of the algorithm.
By default, the Pathfinder algorithm runs 4 single-path Pathfinders in parallel, then uses importance resampling on the set of returned draws to produce the specified number of draws.
Path [1] :Initial log joint density = -11.543343
Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
5 -6.748e+00 1.070e-03 1.707e-05 1.000e+00 1.000e+00 126 -6.220e+00 -6.220e+00
Path [1] :Best Iter: [5] ELBO (-6.219833) evaluations: (126)
Path [2] :Initial log joint density = -7.443345
Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
5 -6.748e+00 9.936e-05 3.738e-07 1.000e+00 1.000e+00 126 -6.164e+00 -6.164e+00
Path [2] :Best Iter: [5] ELBO (-6.164015) evaluations: (126)
Path [3] :Initial log joint density = -18.986308
Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
5 -6.748e+00 2.996e-04 4.018e-06 1.000e+00 1.000e+00 126 -6.201e+00 -6.201e+00
Path [3] :Best Iter: [5] ELBO (-6.200559) evaluations: (126)
Path [4] :Initial log joint density = -8.304453
Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
5 -6.748e+00 2.814e-04 2.034e-06 1.000e+00 1.000e+00 126 -6.221e+00 -6.221e+00
Path [4] :Best Iter: [3] ELBO (-6.161276) evaluations: (126)
Total log probability function evaluations:8404Pathfinder Configuration
num_psis_draws- Final number of draws from multi-path pathfinder. Must be a positive integer. Default value is \(1000\).num_paths- Number of single pathfinders. Must be a positive integer. Default value is \(4\).save_single_paths- Whentrue, save outputs from single pathfinders. Valid values:[true, false]. Default isfalse.max_lbfgs_iters- Maximum number of L-BFGS iterations. Must be a positive integer. Default value is \(1000\).num_draws- Number of approximate posterior draws for each single pathfinder. Must be a positive integer. Default value is \(1000\). Can differ fromnum_psis_draws.num_elbo_draws- Number of Monte Carlo draws to evaluate ELBO. Must be a positive integer. Default value is \(25\).psis_resample- Iftrue, perform psis resampling on samples returned from individual pathfinders. Iffalse, returns allnum_paths * num_draws samplesdraws from the individual pathfinders. Valid values:[true, false]. Default istrue.calculate_lp- Iftrue, log probabilities of the approximate draws are calculated and returned with the output. Iffalse, each pathfinder will only calculate the lp values needed for the ELBO calculation. If False, psis resampling cannot be performed and the algorithm returnsnum_paths * num_draws samples. The output will still contain any lp values used when calculating ELBO scores within L-BFGS iterations. Valid values:[true, false]. Default istrue.
L-BFGS Configuration
Arguments init_alpha through history_size are the full set of arguments to the L-BFGS optimizer and have the same defaults for optimization.
Multi-path Pathfinder CSV files
By default, the pathfinder method uses 4 independent Pathfinder runs, each of which produces 1000 approximate draws, which are then importance resampled down to 1000 final draws. The importance resampled draws are output as a StanCSV file.
The CSV files have the following structure:
The initial CSV comment rows contain the complete set of CmdStan configuration options.
...
# method = pathfinder
# pathfinder
# init_alpha = 0.001 (Default)
# tol_obj = 9.9999999999999998e-13 (Default)
# tol_rel_obj = 10000 (Default)
# tol_grad = 1e-08 (Default)
# tol_rel_grad = 10000000 (Default)
# tol_param = 1e-08 (Default)
# history_size = 5 (Default)
# num_psis_draws = 1000 (Default)
# num_paths = 4 (Default)
# psis_resample = 1 (Default)
# calculate_lp = 1 (Default)
# save_single_paths = 0 (Default)
# max_lbfgs_iters = 1000 (Default)
# num_draws = 1000 (Default)
# num_elbo_draws = 25 (Default)
...Next is the column header line, followed the set of approximate draws. The Pathfinder algorithm first outputs lp_approx__, the log density in the approximating distribution, and lp__, the log density in the target distribution, followed by estimates of the model parameters, transformed parameters, and generated quantities.
lp_approx__,lp__,theta
-2.4973, -8.2951, 0.0811852
-0.87445, -7.06526, 0.160207
-0.812285, -7.07124, 0.35819
...The final lines are comment lines which give timing information.
# Elapsed Time: 0.016000 seconds (Pathfinders)
# 0.003000 seconds (PSIS)
# 0.019000 seconds (Total)Pathfinder provides option save_single_paths which will save output from the single-path Pathfinder runs.
Single-path Pathfinder Outputs.
The boolean option save_single_paths is used to save both the draws and the ELBO iterations from the individual Pathfinder runs. When save_single_paths is true, the draws from each are saved to StanCSV files with the same format as the PSIS sample and the ELBO evaluations along the L-BFGS trajectory for each are saved as JSON. Given an output file name, CmdStan adds suffixes to the base filename to distinguish between the output files. For the default output file name output.csv and default number of runs (4), the resulting CSV files are
output.csv
output_path_1.csv
output_path_1.json
output_path_2.csv
output_path_2.json
output_path_3.csv
output_path_3.json
output_path_4.csv
output_path_4.jsonThe individual sample CSV files have the same structure as the PSIS sample CSV file. The JSON files contain information from each ELBO iteration.
To see how this works, we run Pathfinder on the centered-parameterization of the eight-schools model, where the posterior distribution has a funnel shape:
> ./eight_schools pathfinder save_single_paths=true data file=eight_schools.data.jsonEach JSON file records the approximations to the target density at each point along the trajectory of the L-BFGS optimization algorithms.
{
"0": {
"iter": 0,
"unconstrained_parameters": [1.00595, -0.503687, 1.79367, 0.99083, 0.498077, -0.65816, 1.49176, -1.22647, 1.62911, 0.767445],
"grads": [-0.868919, 0.45198, -0.107675, -0.0123304, 0.163172, 0.354362, -0.108746, 0.673306, -0.102268, -4.51445]
},
"1": {
"iter": 1,
"unconstrained_parameters": [1.00595, -0.503687, 1.79367, 0.99083, 0.498077, -0.65816, 1.49176, -1.22647, 1.62911, 0.767445],
"grads": [-0.868919, 0.45198, -0.107675, -0.0123304, 0.163172, 0.354362, -0.108746, 0.673306, -0.102268, -4.51445],
"history_size": 1,
"lbfgs_success": true,
"pathfinder_success": true,
"x_center": [0.126047, -0.065048, 1.55708, 0.958509, 0.628075, -0.217041, 1.32032, -0.561338, 1.42988, 1.23213],
"logDetCholHk": -2.6839,
"L_approx": [[-0.0630456, -0.0187959], [0, 1.08328]],
"Qk": [[-0.361073, 0.5624], [0.183922, -0.279474], [-0.0708175, 0.15715], [-0.00917823, 0.0215802], [0.0606019, -0.0814513], [0.164071, -0.285769], [-0.057723, 0.112428], [0.276376, -0.424348], [-0.0620524, 0.131786], [-0.846488, -0.531094]],
"alpha": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
"full": false,
"lbfgs_note": ""
},
...,
"171": {
"iter": 171,
"unconstrained_parameters": [1.60479, 1.60479, 1.60479, 1.60479, 1.60479, 1.60479, 1.60479, 1.60479, 1.60479, -35.7821],
"grads": [2.66927e+15, -0.117312, -0.0639521, -2.66927e+15, -0.0445885, 0.0321579, 0.00499827, -0.163952, -0.032084, 6.4073],
"history_size": 5,
"lbfgs_success": true,
"pathfinder_success": true,
"x_center": [5.58876e+15, 5.58876e+15, 5.58876e+15, 5.58876e+15, 5.58876e+15, 5.58876e+15, 5.58876e+15, 5.58876e+15, 5.58876e+15, -2.02979e+17],
"logDetCholHk": 299.023,
"L_approx": [[4.6852e+06, 4.6852e+06, 4.6852e+06, 4.6852e+06, 4.6852e+06, 4.6852e+06, 4.6852e+06, 4.6852e+06, 4.6852e+06, -1.70162e+08], [0, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, -7.97244e+14], [0, 0, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, -7.97244e+14], [0, 0, 0, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, -7.97244e+14], [0, 0, 0, 0, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, -7.97244e+14], [0, 0, 0, 0, 0, 2.19511e+13, 2.19511e+13, 2.19511e+13, 2.19511e+13, -7.97244e+14], [0, 0, 0, 0, 0, 0, 2.19511e+13, 2.19511e+13, 2.19511e+13, -7.97244e+14], [0, 0, 0, 0, 0, 0, 0, 2.19511e+13, 2.19511e+13, -7.97244e+14], [0, 0, 0, 0, 0, 0, 0, 0, 2.19511e+13, -7.97244e+14], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2.89552e+16]],
"Qk": [],
"alpha": [1.11027e-12, 2.24669e-12, 2.05603e-12, 3.71177e-12, 5.7855e-12, 1.80169e-12, 3.40291e-12, 2.29699e-12, 3.43423e-12, 1.25815e-08],
"full": true,
"lbfgs_note": ""
},
"172": {
"iter": 172,
"unconstrained_parameters": [1.60531, 1.60531, 1.60531, 1.60531, 1.60531, 1.60531, 1.60531, 1.60531, 1.60531, -35.801],
"grads": [-0, -0.11731, -0.0639469, 0.0179895, -0.0445842, 0.0321643, 0.00500256, -0.163947, -0.0320824, 7],
"history_size": 5,
"lbfgs_success": false,
"pathfinder_success": false,
"lbfgs_note": ""
}
}Option num_paths=1 runs one single-path Pathfinder and output CSV file contains the draws from that run without PSIS reweighting. The combination of arguments num_paths=1 save_single_paths=true creates just two output files, the CSV sample and the set of ELBO iterations. In this case, the default output file name is “output.csv” and the default diagnostic file name is “output.json”.