Repository: stan-dev/cmdstanpy
Pull request #771 (2024-11-25 15:09:21)

CmdStan 2.36 no longer requires fixed_param hacks
#### Submission Checklist

- [x] Run unit tests
- [x] Declare copyright holder and open-source license: see below

#### Summary

CmdStan 2.36 allows the HMC samplers with 0 parameters

#### Copyright and Licensing

Please list the copyright holder for the work you are submitting (this will be you or your assignee, such as a university or company):
Simons Foundation


By submitting this pull request, the copyright holder is agreeing to license the submitted work under the following licenses:
- Code: BSD 3-clause (https://opensource.org/licenses/BSD-3-Clause)




Patch:
diff --git a/cmdstanpy/model.py b/cmdstanpy/model.py
index 8a3d067..1151988 100644
--- a/cmdstanpy/model.py
+++ b/cmdstanpy/model.py
@@ -205,9 +205,9 @@ class CmdStanModel:
                 self._compiler_options.add_include_path(path)
 
             # try to detect models w/out parameters, needed for sampler
-            if not cmdstan_version_before(
-                2, 27
-            ):  # unknown end of version range
+            if (not cmdstan_version_before(2, 27)) and cmdstan_version_before(
+                2, 36
+            ):
                 try:
                     model_info = self.src_info()
                     if 'parameters' in model_info:
diff --git a/cmdstanpy/stanfit/mcmc.py b/cmdstanpy/stanfit/mcmc.py
index 0bc1e59..8c93c8e 100644
--- a/cmdstanpy/stanfit/mcmc.py
+++ b/cmdstanpy/stanfit/mcmc.py
@@ -412,7 +412,7 @@ class CmdStanMCMC:
                     self._step_size[chain] = float(step_size.strip())
                     if self._metadata.cmdstan_config['metric'] != 'unit_e':
                         line = fd.readline().strip()  # metric type
-                        line = fd.readline().lstrip(' #\t')
+                        line = fd.readline().lstrip(' #\t').rstrip()
                         num_unconstrained_params = len(line.split(','))
                         if chain == 0:  # can't allocate w/o num params
                             if self.metric_type == 'diag_e':
@@ -429,18 +429,21 @@ class CmdStanMCMC:
                                     ),
                                     dtype=float,
                                 )
-                        if self.metric_type == 'diag_e':
-                            xs = line.split(',')
-                            self._metric[chain, :] = [float(x) for x in xs]
-                        else:
-                            xs = line.split(',')
-                            self._metric[chain, 0, :] = [float(x) for x in xs]
-                            for i in range(1, num_unconstrained_params):
-                                line = fd.readline().lstrip(' #\t').strip()
+                        if line:
+                            if self.metric_type == 'diag_e':
                                 xs = line.split(',')
-                                self._metric[chain, i, :] = [
+                                self._metric[chain, :] = [float(x) for x in xs]
+                            else:
+                                xs = line.strip().split(',')
+                                self._metric[chain, 0, :] = [
                                     float(x) for x in xs
                                 ]
+                                for i in range(1, num_unconstrained_params):
+                                    line = fd.readline().lstrip(' #\t').rstrip()
+                                    xs = line.split(',')
+                                    self._metric[chain, i, :] = [
+                                        float(x) for x in xs
+                                    ]
                     else:  # unit_e changed in 2.34 to have an extra line
                         pos = fd.tell()
                         line = fd.readline().strip()


Test patch:
diff --git a/test/data/no_param_hmc_sample.csv b/test/data/no_param_hmc_sample.csv
new file mode 100644
index 0000000..91999e4
--- /dev/null
+++ b/test/data/no_param_hmc_sample.csv
@@ -0,0 +1,157 @@
+# stan_version_major = 2
+# stan_version_minor = 35
+# stan_version_patch = 0
+# model = datagen_poisson_glm_model
+# start_datetime = 2024-12-02 14:45:12 UTC
+# method = sample (Default)
+#   sample
+#     num_samples = 100
+#     num_warmup = 1000 (Default)
+#     save_warmup = false (Default)
+#     thin = 1 (Default)
+#     adapt
+#       engaged = true (Default)
+#       gamma = 0.05 (Default)
+#       delta = 0.8 (Default)
+#       kappa = 0.75 (Default)
+#       t0 = 10 (Default)
+#       init_buffer = 75 (Default)
+#       term_buffer = 50 (Default)
+#       window = 25 (Default)
+#       save_metric = false (Default)
+#     algorithm = hmc (Default)
+#       hmc
+#         engine = nuts (Default)
+#           nuts
+#             max_depth = 10 (Default)
+#         metric = diag_e (Default)
+#         metric_file =  (Default)
+#         stepsize = 1 (Default)
+#         stepsize_jitter = 0 (Default)
+#     num_chains = 1 (Default)
+# id = 1 (Default)
+# data
+#   file =  (Default)
+# init = 2 (Default)
+# random
+#   seed = 2399056448 (Default)
+# output
+#   file = no_param_hmc_sample.csv
+#   diagnostic_file =  (Default)
+#   refresh = 100 (Default)
+#   sig_figs = -1 (Default)
+#   profile_file = profile.csv (Default)
+#   save_cmdstan_config = false (Default)
+# num_threads = 1 (Default)
+# stanc_version = %%NAME%%3 %%VERSION%%
+# stancflags = 
+lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,N,y_sim.1,y_sim.2,y_sim.3,y_sim.4,y_sim.5,y_sim.6,y_sim.7,y_sim.8,y_sim.9,y_sim.10,y_sim.11,y_sim.12,y_sim.13,y_sim.14,y_sim.15,y_sim.16,y_sim.17,y_sim.18,y_sim.19,y_sim.20,x_sim.1,x_sim.2,x_sim.3,x_sim.4,x_sim.5,x_sim.6,x_sim.7,x_sim.8,x_sim.9,x_sim.10,x_sim.11,x_sim.12,x_sim.13,x_sim.14,x_sim.15,x_sim.16,x_sim.17,x_sim.18,x_sim.19,x_sim.20,pop_sim.1,pop_sim.2,pop_sim.3,pop_sim.4,pop_sim.5,pop_sim.6,pop_sim.7,pop_sim.8,pop_sim.9,pop_sim.10,pop_sim.11,pop_sim.12,pop_sim.13,pop_sim.14,pop_sim.15,pop_sim.16,pop_sim.17,pop_sim.18,pop_sim.19,pop_sim.20,alpha_sim,beta_sim,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,eta.9,eta.10,eta.11,eta.12,eta.13,eta.14,eta.15,eta.16,eta.17,eta.18,eta.19,eta.20
+# Adaptation terminated
+# Step size = nan
+# Diagonal elements of inverse mass matrix:
+# 
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+0,1,nan,1,1,0,0,20,581,1103,1129,283,1230,4192,1839,844,1238,2477,1282,2452,1746,2629,812,2847,902,916,941,739,0.39395,0.0314425,0.756811,0.0111658,0.461562,0.978681,0.341407,0.0971613,0.208556,0.825811,0.173667,0.759563,0.271476,0.648194,0.244068,0.627351,0.618649,0.148487,0.233893,0.108801,717.728,2185.49,780.291,561.816,1377.49,2157.12,2317.09,1419.69,1972.64,1580.02,2113.71,1715.22,2450.42,2125.18,1236.29,2469.85,794.081,1557.28,1438.47,1300.99,-0.756643,1.46926,6.39826,6.97915,7.01498,5.59094,7.14953,8.35783,7.49304,6.64431,7.13691,7.82188,7.15472,7.80665,7.44624,7.85734,6.72183,7.97701,6.8295,6.81222,6.85834,6.5741
+0,1,nan,1,1,0,0,20,1373,1299,1899,2210,1382,1223,2204,1655,1675,1925,843,839,1476,2194,2426,1460,2501,1446,2581,847,0.643077,0.381267,0.946283,0.703033,0.095629,0.307798,0.945593,0.099031,0.14563,0.932409,0.472038,0.861157,0.0255042,0.456745,0.708671,0.95804,0.676515,0.591462,0.987416,0.813072,1154.36,1494.78,1217.4,1797.98,2064.03,1514.27,1428.15,2407.78,2352.82,1324.34,838.053,590.124,2328.28,2319.21,1986.88,966.32,2081.28,1297.31,1599.48,617.481,-0.489505,0.982602,7.19368,7.19486,7.54479,7.69571,7.23688,7.13563,7.70377,7.39426,7.41696,7.61535,6.7054,6.737,7.28844,7.70828,7.80116,7.32536,7.81598,7.25972,7.85817,6.73507
+0,1,nan,1,1,0,0,20,310,87,123,296,28,121,161,227,159,516,256,399,193,196,69,391,118,79,66,110,0.390018,0.70064,0.32598,0.406197,0.791016,0.774308,0.767359,0.543944,0.686644,0.164031,0.359296,0.188146,0.693511,0.72835,0.539191,0.0909529,0.89735,0.803991,0.95464,0.710277,2239.05,1210.79,872.915,2086.95,517.958,1913.32,2309.62,2166.36,2153.96,2384.95,1611.39,2038.3,2387.84,2396.68,726.365,1501.82,2084.77,1242.07,1815.03,1550.1,-1.25184,-1.92127,5.71264,4.50107,4.8937,5.61121,3.4783,4.8171,5.0187,5.3839,5.104,6.20995,5.44271,6.00655,5.19388,5.13064,4.30028,5.88785,4.66652,4.32801,4.4179,4.7296
+0,1,nan,1,1,0,0,20,4024,7503,6741,2845,4875,3461,15294,1400,10925,4005,3092,5590,5670,1376,4621,11754,13340,4829,6414,8023,0.642358,0.387393,0.559959,0.29301,0.445057,0.407473,0.887511,0.162736,0.67213,0.186717,0.483146,0.729516,0.642176,0.149805,0.0550886,0.655179,0.917189,0.0867304,0.458533,0.445872,792.519,2122.29,1489.28,928.016,1282.23,948.487,2156.62,552.692,2076.28,1445.07,764.768,979.45,1117.53,545.479,2042.41,2305.25,1812.93,2030.84,1653.67,2091.94,0.743484,1.36561,8.29591,8.93276,8.81422,7.97667,8.50761,8.1548,9.63177,7.28052,9.29968,8.27438,8.04284,8.62671,8.63932,7.24972,8.4406,9.38115,9.49871,8.47813,8.78041,8.99822
+0,1,nan,1,1,0,0,20,289,595,260,848,529,344,206,761,322,348,638,569,368,221,804,202,199,544,298,627,0.0243731,0.853609,0.242478,0.614938,0.221023,0.251662,0.538739,0.515453,0.342744,0.400408,0.539081,0.175395,0.497547,0.0628076,0.886734,0.621373,0.527338,0.0938144,0.396867,0.159362,874.374,1823.12,854.117,2455.26,1727.48,997.449,607.616,2334.29,964.389,1077.37,1901.59,1856.4,1168.64,630.898,2240.53,573.214,616.172,1821.26,922.987,1941.08,-1.17363,0.121402,5.60284,6.4383,5.60588,6.70701,6.30762,5.76212,5.30132,6.64441,5.73947,5.85726,6.44226,6.37406,5.95036,5.28114,6.64849,5.25306,5.31391,6.34504,5.70216,6.41671
+0,1,nan,1,1,0,0,20,275,320,890,865,1267,484,676,728,349,371,1509,202,1198,484,1080,644,199,303,490,1519,0.153452,0.504129,0.416003,0.416251,0.633733,0.945822,0.29985,0.326243,0.0282175,0.139667,0.789661,0.133303,0.863943,0.50078,0.693518,0.87538,0.0279258,0.324871,0.810333,0.919699,888.302,664.919,2343.93,2264.05,2335.06,619.574,1905.42,2049.68,1365.36,1313.12,2368.45,773.413,1665.49,977.414,1924.97,892.521,836.037,810.874,687.176,1965.04,-1.4587,1.25338,5.52295,5.67283,6.8223,6.78793,7.0914,6.15581,6.46959,6.57565,5.79584,5.89652,7.30104,5.3592,7.04202,6.05388,6.97321,6.43253,5.30498,5.6466,6.08955,7.2773
+0,1,nan,1,1,0,0,20,247,777,818,1110,295,358,913,536,660,1116,512,1199,528,528,395,484,930,448,565,494,0.321914,0.479373,0.145062,0.739594,0.0331412,0.6056,0.492783,0.539423,0.0227995,0.401186,0.10937,0.725921,0.184788,0.715367,0.544223,0.640477,0.263407,0.882402,0.586073,0.423947,548.724,1608.33,1828.02,2172.98,615.184,760.302,1849.36,1092.44,1444.75,2343.3,1064.36,2380.22,1086.08,1083.24,778.04,1022.33,2019.35,812.007,1190.58,1031.14,-0.806579,0.169506,5.55558,6.65763,6.729,7.00264,5.62096,5.92979,6.79955,6.28102,6.47298,7.02074,6.18209,7.09142,6.21508,6.30239,5.94245,6.23183,6.8486,6.0425,6.37496,6.2037
+0,1,nan,1,1,0,0,20,391,547,910,1086,794,1030,1570,963,711,661,744,2127,1248,594,1363,1020,594,871,469,647,0.955185,0.375997,0.294382,0.623574,0.628965,0.0151622,0.156802,0.634918,0.756372,0.605548,0.150361,0.1057,0.219394,0.762905,0.371702,0.210037,0.70016,0.790868,0.586307,0.735489,1129.96,826.025,1219.34,2152.35,1695.8,1028.88,1879.42,1962.07,1714.5,1271.05,883.67,2439.81,1638.45,1501.95,2135.28,1255.73,1314.92,2138.92,848.72,1520.56,-0.00149578,-1.14881,5.93112,6.28318,6.76638,6.95645,6.71186,6.91731,7.35709,6.85086,6.57646,6.45045,6.60985,7.67675,7.14797,6.4366,7.23784,6.89268,6.37569,6.75801,6.06868,6.4804
+0,1,nan,1,1,0,0,20,440,809,634,404,465,576,1447,1383,1556,761,430,1047,1207,644,1436,847,1426,1466,697,995,0.727459,0.966522,0.662259,0.822898,0.708831,0.410689,0.451146,0.925878,0.60364,0.407197,0.942708,0.403372,0.631485,0.854492,0.93927,0.123669,0.923239,0.750658,0.737335,0.771862,702.291,1166.76,964.548,619.941,688.07,965.222,2328.8,2041.58,2401.14,1227.56,623.957,1657.76,1848.88,935.851,2014.09,1442.56,2153.12,2293.33,1053.01,1523.97,-0.547,0.167672,6.12932,6.67705,6.4357,6.0206,6.10574,6.39422,7.28175,7.22972,7.33791,6.63406,6.04715,6.93386,7.08122,6.43773,7.21841,6.74791,7.28247,7.31662,6.53603,6.91149
+# 
+#  Elapsed Time: 0 seconds (Warm-up)
+#                0.002 seconds (Sampling)
+#                0.002 seconds (Total)
+# 
diff --git a/test/test_model.py b/test/test_model.py
index 55c2bfe..29995ff 100644
--- a/test/test_model.py
+++ b/test/test_model.py
@@ -95,10 +95,13 @@ def test_exe_only() -> None:
     assert not model2._fixed_param
 
 
-def test_fixed_param() -> None:
+def test_legacy_fixed_param() -> None:
     stan = os.path.join(DATAFILES_PATH, 'datagen_poisson_glm.stan')
     model = CmdStanModel(stan_file=stan)
-    assert model._fixed_param
+    if cmdstan_version_before(2, 36):
+        assert model._fixed_param
+    else:
+        assert not model._fixed_param
 
 
 def test_model_pedantic(caplog: pytest.LogCaptureFixture) -> None:
diff --git a/test/test_sample.py b/test/test_sample.py
index 369132d..944ce7a 100644
--- a/test/test_sample.py
+++ b/test/test_sample.py
@@ -642,13 +642,18 @@ def test_fixed_param_good() -> None:
     assert datagen_fit.step_size is None
 
 
-def test_fixed_param_unspecified() -> None:
+def test_sample_no_params() -> None:
     stan = os.path.join(DATAFILES_PATH, 'datagen_poisson_glm.stan')
     datagen_model = CmdStanModel(stan_file=stan)
     datagen_fit = datagen_model.sample(iter_sampling=100, show_progress=False)
-    assert datagen_fit.step_size is None
     summary = datagen_fit.summary()
-    assert 'lp__' not in list(summary.index)
+
+    if cmdstan_version_before(2, 36):
+        assert 'lp__' not in list(summary.index)
+        assert datagen_fit.step_size is None
+    else:
+        assert 'lp__' in list(summary.index)
+        assert np.isnan(datagen_fit.step_size).all()
 
     exe_only = os.path.join(DATAFILES_PATH, 'exe_only')
     shutil.copyfile(datagen_model.exe_file, exe_only)
@@ -656,9 +661,14 @@ def test_fixed_param_unspecified() -> None:
     datagen2_model = CmdStanModel(exe_file=exe_only)
     datagen2_fit = datagen2_model.sample(iter_sampling=200, show_console=True)
     assert datagen2_fit.chains == 4
-    assert datagen2_fit.step_size is None
     summary = datagen2_fit.summary()
-    assert 'lp__' not in list(summary.index)
+
+    if cmdstan_version_before(2, 36):
+        assert datagen2_fit.step_size is None
+        assert 'lp__' not in list(summary.index)
+    else:
+        assert np.isnan(datagen2_fit.step_size).all()
+        assert 'lp__' in list(summary.index)
 
 
 def test_index_bounds_error() -> None:
@@ -823,7 +833,7 @@ def test_validate_good_run() -> None:
     assert 'Treedepth satisfactory for all transitions.' in diagnostics
     assert 'No divergent transitions found.' in diagnostics
     assert 'E-BFMI satisfactory' in diagnostics
-    assert 'Effective sample size satisfactory.' in diagnostics
+    assert 'effective sample size satisfactory' in diagnostics.lower()
 
 
 def test_validate_big_run() -> None:
@@ -986,6 +996,12 @@ def test_from_csv_fixed_param() -> None:
     assert fixed_param_sample.draws_pd().shape == (100, 88)
 
 
+def test_from_csv_no_param_hmc() -> None:
+    csv_path = os.path.join(DATAFILES_PATH, 'no_param_hmc_sample.csv')
+    no_parameters_sample = from_csv(path=csv_path)
+    assert no_parameters_sample.draws_pd().shape == (100, 93)
+
+
 def test_custom_metric() -> None:
     stan = os.path.join(DATAFILES_PATH, 'bernoulli.stan')
     jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
@@ -1621,33 +1637,18 @@ def test_validate_sample_sig_figs(stanfile='bernoulli.stan'):
 
 
 def test_validate_summary_sig_figs() -> None:
-    # construct CmdStanMCMC from logistic model output, config
-    exe = os.path.join(DATAFILES_PATH, 'logistic' + EXTENSION)
-    rdata = os.path.join(DATAFILES_PATH, 'logistic.data.R')
-    sampler_args = SamplerArgs(iter_sampling=100)
-    cmdstan_args = CmdStanArgs(
-        model_name='logistic',
-        model_exe=exe,
-        chain_ids=[1, 2, 3, 4],
-        seed=12345,
-        data=rdata,
-        output_dir=DATAFILES_PATH,
-        sig_figs=17,
-        method_args=sampler_args,
+    # construct CmdStanMCMC from logistic model output
+    fit = from_csv(
+        [
+            os.path.join(DATAFILES_PATH, 'logistic_output_1.csv'),
+            os.path.join(DATAFILES_PATH, 'logistic_output_2.csv'),
+            os.path.join(DATAFILES_PATH, 'logistic_output_3.csv'),
+            os.path.join(DATAFILES_PATH, 'logistic_output_4.csv'),
+        ]
     )
-    runset = RunSet(args=cmdstan_args, chains=4)
-    runset._csv_files = [
-        os.path.join(DATAFILES_PATH, 'logistic_output_1.csv'),
-        os.path.join(DATAFILES_PATH, 'logistic_output_2.csv'),
-        os.path.join(DATAFILES_PATH, 'logistic_output_3.csv'),
-        os.path.join(DATAFILES_PATH, 'logistic_output_4.csv'),
-    ]
-    retcodes = runset._retcodes
-    for i in range(len(retcodes)):
-        runset._set_retcode(i, 0)
-    fit = CmdStanMCMC(runset)
 
     sum_default = fit.summary()
+
     beta1_default = format(sum_default.iloc[1, 0], '.18g')
     assert beta1_default.startswith('1.3')