New gq tests

Author: WardBrian

cmdstanpy/stanfit/gq.py

@@ -690,12 +690,14 @@ def _previous_draws(self, inc_warmup: bool) -> np.ndarray:
             return np.atleast_2d(  # type: ignore
                 p_fit.optimized_params_np,
             )[:, None]
-        else:  # CmdStanVB:
+        elif isinstance(p_fit, CmdStanVB):
             if inc_warmup:
                 return np.vstack(
                     [p_fit.variational_params_np, p_fit.variational_sample]
                 )[:, None]
             return p_fit.variational_sample[:, None]
+        else:  # CmdStanLaplace, CmdStanPathfinder
+            return p_fit.draws()[:, None, :]
 
     def _previous_draws_pd(
         self, vars: list[str], inc_warmup: bool
@@ -714,8 +716,12 @@ def _previous_draws_pd(
                 return p_fit.optimized_iterations_pd[sel]  # type: ignore
             else:
                 return p_fit.optimized_params_pd[sel]
-        else:  # CmdStanVB:
+        elif isinstance(p_fit, CmdStanVB):
             return p_fit.variational_sample_pd[sel]
+        elif isinstance(p_fit, CmdStanLaplace):
+            return p_fit.draws_pd(vars or None)
+        else:  # CmdStanPathfinder
+            return pd.DataFrame(p_fit.draws(), columns=p_fit.column_names)[sel]
 
     def save_csvfiles(self, dir: str | None = None) -> None:
         """

test/test_generate_quantities.py

@@ -771,3 +771,80 @@ def test_vb_xarray() -> None:
     bern_gqs = model.generate_quantities(data=jdata, previous_fit=bern_fit)
     with pytest.raises(RuntimeError, match="via Sampling"):
         _ = bern_gqs.draws_xr()
+
+
+def test_from_pathfinder() -> None:
+    stan = os.path.join(DATAFILES_PATH, 'bernoulli.stan')
+    bern_model = CmdStanModel(stan_file=stan)
+    jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
+    bern_fit = bern_model.pathfinder(
+        data=jdata,
+        seed=12345,
+    )
+
+    # gq_model
+    stan = os.path.join(DATAFILES_PATH, 'bernoulli_ppc.stan')
+    model = CmdStanModel(stan_file=stan)
+
+    bern_gqs = model.generate_quantities(data=jdata, previous_fit=bern_fit)
+
+    assert bern_gqs.runset._args.method == Method.GENERATE_QUANTITIES
+    assert 'CmdStanGQ: model=bernoulli_ppc' in repr(bern_gqs)
+    assert 'method=generate_quantities' in repr(bern_gqs)
+    assert bern_gqs.runset.chains == 1
+    assert bern_gqs.runset._retcode(0) == 0
+    csv_file = bern_gqs.runset.csv_files[0]
+    assert os.path.exists(csv_file)
+
+    assert bern_gqs.draws().shape == (1000, 1, 10)
+    assert bern_gqs.draws(inc_sample=True).shape == (1000, 1, 14)
+
+    # draws_pd()
+    assert bern_gqs.draws_pd().shape == (1000, 13)
+
+    # stan_variable
+    theta = bern_gqs.stan_variable(var='theta')
+    assert theta.shape == (1000,)
+    y_rep = bern_gqs.stan_variable(var='y_rep')
+    assert y_rep.shape == (1000, 10)
+
+
+def test_from_laplace() -> None:
+    stan = os.path.join(DATAFILES_PATH, 'bernoulli.stan')
+    bern_model = CmdStanModel(stan_file=stan)
+    jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')
+    bern_fit = bern_model.laplace_sample(
+        data=jdata,
+        seed=12345,
+    )
+
+    # gq_model
+    stan = os.path.join(DATAFILES_PATH, 'bernoulli_ppc.stan')
+    model = CmdStanModel(stan_file=stan)
+
+    bern_gqs = model.generate_quantities(data=jdata, previous_fit=bern_fit)
+
+    assert bern_gqs.runset._args.method == Method.GENERATE_QUANTITIES
+    assert 'CmdStanGQ: model=bernoulli_ppc' in repr(bern_gqs)
+    assert 'method=generate_quantities' in repr(bern_gqs)
+    assert bern_gqs.runset.chains == 1
+    assert bern_gqs.runset._retcode(0) == 0
+    csv_file = bern_gqs.runset.csv_files[0]
+    assert os.path.exists(csv_file)
+
+    assert bern_gqs.draws().shape == (1000, 1, 10)
+    assert bern_gqs.draws(inc_sample=True).shape == (1000, 1, 13)
+
+    # draws_pd()
+    assert bern_gqs.draws_pd().shape == (1000, 13)
+    assert (
+        bern_gqs.draws_pd(inc_sample=True).shape[1]
+        == bern_gqs.previous_fit.draws_pd().shape[1]
+        + bern_gqs.draws_pd().shape[1]
+    )
+
+    # stan_variable
+    theta = bern_gqs.stan_variable(var='theta')
+    assert theta.shape == (1000,)
+    y_rep = bern_gqs.stan_variable(var='y_rep')
+    assert y_rep.shape == (1000, 10)