Merge pull request #718 from stan-dev/fix/717-draws_pd-diagnostic-filtering

Allow the 'vars' argument to draws_pd to filter new columns

Author: WardBrian

cmdstanpy/stanfit/gq.py

@@ -323,6 +323,8 @@ def draws_pd(
 
         self._assemble_generated_quantities()
 
+        all_columns = ['chain__', 'iter__', 'draw__'] + list(self.column_names)
+
         gq_cols: List[str] = []
         mcmc_vars: List[str] = []
         if vars is not None:
@@ -341,10 +343,12 @@ def draws_pd(
                             info.start_idx : info.end_idx
                         ]
                     )
+                elif var in ['chain__', 'iter__', 'draw__']:
+                    gq_cols.append(var)
                 else:
                     raise ValueError('Unknown variable: {}'.format(var))
         else:
-            gq_cols = list(self.column_names)
+            gq_cols = all_columns
             vars_list = gq_cols
 
         previous_draws_pd = self._previous_draws_pd(mcmc_vars, inc_warmup)
@@ -369,13 +373,9 @@ def draws_pd(
         )
         draws = np.concatenate([chains_col, iter_col, draw_col, draws], axis=2)
 
-        vars_list = ['chain__', 'iter__', 'draw__'] + vars_list
-        if gq_cols:
-            gq_cols = ['chain__', 'iter__', 'draw__'] + gq_cols
-
         draws_pd = pd.DataFrame(
             data=flatten_chains(draws),
-            columns=['chain__', 'iter__', 'draw__'] + list(self.column_names),
+            columns=all_columns,
         )
 
         if inc_sample and mcmc_vars:

cmdstanpy/stanfit/mcmc.py

@@ -615,10 +615,12 @@ def draws_pd(
                     cols.extend(
                         self.column_names[info.start_idx : info.end_idx]
                     )
+                elif var in ['chain__', 'iter__', 'draw__']:
+                    cols.append(var)
                 else:
                     raise ValueError(f'Unknown variable: {var}')
         else:
-            cols = list(self.column_names)
+            cols = ['chain__', 'iter__', 'draw__'] + list(self.column_names)
 
         draws = self.draws(inc_warmup=inc_warmup)
         # add long-form columns for chain, iteration, draw
@@ -640,8 +642,6 @@ def draws_pd(
         )
         draws = np.concatenate([chains_col, iter_col, draw_col, draws], axis=2)
 
-        cols = ['chain__', 'iter__', 'draw__'] + cols
-
         return pd.DataFrame(
             data=flatten_chains(draws),
             columns=['chain__', 'iter__', 'draw__'] + list(self.column_names),

test/test_generate_quantities.py

@@ -85,9 +85,11 @@ def test_from_csv_files(caplog: pytest.LogCaptureFixture) -> None:
         - 3  # chain, iter, draw duplicates
     )
 
-    assert list(bern_gqs.draws_pd(vars=['y_rep']).columns) == (
-        ["chain__", "iter__", "draw__"] + column_names
-    )
+    assert list(bern_gqs.draws_pd(vars=['y_rep']).columns) == (column_names)
+
+    assert list(
+        bern_gqs.draws_pd(vars=["chain__", "iter__", "draw__", 'y_rep']).columns
+    ) == (["chain__", "iter__", "draw__"] + column_names)
 
 
 def test_pd_xr_agreement():
@@ -315,9 +317,9 @@ def test_save_warmup(caplog: pytest.LogCaptureFixture) -> None:
     assert bern_gqs.draws_pd(inc_warmup=True).shape == (800, 13)
     assert bern_gqs.draws_pd(vars=['y_rep'], inc_warmup=False).shape == (
         400,
-        13,
+        10,
     )
-    assert bern_gqs.draws_pd(vars='y_rep', inc_warmup=False).shape == (400, 13)
+    assert bern_gqs.draws_pd(vars='y_rep', inc_warmup=False).shape == (400, 10)
 
     theta = bern_gqs.stan_variable(var='theta')
     assert theta.shape == (400,)

test/test_sample.py

@@ -778,24 +778,19 @@ def test_validate_good_run() -> None:
         fit.runset.chains * fit.num_draws_sampling,
         len(fit.column_names) + 3,
     )
-    assert fit.draws_pd(vars=['theta']).shape == (400, 4)
-    assert fit.draws_pd(vars=['lp__', 'theta']).shape == (400, 5)
-    assert fit.draws_pd(vars=['theta', 'lp__']).shape == (400, 5)
-    assert fit.draws_pd(vars='theta').shape == (400, 4)
+    assert fit.draws_pd(vars=['theta']).shape == (400, 1)
+    assert fit.draws_pd(vars=['lp__', 'theta']).shape == (400, 2)
+    assert fit.draws_pd(vars=['theta', 'lp__']).shape == (400, 2)
+    assert fit.draws_pd(vars='theta').shape == (400, 1)
 
     assert list(fit.draws_pd(vars=['theta', 'lp__']).columns) == [
-        'chain__',
-        'iter__',
-        'draw__',
         'theta',
         'lp__',
     ]
-    assert list(fit.draws_pd(vars=['lp__', 'theta']).columns) == [
-        'chain__',
-        'iter__',
-        'draw__',
+    assert list(fit.draws_pd(vars=['lp__', 'theta', 'iter__']).columns) == [
         'lp__',
         'theta',
+        'iter__',
     ]
 
     summary = fit.summary()
@@ -854,7 +849,7 @@ def test_validate_big_run() -> None:
     assert fit.step_size.shape == (2,)
     assert fit.metric.shape == (2, 2095)
     assert fit.draws().shape == (1000, 2, 2102)
-    assert fit.draws_pd(vars=['phi']).shape == (2000, 2098)
+    assert fit.draws_pd(vars=['phi']).shape == (2000, 2095)
     with raises_nested(ValueError, r'Unknown variable: gamma'):
         fit.draws_pd(vars=['gamma'])