Add test for compare method standard error sorting consistency (#2407)

* Add test for compare method standard error sorting consistency (#2350)

* Fix pylint issues: remove trailing whitespace and add final newline

* Fix Black formatting: add blank line before function definition

* add to changelog

---------

Co-authored-by: Oriol (ProDesk) <[email protected]>

Author: uzairgheewala

CHANGELOG.md

@@ -11,6 +11,7 @@
 - `reference_values` and `labeller` now work together in `plot_pair` ([2437](https://github.com/arviz-devs/arviz/issues/2437))
 - Fix `plot_lm` for multidimensional data ([2408](https://github.com/arviz-devs/arviz/issues/2408))
 - Add [`scipy-stubs`](https://github.com/scipy/scipy-stubs) as a development dependency ([2445](https://github.com/arviz-devs/arviz/pull/2445))
+- Test compare dataframe stays consistent independently of input order ([2407](https://github.com/arviz-devs/arviz/pull/2407))
 
 ### Documentation
 - Added documentation for `reference_values` ([2438](https://github.com/arviz-devs/arviz/pull/2438))

arviz/tests/base_tests/test_stats.py

@@ -14,7 +14,7 @@
 from xarray import DataArray, Dataset
 from xarray_einstats.stats import XrContinuousRV
 
-from ...data import concat, convert_to_inference_data, from_dict, load_arviz_data
+from ...data import concat, convert_to_inference_data, from_dict, load_arviz_data, InferenceData
 from ...rcparams import rcParams
 from ...stats import (
     apply_test_function,
@@ -882,3 +882,44 @@ def test_bayes_factor():
     bf_dict1 = bayes_factor(idata, prior=np.random.normal(0, 10, 5000), var_name="a", ref_val=0)
     assert bf_dict0["BF10"] > bf_dict0["BF01"]
     assert bf_dict1["BF10"] < bf_dict1["BF01"]
+
+
+def test_compare_sorting_consistency():
+    chains, draws = 4, 1000
+
+    # Model 1 - good fit
+    log_lik1 = np.random.normal(-2, 1, size=(chains, draws))
+    posterior1 = Dataset(
+        {"theta": (("chain", "draw"), np.random.normal(0, 1, size=(chains, draws)))},
+        coords={"chain": range(chains), "draw": range(draws)},
+    )
+    log_like1 = Dataset(
+        {"y": (("chain", "draw"), log_lik1)},
+        coords={"chain": range(chains), "draw": range(draws)},
+    )
+    data1 = InferenceData(posterior=posterior1, log_likelihood=log_like1)
+
+    # Model 2 - poor fit (higher variance)
+    log_lik2 = np.random.normal(-5, 2, size=(chains, draws))
+    posterior2 = Dataset(
+        {"theta": (("chain", "draw"), np.random.normal(0, 1, size=(chains, draws)))},
+        coords={"chain": range(chains), "draw": range(draws)},
+    )
+    log_like2 = Dataset(
+        {"y": (("chain", "draw"), log_lik2)},
+        coords={"chain": range(chains), "draw": range(draws)},
+    )
+    data2 = InferenceData(posterior=posterior2, log_likelihood=log_like2)
+
+    # Compare models in different orders
+    comp_dict1 = {"M1": data1, "M2": data2}
+    comp_dict2 = {"M2": data2, "M1": data1}
+
+    comparison1 = compare(comp_dict1, method="bb-pseudo-bma")
+    comparison2 = compare(comp_dict2, method="bb-pseudo-bma")
+
+    assert comparison1.index.tolist() == comparison2.index.tolist()
+
+    se1 = comparison1["se"].values
+    se2 = comparison2["se"].values
+    np.testing.assert_array_almost_equal(se1, se2)