Increase balance package test coverage by talgalili · Pull Request #281 · facebookresearch/balance

talgalili · 2026-01-18T20:53:32Z

Summary:
The coverage report showed 98% overall coverage with 7 files having gaps. These tests cover previously untested edge cases including:

CLI exception handling paths for weighting failures
Sample class design effect diagnostics and IPW model parameters
CBPS optimization convergence warnings and constraint violation exceptions
Plotting functions with missing values, default parameters, and various dist_types
Distance metrics with empty numeric columns

Differential Revision: D90946146

meta-codesync · 2026-01-18T20:53:39Z

@talgalili has exported this pull request. If you are a Meta employee, you can view the originating Diff in D90946146.

Copilot

Pull request overview

This PR increases test coverage for the balance package by adding tests for previously untested edge cases across multiple modules. The changes target 7 files with coverage gaps, focusing on exception handling, diagnostics, convergence warnings, plotting functions with various parameters, and distance metric edge cases.

Changes:

Added tests for CLI exception handling paths when weighting fails, batch processing, and IPW model parameter passing
Added tests for Sample class design effect diagnostics and IPW model parameters in diagnostics output
Added tests for CBPS optimization convergence warnings and constraint violation exceptions using mocking
Added tests for plotting functions covering default parameter behavior, missing value handling, and various dist_types
Added tests for distance metric functions with empty numeric columns after filtering

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 14 comments.

Show a summary per file

File	Description
tests/test_weighted_comparisons_plots.py	Tests for seaborn_plot_dist default names/dist_types, missing value handling, plotly_plot_density without weights, plotly_plot_qq with plot_it=True, and unsupported dist_type error handling
tests/test_stats_and_plots.py	Tests for empty numeric column handling in wasserstein_distance, cramer_von_mises, and kolmogorov_smirnov; tests for conf_int_of_weighted_mean with DataFrame and relative_frequency_table with Series inputs
tests/test_sample.py	Tests for weight_trimming_percentile in str, design_effect_diagnostics edge cases, and IPW model parameters in diagnostics output
tests/test_cli.py	Tests for CLI exception handling with succeed_on_weighting_failure flag, IPW method with model kwargs, batch column processing, and main() function
tests/test_cbps.py	Tests for CBPS optimization convergence warnings and constraint violation exceptions using unittest.mock to control scipy.optimize return values

Copilot · 2026-01-18T20:59:58Z

tests/test_cbps.py

+                result.__getitem__ = lambda self, key: {
+                    "success": np.bool_(True),
+                    "message": "Optimization terminated successfully",
+                    "x": x0,
+                    "fun": 0.01,
+                }[key]


The lambda function for getitem captures a variable named 'self' which shadows the MagicMock instance. This works but is confusing. Consider using a different parameter name (e.g., 'obj' or '_') to avoid shadowing: result.__getitem__ = lambda obj, key: {...}[key]

Copilot · 2026-01-18T20:59:58Z

tests/test_cbps.py

+            try:
+                balance_cbps.cbps(
+                    sample_df,
+                    sample_weights,
+                    target_df,
+                    target_weights,
+                    transformations=None,
+                    cbps_method="over",
+                )
+            except Exception:
+                pass


The test catches all exceptions with a bare 'except Exception: pass', which could hide unexpected failures and make debugging difficult. Consider being more specific about what exceptions are expected, or remove the try/except if assertLogs will properly handle the case.

Suggested change

try:

balance_cbps.cbps(

sample_df,

sample_weights,

target_df,

target_weights,

transformations=None,

cbps_method="over",

)

except Exception:

pass

balance_cbps.cbps(

sample_df,

sample_weights,

target_df,

target_weights,

transformations=None,

cbps_method="over",

)

Copilot · 2026-01-18T20:59:58Z

tests/test_cbps.py

+            try:
+                balance_cbps.cbps(
+                    sample_df,
+                    sample_weights,
+                    target_df,
+                    target_weights,
+                    transformations=None,
+                    cbps_method="over",
+                )
+            except Exception:
+                pass


The test catches all exceptions with a bare 'except Exception: pass', which could hide unexpected failures and make debugging difficult. Consider being more specific about what exceptions are expected, or remove the try/except if assertLogs will properly handle the case.

Suggested change

try:

balance_cbps.cbps(

sample_df,

sample_weights,

target_df,

target_weights,

transformations=None,

cbps_method="over",

)

except Exception:

pass

balance_cbps.cbps(

sample_df,

sample_weights,

target_df,

target_weights,

transformations=None,

cbps_method="over",

)

Copilot · 2026-01-18T20:59:59Z

tests/test_cbps.py

+                result.__getitem__ = lambda self, key: {
+                    "success": np.bool_(False),
+                    "message": "Maximum iterations reached",
+                    "x": x0,
+                    "fun": 100.0,
+                }[key]


The lambda function for getitem captures a variable named 'self' which shadows the MagicMock instance. This works but is confusing. Consider using a different parameter name (e.g., 'obj' or '_') to avoid shadowing: result.__getitem__ = lambda obj, key: {...}[key]

Copilot · 2026-01-18T20:59:59Z

tests/test_cbps.py

+        successful_result.__getitem__ = lambda self, key: {
+            "success": np.bool_(True),
+            "message": "Optimization terminated successfully",
+            "x": np.array([0.1, 0.1]),
+            "fun": 0.01,
+        }[key]


The lambda function for getitem captures a variable named 'self' which shadows the MagicMock instance. This works but is confusing. Consider using a different parameter name (e.g., 'obj' or '_') to avoid shadowing throughout this test class.

Copilot · 2026-01-18T21:00:00Z

tests/test_weighted_comparisons_plots.py

+        """Test seaborn_plot_dist uses plot_qq for numeric variables with qq dist_type.
+
+        Verifies line 714 in weighted_comparisons_plots.py.
+        """


The test uses np.random.randn without setting a seed, which could lead to non-deterministic test behavior. Consider adding np.random.seed before generating random data to ensure reproducibility and test stability.

Suggested change

"""

"""

np.random.seed(0)

Copilot · 2026-01-18T21:00:00Z

tests/test_weighted_comparisons_plots.py

+
+        from balance.stats_and_plots.weighted_comparisons_plots import plotly_plot_qq
+
+        df = pd.DataFrame({"v1": np.random.randn(50), "weight": np.ones(50)})


The test uses np.random.randn without setting a seed, which could lead to non-deterministic test behavior. Consider adding np.random.seed before generating random data to ensure reproducibility and test stability.

Copilot · 2026-01-18T21:00:00Z

tests/test_weighted_comparisons_plots.py

+        """
+        from balance.stats_and_plots.weighted_comparisons_plots import plotly_plot_dist
+
+        df = pd.DataFrame({"v1": np.random.randn(30), "weight": np.ones(30)})


The test uses np.random.randn without setting a seed, which could lead to non-deterministic test behavior. Consider adding np.random.seed before generating random data to ensure reproducibility and test stability.

Copilot · 2026-01-18T21:00:00Z

tests/test_cbps.py

+            self.assertTrue(
+                len(log_context.records) > 0,


assertTrue(a > b) cannot provide an informative message. Using assertGreater(a, b) instead will give more informative messages.

Suggested change

self.assertTrue(

len(log_context.records) > 0,

self.assertGreater(

len(log_context.records),

0,

Copilot · 2026-01-18T21:00:01Z

tests/test_cli.py

+            self.assertTrue(os.path.isfile(diagnostics_out_file))
+
+            output_df = pd.read_csv(out_file)
+            self.assertTrue(len(output_df) > 0)


assertTrue(a > b) cannot provide an informative message. Using assertGreater(a, b) instead will give more informative messages.

Suggested change

self.assertTrue(len(output_df) > 0)

self.assertGreater(len(output_df), 0)

talgalili · 2026-01-18T21:04:49Z

@copilot open a new pull request to apply changes based on the comments in this thread

Summary: The coverage report showed 98% overall coverage with 7 files having gaps. These tests cover previously untested edge cases including: - CLI exception handling paths for weighting failures - Sample class design effect diagnostics and IPW model parameters - CBPS optimization convergence warnings and constraint violation exceptions - Plotting functions with missing values, default parameters, and various dist_types - Distance metrics with empty numeric columns Differential Revision: D90946146

Copilot

Pull request overview

Copilot reviewed 5 out of 5 changed files in this pull request and generated 4 comments.

Comments suppressed due to low confidence (1)

tests/test_cbps.py:1824

'except' clause does nothing but pass and there is no explanatory comment.

Copilot · 2026-01-19T07:31:41Z

tests/test_cbps.py

+    def test_gmm_loss_bal_init_convergence_failure_warning(self) -> None:
+        """Test line 765: Warning when gmm_loss with beta_balance fails.
+
+        Uses mocking to simulate the second gmm_loss optimization failing.


assertTrue(a > b) cannot provide an informative message. Using assertGreater(a, b) instead will give more informative messages.

Copilot · 2026-01-19T07:31:42Z

tests/test_cbps.py

+
+    def test_gmm_loss_glm_init_convergence_failure_warning(self) -> None:
+        """Test line 747: Warning when gmm_loss with gmm_init fails.
+


'except' clause does nothing but pass and there is no explanatory comment.

Copilot · 2026-01-19T07:31:42Z

tests/test_cbps.py

+                or "convergence" in r.getMessage().lower()
+            ]
+            self.assertTrue(
+                len(gmm_warnings) > 0 or len(log_context.records) > 0,


'except' clause does nothing but pass and there is no explanatory comment.

Copilot · 2026-01-19T07:31:42Z

tests/test_cbps.py

+                "success": np.bool_(True),
+                "message": "Success",
+                "x": np.array([1.0]),
+            }


'except' clause does nothing but pass and there is no explanatory comment.

Summary: The coverage report showed 98% overall coverage with 7 files having gaps. These tests cover previously untested edge cases including: - CLI exception handling paths for weighting failures - Sample class design effect diagnostics and IPW model parameters - CBPS optimization convergence warnings and constraint violation exceptions - Plotting functions with missing values, default parameters, and various dist_types - Distance metrics with empty numeric columns Differential Revision: D90946146

Copilot

Pull request overview

Copilot reviewed 5 out of 5 changed files in this pull request and generated 4 comments.

Copilot · 2026-01-19T08:27:29Z

tests/test_stats_and_plots.py

+
+
+class TestRelativeFrequencyTableSeries(balance.testutil.BalanceTestCase):
+    """Test cases for relative_frequency_table with Series (lines 677, 683)."""
+
+    def test_relative_frequency_table_with_series(self) -> None:
+        """Test relative_frequency_table with Series input.
+
+        Verifies lines 676-683 in weighted_stats.py.
+        """
+        from balance.stats_and_plots.weighted_stats import relative_frequency_table
+
+        s = pd.Series(["a", "a", "b", "c"])
+        result = relative_frequency_table(s)
+        self.assertIsInstance(result, pd.DataFrame)
+        self.assertIn("prop", result.columns)
+
+    def test_relative_frequency_table_with_unnamed_series(self) -> None:
+        """Test relative_frequency_table with unnamed Series.
+
+        Verifies lines 679-680 in weighted_stats.py.
+        """
+        from balance.stats_and_plots.weighted_stats import relative_frequency_table
+
+        s = pd.Series(["a", "a", "b"])
+        s.name = None
+        result = relative_frequency_table(s)
+        self.assertIn("group", result.columns)
+
+    def test_relative_frequency_table_raises_on_invalid_type(self) -> None:
+        """Test relative_frequency_table raises on invalid input type.
+
+        Verifies lines 682-683 in weighted_stats.py.
+        """
+        from balance.stats_and_plots.weighted_stats import relative_frequency_table
+
+        with self.assertRaises((TypeError, AttributeError)):
+            relative_frequency_table([1, 2, 3])  # type: ignore


This test class duplicates the exact same tests already present in the TestDescriptiveStatsEdgeCases class (lines 2260-2293). The test_relative_frequency_table_with_series, test_relative_frequency_table_with_unnamed_series, and test_relative_frequency_table_raises_on_invalid_type tests are identical duplicates and should be removed to avoid test redundancy.

Suggested change

class TestRelativeFrequencyTableSeries(balance.testutil.BalanceTestCase):

"""Test cases for relative_frequency_table with Series (lines 677, 683)."""

def test_relative_frequency_table_with_series(self) -> None:

"""Test relative_frequency_table with Series input.

Verifies lines 676-683 in weighted_stats.py.

"""

from balance.stats_and_plots.weighted_stats import relative_frequency_table

s = pd.Series(["a", "a", "b", "c"])

result = relative_frequency_table(s)

self.assertIsInstance(result, pd.DataFrame)

self.assertIn("prop", result.columns)

def test_relative_frequency_table_with_unnamed_series(self) -> None:

"""Test relative_frequency_table with unnamed Series.

Verifies lines 679-680 in weighted_stats.py.

"""

from balance.stats_and_plots.weighted_stats import relative_frequency_table

s = pd.Series(["a", "a", "b"])

s.name = None

result = relative_frequency_table(s)

self.assertIn("group", result.columns)

def test_relative_frequency_table_raises_on_invalid_type(self) -> None:

"""Test relative_frequency_table raises on invalid input type.

Verifies lines 682-683 in weighted_stats.py.

"""

from balance.stats_and_plots.weighted_stats import relative_frequency_table

with self.assertRaises((TypeError, AttributeError)):

relative_frequency_table([1, 2, 3]) # type: ignore

Copilot · 2026-01-19T08:27:30Z

tests/test_cbps.py

+            result.__getitem__ = lambda self, key: {
+                "success": np.bool_(False),
+                "message": "Did not converge to a solution satisfying the constraints",
+                "x": x0,
+                "fun": 100.0,
+            }[key]


The lambda function used in mock_minimize has a closure issue. The lambda captures 'self' from the outer scope which shadows the 'self' parameter in the lambda. This can lead to unexpected behavior. Consider using a different approach, such as using result.configure_mock or setting attributes directly on the MagicMock object instead of overriding getitem with a lambda.

Copilot · 2026-01-19T08:27:30Z

tests/test_cbps.py

+                result.__getitem__ = lambda self, key: {
+                    "success": np.bool_(True),
+                    "message": "Success",
+                    "x": x0,
+                    "fun": 1.0,
+                }[key]
+            else:
+                # Both GMM optimizations fail with constraint violation
+                result.__getitem__ = lambda self, key: {
+                    "success": np.bool_(False),
+                    "message": "Did not converge to a solution satisfying the constraints",
+                    "x": x0,
+                    "fun": 100.0,
+                }[key]


The lambda function used in mock_minimize has a closure issue. The lambda captures 'self' from the outer scope which shadows the 'self' parameter in the lambda. This can lead to unexpected behavior. Consider using a different approach, such as using result.configure_mock or setting attributes directly on the MagicMock object instead of overriding getitem with a lambda.

Suggested change

result.__getitem__ = lambda self, key: {

"success": np.bool_(True),

"message": "Success",

"x": x0,

"fun": 1.0,

}[key]

else:

# Both GMM optimizations fail with constraint violation

result.__getitem__ = lambda self, key: {

"success": np.bool_(False),

"message": "Did not converge to a solution satisfying the constraints",

"x": x0,

"fun": 100.0,

}[key]

result_data = {

"success": np.bool_(True),

"message": "Success",

"x": x0,

"fun": 1.0,

}

result.__getitem__.side_effect = result_data.__getitem__

else:

# Both GMM optimizations fail with constraint violation

result_data = {

"success": np.bool_(False),

"message": "Did not converge to a solution satisfying the constraints",

"x": x0,

"fun": 100.0,

}

result.__getitem__.side_effect = result_data.__getitem__

Copilot · 2026-01-19T08:27:30Z

tests/test_weighted_comparisons_plots.py

+        df1 = pd.DataFrame({"v1": np.random.randn(50)})
+        df2 = pd.DataFrame({"v1": np.random.randn(50)})


Tests should be deterministic and stable. Set a random seed before calling np.random.randn() to ensure reproducible test results. This aligns with the project's guideline to "avoid order/time dependence and uncontrolled randomness."

Suggested change

df1 = pd.DataFrame({"v1": np.random.randn(50)})

df2 = pd.DataFrame({"v1": np.random.randn(50)})

rng = np.random.RandomState(0)

df1 = pd.DataFrame({"v1": rng.randn(50)})

df2 = pd.DataFrame({"v1": rng.randn(50)})

Summary: The coverage report showed 98% overall coverage with 7 files having gaps. These tests cover previously untested edge cases including: - CLI exception handling paths for weighting failures - Sample class design effect diagnostics and IPW model parameters - CBPS optimization convergence warnings and constraint violation exceptions - Plotting functions with missing values, default parameters, and various dist_types - Distance metrics with empty numeric columns Differential Revision: D90946146

Copilot

Pull request overview

Copilot reviewed 9 out of 9 changed files in this pull request and generated 1 comment.

Copilot · 2026-01-19T12:15:11Z

tests/test_cbps.py

+                self.assertTrue(
+                    len(log_context.records) > 0,


assertTrue(a > b) cannot provide an informative message. Using assertGreater(a, b) instead will give more informative messages.

Suggested change

self.assertTrue(

len(log_context.records) > 0,

self.assertGreater(

len(log_context.records),

0,

Summary: Adds comprehensive tests covering previously untested edge cases identified in the coverage report. The 7 files with coverage gaps now have tests for: - **CLI**: Exception handling paths for weighting failures - **Sample class**: Design effect diagnostics and IPW model parameters - **CBPS**: Optimization convergence warnings and constraint violation exceptions - **Plotting**: Functions with missing values, default parameters, and various dist_types - **Distance metrics**: Empty numeric columns edge case This improves the overall test coverage reliability and catches potential regressions in error handling paths. Differential Revision: D90946146

Copilot

Pull request overview

Copilot reviewed 11 out of 11 changed files in this pull request and generated 2 comments.

.github/workflows/build-and-test.yml

tests/test_util_input_validation.py

Summary: Adds comprehensive tests covering previously untested edge cases identified in the coverage report. The 7 files with coverage gaps now have tests for: - **CLI**: Exception handling paths for weighting failures - **Sample class**: Design effect diagnostics and IPW model parameters - **CBPS**: Optimization convergence warnings and constraint violation exceptions - **Plotting**: Functions with missing values, default parameters, and various dist_types - **Distance metrics**: Empty numeric columns edge case This improves the overall test coverage reliability and catches potential regressions in error handling paths. Differential Revision: D90946146

Copilot

Pull request overview

Copilot reviewed 11 out of 11 changed files in this pull request and generated no new comments.

Summary: Adds comprehensive tests covering previously untested edge cases identified in the coverage report. The 7 files with coverage gaps now have tests for: - **CLI**: Exception handling paths for weighting failures - **Sample class**: Design effect diagnostics and IPW model parameters - **CBPS**: Optimization convergence warnings and constraint violation exceptions - **Plotting**: Functions with missing values, default parameters, and various dist_types - **Distance metrics**: Empty numeric columns edge case This improves the overall test coverage reliability and catches potential regressions in error handling paths. Differential Revision: D90946146

Copilot

Pull request overview

Copilot reviewed 13 out of 13 changed files in this pull request and generated 1 comment.

balance/utils/input_validation.py

talgalili · 2026-01-19T14:39:06Z

FYI @neuralsorcerer we got to 100% test coverage!

neuralsorcerer · 2026-01-19T15:01:34Z

Thank you for the work, @talgalili now that we’ve reached 100% test coverage, what do you think about moving our core computations to Rust for better performance? We could use maturin + pyo3 bindings to compile into python package.

talgalili · 2026-01-19T15:06:50Z

@neuralsorcerer I have no idea how that would 'play' with Meta's internal systems.
Could you please open a dedicated issue and give details of what you had in mind?

I have a longer roadmap in mind (roughly outlines in milestones 0.16 and above). But I need to get internal Meta alignment on it.
This could take a couple of weeks to complete.

If you are looking for a next step issue, please reach out via email and let's discuss ordering. 🙇

meta-codesync · 2026-01-19T15:10:44Z

This pull request has been merged in d0469bc.

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This was referenced Jan 19, 2026

[BUG] Fix " Perfect separation or prediction detected, parameter may not be identified" #279

Closed

[FEATURE] reach 100% test coverage #183

Closed

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balance/utils/input_validation.py Show resolved Hide resolved


		from balance.stats_and_plots.weighted_comparisons_plots import plotly_plot_qq

		df = pd.DataFrame({"v1": np.random.randn(50), "weight": np.ones(50)})

	self.assertTrue(len(output_df) > 0)
	self.assertGreater(len(output_df), 0)


		def test_gmm_loss_glm_init_convergence_failure_warning(self) -> None:
		"""Test line 747: Warning when gmm_loss with gmm_init fails.

		df1 = pd.DataFrame({"v1": np.random.randn(50)})
		df2 = pd.DataFrame({"v1": np.random.randn(50)})

-        df1 = pd.DataFrame({"v1": np.random.randn(50)})
-        df2 = pd.DataFrame({"v1": np.random.randn(50)})
+        rng = np.random.RandomState(0)
+        df1 = pd.DataFrame({"v1": rng.randn(50)})
+        df2 = pd.DataFrame({"v1": rng.randn(50)})

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