Improve robustness to numerical errors in kdeplot

Closes #2762

doc/releases/v0.12.0.txt

@@ -79,6 +79,8 @@ Other updates
 
 - |Fix| Subplot titles will no longer be reset when calling :meth:`FacetGrid.map` or :meth:`FacetGrid.map_dataframe` (:pr:`2705`).
 
+- |Fix| Improved robustness to numerical errors in :func:`kdeplot` (:pr:`2862`).
+
 - |Dependencies| Made `scipy` an optional dependency and added `pip install seaborn[all]` as a method for ensuring the availability of compatible `scipy` and `statsmodels` libraries at install time. This has a few minor implications for existing code, which are explained in the Github pull request (:pr:`2398`).
 
 - |Dependencies| Following `NEP29 <https://numpy.org/neps/nep-0029-deprecation_policy.html>`_, dropped support for Python 3.6 and bumped the minimally-supported versions of the library dependencies.

seaborn/distributions.py

@@ -334,16 +334,6 @@ def _compute_univariate_density(
             # Extract the data points from this sub set and remove nulls
             observations = sub_data[data_variable]
 
-            observation_variance = observations.var()
-            if math.isclose(observation_variance, 0) or np.isnan(observation_variance):
-                msg = (
-                    "Dataset has 0 variance; skipping density estimate. "
-                    "Pass `warn_singular=False` to disable this warning."
-                )
-                if warn_singular:
-                    warnings.warn(msg, UserWarning)
-                continue
-
             # Extract the weights for this subset of observations
             if "weights" in self.variables:
                 weights = sub_data["weights"]
@@ -353,7 +343,23 @@ def _compute_univariate_density(
                 part_weight = len(sub_data)
 
             # Estimate the density of observations at this level
-            density, support = estimator(observations, weights=weights)
+            variance = np.nan_to_num(observations.var())
+            skip = math.isclose(variance, 0)
+            try:
+                density, support = estimator(observations, weights=weights)
+            except np.linalg.LinAlgError:
+                # Convoluted approach needed because numerical failures
+                # can manifest in a few different ways.
+                skip = True
+            finally:
+                if skip:
+                    msg = (
+                        "Dataset has 0 variance; skipping density estimate. "
+                        "Pass `warn_singular=False` to disable this warning."
+                    )
+                    if warn_singular:
+                        warnings.warn(msg, UserWarning)
+                    continue
 
             if log_scale:
                 support = np.power(10, support)
@@ -1054,29 +1060,34 @@ def plot_bivariate_density(
 
         for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True):
 
-            # Extract the data points from this sub set and remove nulls
+            # Extract the data points from this sub set
             observations = sub_data[["x", "y"]]
+            variance = observations.var().fillna(0).min()
+            observations = observations["x"], observations["y"]
 
             # Extract the weights for this subset of observations
             if "weights" in self.variables:
                 weights = sub_data["weights"]
             else:
                 weights = None
 
-            # Check that KDE will not error out
-            variance = observations[["x", "y"]].var()
-            if any(math.isclose(x, 0) for x in variance) or variance.isna().any():
-                msg = (
-                    "Dataset has 0 variance; skipping density estimate. "
-                    "Pass `warn_singular=False` to disable this warning."
-                )
-                if warn_singular:
-                    warnings.warn(msg, UserWarning)
-                continue
-
             # Estimate the density of observations at this level
-            observations = observations["x"], observations["y"]
-            density, support = estimator(*observations, weights=weights)
+            skip = math.isclose(variance, 0)
+            try:
+                density, support = estimator(*observations, weights=weights)
+            except np.linalg.LinAlgError:
+                # Testing for 0 variance doesn't catch all cases where scipy raises,
+                # but we can also get a ValueError, so we need this convoluted approach
+                skip = True
+            finally:
+                if skip:
+                    msg = (
+                        "KDE cannot be estimated (0 variance or perfect covariance). "
+                        "Pass `warn_singular=False` to disable this warning."
+                    )
+                    if warn_singular:
+                        warnings.warn(msg, UserWarning, stacklevel=3)
+                    continue
 
             # Transform the support grid back to the original scale
             xx, yy = support

tests/test_distributions.py

@@ -1,4 +1,5 @@
 import itertools
+import warnings
 
 import numpy as np
 import matplotlib as mpl
@@ -411,9 +412,15 @@ def test_singular_data(self):
             ax = kdeplot(x=[5])
         assert not ax.lines
 
-        with pytest.warns(None) as record:
+        with pytest.warns(UserWarning):
+            # https://github.com/mwaskom/seaborn/issues/2762
+            ax = kdeplot(x=[1929245168.06679] * 18)
+        assert not ax.lines
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", UserWarning)
             ax = kdeplot(x=[5], warn_singular=False)
-        assert not record
+        assert not ax.lines
 
     def test_variable_assignment(self, long_df):
 
@@ -930,9 +937,10 @@ def test_singular_data(self):
             ax = dist.kdeplot(x=[5], y=[6])
         assert not ax.lines
 
-        with pytest.warns(None) as record:
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", UserWarning)
             ax = kdeplot(x=[5], y=[7], warn_singular=False)
-        assert not record
+        assert not ax.lines
 
     def test_fill_artists(self, long_df):