fix: remove dead branch in WealthPercentileTransformer.fit (#168)

CHANGELOG.md

@@ -15,6 +15,10 @@ Format: [Keep a Changelog](https://keepachangelog.com/en/1.1.0/)
   project's standing rules.
 
 ### Added
+- Tests pinning the two untested branches of `WealthPercentileTransformer`:
+  the all-missing column path (returns NaN ranks, keeps a stable output
+  width, raises no warning) and the partially-missing column path (NaN
+  input rows get NaN rank, observed rows get numeric rank). Closes #169.
 - **`datasets.make_donor_panel`** (Tier 2, Beta): a seeded multi-year donor
   panel returning gift-level rows rather than one aggregated row per donor.
   `generate_synthetic_donor_data` cannot demonstrate `RFMTransformer` (needs a

CONTRIBUTORS.md

@@ -45,6 +45,10 @@ contribution. Code, docs, tests, and review all count.
   single-class fallback coverage for
   `PlannedGivingIntentScorer.predict_intent_score`
   ([#149](https://github.com/PhilanthroPy-Project/PhilanthroPy/pull/149)).
+- **Lars** ([@Larslllllll](https://github.com/Larslllllll)): added the missing
+  unit-test coverage for `WealthPercentileTransformer`'s all-missing and
+  partially-missing column branches in `WealthPercentileTransformer` (closes
+  [#169](https://github.com/PhilanthroPy-Project/PhilanthroPy/issues/169)).
 
 ## Getting listed
 

philanthropy/preprocessing/_wealth_percentile.py

@@ -43,9 +43,9 @@ def fit(self, X, y=None):
         """
         X = validate_data(self, X, ensure_all_finite="allow-nan", reset=True)
 
-        if hasattr(X, "columns"):
-             self.feature_names_in_ = np.array(X.columns.tolist(), dtype=object)
-        elif not hasattr(self, "feature_names_in_"):
+        # validate_data has already set feature_names_in_ when input was a DataFrame,
+        # so we only need to handle the array-input path here.
+        if not hasattr(self, "feature_names_in_"):
              self.feature_names_in_ = np.array([f"x{i}" for i in range(X.shape[1])], dtype=object)
 
         # Use feature_names_in_ to resolve columns

tests/test_preprocessing.py

@@ -32,6 +32,7 @@
     EncounterTransformer,
     FiscalYearTransformer,
     WealthScreeningImputer,
+    WealthPercentileTransformer,
 )
 
 # Property-based tests for these transformers live in tests/test_properties.py.
@@ -723,3 +724,93 @@ def test_crm_cleaner_on_unnamed_ndarrays_skips_named_columns(self):
         X = np.array([["2023-01-01", "1250.50"]], dtype=object)
         out = np.asarray(CRMCleaner().fit(X).transform(X))
         assert out.shape == (1, 2)
+
+
+# ===========================================================================
+# 2. WealthPercentileTransformer: edge-case unit tests
+# ===========================================================================
+
+
+class TestWealthPercentileTransformer:
+    """Unit tests for WealthPercentileTransformer edge cases.
+
+    These tests pin the behaviour of columns that are entirely missing or
+    partially missing, so the guard branches remain stable under future edits.
+    """
+
+    def test_all_missing_column_yields_nan_ranks(self):
+        # An all-NaN wealth column should store an empty reference array and
+        # return a column of NaN on transform -- NOT a RuntimeWarning or a
+        # column of inf.  The output width is also preserved so downstream
+        # pipelines have a stable shape regardless of whether the wealth
+        # screen matched anything.
+        df = pd.DataFrame({
+            "net_worth": [np.nan, np.nan, np.nan],
+            "other": [1.0, 2.0, 3.0],
+        })
+        t = WealthPercentileTransformer().set_output(transform="pandas")
+        t.fit(df)
+
+        # The guard branch stores an empty array
+        assert t.percentile_lookup_["net_worth"].size == 0
+
+        # Transform produces NaN in the rank column, other column unchanged
+        out = t.transform(df)
+        assert out["net_worth_pct_rank"].isna().all()
+        assert out["other"].tolist() == [1.0, 2.0, 3.0]
+
+        # The rank column is still named and present (stable output width)
+        assert "net_worth_pct_rank" in out.columns
+        assert "net_worth_pct_rank" in list(t.get_feature_names_out())
+        assert len(t.get_feature_names_out()) == 3  # net_worth, other, net_worth_pct_rank
+
+        # No warning should be raised (would surface as an error here)
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            t.transform(df)  # must not warn
+
+    def test_partially_missing_column_ranks_only_observed_values(self):
+        # When a wealth column has some NaN and some real values, only the
+        # real values contribute to the reference distribution; NaN rows get
+        # NaN in the rank column while non-NaN rows receive a numeric rank.
+        df = pd.DataFrame({
+            "net_worth": [100_000.0, np.nan, 200_000.0, np.nan, 150_000.0],
+            "other": [1.0, 2.0, 3.0, 4.0, 5.0],
+        })
+        t = WealthPercentileTransformer().set_output(transform="pandas")
+        out = t.fit_transform(df)
+
+        assert "net_worth_pct_rank" in out.columns
+        # NaN input rows produce NaN rank
+        assert pd.isna(out.loc[1, "net_worth_pct_rank"])
+        assert pd.isna(out.loc[3, "net_worth_pct_rank"])
+        # Non-NaN input rows produce numeric rank
+        assert not pd.isna(out.loc[0, "net_worth_pct_rank"])
+        assert not pd.isna(out.loc[2, "net_worth_pct_rank"])
+        assert not pd.isna(out.loc[4, "net_worth_pct_rank"])
+        # Other column passes through unchanged
+        assert out["other"].tolist() == [1.0, 2.0, 3.0, 4.0, 5.0]
+
+    def test_wealth_percentile_feature_names_match_for_frame_and_array_input(self):
+        # Fit one transformer on a DataFrame and another on df.to_numpy(),
+        # then assert both produce the documented get_feature_names_out() shape:
+        # - DataFrame-fitted keeps real column names
+        # - array-fitted falls back to x0 .. xn
+        # This pins the behaviour the deleted hasattr(X, "columns") branch provided,
+        # so the deletion is provably safe rather than merely plausible.
+        df = pd.DataFrame({
+            "net_worth": [1.0, 2.0, 3.0],
+            "other": [1.0, 2.0, 3.0],
+        })
+        t_frame = WealthPercentileTransformer().set_output(transform="pandas")
+        t_array = WealthPercentileTransformer().set_output(transform="pandas")
+
+        t_frame.fit(df)
+        t_array.fit(df.to_numpy())
+
+        # DataFrame-fitted transformer keeps real column names
+        assert list(t_frame.get_feature_names_out()) == ["net_worth", "other", "net_worth_pct_rank"]
+
+        # Array-fitted transformer falls back to x0, x1
+        assert list(t_array.get_feature_names_out()) == ["x0", "x1"]
+