Adds bool as supported type for comparisons

Relevant to biocore#27
gibsramen · Jan 10, 2023 · f6ef1b9 · f6ef1b9
1 parent 9cfd089
commit f6ef1b9
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Showing 2 changed files with 15 additions and 3 deletions.
diff --git a/evident/data_handler.py b/evident/data_handler.py
@@ -17,6 +17,8 @@
                     calculate_rm_anova_power)
 from .utils import _listify, _check_sample_overlap
 
+SUPP_TYPES = [np.dtype("object"), np.dtype("bool")]
+
 
 class _BaseDataHandler(ABC):
     """Abstract class for handling data and metadata."""
@@ -48,7 +50,7 @@ def __init__(
         warn_msg_level_count = False
         for col in cat_columns:
             # Drop non-categorical columns
-            if metadata[col].dtype != np.dtype("object"):
+            if metadata[col].dtype not in SUPP_TYPES:
                 cols_to_drop.append(col)
                 continue
 
@@ -120,7 +122,7 @@ def calculate_effect_size(
         :returns: Effect size
         :rtype: evident.results.EffectSizeResult
         """
-        if self.metadata[column].dtype != np.dtype("object"):
+        if self.metadata[column].dtype not in SUPP_TYPES:
             raise exc.NonCategoricalColumnError(self.metadata[column])
 
         column_choices = self.metadata[column].dropna().unique()
@@ -353,7 +355,7 @@ def _create_partial_power_func(
         :returns: Stem of power function based on chosen column
         :rtype: partial function
         """
-        if self.metadata[column].dtype != np.dtype("object"):
+        if self.metadata[column].dtype not in SUPP_TYPES:
             raise exc.NonCategoricalColumnError(self.metadata[column])
 
         column_choices = self.metadata[column].dropna().unique()

diff --git a/evident/tests/test_data_handler.py b/evident/tests/test_data_handler.py
@@ -91,6 +91,16 @@ def test_alpha_data_nan(self, alpha_mock):
         assert exp_warn_msg_1 == warn_msg_1
         assert exp_warn_msg_2 == warn_msg_2
 
+    def test_bool(self, alpha_mock):
+        data = alpha_mock.data
+        md = alpha_mock.metadata
+        md["classification_bool"] = md["classification"] == "B1"
+
+        adh = UnivariateDataHandler(data, md)
+        es1 = adh.calculate_effect_size("classification")
+        es2 = adh.calculate_effect_size("classification_bool")
+        assert es1.effect_size == es2.effect_size
+
 
 class TestBetaDiv:
     def test_init_beta_div_handler(self):