Bump version to 0.5.1; update metric name formatting in bootstrap_ccr…

…am and permutation_test_ccram functions for clarity; enhance tests to reflect changes
dmavani25 · Jan 30, 2025 · 96d1256 · 96d1256
1 parent 0d856fc
commit 96d1256
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Showing 7 changed files with 413 additions and 349 deletions.
diff --git a/discopula/__init__.py b/discopula/__init__.py
@@ -9,7 +9,7 @@
         permutation_test_ccram,
     )
 
-__version__ = "0.5.0"
+__version__ = "0.5.1"
 __all__ = [
   "GenericCheckerboardCopula",
   "gen_contingency_to_case_form",

diff --git a/discopula/checkerboard/gencopula.py b/discopula/checkerboard/gencopula.py
@@ -345,36 +345,37 @@ def get_category_predictions_multi(
 
         return result
 
-    def calculate_scores(self, axis):
-        """Calculate checkerboard scores for the specified axis.
+    def calculate_scores(self, var_index):
+        """Calculate checkerboard scores for the specified variable index.
         
         Parameters
         ----------
-        axis : int
+        var_index : int
             1-Indexed axis of the variable for which to calculate scores
             
         Returns
         -------
         numpy.ndarray
             Array containing checkerboard scores for the given axis
         """
-        parsed_axis = axis - 1
+        parsed_axis = var_index - 1
         return self.scores[parsed_axis]
 
-    def calculate_variance_S(self, axis):
-        """Calculate the variance of score S for the specified axis.
+    def calculate_variance_S(self, var_index):
+        """Calculate the variance of score S for the specified variable index.
         
         Parameters
         ----------
-        axis : int
-            Index of the axis for which to calculate variance
+        var_index : int
+            1-Indexed axis of the variable for which to calculate variance
             
         Returns
         -------
         float
             Variance of score S for the given axis
         """
-        return self._calculate_sigma_sq_S_vectorized(axis)
+        parsed_axis = var_index - 1
+        return self._calculate_sigma_sq_S_vectorized(parsed_axis)
 
     def _calculate_conditional_pmf(self, target_axis, given_axes):
         """Helper Function: Calculate conditional PMF P(target|given)."""

diff --git a/discopula/checkerboard/genstatsim.py b/discopula/checkerboard/genstatsim.py
@@ -93,8 +93,8 @@ def bootstrap_ccram(contingency_table: np.ndarray,
         predictors = [predictors]
 
     # Format metric name
-    predictors_str = ",".join(map(str, predictors))
-    metric_name = f"{'SCCRAM' if scaled else 'CCRAM'} ({predictors_str})->{response}"
+    predictors_str = ",".join(f"X{i}" for i in predictors)
+    metric_name = f"{'SCCRAM' if scaled else 'CCRAM'} ({predictors_str}) to X{response}"
 
     # Calculate observed value
     gen_copula = GenericCheckerboardCopula.from_contingency_table(contingency_table)
@@ -439,8 +439,9 @@ def permutation_test_ccram(contingency_table: np.ndarray,
         parsed_predictors.append(pred_axis - 1)
     parsed_response = response - 1
 
-    predictors_str = ",".join(map(str, predictors))
-    metric_name = f"{'SCCRAM' if scaled else 'CCRAM'} ({predictors_str})->{response}"
+    # Format metric name
+    predictors_str = ",".join(f"X{i}" for i in predictors)
+    metric_name = f"{'SCCRAM' if scaled else 'CCRAM'} ({predictors_str}) to X{response}"
 
     cases = gen_contingency_to_case_form(contingency_table)
     source_data = [cases[:, axis] for axis in parsed_predictors]