fix unit tests

src/seismometer/data/performance.py

@@ -66,7 +66,7 @@ def __call__(self, dataframe: pd.DataFrame, **kwargs) -> dict[str, float]:
 
 @export
 class BinaryClassifierMetricGenerator(MetricGenerator):
-    def __init__(self, rho: float = DEFAULT_RHO):
+    def __init__(self, rho: float = None):
         """
         A class that generates Binary classifier metrics from a dataframe.
         Keeps track of available metric names as well as the function to call to generate them.
@@ -76,6 +76,7 @@ def __init__(self, rho: float = DEFAULT_RHO):
         rho : float, optional
             The relative risk reduction for NNT calculation, by default DEFAULT_RHO.
         """
+        rho = rho or DEFAULT_RHO
         metric_names = STATNAMES + [f"NNT@{rho:0.3n}"]
 
         metric_fn = partial(calculate_binary_stats, rho=rho)
@@ -147,7 +148,7 @@ def calculate_binary_stats(
     target_col: str,
     score_col: str,
     score_threshold: float = 0.5,
-    rho: float = DEFAULT_RHO,
+    rho: float = None,
 ) -> dict[str, float]:
     """
     Generates binary classifier metrics from a dataframe, as a specific threshold
@@ -165,6 +166,7 @@ def calculate_binary_stats(
     rho : float, optional
         The relative risk reduction for NNT calculation, by default DEFAULT_RHO.
     """
+    rho = rho or DEFAULT_RHO
     score_threshold_integer = int(score_threshold * 100)
     y_true = dataframe[target_col]
     y_pred = dataframe[score_col]
@@ -178,7 +180,7 @@ def calculate_bin_stats(
     y_pred: Optional[pd.Series] = None,
     keep_score_values: bool = False,
     not_point_thresholds: bool = False,
-    rho: float = DEFAULT_RHO,
+    rho: float = None,
 ) -> pd.DataFrame:
     """
     Calculate summary statistics from y_true and y_pred (y_proba[:,1] for binary classification) arrays.
@@ -201,6 +203,7 @@ def calculate_bin_stats(
     -------
     pd.DataFrame of stats, rows for each threshold value between 0 and 100 with columns for basic statistics.
     """
+    rho = rho or DEFAULT_RHO
     y_true = y_true.astype(float)  # Expect numeric labels
 
     keep = ~(np.isnan(y_true) | np.isnan(y_pred))
@@ -310,8 +313,7 @@ def calculate_nnt(arr: np.ndarray, rho: Optional[Number | None] = None) -> np.nd
     .. [#med_metrics] eotles/med_metrics: Initial public release. Zenodo; 2024.
            http://dx.doi.org/10.5281/ZENODO.10514448
     """
-    if rho is None:
-        rho = DEFAULT_RHO
+    rho = rho or DEFAULT_RHO
 
     # Divide by zero is ok
     with np.errstate(invalid="ignore", divide="ignore"):