style: black on src n test

src/skmiscpy/cbs.py

@@ -34,7 +34,7 @@ def compute_smd(
         The name of the column containing weights. Defaults to None.
 
     std_binary: bool
-        Should the mean differences for binary variables (i.e., difference in proportion) 
+        Should the mean differences for binary variables (i.e., difference in proportion)
         be standardized or not. Default is False. See notes.
 
     estimand : str, optional
@@ -53,17 +53,17 @@ def compute_smd(
     -----
 
     The mean differences for continuous variables are standardized so that they are on the same scale
-    and so that they can be compared across variables, and they allow for a simple interpretation even 
-    when the details of the variable's original scale are unclear to the analyst. 
-    
-    None of these advantages are passed to binary variables because binary variables are already on the 
-    same scale (i.e., a proportion), and the scale is easily interpretable. In addition, the details of 
-    standardizing the proportion difference of a binary variable involve dividing the proportion difference 
-    by a variance, but the variance of a binary variable is a function of its proportion. Standardizing 
-    the proportion difference of a binary variable can yield the following counterintuitive result: 
-    if P\ :sub:`T`\ = 0.2 and P\ :sub:`C`\ = 0.3, the standardized difference in proportion would be 
-    different from that if P\ :sub:`T`\ = 0.5 and P\ :sub:`C`\ = 0.6, even though the expectation is that 
-    the balance statistic should be the same for both scenarios because both would yield the same degree 
+    and so that they can be compared across variables, and they allow for a simple interpretation even
+    when the details of the variable's original scale are unclear to the analyst.
+
+    None of these advantages are passed to binary variables because binary variables are already on the
+    same scale (i.e., a proportion), and the scale is easily interpretable. In addition, the details of
+    standardizing the proportion difference of a binary variable involve dividing the proportion difference
+    by a variance, but the variance of a binary variable is a function of its proportion. Standardizing
+    the proportion difference of a binary variable can yield the following counterintuitive result:
+    if P\ :sub:`T`\ = 0.2 and P\ :sub:`C`\ = 0.3, the standardized difference in proportion would be
+    different from that if P\ :sub:`T`\ = 0.5 and P\ :sub:`C`\ = 0.6, even though the expectation is that
+    the balance statistic should be the same for both scenarios because both would yield the same degree
     of bias in the effect estimate.
 
 
@@ -145,7 +145,7 @@ def _calc_smd_covar(
     covar: str,
     wt_var: str = None,
     estimand: str = "ATE",
-    std_binary: bool = False
+    std_binary: bool = False,
 ) -> float:
     """
     Calculate the Standardized Mean Difference (SMD) for a covariate between two groups.
@@ -162,10 +162,10 @@ def _calc_smd_covar(
         The column name of the weights. If None, only the unadjusted SMD is calculated. Defaults to None.
     estimand : str, optional
         The causal estimand to use. Defaults to "ATE" (Average Treatment Effect). Currently supported
-        options are "ATT" (Average Treatment Effect among the Treated) and 
+        options are "ATT" (Average Treatment Effect among the Treated) and
         "ATC" (Average Treatment Effect among the Control group).
     std_binary: bool
-        Should the mean differences for binary variables (i.e., difference in proportion) 
+        Should the mean differences for binary variables (i.e., difference in proportion)
         be standardized or not. Default is False. See notes.
 
     Returns
@@ -214,7 +214,9 @@ def _calc_smd_covar(
         if data[covar].dropna().nunique() == 2:
             _check_proportion_within_range(wt_m1, wt_bin_custom_msg_1)
             _check_proportion_within_range(wt_m0, wt_bin_custom_msg_0)
-            return _calc_smd_bin_covar(estimand, m1=m1, m0=m0, wt_m1=wt_m1, wt_m0=wt_m0, std_binary=std_binary)
+            return _calc_smd_bin_covar(
+                estimand, m1=m1, m0=m0, wt_m1=wt_m1, wt_m0=wt_m0, std_binary=std_binary
+            )
         else:
             return _calc_smd_cont_covar(
                 estimand, m1=wt_m1, m0=wt_m0, s2_1=s2_1, s2_0=s2_0
@@ -348,7 +350,11 @@ def _calc_smd_cont_covar(estimand, *args, **kwargs):
 
 
 def _calc_smd_bin_covar_ate(
-    m1: float, m0: float, wt_m1: float = None, wt_m0: float = None, std_binary: bool = False
+    m1: float,
+    m0: float,
+    wt_m1: float = None,
+    wt_m0: float = None,
+    std_binary: bool = False,
 ) -> float:
     """
     Calculate the Standardized Mean Difference (SMD) for binary covariates using the Average Treatment Effect (ATE).
@@ -406,7 +412,11 @@ def _calc_smd_cont_covar_ate(m1: float, m0: float, s2_1: float, s2_0: float) ->
 
 
 def _calc_smd_bin_covar_att(
-    m1: float, m0: float, wt_m1: float = None, wt_m0: float = None, std_binary: bool = False
+    m1: float,
+    m0: float,
+    wt_m1: float = None,
+    wt_m0: float = None,
+    std_binary: bool = False,
 ) -> float:
     """
     Calculate the standardized mean difference (SMD) for binary covariates
@@ -419,7 +429,7 @@ def _calc_smd_bin_covar_att(
     m0 : float
         The mean of the covariate for the control group. Must be between 0 and 1.
     wt_m1 : float, optional
-        The weighted mean of the covariate for the treatment group. 
+        The weighted mean of the covariate for the treatment group.
         If not provided, `m1` is used. Must be between 0 and 1.
     wt_m0 : float, optional
         The weighted mean of the covariate for the control group. I
@@ -432,15 +442,19 @@ def _calc_smd_bin_covar_att(
     """
     wt_m1 = m1 if wt_m1 is None else wt_m1
     wt_m0 = m0 if wt_m0 is None else wt_m0
-    
+
     std_factor = np.sqrt(m1 * (1 - m1)) if std_binary else 1
 
     smd = _calc_raw_smd(a=wt_m1, b=wt_m0, std_factor=std_factor)
     return smd
 
 
 def _calc_smd_bin_covar_atc(
-    m1: float, m0: float, wt_m1: float = None, wt_m0: float = None, std_binary: bool = False
+    m1: float,
+    m0: float,
+    wt_m1: float = None,
+    wt_m0: float = None,
+    std_binary: bool = False,
 ) -> float:
     """
     Calculate the standardized mean difference (SMD) for binary covariates
@@ -453,7 +467,7 @@ def _calc_smd_bin_covar_atc(
     m0 : float
         The mean of the covariate for the control group. Must be between 0 and 1.
     wt_m1 : float, optional
-        The weighted mean of the covariate for the treatment group. 
+        The weighted mean of the covariate for the treatment group.
         If not provided, `m1` is used. Must be between 0 and 1.
     wt_m0 : float, optional
         The weighted mean of the covariate for the control group. I
@@ -485,10 +499,10 @@ def _calc_smd_cont_covar_att(m1: float, m0: float, s2_1: float, s2_0: float) ->
     m0 : float
         The mean of the covariate for control group (group 0).
     s2_1 : float
-        The variance of the covariate for treated group (group 1). 
+        The variance of the covariate for treated group (group 1).
         Must be strictly positive.
     s2_0 : float
-        The variance of the covariate for control group (group 0). 
+        The variance of the covariate for control group (group 0).
         Must be strictly positive.
 
     Returns
@@ -513,10 +527,10 @@ def _calc_smd_cont_covar_atc(m1: float, m0: float, s2_1: float, s2_0: float) ->
     m0 : float
         The mean of the covariate for control group (group 0).
     s2_1 : float
-        The variance of the covariate for treated group (group 1). 
+        The variance of the covariate for treated group (group 1).
         Must be strictly positive.
     s2_0 : float
-        The variance of the covariate for control group (group 0). 
+        The variance of the covariate for control group (group 0).
         Must be strictly positive.
 
     Returns
@@ -553,4 +567,4 @@ def _calc_raw_smd(a: float, b: float, std_factor: float) -> float:
     The raw SMD is calculated as the absolute difference between `a` and `b` divided by `std_factor`.
     """
     raw_smd = abs(a - b) / std_factor
-    return raw_smd
+    return raw_smd

tests/test_smd.py

@@ -338,22 +338,25 @@ def test_compute_smd_att_atc(sample_data):
 
 # --- Test std_binary param ----
 
+
 def test_std_binary_calc_smd_covar(sample_data):
     smd = _calc_smd_covar(
         data=sample_data,
-        group='group',
-        covar='binary_var',
+        group="group",
+        covar="binary_var",
     )
     expected = np.float64(0.3333333)
     np.testing.assert_allclose(smd, expected, rtol=1e-4, atol=0)
 
 
 def test_compute_smd_invalid_std_binary_type(sample_data):
-    with pytest.raises(TypeError, match="The `std_binary` parameter must be of type bool"):
+    with pytest.raises(
+        TypeError, match="The `std_binary` parameter must be of type bool"
+    ):
         compute_smd(
-            sample_data, 
-            group='group', 
-            vars=["binary_var"], 
+            sample_data,
+            group="group",
+            vars=["binary_var"],
             wt_var="weights",
-            std_binary="True"
-        )
+            std_binary="True",
+        )