ensuring boolean columns get cast to float for statsmodels

ISLP/models/model_spec.py

@@ -107,7 +107,6 @@ def fit(self, X, y=None):
         cats = self.encoder_.categories_[0]
         column_names = [str(n) for n in cats]
 
-
         if isinstance(X, pd.DataFrame): # expecting a column, we take .iloc[:,0]
             X = X.iloc[:,0]
 
@@ -635,18 +634,23 @@ def build_model(column_info,
         if isinstance(X, (pd.Series, pd.DataFrame)):
             df = pd.concat(dfs, axis=1)
             df.index = X.index
-            return df
         else:
-            return np.column_stack(dfs)
+            return np.column_stack(dfs).astype(float)
     else:  # return a 0 design
         zero = np.zeros(X.shape[0])
         if isinstance(X, (pd.Series, pd.DataFrame)):
             df = pd.DataFrame({'zero': zero})
             df.index = X.index
-            return df
         else:
             return zero
 
+    # if we reach here, we will be returning a DataFrame
+
+    for col in df.columns:
+        if df[col].dtype == bool:
+            df[col] = df[col].astype(float)
+    return df
+
 def derived_feature(variables, encoder=None, name=None, use_transform=True):
     """
     Create a Feature, optionally

tests/models/test_boolean_columns.py

@@ -0,0 +1,23 @@
+import pandas as pd
+import statsmodels.api as sm
+import numpy as np
+from itertools import combinations
+
+from ISLP.models import ModelSpec as MS
+
+rng = np.random.default_rng(0)
+
+df = pd.DataFrame({'A':rng.standard_normal(10),
+                  'B':np.array([1,2,3,2,1,1,1,3,2,1], int),
+                  'C':np.array([True,False,False,True,True]*2, bool),
+                  'D':rng.standard_normal(10)})
+Y = rng.standard_normal(10)
+
+def test_all():
+
+    for i in range(1, 5):
+        for comb in combinations(['A','B','C','D'], i):
+
+            X = MS(comb).fit_transform(df)
+            sm.OLS(Y, X).fit() 
+