float.patch

diff --git a/utils/tabular/features/abstract_feature_generator.py b/utils/tabular/features/abstract_feature_generator.py
index 155cc1f..2fbcdf8 100644
--- a/utils/tabular/features/abstract_feature_generator.py
+++ b/utils/tabular/features/abstract_feature_generator.py
@@ -89,20 +89,23 @@ class AbstractFeatureGenerator:
             self.features_to_remove += self.banned_features
         X_index = copy.deepcopy(X.index)
         X.columns = X.columns.astype(str)  # Ensure all column names are strings
+
+        # populate self.features_init, self.feature_type_family, self.features_to_remove
         self.get_feature_types(X)
         X = X.drop(self.features_to_remove, axis=1, errors='ignore')
         self.features_init_to_keep = copy.deepcopy(list(X.columns))
-        self.features_init_types = X.dtypes.to_dict()
+        self.features_init_types = {featname: typ for typ, featname_list in self.feature_type_family.items() for featname in featname_list}
         self.feature_type_family_init_raw = get_type_groups_df(X)
 
         X.reset_index(drop=True, inplace=True)
         X_features = self.generate_features(X)
+        object_column_set = set(self.feature_type_family.get('object', []))
         for column in X_features:
             unique_value_count = len(X_features[column].unique())
             if unique_value_count == 1:
                 self.features_to_remove_post.append(column)
             # TODO: Consider making 0.99 a parameter to FeatureGenerator
-            elif 'object' in self.feature_type_family and column in self.feature_type_family['object'] and (unique_value_count / X_len > 0.99):
+            elif column in object_column_set and (unique_value_count / X_len > 0.99):
                 self.features_to_remove_post.append(column)
 
         self.features_binned = list(set(self.features_binned) - set(self.features_to_remove_post))
@@ -439,7 +442,7 @@ class AbstractFeatureGenerator:
     # TODO: add option for user to specify dtypes on load
     @staticmethod
     def get_type_family(dtype):
-        return get_type_family(dtype=dtype)
+        return get_type_family(dtype)
 
     @staticmethod
     def word_count(string):
diff --git a/utils/tabular/features/auto_ml_feature_generator.py b/utils/tabular/features/auto_ml_feature_generator.py
index 78ebf5c..ec6200f 100644
--- a/utils/tabular/features/auto_ml_feature_generator.py
+++ b/utils/tabular/features/auto_ml_feature_generator.py
@@ -62,7 +62,7 @@ class AutoMLFeatureGenerator(AbstractFeatureGenerator):
             self._compute_feature_transformations()
         X_features = pd.DataFrame(index=X.index)
         for column in X.columns:
-            if X[column].dtype.name == 'object':
+            if self.features_init_types[column] == 'object':
                 X[column].fillna('', inplace=True)
             else:
                 X[column].fillna(np.nan, inplace=True)
diff --git a/utils/tabular/features/utils.py b/utils/tabular/features/utils.py
index 86c9320..765e966 100644
--- a/utils/tabular/features/utils.py
+++ b/utils/tabular/features/utils.py
@@ -6,27 +6,35 @@ import numpy as np
 logger = logging.getLogger(__name__)
 
 
-def get_type_family(dtype):
+def get_type_family(dtype_toplevel):
     """From dtype, gets the dtype family."""
+    # check if dtype is Sparse dtype extension from pandas
+    is_sparse = dtype_toplevel.name.startswith('Sparse[')
+    dtype = dtype_toplevel.subtype if is_sparse else dtype_toplevel
+    ret = None
     try:
         if dtype.name is 'category':
-            return 'category'
+            ret = 'category'
         if 'datetime' in dtype.name:
-            return 'datetime'
+            ret = 'datetime'
         elif np.issubdtype(dtype, np.integer):
-            return 'int'
+            ret = 'int'
         elif np.issubdtype(dtype, np.floating):
-            return 'float'
+            ret = 'float'
     except Exception as err:
         logger.exception(f'Warning: dtype {dtype} is not recognized as a valid dtype by numpy! AutoGluon may incorrectly handle this feature...')
         logger.exception(err)
-
-    if dtype.name in ['bool', 'bool_']:
-        return 'bool'
-    elif dtype.name in ['str', 'string', 'object']:
-        return 'object'
-    else:
-        return dtype.name
+    if ret is None:
+        if dtype.name in ['bool', 'bool_']:
+            ret = 'bool'
+        elif dtype.name in ['str', 'string', 'object']:
+            ret = 'object'
+        else:
+            ret = dtype.name
+    # forget that we are sparse because storage does not
+    # affect semantics.
+    # ~~f'Sparse[{ret}]' if is_sparse else ret~~
+    return ret
 
 
 def get_type_groups_df(df):