added check numeric dtype

smarttree/_check.py

@@ -317,16 +317,16 @@ def _check__feature_na_mode(feature_na_mode):
 
 
 def check__data(
+    X,
     *,
-    X=None,
     y=None,
     num_features=None,
     cat_features=None,
     rank_features=None,
     all_features=None,
 ):
-    if X is not None:
-        _check__X(X)
+    _check__X(X)
+    X = X.copy()
 
     if y is not None:
         _check__y(y)
@@ -346,6 +346,11 @@ def check__data(
     if all_features is not None:
         _check__all_features_in(X, all_features)
 
+    if y is not None:
+        return X, y
+    else:
+        return X
+
 
 def _check__X(X):
     if not isinstance(X, pd.DataFrame):
@@ -369,9 +374,17 @@ def _check__num_features_in(X, num_features):
     for num_feature in num_features:
         if num_feature not in X.columns:
             raise ValueError(
-                f"`num_features` contain feature {num_feature!r},"
+                f"`num_features` contain feature '{num_feature}',"
                 " which isnt present in the training data."
             )
+        if not pd.api.types.is_numeric_dtype(X[num_feature]):
+            try:
+                X[num_feature] = pd.to_numeric(X[num_feature])
+            except ValueError:
+                raise ValueError(
+                    f"`num_features` contain feature '{num_feature}',"
+                    " which isnt numeric or convertable to numeric."
+                )
 
 
 def _check__cat_features_in(X, cat_features):

smarttree/_classes.py

@@ -495,7 +495,7 @@ def fit(self, X: pd.DataFrame, y: pd.Series) -> Self:
             y: pd.Series
               The target values.
         """
-        check__data(
+        X, y = check__data(
             X=X,
             y=y,
             num_features=self.num_features,
@@ -627,7 +627,7 @@ def predict_proba(self, X: pd.DataFrame) -> NDArray[np.floating]:
             ndarray: The class probabilities of the input samples. The order of
             the classes corresponds to that in the attribute :term:`class_names`.
         """
-        check__data(X=X, all_features=self.all_features)
+        X = check__data(X=X, all_features=self.all_features)
 
         X = self.__preprocess(X)
 
@@ -699,7 +699,7 @@ def score(
         sample_weight: pd.Series | None = None,
     ) -> float | np.floating:
         """Returns the accuracy metric."""
-        check__data(X=X, y=y, all_features=self.all_features)
+        X, y = check__data(X=X, y=y, all_features=self.all_features)
         return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
 
     @lru_cache

tests/decision_tree/base/test__check_data.py

@@ -71,10 +71,21 @@ def decision_tree():
                 ),
             ),
         ),
+        (
+            "not_num_dtype",
+            "valid",
+            pytest.raises(
+                ValueError,
+                match=(
+                    f"`num_features` contain feature '{NUM_FEATURE}',"
+                    " which isnt numeric or convertable to numeric."
+                ),
+            ),
+        ),
     ],
     ids=[
         "valid", "not_series", "short", "not_df", "contain_na",
-        "missing_num", "missing_cat", "missing_rank",
+        "missing_num", "missing_cat", "missing_rank", "not_num_dtype",
     ],
 )
 def test__check_data__fit(X, y, X_scenario, y_scenario, decision_tree, expected_context):
@@ -85,6 +96,9 @@ def test__check_data__fit(X, y, X_scenario, y_scenario, decision_tree, expected_
         "missing_num": X[SELECTED].drop(columns=NUM_FEATURE),
         "missing_cat": X[SELECTED].drop(columns=CAT_FEATURE),
         "missing_rank": X[SELECTED].drop(columns=RANK_FEATURE),
+        "not_num_dtype": X[SELECTED].rename(
+            columns={NUM_FEATURE: CAT_FEATURE, CAT_FEATURE: NUM_FEATURE}
+        )
     }
     y_map = {
         "valid": y,