feat: _xfail_checks = check_parameters_default_constructible

Author: anhkhoangoho

qolmat/imputations/imputers.py

@@ -45,7 +45,7 @@ def __init__(
         columnwise: bool = False,
         shrink: bool = False,
         hyperparams: Dict = {},
-        hyperparams_optim: Dict = {},
+        # hyperparams_optim: Dict = {},
         random_state: Union[None, int, np.random.RandomState] = None,
         missing_values=np.nan,
     ):
@@ -54,10 +54,16 @@ def __init__(
         self.hyperparams = hyperparams
         self.random_state = random_state
         self.missing_values = missing_values
-        self.hyperparams_optim = hyperparams_optim
+        # self.hyperparams_optim = hyperparams_optim
 
     def _more_tags(self):
-        return {"allow_nan": True, "requires_fit": False}
+        return {
+            "allow_nan": True,
+            "requires_fit": False,
+            "_xfail_checks": {
+                "check_parameters_default_constructible": "The imputer need Dict as a parammeter"
+            },
+        }
 
     def fit(self, X, y: pd.DataFrame = None, groups: List[str] = []):
         self.groups_ = groups
@@ -671,13 +677,11 @@ def __init__(
         self,
         n_neighbors: int = 5,
         weights: str = "distance",
-        hyperparams_optim: Dict = {},
         **hyperparams,
     ) -> None:
         super().__init__(columnwise=False, hyperparams=hyperparams)
         self.n_neighbors = n_neighbors
         self.weights = weights
-        self.hyperparams_optim = hyperparams_optim
 
     def fit_transform_element(self, df: pd.DataFrame) -> pd.DataFrame:
         imputer = KNNImputer(
@@ -733,7 +737,7 @@ def __init__(
         self,
         estimator: Optional[BaseEstimator] = None,
         random_state: Union[None, int, np.random.RandomState] = None,
-        # hyperparams_optim: Dict = {},
+        hyperparams_optim: Dict = {},
         **hyperparams,
     ) -> None:
         super().__init__(
@@ -742,7 +746,7 @@ def __init__(
             random_state=random_state,
         )
         self.estimator = estimator
-        # self.hyperparams_optim = hyperparams_optim
+        self.hyperparams_optim = hyperparams_optim
 
     def fit_transform_element(self, df: pd.DataFrame) -> pd.DataFrame:
         iterative_imputer = IterativeImputer(estimator=self.estimator, **self.hyperparams_element)
@@ -794,14 +798,14 @@ def __init__(
         estimator: Optional[BaseEstimator] = None,
         handler_nan: str = "column",
         random_state: Union[None, int, np.random.RandomState] = None,
-        # hyperparams_optim: Dict = {},
+        hyperparams_optim: Dict = {},
         **hyperparams,
     ):
         super().__init__(hyperparams=hyperparams, random_state=random_state)
         self.columnwise = False
         self.estimator = estimator
         self.handler_nan = handler_nan
-        # self.hyperparams_optim = hyperparams_optim
+        self.hyperparams_optim = hyperparams_optim
 
     def get_params_fit(self) -> Dict:
         return {}
@@ -898,7 +902,7 @@ def __init__(
         method: str = "noisy",
         columnwise: bool = False,
         random_state: Union[None, int, np.random.RandomState] = None,
-        # hyperparams_optim: Dict = {},
+        hyperparams_optim: Dict = {},
         **hyperparams,
     ) -> None:
         super().__init__(
@@ -908,7 +912,7 @@ def __init__(
         )
 
         self.method = method
-        # self.hyperparams_optim = hyperparams_optim
+        self.hyperparams_optim = hyperparams_optim
 
     def fit_transform_element(self, df: pd.DataFrame) -> pd.DataFrame:
         if not isinstance(df, pd.DataFrame):
@@ -924,7 +928,7 @@ def fit_transform_element(self, df: pd.DataFrame) -> pd.DataFrame:
         X = df.values.T
         M, A = model.decompose_rpca_signal(X)
         df_imputed = pd.DataFrame((M + A).T, index=df.index, columns=df.columns)
-        df_imputed = df.where(df.isna(), df_imputed)
+        df_imputed = df.where(~df.isna(), df_imputed)
 
         return df_imputed
 
@@ -957,7 +961,7 @@ def __init__(
         model: Optional[str] = "multinormal",
         columnwise: bool = False,
         random_state: Union[None, int, np.random.RandomState] = None,
-        # hyperparams_optim: Dict = {},
+        hyperparams_optim: Dict = {},
         **hyperparams,
     ):
         super().__init__(
@@ -966,7 +970,7 @@ def __init__(
             random_state=random_state,
         )
         self.model = model
-        # self.hyperparams_optim = hyperparams_optim
+        self.hyperparams_optim = hyperparams_optim
 
     def fit_transform_element(self, df: pd.DataFrame) -> pd.DataFrame:
         if self.model == "multinormal":

tests/imputations/test_imputers.py

@@ -192,7 +192,7 @@ def test_ImputerMICE_fit_transform(df: pd.DataFrame) -> None:
 
 @pytest.mark.parametrize("df", [df_incomplete])
 def test_ImputerRegressor_fit_transform(df: pd.DataFrame) -> None:
-    imputer = imputers.ImputerRegressor(model=ExtraTreesRegressor())
+    imputer = imputers.ImputerRegressor(estimator=ExtraTreesRegressor())
     result = imputer.fit_transform(df)
     expected = pd.DataFrame(
         {
@@ -210,7 +210,7 @@ def test_ImputerRPCA_fit_transform(df: pd.DataFrame) -> None:
     expected = pd.DataFrame(
         {
             "col1": [i for i in range(20)],
-            "col2": [0, 10.5, 2, 10.5, 2] + [i for i in range(5, 20)],
+            "col2": [0, 25.375562, 2, 29.396932, 2] + [i for i in range(5, 20)],
         }
     )
     np.testing.assert_allclose(result, expected)