adding super().__init__() to all classes that inherit from BaseEstimator

applications/aws_dashboard/requirements.txt

@@ -8,7 +8,7 @@ sagemaker >= 2.143
 cryptography>=42.0.5
 ipython>=8.17.2
 xgboost>=2.0.3
-scikit-learn >=1.4.2, <=1.52
+scikit-learn >=1.4.2
 joblib>=1.3.2
 requests>=2.32.0
 plotly >= 5.18.0

pyproject.toml

@@ -36,7 +36,7 @@ dependencies = [
     "ipython >= 8.17.2",
     "pyreadline3; sys_platform == 'win32'",
     "xgboost >= 2.0.3",
-    "scikit-learn >=1.4.2, <=1.52",
+    "scikit-learn >=1.4.2",
     "joblib >= 1.3.2",
     "requests >= 2.26.0"
 ]

requirements.txt

@@ -9,6 +9,6 @@ cryptography>=42.0.5
 ipython>=8.17.2
 pyreadline3; sys_platform == 'win32'
 xgboost>=2.0.3
-scikit-learn >=1.4.2, <=1.52
+scikit-learn >=1.4.2
 joblib>=1.3.2
 requests>=2.32.0

src/sageworks/algorithms/dataframe/quantile_regression.py

@@ -27,6 +27,7 @@ def __init__(
         self.q_models = {}
         self.quantiles = quantiles
         self.rmse_model = None
+        super().__init__()
 
     def fit(self, X: pd.DataFrame, y: pd.Series) -> BaseEstimator:
         """

src/sageworks/algorithms/dataframe/residuals_calculator.py

@@ -42,6 +42,7 @@ def __init__(
         self.endpoint = endpoint  # Use this endpoint for inference if provided
         self.X = None
         self.y = None
+        super().__init__()
 
     def fit(self, X: pd.DataFrame, y: pd.Series) -> BaseEstimator:
         """

src/sageworks/algorithms/dataframe/target_gradients.py

@@ -29,6 +29,7 @@ def __init__(self, n_neighbors: int = 2):
         self.knn = KNeighborsRegressor(n_neighbors=self.n_neighbors, algorithm="ball_tree", metric="euclidean")
         self.X = None
         self.y = None
+        super().__init__()
 
     def fit(self, X: pd.DataFrame, y: pd.Series) -> BaseEstimator:
         """