Updated the utils

autoqild/utilities/utils.py

@@ -228,7 +228,7 @@ def check_and_delete_corrupt_h5_file(file_path, logger):
         logger.info(f"File does not exist '{basename}'")
 
 
-def standardize_features(x_train, x_test):
+def standardize_features(x_train, x_test, scaler=RobustScaler):
     """Standardize the features in the training and test sets using
     RobustScaler as a default.
 
@@ -246,89 +246,8 @@ def standardize_features(x_train, x_test):
     x_test : array-like
         Standardized test set features.
     """
-    standardize = Standardize()
+    standardize = scaler()
     x_train = standardize.fit_transform(x_train)
     x_test = standardize.transform(x_test)
     return x_train, x_test
 
-
-class Standardize:
-    """A class for standardizing features using a specified scaler.
-
-    Parameters
-    ----------
-    scalar : object, optional
-        The scaling class to use (default is "RobustScaler").
-
-    Attributes
-    ----------
-    n_features : list or None
-        The list of feature names if `X` is a dictionary.
-    scalars : dict
-        A dictionary of scalers for each feature if `X` is a dictionary.
-    """
-
-    def __init__(self, scalar=RobustScaler):
-        self.scalar = scalar
-        self.n_features = None
-        self.scalars = dict()
-
-    def fit(self, X):
-        """Fit the scaler to the data.
-
-        Parameters
-        ----------
-        X : array-like or dict
-            The data to fit the scaler on.
-
-        Returns
-        -------
-        self : object
-            Fitted scaler.
-        """
-        if isinstance(X, dict):
-            self.n_features = list(X.keys())
-            for k, x in X.items():
-                scalar = self.scalar()
-                self.scalars[k] = scalar.fit(x)
-        if isinstance(X, (np.ndarray, np.generic)):
-            self.scalar = self.scalar()
-            self.scalar.fit(X)
-            self.n_features = X.shape[-1]
-
-    def transform(self, X):
-        """Apply the scaling transformation to the data.
-
-        Parameters
-        ----------
-        X : array-like or dict
-            The data to transform.
-
-        Returns
-        -------
-        X : array-like or dict
-            The transformed data.
-        """
-        if isinstance(X, dict):
-            for n in self.n_features:
-                X[n] = self.scalars[n].transform(X[n])
-        if isinstance(X, (np.ndarray, np.generic)):
-            X = self.scalar.transform(X)
-        return X
-
-    def fit_transform(self, X):
-        """Fit the scaler and transform the data.
-
-        Parameters
-        ----------
-        X : array-like or dict
-            The data to fit and transform.
-
-        Returns
-        -------
-        X : array-like or dict
-            The transformed data.
-        """
-        self.fit(X)
-        X = self.transform(X)
-        return X

docs/source/references.rst

@@ -26,6 +26,6 @@ List of references for the implemented learning algorithms, AutoML tools and bas
 -------------------------
 🚀 Baseline MI Estimators
 -------------------------
-- `Gaussain Mixture Models <https://ieeexplore.ieee.org/document/6889561>`_: Polo et al. (2022)
+- `Gaussain Mixture Model (GMM) <https://ieeexplore.ieee.org/document/6889561>`_: Polo et al. (2022)
 - `Mutual Information Neural Estimation (MINE) <https://proceedings.mlr.press/v80/belghazi18a/belghazi18a.pdf>`_: Belghazi et al. (2018)
 - `PC-softmax <https://arxiv.org/abs/1911.10688>`_: Qin et al. (2020)