Update sklearn api (#60)

* update interface in air_pls

* update airpls

* update arpls api

* fix documentation in index_selector

* fix typo in index_selector

* fix api in constant baseline correction

* update cubic splines

* update api in linear correction

* update init for linear correction

* fix api in non negative

* fix api in polynomial and in subtract reference

* fix norris williams

* fix api in savitzky golay

* fix minmax scaler

* fix norm scaler

* fix point scaler

* fix emsc

* update msc

* update rnv

* update snv

* update mean filter

* update median filter

* update savgol filter

* update whittaker

chemotools/baseline/__init__.py

@@ -1,8 +1,8 @@
-from .air_pls import AirPls
-from .ar_pls import ArPls
-from .constant_baseline_correction import ConstantBaselineCorrection
-from .cubic_spline_correction import CubicSplineCorrection
-from .linear_correction import LinearCorrection
-from .non_negative import NonNegative
-from .polynomial_correction import PolynomialCorrection
-from .subtract_reference import SubtractReference
+from ._air_pls import AirPls
+from ._ar_pls import ArPls
+from ._constant_baseline_correction import ConstantBaselineCorrection
+from ._cubic_spline_correction import CubicSplineCorrection
+from ._linear_correction import LinearCorrection
+from ._non_negative import NonNegative
+from ._polynomial_correction import PolynomialCorrection
+from ._subtract_reference import SubtractReference

chemotools/baseline/air_pls.py → chemotools/baseline/_air_pls.py

@@ -30,14 +30,6 @@ class AirPls(OneToOneFeatureMixin, BaseEstimator, TransformerMixin):
         The number of iterations used to calculate the baseline. Increasing the number of iterations can improve the
         accuracy of the baseline correction, but also increases the computation time.
 
-    Attributes
-    ----------
-    n_features_in_ : int
-        The number of features in the input data.
-
-    _is_fitted : bool
-        A flag indicating whether the estimator has been fitted to data.
-
     Methods
     -------
     fit(X, y=None)
@@ -85,13 +77,7 @@ def fit(self, X: np.ndarray, y=None) -> "AirPls":
             Returns the instance itself.
         """
         # Check that X is a 2D array and has only finite values
-        X = check_input(X)
-
-        # Set the number of features
-        self.n_features_in_ = X.shape[1]
-
-        # Set the fitted attribute to True
-        self._is_fitted = True
+        X = self._validate_data(X)
 
         return self
 
@@ -113,7 +99,7 @@ def transform(self, X: np.ndarray, y=None) -> np.ndarray:
         """
 
         # Check that the estimator is fitted
-        check_is_fitted(self, "_is_fitted")
+        check_is_fitted(self, "n_features_in_")
 
         # Check that X is a 2D array and has only finite values
         X = check_input(X)

chemotools/baseline/ar_pls.py → chemotools/baseline/_ar_pls.py

@@ -29,13 +29,6 @@ class ArPls(OneToOneFeatureMixin, BaseEstimator, TransformerMixin):
     nr_iterations : int, optional (default=100)
         The maximum number of iterations for the weight updating scheme.
 
-    Attributes
-    ----------
-    n_features_in_ : int
-        The number of input features.
-
-    _is_fitted : bool
-        Whether the estimator has been fitted.
 
     Methods
     -------
@@ -86,13 +79,7 @@ def fit(self, X: np.ndarray, y=None) -> "ArPls":
         """
 
         # Check that X is a 2D array and has only finite values
-        X = check_input(X)
-
-        # Set the number of features
-        self.n_features_in_ = X.shape[1]
-
-        # Set the fitted attribute to True
-        self._is_fitted = True
+        X = self._validate_data(X)
 
         return self
 
@@ -114,7 +101,7 @@ def transform(self, X: np.ndarray, y=None) -> np.ndarray:
         """
 
         # Check that the estimator is fitted
-        check_is_fitted(self, "_is_fitted")
+        check_is_fitted(self, "n_features_in_")
 
         # Check that X is a 2D array and has only finite values
         X = check_input(X)

chemotools/baseline/constant_baseline_correction.py → chemotools/baseline/_constant_baseline_correction.py

@@ -30,12 +30,6 @@ class ConstantBaselineCorrection(OneToOneFeatureMixin, BaseEstimator, Transforme
     end_index_ : int
         The index of the end of the range. It is 1 if the wavenumbers are not provided.
 
-    n_features_in_ : int
-        The number of features in the input data.
-
-    _is_fitted : bool
-        Whether the transformer has been fitted to data.
-
     Methods
     -------
     fit(X, y=None)
@@ -46,7 +40,10 @@ class ConstantBaselineCorrection(OneToOneFeatureMixin, BaseEstimator, Transforme
     """
 
     def __init__(
-        self, start: int = 0, end: int = 1, wavenumbers: np.ndarray = None,
+        self,
+        start: int = 0,
+        end: int = 1,
+        wavenumbers: np.ndarray = None,
     ) -> None:
         self.start = start
         self.end = end
@@ -70,13 +67,7 @@ def fit(self, X: np.ndarray, y=None) -> "ConstantBaselineCorrection":
             The fitted transformer.
         """
         # Check that X is a 2D array and has only finite values
-        X = check_input(X)
-
-        # Set the number of features
-        self.n_features_in_ = X.shape[1]
-
-        # Set the fitted attribute to True
-        self._is_fitted = True
+        X = self._validate_data(X)
 
         # Set the start and end indices
         if self.wavenumbers is None:
@@ -109,7 +100,7 @@ def transform(self, X: np.ndarray, y=0, copy=True) -> np.ndarray:
             The transformed input data.
         """
         # Check that the estimator is fitted
-        check_is_fitted(self, "_is_fitted")
+        check_is_fitted(self, ["start_index_", "end_index_"])
 
         # Check that X is a 2D array and has only finite values
         X = check_input(X)

chemotools/baseline/cubic_spline_correction.py → chemotools/baseline/_cubic_spline_correction.py

@@ -5,9 +5,10 @@
 
 from chemotools.utils.check_inputs import check_input
 
+
 class CubicSplineCorrection(OneToOneFeatureMixin, BaseEstimator, TransformerMixin):
     """
-    A transformer that corrects a baseline by subtracting a cubic spline through the 
+    A transformer that corrects a baseline by subtracting a cubic spline through the
     points defined by the indices.
 
     Parameters
@@ -32,6 +33,7 @@ class CubicSplineCorrection(OneToOneFeatureMixin, BaseEstimator, TransformerMixi
         Transform the input data by subtracting the constant baseline value.
 
     """
+
     def __init__(self, indices: list = None) -> None:
         self.indices = indices
 
@@ -53,13 +55,7 @@ def fit(self, X: np.ndarray, y=None) -> "CubicSplineCorrection":
             The fitted transformer.
         """
         # Check that X is a 2D array and has only finite values
-        X = check_input(X)
-
-        # Set the number of features
-        self.n_features_in_ = X.shape[1]
-
-        # Set the fitted attribute to True
-        self._is_fitted = True
+        X = self._validate_data(X)
 
         if self.indices is None:
             self.indices_ = [0, len(X[0]) - 1]
@@ -89,15 +85,17 @@ def transform(self, X: np.ndarray, y=None, copy=True):
             The transformed data.
         """
         # Check that the estimator is fitted
-        check_is_fitted(self, "_is_fitted")
+        check_is_fitted(self, "indices_")
 
         # Check that X is a 2D array and has only finite values
         X = check_input(X)
         X_ = X.copy()
 
         # Check that the number of features is the same as the fitted data
         if X_.shape[1] != self.n_features_in_:
-            raise ValueError(f"Expected {self.n_features_in_} features but got {X_.shape[1]}")
+            raise ValueError(
+                f"Expected {self.n_features_in_} features but got {X_.shape[1]}"
+            )
 
         # Calculate spline baseline correction
         for i, x in enumerate(X_):
@@ -106,7 +104,7 @@ def transform(self, X: np.ndarray, y=None, copy=True):
 
     def _spline_baseline_correct(self, x: np.ndarray) -> np.ndarray:
         indices = self.indices_
-        intensity = x[indices]  
+        intensity = x[indices]
         spl = CubicSpline(indices, intensity)
-        baseline = spl(range(len(x)))      
-        return x - baseline
+        baseline = spl(range(len(x)))
+        return x - baseline

chemotools/baseline/linear_correction.py → chemotools/baseline/_linear_correction.py

@@ -10,17 +10,6 @@ class LinearCorrection(OneToOneFeatureMixin, BaseEstimator, TransformerMixin):
     A transformer that corrects a baseline by subtracting a linear baseline through the
     initial and final points of the spectrum.
 
-    Parameters
-    ----------
-
-    Attributes
-    ----------
-    n_features_in_ : int
-        The number of features in the input data.
-
-    _is_fitted : bool
-        Whether the transformer has been fitted to data.
-
     Methods
     -------
     fit(X, y=None)
@@ -68,13 +57,7 @@ def fit(self, X: np.ndarray, y=None) -> "LinearCorrection":
             The fitted transformer.
         """
         # Check that X is a 2D array and has only finite values
-        X = check_input(X)
-
-        # Set the number of features
-        self.n_features_in_ = X.shape[1]
-
-        # Set the fitted attribute to True
-        self._is_fitted = True
+        X = self._validate_data(X)
 
         return self
 
@@ -99,7 +82,7 @@ def transform(self, X: np.ndarray, y=0, copy=True) -> np.ndarray:
             The transformed data.
         """
         # Check that the estimator is fitted
-        check_is_fitted(self, "_is_fitted")
+        check_is_fitted(self, "n_features_in_")
 
         # Check that X is a 2D array and has only finite values
         X = check_input(X)

chemotools/baseline/non_negative.py → chemotools/baseline/_non_negative.py

@@ -14,14 +14,6 @@ class NonNegative(OneToOneFeatureMixin, BaseEstimator, TransformerMixin):
     mode : str, optional
         The mode to use for the non-negative values. Can be "zero" or "abs".
 
-    Attributes
-    ----------
-    n_features_in_ : int
-        The number of features in the input data.
-
-    _is_fitted : bool
-        Whether the transformer has been fitted to data.
-
     Methods
     -------
     fit(X, y=None)
@@ -52,13 +44,7 @@ def fit(self, X: np.ndarray, y=None) -> "NonNegative":
             The fitted transformer.
         """
         # Check that X is a 2D array and has only finite values
-        X = check_input(X)
-
-        # Set the number of features
-        self.n_features_in_ = X.shape[1]
-
-        # Set the fitted attribute to True
-        self._is_fitted = True
+        X = self._validate_data(X)
 
         return self
 
@@ -80,7 +66,7 @@ def transform(self, X: np.ndarray, y=None) -> np.ndarray:
             The transformed data.
         """
         # Check that the estimator is fitted
-        check_is_fitted(self, "_is_fitted")
+        check_is_fitted(self, "n_features_in_")
 
         # Check that X is a 2D array and has only finite values
         X = check_input(X)

chemotools/baseline/polynomial_correction.py → chemotools/baseline/_polynomial_correction.py

@@ -4,9 +4,10 @@
 
 from chemotools.utils.check_inputs import check_input
 
+
 class PolynomialCorrection(OneToOneFeatureMixin, BaseEstimator, TransformerMixin):
     """
-    A transformer that subtracts a polynomial baseline from the input data. The polynomial is 
+    A transformer that subtracts a polynomial baseline from the input data. The polynomial is
     fitted to the points in the spectrum specified by the indices parameter.
 
     Parameters
@@ -18,14 +19,6 @@ class PolynomialCorrection(OneToOneFeatureMixin, BaseEstimator, TransformerMixin
         The indices of the points in the spectrum to fit the polynomial to. Defaults to None,
         which fits the polynomial to all points in the spectrum (equivalent to detrend).
 
-    Attributes
-    ----------
-    n_features_in_ : int
-        The number of features in the input data.
-
-    _is_fitted : bool
-        Whether the transformer has been fitted to data.
-
     Methods
     -------
     fit(X, y=None)
@@ -37,6 +30,7 @@ class PolynomialCorrection(OneToOneFeatureMixin, BaseEstimator, TransformerMixin
     _baseline_correct_spectrum(x)
         Subtract the polynomial baseline from a single spectrum.
     """
+
     def __init__(self, order: int = 1, indices: list = None) -> None:
         self.order = order
         self.indices = indices
@@ -59,22 +53,16 @@ def fit(self, X: np.ndarray, y=None) -> "PolynomialCorrection":
             The fitted transformer.
         """
         # Check that X is a 2D array and has only finite values
-        X = check_input(X)
-
-        # Set the number of features
-        self.n_features_in_ = X.shape[1]
-
-        # Set the fitted attribute to True
-        self._is_fitted = True
+        X = self._validate_data(X)
 
         if self.indices is None:
             self.indices_ = range(0, len(X[0]))
         else:
             self.indices_ = self.indices
 
         return self
-    
-    def transform(self, X: np.ndarray, y:int=0, copy:bool=True) -> np.ndarray:
+
+    def transform(self, X: np.ndarray, y: int = 0, copy: bool = True) -> np.ndarray:
         """
         Transform the input data by subtracting the polynomial baseline.
 
@@ -95,21 +83,23 @@ def transform(self, X: np.ndarray, y:int=0, copy:bool=True) -> np.ndarray:
             The transformed data.
         """
         # Check that the estimator is fitted
-        check_is_fitted(self, "_is_fitted")
+        check_is_fitted(self, "indices_")
 
         # Check that X is a 2D array and has only finite values
         X = check_input(X)
         X_ = X.copy()
 
         # Check that the number of features is the same as the fitted data
         if X_.shape[1] != self.n_features_in_:
-            raise ValueError(f"Expected {self.n_features_in_} features but got {X_.shape[1]}")
+            raise ValueError(
+                f"Expected {self.n_features_in_} features but got {X_.shape[1]}"
+            )
 
         # Calculate polynomial baseline correction
         for i, x in enumerate(X_):
             X_[i] = self._baseline_correct_spectrum(x)
         return X_.reshape(-1, 1) if X_.ndim == 1 else X_
-    
+
     def _baseline_correct_spectrum(self, x: np.ndarray) -> np.ndarray:
         """
         Subtract the polynomial baseline from a single spectrum.
@@ -126,5 +116,5 @@ def _baseline_correct_spectrum(self, x: np.ndarray) -> np.ndarray:
         """
         intensity = x[self.indices_]
         poly = np.polyfit(self.indices_, intensity, self.order)
-        baseline = [np.polyval(poly, i) for i in range(0, len(x))]      
-        return x - baseline
+        baseline = [np.polyval(poly, i) for i in range(0, len(x))]
+        return x - baseline