Merge pull request #28 from paucablop/add-scale-by-index

Add scale by index

chemotools/scale/__init__.py

@@ -1,2 +1,3 @@
-from .min_max_normalize import MinMaxScaler
-from .l_normalize import LNormalize
+from .index_scaler import IndexScaler
+from .min_max_scaler import MinMaxScaler
+from .norm_scaler import NormScaler

chemotools/scale/index_scaler.py

@@ -0,0 +1,41 @@
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.utils.validation import check_is_fitted
+
+from chemotools.utils.check_inputs import check_input
+
+
+class IndexScaler(BaseEstimator, TransformerMixin):
+    def __init__(self, index: int = 0):
+        self.index = index
+
+
+    def fit(self, X: np.ndarray, y=None) -> "IndexScaler":
+        # 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
+
+        return self
+
+    def transform(self, X: np.ndarray, y=None) -> np.ndarray:
+        # Check that the estimator is fitted
+        check_is_fitted(self, "_is_fitted")
+
+        # 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]}")
+
+        # Scale the data by index
+        for i, x in enumerate(X_):
+            X_[i] = x / x[self.index]
+
+        return X_.reshape(-1, 1) if X_.ndim == 1 else X_

chemotools/scale/l_normalize.py → chemotools/scale/norm_scaler.py

@@ -5,11 +5,11 @@
 from chemotools.utils.check_inputs import check_input
 
 
-class LNormalize(BaseEstimator, TransformerMixin):
+class NormScaler(BaseEstimator, TransformerMixin):
     def __init__(self, l_norm: int = 2):
         self.l_norm = l_norm
 
-    def fit(self, X: np.ndarray, y=None) -> "LNormalize":
+    def fit(self, X: np.ndarray, y=None) -> "NormScaler":
         # Check that X is a 2D array and has only finite values
         X = check_input(X)
 

index.md

@@ -36,6 +36,7 @@ Table of contents
         * [Non-negative](#non-negative)
         * [Subtract reference spectrum](#subtract-reference-spectrum)
    * [Scale](#scale)
+        * [Index scaler](#index-scaler)
         * [Min-max scaler](#minmax-scaler)
         * [L-Norm scaler](#l-norm-scaler)
    * [Smooth](#smooth)

tests/test_functionality.py

@@ -2,7 +2,7 @@
 
 from chemotools.baseline import AirPls, LinearCorrection, NonNegative, SubtractReference
 from chemotools.derivative import NorrisWilliams, SavitzkyGolay
-from chemotools.scale import LNormalize, MinMaxScaler
+from chemotools.scale import IndexScaler, MinMaxScaler, NormScaler
 from chemotools.scatter import MultiplicativeScatterCorrection, StandardNormalVariate
 from chemotools.smooth import MeanFilter, MedianFilter, WhittakerSmooth
 from tests.fixtures import (
@@ -26,11 +26,20 @@ def test_air_pls(spectrum, reference_airpls):
     # Assert
     assert np.allclose(spectrum_corrected[0], reference_airpls[0], atol=1e-8)
 
+def test_index_scaler(spectrum):
+    # Arrange
+    index_scaler = IndexScaler(index=0)
+    reference_spectrum = [value/spectrum[0][0] for value in spectrum[0]]
+    # Act
+    spectrum_corrected = index_scaler.fit_transform(spectrum)
+
+    # Assert
+    assert np.allclose(spectrum_corrected[0], reference_spectrum, atol=1e-8)
 
 def test_l1_norm(spectrum):
     # Arrange
     norm = 1
-    l1_norm = LNormalize(l_norm=norm)
+    l1_norm = NormScaler(l_norm=norm)
     spectrum_norm = np.linalg.norm(spectrum[0], ord=norm)
 
     # Act
@@ -43,7 +52,7 @@ def test_l1_norm(spectrum):
 def test_l2_norm(spectrum):
     # Arrange
     norm = 2
-    l1_norm = LNormalize(l_norm=norm)
+    l1_norm = NormScaler(l_norm=norm)
     spectrum_norm = np.linalg.norm(spectrum[0], ord=norm)
 
     # Act

tests/test_sklearn_compliance.py

@@ -2,7 +2,7 @@
 
 from chemotools.baseline import AirPls, CubicSplineCorrection, LinearCorrection, NonNegative, PolynomialCorrection, SubtractReference
 from chemotools.derivative import NorrisWilliams, SavitzkyGolay
-from chemotools.scale import MinMaxScaler, LNormalize
+from chemotools.scale import IndexScaler, MinMaxScaler, NormScaler
 from chemotools.scatter import MultiplicativeScatterCorrection, StandardNormalVariate
 from chemotools.smooth import MeanFilter, MedianFilter, SavitzkyGolayFilter, WhittakerSmooth
 
@@ -23,6 +23,13 @@ def test_compliance_cubic_spline_correction():
     # Act & Assert
     check_estimator(transformer)
 
+# IndexScaler
+def test_compliance_index_scaler():
+    # Arrange
+    transformer = IndexScaler()
+    # Act & Assert
+    check_estimator(transformer)
+
 # LinearCorrection
 def test_compliance_linear_correction():
     # Arrange
@@ -33,7 +40,7 @@ def test_compliance_linear_correction():
 # LNormalize
 def test_compliance_l_norm():
     # Arrange
-    transformer = LNormalize()
+    transformer = NormScaler()
     # Act & Assert
     check_estimator(transformer)