"""
The :mod:`chemotools.derivative._norris_william` module implements the Norris-Williams
transformer to calculate the Norris-Williams derivative of spectral data.
"""
# Author: Pau Cabaneros
# License: MIT
from numbers import Integral
import numpy as np
from scipy.ndimage import convolve1d
from sklearn.base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin
from sklearn.utils._param_validation import Interval, StrOptions
from sklearn.utils.validation import check_is_fitted, validate_data
from chemotools._deprecation import (
DEPRECATED_PARAMETER,
deprecated_parameter_constraint,
resolve_renamed_parameter,
)
[docs]
class NorrisWilliams(TransformerMixin, OneToOneFeatureMixin, BaseEstimator):
"""
A transformer that calculates the Norris-Williams derivative of the input data.
Parameters
----------
window_length : int, optional, default=5
The size of the window to use for the derivative
calculation. Must be odd. Default is 5.
gap_size : int, optional, default=3
The size of the gap to use for the derivative
calculation. Must be odd. Default is 3.
deriv : int, optional, default=1
The order of the derivative to calculate. Can be 1 or 2. Default is 1.
mode : str, optional, default="nearest"
The mode to use for the derivative calculation. Can be "nearest", "constant",
"reflect", "wrap", "mirror" or "interp". Default is "nearest".
window_size : int, optional
Deprecated alias for ``window_length``.
derivative_order : int, optional
Deprecated alias for ``deriv``.
Attributes
----------
n_features_in_ : int
The number of features in the input data.
References
----------
[1] Åsmund Rinnan, Frans van den Berg, Søren Balling Engelsen,
"Review of the most common pre-processing techniques for near-infrared spectra,"
TrAC Trends in Analytical Chemistry 28 (10) 1201-1222 (2009).
Examples
--------
>>> from chemotools.derivative import NorrisWilliams
>>> from chemotools.datasets import load_fermentation_train
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Instantiate the transformer
>>> transformer = NorrisWilliams(window_size=5, gap_size=3)
NorrisWilliams()
>>> transformer.fit(X)
>>> # Calculate Norris-Williams derivative
>>> X_corrected = transformer.transform(X)
"""
_parameter_constraints: dict = {
"window_length": [Interval(Integral, 3, None, closed="left")],
"gap_size": [Interval(Integral, 1, None, closed="left")],
"deriv": [Interval(Integral, 1, 2, closed="both")],
"mode": [
StrOptions({"nearest", "constant", "reflect", "wrap", "mirror", "interp"})
],
"window_size": [
Interval(Integral, 3, None, closed="left"),
deprecated_parameter_constraint(),
],
"derivative_order": [
Interval(Integral, 1, 2, closed="both"),
deprecated_parameter_constraint(),
],
}
def __init__(
self,
window_length: int = 5,
gap_size: int = 3,
deriv: int = 1,
mode="nearest",
window_size=DEPRECATED_PARAMETER,
derivative_order=DEPRECATED_PARAMETER,
):
self.window_length = window_length
self.gap_size = gap_size
self.deriv = deriv
self.mode = mode
self.window_size = window_size
self.derivative_order = derivative_order
[docs]
def fit(self, X: np.ndarray, y=None) -> "NorrisWilliams":
"""
Fit the transformer to the input data.
Parameters
----------
X : np.ndarray of shape (n_samples, n_features)
The input data to fit the transformer to.
y : None
Ignored to align with API.
Returns
-------
self : NorrisWilliams
The fitted transformer.
"""
# Check that X is a 2D array and has only finite values
X = validate_data(
self, X, y="no_validation", ensure_2d=True, reset=True, dtype=np.float64
)
self.window_length_ = resolve_renamed_parameter(
new_name="window_length",
new_value=self.window_length,
new_default=5,
old_name="window_size",
old_value=self.window_size,
)
self.deriv_ = resolve_renamed_parameter(
new_name="deriv",
new_value=self.deriv,
new_default=1,
old_name="derivative_order",
old_value=self.derivative_order,
)
return self
def _smoothing_kernel(self):
return np.ones(self.window_length_) / self.window_length_
def _first_derivative_kernel(self):
array = np.zeros(self.gap_size)
array[0] = 1 / (self.gap_size)
array[-1] = -1 / (self.gap_size)
return array
def _second_derivative_kernel(self):
array = np.zeros(self.gap_size)
array[0] = 1 / (self.gap_size)
array[-1] = 1 / (self.gap_size)
array[int((self.gap_size - 1) / 2)] = -2 / self.gap_size
return array
def _spectrum_first_derivative(self, X):
# Apply filter of data
smoothing_kernel = self._smoothing_kernel()
first_derivative_kernel = self._first_derivative_kernel()
smoothed = convolve1d(X, smoothing_kernel, mode=self.mode)
derivative = convolve1d(smoothed, first_derivative_kernel, mode=self.mode)
return derivative
def _spectrum_second_derivative(self, X):
# Apply filter of data
smoothing_kernel = self._smoothing_kernel()
second_derivative_kernel = self._second_derivative_kernel()
smoothed = convolve1d(X, smoothing_kernel, mode=self.mode)
derivative = convolve1d(smoothed, second_derivative_kernel, mode=self.mode)
return derivative
