chemotools.scatter._standard_normal_variate 源代码

"""
The :mod:`chemotools.scatter._standard_normal_variate` module
implements the Standard Normal Variate (SNV) transformation.
"""

# Authors: Pau Cabaneros
# License: MIT

import warnings

import numpy as np
from sklearn.base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin
from sklearn.utils.validation import check_is_fitted, validate_data

from chemotools._doc_mixin import DocLinkMixin




class StandardNormalVariate(
    DocLinkMixin, TransformerMixin, OneToOneFeatureMixin, BaseEstimator
):
    """
    A transformer that calculates the standard normal variate of the input data.

    Parameters
    ----------
    None
        The transformer has no constructor hyperparameters.

    Attributes
    ----------
    n_features_in_ : int
        The number of features in the training data.

    Raises
    ------
    UserWarning
        If the standard deviation of a spectrum is zero
        (spectrum is flat), a warning is raised
        indicating that the result will contain NaNs.

    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.datasets import load_fermentation_train
    >>> from chemotools.scatter import StandardNormalVariate
    >>> # Load sample data
    >>> X, _ = load_fermentation_train()
    >>> # Initialize StandardNormalVariate
    >>> snv = StandardNormalVariate()
    StandardNormalVariate()
    >>> # Fit and transform the data
    >>> X_scaled = snv.fit_transform(X)
    """

    _parameter_constraints: dict = {}



    def fit(self, X: np.ndarray, y=None) -> "StandardNormalVariate":
        """
        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 : StandardNormalVariate
            The fitted transformer.
        """
        # Validate the input parameters
        self._validate_params()

        # 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
        )
        return self




    def transform(self, X: np.ndarray, y=None) -> np.ndarray:
        """
        Transform the input data by calculating the standard normal variate.

        Parameters
        ----------
        X : np.ndarray of shape (n_samples, n_features)
            The input data to transform.

        y : None
            Ignored to align with API.

        Returns
        -------
        X_transformed : np.ndarray of shape (n_samples, n_features)
            The transformed data.
        """
        # Check that the estimator is fitted
        check_is_fitted(self, "n_features_in_")

        # Check that X is a 2D array and has only finite values
        X_ = validate_data(
            self,
            X,
            y="no_validation",
            ensure_2d=True,
            copy=True,
            reset=False,
            dtype=np.float64,
        )

        # Calculate the standard normal variate
        mean = X_.mean(axis=1, keepdims=True)
        std = X_.std(axis=1, keepdims=True)

        if np.any(std == 0):
            warnings.warn(
                "Standard deviation is zero in SNV. "
                "This indicates a flat signal and will "
                "result in NaNs.",
                UserWarning,
                stacklevel=2,
            )

        return (X_ - mean) / std