"""
The :mod: `chemotools.adaptation._direct_standardization`
module implements the Direct Standardization (DS) transformer
"""
# Authors: Ruggero Guerrini
# 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
[ドキュメント]
class DirectStandardization(
DocLinkMixin, OneToOneFeatureMixin, TransformerMixin, BaseEstimator
):
"""
Direct Standardization (DS) is a transformer used for domain adaptation (calibration
) applications. The transformer uses least squares to find a linear map from
the target instrument space to the source instrument space, following the
implementation by [1]_.
Attributes
----------
T_ : np.ndarray of shape (n_features, n_features)
Linear transformation matrix mapping target instrument space to source
instrument space.
x_source_provided_ : bool
Boolean value to flag if X_source was provided during fitting
Raises
------
ValueError
If X and X_source do not have the same shape.
See Also
--------
PiecewiseDirectStandardization : Localized version using windowed PLS regression.
References
----------
.. [1] Wang, Yongdong., Veltkamp, D. J., & Kowalski, B. R. (1991),
Multivariate instrument standardization,
Analytical Chemistry, 63(23), Pages 2750–2756,
https://doi.org/10.1021/ac00023a016.
Examples
--------
**Basic usage**
>>> import numpy as np
>>> from chemotools.adaptation import DirectStandardization
>>>
>>> rng = np.random.default_rng(17)
>>> X_source = rng.normal(size=(100, 20))
>>> X_target = X_source * 2 - rng.normal(size=(100, 20)) * 0.02
>>>
>>> ds = DirectStandardization().fit(X_target, X_source=X_source)
>>> X_transf = ds.transform(X_target)
"""
# Fitted attributes (set during fit, typed for type checkers)
n_features_in_: int
T_: np.ndarray
x_source_provided_: bool
_parameter_constraints: dict = {}
[ドキュメント]
def fit(
self, X: np.ndarray, y=None, *, X_source: np.ndarray | None = None
) -> "DirectStandardization":
"""
Fit the Direct Standardization model.
Parameters
----------
X : np.ndarray of shape (n_samples, n_features)
Data from the target instrument.
y : None
Ignored to align with API.
X_source : np.ndarray of shape (n_samples, n_features), optional
Data from the source instrument. If None, the transformer defaults to
an identity transformation.
Returns
-------
self : DirectStandardization
"""
# Validate the input parameters
self._validate_params()
# Check that X is a 2D array and has only finite values
X = validate_data(self, X, ensure_2d=True, reset=True, dtype=np.float64)
# If X_source is None, default to identity transformation
if X_source is None:
warnings.warn(
"X_source is None, the transformer will act as an identity "
"transformation."
)
self.T_ = np.eye(X.shape[1])
self.x_source_provided_ = False
return self
# Check that X_source is a 2D array and has only finite values
X_source = validate_data(
self, X_source, ensure_2d=True, reset=False, dtype=np.float64
)
# Check consistency between X and X_source
if X_source.shape != X.shape:
raise ValueError(
f"X and X_source must have the same shape, "
f"got X={X.shape} and X_source={X_source.shape}."
)
self.T_, _, _, _ = np.linalg.lstsq(X, X_source, rcond=None)
self.x_source_provided_ = True
return self
