"""
The :mod:`chemotools.feature_selection._range_cut` module implements the RangeCut
to select specific features from spectral data based on start and end indices or
x-axis values.
"""
from typing import Optional
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.feature_selection._base import SelectorMixin
from sklearn.utils._param_validation import Integral, Interval
from sklearn.utils.validation import check_is_fitted, validate_data
from chemotools._axis_mixin import XAxisMixin
from chemotools._deprecation import (
DEPRECATED_PARAMETER,
deprecated_parameter_constraint,
)
[docs]
class RangeCut(XAxisMixin, SelectorMixin, BaseEstimator):
"""Select a contiguous spectral region by index or by x-axis value.
The range can be specified in two ways:
* By integer indices (``start`` and ``end``)
* By x-axis values (``start`` and ``end`` interpreted against the provided
``x_axis`` array)
If ``x_axis`` is supplied, the closest indices to the given start / end
x-axis values are located. Otherwise numeric ``start`` / ``end`` are
treated directly as indices. X-axis values must be in ascending order.
Parameters
----------
start : int, default=0
Index or x-axis value of the start of the range.
end : int, default=-1
Index or x-axis value of the end of the range.
x_axis : array-like, optional
X-axis values corresponding to columns. Must be ascending if provided.
wavenumbers : array-like, optional
Deprecated alias for ``x_axis``. Use ``x_axis`` instead.
Attributes
----------
start_index_ : int
Resolved start index.
end_index_ : int
Resolved end index.
x_axis_ : array-like or None
Selected x-axis values (if provided), else ``None``.
wavenumbers_ : array-like or None
Deprecated alias for ``x_axis_``.
Examples
--------
>>> from chemotools.feature_selection import RangeCut
>>> from chemotools.datasets import load_fermentation_train
>>> X, _ = load_fermentation_train()
>>> wavenumbers = X.columns.values
>>> rc = RangeCut(start=1000, end=2000, x_axis=wavenumbers)
>>> rc.fit(X)
RangeCut(start=1000, end=2000, x_axis=wavenumbers)
>>> X_cut = rc.transform(X)
>>> X_cut.shape
(21, 616)
"""
_parameter_constraints: dict = {
"start": Interval(Integral, 0, None, closed="left"),
"end": [Integral],
"x_axis": ["array-like", None],
"wavenumbers": ["array-like", None, deprecated_parameter_constraint()],
}
def __init__(
self,
start: int = 0,
end: int = -1,
x_axis: Optional[np.ndarray] = None,
wavenumbers=DEPRECATED_PARAMETER,
):
self.start = start
self.end = end
self.x_axis = x_axis
self.wavenumbers = wavenumbers
[docs]
def fit(self, X: np.ndarray, y=None) -> "RangeCut":
"""
Fit the transformer to the input data.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The input data to fit the transformer to.
y : None
Ignored to align with API.
Returns
-------
self : RangeCut
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
)
axis_values = self._resolve_x_axis(self.x_axis, self.wavenumbers)
# Set the start and end indices
if axis_values is None:
self.start_index_ = self.start
self.end_index_ = self.end
self.x_axis_ = None
self.wavenumbers_ = None
else:
axis = np.asarray(axis_values)
self.start_index_ = self._find_index(self.start, axis)
self.end_index_ = self._find_index(self.end, axis)
self.x_axis_ = axis_values[self.start_index_ : self.end_index_]
self.wavenumbers_ = self.x_axis_
return self
def _get_support_mask(self):
"""
Get the boolean mask indicating which features are selected.
Returns
-------
mask : np.ndarray of shape (n_features,)
The boolean mask indicating which features are selected.
"""
# Check that the estimator is fitted
check_is_fitted(self, ["start_index_", "end_index_"])
# Create the mask
mask = np.zeros(self.n_features_in_, dtype=bool) # type: ignore[unresolved-attribute] # sklearn fitted attribute
mask[self.start_index_ : self.end_index_] = True
return mask
