"""
The :mod:`chemotools.smooth._mean_filter` module implements
the Mean Filter (MF) transformation.
"""
# Authors: Pau Cabaneros
# License: MIT
from numbers import Integral
import numpy as np
from scipy.ndimage import uniform_filter1d
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 MeanFilter(TransformerMixin, OneToOneFeatureMixin, BaseEstimator):
"""
A transformer that calculates the mean filter of the input data.
Parameters
----------
window_length : int, optional, default=3
The size of the window to use for the mean filter. Must be odd. Default is 3.
mode : str, optional, default="nearest"
The mode to use for the mean filter. Can be "nearest", "constant", "reflect",
"wrap", "mirror" or "interp". Default is "nearest".
window_size : int, optional
Deprecated alias for ``window_length``.
Attributes
----------
n_features_in_ : int
The number of features in the training data.
Examples
--------
>>> from chemotools.datasets import load_fermentation_train
>>> from chemotools.smooth import MeanFilter
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Initialize MeanFilter
>>> mf = MeanFilter()
MeanFilter()
>>> # Fit and transform the data
>>> X_smoothed = mf.fit_transform(X)
"""
_parameter_constraints: dict = {
"window_length": [Interval(Integral, 3, None, closed="left")],
"mode": [
StrOptions({"nearest", "constant", "reflect", "wrap", "mirror", "interp"})
],
"window_size": [
Interval(Integral, 3, None, closed="left"),
deprecated_parameter_constraint(),
],
}
def __init__(
self, window_length: int = 3, mode="nearest", window_size=DEPRECATED_PARAMETER
) -> None:
self.window_length = window_length
self.window_size = window_size
self.mode = mode
[docs]
def fit(self, X: np.ndarray, y=None) -> "MeanFilter":
"""
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 : MeanFilter
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=3,
old_name="window_size",
old_value=self.window_size,
)
return self
