bci_essentials.utils.reduce_to_single_channel

This module contains a custom transformer that reduces the dimensionality of the input data by removing all but the first channel to turn it into a 2D array.

 1"""This module contains a custom transformer that reduces the dimensionality of the input data by removing all but the first channel to turn it into a 2D array."""
 2
 3from sklearn.base import BaseEstimator, TransformerMixin
 4
 5
 6class ReduceToSingleChannel(BaseEstimator, TransformerMixin):
 7    def __init__(self):
 8        pass
 9
10    def fit(self, X, y=None):
11        # No fitting necessary for this transformer
12        return self
13
14    def transform(self, X):
15        if X.ndim == 3:
16            X = X[:, 0, :]
17        return X
class ReduceToSingleChannel(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin): 
 7class ReduceToSingleChannel(BaseEstimator, TransformerMixin):
 8    def __init__(self):
 9        pass
10
11    def fit(self, X, y=None):
12        # No fitting necessary for this transformer
13        return self
14
15    def transform(self, X):
16        if X.ndim == 3:
17            X = X[:, 0, :]
18        return X

Base class for all estimators in scikit-learn.

Inheriting from this class provides default implementations of:

  • setting and getting parameters used by GridSearchCV and friends;
  • textual and HTML representation displayed in terminals and IDEs;
  • estimator serialization;
  • parameters validation;
  • data validation;
  • feature names validation.

Read more in the :ref:User Guide <rolling_your_own_estimator>.

Notes

All estimators should specify all the parameters that can be set at the class level in their __init__ as explicit keyword arguments (no *args or **kwargs).

Examples
>>> import numpy as np
>>> from sklearn.base import BaseEstimator
>>> class MyEstimator(BaseEstimator):
...     def __init__(self, *, param=1):
...         self.param = param
...     def fit(self, X, y=None):
...         self.is_fitted_ = True
...         return self
...     def predict(self, X):
...         return np.full(shape=X.shape[0], fill_value=self.param)
>>> estimator = MyEstimator(param=2)
>>> estimator.get_params()
{'param': 2}
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> y = np.array([1, 0, 1])
>>> estimator.fit(X, y).predict(X)
array([2, 2, 2])
>>> estimator.set_params(param=3).fit(X, y).predict(X)
array([3, 3, 3])
def fit(self, X, y=None): 
11    def fit(self, X, y=None):
12        # No fitting necessary for this transformer
13        return self
def transform(self, X): 
15    def transform(self, X):
16        if X.ndim == 3:
17            X = X[:, 0, :]
18        return X