mne.decoding.Scaler¶
-
class
mne.decoding.
Scaler
(info=None, scalings=None, with_mean=True, with_std=True)[source]¶ Standardize channel data.
This class scales data for each channel. It differs from scikit-learn classes (e.g.,
sklearn.preprocessing.StandardScaler
) in that it scales each channel by estimating μ and σ using data from all time points and epochs, as opposed to standardizing each feature (i.e., each time point for each channel) by estimating using μ and σ using data from all epochs.- Parameters
- infoinstance of
Info
|None
The measurement info. Only necessary if
scalings
is a dict or None.- scalings
dict
,str
, default None. Scaling method to be applied to data channel wise.
if scalings is None (default), scales mag by 1e15, grad by 1e13, and eeg by 1e6.
if scalings is
dict
, keys are channel types and values are scale factors.if
scalings=='median'
,sklearn.preprocessing.RobustScaler
is used (requires sklearn version 0.17+).if
scalings=='mean'
,sklearn.preprocessing.StandardScaler
is used.
- with_meanbool, default
True
If True, center the data using mean (or median) before scaling. Ignored for channel-type scaling.
- with_stdbool, default
True
If True, scale the data to unit variance (
scalings='mean'
), quantile range (scalings='median
), or using channel type ifscalings
is a dict or None).
- infoinstance of
Methods
__hash__
(self, /)Return hash(self).
fit
(self, epochs_data[, y])Standardize data across channels.
fit_transform
(self, epochs_data[, y])Fit to data, then transform it.
get_params
(self[, deep])Get parameters for this estimator.
inverse_transform
(self, epochs_data)Invert standardization of data across channels.
set_params
(self, \*\*params)Set the parameters of this estimator.
transform
(self, epochs_data)Standardize data across channels.
-
__hash__
(self, /)¶ Return hash(self).
-
fit_transform
(self, epochs_data, y=None)[source]¶ Fit to data, then transform it.
Fits transformer to epochs_data and y and returns a transformed version of epochs_data.
- Parameters
- Returns
- X
array
, shape (n_epochs, n_channels, n_times) The data concatenated over channels.
- X
Notes
This function makes a copy of the data before the operations and the memory usage may be large with big data.
-
inverse_transform
(self, epochs_data)[source]¶ Invert standardization of data across channels.
- Parameters
- epochs_data
array
, shape (n_epochs, n_channels, n_times) The data.
- epochs_data
- Returns
- X
array
, shape (n_epochs, n_channels, n_times) The data concatenated over channels.
- X
Notes
This function makes a copy of the data before the operations and the memory usage may be large with big data.
-
set_params
(self, **params)[source]¶ Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object. Returns ——- self
-
transform
(self, epochs_data)[source]¶ Standardize data across channels.
- Parameters
- epochs_data
array
, shape (n_epochs, n_channels, n_times) The data.
- epochs_data
- Returns
- X
array
, shape (n_epochs, n_channels, n_times) The data concatenated over channels.
- X
Notes
This function makes a copy of the data before the operations and the memory usage may be large with big data.