Scikit Modules

Scikit Modules#

class brainmaze_eeg.scikit_modules.FeatureAugmentorModule#

Feature augmentation using an ‘augment_features’ function from the ‘PiesUtils’ package. See the code for additional details.

fit(X=None, Y=None)#
fit_transform(X, Y=None)#
transform(X)#
class brainmaze_eeg.scikit_modules.Log10Module#
fit(X, Y=None)#
fit_transform(X, Y=None)#
transform(X, Y=None)#
class brainmaze_eeg.scikit_modules.LogModule#
fit(X, Y=None)#
fit_transform(X, Y=None)#
transform(X, Y=None)#
class brainmaze_eeg.scikit_modules.PCAModule(var_threshold=0.98)#
fit(X, y=None)#

Fit the model with X.

Parameters:

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

  • y (Ignored) – Ignored.

Returns:

self – Returns the instance itself.

Return type:

object

fit_transform(X, y=None)#

Fit the model with X and apply the dimensionality reduction on X.

Parameters:

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

  • y (Ignored) – Ignored.

Returns:

X_new – Transformed values.

Return type:

ndarray of shape (n_samples, n_components)

Notes

This method returns a Fortran-ordered array. To convert it to a C-ordered array, use ‘np.ascontiguousarray’.

class brainmaze_eeg.scikit_modules.PCAModuleSVD(var_threshold=0.98)#
fit(X, Y=None)#
fit_transform(X, Y=None)#
transform(X, Y=None)#
class brainmaze_eeg.scikit_modules.ZScoreModule(trainable=False, continuous_learning=False, multi_class=False)#

Z-score normalization compatible with scikit.pipeline.Pipeline Enables continuous learning - enabling continuous adaptation.

Modes

  • Zscore normalization

  • Zscore normalization with fixed mean and std values based on the initial training dataset

    • Possible category-wise normalization with mean and std values estimated from the training dataset - number of features is multiplied by number of categories

  • Zscore normalization with an initial mean and std values trained on the training dataset - adaptation during inference

    https://stats.stackexchange.com/questions/211837/variance-of-subsample

continuous_learning#

If true - An instance updates mean and variance values during each prediction step. Initial outlier filtering is recommended

Type:

bool

trainable#

If false - An instance normalizes inference data based on their current mean value and std If true - An instance remembers mean and variance values of training data

Type:

bool

multi_class#

If true - An instance performs normalization for each training class separately Number of output features is multiplied by a number of training categories

Type:

bool

mean#

Trained mean values for each feature. In case multi_class == True -> list of numpy ndarrays for each category

Type:

numpy ndarray / list

std#
Type:

numpy ndarray

N#
Type:

int

fit(X=None, Y=None)#
Parameters:

  • X (numpy ndarray) – shape[n_samples, n_features]

  • Y (list or numpy array, optional) – category reference for each sample - required only for option with multi_class normalization

Return type:

None

fit_transform(X=None, Y=None)#
Parameters:

  • X (numpy ndarray) – shape[n_samples, n_features]

  • Y (list or numpy array, optional) – category reference for each sample - required only for option with multi_class normalization

Returns:

transformed_data – shape[n_samples, n_features]

Return type:

numpy ndarray

transform(X=None)#
Parameters:

X (numpy ndarray) – shape[n_samples, n_features]

Returns:

transformed_data – shape[n_samples, n_features]

Return type:

numpy ndarray

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