Feature¶

class best.modules.feature.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 best.modules.feature.Log10Module¶

fit(X, Y=None)¶

fit_transform(X, Y=None)¶

transform(X, Y=None)¶

class best.modules.feature.LogModule¶

fit(X, Y=None)¶

fit_transform(X, Y=None)¶

transform(X, Y=None)¶

class best.modules.feature.PCAModule(var_threshold=0.98)¶

fit(X, y=None)¶

Fit the model with X.

Parameters

X (array-like 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) –

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 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) –

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 best.modules.feature.PCAModuleSVD(var_threshold=0.98)¶

fit(X, Y=None)¶

fit_transform(X, Y=None)¶

transform(X, Y=None)¶

class best.modules.feature.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

Returns

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