StandardScaler

class dtaianomaly.preprocessing.StandardScaler(min_std: float = 1e-09)[source]

Standard scale the data: rescale to zero mean, unit variance.

Rescale to zero mean and unit variance. A mean value and standard deviation is computed on a training set, after which these values can be used to transform a new time series. Therefore, there is no guarantee that the values of the transformed test set will actually have zero mean and unit variance. For multivariate time series, each attribute will be normalized independently, i.e., the mean and std of each attribute in the transformed time series will 1.0 and 0.0, respectively.

Parameters:

min_stdfloat, default = 1e-9: The minimum std required to actually Z-normalize an attribute. If the standard deviation is below this value, then no normalization will be applied. This prevents amplifying noise in the data.

Attributes:

mean_array-like of shape (n_attributes): The mean value in each attribute of the training data.
std_array-like of shape (n_attributes): The standard deviation in each attribute of the training data.

Raises:

NotFittedError: If the transform method is called before fitting this StandardScaler.

Examples

>>> from dtaianomaly.preprocessing import StandardScaler
>>> from dtaianomaly.data import demonstration_time_series
>>> X, y = demonstration_time_series()
>>> preprocessor = StandardScaler()
>>> X_, y_ = preprocessor.fit_transform(X, y)

check_is_fitted() → None

Raise an error if this object is not fitted.

Check whether this object is fitted, and if it is not fitted, an exception is thrown.

Raises:

NotFittedError: If this object is not fitted.

fit(X: ndarray, y: ndarray = None) → Preprocessor

Fit this preprocessor.

First checks the inputs with check_preprocessing_inputs(), and then fits this preprocessor.

Parameters:

Xarray-like of shape (n_samples, n_attributes): Raw time series.
yarray-like, default=None: Ground-truth information.

Returns:

Preprocessor: Returns the fitted instance self.

fit_transform(X: ~numpy.ndarray, y: ~numpy.ndarray = None) -> (<class 'numpy.ndarray'>, numpy.ndarray | None)

Fit this preprocessor and transform the given time series.

First checks the inputs with check_preprocessing_inputs(), and then chains the fit and transform methods on the given data, i.e., first fit this preprocessor on the given X and y, after which the given X and y will be transformed.

Parameters:

Xarray-like of shape (n_samples, n_attributes): Raw time series.
yarray-like of shape (n_samples), default=None: Ground-truth information.

Returns:

X_transformednp.ndarray of shape (n_samples, n_attributes): Preprocessed raw time series.
y_transformednp.ndarray of shape (n_samples): The transformed ground truth. If no ground truth was provided (y=None), then None will be returned as well.

is_fitted() → bool

Check whether this object is fitted.

Check whether all the attributes of this object that end with an underscore (‘_’) has been initialized.

Returns:

bool: True if and only if all the attributes of this object ending with ‘_’ are initialized.

requires_fitting() → bool

Check whether this object requires fitting.

Check whether any of the attributes of this object ends with an underscore (‘_’), which indicates that the attribute is set when the object is fitted. Note that this method does not check whether the object is fitted, i.e., whether the attributes have been set.

Returns:

bool: True if and only if this object has attributes that end with ‘_’.

transform(X: ~numpy.ndarray, y: ~numpy.ndarray = None) -> (<class 'numpy.ndarray'>, numpy.ndarray | None)

Transform the given time series.

First checks the inputs with check_preprocessing_inputs(), and then transforms (i.e., preprocesses) the given time series.

Parameters:

Xarray-like of shape (n_samples, n_attributes): Raw time series.
yarray-like of shape (n_samples), default=None: Ground-truth information.

Returns:

X_transformednp.ndarray of shape (n_samples, n_attributes): Preprocessed raw time series.
y_transformednp.ndarray of shape (n_samples): The transformed ground truth. If no ground truth was provided (y=None), then None will be returned as well.