tsgm.models.augmentations

Module Contents

class GaussianNoise(per_feature: bool = True)

Bases: BaseAugmenter

Apply noise to the input time series. Args:

variance ((float, float) or float): variance range for noise. If var_limit is a single float, the range

will be (0, var_limit). Default: (10.0, 50.0).

mean (float): mean of the noise. Default: 0 per_feature (bool): if set to True, noise will be sampled for each feature independently.

Otherwise, the noise will be sampled once for all features. Default: True

generate(X: tensorflow.python.types.core.TensorLike, y: tensorflow.python.types.core.TensorLike | None = None, n_samples: int = 1, mean: float = 0, variance: float = 1.0) → AugmentationOutput

Generate synthetic data with Gaussian noise.

Parameters:

• X (TensorLike) – Input data tensor of shape (n_data, n_timesteps, n_features).

• y (Optional[TensorLike]) – Optional labels tensor. If provided, labels will also be returned

• n_samples (int) – Number of augmented samples to generate. Default is 1.

• mean (float) – The mean of the noise. Default is 0.

• variance (float) – The variance of the noise. Default is 1.0.

Returns:

Augmented data tensor of shape (n_samples, n_timesteps, n_features) and optionally augmented labels if ‘y’ is provided.

Return type:

Union[TensorLike, Tuple[TensorLike, TensorLike]]

class SliceAndShuffle(per_feature: bool = False)

Bases: BaseAugmenter

Slice the time series in k pieces and create a new time series by shuffling. Args:

per_feature (bool): if set to True, each time series is sliced independently.

Otherwise, all features are sliced in the same way. Default: True

generate(X: tensorflow.python.types.core.TensorLike, y: tensorflow.python.types.core.TensorLike | None = None, n_samples: int = 1, n_segments: int = 2) → AugmentationOutput

Generate synthetic data using Slice-And-Shuffle strategy. Slices are randomly selected.

Parameters:

• X (TensorLike) – Input data tensor of shape (n_data, n_timesteps, n_features).

• y (Optional[TensorLike]) – Optional labels tensor. If provided, labels will also be returned

• n_segments (int) – The number of slices, default is 2.

• n_samples (int) – Number of augmented samples to generate. Default is 1.

Returns:

Augmented data tensor of shape (n_samples, n_timesteps, n_features) and optionally augmented labels if ‘y’ is provided.

Return type:

Union[TensorLike, Tuple[TensorLike, TensorLike]]

class Shuffle

Bases: BaseAugmenter

Shuffles time series features. Shuffling is beneficial when each feature corresponds to interchangeable sensors.

generate(X: tensorflow.python.types.core.TensorLike, y: tensorflow.python.types.core.TensorLike | None = None, n_samples: int = 1) → AugmentationOutput

Generate synthetic data using Shuffle strategy. Features are randomly shuffled to generate novel samples.

Parameters:

• X (TensorLike) – Input data tensor of shape (n_data, n_timesteps, n_features).

• y (Optional[TensorLike]) – Optional labels tensor. If provided, labels will also be returned

• n_samples (int) – Number of augmented samples to generate. Default is 1.

Returns:

Augmented data tensor of shape (n_samples, n_timesteps, n_features) and optionally augmented labels if ‘y’ is provided.

Return type:

Union[TensorLike, Tuple[TensorLike, TensorLike]]

class MagnitudeWarping

Bases: BaseAugmenter

Magnitude warping changes the magnitude of each sample by convolving the data window with a smooth curve varying around one https://dl.acm.org/doi/pdf/10.1145/3136755.3136817

generate(X: tensorflow.python.types.core.TensorLike, y: tensorflow.python.types.core.TensorLike | None = None, n_samples: int = 1, sigma: float = 0.2, n_knots: int = 4) → AugmentationOutput

Generates augmented samples via MagnitudeWarping for (X, y)

Parameters:

• X (TensorLike) – Input data tensor of shape (n_data, n_timesteps, n_features).

• y (Optional[TensorLike]) – Optional labels tensor. If provided, labels will also be returned

• n_samples (int) – Number of augmented samples to generate. Default is 1.

• sigma (float) – Standard deviation for the random warping. Default is 0.2.

• n_knots (int) – Number of knots used for warping curve. Default is 4.

Returns:

Augmented data tensor of shape (n_samples, n_timesteps, n_features) and optionally augmented labels if ‘y’ is provided.

Return type:

Union[TensorLike, Tuple[TensorLike, TensorLike]]

class WindowWarping

Bases: BaseAugmenter

https://halshs.archives-ouvertes.fr/halshs-01357973/document

generate(X: tensorflow.python.types.core.TensorLike, y: tensorflow.python.types.core.TensorLike | None = None, window_ratio: float = 0.2, scales: Tuple = (0.25, 1.0), n_samples: int = 1) → AugmentationOutput

Generates augmented samples via MagnitudeWarping for (X, y)

Parameters:

• X (TensorLike) – Input data tensor of shape (n_data, n_timesteps, n_features).

• y (Optional[TensorLike]) – Optional labels tensor. If provided, labels will also be returned

• window_ratio (float) – The ratio of the window size relative to the total number of timesteps. Default is 0.2.

• scale (tuple) – A tuple specifying the scale range for warping. Default is (0.25, 1.0).

• n_samples (int) – Number of augmented samples to generate. Default is 1.

Returns:

Augmented data tensor of shape (n_samples, n_timesteps, n_features) and optionally augmented labels if ‘y’ is provided.

Return type:

Union[TensorLike, Tuple[TensorLike, TensorLike]]

class DTWBarycentricAveraging

Bases: BaseAugmenter

DTW Barycenter Averaging (DBA) [1] method estimated through

Expectation-Maximization algorithm [2] as in https://github.com/tslearn-team/tslearn/

References

[1]

F. Petitjean, A. Ketterlin & P. Gancarski. A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognition, Elsevier, 2011, Vol. 44, Num. 3, pp. 678-693

[2]

D. Schultz and B. Jain. Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces. Pattern Recognition, 74, 340-358.

generate(X: tensorflow.python.types.core.TensorLike, y: tensorflow.python.types.core.TensorLike | None = None, n_samples: int = 1, num_initial_samples: int | None = None, initial_timeseries: List[tensorflow.python.types.core.TensorLike] | None = None, initial_labels: List[int] | None = None, **kwargs) → AugmentationOutput

Parameters

X : TensorLike, the timeseries dataset y : TensorLike or None, the classes n_samples : int, number of samples to generate (per class, if y is given) num_initial_samples : int or None (default: None)

The number of timeseries to draw (per class) from the dataset before computing DTW_BA. If None, use the entire set (per class).

initial_timeseriesarray or None (default: None)

Initial timesteries to start from for the optimization process, with shape (original_size, d). In case y is given, the shape of initial_timeseries is assumed to be (n_classes, original_size, d)

initial_labels: array or None (default: None)

Labels for samples from initial_timeseries

Returns

np.array of shape (n_samples, original_size, d) if y is None

or (n_classes * n_samples, original_size, d), and np.array of labels (or None)