ballet.eng.external.skits module

class ballet.eng.external.skits.AutoregressiveTransformer(num_lags=5, pred_stride=1)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
inverse_transform(X)[source]
transform(X, y=None)[source]
class ballet.eng.external.skits.DifferenceTransformer(period=1)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
inverse_transform(X)[source]
needs_refit = True
transform(X, y=None, refit=False)[source]
class ballet.eng.external.skits.FourierTransformer(period=10, max_order=10, step_size=1)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
transform(X, y=None)[source]
class ballet.eng.external.skits.HorizonTransformer(horizon=2)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
fit_transform(X, y=None)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

  • X (array-like of shape (n_samples, n_features)) – Input samples.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

  • **fit_params (dict) – Additional fit parameters.

Returns

X_new – Transformed array.

Return type

ndarray array of shape (n_samples, n_features_new)

inverse_transform(X, y=None)[source]
needs_refit = True
transform(X, y=None, refit=False)[source]
y_only = True
class ballet.eng.external.skits.IntegratedTransformer(num_lags=1, pred_stride=1)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
transform(X, y=None)[source]
class ballet.eng.external.skits.LogTransformer[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
inverse_transform(X)[source]
needs_refit = False
transform(X, y=None, refit=False)[source]
class ballet.eng.external.skits.ReversibleImputer(y_only=False)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
inverse_transform(X)[source]
needs_refit = True
transform(X, y=None, refit=False)[source]
class ballet.eng.external.skits.RollingMeanTransformer(window=5)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
transform(X, y=None)[source]
class ballet.eng.external.skits.SeasonalTransformer(seasonal_period=1, pred_stride=1)[source]

Bases: skits.feature_extraction.AutoregressiveTransformer

class ballet.eng.external.skits.TrendTransformer(shift=0)[source]

Bases: sklearn.base.BaseEstimator, sklearn.base.TransformerMixin

fit(X, y=None)[source]
transform(X, y=None)[source]