gtime.feature_extraction.Shift

class gtime.feature_extraction.Shift(shift: int = 1)

Perform a shift of a DataFrame of size equal to shift.

Parameters
shiftint, optional, default: 1

How much to shift.

Notes

The shift parameter can also accept negative values. However, this should be used carefully, since if the resulting feature is used for training or testing it might generate a leak from the feature.

Examples

>>> import pandas as pd
>>> from gtime.feature_extraction import Shift
>>> ts = pd.DataFrame([0, 1, 2, 3, 4, 5])
>>> shift = Shift(shift=3)
>>> shift.fit_transform(ts)
   0__Shift
0       NaN
1       NaN
2       NaN
3       0.0
4       1.0
5       2.0

Methods

fit(self, X[, y])

Fit the estimator.

fit_transform(self, X[, y])

Fit to data, then transform it.

get_feature_names(self)

Return feature names for output features.

get_params(self[, deep])

Get parameters for this estimator.

set_params(self, \*\*params)

Set the parameters of this estimator.

transform(self, time_series)

Create a shifted version of time_series.

__init__(self, shift:int=1)

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)

Fit the estimator.

Parameters
Xpd.DataFrame, shape (n_samples, n_features)

Input data.

yNone

There is no need of a target in a transformer, yet the pipeline API requires this parameter.

Returns
selfobject

Returns self.

fit_transform(self, X, y=None, **fit_params)

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
Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.

**fit_paramsdict

Additional fit parameters.

Returns
X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_feature_names(self)

Return feature names for output features.

Returns
output_feature_namesndarray, shape (n_output_features,)

Array of feature names.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfobject

Estimator instance.

transform(self, time_series:pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Create a shifted version of time_series.

Parameters
time_seriespd.DataFrame, shape (n_samples, 1), required

The DataFrame to shift.

Returns
time_series_tpd.DataFrame, shape (n_samples, 1)

The shifted version of the original time_series.