gtime.feature_extraction.Exogenous

class gtime.feature_extraction.Exogenous(exogenous_time_series: pandas.core.frame.DataFrame, method: Optional[str] = None)

Reindex exogenous_time_series with the index of time_series. To check the documentation of pandas.DataFrame.reindex and to see which type of method are available, please refer to the pandas documentation.

Parameters

exogenous_time_seriespd.DataFrame, shape (n_samples, 1), required

The time series to reindex

methodstr, optional, default: None

The method used to re-index. This must be a method used by the pandas.DataFrame.reindex method.

Examples

>>> import pandas as pd
>>> from gtime.feature_extraction import Exogenous
>>> ts = pd.DataFrame([0, 1, 2, 3, 4, 5], index=[3, 4, 5, 6, 7, 8])
>>> exog_ts = pd.DataFrame([10, 8, 1, 3, 2, 7])
>>> exog = Exogenous(exog_ts)
>>> exog.fit_transform(ts)
   0__Exogenous
3           3.0
4           2.0
5           7.0
6           NaN
7           NaN
8           NaN

>>> exog = Exogenous(exog_ts, method="nearest")
>>> exog.fit_transform(ts)
   0__Exogenous
3             3
4             2
5             7
6             7
7             7
8             7

Methods

fit(self, time_series[, 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)	Reindex the exogenous_time_series with the index of time_series.

__init__(self, exogenous_time_series:pandas.core.frame.DataFrame, method:Union[str, NoneType]=None)

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

fit(self, time_series, y=None)

Fit the estimator.

Parameters

time_seriespd.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

Reindex the exogenous_time_series with the index of time_series.

Parameters

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

The input DataFrame. Used only for its index.

Returns

time_series_tpd.DataFrame, shape (n_samples, 1)

The original exogenous_time_series, re-indexed with the index of time_series.