gtime.feature_extraction.Exogenous

class gtime.feature_extraction.Exogenous

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.

Examples

>>> import pandas as pd
>>> from gtime.feature_extraction import Exogenous
>>> ts = pd.DataFrame({'exogenous': [10, 8, 1, 3, 2, 7]},  index=[3, 4, 5, 6, 7, 8])
>>> exog = Exogenous()
>>> exog.fit_transform(ts)
    exogenous__Exogenous
3                    10
4                     8
5                     1
6                     3
7                     2
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) It returns the input time series adding the class name to it
__init__(self, /, *args, **kwargs)

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

fit(self, time_series, y=None)

Fit the estimator.

Parameters:
time_series : pd.DataFrame, shape (n_samples, n_features)

Input data.

y : None

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

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:
X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

**fit_params : dict

Additional fit parameters.
Returns:
X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.
get_feature_names(self)

Return feature names for output features.

Returns:
output_feature_names : ndarray, shape (n_output_features,)

Array of feature names.
get_params(self, deep=True)

Get parameters for this estimator.

Parameters:
deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:
params : mapping 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:
**params : dict

Estimator parameters.
Returns:
self : object

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

It returns the input time series adding the class name to it

Parameters:
time_series : pd.DataFrame, shape (n_samples, n_features), required

The input DataFrame.
Returns:
time_series_t : pd.DataFrame, shape (n_samples, n_features)

The original exogenous_time_series, adding the class name to it