`gtime.feature_generation`.PeriodicSeasonal¶

class gtime.feature_generation.PeriodicSeasonal(period: Union[pandas._libs.tslibs.timedeltas.Timedelta, str] = '365 days', amplitude: float = 0.5, start_date: Union[pandas._libs.tslibs.timestamps.Timestamp, str, None] = None, length: Optional[int] = 50, index_period: Union[pandas.core.indexes.datetimes.DatetimeIndex, int, None] = None)¶

Create a sinusoid from a given date and with a given period and amplitude.

Parameters:

period : Union[pd.Timedelta, str], optional, default: '365 days': The period of the generated time series.
amplitude : float, optional, default: 0.5: The amplitude of the time series.
start_date : Union[pd.Timestamp, str], optional, default: None: The date from which to start generating the feature. This is used only if X is not passed in the transform method, otherwise the start date is inferred from it.
length : int, optional, default: 50: The length of the sinusoid. This is used only if X is not passed in the transform method, otherwise the length is inferred from it.
index_period : Union[DatetimeIndex, int], optional, default: None: The period of the index of the output DataFrame. This is used only if X is not passed in the transform method, otherwise the index period is taken from it.

Examples

>>> import pandas as pd
>>> from gtime.feature_generation import PeriodicSeasonal
>>> X = pd.DataFrame(range(0, 10), index=pd.date_range(start='2019-04-18',  end='2019-04-27', freq='d'))
>>> periodic = PeriodicSeasonal()
>>> periodic.fit_transform(X)
            0__PeriodicSeasonal
2019-04-18             0.000000
2019-04-19             0.008607
2019-04-20             0.017211
2019-04-21             0.025810
2019-04-22             0.034401
2019-04-23             0.042982
2019-04-24             0.051551
2019-04-25             0.060104
2019-04-26             0.068639
2019-04-27             0.077154

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, NoneType] = None)	Generate a sinusoid, with the given `period`, `amplitude` and `length`, starting from the selected `start_date`.

__init__(self, period: Union[pandas._libs.tslibs.timedeltas.Timedelta, str] = '365 days', amplitude: float = 0.5, start_date: Union[pandas._libs.tslibs.timestamps.Timestamp, str, NoneType] = None, length: Union[int, NoneType] = 50, index_period: Union[pandas.core.indexes.datetimes.DatetimeIndex, int, NoneType] = None)¶: Initialize self. See help(type(self)) for accurate signature.

fit(self, X: pandas.core.frame.DataFrame, y=None) → 'PeriodicSeasonal'¶

Fit the estimator.

Parameters:	X : pd.DataFrame, shape (n_samples, n_features), required Input data. y : None There is no need of a target in a transformer, yet the pipeline API requires this parameter.
Returns:	self : PeriodicSeasonal 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: Union[pandas.core.frame.DataFrame, NoneType] = None) → pandas.core.frame.DataFrame¶

Generate a sinusoid, with the given period, amplitude and length, starting from the selected start_date. If time_series is not None, the start_date is replaced by the start date of the time series and the output sinusoid will have the same index as time_series.

Parameters:	time_series : pd.DataFrame, shape (n_samples, 1), optional, default: `None` The input DataFrame, If passed, the output DataFrame is going to have the same index as `time_series`. If is not passed, then the `start_date` and `index_period` must have been passed in the constructor when the object was instantiated.
Returns:	periodic_feature : pd.DataFrame, shape (n_samples, 1) The DataFrame containing the generated period feature.
Raises:	ValueError Raised if `time_series` is not passed and the `start_date` or the `index_period` are not present.

gtime.feature_generation.PeriodicSeasonal¶

`gtime.feature_generation`.PeriodicSeasonal¶