`gtime.feature_generation`.Constant¶

class gtime.feature_generation.Constant(constant: int = 0, length: int = None)¶

Generate a pd.DataFrame with one column, of the same length as the input X and containing the value constant across the whole column.

Parameters:	constant : int, optional, default: `2` The value to use to generate the constant column of the `pd.DataFrame`. length : int, optional, default: `50` The length of the DataFrame to generate. This is used only if X is not passed in the `transform` method, otherwise the length is inferred from it.

Examples

>>> import pandas as pd
>>> from gtime.feature_generation import Constant
>>> X = pd.DataFrame(range(0, 5), index=pd.date_range(start='2019-04-18',  end='2019-04-22', freq='d'))
>>> constant = Constant(constant=3)
>>> constant.fit_transform(X)
            0__Constant
2019-04-18          3.0
2019-04-19          3.0
2019-04-20          3.0
2019-04-21          3.0
2019-04-22          3.0

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, NoneType] = None)	Generate a `pd.DataFrame` with one column with the same length as `time_series` and with the same index, containing a value equal to `constant`.

__init__(self, constant: int = 0, length: int = None)¶: Initialize self. See help(type(self)) for accurate signature.

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

Fit the estimator.

Parameters:	X : 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 : Constant 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 pd.DataFrame with one column with the same length as time_series and with the same index, containing a value equal to constant.

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`.
Returns:	constant_series_renamed : pd.DataFrame, shape (length, 1) A constant series, with the same length of `X` and with the same index.

gtime.feature_generation.Constant¶

`gtime.feature_generation`.Constant¶