gtime.feature_extraction.Polynomial

class gtime.feature_extraction.Polynomial(degree: int = 2)

Compute the polynomial feature_extraction, of a degree equal to the input degree. Wrapper of sklearn.preprocessing.PolynomialFeatures but returns a pd.DataFrame.

Parameters:
degree : int, optional, default: 2

The degree of the polynomial feature_extraction.
Attributes:
powers_

Examples

>>> import pandas as pd
>>> from gtime.feature_extraction import Polynomial
>>> ts = pd.DataFrame([0, 1, 2, 3, 4, 5])
>>> pol = Polynomial(degree=3)
>>> pol.fit_transform(ts)
   0__Polynomial  1__Polynomial  2__Polynomial  3__Polynomial
0            1.0            0.0            0.0            0.0
1            1.0            1.0            1.0            1.0
2            1.0            2.0            4.0            8.0
3            1.0            3.0            9.0           27.0
4            1.0            4.0           16.0           64.0
5            1.0            5.0           25.0          125.0

Methods

fit(self, time_series[, y]) Fit the estimator.
fit_transform(self, X[, y]) Fit to data, then transform it.
get_feature_names(self[, input_features]) 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) Compute the polynomial feature_extraction of time_series, up to a degree equal to degree.
__init__(self, degree: int = 2)

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

fit(self, time_series: pandas.core.frame.DataFrame, 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, input_features=None)

Return feature names for output features

Parameters:
input_features : list of string, length n_features, optional

String names for input features if available. By default, “x0”, “x1”, … “xn_features” is used.
Returns:
output_feature_names : list of string, length n_output_features
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

Compute the polynomial feature_extraction of time_series, up to a degree equal to degree.

Parameters:
time_series : pd.DataFrame, shape (n_samples, 1), required

The input DataFrame. Used only for its index.
Returns:
time_series_t : pd.DataFrame, shape (n_samples, 1)

The computed polynomial feature_extraction.