giotto.time_series.Labeller

class giotto.time_series.Labeller(width=10, func=<function std>, func_params={}, percentiles=None, n_steps_future=1)

Target creation from sliding windows over a time series.

Useful to define a time series forecasting task in which labels are obtained from future values of the input time series, via the application of a function to time windows.

Parameters

widthint, optional, default: 10

Width of each sliding window. Each window contains width + 1 objects from the original time series.

funccallable, optional, default: numpy.std

Function to be applied to each window.

func_paramsdict, optional, default: {}

Additional keyword arguments for func.

percentileslist of ordered floats between 0 and 1 or None, optional, default: None

If None, creates a target for a regression task. Otherwise, creates a target for an n-class classification task where n = len(percentiles) + 1.

n_steps_futureint, optional, default: 1

Number of steps in the future for the predictive task.

Attributes

thresholds_list of floats or None if percentiles is None

Values corresponding to each percentile, based on data seen in fit.

Examples

>>> import numpy as np
>>> from giotto.time_series import Labeller
>>> # Create a time series
>>> X = np.arange(10).reshape(-1, 1)
>>> labeller = Labeller(width=2)
>>> # Fit and transform X
>>> X, y = labeller.fit_transform_resample(X, X)
>>> print(X)
[[1]
 [2]
 [3]
 [4]
 [5]
 [6]
 [7]
 [8]]
>>> print(y)
[0.81649658 0.81649658 0.81649658 0.81649658 0.81649658 0.81649658
 0.81649658 0.81649658]

Methods

fit(self, X[, y])	Compute thresholds_ and return the estimator.
fit_transform(self, X[, y])	Fit to data, then transform it.
fit_transform_resample(self, X, y, …)	Fit to data, then transform the input and resample the target.
get_params(self[, deep])	Get parameters for this estimator.
resample(self, y[, X])	Resample y.
set_params(self, \*\*params)	Set the parameters of this estimator.
transform(self, X[, y])	Cuts X so it is aligned with y.
transform_resample(self, X, y)	Fit to data, then transform it.

__init__(self, width=10, func=<function std at 0x10f7bfe18>, func_params={}, percentiles=None, n_steps_future=1)

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

fit(self, X, y=None)

Compute thresholds_ and return the estimator.

Parameters

Xndarray, shape (n_samples,) or (n_samples, 1)

Time series to build a target for.

yNone

There is no need for a target in a transformer, yet the pipeline API requires this parameter.

Returns

selfobject

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

Xndarray of shape (n_samples, …)

Input data.

yNone

There is no need for a target in a transformer, yet the pipeline API requires this parameter.

Returns

Xtnumpy array of shape (n_samples, …)

Transformed input.

fit_transform_resample(self, X, y, **fit_params)

Fit to data, then transform the input and resample the target. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X ans a resampled version of y.

Parameters

Xndarray of shape (n_samples, …)

Input data.

yndarray of shape (n_samples, )

Target data.

Returns

Xtndarray of shape (n_samples, …)

Transformed input.

yrndarray of shape (n_samples, …)

Resampled target.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepboolean, optional

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.

resample(self, y, X=None)

Resample y.

Parameters

yndarray, shape (n_samples,)

Time series to build a target for.

XNone

There is no need for X, yet the pipeline API requires this parameter.

Returns

yrndarray, shape (n_samples_new,)

Target for the prediction task.

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.

Returns

self

transform(self, X, y=None)

Cuts X so it is aligned with y.

Parameters

Xndarray, shape (n_samples,) or (n_samples, 1)

Time series to build a target for.

yNone

There is no need for a target, yet the pipeline API requires this parameter.

Returns

Xtndarray, shape (n_samples_new, 1)

The cut input time series.

transform_resample(self, X, y)

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

Xndarray of shape (n_samples, …)

Input data.

yndarray of shape (n_samples, )

Target data.

Returns

Xtndarray of shape (n_samples, …)

Transformed input.