gtime.forecasting.GARFF

class gtime.forecasting.GARFF(estimator)

Generalized Auto Regression model with feedforward training. This model is a wrapper of sklearn.multioutput.RegressorChain but returns a pd.DataFrame.

Fit one model for each target variable contained in the y matrix, also using the predictions of the previous model.

Parameters
estimatorestimator object, required

The model used to make the predictions step by step. Regressor object such as derived from RegressorMixin.

Notes

sklearn.multioutput.RegressorChain order, cv and random_state parameters were set to None due to target order importance in a time-series forecasting context.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from gtime.forecasting import GARFF
>>> from sklearn.ensemble import RandomForestRegressor
>>> time_index = pd.date_range("2020-01-01", "2020-01-30")
>>> X = pd.DataFrame(np.random.random((30, 5)), index=time_index)
>>> y_columns = ["y_1", "y_2", "y_3"]
>>> y = pd.DataFrame(np.random.random((30, 3)), index=time_index, columns=y_columns)
>>> X_train, y_train = X[:20], y[:20]
>>> X_test, y_test = X[20:], y[20:]
>>> random_forest = RandomForestRegressor()
>>> garff = GARFF(estimator=random_forest)
>>> garff.fit(X_train, y_train)
>>> predictions = garff.predict(X_test)
>>> predictions.shape
(10, 3)

Methods

fit(self, X, y)

Fit the models, one for each time step.

get_params(self[, deep])

Get parameters for this estimator.

predict(self, X)

For each row in X, make a prediction for each fitted model, from 1 to horizon.

score(self, X, y[, sample_weight])

Return the coefficient of determination R^2 of the prediction.

set_params(self, \*\*params)

Set the parameters of this estimator.

__init__(self, estimator)

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

fit(self, X:pandas.core.frame.DataFrame, y:pandas.core.frame.DataFrame)

Fit the models, one for each time step. Each model is trained on the initial set of features and on the true values of the previous steps.

Parameters
Xpd.DataFrame, shape (n_samples, n_features), required

The data.

ypd.DataFrame, shape (n_samples, horizon), required

The matrix containing the target variables.

Returns
selfobject

The fitted object.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters
deepbool, default=True

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.

predict(self, X:pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

For each row in X, make a prediction for each fitted model, from 1 to horizon.

Parameters
Xpd.DataFrame, shape (n_samples, n_features)

The data.

Returns
y_p_dfpd.DataFrame, shape (n_samples, horizon)

The predictions, one for each timestep in horizon.

score(self, X, y, sample_weight=None)

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters
Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns
scorefloat

R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

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
**paramsdict

Estimator parameters.

Returns
selfobject

Estimator instance.