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 apd.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 timeseries 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("20200101", "20200130") >>> 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 tohorizon
.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 tohorizon
. 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
 Xarraylike 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.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike 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 usemultioutput='uniform_average'
from version 0.23 to keep consistent withr2_score
. This will influence thescore
method of all the multioutput regressors (except forMultiOutputRegressor
). To specify the default value manually and avoid the warning, please either callr2_score
directly or make a custom scorer withmake_scorer
(the builtin scorer'r2'
usesmultioutput='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.