gtime.regressors.LinearRegressor

class gtime.regressors.LinearRegressor(loss=<function mean_squared_error>)

Implementation of a LinearRegressor that takes a custom loss function.

Parameters:

loss : Callable, optional, default: mean_squared_error

The loss function to use when fitting the model. The loss function must accept y_true, y_pred and return a single real number.

Examples

>>> from gtime.regressors.linear_regressor import LinearRegressor
>>> from gtime.metrics import max_error
>>> import numpy as np
>>> import pandas as pd
>>> X = np.random.random((100, 10))
>>> y = np.random.random(100)
>>> lr = LinearRegressor(loss=max_error)
>>> X_train, y_train = X[:90], y[:90]
>>> X_test, y_test = X[90:], y[90:]
>>> x0 = [0]*11
>>> lr.fit(X_train, y_train, x0=x0)
>>> lr.predict(X_test)
array([0.62987155, 0.46971378, 0.50421395, 0.5543149 , 0.50848151,
       0.54768797, 0.50968854, 0.50500384, 0.58069366, 0.54912972])

Methods

fit(self, X, y, \*\*kwargs)	Fit the linear model on X and y on the given loss function.To do the minimization, the scipy.optimize.minimize function is used.
predict(self, X)	Predict the y values associated to the features X.

__init__(self, loss=<function mean_squared_error at 0x7f5c9a835b70>)

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

fit(self, X: pandas.core.frame.DataFrame, y: pandas.core.frame.DataFrame, **kwargs) → 'LinearRegressor'

Fit the linear model on X and y on the given loss function.To do the minimization, the scipy.optimize.minimize function is used. To have more details and check which kind of options are available, please refer to the scipy documentation.

Parameters:

X : pd.DataFrame, shape (n_samples, n_features), required

The X matrix used as features in the fitting procedure.

y : pd.DataFrame, shape (n_samples, 1), required

The y matrix to use as target values in the fitting procedure.

kwargs: dict, optional.

Optional arguments to pass to the minimize function of scipy.

Returns:

self: LinearRegressor

The fitted model.

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

Predict the y values associated to the features X.

Parameters:

X : pd.DataFrame, shape (n_samples, n_features), required

The features used to predict.

Returns:

predictions : pd.DataFrame, shape (n_samples, 1)

The predictions of the model