Module iaf.fit.metrics

Implementation of simple metrics.

Functions

def calc_sse(y: numpy.ndarray, y_hat: numpy.ndarray)

Calculate the Sum of Squared Errors between prediction y_hat and data y.

Parameters

y : np.ndarray

Array of target values.

y_hat : np.ndarray

Array of predicted values.

Returns

sse : float

Sum of squared errors.

def calc_sse_weighed(y: numpy.ndarray, y_hat: numpy.ndarray, se: numpy.ndarray)

Calculate the Sum of Squared Errors between prediction y_hat and data y weighed by the standard error vector se.

Parameters

y : np.ndarray

Array of target values.

y_hat : np.ndarray

Array of predicted values.

se : np.ndarray

Array of standard error values.

Returns

sse : float

Weighted sum of squared errors.

def r_squared(y: numpy.ndarray, y_hat: numpy.ndarray)

Calculate the coefficient of determination R^2 for vectors y_hat and y.

Parameters

y : np.ndarray

Array of target values.

y_hat : np.ndarray

Array of predicted values.

Returns

r_squared : float

Coefficient of determination R^2

def r_squared_adj(y: numpy.ndarray, y_hat: numpy.ndarray, p: int)

Calculate the adjusted coefficient of determination R^2 for vectors y_hat and y and number of parameters p.

Parameters

y : np.ndarray

Array of target values.

y_hat : np.ndarray

Array of predicted values.

p : int

Number of parameters.

Returns

r_squared : float

Adjusted coefficient of determination R^2