compute_gof

class compute_gof(actual, predicted, normalize=True, use_frac=False, use_squared=False, as_scalar='none', eps=1e-09, skestimator=None, estimator=None, **kwargs)

Calculate the goodness of fit. By default use normalized absolute error, but highly customizable. For example, mean squared error is equivalent to setting normalize=False, use_squared=True, as_scalar=’mean’.

Parameters:

• actual (arr) – array of actual (data) points

• predicted (arr) – corresponding array of predicted (model) points

• normalize (bool) – whether to divide the values by the largest value in either series

• use_frac (bool) – convert to fractional mismatches rather than absolute

• use_squared (bool) – square the mismatches

• as_scalar (str) – return as a scalar instead of a time series: choices are sum, mean, median

• eps (float) – to avoid divide-by-zero

• skestimator (str) – if provided, use this scikit-learn estimator instead

• estimator (func) – if provided, use this custom estimator instead

• kwargs (dict) – passed to the scikit-learn or custom estimator

Returns:

array of goodness-of-fit values, or a single value if as_scalar is True

Return type:

gofs (arr)

Examples:

x1 = np.cumsum(np.random.random(100))
x2 = np.cumsum(np.random.random(100))

e1 = compute_gof(x1, x2) # Default, normalized absolute error
e2 = compute_gof(x1, x2, normalize=False, use_frac=False) # Fractional error
e3 = compute_gof(x1, x2, normalize=False, use_squared=True, as_scalar='mean') # Mean squared error
e4 = compute_gof(x1, x2, skestimator='mean_squared_error') # Scikit-learn's MSE method
e5 = compute_gof(x1, x2, as_scalar='median') # Normalized median absolute error -- highly robust