LogLoss

class LogLoss(multioutput='uniform_average', multivariate=False)[source]

Logarithmic loss for distributional predictions.

For a predictive distribution \(d\) with pdf \(p_d\) and a ground truth value \(y\), the logarithmic loss is defined as \(L(y, d) := -\log p_d(y)\).

Parameters:

multioutput‘uniform_average’ (default), 1D array-like, or ‘raw_values’

Whether and how to aggregate metric for multivariate (multioutput) data.

If 'uniform_average' (default), errors of all outputs are averaged with uniform weight.
If 1D array-like, errors are averaged across variables, with values used as averaging weights (same order).
If 'raw_values', does not average across variables (outputs), per-variable errors are returned.

multivariatebool, optional, default=False

if True, behaves as multivariate log-loss: the log-loss is computed for entire row, results one score per row
if False, is univariate log-loss: the log-loss is computed per variable marginal, results in many scores per row

__call__(y_true, y_pred, **kwargs)

Calculate metric value using underlying metric function.

__call__(y_true, y_pred, **kwargs)[source]

Calculate metric value using underlying metric function.

Parameters:

y_truepd.Series, pd.DataFrame or np.array of shape (fh,) or (fh, n_outputs) where fh is the forecasting horizon: Ground truth (correct) target values.
y_predreturn object of probabilistic prediction method scitype:y_pred: must be at fh and for variables equal to those in y_true.

Returns:

lossfloat or 1-column pd.DataFrame with calculated metric value(s)

metric is always averaged (arithmetic) over fh values if multioutput = “raw_values”,

will have a column level corresponding to variables in y_true

if multioutput = multioutput = “uniform_average” or or array-like: entries will be averaged over output variable column
if score_average = False,: will have column levels corresponding to quantiles/intervals
if score_average = True,: entries will be averaged over quantiles/interval column