List of Built-in Metrics
========================

This package provides some commonly used metrics. They are implemented in a pre-defined protocols in order to be called by experiments classes. Here is an example.

.. code:: python

    def accuracy_score(y_true, y_pred, param_dict=None):
        """Accuracy classification score.

        Parameters
        ----------
        y_true : 1d array-like, or label indicator array / sparse matrix
            Ground truth (correct) _labels.

        y_pred : 1d array-like, or label indicator array / sparse matrix
            Predicted _labels, as returned by a classifier.

        param_dict: dict
            A dictory saving the parameters including::

                sample_weight : array-like of shape = [n_samples], optional Sample 
                weights.

        Returns
        -------
        score : float
        """
        # codes...


These functions always have three parameters: **y_true** for ground-truth labels, *y_pred* for prediction returned by an estimator, **param_dict** is a dict that stores other parameters used in the function.

The built-in metrics include:

.. code:: python

    'accuracy_score',
    'zero_one_loss',
    'roc_auc_score',
    'get_fps_tps_thresholds',
    'f1_score',
    'hamming_loss',
    'one_error',
    'coverage_error',
    'label_ranking_loss',
    'label_ranking_average_precision_score',
    'micro_auc_score',
    'Average_precision_score',
    'minus_mean_square_error'

Please refer to ``s3l.metrics.performance`` for more details.