def compute()

in tensorflow_similarity/classification_metrics/precision.py [0:0]

• 14 lines of code
• 7 McCabe index (conditional complexity)

    def compute(self,
                tp: FloatTensor,
                fp: FloatTensor,
                tn: FloatTensor,
                fn: FloatTensor,
                count: int) -> FloatTensor:
        """Compute the classification metric.

        The `compute()` method supports computing the metric for a set of
        values, where each value represents the counts at a specific distance
        threshold.

        Args:
            tp: A 1D FloatTensor containing the count of True Positives at each
            distance threshold.

            fp: A 1D FloatTensor containing the count of False Positives at
            each distance threshold.

            tn: A 1D FloatTensor containing the count of True Negatives at each
            distance threshold.

            fn: A 1D FloatTensor containing the count of False Negatives at
            each distance threshold.

            count: The total number of queries

        Returns:
            A 1D FloatTensor containing the metric at each distance threshold.
        """
        p: FloatTensor = tf.math.divide_no_nan(tp, tp + fp)

        # If all queries return empty result sets we have a recall of zero. In
        # this case the precision should be 1.0 (see
        # https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-ranked-retrieval-results-1.html#fig:precision-recall).
        # The following accounts for the and sets the first precision value to
        # 1.0 if the first recall and precision are both zero.
        if (tp + fp)[0] == 0.0 and len(p) > 1:
            initial_precision = tf.constant(
                    [tf.constant([1.0]), tf.zeros(len(p)-1)],
                    axis=0
            )
            p = p + initial_precision

        return p