tf.constant() has no keyword "axis" in precision.py

[Peter Doggart]

When I'm running model.calibrate() using matcher="match_majority_vote" and k=5, I get a TypeError from precision.py.

x_cal_data, y_cal = train_ds.get_slice(begin=0, size=1000)
calibration = model.calibrate(
    x_cal_data,
    y_cal,
    calibration_metric="f1",
    matcher="match_majority_vote",#"match_nearest",
    extra_metrics=["precision", "recall", "binary_accuracy"],
    verbose=1,
    k=5
)

<Huge stack trace>
---> 87     initial_precision = tf.constant([tf.constant([1.0]), tf.zeros(len(p) - 1)], axis=0)
     88     p = p + initial_precision
     90 return p

TypeError: constant() got an unexpected keyword argument 'axis'

The bit of code in question is:

        # 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

Looking at the tf.constant() docs there is no axis argument:

tf.constant(
    value, dtype=None, shape=None, name='Const'
)

Is this a bug, or am I missing something? I'm not overly familiar with the internal TF types. More than happy to put in a PR to fix if it is a bug!

[Owen Vallis]

Thanks for raising this and to @abhisharsinha for pushing a fix. I'll cherry pick this from dev and merge it to master.

[Owen Vallis]

Patch available in v0.16.8