The woe_1d function wasn't running y.reset_index() in
the case where its y input was already a pd.Series. This
meant that it would return incorrect values when indexes
in X and y were noncontinuous.

Added a check for this condition to tests/test_metrics.py
as well.

The scikit-learn WOEEncoder uses ln(%bad / %good) instead of
ln(%good / %bad) for some reason, which flips all of the signs.
A previous fix switched the computation of WOE in the summary
table to match. However, this fix caused the IV values to be
negative in summary tables, which I found surprising.

I added an abs() call to the IV calculation to correct this
cosmetic defect.

Tests were failing in Pandas 2.0 due to the use of
DataFrame.append, which has been removed. I fixed it
by changing this to a pd.concat call.