Refactor APLR Wrappers for Robustness and Maintainability (#638)

* docs

Signed-off-by: mathias-von-ottenbreit <ottenbreitdatascience@gmail.com>

* aplr wrapper

Signed-off-by: mathias-von-ottenbreit <ottenbreitdatascience@gmail.com>

* added tests

Signed-off-by: mathias-von-ottenbreit <ottenbreitdatascience@gmail.com>

* formatting

Signed-off-by: mathias-von-ottenbreit <ottenbreitdatascience@gmail.com>

* new refactor

Signed-off-by: mathias-von-ottenbreit <ottenbreitdatascience@gmail.com>

* Trigger CI

Signed-off-by: mathias-von-ottenbreit <ottenbreitdatascience@gmail.com>

---------

Signed-off-by: mathias-von-ottenbreit <ottenbreitdatascience@gmail.com>

Author: mathias-von-ottenbreit

docs/interpret/aplr.ipynb

@@ -93,7 +93,7 @@
    "source": [
     "<h2>Further Resources</h2>\n",
     "\n",
-    "Below are more detailed code examples from the backing repository for APLR. This will also work when using APLR through `InterpretML` if you change `from aplr import` to `from interpret.glassbox import`.\n",
+    "Below are more detailed code examples from the backing repository for APLR. This will also work when using APLR through `InterpretML` if you change `from aplr import` to `from interpret.glassbox import`. For better precision in global explanation plots you may consider using the plotting functionality in native APLR, introduced in APLR version 10.13.0.\n",
     "- [Regression code example](https://github.com/ottenbreit-data-science/aplr/blob/main/examples/train_aplr_regression.py)\n",
     "- [Classification code example](https://github.com/ottenbreit-data-science/aplr/blob/main/examples/train_aplr_classification.py)"
    ]