rework top-level docstrings into init

menelaus/experimental/__init__.py

@@ -0,0 +1,15 @@
+"""
+The experimental module is for live, but untested / informally-released code. Currently this houses
+classes and functions meant to apply drift detection to NLP data. This is achieved by implementing
+three components:
+    * Transform functions, which are curried functions initialized with a certain configuration,
+    and called in some sequence to transform an initial batch of data into a final formatted data
+    representation. Applying transforms helps compare two sets of data, and convert data into a format
+    accepted by some ``Alarm`` type.
+    * ``Alarm`` classes, which are parameterized objects with an ``evaluate()`` function that takes 2 
+    data representations (*i.e.*, transformed data) and determines whether drift has occurred according 
+    to some statistical test, principle, or heuristic. 
+    * A ``Detector`` class which accepts a list of transforms to apply, an initialied alarm scheme,
+    and can step through newly-seen data sequentially and report drift as it occurs. Importantly, this
+    class can recalibrate the source or reference data as drift is discovered.
+"""

menelaus/experimental/transform.py

@@ -1,10 +1,3 @@
-""" 
-Contains transform functions, which are curried functions initialized with a certain configuration,
-and called in some sequence to transform an initial batch of data into a final formatted data
-representation. Applying transforms helps compare two sets of data, and convert data into a format
-accepted by some ``Alarm`` type.
-"""
-
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.layers import Flatten, Dense, InputLayer