Fklearn version 2.2.0 - changelog (#210)

* Changelog for S-Learner and Validator bugfix

* fix missing newline

* bump version

* updated changelog with T-Learner information

CHANGELOG.md

@@ -1,5 +1,19 @@
 # Changelog
 
+## [2.2.0] - 2022-08-25
+- **Enhancement**
+  - Including Classification S-Learner and T-Learner models to the causal cate learning library.
+- **Bug Fix**
+  - Fix validator behavior when receiving data containing gaps and a time based split function that
+    could generate empty 
+    training and testing folds and then break. 
+    The argument `drop_empty_folds` can be set to `True` to drop invalid folds from validation and 
+    store them in the 
+    log.
+- **Documentation**
+  - Including Classification S-Learner and T-Learner documentation, also changing validator documentation to 
+    reflect changes.
+
 ## [2.1.0] - 2022-07-25
 - **Enhancement**
     - Add optional parameter `return_eval_logs_on_train` to the `validator` function,

docs/source/api/fklearn.causal.cate_learning.rst

@@ -12,6 +12,14 @@ fklearn.causal.cate\_learning.double\_machine\_learning module
     :undoc-members:
     :show-inheritance:
 
+fklearn.causal.cate\_learning.meta\_learners module
+---------------------------------------------------
+
+.. automodule:: fklearn.causal.cate_learning.meta_learners
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 
 Module contents
 ---------------

src/fklearn/causal/cate_learning/meta_learners.py

@@ -185,8 +185,11 @@ def causal_s_classification_learner(
     of a new sample for both scenarios, i.e., with T = 0 and T = 1. The CATE τ
     is defined as τ(xi) = M(X=xi, T=1) - M(X=xi, T=0), being M a Machine Learning
     Model.
-    References:
+
+    **References:**
+
     [1] https://matheusfacure.github.io/python-causality-handbook/21-Meta-Learners.html
+
     [2] https://causalml.readthedocs.io/en/latest/methodology.html
     Parameters
     ----------

src/fklearn/resources/VERSION

@@ -1 +1 @@
-2.0.0
+2.2.0