included missing init to the causal exceptions (#212)

* included missing init

* including missing type

* fix lint

* Included return type

* exceptions.py: Fixed exceptions typing.

* updating changelog and version

* fix docstring

* including docstring updates

* fix failing test

Co-authored-by: Giulio Santo <giulio.santo@nubank.com.br>

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Changelog
 
+## [2.2.1] - 2022-09-06
+- **Bug Fix**
+  - Including a necessary init file to allow the import of the causal cate learners.
+  - Fix a docstring issue where the description of causal learners were not showing all parameters.
+
 ## [2.2.0] - 2022-08-25
 - **Enhancement**
   - Including Classification S-Learner and T-Learner models to the causal cate learning library.

src/fklearn/causal/cate_learning/meta_learners.py

@@ -191,6 +191,7 @@ def causal_s_classification_learner(
     [1] https://matheusfacure.github.io/python-causality-handbook/21-Meta-Learners.html
 
     [2] https://causalml.readthedocs.io/en/latest/methodology.html
+
     Parameters
     ----------
     df : pd.DataFrame
@@ -369,34 +370,29 @@ def causal_t_classification_learner(
     and $M_{1}$ are traditional Machine Learning models such as a LightGBM Classifier and
     that $x_{i}$ is the feature set of sample $i$.
 
-    References:
+    **References:**
+
     [1] https://matheusfacure.github.io/python-causality-handbook/21-Meta-Learners.html
+
     [2] https://causalml.readthedocs.io/en/latest/methodology.html
 
     Parameters
     ----------
-
     df : pd.DataFrame
         A Pandas' DataFrame with features and target columns.
         The model will be trained to predict the target column
         from the features.
-
     treatment_col: str
         The name of the column in `df` which contains the names of
         the treatments and control to which each data sample was subjected.
-
     control_name: str
         The name of the control group.
-
     prediction_column : str
         The name of the column with the predictions from the provided learner.
-
     learner: LearnerFnType
         A fklearn classification learner function.
-
     treatment_learner: LearnerFnType
         An optional fklearn classification learner function.
-
     learner_transformers: List[LearnerFnType]
         A list of fklearn transformer functions to be applied after the learner and before estimating the CATE.
         This parameter may be useful, for example, to estimate the CATE with calibrated classifiers.
@@ -447,6 +443,6 @@ def p(new_df: pd.DataFrame) -> pd.DataFrame:
     return p, p(df), log
 
 
-causal_t_classification_learner.__doc__ = learner_return_docstring(
+causal_t_classification_learner.__doc__ += learner_return_docstring(
     "Causal T-Learner Classifier"
 )

src/fklearn/exceptions/exceptions.py

@@ -1,17 +1,31 @@
+from typing import Any, Dict, List
+
+
 class MultipleTreatmentsError(Exception):
-    def __init__(self, msg="Data contains multiple treatments.", *args, **kwargs):
+    def __init__(
+        self,
+        msg: str = "Data contains multiple treatments.",
+        *args: List[Any],
+        **kwargs: Dict[str, Any]
+    ) -> None:
         super().__init__(msg, *args, **kwargs)
 
 
 class MissingControlError(Exception):
     def __init__(
-        self, msg="Data does not contain the specified control.", *args, **kwargs
-    ):
+        self,
+        msg: str = "Data does not contain the specified control.",
+        *args: List[Any],
+        **kwargs: Dict[str, Any]
+    ) -> None:
         super().__init__(msg, *args, **kwargs)
 
 
 class MissingTreatmentError(Exception):
     def __init__(
-        self, msg="Data does not contain the specified treatment.", *args, **kwargs
-    ):
+        self,
+        msg: str = "Data does not contain the specified treatment.",
+        *args: List[Any],
+        **kwargs: Dict[str, Any]
+    ) -> None:
         super().__init__(msg, *args, **kwargs)

src/fklearn/resources/VERSION

@@ -1 +1 @@
-2.2.0
+2.2.1

tests/validation/test_evaluators.py

@@ -469,7 +469,7 @@ def test_exponential_coefficient_evaluator():
 
     result = exponential_coefficient_evaluator(predictions)
 
-    assert result['exponential_coefficient_evaluator__target'] == a1
+    assert result['exponential_coefficient_evaluator__target'] == pytest.approx(a1)
 
 
 def test_logistic_coefficient_evaluator():