Merge pull request #172 from CITCOM-project/pylint-docstring-params

Add docstring params to pylint checks

Author: christopher-wild

.pylintrc

@@ -75,7 +75,7 @@ limit-inference-results=100
 
 # List of plugins (as comma separated values of python module names) to load,
 # usually to register additional checkers.
-load-plugins=
+load-plugins=pylint.extensions.docparams
 
 # Pickle collected data for later comparisons.
 persistent=yes

causal_testing/json_front/json_class.py

@@ -57,7 +57,7 @@ def set_paths(self, json_path: str, dag_path: str, data_paths: str):
         Takes a path of the directory containing all scenario specific files and creates individual paths for each file
         :param json_path: string path representation to .json file containing test specifications
         :param dag_path: string path representation to the .dot file containing the Causal DAG
-        :param data_path: string path representation to the data file
+        :param data_paths: string path representation to the data files
         """
         self.paths = JsonClassPaths(json_path=json_path, dag_path=dag_path, data_paths=data_paths)
 

causal_testing/specification/causal_dag.py

@@ -9,6 +9,8 @@
 
 import networkx as nx
 
+from causal_testing.testing.base_test_case import BaseTestCase
+
 from .scenario import Scenario
 from .variable import Output
 
@@ -496,7 +498,7 @@ def depends_on_outputs(self, node: Node, scenario: Scenario) -> bool:
             return True
         return any((self.depends_on_outputs(n, scenario) for n in self.graph.predecessors(node)))
 
-    def identification(self, base_test_case):
+    def identification(self, base_test_case: BaseTestCase):
         """Identify and return the minimum adjustment set
 
         :param base_test_case: A base test case instance containing the outcome_variable and the

causal_testing/testing/estimators.py

@@ -158,10 +158,10 @@ def _run_logistic_regression(self, data) -> RegressionResultsWrapper:
         model = smf.logit(formula=self.formula, data=data).fit(disp=0)
         return model
 
-    def estimate(self, data: pd.DataFrame, adjustment_config=None) -> RegressionResultsWrapper:
+    def estimate(self, data: pd.DataFrame, adjustment_config:dict = None) -> RegressionResultsWrapper:
         """add terms to the dataframe and estimate the outcome from the data
         :param data: A pandas dataframe containing execution data from the system-under-test.
-
+        :param adjustment_config: Dictionary containing the adjustment configuration of the adjustment set
         """
         if adjustment_config is None:
             adjustment_config = {}