black formatting

Author: christopher-wild

causal_testing/generation/abstract_causal_test_case.py

@@ -1,4 +1,5 @@
 """This module contains the class AbstractCausalTestCase, which generates concrete test cases"""
+
 import itertools
 import logging
 from enum import Enum

causal_testing/json_front/json_class.py

@@ -108,9 +108,9 @@ def _create_abstract_test_case(self, test, mutates, effects):
                 self.scenario.variables[variable]: effects[effect]
                 for variable, effect in test["expected_effect"].items()
             },
-            effect_modifiers={self.scenario.variables[v] for v in test["effect_modifiers"]}
-            if "effect_modifiers" in test
-            else {},
+            effect_modifiers=(
+                {self.scenario.variables[v] for v in test["effect_modifiers"]} if "effect_modifiers" in test else {}
+            ),
             estimate_type=test["estimate_type"],
             effect=test.get("effect", "total"),
         )

causal_testing/specification/metamorphic_relation.py

@@ -73,9 +73,11 @@ def generate_follow_up(self, n_tests: int, min_val: float, max_val: float, seed:
                 source_follow_up_test_inputs[[follow_up_input]]
                 .rename(columns={follow_up_input: self.treatment_var})
                 .to_dict(orient="records"),
-                test_inputs.to_dict(orient="records")
-                if not test_inputs.empty
-                else [{}] * len(source_follow_up_test_inputs),
+                (
+                    test_inputs.to_dict(orient="records")
+                    if not test_inputs.empty
+                    else [{}] * len(source_follow_up_test_inputs)
+                ),
             )
         ]
 

causal_testing/specification/scenario.py

@@ -1,4 +1,5 @@
 """This module holds the Scenario Class"""
+
 from collections.abc import Iterable, Mapping
 
 from tabulate import tabulate

causal_testing/surrogate/causal_surrogate_assisted.py

@@ -19,7 +19,7 @@ class SimulationResult:
     relationship: str
 
 
-class SearchAlgorithm(ABC): # pylint: disable=too-few-public-methods
+class SearchAlgorithm(ABC):  # pylint: disable=too-few-public-methods
     """Class to be inherited with the search algorithm consisting of a search function and the fitness function of the
     space to be searched"""
 

causal_testing/surrogate/surrogate_search_algorithms.py

@@ -1,4 +1,5 @@
 """Module containing implementation of search algorithm for surrogate search """
+
 # Fitness functions are required to be iteratively defined, including all variables within.
 
 from operator import itemgetter
@@ -26,7 +27,7 @@ def __init__(self, delta=0.05, config: dict = None) -> None:
 
     # pylint: disable=too-many-locals
     def search(
-            self, surrogate_models: list[CubicSplineRegressionEstimator], specification: CausalSpecification
+        self, surrogate_models: list[CubicSplineRegressionEstimator], specification: CausalSpecification
     ) -> list:
         solutions = []
 
@@ -47,7 +48,8 @@ def fitness_function(ga, solution, idx):  # pylint: disable=unused-argument
                 ate = surrogate.estimate_ate_calculated(adjustment_dict)
                 if len(ate) > 1:
                     raise ValueError(
-                        "Multiple ate values provided but currently only single values supported in this method")
+                        "Multiple ate values provided but currently only single values supported in this method"
+                    )
                 return contradiction_function(ate[0])
 
             gene_types, gene_space = self.create_gene_types(surrogate, specification)
@@ -84,7 +86,7 @@ def fitness_function(ga, solution, idx):  # pylint: disable=unused-argument
 
     @staticmethod
     def create_gene_types(
-            surrogate_model: CubicSplineRegressionEstimator, specification: CausalSpecification
+        surrogate_model: CubicSplineRegressionEstimator, specification: CausalSpecification
     ) -> tuple[list, list]:
         """Generate the gene_types and gene_space for a given fitness function and specification
         :param surrogate_model: Instance of a CubicSplineRegressionEstimator

causal_testing/testing/base_test_case.py

@@ -1,4 +1,5 @@
 """This module contains the BaseTestCase dataclass, which stores the information required for identification"""
+
 from dataclasses import dataclass
 from causal_testing.specification.variable import Variable
 from causal_testing.testing.effect import Effect

causal_testing/testing/causal_test_adequacy.py

@@ -1,6 +1,7 @@
 """
 This module contains code to measure various aspects of causal test adequacy.
 """
+
 from itertools import combinations
 from copy import deepcopy
 import pandas as pd

causal_testing/testing/causal_test_case.py

@@ -1,4 +1,5 @@
 """This module contains the CausalTestCase class, a class that holds the information required for a causal test"""
+
 import logging
 from typing import Any
 import numpy as np

causal_testing/testing/causal_test_outcome.py

@@ -29,10 +29,12 @@ class SomeEffect(CausalTestOutcome):
     def apply(self, res: CausalTestResult) -> bool:
         if res.test_value.type == "risk_ratio":
             return any(
-                1 < ci_low < ci_high or ci_low < ci_high < 1 for ci_low, ci_high in zip(res.ci_low(), res.ci_high()))
-        if res.test_value.type in ('coefficient', 'ate'):
+                1 < ci_low < ci_high or ci_low < ci_high < 1 for ci_low, ci_high in zip(res.ci_low(), res.ci_high())
+            )
+        if res.test_value.type in ("coefficient", "ate"):
             return any(
-                0 < ci_low < ci_high or ci_low < ci_high < 0 for ci_low, ci_high in zip(res.ci_low(), res.ci_high()))
+                0 < ci_low < ci_high or ci_low < ci_high < 0 for ci_low, ci_high in zip(res.ci_low(), res.ci_high())
+            )
 
         raise ValueError(f"Test Value type {res.test_value.type} is not valid for this TestOutcome")
 
@@ -51,17 +53,19 @@ def __init__(self, atol: float = 1e-10, ctol: float = 0.05):
 
     def apply(self, res: CausalTestResult) -> bool:
         if res.test_value.type == "risk_ratio":
-            return any(ci_low < 1 < ci_high or np.isclose(value, 1.0, atol=self.atol) for ci_low, ci_high, value in
-                       zip(res.ci_low(), res.ci_high(), res.test_value.value))
-        if res.test_value.type in ('coefficient', 'ate'):
+            return any(
+                ci_low < 1 < ci_high or np.isclose(value, 1.0, atol=self.atol)
+                for ci_low, ci_high, value in zip(res.ci_low(), res.ci_high(), res.test_value.value)
+            )
+        if res.test_value.type in ("coefficient", "ate"):
             value = res.test_value.value if isinstance(res.ci_high(), Iterable) else [res.test_value.value]
             return (
-                    sum(
-                        not ((ci_low < 0 < ci_high) or abs(v) < self.atol)
-                        for ci_low, ci_high, v in zip(res.ci_low(), res.ci_high(), value)
-                    )
-                    / len(value)
-                    < self.ctol
+                sum(
+                    not ((ci_low < 0 < ci_high) or abs(v) < self.atol)
+                    for ci_low, ci_high, v in zip(res.ci_low(), res.ci_high(), value)
+                )
+                / len(value)
+                < self.ctol
             )
 
         raise ValueError(f"Test Value type {res.test_value.type} is not valid for this TestOutcome")