Adjust format based on new formatter version

The new black formatter version (24.3.0) requires new changes, which are addressed in this commit.

Signed-off-by: Patrick Bloebaum <bloebp@amazon.com>

dowhy/causal_estimators/causalml.py

@@ -11,11 +11,9 @@
 
 
 class _CausalmlEstimator(Protocol):
-    def estimate_ate(self, *args, **kwargs):
-        ...
+    def estimate_ate(self, *args, **kwargs): ...
 
-    def fit_predict(self, *args, **kwargs):
-        ...
+    def fit_predict(self, *args, **kwargs): ...
 
 
 logger = logging.getLogger(__name__)

dowhy/causal_estimators/econml.py

@@ -13,20 +13,15 @@
 
 
 class _EconmlEstimator(Protocol):
-    def fit(self, *args, **kwargs):
-        ...
+    def fit(self, *args, **kwargs): ...
 
-    def effect(self, *args, **kwargs):
-        ...
+    def effect(self, *args, **kwargs): ...
 
-    def effect_interval(self, *args, **kwargs):
-        ...
+    def effect_interval(self, *args, **kwargs): ...
 
-    def effect_inference(self, *args, **kwargs):
-        ...
+    def effect_inference(self, *args, **kwargs): ...
 
-    def shap_values(self, *args, **kwargs):
-        ...
+    def shap_values(self, *args, **kwargs): ...
 
 
 class Econml(CausalEstimator):

dowhy/causal_graph.py

@@ -11,7 +11,6 @@
 
 
 class CausalGraph:
-
     """Class for creating and modifying the causal graph.
 
     Accepts a networkx DiGraph, a :py:class:`ProbabilisticCausalModel <dowhy.gcm.ProbabilisticCausalModel`, a graph string (or a text file) in gml format (preferred) or dot format. Graphviz-like attributes can be set for edges and nodes. E.g. style="dashed" as an edge attribute ensures that the edge is drawn with a dashed line.

dowhy/causal_identifier/identified_estimand.py

@@ -7,7 +7,6 @@
 
 
 class IdentifiedEstimand:
-
     """Class for storing a causal estimand, typically as a result of the identification step."""
 
     def __init__(

dowhy/causal_model.py

@@ -1,6 +1,7 @@
 """ Module containing the main model class for the dowhy package.
 
 """
+
 import logging
 import typing
 import warnings

dowhy/causal_refuter.py

@@ -20,7 +20,6 @@ class SignificanceTestType(Enum):
 
 
 class CausalRefuter:
-
     """Base class for different refutation methods.
 
     Subclasses implement specific refutations methods.

dowhy/causal_refuters/add_unobserved_common_cause.py

@@ -29,7 +29,6 @@
 
 
 class AddUnobservedCommonCause(CausalRefuter):
-
     """Add an unobserved confounder for refutation.
 
     AddUnobservedCommonCause class supports three methods:

dowhy/causal_refuters/overrule/ruleset.py

@@ -7,6 +7,7 @@
 Overlap in Observational Studies. In S. Chiappa & R. Calandra (Eds.), Proceedings of the Twenty Third International
 Conference on Artificial Intelligence and Statistics (Vol. 108, pp. 788–798). PMLR. https://arxiv.org/abs/1907.04138
 """
+
 from typing import Callable, Dict, List, Optional
 
 import numpy as np

dowhy/gcm/auto.py

@@ -157,7 +157,7 @@ def __str__(self):
                     % self._nodes[node]["model_performances"][0][2]
                 )
 
-                for (model, performance, metric_name) in self._nodes[node]["model_performances"]:
+                for model, performance, metric_name in self._nodes[node]["model_performances"]:
                     summary_strings.append("%s: %s" % (str(model()).replace("()", ""), str(performance)))
 
         summary_strings.append(
@@ -344,7 +344,7 @@ def assign_causal_mechanisms(
                 + ",N).",
             )
 
-        for (model, performance, metric_name) in model_performances:
+        for model, performance, metric_name in model_performances:
             auto_assignment_summary.add_model_performance(node, model, performance, metric_name)
 
     return auto_assignment_summary

dowhy/gcm/falsify.py

@@ -1,4 +1,5 @@
 """This module provides functionality to falsify a user-given DAG given observed data."""
+
 import warnings
 from dataclasses import dataclass, field
 from enum import Enum, auto

dowhy/gcm/feature_relevance.py

@@ -2,6 +2,7 @@
 be blackbox prediction models, it is also possible to explain causal mechanisms with respect to the direct parents.
 In these cases, it would be possible to incorporate the noise to represent the part of the generation process that
 cannot be explained by the parents."""
+
 from typing import Any, Callable, Dict, Optional, Tuple, Union
 
 import numpy as np
@@ -258,9 +259,9 @@ def single_sample_set_function(subset: np.ndarray) -> Union[np.ndarray, float]:
             baseline_samples=baseline_samples,
             baseline_feature_indices=np.arange(0, feature_samples.shape[1])[subset == 1],
             return_averaged_results=False,
-            feature_perturbation="randomize_columns_jointly"
-            if randomize_features_jointly
-            else "randomize_columns_independently",
+            feature_perturbation=(
+                "randomize_columns_jointly" if randomize_features_jointly else "randomize_columns_independently"
+            ),
             max_batch_size=max_batch_size,
         )
 

dowhy/gcm/independence_test/regression.py

@@ -1,6 +1,7 @@
 """ Regression based (conditional) independence test. Testing independence via regression, i.e. if a variable has
 information about another variable, then they are dependent.
 """
+
 from typing import Callable, List, Optional, Union
 
 import numpy as np

dowhy/gcm/influence.py

@@ -1,4 +1,5 @@
 """This module provides functions to estimate causal influences."""
+
 import logging
 import warnings
 from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast

dowhy/gcm/shapley.py

@@ -592,7 +592,7 @@ def parallel_job(input_subset: Tuple[int], parallel_random_seed: int) -> Union[f
     )
 
     subset_to_result_map = {}
-    for (subset, result) in zip(evaluation_subsets, subset_results):
+    for subset, result in zip(evaluation_subsets, subset_results):
         subset_to_result_map[subset] = result
 
     return subset_to_result_map

dowhy/gcm/validation.py

@@ -1,4 +1,5 @@
 """Contains a method to reject the causal graph and validate causal mechanisms such as post non-linear models."""
+
 from enum import Enum, auto
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 

dowhy/graph.py

@@ -1,4 +1,5 @@
 """This module defines the fundamental interfaces and functions related to causal graphs."""
+
 import itertools
 import logging
 import re

dowhy/interpreters/confounder_distribution_interpreter.py

@@ -64,7 +64,6 @@ def discrete_dist_plot(labels, not_treated_counts, treated_counts, ax, title, va
         ax.legend()
 
     def interpret(self, data: pd.DataFrame):
-
         """
         Shows distribution changes for confounding variables before and after applying inverse propensity weights.
         """

dowhy/utils/dgps/linear_dgp.py

@@ -63,18 +63,10 @@ def generate_data(self, sample_size):
     def generation_process(self):
         self.weights["confounder=>treatment"] = self.generate_weights((len(self.confounder), len(self.treatment)))
         self.weights["confounder=>treatment"][0,] = (
-            self.weights["confounder=>treatment"][
-                0,
-            ]
-            + 100
+            self.weights["confounder=>treatment"][0,] + 100
         )  # increasing weight of the first confounder
         self.weights["confounder=>outcome"] = self.generate_weights((len(self.confounder), len(self.outcome)))
-        self.weights["confounder=>outcome"][0,] = (
-            self.weights["confounder=>outcome"][
-                0,
-            ]
-            + 100
-        )
+        self.weights["confounder=>outcome"][0,] = self.weights["confounder=>outcome"][0,] + 100
         self.weights["effect_modifier=>outcome"] = self.generate_weights((len(self.effect_modifier), len(self.outcome)))
         self.weights["treatment=>outcome"] = self.generate_weights((len(self.treatment), len(self.outcome)))
 

dowhy/utils/graphviz_plotting.py

@@ -30,7 +30,7 @@ def plot_causal_graph_graphviz(
         layout_prog = "dot"
 
     max_strength = 0.0
-    for (source, target, strength) in causal_graph.edges(data="CAUSAL_STRENGTH", default=None):
+    for source, target, strength in causal_graph.edges(data="CAUSAL_STRENGTH", default=None):
         if (source, target) not in causal_strengths:
             causal_strengths[(source, target)] = strength
         if causal_strengths[(source, target)] is not None:
@@ -46,7 +46,7 @@ def plot_causal_graph_graphviz(
         else:
             pygraphviz_graph.add_node(node)
 
-    for (source, target) in causal_graph.edges():
+    for source, target in causal_graph.edges():
         causal_strength = causal_strengths[(source, target)]
         color = colors[(source, target)]
         if causal_strength is not None:

dowhy/utils/networkx_plotting.py

@@ -28,7 +28,7 @@ def plot_causal_graph_networkx(
         colors = deepcopy(colors)
 
     max_strength = 0.0
-    for (source, target, strength) in causal_graph.edges(data="CAUSAL_STRENGTH", default=None):
+    for source, target, strength in causal_graph.edges(data="CAUSAL_STRENGTH", default=None):
         if (source, target) not in causal_strengths:
             causal_strengths[(source, target)] = strength