Repository: mckinsey/causalnex (language: python)

Issue:
Release 0.9.2
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## Motivation and Context
Why was this PR created?

## How has this been tested?
What testing strategies have you used?

## Checklist

- [ ] Read the [contributing](/CONTRIBUTING.md) guidelines
- [ ] Opened this PR as a 'Draft Pull Request' if it is work-in-progress
- [ ] Updated the documentation to reflect the code changes
- [ ] Added a description of this change and added my name to the list of supporting contributions in the [`RELEASE.md`](/RELEASE.md) file
- [ ] Added tests to cover my changes
- [ ] Assigned myself to the PR



Discussion / hints:

Fix patch:
diff --git a/causalnex/__init__.py b/causalnex/__init__.py
index a5cd814..9ab44d7 100644
--- a/causalnex/__init__.py
+++ b/causalnex/__init__.py
@@ -30,6 +30,6 @@
 causalnex toolkit for causal reasoning (Bayesian Networks / Inference)
 """
 
-__version__ = "0.9.1"
+__version__ = "0.9.2"
 
 __all__ = ["structure", "discretiser", "evaluation", "inference", "network", "plots"]


Test patch:
diff --git a/tests/structure/test_sklearn.py b/tests/structure/test_sklearn.py
index d7485fe..21790c3 100644
--- a/tests/structure/test_sklearn.py
+++ b/tests/structure/test_sklearn.py
@@ -35,11 +35,9 @@
 from IPython.display import Image
 from matplotlib.axes import Axes
 from matplotlib.figure import Figure
-from sklearn.datasets import load_diabetes
 from sklearn.exceptions import NotFittedError
 from sklearn.gaussian_process.kernels import RBF
 from sklearn.model_selection import KFold, cross_val_score
-from sklearn.utils import Bunch
 
 from causalnex.structure import data_generators as dg
 from causalnex.structure.pytorch import DAGClassifier, DAGRegressor
@@ -489,28 +487,3 @@ def test_independent_predictions(hidden_layer_units):
     assert np.isclose(pred_alone[0], pred_joint0[0])
     assert np.isclose(pred_alone[0], pred_joint1[0])
     assert np.isclose(pred_joint0[0], pred_joint1[0])
-
-
-def test_tabu_regressor():
-    torch.manual_seed(42)
-    data: Bunch = load_diabetes()
-    X = data.data  # pylint: disable=no-member
-    y = data.target  # pylint: disable=no-member
-    names = data["feature_names"]
-
-    reg = DAGRegressor(
-        threshold=0.0,
-        alpha=0.0001,
-        beta=0.2,
-        hidden_layer_units=[2],
-        standardize=True,
-        enforce_dag=True,
-        tabu_child_nodes=["age", "sex", "bmi"],
-    )
-
-    _ = cross_val_score(reg, X, y, cv=KFold(n_splits=3, shuffle=True, random_state=42))
-
-    X = pd.DataFrame(X, columns=names)
-    y = pd.Series(y, name="DPROG")
-    reg.fit(X, y)
-    reg.plot_dag(enforce_dag=True)