Upper bound SciKit-Learn to address freeze in causal (#1432)

<!--- Provide a general summary of your changes in the Title above -->

## Description

Replaces #1429  to address #1430 . Causal analysis is getting stuck with the latest release of SciKit-Learn. This contains:

- Test case which gets stuck with SciKit-Learn 1.1.0
- Upper bound on SciKit-Learn in `requirements.txt`

## Checklist

<!--- Make sure to satisfy all the criteria listed below. -->

- [x] I have added screenshots above for all UI changes.
- [x] Documentation was updated if it was needed.
- [x] New tests were added or changes were manually verified.


Signed-off-by: Richard Edgar <[email protected]>

responsibleai/requirements.txt

@@ -7,7 +7,7 @@ lightgbm>=2.0.11
 numpy>=1.17.2
 numba<0.54.0
 pandas>=0.25.1
-scikit-learn>=0.22.1
+scikit-learn>=0.22.1,<1.1 # See PR 1429 about upper bound
 scipy>=1.4.1
 semver~=2.13.0
 

responsibleai/tests/causal/test_causal_general.py

@@ -0,0 +1,51 @@
+# Copyright (c) Microsoft Corporation
+# Licensed under the MIT License.
+
+
+from responsibleai import RAIInsights
+
+from ..causal_manager_validator import _check_causal_result
+from ..common_utils import create_adult_income_dataset
+
+
+def test_causal_classification_scikitlearn_issue():
+    # This test gets stuck on SciKit-Learn v1.1.0
+    # See PR #1429
+    data_train, data_test, _, _, categorical_features, \
+        _, target_name, classes = create_adult_income_dataset()
+
+    rai_i = RAIInsights(
+        model=None,
+        train=data_train,
+        test=data_test,
+        task_type='classification',
+        target_column=target_name,
+        categorical_features=categorical_features,
+        classes=classes
+    )
+    assert rai_i is not None
+
+    treatment_features = ["age", "gender"]
+    cat_expansion = 49
+    rai_i.causal.add(treatment_features=treatment_features,
+                     heterogeneity_features=["marital_status"],
+                     nuisance_model="automl",
+                     heterogeneity_model="forest",
+                     alpha=0.06,
+                     upper_bound_on_cat_expansion=cat_expansion,
+                     treatment_cost=[0.1, 0.2],
+                     min_tree_leaf_samples=2,
+                     skip_cat_limit_checks=False,
+                     categories="auto",
+                     n_jobs=1,
+                     verbose=1,
+                     random_state=100,
+                     )
+
+    rai_i.compute()
+
+    results = rai_i.causal.get()
+    assert results is not None
+    assert isinstance(results, list)
+    assert len(results) == 1
+    _check_causal_result(results[0])