LightGBM notebook update (#690)

* version update in notebook

* comment about optuna install

* monotone constraints

notebook/automl_lightgbm.ipynb

@@ -39,7 +39,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install flaml[notebook]==1.0.8"
+    "%pip install flaml[notebook]==1.0.10"
    ]
   },
   {
@@ -651,6 +651,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# uncomment the following line if optuna is not installed\n",
     "# %pip install optuna==2.8.0"
    ]
   },

website/docs/Use-Cases/Task-Oriented-AutoML.md

@@ -281,7 +281,9 @@ Some constraints on the estimator can be implemented via the custom learner. For
 class MonotonicXGBoostEstimator(XGBoostSklearnEstimator):
     @classmethod
     def search_space(**args):
-        return super().search_space(**args).update({"monotone_constraints": "(1, -1)"})
+        space = super().search_space(**args)
+        space.update({"monotone_constraints": {"domain": "(1, -1)"}})
+        return space
 ```
 
 It adds a monotonicity constraint to XGBoost. This approach can be used to set any constraint that is an argument in the underlying estimator's constructor.