add ability to specify development options in the EBM benchmarking notebook

Author: paulbkoch

docs/benchmarks/ebm-benchmark.ipynb

@@ -206,6 +206,7 @@
     "    max_samples = None\n",
     "    n_calibration_folds = 4  # 4 uses all cores on the containers\n",
     "\n",
+    "    from interpret.develop import set_option\n",
     "    from interpret.glassbox import ExplainableBoostingClassifier, ExplainableBoostingRegressor\n",
     "    from xgboost import XGBClassifier, XGBRegressor, XGBRFClassifier, XGBRFRegressor\n",
     "    from lightgbm import LGBMClassifier, LGBMRegressor\n",
@@ -348,8 +349,20 @@
     "    # Specify method\n",
     "    if trial.task.problem in [\"binary\", \"multiclass\"]:\n",
     "        if trial.method == \"ebm\" or trial.method == \"ebm_opt\" and trial.task.name in ebm_classification_non_opt:\n",
+    "            for param, val in ebm_params.copy().items():\n",
+    "                try:\n",
+    "                    set_option(param, val)\n",
+    "                    del ebm_params[param]\n",
+    "                except:\n",
+    "                    pass\n",
     "            est = ExplainableBoostingClassifier(**ebm_params)\n",
     "        elif trial.method == \"ebm_opt\":\n",
+    "            for param, val in ebm_params.copy().items():\n",
+    "                try:\n",
+    "                    set_option(param, val)\n",
+    "                    del ebm_params[param]\n",
+    "                except:\n",
+    "                    pass\n",
     "            # TODO: change these optimization parameters\n",
     "            param_grid = {\n",
     "                'smoothing_rounds': optuna.distributions.IntDistribution(1, 4000, log=True),\n",
@@ -513,8 +526,20 @@
     "            raise Exception(f\"Unrecognized classification method name {trial.method}\")\n",
     "    elif trial.task.problem == \"regression\":\n",
     "        if trial.method == \"ebm\":\n",
+    "            for param, val in ebm_params.copy().items():\n",
+    "                try:\n",
+    "                    set_option(param, val)\n",
+    "                    del ebm_params[param]\n",
+    "                except:\n",
+    "                    pass\n",
     "            est = ExplainableBoostingRegressor(**ebm_params)\n",
     "        elif trial.method == \"ebm_opt\":\n",
+    "            for param, val in ebm_params.copy().items():\n",
+    "                try:\n",
+    "                    set_option(param, val)\n",
+    "                    del ebm_params[param]\n",
+    "                except:\n",
+    "                    pass\n",
     "            if trial.task.name in {\"Allstate_Claims_Severity\"}:\n",
     "                # TODO: tweak\n",
     "                max_samples = 5000  # crashes or fit time too long without subsampling\n",