Update TabRepo_Reproducibility.ipynb (#91)

examples/TabRepo_Reproducibility.ipynb

@@ -655,10 +655,10 @@
         "config = \"CatBoost_r1_BAG_L1\"\n",
         "config_key = config.rsplit(\"_BAG_L1\", 1)[0]\n",
         "config_type = config_hyperparameters[config_key][\"model_type\"]\n",
-        "config_hyperparameters = config_hyperparameters[config_key][\"hyperparameters\"]\n",
+        "conf_hyperparameters = config_hyperparameters[config_key][\"hyperparameters\"]\n",
         "print(f\"Config: {config}\\n\"\n",
         "      f\"\\tType           : {config_type}\\n\"\n",
-        "      f\"\\tHyperparameters: {config_hyperparameters}\")\n",
+        "      f\"\\tHyperparameters: {conf_hyperparameters}\")\n",
         "\n",
         "metrics = repo.metrics(datasets=[\"sensory\", \"Moneyball\"], configs=[\"CatBoost_r1_BAG_L1\", \"LightGBM_r41_BAG_L1\"])\n",
         "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None, \"display.width\", 1000):\n",
@@ -676,7 +676,7 @@
         "y_val = repo.labels_val(dataset=dataset, fold=0)\n",
         "print(f\"Ground Truth Val (dataset={dataset}, fold=0):\\n{y_val[:10]}\")\n",
         "\n",
-        "df_ranks, df_ensemble_weights = repo.evaluate_ensemble(datasets=[dataset], configs=configs, ensemble_size=100)\n",
+        "df_ranks, df_ensemble_weights = repo.evaluate_ensembles(datasets=[dataset], ensemble_size=100)\n",
         "print(f\"Ensemble rank per task:\\n{df_ranks}\")\n",
         "\n",
         "df_ensemble_weights_mean_sorted = df_ensemble_weights.mean(axis=0).sort_values(ascending=False)\n",