support latest xgboost version (#599)

* support latest xgboost version

* Update test_classification.py

* Update 

Exists problems when installing xgb1.6.1 in py3.6

* cleanup

* xgboost version

* remove time_budget_s in test

* remove redundancy

* stop support of python 3.6

Co-authored-by: zsk <shaokunzhang529@gmail.com>
Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

notebook/automl_classification.ipynb

@@ -26,7 +26,7 @@
     "\n",
     "In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library.\n",
     "\n",
-    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` option:\n",
     "```bash\n",
     "pip install flaml[notebook]\n",
     "```"

notebook/automl_lightgbm.ipynb

@@ -27,7 +27,7 @@
     "\n",
     "In this notebook, we demonstrate how to use FLAML library to tune hyperparameters of LightGBM with a regression example.\n",
     "\n",
-    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` option:\n",
     "```bash\n",
     "pip install flaml[notebook]\n",
     "```"

notebook/automl_nlp.ipynb

@@ -26,7 +26,7 @@
     "\n",
     "In this notebook, we demonstrate how to use the FLAML library to fine tune an NLP language model with hyperparameter search. We have tested this notebook on a server with 4 NVidia V100 GPU (32GB) and 400GB CPU Ram.\n",
     "\n",
-    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `nlp,ray,notebook` and `blendsearch` option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `nlp,ray,notebook` and `blendsearch` option:\n",
     "```bash\n",
     "pip install flaml[nlp,ray,notebook,blendsearch];\n",
     "```"

notebook/automl_time_series_forecast.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "In this notebook, we demonstrate how to use FLAML library for time series forecasting tasks: univariate time series forecasting (only time), multivariate time series forecasting (with exogneous variables) and forecasting discrete values.\n",
     "\n",
-    "FLAML requires Python>=3.6. To run this notebook example, please install flaml with the notebook and forecast option:\n"
+    "FLAML requires Python>=3.7. To run this notebook example, please install flaml with the notebook and forecast option:\n"
    ]
   },
   {

notebook/automl_xgboost.ipynb

@@ -27,7 +27,7 @@
     "\n",
     "In this notebook, we demonstrate how to use FLAML library to tune hyperparameters of XGBoost with a regression example.\n",
     "\n",
-    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` option:\n",
     "```bash\n",
     "pip install flaml[notebook]\n",
     "```"

notebook/autovw.ipynb

@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n",
+    "Copyright (c) Microsoft Corporation. All rights reserved. \n",
     "\n",
     "Licensed under the MIT License.\n",
     "\n",
@@ -22,7 +22,7 @@
     "\n",
     "*ChaCha for online AutoML. Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. To appear in ICML 2021.*\n",
     "\n",
-    "AutoVW is implemented in FLAML. FLAML requires `Python>=3.6`. To run this notebook example, please install:"
+    "AutoVW is implemented in FLAML. FLAML requires `Python>=3.7`. To run this notebook example, please install:"
    ]
   },
   {

notebook/integrate_azureml.ipynb

@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n",
+    "Copyright (c) Microsoft Corporation. All rights reserved. \n",
     "\n",
     "Licensed under the MIT License.\n",
     "\n",
@@ -27,7 +27,7 @@
     "\n",
     "In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library together with AzureML.\n",
     "\n",
-    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the [azureml] option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [azureml] option:\n",
     "```bash\n",
     "pip install flaml[azureml]\n",
     "```"

notebook/integrate_sklearn.ipynb

@@ -39,7 +39,7 @@
     "\n",
     "In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library.\n",
     "\n",
-    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` option:\n",
     "```bash\n",
     "pip install flaml[notebook]\n",
     "```"

notebook/research/acl2021.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Copyright (c) 2020-2021. All rights reserved.\n",
+    "Copyright (c). All rights reserved.\n",
     "\n",
     "Licensed under the MIT License.\n",
     "\n",
@@ -22,7 +22,7 @@
     "\n",
     "*Running this notebook takes about one hour.\n",
     "\n",
-    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` and `nlp` options:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `notebook` and `nlp` options:\n",
     "\n",
     "```bash\n",
     "pip install flaml[nlp]==0.7.1 # in higher version of flaml, the API for nlp tasks changed\n",
@@ -364,10 +364,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[2m\u001B[36m(pid=50964)\u001B[0m {'eval_loss': 0.5942569971084595, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10434782608695652}\n",
-      "\u001B[2m\u001B[36m(pid=50964)\u001B[0m {'eval_loss': 0.5942569971084595, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10434782608695652}\n",
-      "\u001B[2m\u001B[36m(pid=50948)\u001B[0m {'eval_loss': 0.649192214012146, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.2}\n",
-      "\u001B[2m\u001B[36m(pid=50948)\u001B[0m {'eval_loss': 0.649192214012146, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.2}\n"
+      "\u001b[2m\u001b[36m(pid=50964)\u001b[0m {'eval_loss': 0.5942569971084595, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10434782608695652}\n",
+      "\u001b[2m\u001b[36m(pid=50964)\u001b[0m {'eval_loss': 0.5942569971084595, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10434782608695652}\n",
+      "\u001b[2m\u001b[36m(pid=50948)\u001b[0m {'eval_loss': 0.649192214012146, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.2}\n",
+      "\u001b[2m\u001b[36m(pid=50948)\u001b[0m {'eval_loss': 0.649192214012146, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.2}\n"
      ]
     },
     {
@@ -485,12 +485,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[2m\u001B[36m(pid=54411)\u001B[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=54411)\u001B[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=54411)\u001B[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=54417)\u001B[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=54417)\u001B[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=54417)\u001B[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n"
+      "\u001b[2m\u001b[36m(pid=54411)\u001b[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=54411)\u001b[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=54411)\u001b[0m {'eval_loss': 0.624100387096405, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=54417)\u001b[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=54417)\u001b[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=54417)\u001b[0m {'eval_loss': 0.5938675999641418, 'eval_accuracy': 0.7156862745098039, 'eval_f1': 0.8258258258258258, 'epoch': 0.5}\n"
      ]
     },
     {
@@ -590,18 +590,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[2m\u001B[36m(pid=57835)\u001B[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
-      "\u001B[2m\u001B[36m(pid=57835)\u001B[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
-      "\u001B[2m\u001B[36m(pid=57835)\u001B[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
-      "\u001B[2m\u001B[36m(pid=57835)\u001B[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
-      "\u001B[2m\u001B[36m(pid=57836)\u001B[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
-      "\u001B[2m\u001B[36m(pid=57836)\u001B[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
-      "\u001B[2m\u001B[36m(pid=57836)\u001B[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
-      "\u001B[2m\u001B[36m(pid=57836)\u001B[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
-      "\u001B[2m\u001B[36m(pid=57839)\u001B[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=57839)\u001B[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=57839)\u001B[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=57839)\u001B[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n"
+      "\u001b[2m\u001b[36m(pid=57835)\u001b[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
+      "\u001b[2m\u001b[36m(pid=57835)\u001b[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
+      "\u001b[2m\u001b[36m(pid=57835)\u001b[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
+      "\u001b[2m\u001b[36m(pid=57835)\u001b[0m {'eval_loss': 0.5822290778160095, 'eval_accuracy': 0.7058823529411765, 'eval_f1': 0.8181818181818181, 'epoch': 0.5043478260869565}\n",
+      "\u001b[2m\u001b[36m(pid=57836)\u001b[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
+      "\u001b[2m\u001b[36m(pid=57836)\u001b[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
+      "\u001b[2m\u001b[36m(pid=57836)\u001b[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
+      "\u001b[2m\u001b[36m(pid=57836)\u001b[0m {'eval_loss': 0.6087244749069214, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.10344827586206896}\n",
+      "\u001b[2m\u001b[36m(pid=57839)\u001b[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=57839)\u001b[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=57839)\u001b[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=57839)\u001b[0m {'eval_loss': 0.5486209392547607, 'eval_accuracy': 0.7034313725490197, 'eval_f1': 0.8141321044546851, 'epoch': 0.5}\n"
      ]
     },
     {
@@ -701,21 +701,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=61251)\u001B[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
-      "\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
-      "\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
-      "\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
-      "\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
-      "\u001B[2m\u001B[36m(pid=61255)\u001B[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
-      "\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
-      "\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
-      "\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
-      "\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
-      "\u001B[2m\u001B[36m(pid=61236)\u001B[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n"
+      "\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=61251)\u001b[0m {'eval_loss': 0.6236899495124817, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.5}\n",
+      "\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
+      "\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
+      "\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
+      "\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
+      "\u001b[2m\u001b[36m(pid=61255)\u001b[0m {'eval_loss': 0.6249027848243713, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.3}\n",
+      "\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
+      "\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
+      "\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
+      "\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n",
+      "\u001b[2m\u001b[36m(pid=61236)\u001b[0m {'eval_loss': 0.6138392686843872, 'eval_accuracy': 0.6838235294117647, 'eval_f1': 0.8122270742358079, 'epoch': 0.20689655172413793}\n"
      ]
     },
     {
@@ -806,4 +806,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

notebook/zeroshot_lightgbm.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "In this notebook, we demonstrate a basic use case of zero-shot AutoML with FLAML.\n",
     "\n",
-    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml and openml:"
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml and openml:"
    ]
   },
   {