feat: xgboost notebook (#246)

* feat: friday update

* feat: friday update

* feat: hpt notebook

* feat: hpt notebook

* feat: sklearn

* feat: sklearn

* feat: xgb training

* feat: xgb training

notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb

@@ -87,8 +87,9 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- Training using a single Python script.\n",
-        "- Training using a Python package."
+        "- Training using a Python package.\n",
+        "- Report accuracy when hyperparameter tuning.\n",
+        "- Create a `Vertex AI Model` resource."
       ]
     },
     {
@@ -519,7 +520,9 @@
         "\n",
         "1. Save the in-memory model to the local filesystem in pickle format (e.g., model.pkl).\n",
         "2. Create a Cloud Storage storage client.\n",
-        "3. Upload the pickle file as a blob to the specified Cloud Storage location using the Cloud Storage storage client."
+        "3. Upload the pickle file as a blob to the specified Cloud Storage location using the Cloud Storage storage client.\n",
+        "\n",
+        "*Note*: You can do hyperparameter tuning with a XGBoost model."
       ]
     },
     {
@@ -569,7 +572,7 @@
         "setup_cfg = \"[egg_info]\\n\\ntag_build =\\n\\ntag_date = 0\"\n",
         "! echo \"$setup_cfg\" > custom/setup.cfg\n",
         "\n",
-        "setup_py = \"import setuptools\\n\\nsetuptools.setup(\\n\\n    install_requires=[\\n\\n        'wget',\\n\\n    ],\\n\\n    packages=setuptools.find_packages())\"\n",
+        "setup_py = \"import setuptools\\n\\nsetuptools.setup(\\n\\n    install_requires=[\\n\\n        'wget',\\n\\n        'cloudml-hypertune',\\n\\n    ],\\n\\n    packages=setuptools.find_packages())\"\n",
         "! echo \"$setup_py\" > custom/setup.py\n",
         "\n",
         "pkg_info = \"Metadata-Version: 1.0\\n\\nName: News Aggregation text classification\\n\\nVersion: 0.0.0\\n\\nSummary: Demostration training script\\n\\nHome-page: www.google.com\\n\\nAuthor: Google\\n\\nAuthor-email: aferlitsch@google.com\\n\\nLicense: Public\\n\\nDescription: Demo\\n\\nPlatform: Vertex\"\n",
@@ -600,6 +603,9 @@
         "    - Builds the corresponding model architecture.\n",
         "- Training (`train_model()`):\n",
         "    - Trains the model\n",
+        "- Evaluation (`evaluate_model()`):\n",
+        "    - Evaluates the model.\n",
+        "    - If hyperparameter tuning, reports the metric for accuracy.\n",
         "- Model artifact saving\n",
         "    - Saves the model artifacts and evaluation metrics where the Cloud Storage location specified by `model-dir`."
       ]
@@ -627,6 +633,7 @@
         "from sklearn.model_selection import train_test_split\n",
         "from sklearn.naive_bayes import MultinomialNB\n",
         "from sklearn.pipeline import Pipeline\n",
+        "import hypertune\n",
         "\n",
         "parser = argparse.ArgumentParser()\n",
         "parser.add_argument('--model-dir', dest='model_dir',\n",
@@ -682,6 +689,13 @@
         ") -> float:\n",
         "    score = model.score(X_test, y_test)\n",
         "    logging.info(f\"Evaluation completed with model score: {score}\")\n",
+        "\n",
+        "    # report metric for hyperparameter tuning\n",
+        "    hpt = hypertune.HyperTune()\n",
+        "    hpt.report_hyperparameter_tuning_metric(\n",
+        "        hyperparameter_metric_tag='accuracy',\n",
+        "        metric_value=score\n",
+        "    )\n",
         "    return score\n",
         "\n",
         "def export_model_to_gcs(fitted_pipeline: Pipeline, gcs_uri: str) -> str:\n",