3_model_training_deployment_yala.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. ML Modeling\n",
    "\n",
    "- In this notebook, we will illustrate how to train an XGBoost model with the diamonds dataset using the Snowpark ML Model API. \n",
    "- We also show how to do inference and deploy the model as a UDF.\n",
    "\n",
    "The Snowpark ML Model API currently supports sklearn, xgboost, and lightgbm models."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Snowpark for Python\n",
    "from snowflake.snowpark import Session\n",
    "from snowflake.snowpark.version import VERSION\n",
    "from snowflake.snowpark.functions import udf\n",
    "import snowflake.snowpark.functions as F\n",
    "\n",
    "# Snowpark ML\n",
    "from snowflake.ml.modeling.xgboost import XGBRegressor\n",
    "from snowflake.ml.modeling.model_selection import GridSearchCV\n",
    "from snowflake.ml.registry import model_registry\n",
    "from snowflake.ml._internal.utils import identifier\n",
    "\n",
    "# data science libs\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from snowflake.ml.modeling.metrics import mean_absolute_percentage_error\n",
    "\n",
    "# misc\n",
    "import json\n",
    "import joblib\n",
    "import cachetools\n",
    "\n",
    "# warning suppresion\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Establish a Secure Connection to Snowflake\n",
    "\n",
    "*Other connection options include Username/Password, MFA, OAuth, Okta, SSO. For more information, refer to the [Python Connector](https://docs.snowflake.com/en/developer-guide/python-connector/python-connector-example) documentation.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Connection Established with the following parameters:\n",
      "User                        : SIKHADAS\n",
      "Role                        : \"ACCOUNTADMIN\"\n",
      "Database                    : \"ML_HOL_DB\"\n",
      "Schema                      : \"ML_HOL_SCHEMA\"\n",
      "Warehouse                   : \"ML_HOL_WH\"\n",
      "Snowflake version           : 7.34.0\n",
      "Snowpark for Python version : 1.6.1\n"
     ]
    }
   ],
   "source": [
    "# Make a Snowpark Connection\n",
    "\n",
    "################################################################################################################\n",
    "#  You can also use the SnowSQL Client to configure your connection params:\n",
    "#  https://docs.snowflake.com/en/user-guide/snowsql-install-config.html\n",
    "#\n",
    "#  >>> from snowflake.ml.utils import connection_params\n",
    "#  >>> session = Session.builder.configs(connection_params.SnowflakeLoginOptions()\n",
    "#  >>> ).create()   \n",
    "#\n",
    "#  NOTE: If you have named connection params then specify the connection name\n",
    "#  Example:\n",
    "#  \n",
    "#  >>> session = Session.builder.configs(\n",
    "#  >>> connection_params.SnowflakeLoginOptions(connection_name='connections.snowml')\n",
    "#  >>> ).create()\n",
    "#\n",
    "#################################################################################################################\n",
    "\n",
    "# Edit the connection.json before creating the session object below\n",
    "# Create Snowflake Session object\n",
    "connection_parameters = json.load(open('connection.json'))\n",
    "session = Session.builder.configs(connection_parameters).create()\n",
    "session.sql_simplifier_enabled = True\n",
    "\n",
    "snowflake_environment = session.sql('SELECT current_user(), current_version()').collect()\n",
    "snowpark_version = VERSION\n",
    "\n",
    "# Current Environment Details\n",
    "print('\\nConnection Established with the following parameters:')\n",
    "print('User                        : {}'.format(snowflake_environment[0][0]))\n",
    "print('Role                        : {}'.format(session.get_current_role()))\n",
    "print('Database                    : {}'.format(session.get_current_database()))\n",
    "print('Schema                      : {}'.format(session.get_current_schema()))\n",
    "print('Warehouse                   : {}'.format(session.get_current_warehouse()))\n",
    "print('Snowflake version           : {}'.format(snowflake_environment[0][1]))\n",
    "print('Snowpark for Python version : {}.{}.{}'.format(snowpark_version[0],snowpark_version[1],snowpark_version[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the table name where we stored the diamonds dataset\n",
    "# **nChange this only if you named your table something else in the data ingest notebook **\n",
    "DEMO_TABLE = 'diamonds' \n",
    "input_tbl = f\"{session.get_current_database()}.{session.get_current_schema()}.{DEMO_TABLE}\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data & preprocessing pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"  |\"COLOR\"  |\"CLARITY\"  |\"DEPTH\"  |\"TABLE_PCT\"  |\"PRICE\"  |\"X\"   |\"Y\"   |\"Z\"   |\"CUT\"      |\n",
      "----------------------------------------------------------------------------------------------------\n",
      "|0.23     |E        |SI2        |61.5     |55.0         |326.0    |3.95  |3.98  |2.43  |IDEAL      |\n",
      "|0.21     |E        |SI1        |59.8     |61.0         |326.0    |3.89  |3.84  |2.31  |PREMIUM    |\n",
      "|0.23     |E        |VS1        |56.9     |65.0         |327.0    |4.05  |4.07  |2.31  |GOOD       |\n",
      "|0.29     |I        |VS2        |62.4     |58.0         |334.0    |4.2   |4.23  |2.63  |PREMIUM    |\n",
      "|0.31     |J        |SI2        |63.3     |58.0         |335.0    |4.34  |4.35  |2.75  |GOOD       |\n",
      "|0.24     |J        |VVS2       |62.8     |57.0         |336.0    |3.94  |3.96  |2.48  |VERY_GOOD  |\n",
      "|0.24     |I        |VVS1       |62.3     |57.0         |336.0    |3.95  |3.98  |2.47  |VERY_GOOD  |\n",
      "|0.26     |H        |SI1        |61.9     |55.0         |337.0    |4.07  |4.11  |2.53  |VERY_GOOD  |\n",
      "|0.22     |E        |VS2        |65.1     |61.0         |337.0    |3.87  |3.78  |2.49  |FAIR       |\n",
      "|0.23     |H        |VS1        |59.4     |61.0         |338.0    |4.0   |4.05  |2.39  |VERY_GOOD  |\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load in the data\n",
    "diamonds_df = session.table(input_tbl)\n",
    "diamonds_df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorize all the features for modeling\n",
    "CATEGORICAL_COLUMNS = [\"CUT\", \"COLOR\", \"CLARITY\"]\n",
    "CATEGORICAL_COLUMNS_OE = [\"CUT_OE\", \"COLOR_OE\", \"CLARITY_OE\"] # To name the ordinal encoded columns\n",
    "NUMERICAL_COLUMNS = [\"CARAT\", \"DEPTH\", \"TABLE_PCT\", \"X\", \"Y\", \"Z\"]\n",
    "\n",
    "LABEL_COLUMNS = ['PRICE']\n",
    "OUTPUT_COLUMNS = ['PREDICTED_PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the preprocessing pipeline object\n",
    "PIPELINE_FILE = 'preprocessing_pipeline.joblib'\n",
    "preprocessing_pipeline = joblib.load(PIPELINE_FILE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build a simple XGBoost Regression model\n",
    "\n",
    "What's happening here? \n",
    "\n",
    "- The `model.fit()` function actually creates a temporary stored procedure in the background. This also means that the model training is a single-node operation. Be sure to use a [Snowpark Optimized Warehouse](https://docs.snowflake.com/en/user-guide/warehouses-snowpark-optimized) if you need more memory. We are just using an XS Standard Virtual Warehouse here, which we created at the beginning of this quickstart.\n",
    "- The `model.predict()` function actualls creates a temporary vectorized UDF in the background, which means the input DataFrame is batched as Pandas DataFrames and inference is parallelized across the batches of data.\n",
    "\n",
    "You can check the query history once you execute the following cell to check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test sets\n",
    "diamonds_train_df, diamonds_test_df = diamonds_df.random_split(weights=[0.9, 0.1], seed=0)\n",
    "\n",
    "# Run the train and test sets through the Pipeline object we defined earlier\n",
    "train_df = preprocessing_pipeline.fit(diamonds_train_df).transform(diamonds_train_df)\n",
    "test_df = preprocessing_pipeline.transform(diamonds_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the XGBRegressor\n",
    "regressor = XGBRegressor(\n",
    "    input_cols=CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS,\n",
    "    label_cols=LABEL_COLUMNS,\n",
    "    output_cols=OUTPUT_COLUMNS\n",
    ")\n",
    "\n",
    "# Train\n",
    "regressor.fit(train_df)\n",
    "\n",
    "# Predict\n",
    "result = regressor.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUT_OE</th>\n",
       "      <th>COLOR_OE</th>\n",
       "      <th>CLARITY_OE</th>\n",
       "      <th>CARAT</th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>TABLE_PCT</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>PREDICTED_PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.075212</td>\n",
       "      <td>0.082390</td>\n",
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       "      <td>0.066893</td>\n",
       "      <td>0.077673</td>\n",
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       "      <td>0.006237</td>\n",
       "      <td>0.486111</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.368715</td>\n",
       "      <td>0.067402</td>\n",
       "      <td>0.075472</td>\n",
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       "      <td>0.071986</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5407</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.103950</td>\n",
       "      <td>0.505556</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.528864</td>\n",
       "      <td>0.097284</td>\n",
       "      <td>0.109748</td>\n",
       "      <td>2636.223389</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.452778</td>\n",
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       "      <td>0.099321</td>\n",
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       "      <td>0.095925</td>\n",
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       "    <tr>\n",
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       "      <td>0.097284</td>\n",
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       "<p>5412 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CUT_OE  COLOR_OE  CLARITY_OE     CARAT     DEPTH  TABLE_PCT         X  \\\n",
       "0        2.0       6.0         5.0  0.020790  0.547222   0.307692  0.391993   \n",
       "1        2.0       6.0         5.0  0.022869  0.455556   0.365385  0.408752   \n",
       "2        1.0       5.0         3.0  0.008316  0.541667   0.269231  0.369646   \n",
       "3        2.0       0.0         4.0  0.006237  0.486111   0.346154  0.368715   \n",
       "4        1.0       4.0         5.0  0.020790  0.552778   0.307692  0.398510   \n",
       "...      ...       ...         ...       ...       ...        ...       ...   \n",
       "5407     2.0       1.0         5.0  0.103950  0.505556   0.269231  0.528864   \n",
       "5408     1.0       5.0         3.0  0.116424  0.452778   0.365385  0.552142   \n",
       "5409     2.0       1.0         4.0  0.103950  0.550000   0.326923  0.520484   \n",
       "5410     0.0       3.0         3.0  0.106029  0.511111   0.250000  0.536313   \n",
       "5411     0.0       0.0         6.0  0.114345  0.533333   0.230769  0.542831   \n",
       "\n",
       "             Y         Z  PREDICTED_PRICE  \n",
       "0     0.072496  0.083648       322.593506  \n",
       "1     0.075212  0.082390       267.302460  \n",
       "2     0.066893  0.077673       385.477814  \n",
       "3     0.067402  0.075472       434.468170  \n",
       "4     0.071986  0.084277       566.351562  \n",
       "...        ...       ...              ...  \n",
       "5407  0.097284  0.109748      2636.223389  \n",
       "5408  0.099321  0.109748      2343.046143  \n",
       "5409  0.095925  0.111006      2690.643066  \n",
       "5410  0.097284  0.111006      3194.863525  \n",
       "5411  0.099660  0.114465      2852.860352  \n",
       "\n",
       "[5412 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just to illustrate, we can also pass in a Pandas DataFrame to Snowpark ML's model.predict()\n",
    "regressor.predict(test_df[CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS].to_pandas())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's analyze the results using Snowpark ML's MAPE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n",
      "|\"PRICE\"  |\"PREDICTED_PRICE\"  |\n",
      "-------------------------------\n",
      "|351.0    |322.593505859375   |\n",
      "|353.0    |267.3024597167969  |\n",
      "|355.0    |385.4778137207031  |\n",
      "|357.0    |434.4681701660156  |\n",
      "|554.0    |566.3515625        |\n",
      "|554.0    |492.4712219238281  |\n",
      "|2757.0   |2734.483642578125  |\n",
      "|2759.0   |2950.267578125     |\n",
      "|2759.0   |2802.850830078125  |\n",
      "|2762.0   |2944.725341796875  |\n",
      "-------------------------------\n",
      "\n",
      "Mean absolute percentage error: 0.07117102331735714\n"
     ]
    }
   ],
   "source": [
    "mape = mean_absolute_percentage_error(df=result, \n",
    "                                        y_true_col_names=\"PRICE\", \n",
    "                                        y_pred_col_names=\"PREDICTED_PRICE\")\n",
    "\n",
    "result.select(\"PRICE\", \"PREDICTED_PRICE\").show()\n",
    "print(f\"Mean absolute percentage error: {mape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot actual vs predicted \n",
    "g = sns.relplot(data=result[\"PRICE\", \"PREDICTED_PRICE\"].to_pandas().astype(\"float64\"), x=\"PRICE\", y=\"PREDICTED_PRICE\", kind=\"scatter\")\n",
    "g.ax.axline((0,0), slope=1, color=\"r\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, let's use Snowpark ML's `GridSearchCV()` function to find optimal model parameters\n",
    "\n",
    "*Note: This takes ~2.5 min to run.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<snowflake.ml.modeling.model_selection.grid_search_cv.GridSearchCV at 0x7f9063305d30>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search = GridSearchCV(\n",
    "    estimator=XGBRegressor(),\n",
    "    param_grid={\n",
    "        \"n_estimators\":[100, 200, 300, 400, 500],\n",
    "        \"learning_rate\":[0.1, 0.2, 0.3, 0.4, 0.5],\n",
    "    },\n",
    "    n_jobs = -1,\n",
    "    scoring=\"neg_mean_absolute_percentage_error\",\n",
    "    input_cols=CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS,\n",
    "    label_cols=LABEL_COLUMNS,\n",
    "    output_cols=OUTPUT_COLUMNS\n",
    ")\n",
    "\n",
    "# Train\n",
    "grid_search.fit(train_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the best estimator has the following parameters: `n_estimators=500` & `learning_rate=0.4`.\n",
    "\n",
    "We can use `to_sklearn()` in order to get the actual xgboost model object, which gives us access to all its attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=500, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.to_sklearn().best_estimator_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also analyze the grid search results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600.861x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze grid search results\n",
    "gs_results = grid_search.to_sklearn().cv_results_\n",
    "n_estimators_val = []\n",
    "learning_rate_val = []\n",
    "for param_dict in gs_results[\"params\"]:\n",
    "    n_estimators_val.append(param_dict[\"n_estimators\"])\n",
    "    learning_rate_val.append(param_dict[\"learning_rate\"])\n",
    "mape_val = gs_results[\"mean_test_score\"]*-1\n",
    "\n",
    "gs_results_df = pd.DataFrame(data={\n",
    "    \"n_estimators\":n_estimators_val,\n",
    "    \"learning_rate\":learning_rate_val,\n",
    "    \"mape\":mape_val})\n",
    "\n",
    "sns.relplot(data=gs_results_df, x=\"learning_rate\", y=\"mape\", hue=\"n_estimators\", kind=\"line\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is consistent with the `learning_rate=0.4` and `n_estimator=500` chosen as the best estimator with the lowest MAPE."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's predict and analyze the results from using the best estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------\n",
      "|\"PRICE\"  |\"PREDICTED_PRICE\"   |\n",
      "--------------------------------\n",
      "|351.0    |353.2740173339844   |\n",
      "|353.0    |343.84271240234375  |\n",
      "|355.0    |431.1207275390625   |\n",
      "|357.0    |355.054443359375    |\n",
      "|554.0    |560.3695068359375   |\n",
      "|554.0    |527.7815551757812   |\n",
      "|2757.0   |2796.111083984375   |\n",
      "|2759.0   |2878.782958984375   |\n",
      "|2759.0   |3021.80224609375    |\n",
      "|2762.0   |2838.24658203125    |\n",
      "--------------------------------\n",
      "\n",
      "Mean absolute percentage error: 0.06614988993792766\n"
     ]
    }
   ],
   "source": [
    "# Predict\n",
    "result = grid_search.predict(test_df)\n",
    "\n",
    "# Analyze results\n",
    "mape = mean_absolute_percentage_error(df=result, \n",
    "                                        y_true_col_names=\"PRICE\", \n",
    "                                        y_pred_col_names=\"PREDICTED_PRICE\")\n",
    "\n",
    "result.select(\"PRICE\", \"PREDICTED_PRICE\").show()\n",
    "print(f\"Mean absolute percentage error: {mape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot actual vs predicted \n",
    "g = sns.relplot(data=result[\"PRICE\", \"PREDICTED_PRICE\"].to_pandas().astype(\"float64\"), x=\"PRICE\", y=\"PREDICTED_PRICE\", kind=\"scatter\")\n",
    "g.ax.axline((0,0), slope=1, color=\"r\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model deployment using Model Registry"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, with Snowpark ML's model registry, we have a Snowflake native model versioning and deployment framework. This allows us to log models, tag parameters and metrics, track metadata, create versions, and ultimately deploy models into a Snowflake warehouse or Snowpark Container Service for batch scoring tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's save our optimal model first and its metadata\n",
    "optimal_model = grid_search.to_sklearn().best_estimator_\n",
    "optimal_n_estimators = grid_search.to_sklearn().best_estimator_.n_estimators\n",
    "optimal_learning_rate = grid_search.to_sklearn().best_estimator_.learning_rate\n",
    "\n",
    "optimal_mape = gs_results_df.loc[(gs_results_df['n_estimators']==optimal_n_estimators) &\n",
    "                                 (gs_results_df['learning_rate']==optimal_learning_rate), 'mape'].values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will log our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "create_model_registry() is in private preview since 0.2.0. Do not use it in production. \n",
      "WARNING:absl:The database ML_HOL_DB already exists. Skipping creation.\n",
      "WARNING:absl:The schema ML_HOL_DB.ML_HOL_SCHEMA already exists. Skipping creation.\n",
      "WARNING:snowflake.snowpark:ModelRegistry.log_model() is in private preview since 0.2.0. Do not use it in production. \n",
      "WARNING:snowflake.snowpark:ModelRegistry.list_models() is in private preview since 0.2.0. Do not use it in production. \n",
      "WARNING:snowflake.snowpark:ModelRegistry.set_metric() is in private preview since 0.2.0. Do not use it in production. \n",
      "WARNING:snowflake.snowpark:ModelRegistry.get_metrics() is in private preview since 0.2.0. Do not use it in production. \n"
     ]
    }
   ],
   "source": [
    "# Get sample input data to pass into the registry logging function\n",
    "X = train_df.select(CATEGORICAL_COLUMNS_OE+NUMERICAL_COLUMNS).limit(100)\n",
    "\n",
    "db = identifier._get_unescaped_name(session.get_current_database())\n",
    "schema = identifier._get_unescaped_name(session.get_current_schema())\n",
    "\n",
    "# Define model name and version\n",
    "model_name = \"diamonds_model\"\n",
    "model_version = 1\n",
    "\n",
    "# Create a registry and log the model\n",
    "registry = model_registry.ModelRegistry(session=session, database_name=db, schema_name=schema, create_if_not_exists=True)\n",
    "\n",
    "registry.log_model(\n",
    "    model_name=model_name,\n",
    "    model_version=model_version,\n",
    "    model=optimal_model,\n",
    "    sample_input_data=X,\n",
    "    options={\"embed_local_ml_library\": True, # This option is enabled to pull latest dev code changes.\n",
    "             \"relax\": True} # relax dependencies\n",
    ")\n",
    "\n",
    "# Add evaluation metric\n",
    "registry.set_metric(model_name=model_name, model_version=model_version, metric_name=\"mean_abs_pct_err\", metric_value=optimal_mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CREATION_CONTEXT</th>\n",
       "      <th>CREATION_ENVIRONMENT_SPEC</th>\n",
       "      <th>CREATION_ROLE</th>\n",
       "      <th>CREATION_TIME</th>\n",
       "      <th>ID</th>\n",
       "      <th>INPUT_SPEC</th>\n",
       "      <th>NAME</th>\n",
       "      <th>OUTPUT_SPEC</th>\n",
       "      <th>RUNTIME_ENVIRONMENT_SPEC</th>\n",
       "      <th>TRAINING_DATASET_ID</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>URI</th>\n",
       "      <th>VERSION</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>METRICS</th>\n",
       "      <th>TAGS</th>\n",
       "      <th>REGISTRATION_TIMESTAMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>{\\n  \"python\": \"3.9.0\"\\n}</td>\n",
       "      <td>\"ACCOUNTADMIN\"</td>\n",
       "      <td>2023-09-29 11:33:52.080000-07:00</td>\n",
       "      <td>ae690e085ef611ee95ec9e6c7f2f9bc3</td>\n",
       "      <td>None</td>\n",
       "      <td>diamonds_model</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>xgboost</td>\n",
       "      <td>sfc://ML_HOL_DB.ML_HOL_SCHEMA.SNOWML_MODEL_AE6...</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>{\\n  \"mean_abs_pct_err\": 0.16197244497750451\\n}</td>\n",
       "      <td>None</td>\n",
       "      <td>2023-09-29 11:33:54.462000-07:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CREATION_CONTEXT  CREATION_ENVIRONMENT_SPEC   CREATION_ROLE  \\\n",
       "0             None  {\\n  \"python\": \"3.9.0\"\\n}  \"ACCOUNTADMIN\"   \n",
       "\n",
       "                     CREATION_TIME                                ID  \\\n",
       "0 2023-09-29 11:33:52.080000-07:00  ae690e085ef611ee95ec9e6c7f2f9bc3   \n",
       "\n",
       "  INPUT_SPEC            NAME OUTPUT_SPEC RUNTIME_ENVIRONMENT_SPEC  \\\n",
       "0       None  diamonds_model        None                     None   \n",
       "\n",
       "  TRAINING_DATASET_ID     TYPE  \\\n",
       "0                None  xgboost   \n",
       "\n",
       "                                                 URI VERSION DESCRIPTION  \\\n",
       "0  sfc://ML_HOL_DB.ML_HOL_SCHEMA.SNOWML_MODEL_AE6...       1        None   \n",
       "\n",
       "                                           METRICS  TAGS  \\\n",
       "0  {\\n  \"mean_abs_pct_err\": 0.16197244497750451\\n}  None   \n",
       "\n",
       "            REGISTRATION_TIMESTAMP  \n",
       "0 2023-09-29 11:33:54.462000-07:00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's confirm it was added\n",
    "registry.list_models().to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we're ready to deploy to a Snowflake Warehouse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:snowflake.snowpark:ModelRegistry.deploy() is in private preview since 0.2.0. Do not use it in production. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated UDF file is persisted at: /var/folders/qs/p8635t1d7zlg1nvn502qb4g40000gn/T/tmpkl4bc928.py\n",
      "ML_HOL_DB.ML_HOL_SCHEMA.diamonds_model1_UDF is deployed to warehouse.\n"
     ]
    }
   ],
   "source": [
    "# Pick a deployment name and deploy\n",
    "model_deployment_name = model_name + f\"{model_version}\" + \"_UDF\"\n",
    "\n",
    "registry.deploy(model_name=model_name,\n",
    "                model_version=model_version,\n",
    "                deployment_name=model_deployment_name, \n",
    "                target_method=\"predict\", \n",
    "                permanent=True, \n",
    "                options={\"relax_version\": True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:snowflake.snowpark:ModelRegistry.list_deployments() is in private preview since 1.0.1. Do not use it in production. \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MODEL_NAME</th>\n",
       "      <th>MODEL_VERSION</th>\n",
       "      <th>DEPLOYMENT_NAME</th>\n",
       "      <th>CREATION_TIME</th>\n",
       "      <th>TARGET_METHOD</th>\n",
       "      <th>TARGET_PLATFORM</th>\n",
       "      <th>SIGNATURE</th>\n",
       "      <th>OPTIONS</th>\n",
       "      <th>STAGE_PATH</th>\n",
       "      <th>ROLE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>diamonds_model</td>\n",
       "      <td>1</td>\n",
       "      <td>diamonds_model1_UDF</td>\n",
       "      <td>2023-09-29 11:34:10.169000-07:00</td>\n",
       "      <td>predict</td>\n",
       "      <td>warehouse</td>\n",
       "      <td>{\\n  \"inputs\": [\\n    {\\n      \"name\": \"CUT_OE...</td>\n",
       "      <td>{\\n  \"permanent_udf_stage_location\": \"@ML_HOL_...</td>\n",
       "      <td>@ML_HOL_DB.ML_HOL_SCHEMA._SYSTEM_REGISTRY_DEPL...</td>\n",
       "      <td>\"ACCOUNTADMIN\"</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MODEL_NAME MODEL_VERSION      DEPLOYMENT_NAME  \\\n",
       "0  diamonds_model             1  diamonds_model1_UDF   \n",
       "\n",
       "                     CREATION_TIME TARGET_METHOD TARGET_PLATFORM  \\\n",
       "0 2023-09-29 11:34:10.169000-07:00       predict       warehouse   \n",
       "\n",
       "                                           SIGNATURE  \\\n",
       "0  {\\n  \"inputs\": [\\n    {\\n      \"name\": \"CUT_OE...   \n",
       "\n",
       "                                             OPTIONS  \\\n",
       "0  {\\n  \"permanent_udf_stage_location\": \"@ML_HOL_...   \n",
       "\n",
       "                                          STAGE_PATH            ROLE  \n",
       "0  @ML_HOL_DB.ML_HOL_SCHEMA._SYSTEM_REGISTRY_DEPL...  \"ACCOUNTADMIN\"  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's confirm it was added\n",
    "registry.list_deployments(model_name, model_version).to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the deployed model to perform inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:snowflake.snowpark:ModelReference.predict() is in private preview since 0.2.0. Do not use it in production. \n",
      "WARNING:snowflake.snowpark:ModelRegistry.get_deployment() is in private preview since 1.0.1. Do not use it in production. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|\"CARAT\"               |\"DEPTH\"              |\"TABLE_PCT\"          |\"X\"                  |\"Y\"                  |\"Z\"                  |\"CUT_OE\"  |\"COLOR_OE\"  |\"CLARITY_OE\"  |\"COLOR\"  |\"CLARITY\"  |\"PRICE\"  |\"CUT\"      |\"PREDICTED_PRICE\"   |\n",
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "|0.02079002079002079   |0.5472222222222223   |0.3076923076923076   |0.3919925512104283   |0.07249575551782682  |0.08364779874213837  |2.0       |6.0         |5.0           |J        |SI1        |351.0    |VERY_GOOD  |353.2740173339844   |\n",
      "|0.02286902286902287   |0.4555555555555555   |0.3653846153846153   |0.40875232774674114  |0.07521222410865874  |0.08238993710691825  |2.0       |6.0         |5.0           |J        |SI1        |353.0    |VERY_GOOD  |343.84271240234375  |\n",
      "|0.008316008316008316  |0.5416666666666665   |0.2692307692307693   |0.36964618249534453  |0.06689303904923599  |0.07767295597484278  |1.0       |5.0         |3.0           |I        |VS1        |355.0    |PREMIUM    |431.1207275390625   |\n",
      "|0.006237006237006237  |0.48611111111111094  |0.34615384615384615  |0.3687150837988827   |0.06740237691001698  |0.07547169811320754  |2.0       |0.0         |4.0           |D        |VS2        |357.0    |VERY_GOOD  |355.054443359375    |\n",
      "|0.02079002079002079   |0.5527777777777776   |0.3076923076923076   |0.3985102420856611   |0.07198641765704585  |0.08427672955974844  |1.0       |4.0         |5.0           |H        |SI1        |554.0    |PREMIUM    |560.3695068359375   |\n",
      "|0.008316008316008316  |0.4916666666666667   |0.28846153846153844  |0.3789571694599628   |0.06859083191850594  |0.07735849056603773  |1.0       |4.0         |2.0           |H        |VVS2       |554.0    |PREMIUM    |527.7815551757812   |\n",
      "|0.10395010395010396   |0.5166666666666666   |0.2500000000000001   |0.5307262569832403   |0.09626485568760612  |0.11006289308176101  |0.0       |3.0         |4.0           |G        |VS2        |2757.0   |IDEAL      |2796.111083984375   |\n",
      "|0.10602910602910603   |0.5388888888888888   |0.2692307692307693   |0.5288640595903166   |0.0972835314091681   |0.1119496855345912   |2.0       |1.0         |4.0           |E        |VS2        |2759.0   |VERY_GOOD  |2878.782958984375   |\n",
      "|0.158004158004158     |0.6472222222222221   |0.3653846153846153   |0.5837988826815642   |0.10101867572156198  |0.1279874213836478   |4.0       |2.0         |6.0           |F        |SI2        |2759.0   |FAIR       |3021.80224609375    |\n",
      "|0.11226611226611227   |0.536111111111111    |0.21153846153846156  |0.5400372439478585   |0.09898132427843803  |0.11383647798742139  |0.0       |1.0         |5.0           |E        |SI1        |2762.0   |IDEAL      |2838.24658203125    |\n",
      "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# We can always get a reference to our registry using this function call\n",
    "model_ref = model_registry.ModelReference(registry=registry, model_name=model_name, model_version=model_version)\n",
    "\n",
    "# We can then use the deployed model to perform inference\n",
    "result_sdf = model_ref.predict(deployment_name=model_deployment_name, data=test_df)\n",
    "result_sdf.rename(F.col('\"output_feature_0\"'),\"PREDICTED_PRICE\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do some clean up now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:snowflake.snowpark:ModelRegistry.delete_deployment() is in private preview since 1.0.1. Do not use it in production. \n",
      "WARNING:snowflake.snowpark:ModelRegistry.delete_model() is in private preview since 0.2.0. Do not use it in production. \n"
     ]
    }
   ],
   "source": [
    "# Clean up\n",
    "registry.delete_deployment(model_name=model_name, model_version=model_version, deployment_name=model_deployment_name)\n",
    "registry.delete_model(model_name=model_name, model_version=model_version, delete_artifact=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "Empty DataFrame\n",
       "Columns: [MODEL_NAME, MODEL_VERSION, DEPLOYMENT_NAME, CREATION_TIME, TARGET_METHOD, TARGET_PLATFORM, SIGNATURE, OPTIONS, STAGE_PATH, ROLE]\n",
       "Index: []"
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     },
     "execution_count": 24,
     "metadata": {},
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   "source": [
    "registry.list_deployments(model_name, model_version).to_pandas()"
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  {
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   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CREATION_CONTEXT</th>\n",
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       "      <th>CREATION_TIME</th>\n",
       "      <th>ID</th>\n",
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       "Columns: [CREATION_CONTEXT, CREATION_ENVIRONMENT_SPEC, CREATION_ROLE, CREATION_TIME, ID, INPUT_SPEC, NAME, OUTPUT_SPEC, RUNTIME_ENVIRONMENT_SPEC, TRAINING_DATASET_ID, TYPE, URI, VERSION, DESCRIPTION, METRICS, TAGS, REGISTRATION_TIMESTAMP]\n",
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "registry.list_models().to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "session.close()"
   ]
  }
 ],
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