ML Lineage Workflows/ML Lineage Workflows.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0bb54abc",
   "metadata": {},
   "source": [
    "- **Required Versions**:\n",
    "  - Snowflake: 8.27 or higher and Enterprise Edition or higher \n",
    "  - Snowflake ML Python: **1.6.0** or higher\n",
    "  - Snowpark Python: **1.21.0** or higher\n",
    "\n",
    "- **Last Updated**: 8/1/2024"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeae3429",
   "metadata": {},
   "source": [
    "# ML Lineage workflows {-}\n",
    "\n",
    "This notebook showcases various machine learning workflows, delving into the lineage of each process. It highlights essential features of Snowflake's ML, including [Snowflake Feature Store](https://docs.snowflake.com/en/developer-guide/snowpark-ml/feature-store/overview), [Dataset](https://docs.snowflake.com/en/developer-guide/snowpark-ml/dataset), ML Lineage, [Snowpark ML Modeling](https://docs.snowflake.com/en/developer-guide/snowpark-ml/modeling) and [Snowflake Model Registry](https://docs.snowflake.com/en/developer-guide/snowpark-ml/model-registry/overview). \n",
    "\n",
    "\n",
    "Note: there may be a delay in the availability of the newest snowflake-ml-python package in the Snowflake Conda channel. To install the latest snowflake-ml-python package which includes all of necessary components used in this notebook, please follow the install instructions [here](https://docs.snowflake.com/LIMITEDACCESS/snowpark-ml-library-update).\n",
    "\n",
    "**Table of contents**\n",
    "\n",
    "1. [Set up environment](#set-up-environment)   \n",
    "    1.1. [Connect to Snowflake](#connect-to-snowflake)  \n",
    "    1.2. [Select your example](#select-your-example)  \n",
    "\n",
    "2. [Feature View Lineage](#feature-view-lineage)  \n",
    "\n",
    "3. [Training Data Lineage](#training-data-lineage)  \n",
    "   3.1. [Training data from feature views](#training-data-from-feature-views)    \n",
    "       3.1.1. [Dataset as training data](#dataset-as-training-data)   \n",
    "       3.1.2. [Tables as training data](#tables-as-training-data)   \n",
    "   3.2. [Training data from source tables](#training-data-from-source-tables)  \n",
    "      3.2.1. [Dataset from source table as training data](#dataset-from-source-table-as-training-data)  \n",
    "\n",
    "4. [Model Lineage](#model-lineage)  \n",
    "   4.1. [Model trained in Snowflake ecosystem](#model-trained-in-snowflake-ecosystem)  \n",
    "      4.1.1. [Model trained using dataset](#model-trained-using-dataset)  \n",
    "      4.1.2. [Model trained using source tables](#model-trained-using-source-tables)  \n",
    "   4.2. [Model trained in non-Snowflake ecosystem](#model-trained-in-non-snowflake-ecosystem)\n",
    "\n",
    "5. [Visualization of lineage](#visualization-of-lineage)\n",
    "\n",
    "6. [Clean up notebook](#clean-up-notebook)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f16e6a8",
   "metadata": {},
   "source": [
    "## 1. Set up environment {-}\n",
    "<a id='set-up-environment'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb535ff6",
   "metadata": {},
   "source": [
    "### 1.1 Connect to Snowflake {-}\n",
    "\n",
    "Let's start with setting up our test environment. We will create a session and a schema. The schema `FS_DEMO_SCHEMA` will be used as the Feature Store. It will be cleaned up at the end of the demo. You need to fill the `connection_parameters` with your Snowflake connection information. Follow this **[guide](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-session)** for more details about how to connect to Snowflake.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9622928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<snowflake.snowpark.session.Session: account=\"ax_test_qa3\", role=\"ACCOUNTADMIN\", database=\"LINEAGE_DEMO_DB\", schema=\"PUBLIC\", warehouse=\"AX_XL\">\n"
     ]
    }
   ],
   "source": [
    "from snowflake.snowpark import Session, context, exceptions\n",
    "\n",
    "try:\n",
    "    # Retrieve active session if in Snowpark Notebook\n",
    "    session = context.get_active_session()\n",
    "except exceptions.SnowparkSessionException:\n",
    "    # ACTION REQUIRED: Need to manually configure Snowflake connection if using Jupyter\n",
    "    connection_parameters = {\n",
    "        \"account\": \"<your snowflake account>\",\n",
    "        \"user\": \"<your snowflake user>\",\n",
    "        \"password\": \"<your snowflake password>\",\n",
    "        \"role\": \"<your snowflake role>\",\n",
    "        \"warehouse\": \"<your snowflake warehouse>\",\n",
    "        \"database\": \"<your snowflake database>\",\n",
    "        \"schema\": \"<your snowflake schema>\",\n",
    "    }\n",
    "\n",
    "\n",
    "    session = Session.builder.configs(connection_parameters).create()\n",
    "    # Add a query tag to the session. This helps with troubleshooting and performance monitoring.\n",
    "    session.query_tag = {\"origin\":\"sf_sit-is\", \n",
    "                        \"name\":\"aiml_notebooks_lineage\", \n",
    "                        \"version\":{\"major\":1, \"minor\":0},\n",
    "                        \"attributes\":{\"is_quickstart\":1, \"source\":\"notebook\"}}\n",
    "    print(session)\n",
    "\n",
    "assert session.get_current_database() != None, \"Session must have a database for the demo.\"\n",
    "assert session.get_current_warehouse() != None, \"Session must have a warehouse for the demo.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f38d70d-ccc2-40b9-8020-50e3ad3ff165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(status='Schema MODEL_SCHEMA successfully created.')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The schema where Feature Store lives.\n",
    "FS_DEMO_SCHEMA = \"FEATURE_STORE\"\n",
    "# The schema model lives.\n",
    "MODEL_DEMO_SCHEMA = \"MODEL_SCHEMA\"\n",
    "\n",
    "# Make sure your role has CREATE SCHEMA privileges or USAGE privileges on the schema if it already exists.\n",
    "session.sql(f\"CREATE OR REPLACE SCHEMA {FS_DEMO_SCHEMA}\").collect()\n",
    "session.sql(f\"CREATE OR REPLACE SCHEMA {MODEL_DEMO_SCHEMA}\").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3f89f3-d84d-4226-830d-3a967499fed7",
   "metadata": {},
   "source": [
    "<a id='select-your-example'></a>\n",
    "\n",
    "### 1.2 Select your example {-}\n",
    "\n",
    "We have prepared some examples that you can find in our [open source repo](https://github.com/snowflakedb/snowflake-ml-python/tree/main/snowflake/ml/feature_store/examples). Each example contains the source dataset, feature view and entity definitions which will be used in this demo. `ExampleHelper` (included in snowflake-ml-python) will setup everything with simple APIs and you don't have to worry about the details.\n",
    "\n",
    "`load_example()` will load the source data into Snowflake tables. In the example below, we are using the “wine_quality_features” example. You can replace this with any example listed above. Execution of the cell below may take some time depending on the size of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2cbb04de-193c-44e1-b400-802e22eb6941",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.feature_store.examples.example_helper import ExampleHelper\n",
    "\n",
    "example_helper = ExampleHelper(session, session.get_current_database(), FS_DEMO_SCHEMA)\n",
    "source_tables = example_helper.load_example('wine_quality_features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5a73b0c-41dd-47f7-b7a1-1da492d23b5e",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WINE_ID</th>\n",
       "      <th>FIXED_ACIDITY</th>\n",
       "      <th>VOLATILE_ACIDITY</th>\n",
       "      <th>CITRIC_ACID</th>\n",
       "      <th>RESIDUAL_SUGAR</th>\n",
       "      <th>CHLORIDES</th>\n",
       "      <th>FREE_SULFUR_DIOXIDE</th>\n",
       "      <th>TOTAL_SULFUR_DIOXIDE</th>\n",
       "      <th>DENSITY</th>\n",
       "      <th>PH</th>\n",
       "      <th>SULPHATES</th>\n",
       "      <th>ALCOHOL</th>\n",
       "      <th>QUALITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25</td>\n",
       "      <td>67</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15</td>\n",
       "      <td>54</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17</td>\n",
       "      <td>60</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WINE_ID  FIXED_ACIDITY  VOLATILE_ACIDITY  CITRIC_ACID  RESIDUAL_SUGAR  \\\n",
       "0        1            7.4              0.70         0.00             1.9   \n",
       "1        2            7.8              0.88         0.00             2.6   \n",
       "2        3            7.8              0.76         0.04             2.3   \n",
       "3        4           11.2              0.28         0.56             1.9   \n",
       "4        5            7.4              0.70         0.00             1.9   \n",
       "\n",
       "   CHLORIDES  FREE_SULFUR_DIOXIDE  TOTAL_SULFUR_DIOXIDE  DENSITY    PH  \\\n",
       "0      0.076                   11                    34   0.9978  3.51   \n",
       "1      0.098                   25                    67   0.9968  3.20   \n",
       "2      0.092                   15                    54   0.9970  3.26   \n",
       "3      0.075                   17                    60   0.9980  3.16   \n",
       "4      0.076                   11                    34   0.9978  3.51   \n",
       "\n",
       "   SULPHATES  ALCOHOL  QUALITY  \n",
       "0       0.56      9.4        5  \n",
       "1       0.68      9.8        5  \n",
       "2       0.65      9.8        5  \n",
       "3       0.58      9.8        6  \n",
       "4       0.56      9.4        5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.table(source_tables[0]).limit(5).to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3038124f-123b-4d22-9058-4fe2e57af6fe",
   "metadata": {},
   "source": [
    "<a id='feature-view-lineage'></a>\n",
    "\n",
    "## 2. Feature View Lineage {-}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ece7a2b",
   "metadata": {},
   "source": [
    "Create a new feature store and register and entities and feature views. More details on feature store APIs can be found [here](https://docs.snowflake.com/en/developer-guide/snowpark-ml/feature-store/overview)). For the detailed workflow of feature store refer to the notebook [here](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/End-to-end%20ML%20with%20Feature%20Store%20and%20Model%20Registry/End-to-end%20ML%20with%20Feature%20Store%20and%20Model%20Registry.ipynb) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe850ccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------\n",
      "|\"NAME\"               |\"VERSION\"  |\"DESC\"                                              |\"REFRESH_FREQ\"  |\n",
      "---------------------------------------------------------------------------------------------------------\n",
      "|WINE_FEATURES        |1.0        |Managed feature view about wine quality which r...  |1 day           |\n",
      "|EXTRA_WINE_FEATURES  |1.0        |Static feature view about wine quality which ne...  |NULL            |\n",
      "---------------------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from snowflake.ml.feature_store import (\n",
    "    FeatureStore,\n",
    "    FeatureView,\n",
    "    Entity,\n",
    "    CreationMode\n",
    ")\n",
    "\n",
    "fs = FeatureStore(\n",
    "    session=session, \n",
    "    database=session.get_current_database(), \n",
    "    name=FS_DEMO_SCHEMA, \n",
    "    default_warehouse=session.get_current_warehouse(),\n",
    "    creation_mode=CreationMode.CREATE_IF_NOT_EXIST,\n",
    ")\n",
    "\n",
    "all_entities = []\n",
    "for e in example_helper.load_entities():\n",
    "    entity = fs.register_entity(e)\n",
    "    all_entities.append(entity)\n",
    "\n",
    "all_feature_views = []\n",
    "for fv in example_helper.load_draft_feature_views():\n",
    "    rf = fs.register_feature_view(\n",
    "        feature_view=fv,\n",
    "        version='1.0'\n",
    "    )\n",
    "    all_feature_views.append(rf)\n",
    "\n",
    "fs.list_feature_views().select('name', 'version', 'desc', 'refresh_freq').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3bbf57e",
   "metadata": {},
   "source": [
    "##### Query Lineage\n",
    "\n",
    "\n",
    "\n",
    "Query the upstream lineage of the feature views we just created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b9cec24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LineageNode.lineage() is in private preview since 1.5.3. Do not use it in production. \n",
      "Lineage.trace() is in private preview since 1.16.0. Do not use it in production. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upstream Lineage of feature view 'EXTRA_WINE_FEATURES'\n",
      "[LineageNode(\n",
      "  name='LINEAGE_DEMO_DB.FEATURE_STORE.WINEDATA',\n",
      "  version='None',\n",
      "  domain='table',\n",
      "  status='ACTIVE',\n",
      "  created_on='2024-08-01 22:44:14'\n",
      ")]\n",
      "Upstream Lineage of feature view 'WINE_FEATURES'\n",
      "[LineageNode(\n",
      "  name='LINEAGE_DEMO_DB.FEATURE_STORE.WINEDATA',\n",
      "  version='None',\n",
      "  domain='table',\n",
      "  status='ACTIVE',\n",
      "  created_on='2024-08-01 22:44:14'\n",
      ")]\n"
     ]
    }
   ],
   "source": [
    "for fv in all_feature_views:\n",
    "    print(\"Upstream Lineage of feature view '\" + fv.name + \"'\")\n",
    "    print(fv.lineage(direction='upstream'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc1a7dc",
   "metadata": {},
   "source": [
    "<a id='training-data-lineage'></a>\n",
    "\n",
    "## 3. Training Data Lineage  {-}\n",
    "\n",
    "Next step in ML workflows will be generating training data that is needed to train the model. There are 2 ways to generate training data. \n",
    "1. Using feature views.\n",
    "2. Using source tables directly.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c705ac1",
   "metadata": {},
   "source": [
    "<a id='training-data-from-feature-views'></a>\n",
    "\n",
    "### 3.1 Training Data from Feature views  {-}\n",
    "\n",
    "Lets explore the workflow of creating training data sets using the feature views. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec901a65-f3ee-4c12-b8ff-ec0f630e5372",
   "metadata": {},
   "source": [
    "Retrieve some metadata columns that are essential when generating training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6c80b76-99a4-4eb0-b0cb-583afa434ecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_cols = example_helper.get_label_cols()\n",
    "timestamp_col = example_helper.get_training_data_timestamp_col()\n",
    "excluded_cols = example_helper.get_excluded_cols()\n",
    "join_keys = [key for entity in all_entities for key in entity.join_keys]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f66ad1d-5ad1-4ad8-92f9-c65be491e0c3",
   "metadata": {},
   "source": [
    "Create a spine dataframe that's sampled from source table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ea00fe3a-ac16-45d7-95c0-7ea7ed344e79",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>QUALITY</th>\n",
       "      <th>WINE_ID</th>\n",
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       "      <th>507</th>\n",
       "      <td>6</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>6</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>5</td>\n",
       "      <td>1132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>5</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>5</td>\n",
       "      <td>1568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     QUALITY  WINE_ID\n",
       "0          6      544\n",
       "1          5      978\n",
       "2          5      679\n",
       "3          5     1459\n",
       "4          5        5\n",
       "..       ...      ...\n",
       "507        6      508\n",
       "508        6      624\n",
       "509        5     1132\n",
       "510        5      511\n",
       "511        5     1568\n",
       "\n",
       "[512 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_count = 512\n",
    "source_df = session.sql(f\"\"\"\n",
    "    select {','.join(label_cols)}, \n",
    "            {','.join(join_keys)} \n",
    "            {',' + timestamp_col if timestamp_col is not None else ''} \n",
    "    from {source_tables[0]}\"\"\")\n",
    "spine_df = source_df.sample(n=sample_count)\n",
    "# preview spine dataframe\n",
    "spine_df.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e69a065",
   "metadata": {},
   "source": [
    "<a id='dataset-as-training-data'></a>\n",
    "\n",
    "#### 3.1.1 Dataset as training data  {-}\n",
    "\n",
    "\n",
    "\n",
    "[Snowflake Dataset](https://docs.snowflake.com/en/developer-guide/snowpark-ml/dataset) generated from feature views created above. Dataset is a readonly objects helps in reproducability of the ML model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3a03f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dataset = fs.generate_dataset(\n",
    "    name=\"my_dataset\",\n",
    "    spine_df=spine_df, \n",
    "    features=all_feature_views,\n",
    "    version=\"4.0\",\n",
    "    spine_timestamp_col=timestamp_col,\n",
    "    spine_label_cols=label_cols,\n",
    "    exclude_columns=excluded_cols,\n",
    "    desc=\"This is the dataset joined spine dataframe with feature views\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac0d146",
   "metadata": {},
   "source": [
    "##### Query Lineage\n",
    "\n",
    "\n",
    "\n",
    "Query Upstream lineage of the dataset we just generated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5023adaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LineageNode(\n",
       "   name='LINEAGE_DEMO_DB.FEATURE_STORE.WINEDATA',\n",
       "   version='None',\n",
       "   domain='table',\n",
       "   status='ACTIVE',\n",
       "   created_on='2024-08-01 22:44:14'\n",
       " ),\n",
       " FeatureView(_name=EXTRA_WINE_FEATURES, _entities=[Entity(name=WINE, join_keys=['WINE_ID'], owner=None, desc=Wine ID column.)], _feature_df=<snowflake.snowpark.dataframe.DataFrame object at 0x1711bc3d0>, _timestamp_col=None, _desc=Static feature view about wine quality which never refresh., _infer_schema_df=<snowflake.snowpark.dataframe.DataFrame object at 0x1711fd910>, _query=SELECT \"WINE_ID\", \"SULPHATES\", \"ALCOHOL\" FROM \"LINEAGE_DEMO_DB\".FEATURE_STORE.winedata, _version=1.0, _status=FeatureViewStatus.STATIC, _feature_desc=OrderedDict([('SULPHATES', ''), ('ALCOHOL', '')]), _refresh_freq=None, _database=LINEAGE_DEMO_DB, _schema=FEATURE_STORE, _warehouse=None, _refresh_mode=None, _refresh_mode_reason=None, _owner=ACCOUNTADMIN, _lineage_node_name=LINEAGE_DEMO_DB.FEATURE_STORE.EXTRA_WINE_FEATURES, _lineage_node_domain=feature_view, _lineage_node_version=1.0, _lineage_node_status=None, _lineage_node_created_on=None, _session=<snowflake.snowpark.session.Session: account=\"ax_test_qa3\", role=\"ACCOUNTADMIN\", database=\"LINEAGE_DEMO_DB\", schema=\"MODEL_SCHEMA\", warehouse=\"AX_XL\">),\n",
       " FeatureView(_name=WINE_FEATURES, _entities=[Entity(name=WINE, join_keys=['WINE_ID'], owner=None, desc=Wine ID column.)], _feature_df=<snowflake.snowpark.dataframe.DataFrame object at 0x1711a1550>, _timestamp_col=None, _desc=Managed feature view about wine quality which refreshes everyday., _infer_schema_df=<snowflake.snowpark.dataframe.DataFrame object at 0x1711a12b0>, _query=SELECT \"WINE_ID\", \"FIXED_ACIDITY\", \"CITRIC_ACID\", \"CHLORIDES\", \"TOTAL_SULFUR_DIOXIDE\", \"PH\", (\"FIXED_ACIDITY\" * \"CITRIC_ACID\") AS \"MY_NEW_FEATURE\" FROM \"LINEAGE_DEMO_DB\".FEATURE_STORE.winedata, _version=1.0, _status=FeatureViewStatus.ACTIVE, _feature_desc=OrderedDict([('FIXED_ACIDITY', ''), ('CITRIC_ACID', ''), ('CHLORIDES', ''), ('TOTAL_SULFUR_DIOXIDE', ''), ('PH', ''), ('MY_NEW_FEATURE', '')]), _refresh_freq=1 day, _database=LINEAGE_DEMO_DB, _schema=FEATURE_STORE, _warehouse=AX_XL, _refresh_mode=INCREMENTAL, _refresh_mode_reason=None, _owner=ACCOUNTADMIN, _lineage_node_name=LINEAGE_DEMO_DB.FEATURE_STORE.WINE_FEATURES, _lineage_node_domain=feature_view, _lineage_node_version=1.0, _lineage_node_status=None, _lineage_node_created_on=None, _session=<snowflake.snowpark.session.Session: account=\"ax_test_qa3\", role=\"ACCOUNTADMIN\", database=\"LINEAGE_DEMO_DB\", schema=\"MODEL_SCHEMA\", warehouse=\"AX_XL\">)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dataset.lineage(direction=\"upstream\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d4469c8",
   "metadata": {},
   "source": [
    "Query the downstream of feature views we used to create the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7cb60b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downstream Lineage of feature view 'EXTRA_WINE_FEATURES'\n",
      "[Dataset(\n",
      "  name='LINEAGE_DEMO_DB.FEATURE_STORE.MY_DATASET',\n",
      "  version='4.0',\n",
      ")]\n",
      "Downstream Lineage of feature view 'WINE_FEATURES'\n",
      "[Dataset(\n",
      "  name='LINEAGE_DEMO_DB.FEATURE_STORE.MY_DATASET',\n",
      "  version='4.0',\n",
      ")]\n"
     ]
    }
   ],
   "source": [
    "for fv in all_feature_views:\n",
    "    print(\"Downstream Lineage of feature view '\" + fv.name + \"'\")\n",
    "    print(fv.lineage(direction='downstream'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd71162",
   "metadata": {},
   "source": [
    "<a id='tables-as-training-data'></a>\n",
    "\n",
    "#### 3.1.2 Tables as Training data  {-}\n",
    "\n",
    "Alternatively, you can create a regular table as a dataset from feature views. The downside is that tables are mutable, so reproducibility cannot be guaranteed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5eefec90",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_table_data = fs.generate_dataset(\n",
    "    name=\"my_table_dataset\",\n",
    "    spine_df=spine_df, \n",
    "    features=all_feature_views,\n",
    "    version=\"4.0\",\n",
    "    spine_timestamp_col=timestamp_col,\n",
    "    spine_label_cols=label_cols,\n",
    "    exclude_columns=excluded_cols,\n",
    "    desc=\"This is the dataset joined spine dataframe with feature views\",\n",
    "    output_type=\"table\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e480986",
   "metadata": {},
   "source": [
    "##### Query Lineage\n",
    "\n",
    "\n",
    "You can also explore lineage of generated table from the feature view. You filter the results to see just table entities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a253f3b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downstream Lineage of feature view 'EXTRA_WINE_FEATURES'\n",
      "[LineageNode(\n",
      "  name='LINEAGE_DEMO_DB.FEATURE_STORE.MY_TABLE_DATASET_4',\n",
      "  version='None',\n",
      "  domain='table',\n",
      "  status='ACTIVE',\n",
      "  created_on='2024-08-01 22:44:28'\n",
      ")]\n",
      "Downstream Lineage of feature view 'WINE_FEATURES'\n",
      "[LineageNode(\n",
      "  name='LINEAGE_DEMO_DB.FEATURE_STORE.MY_TABLE_DATASET_4',\n",
      "  version='None',\n",
      "  domain='table',\n",
      "  status='ACTIVE',\n",
      "  created_on='2024-08-01 22:44:28'\n",
      ")]\n"
     ]
    }
   ],
   "source": [
    "for fv in all_feature_views:\n",
    "    print(\"Downstream Lineage of feature view '\" + fv.name + \"'\")\n",
    "    print(fv.lineage(direction='downstream', domain_filter=[\"table\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb38ac0b",
   "metadata": {},
   "source": [
    "<a id='training-data-from-source-tables'></a>\n",
    "\n",
    "\n",
    "### 3.2 Training Data from source tables  {-}\n",
    "\n",
    "We will explore the workflow of creating training dataset directly from source tables instead of feature views. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761f1296",
   "metadata": {},
   "source": [
    "<a id='dataset-from-source-table-as-training-data'></a>\n",
    "\n",
    "\n",
    "#### 3.2.1 Dataset from source table as training data\n",
    "\n",
    "\n",
    "Create the dataset from a source table. Lineage works in a similar way even when its trained with source view or a stage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35b8b59a-dcae-41a4-8954-db6719c5cf60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml import dataset\n",
    "\n",
    "my_table_based_dataset = dataset.create_from_dataframe(\n",
    "            session=session,\n",
    "            name=\"my_dataset_from_table\",\n",
    "            version=\"v1\",\n",
    "            input_dataframe=session.table(source_tables[0]),\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a4a2444",
   "metadata": {},
   "source": [
    "##### Query Lineage\n",
    "\n",
    "\n",
    "Query the upstream lineage of the dataset we just created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "190cac1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LineageNode(\n",
       "   name='LINEAGE_DEMO_DB.FEATURE_STORE.WINEDATA',\n",
       "   version='None',\n",
       "   domain='table',\n",
       "   status='ACTIVE',\n",
       "   created_on='2024-08-01 22:44:14'\n",
       " )]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_table_based_dataset.lineage(direction=\"upstream\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d0acfc",
   "metadata": {},
   "source": [
    "<a id='model-lineage'></a>\n",
    "\n",
    "\n",
    "## 4. Model Lineage  {-}\n",
    "\n",
    "Now let's train a simple random forest model, and evaluate the prediction accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03addbc",
   "metadata": {},
   "source": [
    "Let's create a registry to save the trained models. All models need to be logged into the registry for their lineage to be tracked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf1209a6-4c8c-4441-9c5d-7688f4ec0233",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.registry import Registry\n",
    "\n",
    "registry = Registry(\n",
    "    session=session, \n",
    "    database_name=session.get_current_database(), \n",
    "    schema_name=MODEL_DEMO_SCHEMA,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec35fef9",
   "metadata": {},
   "source": [
    "<a id='model-trained-in-snowflake-ecosystem'></a>\n",
    "\n",
    "\n",
    "### 4.1 Model trained in snowflake ecosystem  {-}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea11101",
   "metadata": {},
   "source": [
    "Lets define a training function that uses Random forest to build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd5035d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.modeling.ensemble import RandomForestRegressor\n",
    "from snowflake.ml.modeling import metrics as snowml_metrics\n",
    "from snowflake.snowpark.functions import abs as sp_abs, mean, col\n",
    "\n",
    "def train_model_using_snowpark_ml(training_data_df):\n",
    "    train, test = training_data_df.random_split([0.8, 0.2], seed=42)\n",
    "    feature_columns = list(set(training_data_df.columns) - set(label_cols) - set(join_keys) - set([timestamp_col]))\n",
    "    print(f\"feature cols: {feature_columns}\")\n",
    "    \n",
    "    rf = RandomForestRegressor(\n",
    "        input_cols=feature_columns, label_cols=label_cols, \n",
    "        max_depth=3, n_estimators=20, random_state=42\n",
    "    )\n",
    "\n",
    "    rf.fit(train)\n",
    "    predictions = rf.predict(test)\n",
    "\n",
    "    output_label_names = ['OUTPUT_' + col for col in label_cols]\n",
    "    mse = snowml_metrics.mean_squared_error(\n",
    "        df=predictions, \n",
    "        y_true_col_names=label_cols, \n",
    "        y_pred_col_names=output_label_names\n",
    "    )\n",
    "\n",
    "    accuracy = 100 - snowml_metrics.mean_absolute_percentage_error(\n",
    "        df=predictions,\n",
    "        y_true_col_names=label_cols,\n",
    "        y_pred_col_names=output_label_names\n",
    "    )\n",
    "\n",
    "    print(f\"MSE: {mse}, Accuracy: {accuracy}\")\n",
    "    return rf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574a810b",
   "metadata": {},
   "source": [
    "<a id='model-trained-using-dataset'></a>\n",
    "\n",
    "\n",
    "#### 4.1.1 Model trained using Dataset  {-}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1cf2f4-59b3-40da-8c43-bee27129105d",
   "metadata": {},
   "source": [
    "Convert dataset to a snowpark dataframe and examine all the features in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5f3c71aa-1c6b-4bf4-83f9-2176dc249f83",
   "metadata": {},
   "outputs": [
    {
     "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>QUALITY</th>\n",
       "      <th>SULPHATES</th>\n",
       "      <th>ALCOHOL</th>\n",
       "      <th>FIXED_ACIDITY</th>\n",
       "      <th>CITRIC_ACID</th>\n",
       "      <th>CHLORIDES</th>\n",
       "      <th>TOTAL_SULFUR_DIOXIDE</th>\n",
       "      <th>PH</th>\n",
       "      <th>MY_NEW_FEATURE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.076</td>\n",
       "      <td>34</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.098</td>\n",
       "      <td>67</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.075</td>\n",
       "      <td>66</td>\n",
       "      <td>3.40</td>\n",
       "      <td>2.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.70</td>\n",
       "      <td>11.1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.062</td>\n",
       "      <td>20</td>\n",
       "      <td>3.28</td>\n",
       "      <td>3.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>10.7</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.093</td>\n",
       "      <td>80</td>\n",
       "      <td>3.30</td>\n",
       "      <td>5.782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>4</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.081</td>\n",
       "      <td>24</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>5</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.064</td>\n",
       "      <td>34</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>7</td>\n",
       "      <td>0.68</td>\n",
       "      <td>11.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.084</td>\n",
       "      <td>43</td>\n",
       "      <td>3.04</td>\n",
       "      <td>9.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>6</td>\n",
       "      <td>0.48</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.074</td>\n",
       "      <td>92</td>\n",
       "      <td>3.39</td>\n",
       "      <td>4.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>6</td>\n",
       "      <td>0.63</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.090</td>\n",
       "      <td>62</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     QUALITY  SULPHATES  ALCOHOL  FIXED_ACIDITY  CITRIC_ACID  CHLORIDES  \\\n",
       "0          5       0.56      9.4            7.4         0.00      0.076   \n",
       "1          5       0.68      9.8            7.8         0.00      0.098   \n",
       "2          5       0.64      9.5            7.6         0.29      0.075   \n",
       "3          5       0.70     11.1            7.9         0.40      0.062   \n",
       "4          7       0.76     10.7           11.8         0.49      0.093   \n",
       "..       ...        ...      ...            ...          ...        ...   \n",
       "507        4       0.46      9.6            8.1         0.00      0.081   \n",
       "508        5       0.64      9.7            6.7         0.08      0.064   \n",
       "509        7       0.68     11.4           13.3         0.75      0.084   \n",
       "510        6       0.48     10.5            5.6         0.78      0.074   \n",
       "511        6       0.63     10.2           10.0         0.31      0.090   \n",
       "\n",
       "     TOTAL_SULFUR_DIOXIDE    PH  MY_NEW_FEATURE  \n",
       "0                      34  3.51           0.000  \n",
       "1                      67  3.20           0.000  \n",
       "2                      66  3.40           2.204  \n",
       "3                      20  3.28           3.160  \n",
       "4                      80  3.30           5.782  \n",
       "..                    ...   ...             ...  \n",
       "507                    24  3.38           0.000  \n",
       "508                    34  3.33           0.536  \n",
       "509                    43  3.04           9.975  \n",
       "510                    92  3.39           4.368  \n",
       "511                    62  3.18           3.100  \n",
       "\n",
       "[512 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_df = my_dataset.read.to_snowpark_dataframe()\n",
    "assert training_data_df.count() == sample_count\n",
    "# drop rows that have any nulls in value. \n",
    "training_data_df = training_data_df.dropna(how='any')\n",
    "training_data_df.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eee34d3",
   "metadata": {},
   "source": [
    "Train the random forest model using Snowpark-ML and the dataset, then log the model in the registry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "352603a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature cols: ['MY_NEW_FEATURE', 'PH', 'TOTAL_SULFUR_DIOXIDE', 'CITRIC_ACID', 'CHLORIDES', 'SULPHATES', 'FIXED_ACIDITY', 'ALCOHOL']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The version of package 'snowflake-snowpark-python' in the local environment is 1.20.2, which does not fit the criteria for the requirement 'snowflake-snowpark-python'. Your UDF might not work when the package version is different between the server and your local environment.\n",
      "The version of package 'numpy' in the local environment is 1.24.4, which does not fit the criteria for the requirement 'numpy==1.24.3'. Your UDF might not work when the package version is different between the server and your local environment.\n",
      "The version of package 'scikit-learn' in the local environment is 1.3.2, which does not fit the criteria for the requirement 'scikit-learn==1.3.0'. Your UDF might not work when the package version is different between the server and your local environment.\n",
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/site-packages/sklearn/base.py:348: InconsistentVersionWarning: Trying to unpickle estimator DecisionTreeRegressor from version 1.3.0 when using version 1.3.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/site-packages/sklearn/base.py:348: InconsistentVersionWarning: Trying to unpickle estimator RandomForestRegressor from version 1.3.0 when using version 1.3.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "The version of package 'numpy' in the local environment is 1.24.4, which does not fit the criteria for the requirement 'numpy==1.24.3'. Your UDF might not work when the package version is different between the server and your local environment.\n",
      "The version of package 'scikit-learn' in the local environment is 1.3.2, which does not fit the criteria for the requirement 'scikit-learn==1.3.0'. Your UDF might not work when the package version is different between the server and your local environment.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.25267477340674793, Accuracy: 99.92349333548655\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/contextlib.py:113: UserWarning: `relax_version` is not set and therefore defaulted to True. Dependency version constraints relaxed from ==x.y.z to >=x.y, <(x+1). To use specific dependency versions for compatibility, reproducibility, etc., set `options={'relax_version': False}` when logging the model.\n",
      "  return next(self.gen)\n"
     ]
    }
   ],
   "source": [
    "random_forest_model = train_model_using_snowpark_ml(training_data_df) \n",
    "\n",
    "model_version = registry.log_model(\n",
    "    model_name=\"MODEL_TRAINED_ON_DATASET\",\n",
    "    version_name=\"v1\",\n",
    "    model=random_forest_model,\n",
    "    comment=\"Model trained with feature views, dataset\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed343e67",
   "metadata": {},
   "source": [
    "##### Query Lineage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d44553",
   "metadata": {},
   "source": [
    "Query the upstream of the model we just trained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae53f591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Dataset(\n",
       "   name='LINEAGE_DEMO_DB.FEATURE_STORE.MY_DATASET',\n",
       "   version='4.0',\n",
       " )]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = model_version.lineage(direction=\"upstream\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9599921",
   "metadata": {},
   "source": [
    "The model can also be explored as part of the downstream path of the dataset used to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fc4f5bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ModelVersion(\n",
       "   name='MODEL_TRAINED_ON_DATASET',\n",
       "   version='V1',\n",
       " )]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].lineage(direction=\"downstream\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c9e42b7",
   "metadata": {},
   "source": [
    "<a id='model-trained-using-source-tables'></a>\n",
    "\n",
    "\n",
    "#### 4.1.2 Model trained using source tables  {-}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "703ceeda",
   "metadata": {},
   "source": [
    "Train the random forest model using Snowpark-ML and the source tables, then log the model in the registry.\n",
    "\n",
    "Lineage works in a similar way even when its trained with source view or a stage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95fb5cbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature cols: ['VOLATILE_ACIDITY', 'RESIDUAL_SUGAR', 'TOTAL_SULFUR_DIOXIDE', 'CITRIC_ACID', 'PH', 'CHLORIDES', 'SULPHATES', 'DENSITY', 'FIXED_ACIDITY', 'ALCOHOL', 'FREE_SULFUR_DIOXIDE']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/site-packages/sklearn/base.py:348: InconsistentVersionWarning: Trying to unpickle estimator DecisionTreeRegressor from version 1.3.0 when using version 1.3.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/site-packages/sklearn/base.py:348: InconsistentVersionWarning: Trying to unpickle estimator RandomForestRegressor from version 1.3.0 when using version 1.3.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "WARNING:snowflake.snowpark.session:The version of package 'numpy' in the local environment is 1.24.4, which does not fit the criteria for the requirement 'numpy==1.24.3'. Your UDF might not work when the package version is different between the server and your local environment.\n",
      "WARNING:snowflake.snowpark.session:The version of package 'scikit-learn' in the local environment is 1.3.2, which does not fit the criteria for the requirement 'scikit-learn==1.3.0'. Your UDF might not work when the package version is different between the server and your local environment.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.39049050644031114, Accuracy: 99.90829288453038\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/contextlib.py:113: UserWarning: `relax_version` is not set and therefore defaulted to True. Dependency version constraints relaxed from ==x.y.z to >=x.y, <(x+1). To use specific dependency versions for compatibility, reproducibility, etc., set `options={'relax_version': False}` when logging the model.\n",
      "  return next(self.gen)\n"
     ]
    }
   ],
   "source": [
    "table_training_data_df = session.table(source_tables[0])\n",
    "table_training_data_df.dropna(how='any')\n",
    "\n",
    "random_forest_model = train_model_using_snowpark_ml(table_training_data_df) \n",
    "\n",
    "model_version = registry.log_model(\n",
    "    model_name=\"MODEL_TRAINED_ON_TABLE\",\n",
    "    version_name=\"v1\",\n",
    "    model=random_forest_model,\n",
    "    comment=\"Model trained with table\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2af23e31",
   "metadata": {},
   "source": [
    "##### Query Lineage\n",
    "\n",
    "\n",
    "Query the upstream of the model we just trained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "23f99177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LineageNode(\n",
       "   name='LINEAGE_DEMO_DB.MODEL_SCHEMA.SNOWPARK_TEMP_TABLE_Q9VM0X2LP8',\n",
       "   version='None',\n",
       "   domain='table',\n",
       "   status='ACTIVE',\n",
       "   created_on='2024-08-01 22:45:06'\n",
       " )]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = model_version.lineage(direction=\"upstream\")\n",
    "\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d426c9",
   "metadata": {},
   "source": [
    "The model can also be explored as part of the downstream path of the table used to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "21e4cd1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ModelVersion(\n",
       "   name='MODEL_TRAINED_ON_TABLE',\n",
       "   version='V1',\n",
       " )]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[0].lineage(direction=\"downstream\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2ce5b3",
   "metadata": {},
   "source": [
    "<a id='model-trained-in-non-snowflake-ecosystem'></a>\n",
    "\n",
    "\n",
    "### 4.2 Model trained in non-snowflake ecosystem  {-}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33cb95c7",
   "metadata": {},
   "source": [
    "For the workflows such as:\n",
    "A model trained using snowpark.ml but not a Snowpark DataFrame (like pandas).\n",
    "A model trained without using snowpark.ml or a Snowpark DataFrame.\n",
    "A model trained outside of Snowflake.\n",
    "\n",
    "\n",
    "You can still associate the lineage between the source data object and the trained model by passing the snowpark dataframe backed by the source data object to model registry’s log_model API as sample_input_data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "63e5f294",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature cols: ['MY_NEW_FEATURE', 'PH', 'TOTAL_SULFUR_DIOXIDE', 'CITRIC_ACID', 'CHLORIDES', 'SULPHATES', 'FIXED_ACIDITY', 'ALCOHOL']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/contextlib.py:113: UserWarning: `relax_version` is not set and therefore defaulted to True. Dependency version constraints relaxed from ==x.y.z to >=x.y, <(x+1). To use specific dependency versions for compatibility, reproducibility, etc., set `options={'relax_version': False}` when logging the model.\n",
      "  return next(self.gen)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.4896289668292035, Accuracy: 99.89502779572355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/anaconda3/envs/py38_env/lib/python3.8/site-packages/snowflake/ml/model/model_signature.py:69: UserWarning: The sample input has 512 rows, thus a truncation happened before inferring signature. This might cause inaccurate signature inference. If that happens, consider specifying signature manually.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "def train_model_using_sklearn(training_data_df, feature_columns):\n",
    "    train, test = train_test_split(training_data_df, test_size=0.2, random_state=42)\n",
    "        \n",
    "    X_train = train[feature_columns]\n",
    "    y_train = train[label_cols]\n",
    "    \n",
    "    X_test = test[feature_columns]\n",
    "    y_test = test[label_cols]\n",
    "\n",
    "    rf = RandomForestRegressor(max_depth=3, n_estimators=20, random_state=42)\n",
    "    rf.fit(X_train, y_train)\n",
    "\n",
    "    predictions = rf.predict(X_test)\n",
    "\n",
    "    mse = mean_squared_error(y_test, predictions)\n",
    "    accuracy = 100 - mean_absolute_percentage_error(y_test, predictions)\n",
    "    \n",
    "    print(f\"MSE: {mse}, Accuracy: {accuracy}\")\n",
    "    return rf\n",
    "\n",
    "\n",
    "feature_columns = list(set(training_data_df.columns) - set(label_cols) - set(join_keys) - set([timestamp_col]))\n",
    "print(f\"feature cols: {feature_columns}\")\n",
    "\n",
    "sklearn_trained_model = train_model_using_sklearn(training_data_df.to_pandas(), feature_columns)\n",
    "\n",
    "training_data_df = training_data_df.select(feature_columns)\n",
    "model_version = registry.log_model(\n",
    "    model_name=\"MODEL_TRAINED_ON_PANDAS\",\n",
    "    version_name=\"v1\",\n",
    "    model=sklearn_trained_model,\n",
    "    comment=\"Model trained with pandas dataframe\",\n",
    "    # Passing the snowpark dataframe as sample input data\n",
    "    sample_input_data = training_data_df\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05e551d1",
   "metadata": {},
   "source": [
    "##### Query lineage\n",
    "\n",
    "\n",
    "Query the upstream of the model we just trained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ff87faf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Dataset(\n",
       "   name='LINEAGE_DEMO_DB.FEATURE_STORE.MY_DATASET',\n",
       "   version='4.0',\n",
       " )]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = model_version.lineage(direction=\"upstream\")\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95a48d1",
   "metadata": {},
   "source": [
    "The model can also be explored as part of the downstream path of the dataset used to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cb9cbf8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ModelVersion(\n",
      "  name='MODEL_TRAINED_ON_DATASET',\n",
      "  version='V1',\n",
      "), ModelVersion(\n",
      "  name='MODEL_TRAINED_ON_PANDAS',\n",
      "  version='V1',\n",
      ")]\n"
     ]
    }
   ],
   "source": [
    "print(ds[0].lineage(direction=\"downstream\"))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad0070f",
   "metadata": {},
   "source": [
    "<a id='visualization-of-lineage'></a>\n",
    "\n",
    "## 5. Visualization of lineage\n",
    "\n",
    "\n",
    "The below image shows the screenshot of complete visualization of lineages of all the objects we created in the notebook from Snowsight UI. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a31c0e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "# Path to your image file\n",
    "image_path = 'lineage-graph.png'\n",
    "\n",
    "# Display the image\n",
    "display(Image(filename=image_path,width=1000, height=1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e23443",
   "metadata": {},
   "source": [
    "<a id='clean-up-notebook'></a>\n",
    "\n",
    "\n",
    "## 6. Clean up notebook  {-}\n",
    "\n",
    "\n",
    "This cell will drop the schemas have been created at beginning of this notebook, and also drop all objects live in the schemas including source data tables, feature views, datasets, and models.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dee533e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "session.sql(f\"DROP SCHEMA IF EXISTS {FS_DEMO_SCHEMA}\").collect()\n",
    "session.sql(f\"DROP SCHEMA IF EXISTS {MODEL_DEMO_SCHEMA}\").collect()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}