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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Desai","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"ML関数","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/ml-functions","tagIcon":""},"quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"fragmentPath":"/content/dam/snowflake-site/ja/content-fragments/quickstarts/getting-started-with-dataengineering-ml-using-snowpark-python-ja",":type":"snowflake-site/components/quickstart/quickstart-hero","isDeveloperGuidesPage":false,"quickstartHeroBreadcrumbs":[{"title":"Snowpark for Pythonを使用したデータエンジニアリングとMLの入門","url":"https://www.snowflake.com/content/snowflake-site/global/ja/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja","currentPage":true},{"title":"開発者ガイド","url":"https://www.snowflake.com/content/snowflake-site/global/ja/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/ja/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-777531a89e","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-c0752e7666",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-378fe82776","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003E概要\u003C/h2\u003E\n","\u003Cp\u003Eこのガイドを完了すると、未加工データから、組織が広告予算の割り当てを最適化するのに役立つ双方向アプリケーションに移行できるようになります。\u003C/p\u003E\n","\u003Cp\u003Eこのクイックスタートに従って、各ステップで次のような概要を学ぶことができます。\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E環境の設定\u003C/strong\u003E：ステージとテーブルを使用して、S3からSnowflakeに未加工データを取り込み、整理します。\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eデータエンジニアリング\u003C/strong\u003E：Snowpark for Python DataFramesを活用して、グループ化、集約、ピボット、結合などのデータ変換を実行し、下流のアプリケーション用のデータを準備します。\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eデータパイプライン\u003C/strong\u003E：Snowflakeタスクを使用して、データパイプラインコードを、統合された監視を備えた運用パイプラインに変換します。\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E機械学習\u003C/strong\u003E：データを準備し、SnowflakeでSnowpark MLを使用してMLトレーニングを実行し、Snowparkユーザー定義関数（UDF）としてモデルを展開します。\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreamlitアプリケーション\u003C/strong\u003E：Pythonを使用してインタラクティブなアプリケーションを構築し（ウェブ開発の経験は不要）、さまざまな広告費予算のROIの可視化を支援します。\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E上記のテクノロジーについて初めて知る方のために、ドキュメントへのリンクを含む簡単な要約を以下に示します。\u003C/p\u003E\n","\u003Ch3\u003ESnowparkとは\u003C/h3\u003E\n","\u003Cp\u003EPython、Java、Scalaなどの非SQLコードを安全にデプロイして処理するSnowflakeのライブラリとランタイムのセットです。\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E使い慣れたクライアント側ライブラリ\u003C/strong\u003E - Snowparkは、高度に統合されたDataFrame型のプログラミングとOSS互換のAPIをデータ実務者の好みの言語で利用できるようにします。より効率的なMLモデリング（公開プレビュー）とML運用（プライベートプレビュー）のためのSnowpark ML APIも含まれています。\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E柔軟なランタイムコンストラクト\u003C/strong\u003E - Snowparkは、ユーザーがカスタムロジックを取り込んで実行できるようにする柔軟なランタイムコンストラクトを提供します。開発者は、ユーザー定義関数とストアドプロシージャを使用して、データパイプライン、MLモデル、データアプリケーションをシームレスに構築できます。\u003C/p\u003E\n","\u003Cp\u003E詳しくは、\u003Ca href=\"/snowpark/\"\u003ESnowpark\u003C/a\u003Eをご覧ください。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/snowpark.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESnowpark MLとは\u003C/h3\u003E\n","\u003Cp\u003ESnowpark MLは、Snowflakeでより高速かつ直感的なエンドツーエンドのML開発を行うための新しいライブラリです。Snowpark MLには、モデル開発用のSnowpark MLモデリング（公開プレビュー）とモデル展開用のSnowpark ML運用（プライベートプレビュー）の2つのAPIがあります。\u003C/p\u003E\n","\u003Cp\u003Eこのクイックスタートでは、特徴量エンジニアリングをスケールアウトし、SnowflakeでのMLトレーニングの実行を簡素化するSnowpark MLモデリングAPIに焦点を当てます。\u003C/p\u003E\n","\u003Ch3\u003EStreamlitとは\u003C/h3\u003E\n","\u003Cp\u003EStreamlitは、開発者がデータアプリケーションをすばやく簡単に作成、共有、デプロイできるようにする、純粋なPythonの\u003Ca href=\"https://github.com/streamlit/streamlit\"\u003Eオープンソース\u003C/a\u003Eアプリケーションフレームワークです。詳しくは、\u003Ca href=\"https://streamlit.io/\"\u003EStreamlit\u003C/a\u003Eをご覧ください。\u003C/p\u003E\n","\u003Ch3\u003E学習する内容\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowpark DataFramesとAPIを利用してデータを分析し、データエンジニアリングタスクを実行する方法\u003C/li\u003E\u003Cli\u003E厳選されたSnowflake AnacondaチャネルからオープンソースのPythonライブラリを使用する方法\u003C/li\u003E\u003Cli\u003ESnowflakeでSnowpark MLを使用してMLモデルをトレーニングする方法\u003C/li\u003E\u003Cli\u003Eオンライン推論とオフライン推論のそれぞれに、スカラーおよびベクトル化されたSnowpark Pythonユーザー定義関数（UDF）を作成する方法\u003C/li\u003E\u003Cli\u003ESnowflakeタスクを作成してデータパイプラインを自動化する方法\u003C/li\u003E\u003Cli\u003Eユーザー入力に基づく推論にスカラーUDFを使用するStreamlitウェブアプリケーションを作成する方法\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E前提条件\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\"\u003EGit\u003C/a\u003Eがインストールされていること\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.python.org/downloads/\"\u003EPython 3.9\u003C/a\u003Eがインストールされていること\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E開始\u003C/strong\u003Eステップでは、Python環境をPython 3.9で作成することに注意してください。\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda\"\u003EORGADMINによって有効化されたAnacondaパッケージ\u003C/a\u003Eを持つSnowflakeアカウント。Snowflakeアカウントをお持ちでない場合は、\u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003E無料トライアルアカウント\u003C/a\u003Eに登録できます。\u003C/li\u003E\u003Cli\u003Eアカウント管理者の役割を持つSnowflakeアカウントのログイン。環境にこの役割がある場合は、それを使用できます。それ以外の場合は、1）無料トライアルに登録する、2）データベース、スキーマ、テーブル、ステージ、タスク、ユーザー定義関数、ストアドプロシージャを作成できる別の役割を使用する、または3）上記のオブジェクトを作成できる既存のデータベースとスキーマを使用する必要があります。\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E重要：続行する前に、\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#getting-started\"\u003Eこちら\u003C/a\u003Eで説明されているように、ORGADMINによってAnacondaパッケージが有効化されているSnowflakeアカウントがあることを確認してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E環境を設定する\u003C/h2\u003E\n","\u003Ch3\u003Eテーブルを作成し、データを読み込み、ステージを設定する\u003C/h3\u003E\n","\u003Cp\u003E認証情報を使用して\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight.html#\"\u003ESnowsight\u003C/a\u003Eにログインしてテーブルを作成し、Amazon S3からデータを読み込み、Snowflake内部ステージを設定します。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E重要：\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003Eこのセクションで作成したオブジェクトに別の名前を使用する場合は、それに応じて次のセクションのスクリプトとコードを更新してください。\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E以下の各SQLスクリプトブロックについて、ブロック内のすべてのステートメントを選択し、上から順に実行します。\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E次のSQLコマンドを実行して、\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-warehouse.html\"\u003Eウェアハウス\u003C/a\u003E、\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-database.html\"\u003Eデータベース\u003C/a\u003E、\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-schema.html\"\u003Eスキーマ\u003C/a\u003Eを作成します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE DASH_L;\nCREATE OR REPLACE DATABASE DASH_DB;\nCREATE OR REPLACE SCHEMA DASH_SCHEMA;\n\nUSE DASH_DB.DASH_SCHEMA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E次のSQLコマンドを実行し、公的にアクセス可能なS3バケットにホストされているデータからテーブル\u003Cstrong\u003ECAMPAIGN_SPEND\u003C/strong\u003Eを作成します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE file format csvformat\n  skip_header = 1\n  type = 'CSV';\n\nCREATE or REPLACE stage campaign_data_stage\n  file_format = csvformat\n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/campaign_spend/';\n\nCREATE or REPLACE TABLE CAMPAIGN_SPEND (\n  CAMPAIGN VARCHAR(60), \n  CHANNEL VARCHAR(60),\n  DATE DATE,\n  TOTAL_CLICKS NUMBER(38,0),\n  TOTAL_COST NUMBER(38,0),\n  ADS_SERVED NUMBER(38,0)\n);\n\nCOPY into CAMPAIGN_SPEND\n  from @campaign_data_stage;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E次のSQLコマンドを実行し、公的にアクセス可能なS3バケットにホストされているデータからテーブル\u003Cstrong\u003EMONTHLY_REVENUE\u003C/strong\u003Eを作成します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE stage monthly_revenue_data_stage\n  file_format = csvformat\n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/monthly_revenue/';\n\nCREATE or REPLACE TABLE MONTHLY_REVENUE (\n  YEAR NUMBER(38,0),\n  MONTH NUMBER(38,0),\n  REVENUE FLOAT\n);\n\nCOPY into MONTHLY_REVENUE\n  from @monthly_revenue_data_stage;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E次のSQLコマンドを実行して、過去6か月間の予算割り当てとROIを保持するテーブル\u003Cstrong\u003EBUDGET_ALLOCATIONS_AND_ROI\u003C/strong\u003Eを作成します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE TABLE BUDGET_ALLOCATIONS_AND_ROI (\n  MONTH varchar(30),\n  SEARCHENGINE integer,\n  SOCIALMEDIA integer,\n  VIDEO integer,\n  EMAIL integer,\n  ROI float\n)\nCOMMENT = '{"origin":"sf_sit-is", "name":"aiml_notebooks_ad_spend_roi", "version":{"major":1, "minor":0}, "attributes":{"is_quickstart":1, "source":"streamlit"}}';\n\nINSERT INTO BUDGET_ALLOCATIONS_AND_ROI (MONTH, SEARCHENGINE, SOCIALMEDIA, VIDEO, EMAIL, ROI)\nVALUES\n('January',35,50,35,85,8.22),\n('February',75,50,35,85,13.90),\n('March',15,50,35,15,7.34),\n('April',25,80,40,90,13.23),\n('May',95,95,10,95,6.246),\n('June',35,50,35,85,8.22);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E次のコマンドを実行して、ストアドプロシージャ、UDF、MLモデルファイルを格納するためのSnowflake\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage\"\u003E内部ステージ\u003C/a\u003Eを作成します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE dash_sprocs;\nCREATE OR REPLACE STAGE dash_models;\nCREATE OR REPLACE STAGE dash_udfs;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E任意で、Snowsightで\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/setup.sql\"\u003Esetup.sql\u003C/a\u003Eを開き、すべてのSQLステートメントを実行してオブジェクトを作成し、AWS S3からデータを読み込むこともできます。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E重要：このセクションで作成したオブジェクトに別の名前を使用する場合は、それに応じて次のセクションのスクリプトとコードを更新してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E開始する\u003C/h2\u003E\n","\u003Cp\u003Eこのセクションでは、GitHubレポジトリの複製と、Snowpark for Python環境の設定について説明します。\u003C/p\u003E\n","\u003Ch3\u003EGitHubレポジトリを複製する\u003C/h3\u003E\n","\u003Cp\u003E最初のステップは、\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn\"\u003EGitHubレポジトリ\u003C/a\u003Eを複製することです。このレポジトリには、このクイックスタートガイドを正常に完了するために必要なすべてのコードが含まれています。\u003C/p\u003E\n","\u003Cp\u003EHTTPSを使用する場合：\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Eまたは、SSHを使用する場合：\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone git@github.com:Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESnowpark for Python\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eデータエンジニアリング\u003C/strong\u003Eと\u003Cstrong\u003E機械学習\u003C/strong\u003Eのステップを完了するには、以下の説明に従って、すべてをローカルにインストールする（オプション1）か、Hexを使用する（オプション2）を選択します。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E重要：\u003Cstrong\u003EStreamlitアプリケーション\u003C/strong\u003Eを実行するには、Python環境を作成し、「\u003Cstrong\u003Eローカルインストール\u003C/strong\u003E」の説明に従って、Snowpark for Pythonとその他のライブラリをローカルにインストールする必要があります。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003Eオプション1 -- ローカルインストール\u003C/h4\u003E\n","\u003Cp\u003Eこのオプションを使用すると、このクイックスタートガイドのすべてのステップを完了できます。\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eステップ1：\u003C/strong\u003E\u003Ca href=\"https://conda.io/miniconda.html\"\u003Ehttps://conda.io/miniconda.html\u003C/a\u003Eからminicondaインストーラーをダウンロードしてインストールします*（または、Python 3.9では、\u003Ca href=\"https://virtualenv.pypa.io/en/latest/\"\u003Evirtualenv\u003C/a\u003Eなどの他のPython環境を使用することもできます）*。\u003C/p\u003E\n","\u003Cp\u003E**ステップ2：**新しいターミナルウィンドウを開き、同じターミナルウィンドウで次のコマンドを実行します。\u003C/p\u003E\n","\u003Cp\u003E**ステップ3：**同じターミナルウィンドウで次のコマンドを実行して、\u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003EというPython 3.9 conda環境を作成します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda create --name snowpark-de-ml -c https://repo.anaconda.com/pkgs/snowflake python=3.9\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eステップ4：\u003Cstrong\u003E同じターミナルウィンドウで次のコマンドを実行して、conda環境\u003C/strong\u003Esnowpark-de-ml\u003C/strong\u003Eをアクティブ化します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda activate snowpark-de-ml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eステップ5：\u003Cstrong\u003E同じターミナルウィンドウで次のコマンドを実行して、\u003Ca href=\"https://repo.anaconda.com/pkgs/snowflake/\"\u003ESnowflake Anacondaチャンネル\u003C/a\u003EからSnowpark Pythonとその他のライブラリをconda環境\u003C/strong\u003Esnowpark-de-ml\u003C/strong\u003Eにインストールします。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python pandas notebook scikit-learn cachetools\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eステップ6：\u003Cstrong\u003E同じターミナルウィンドウで次のコマンドを実行して、Streamlitライブラリをconda環境\u003C/strong\u003Esnowpark-de-ml\u003C/strong\u003Eにインストールします。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epip install streamlit\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Eステップ7：\u003Cstrong\u003E同じターミナルウィンドウで次のコマンドを実行して、Snowpark MLライブラリをconda環境\u003C/strong\u003Esnowpark-de-ml\u003C/strong\u003Eにインストールします。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epip install snowflake-ml-python\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E**ステップ9：**Snowflakeアカウントの詳細と認証情報で\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003Eを更新します。\u003C/p\u003E\n","\u003Cp\u003E以下は、\u003Cstrong\u003E環境の設定\u003C/strong\u003Eステップで説明したオブジェクト名に基づく***connection.json***のサンプルです。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "account"   : "<your_account_identifier_goes_here>",\n  "user"      : "<your_username_goes_here>",\n  "password"  : "<your_password_goes_here>",\n  "role"      : "ACCOUNTADMIN",\n  "warehouse" : "DASH_L",\n  "database"  : "DASH_DB",\n  "schema"    : "DASH_SCHEMA"\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意：上記の\u003Cstrong\u003Eaccount\u003C/strong\u003Eパラメータには、\u003Cstrong\u003Eアカウント識別子\u003C/strong\u003Eを指定し、snowflakecomputing.comドメイン名は含めないでください。Snowflakeは、接続の作成時にこれを自動的に追加します。詳細については、\u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier.html\"\u003Eドキュメント\u003C/a\u003Eを参照してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003Eオプション2 -- Hexの使用\u003C/h4\u003E\n","\u003Cp\u003E既存の\u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003Eアカウントを使用する場合、または\u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003E30日間の無料トライアルアカウントを作成\u003C/a\u003Eする場合は、Snowpark for Pythonが組み込まれているため、Python環境を作成し、Snowpark for Pythonを他のライブラリとともにラップトップにローカルにインストールする必要はありません。これにより、このクイックスタートガイドの\u003Cstrong\u003Eデータエンジニアリング\u003C/strong\u003Eと\u003Cstrong\u003E機械学習\u003C/strong\u003EのステップをHexで直接完了できるようになります。（Hexでデータエンジニアリングと機械学習のノートブックをロードする詳細については、それぞれの手順を参照してください）。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E重要：\u003Cstrong\u003EStreamlitアプリケーション\u003C/strong\u003Eを実行するには、Python環境を作成し、上記の「\u003Cstrong\u003Eローカルインストール\u003C/strong\u003E」の説明に従って、Snowpark for Pythonとその他のライブラリをローカルにインストールする必要があります。\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Eデータエンジニアリング\u003C/h2\u003E\n","\u003Cp\u003E下記リンク先のノートブックでは、次のデータエンジニアリングタスクを説明しています。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESnowpark PythonからSnowflakeへの安全な接続を確立する\u003C/li\u003E\u003Cli\u003ESnowflakeテーブルからSnowpark DataFramesにデータを読み込む\u003C/li\u003E\u003Cli\u003ESnowpark DataFramesで探索的データ分析を実行する\u003C/li\u003E\u003Cli\u003ESnowpark DataFramesを使用して、複数のテーブルからデータをピボットおよび結合する\u003C/li\u003E\u003Cli\u003ESnowflakeタスクを使用してデータパイプラインタスクを自動化する\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EJupyterまたはVisual Studio Codeのデータエンジニアリングノートブック\u003C/h3\u003E\n","\u003Cp\u003E開始するには、次の手順に従います。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003Eターミナルウィンドウで、このフォルダを参照し、コマンドラインで\u003Ccode\u003Ejupyter notebook\u003C/code\u003Eを実行します（他のツールやVisual Studio CodeなどのIDEを使用することもできます）。\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003ESnowpark_For_Python_DE.ipynb\u003C/a\u003Eのセルを開いて実行します。\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E重要：Jupyterノートブックで、（Python）カーネルが***snowpark-de-ml***に設定されていることを確認してください。これは、\u003Cstrong\u003EGitHubレポジトリの複製\u003C/strong\u003Eステップで作成した環境の名前です。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EHexのデータエンジニアリングノートブック\u003C/h3\u003E\n","\u003Cp\u003E既存の\u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003Eアカウントを使用する場合、または\u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003E30日間の無料トライアルアカウントを作成\u003C/a\u003Eする場合は、次の手順に従ってノートブックをロードし、HexからSnowflakeに接続するためのデータ接続を作成します。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003ESnowpark_For_Python_DE.ipynb\u003C/a\u003Eをプロジェクトとしてアカウントにインポートします。インポートの詳細については、\u003Ca href=\"https://learn.hex.tech/docs/versioning/import-export\"\u003Eドキュメント\u003C/a\u003Eを参照してください。\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E次に、Snowflakeへの接続に\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003Eを使用する代わりに、以下に示すように\u003Ca href=\"https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database\"\u003Eデータ接続\u003C/a\u003Eを作成し、それをデータエンジニアリングノートブックで使用します。\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/hex_data_connection.png\" alt=\"HEXデータ接続\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意：ワークスペース内のプロジェクトやユーザーに対して、共有データ接続を作成することもできます。詳細については、\u003Ca href=\"https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections\"\u003Eドキュメント\u003C/a\u003Eを参照してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003Eノートブックの次のコードスニペットを置き換えます。\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = json.load(open('connection.json'))\nsession = Session.builder.configs(connection_parameters).create()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E以下に置き換えます。\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport hextoolkit\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\nsession = hex_snowflake_conn.get_snowpark_session()\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Eデータパイプライン\u003C/h2\u003E\n","\u003Cp\u003Eデータトランスフォーメーションは、Snowflakeで実行される自動データパイプラインの形式で運用することもできます。\u003C/p\u003E\n","\u003Cp\u003E特に、「\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003Eデータエンジニアリングノートブック\u003C/a\u003E」には、オプションでデータトランスフォーメーションを\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003ESnowflakeタスク\u003C/a\u003Eとして構築して実行する方法を説明しているセクションがあります。\u003C/p\u003E\n","\u003Cp\u003E参考までに、コードスニペットを以下に示します。\u003C/p\u003E\n","\u003Ch3\u003E\u003Cstrong\u003Eルート/親タスク\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003Eこのタスクは、キャンペーン支出データのロードとさまざまな変換の実行を自動化します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef campaign_spend_data_pipeline(session: Session) -> str:\n  # DATA TRANSFORMATIONS\n  # Perform the following actions to transform the data\n\n  # Load the campaign spend data\n  snow_df_spend_t = session.table('campaign_spend')\n\n  # Transform the data so we can see total cost per year/month per channel using group_by() and agg() Snowpark DataFrame functions\n  snow_df_spend_per_channel_t = snow_df_spend_t.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).\n      with_column_renamed('"YEAR(DATE)"',"YEAR").with_column_renamed('"MONTH(DATE)"',"MONTH").sort('YEAR','MONTH')\n\n  # Transform the data so that each row will represent total cost across all channels per year/month using pivot() and sum() Snowpark DataFrame functions\n  snow_df_spend_per_month_t = snow_df_spend_per_channel_t.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH')\n  snow_df_spend_per_month_t = snow_df_spend_per_month_t.select(\n      col("YEAR"),\n      col("MONTH"),\n      col("'search_engine'").as_("SEARCH_ENGINE"),\n      col("'social_media'").as_("SOCIAL_MEDIA"),\n      col("'video'").as_("VIDEO"),\n      col("'email'").as_("EMAIL")\n  )\n\n  # Save transformed data\n  snow_df_spend_per_month_t.write.mode('overwrite').save_as_table('SPEND_PER_MONTH')\n\n# Register data pipelining function as a Stored Procedure so it can be run as a task\nsession.sproc.register(\n  func=campaign_spend_data_pipeline,\n  name="campaign_spend_data_pipeline",\n  packages=['snowflake-snowpark-python'],\n  is_permanent=True,\n  stage_location="@dash_sprocs",\n  replace=True)\n\ncampaign_spend_data_pipeline_task = """\nCREATE OR REPLACE TASK campaign_spend_data_pipeline_task\n    WAREHOUSE = 'DASH_L'\n    SCHEDULE  = '3 MINUTE'\nAS\n    CALL campaign_spend_data_pipeline()\n"""\nsession.sql(campaign_spend_data_pipeline_task).collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003E子/依存タスク\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003Eこのタスクは、月間売上データのロード、さまざまな変換の実行、変換されたキャンペーン支出データとの結合を自動化します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef monthly_revenue_data_pipeline(session: Session) -> str:\n  # Load revenue table and transform the data into revenue per year/month using group_by and agg() functions\n  snow_df_spend_per_month_t = session.table('spend_per_month')\n  snow_df_revenue_t = session.table('monthly_revenue')\n  snow_df_revenue_per_month_t = snow_df_revenue_t.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')\n\n  # Join revenue data with the transformed campaign spend data so that our input features (i.e. cost per channel) and target variable (i.e. revenue) can be loaded into a single table for model training\n  snow_df_spend_and_revenue_per_month_t = snow_df_spend_per_month_t.join(snow_df_revenue_per_month_t, ["YEAR","MONTH"])\n\n  # SAVE in a new table for the next task\n  snow_df_spend_and_revenue_per_month_t.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')\n\n# Register data pipelining function as a Stored Procedure so it can be run as a task\nsession.sproc.register(\n  func=monthly_revenue_data_pipeline,\n  name="monthly_revenue_data_pipeline",\n  packages=['snowflake-snowpark-python'],\n  is_permanent=True,\n  stage_location="@dash_sprocs",\n  replace=True)\n\nmonthly_revenue_data_pipeline_task = """\n  CREATE OR REPLACE TASK monthly_revenue_data_pipeline_task\n      WAREHOUSE = 'DASH_L'\n      AFTER campaign_spend_data_pipeline_task\n  AS\n      CALL monthly_revenue_data_pipeline()\n  """\nsession.sql(monthly_revenue_data_pipeline_task).collect()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意：上記の***monthly_revenue_data_pipeline_task***には、\u003Cstrong\u003EAFTER campaign_spend_data_pipeline_task\u003C/strong\u003E句があり、依存タスクであることに注意してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003Eタスクの開始\u003C/h4\u003E\n","\u003Cp\u003ESnowflakeタスクはデフォルトでは開始されないため、開始/再開するには次のステートメントを実行する必要があります。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task monthly_revenue_data_pipeline_task resume").collect()\nsession.sql("alter task campaign_spend_data_pipeline_task resume").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003Eタスクの中断\u003C/h4\u003E\n","\u003Cp\u003E上記のタスクを再開する場合は、不要なリソース使用を回避するため、次のコマンドを実行してタスクを一時停止してください。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task campaign_spend_data_pipeline_task suspend").collect()\nsession.sql("alter task monthly_revenue_data_pipeline_task suspend").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eタスクの監視\u003C/h3\u003E\n","\u003Cp\u003Eこれらのタスクとその\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro#label-task-dag\"\u003EDAG\u003C/a\u003Eは、\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-tasks#viewing-individual-task-graphs\"\u003ESnowsight\u003C/a\u003Eで次のように表示できます。\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/snowflake_tasks.png\" alt=\"タスクの監視\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003Eタスクのエラー通知\u003C/h3\u003E\n","\u003Cp\u003Eタスクの実行中にエラーが発生したときに、クラウドメッセージングサービスへのプッシュ通知を有効にすることもできます。詳細については、\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-errors\"\u003Eドキュメント\u003C/a\u003Eを参照してください。\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E機械学習\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E前提条件：\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003ESnowpark_For_Python_DE.ipynb\u003C/a\u003Eで説明されているデータエンジニアリングの手順が正常に完了していること。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E下記リンク先のノートブックでは、次の機械学習タスクを説明しています。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESnowpark PythonからSnowflakeへの安全な接続を確立する\u003C/li\u003E\u003Cli\u003ESnowflakeテーブルからSnowpark DataFramesに機能とターゲットを読み込む\u003C/li\u003E\u003Cli\u003Eモデルトレーニングのための機能を準備する\u003C/li\u003E\u003Cli\u003ESnowflakeでSnowpark MLを使用してMLモデルをトレーニングする\u003C/li\u003E\u003Cli\u003Eオンライン推論とオフライン推論のそれぞれに、新しいデータポイントに対する推論用のスカラーおよびベクトル化された（別名バッチ）\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs\"\u003EPythonユーザー定義関数（UDF）\u003C/a\u003Eを作成する\u003C/li\u003E\u003C/ol\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/snowpark_e2e_ml.png\" alt=\"エンドツーエンドML\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EJupyterまたはVisual Studio Codeの機械学習ノートブック\u003C/h3\u003E\n","\u003Cp\u003E開始するには、次の手順に従います。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003Eターミナルウィンドウで、このフォルダを参照し、コマンドラインで\u003Ccode\u003Ejupyter notebook\u003C/code\u003Eを実行します（他のツールやVisual Studio CodeなどのIDEを使用することもできます）。\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb\"\u003ESnowpark_For_Python_ML.ipynb\u003C/a\u003Eを開いて実行します。\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E重要：Jupyterノートブックで、（Python）カーネルが***snowpark-de-ml***に設定されていることを確認してください。これは、\u003Cstrong\u003EGitHubレポジトリの複製\u003C/strong\u003Eステップで作成した環境の名前です。\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EHexの機械学習ノートブック\u003C/h3\u003E\n","\u003Cp\u003E既存の\u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003Eアカウントを使用する場合、または\u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003E30日間の無料トライアルアカウントを作成\u003C/a\u003Eする場合は、次の手順に従ってノートブックをロードし、HexからSnowflakeに接続するためのデータ接続を作成します。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb\"\u003ESnowpark_For_Python_ML.ipynb\u003C/a\u003Eをプロジェクトとしてアカウントにインポートします。インポートの詳細については、\u003Ca href=\"https://learn.hex.tech/docs/versioning/import-export\"\u003Eドキュメント\u003C/a\u003Eを参照してください。\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E次に、Snowflakeへの接続に\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003Eを使用する代わりに、以下に示すように\u003Ca href=\"https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database\"\u003Eデータ接続\u003C/a\u003Eを作成し、それを機械学習ノートブックで使用します。\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/hex_data_connection.png\" alt=\"HEXデータ接続\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E注意：ワークスペース内のプロジェクトやユーザーに対して、共有データ接続を作成することもできます。詳細については、\u003Ca href=\"https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections\"\u003Eドキュメント\u003C/a\u003Eを参照してください。\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003Eノートブックの次のコードスニペットを置き換えます。\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = json.load(open('connection.json'))\nsession = Session.builder.configs(connection_parameters).create()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E以下に置き換えます。\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport hextoolkit\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\nsession = hex_snowflake_conn.get_snowpark_session()\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStreamlitアプリケーション\u003C/h2\u003E\n","\u003Ch3\u003EStreamlitアプリをローカルで実行する\u003C/h3\u003E\n","\u003Cp\u003Eターミナルウィンドウでこのフォルダを参照し、次のコマンドを実行して、Streamlitアプリケーション\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction.py\"\u003ESnowpark_Streamlit_Revenue_Prediction.py\u003C/a\u003Eをマシンのローカルで実行します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Estreamlit run Snowpark_Streamlit_Revenue_Prediction.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E問題がなければ、次のようにアプリが読み込まれた状態でブラウザウィンドウが開きます。\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/app.png\" alt=\"Streamlitアプリ\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStreamlitアプリをSnowflakeで実行する - Streamlit-in-Snowflake（SiS）\u003C/h3\u003E\n","\u003Cp\u003EアカウントでSiSを有効にしている場合は、次の手順に従って、アプリケーションをマシンのローカルではなく、Snowsightで実行します。\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E重要：2023年6月現在、SiSはプライベートプレビュー中です。\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003E左側のナビゲーションメニューで \u003Cstrong\u003E「Streamlit」\u003C/strong\u003E をクリックします。\u003C/li\u003E\u003Cli\u003E右上の \u003Cstrong\u003E「+ Streamlitアプリ」\u003C/strong\u003E をクリックします。\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eアプリ名\u003C/strong\u003E を入力します。\u003C/li\u003E\u003Cli\u003EStreamlitアプリケーションを作成する \u003Cstrong\u003E「ウェアハウス」\u003C/strong\u003E と \u003Cstrong\u003E「アプリの場所」\u003C/strong\u003E（データベースとスキーマ）を選択します。\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E「作成」\u003C/strong\u003E をクリックします。\u003C/li\u003E\u003Cli\u003Eこの時点で、Streamlitのサンプルアプリケーションのコードが提供されます。\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction_SiS.py\"\u003ESnowpark_Streamlit_Revenue_Prediction_SiS.py\u003C/a\u003Eを開き、コードをStreamlitのサンプルアプリケーションにコピーして貼り付けます。\u003C/li\u003E\u003Cli\u003E右上の \u003Cstrong\u003E「実行」\u003C/strong\u003E をクリックします。\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E問題がなければ、以下に示すように、Snowsightに次のアプリが表示されます。\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/app_sis.png\" alt=\"Streamlit-in-Snowflake\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EデータをSnowflakeに保存する\u003C/h3\u003E\n","\u003Cp\u003E両方のアプリケーションで、広告予算スライダーを調整して、それらの割り当ての予測ROIを確認します。 \u003Cstrong\u003E「Snowflakeに保存」\u003C/strong\u003E ボタンをクリックして、現在の割り当てと予測ROIをBUDGET_ALLOCATIONS_AND_ROI Snowflakeテーブルに保存することもできます。\u003C/p\u003E\n","\u003Ch3\u003E2つのStreamlitアプリの違い\u003C/h3\u003E\n","\u003Cp\u003EStreamlitアプリケーションをローカルで実行する場合とSnowflake（SiS）で実行する場合の主な違いは、セッションオブジェクトを作成してアクセスする方法です。\u003C/p\u003E\n","\u003Cp\u003Eローカルで実行する場合は、次のように新しいセッションオブジェクトを作成してアクセスします。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Function to create Snowflake Session to connect to Snowflake\ndef create_session():\n    if "snowpark_session" not in st.session_state:\n        session = Session.builder.configs(json.load(open("connection.json"))).create()\n        st.session_state['snowpark_session'] = session\n    else:\n        session = st.session_state['snowpark_session']\n    return session\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowflake（SiS）で実行する場合は、次のように現在のSessionオブジェクトにアクセスします。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esession = snowpark.session._get_active_session()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Eクリーンアップ\u003C/h2\u003E\n","\u003Cp\u003E「\u003Cstrong\u003Eデータエンジニアリング\u003C/strong\u003E」セクションまたは「\u003Cstrong\u003Eデータパイプライン\u003C/strong\u003E」セクションの一部として、2つのタスク\u003Ccode\u003Emonthly_revenue_data_pipeline_task\u003C/code\u003Eと\u003Ccode\u003Ecampaign_spend_data_pipeline_task\u003C/code\u003Eを開始/再開した場合は、不要なリソース使用を回避するため、次のコマンドを実行してこれらのタスクを一時停止することが重要です。\u003C/p\u003E\n","\u003Cp\u003EノートブックでSnowpark Python APIを使用する場合\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task campaign_spend_data_pipeline_task suspend").collect()\nsession.sql("alter task monthly_revenue_data_pipeline_task suspend").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowsightの場合\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ealter task campaign_spend_data_pipeline_task suspend;\nalter task monthly_revenue_data_pipeline_task suspend;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Eまとめとリソース\u003C/h2\u003E\n","\u003Cp\u003Eおめでとうございます。Snowpark for Pythonとscikit-learnを使用して、データエンジニアリングタスクを正常に実行し、検索、ビデオ、ソーシャルメディア、メールなど複数のチャネルで変動する広告費予算の将来のROI（投資収益率）を予測する線形回帰モデルトレーニングしました。次に、そのモデルを使用して、ユーザー入力に基づいて新しい予算配分の予測を生成するStreamlitアプリケーションを作成しました。\u003C/p\u003E\n","\u003Cp\u003Eこのクイックスタートガイドに関するフィードバックをお待ちしています。こちらの\u003Ca href=\"https://forms.gle/XKd8rXPUNs2G1yM28\"\u003Eフィードバックフォーム\u003C/a\u003Eからフィードバックをお寄せください。\u003C/p\u003E\n","\u003Ch3\u003E学習した内容\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowpark DataFramesとAPIを利用してデータを分析し、データエンジニアリングタスクを実行する方法\u003C/li\u003E\u003Cli\u003E厳選されたSnowflake AnacondaチャネルからオープンソースのPythonライブラリを使用する方法\u003C/li\u003E\u003Cli\u003ESnowflakeでSnowpark MLを使用してMLモデルをトレーニングする方法\u003C/li\u003E\u003Cli\u003Eオンライン推論とオフライン推論のそれぞれに、スカラーおよびベクトル化されたSnowpark Pythonユーザー定義関数（UDF）を作成する方法\u003C/li\u003E\u003Cli\u003ESnowflakeタスクを作成してデータパイプラインとモデルの（再）トレーニングを自動化する方法\u003C/li\u003E\u003Cli\u003E推論にスカラーUDFを使用するStreamlitウェブアプリケーションを作成する方法\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E関連リソース\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn\"\u003EGitHubのソースコード\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003E上級：Snowpark for Pythonデータエンジニアリングガイド\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-snowpark-machine-learning/\"\u003E上級：Snowpark for Python機械学習ガイド\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md\"\u003ESnowpark for Pythonデモ\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html\"\u003ESnowpark for Python開発者ガイド\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/\"\u003EStreamlitドキュメント\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Snowpark for Pythonを使用したデータエンジニアリングとMLの入門",":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\r\n\u003C!-- ------------------------ --\u003E\r\n## 概要\r\n\r\n\r\nこのガイドを完了すると、未加工データから、組織が広告予算の割り当てを最適化するのに役立つ双方向アプリケーションに移行できるようになります。\r\n\r\nこのクイックスタートに従って、各ステップで次のような概要を学ぶことができます。\r\n\r\n- **環境の設定**：ステージとテーブルを使用して、S3からSnowflakeに未加工データを取り込み、整理します。\r\n- **データエンジニアリング**：Snowpark for Python DataFramesを活用して、グループ化、集約、ピボット、結合などのデータ変換を実行し、下流のアプリケーション用のデータを準備します。\r\n- **データパイプライン**：Snowflakeタスクを使用して、データパイプラインコードを、統合された監視を備えた運用パイプラインに変換します。\r\n- **機械学習**：データを準備し、SnowflakeでSnowpark MLを使用してMLトレーニングを実行し、Snowparkユーザー定義関数（UDF）としてモデルを展開します。\r\n- **Streamlitアプリケーション**：Pythonを使用してインタラクティブなアプリケーションを構築し（ウェブ開発の経験は不要）、さまざまな広告費予算のROIの可視化を支援します。\r\n\r\n上記のテクノロジーについて初めて知る方のために、ドキュメントへのリンクを含む簡単な要約を以下に示します。\r\n\r\n### Snowparkとは\r\n\r\nPython、Java、Scalaなどの非SQLコードを安全にデプロイして処理するSnowflakeのライブラリとランタイムのセットです。\r\n\r\n**使い慣れたクライアント側ライブラリ** - Snowparkは、高度に統合されたDataFrame型のプログラミングとOSS互換のAPIをデータ実務者の好みの言語で利用できるようにします。より効率的なMLモデリング（公開プレビュー）とML運用（プライベートプレビュー）のためのSnowpark ML APIも含まれています。\r\n\r\n**柔軟なランタイムコンストラクト** - Snowparkは、ユーザーがカスタムロジックを取り込んで実行できるようにする柔軟なランタイムコンストラクトを提供します。開発者は、ユーザー定義関数とストアドプロシージャを使用して、データパイプライン、MLモデル、データアプリケーションをシームレスに構築できます。\r\n\r\n詳しくは、[Snowpark](/snowpark/)をご覧ください。\r\n\r\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/snowpark.png)\r\n\r\n### Snowpark MLとは\r\n\r\nSnowpark MLは、Snowflakeでより高速かつ直感的なエンドツーエンドのML開発を行うための新しいライブラリです。Snowpark MLには、モデル開発用のSnowpark MLモデリング（公開プレビュー）とモデル展開用のSnowpark ML運用（プライベートプレビュー）の2つのAPIがあります。\r\n\r\nこのクイックスタートでは、特徴量エンジニアリングをスケールアウトし、SnowflakeでのMLトレーニングの実行を簡素化するSnowpark MLモデリングAPIに焦点を当てます。\r\n\r\n### Streamlitとは\r\n\r\nStreamlitは、開発者がデータアプリケーションをすばやく簡単に作成、共有、デプロイできるようにする、純粋なPythonの[オープンソース](https://github.com/streamlit/streamlit)アプリケーションフレームワークです。詳しくは、[Streamlit](https://streamlit.io/)をご覧ください。\r\n\r\n### 学習する内容\r\n\r\n- Snowpark DataFramesとAPIを利用してデータを分析し、データエンジニアリングタスクを実行する方法\r\n- 厳選されたSnowflake AnacondaチャネルからオープンソースのPythonライブラリを使用する方法\r\n- SnowflakeでSnowpark MLを使用してMLモデルをトレーニングする方法\r\n- オンライン推論とオフライン推論のそれぞれに、スカラーおよびベクトル化されたSnowpark Pythonユーザー定義関数（UDF）を作成する方法\r\n- Snowflakeタスクを作成してデータパイプラインを自動化する方法\r\n- ユーザー入力に基づく推論にスカラーUDFを使用するStreamlitウェブアプリケーションを作成する方法\r\n\r\n### 前提条件\r\n\r\n- [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)がインストールされていること\r\n- [Python 3.9](https://www.python.org/downloads/)がインストールされていること\r\n  - **開始**ステップでは、Python環境をPython 3.9で作成することに注意してください。\r\n- [ORGADMINによって有効化されたAnacondaパッケージ](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda)を持つSnowflakeアカウント。Snowflakeアカウントをお持ちでない場合は、[無料トライアルアカウント](https://signup.snowflake.com/?utm_cta=quickstarts_)に登録できます。\r\n- アカウント管理者の役割を持つSnowflakeアカウントのログイン。環境にこの役割がある場合は、それを使用できます。それ以外の場合は、1）無料トライアルに登録する、2）データベース、スキーマ、テーブル、ステージ、タスク、ユーザー定義関数、ストアドプロシージャを作成できる別の役割を使用する、または3）上記のオブジェクトを作成できる既存のデータベースとスキーマを使用する必要があります。\r\n\r\n\u003E 重要：続行する前に、[こちら](https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-packages#getting-started)で説明されているように、ORGADMINによってAnacondaパッケージが有効化されているSnowflakeアカウントがあることを確認してください。\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 環境を設定する\r\n\r\n\r\n### テーブルを作成し、データを読み込み、ステージを設定する\r\n\r\n認証情報を使用して[Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight.html#)にログインしてテーブルを作成し、Amazon S3からデータを読み込み、Snowflake内部ステージを設定します。\r\n\r\n\u003E 重要：\r\n\u003E \r\n\u003E - このセクションで作成したオブジェクトに別の名前を使用する場合は、それに応じて次のセクションのスクリプトとコードを更新してください。\r\n\u003E \r\n\u003E - 以下の各SQLスクリプトブロックについて、ブロック内のすべてのステートメントを選択し、上から順に実行します。\r\n\r\n次のSQLコマンドを実行して、[ウェアハウス](https://docs.snowflake.com/en/sql-reference/sql/create-warehouse.html)、[データベース](https://docs.snowflake.com/en/sql-reference/sql/create-database.html)、[スキーマ](https://docs.snowflake.com/en/sql-reference/sql/create-schema.html)を作成します。\r\n\r\n```sql\r\nUSE ROLE ACCOUNTADMIN;\r\n\r\nCREATE OR REPLACE WAREHOUSE DASH_L;\r\nCREATE OR REPLACE DATABASE DASH_DB;\r\nCREATE OR REPLACE SCHEMA DASH_SCHEMA;\r\n\r\nUSE DASH_DB.DASH_SCHEMA;\r\n```\r\n\r\n次のSQLコマンドを実行し、公的にアクセス可能なS3バケットにホストされているデータからテーブル**CAMPAIGN_SPEND**を作成します。\r\n\r\n```sql\r\nCREATE or REPLACE file format csvformat\r\n  skip_header = 1\r\n  type = 'CSV';\r\n\r\nCREATE or REPLACE stage campaign_data_stage\r\n  file_format = csvformat\r\n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/campaign_spend/';\r\n\r\nCREATE or REPLACE TABLE CAMPAIGN_SPEND (\r\n  CAMPAIGN VARCHAR(60), \r\n  CHANNEL VARCHAR(60),\r\n  DATE DATE,\r\n  TOTAL_CLICKS NUMBER(38,0),\r\n  TOTAL_COST NUMBER(38,0),\r\n  ADS_SERVED NUMBER(38,0)\r\n);\r\n\r\nCOPY into CAMPAIGN_SPEND\r\n  from @campaign_data_stage;\r\n```\r\n\r\n次のSQLコマンドを実行し、公的にアクセス可能なS3バケットにホストされているデータからテーブル**MONTHLY_REVENUE**を作成します。\r\n\r\n```sql\r\nCREATE or REPLACE stage monthly_revenue_data_stage\r\n  file_format = csvformat\r\n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/monthly_revenue/';\r\n\r\nCREATE or REPLACE TABLE MONTHLY_REVENUE (\r\n  YEAR NUMBER(38,0),\r\n  MONTH NUMBER(38,0),\r\n  REVENUE FLOAT\r\n);\r\n\r\nCOPY into MONTHLY_REVENUE\r\n  from @monthly_revenue_data_stage;\r\n```\r\n\r\n次のSQLコマンドを実行して、過去6か月間の予算割り当てとROIを保持するテーブル**BUDGET_ALLOCATIONS_AND_ROI**を作成します。\r\n\r\n```sql\r\nCREATE or REPLACE TABLE BUDGET_ALLOCATIONS_AND_ROI (\r\n  MONTH varchar(30),\r\n  SEARCHENGINE integer,\r\n  SOCIALMEDIA integer,\r\n  VIDEO integer,\r\n  EMAIL integer,\r\n  ROI float\r\n)\r\nCOMMENT = '{\"origin\":\"sf_sit-is\", \"name\":\"aiml_notebooks_ad_spend_roi\", \"version\":{\"major\":1, \"minor\":0}, \"attributes\":{\"is_quickstart\":1, \"source\":\"streamlit\"}}';\r\n\r\nINSERT INTO BUDGET_ALLOCATIONS_AND_ROI (MONTH, SEARCHENGINE, SOCIALMEDIA, VIDEO, EMAIL, ROI)\r\nVALUES\r\n('January',35,50,35,85,8.22),\r\n('February',75,50,35,85,13.90),\r\n('March',15,50,35,15,7.34),\r\n('April',25,80,40,90,13.23),\r\n('May',95,95,10,95,6.246),\r\n('June',35,50,35,85,8.22);\r\n```\r\n\r\n次のコマンドを実行して、ストアドプロシージャ、UDF、MLモデルファイルを格納するためのSnowflake[内部ステージ](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage)を作成します。\r\n\r\n```sql\r\nCREATE OR REPLACE STAGE dash_sprocs;\r\nCREATE OR REPLACE STAGE dash_models;\r\nCREATE OR REPLACE STAGE dash_udfs;\r\n```\r\n\r\n任意で、Snowsightで[setup.sql](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/setup.sql)を開き、すべてのSQLステートメントを実行してオブジェクトを作成し、AWS S3からデータを読み込むこともできます。\r\n\r\n\u003E 重要：このセクションで作成したオブジェクトに別の名前を使用する場合は、それに応じて次のセクションのスクリプトとコードを更新してください。\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 開始する\r\n\r\n\r\nこのセクションでは、GitHubレポジトリの複製と、Snowpark for Python環境の設定について説明します。\r\n\r\n### GitHubレポジトリを複製する\r\n\r\n最初のステップは、[GitHubレポジトリ](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn)を複製することです。このレポジトリには、このクイックスタートガイドを正常に完了するために必要なすべてのコードが含まれています。\r\n\r\nHTTPSを使用する場合：\r\n\r\n```shell\r\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\r\n```\r\n\r\nまたは、SSHを使用する場合：\r\n\r\n```shell\r\ngit clone git@github.com:Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\r\n```\r\n\r\n### Snowpark for Python\r\n\r\n**データエンジニアリング**と**機械学習**のステップを完了するには、以下の説明に従って、すべてをローカルにインストールする（オプション1）か、Hexを使用する（オプション2）を選択します。\r\n\r\n\u003E 重要：**Streamlitアプリケーション**を実行するには、Python環境を作成し、「**ローカルインストール**」の説明に従って、Snowpark for Pythonとその他のライブラリをローカルにインストールする必要があります。\r\n\r\n#### オプション1 -- ローカルインストール\r\n\r\nこのオプションを使用すると、このクイックスタートガイドのすべてのステップを完了できます。\r\n\r\n**ステップ1：**[https://conda.io/miniconda.html](https://conda.io/miniconda.html)からminicondaインストーラーをダウンロードしてインストールします*（または、Python 3.9では、[virtualenv](https://virtualenv.pypa.io/en/latest/)などの他のPython環境を使用することもできます）*。\r\n\r\n**ステップ2：**新しいターミナルウィンドウを開き、同じターミナルウィンドウで次のコマンドを実行します。\r\n\r\n**ステップ3：**同じターミナルウィンドウで次のコマンドを実行して、**snowpark-de-ml**というPython 3.9 conda環境を作成します。\r\n\r\n```python\r\nconda create --name snowpark-de-ml -c https://repo.anaconda.com/pkgs/snowflake python=3.9\r\n```\r\n\r\n**ステップ4：**同じターミナルウィンドウで次のコマンドを実行して、conda環境**snowpark-de-ml**をアクティブ化します。\r\n\r\n```python\r\nconda activate snowpark-de-ml\r\n```\r\n\r\n**ステップ5：**同じターミナルウィンドウで次のコマンドを実行して、[Snowflake Anacondaチャンネル](https://repo.anaconda.com/pkgs/snowflake/)からSnowpark Pythonとその他のライブラリをconda環境**snowpark-de-ml**にインストールします。\r\n\r\n```python\r\nconda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python pandas notebook scikit-learn cachetools\r\n```\r\n\r\n**ステップ6：**同じターミナルウィンドウで次のコマンドを実行して、Streamlitライブラリをconda環境**snowpark-de-ml**にインストールします。\r\n\r\n```python\r\npip install streamlit\r\n```\r\n\r\n**ステップ7：**同じターミナルウィンドウで次のコマンドを実行して、Snowpark MLライブラリをconda環境**snowpark-de-ml**にインストールします。\r\n\r\n```python\r\npip install snowflake-ml-python\r\n```\r\n\r\n**ステップ9：**Snowflakeアカウントの詳細と認証情報で[connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json)を更新します。\r\n\r\n以下は、**環境の設定**ステップで説明したオブジェクト名に基づく***connection.json***のサンプルです。\r\n\r\n```json\r\n{\r\n  \"account\"   : \"\u003Cyour_account_identifier_goes_here\u003E\",\r\n  \"user\"      : \"\u003Cyour_username_goes_here\u003E\",\r\n  \"password\"  : \"\u003Cyour_password_goes_here\u003E\",\r\n  \"role\"      : \"ACCOUNTADMIN\",\r\n  \"warehouse\" : \"DASH_L\",\r\n  \"database\"  : \"DASH_DB\",\r\n  \"schema\"    : \"DASH_SCHEMA\"\r\n}\r\n```\r\n\r\n\u003E 注意：上記の**account**パラメータには、**アカウント識別子**を指定し、snowflakecomputing.comドメイン名は含めないでください。Snowflakeは、接続の作成時にこれを自動的に追加します。詳細については、[ドキュメント](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html)を参照してください。\r\n\r\n#### オプション2 -- Hexの使用\r\n\r\n既存の[Hex](https://app.hex.tech/login)アカウントを使用する場合、または[30日間の無料トライアルアカウントを作成](https://app.hex.tech/signup/quickstart-30)する場合は、Snowpark for Pythonが組み込まれているため、Python環境を作成し、Snowpark for Pythonを他のライブラリとともにラップトップにローカルにインストールする必要はありません。これにより、このクイックスタートガイドの**データエンジニアリング**と**機械学習**のステップをHexで直接完了できるようになります。（Hexでデータエンジニアリングと機械学習のノートブックをロードする詳細については、それぞれの手順を参照してください）。\r\n\r\n\u003E 重要：**Streamlitアプリケーション**を実行するには、Python環境を作成し、上記の「**ローカルインストール**」の説明に従って、Snowpark for Pythonとその他のライブラリをローカルにインストールする必要があります。\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## データエンジニアリング\r\n\r\n\r\n下記リンク先のノートブックでは、次のデータエンジニアリングタスクを説明しています。\r\n\r\n1) Snowpark PythonからSnowflakeへの安全な接続を確立する\r\n2) SnowflakeテーブルからSnowpark DataFramesにデータを読み込む\r\n3) Snowpark DataFramesで探索的データ分析を実行する\r\n4) Snowpark DataFramesを使用して、複数のテーブルからデータをピボットおよび結合する\r\n5) Snowflakeタスクを使用してデータパイプラインタスクを自動化する\r\n\r\n### JupyterまたはVisual Studio Codeのデータエンジニアリングノートブック\r\n\r\n開始するには、次の手順に従います。\r\n\r\n1) ターミナルウィンドウで、このフォルダを参照し、コマンドラインで`jupyter notebook`を実行します（他のツールやVisual Studio CodeなどのIDEを使用することもできます）。\r\n\r\n2) [Snowpark_For_Python_DE.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb)のセルを開いて実行します。\r\n\r\n\u003E 重要：Jupyterノートブックで、（Python）カーネルが***snowpark-de-ml***に設定されていることを確認してください。これは、**GitHubレポジトリの複製**ステップで作成した環境の名前です。\r\n\r\n### Hexのデータエンジニアリングノートブック\r\n\r\n既存の[Hex](https://app.hex.tech/login)アカウントを使用する場合、または[30日間の無料トライアルアカウントを作成](https://app.hex.tech/signup/quickstart-30)する場合は、次の手順に従ってノートブックをロードし、HexからSnowflakeに接続するためのデータ接続を作成します。\r\n\r\n1) [Snowpark_For_Python_DE.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb)をプロジェクトとしてアカウントにインポートします。インポートの詳細については、[ドキュメント](https://learn.hex.tech/docs/versioning/import-export)を参照してください。\r\n\r\n2) 次に、Snowflakeへの接続に[connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json)を使用する代わりに、以下に示すように[データ接続](https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database)を作成し、それをデータエンジニアリングノートブックで使用します。\r\n\r\n![HEXデータ接続](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/hex_data_connection.png)\r\n\r\n\u003E 注意：ワークスペース内のプロジェクトやユーザーに対して、共有データ接続を作成することもできます。詳細については、[ドキュメント](https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections)を参照してください。\r\n\r\n3) ノートブックの次のコードスニペットを置き換えます。\r\n\r\n```python\r\nconnection_parameters = json.load(open('connection.json'))\r\nsession = Session.builder.configs(connection_parameters).create()\r\n```\r\n\r\n**以下に置き換えます。**\r\n\r\n```python\r\nimport hextoolkit\r\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\r\nsession = hex_snowflake_conn.get_snowpark_session()\r\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## データパイプライン\r\n\r\nデータトランスフォーメーションは、Snowflakeで実行される自動データパイプラインの形式で運用することもできます。\r\n\r\n特に、「[データエンジニアリングノートブック](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb)」には、オプションでデータトランスフォーメーションを[Snowflakeタスク](https://docs.snowflake.com/en/user-guide/tasks-intro)として構築して実行する方法を説明しているセクションがあります。\r\n\r\n参考までに、コードスニペットを以下に示します。\r\n\r\n### **ルート/親タスク**\r\n\r\nこのタスクは、キャンペーン支出データのロードとさまざまな変換の実行を自動化します。\r\n\r\n```python\r\ndef campaign_spend_data_pipeline(session: Session) -\u003E str:\r\n  # DATA TRANSFORMATIONS\r\n  # Perform the following actions to transform the data\r\n\r\n  # Load the campaign spend data\r\n  snow_df_spend_t = session.table('campaign_spend')\r\n\r\n  # Transform the data so we can see total cost per year/month per channel using group_by() and agg() Snowpark DataFrame functions\r\n  snow_df_spend_per_channel_t = snow_df_spend_t.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).\r\n      with_column_renamed('\"YEAR(DATE)\"',\"YEAR\").with_column_renamed('\"MONTH(DATE)\"',\"MONTH\").sort('YEAR','MONTH')\r\n\r\n  # Transform the data so that each row will represent total cost across all channels per year/month using pivot() and sum() Snowpark DataFrame functions\r\n  snow_df_spend_per_month_t = snow_df_spend_per_channel_t.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH')\r\n  snow_df_spend_per_month_t = snow_df_spend_per_month_t.select(\r\n      col(\"YEAR\"),\r\n      col(\"MONTH\"),\r\n      col(\"'search_engine'\").as_(\"SEARCH_ENGINE\"),\r\n      col(\"'social_media'\").as_(\"SOCIAL_MEDIA\"),\r\n      col(\"'video'\").as_(\"VIDEO\"),\r\n      col(\"'email'\").as_(\"EMAIL\")\r\n  )\r\n\r\n  # Save transformed data\r\n  snow_df_spend_per_month_t.write.mode('overwrite').save_as_table('SPEND_PER_MONTH')\r\n\r\n# Register data pipelining function as a Stored Procedure so it can be run as a task\r\nsession.sproc.register(\r\n  func=campaign_spend_data_pipeline,\r\n  name=\"campaign_spend_data_pipeline\",\r\n  packages=['snowflake-snowpark-python'],\r\n  is_permanent=True,\r\n  stage_location=\"@dash_sprocs\",\r\n  replace=True)\r\n\r\ncampaign_spend_data_pipeline_task = \"\"\"\r\nCREATE OR REPLACE TASK campaign_spend_data_pipeline_task\r\n    WAREHOUSE = 'DASH_L'\r\n    SCHEDULE  = '3 MINUTE'\r\nAS\r\n    CALL campaign_spend_data_pipeline()\r\n\"\"\"\r\nsession.sql(campaign_spend_data_pipeline_task).collect()\r\n```\r\n\r\n### **子/依存タスク**\r\n\r\nこのタスクは、月間売上データのロード、さまざまな変換の実行、変換されたキャンペーン支出データとの結合を自動化します。\r\n\r\n```python\r\ndef monthly_revenue_data_pipeline(session: Session) -\u003E str:\r\n  # Load revenue table and transform the data into revenue per year/month using group_by and agg() functions\r\n  snow_df_spend_per_month_t = session.table('spend_per_month')\r\n  snow_df_revenue_t = session.table('monthly_revenue')\r\n  snow_df_revenue_per_month_t = snow_df_revenue_t.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')\r\n\r\n  # Join revenue data with the transformed campaign spend data so that our input features (i.e. cost per channel) and target variable (i.e. revenue) can be loaded into a single table for model training\r\n  snow_df_spend_and_revenue_per_month_t = snow_df_spend_per_month_t.join(snow_df_revenue_per_month_t, [\"YEAR\",\"MONTH\"])\r\n\r\n  # SAVE in a new table for the next task\r\n  snow_df_spend_and_revenue_per_month_t.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')\r\n\r\n# Register data pipelining function as a Stored Procedure so it can be run as a task\r\nsession.sproc.register(\r\n  func=monthly_revenue_data_pipeline,\r\n  name=\"monthly_revenue_data_pipeline\",\r\n  packages=['snowflake-snowpark-python'],\r\n  is_permanent=True,\r\n  stage_location=\"@dash_sprocs\",\r\n  replace=True)\r\n\r\nmonthly_revenue_data_pipeline_task = \"\"\"\r\n  CREATE OR REPLACE TASK monthly_revenue_data_pipeline_task\r\n      WAREHOUSE = 'DASH_L'\r\n      AFTER campaign_spend_data_pipeline_task\r\n  AS\r\n      CALL monthly_revenue_data_pipeline()\r\n  \"\"\"\r\nsession.sql(monthly_revenue_data_pipeline_task).collect()\r\n```\r\n\r\n\u003E 注意：上記の***monthly_revenue_data_pipeline_task***には、**AFTER campaign_spend_data_pipeline_task**句があり、依存タスクであることに注意してください。\r\n\r\n#### タスクの開始\r\n\r\nSnowflakeタスクはデフォルトでは開始されないため、開始/再開するには次のステートメントを実行する必要があります。\r\n\r\n```sql\r\nsession.sql(\"alter task monthly_revenue_data_pipeline_task resume\").collect()\r\nsession.sql(\"alter task campaign_spend_data_pipeline_task resume\").collect()\r\n```\r\n\r\n#### タスクの中断\r\n\r\n上記のタスクを再開する場合は、不要なリソース使用を回避するため、次のコマンドを実行してタスクを一時停止してください。\r\n\r\n```sql\r\nsession.sql(\"alter task campaign_spend_data_pipeline_task suspend\").collect()\r\nsession.sql(\"alter task monthly_revenue_data_pipeline_task suspend\").collect()\r\n```\r\n\r\n### タスクの監視\r\n\r\nこれらのタスクとその[DAG](https://docs.snowflake.com/en/user-guide/tasks-intro#label-task-dag)は、[Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight-tasks#viewing-individual-task-graphs)で次のように表示できます。\r\n\r\n---\r\n\r\n![タスクの監視](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/snowflake_tasks.png)\r\n\r\n---\r\n\r\n### タスクのエラー通知\r\n\r\nタスクの実行中にエラーが発生したときに、クラウドメッセージングサービスへのプッシュ通知を有効にすることもできます。詳細については、[ドキュメント](https://docs.snowflake.com/en/user-guide/tasks-errors)を参照してください。\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 機械学習\r\n\r\n\r\n\u003E 前提条件：[Snowpark_For_Python_DE.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb)で説明されているデータエンジニアリングの手順が正常に完了していること。\r\n\r\n下記リンク先のノートブックでは、次の機械学習タスクを説明しています。\r\n\r\n1) Snowpark PythonからSnowflakeへの安全な接続を確立する\r\n2) SnowflakeテーブルからSnowpark DataFramesに機能とターゲットを読み込む\r\n3) モデルトレーニングのための機能を準備する\r\n4) SnowflakeでSnowpark MLを使用してMLモデルをトレーニングする\r\n5) オンライン推論とオフライン推論のそれぞれに、新しいデータポイントに対する推論用のスカラーおよびベクトル化された（別名バッチ）[Pythonユーザー定義関数（UDF）](https://docs.snowflake.com/en/developer-guide/snowpark/python/creating-udfs)を作成する\r\n\r\n---\r\n\r\n![エンドツーエンドML](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/snowpark_e2e_ml.png)\r\n\r\n---\r\n\r\n### JupyterまたはVisual Studio Codeの機械学習ノートブック\r\n\r\n開始するには、次の手順に従います。\r\n\r\n1) ターミナルウィンドウで、このフォルダを参照し、コマンドラインで`jupyter notebook`を実行します（他のツールやVisual Studio CodeなどのIDEを使用することもできます）。\r\n\r\n2) [Snowpark_For_Python_ML.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb)を開いて実行します。\r\n\r\n\u003E 重要：Jupyterノートブックで、（Python）カーネルが***snowpark-de-ml***に設定されていることを確認してください。これは、**GitHubレポジトリの複製**ステップで作成した環境の名前です。\r\n\r\n### Hexの機械学習ノートブック\r\n\r\n既存の[Hex](https://app.hex.tech/login)アカウントを使用する場合、または[30日間の無料トライアルアカウントを作成](https://app.hex.tech/signup/quickstart-30)する場合は、次の手順に従ってノートブックをロードし、HexからSnowflakeに接続するためのデータ接続を作成します。\r\n\r\n1) [Snowpark_For_Python_ML.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb)をプロジェクトとしてアカウントにインポートします。インポートの詳細については、[ドキュメント](https://learn.hex.tech/docs/versioning/import-export)を参照してください。\r\n\r\n2) 次に、Snowflakeへの接続に[connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json)を使用する代わりに、以下に示すように[データ接続](https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database)を作成し、それを機械学習ノートブックで使用します。\r\n\r\n![HEXデータ接続](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/hex_data_connection.png)\r\n\r\n\u003E 注意：ワークスペース内のプロジェクトやユーザーに対して、共有データ接続を作成することもできます。詳細については、[ドキュメント](https://learn.hex.tech/docs/administration/workspace_settings/workspace-assets#shared-data-connections)を参照してください。\r\n\r\n3) ノートブックの次のコードスニペットを置き換えます。\r\n\r\n```python\r\nconnection_parameters = json.load(open('connection.json'))\r\nsession = Session.builder.configs(connection_parameters).create()\r\n```\r\n\r\n**以下に置き換えます。**\r\n\r\n```python\r\nimport hextoolkit\r\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\r\nsession = hex_snowflake_conn.get_snowpark_session()\r\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Streamlitアプリケーション\r\n\r\n\r\n### Streamlitアプリをローカルで実行する\r\n\r\nターミナルウィンドウでこのフォルダを参照し、次のコマンドを実行して、Streamlitアプリケーション[Snowpark_Streamlit_Revenue_Prediction.py](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction.py)をマシンのローカルで実行します。\r\n\r\n```shell\r\nstreamlit run Snowpark_Streamlit_Revenue_Prediction.py\r\n```\r\n\r\n問題がなければ、次のようにアプリが読み込まれた状態でブラウザウィンドウが開きます。\r\n\r\n---\r\n\r\n![Streamlitアプリ](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/app.png)\r\n\r\n---\r\n\r\n### StreamlitアプリをSnowflakeで実行する - Streamlit-in-Snowflake（SiS）\r\n\r\nアカウントでSiSを有効にしている場合は、次の手順に従って、アプリケーションをマシンのローカルではなく、Snowsightで実行します。\r\n\r\n\u003E 重要：2023年6月現在、SiSはプライベートプレビュー中です。\r\n\r\n1) 左側のナビゲーションメニューで **「Streamlit」** をクリックします。\r\n2) 右上の **「+ Streamlitアプリ」** をクリックします。\r\n3) **アプリ名** を入力します。\r\n4) Streamlitアプリケーションを作成する **「ウェアハウス」** と **「アプリの場所」**（データベースとスキーマ）を選択します。\r\n5) **「作成」** をクリックします。\r\n6) この時点で、Streamlitのサンプルアプリケーションのコードが提供されます。[Snowpark_Streamlit_Revenue_Prediction_SiS.py](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction_SiS.py)を開き、コードをStreamlitのサンプルアプリケーションにコピーして貼り付けます。\r\n7) 右上の **「実行」** をクリックします。\r\n\r\n問題がなければ、以下に示すように、Snowsightに次のアプリが表示されます。\r\n\r\n---\r\n\r\n![Streamlit-in-Snowflake](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-ja/app_sis.png)\r\n\r\n---\r\n\r\n### データをSnowflakeに保存する\r\n\r\n両方のアプリケーションで、広告予算スライダーを調整して、それらの割り当ての予測ROIを確認します。 **「Snowflakeに保存」** ボタンをクリックして、現在の割り当てと予測ROIをBUDGET_ALLOCATIONS_AND_ROI Snowflakeテーブルに保存することもできます。\r\n\r\n### 2つのStreamlitアプリの違い\r\n\r\nStreamlitアプリケーションをローカルで実行する場合とSnowflake（SiS）で実行する場合の主な違いは、セッションオブジェクトを作成してアクセスする方法です。\r\n\r\nローカルで実行する場合は、次のように新しいセッションオブジェクトを作成してアクセスします。\r\n\r\n```python\r\n# Function to create Snowflake Session to connect to Snowflake\r\ndef create_session():\r\n    if \"snowpark_session\" not in st.session_state:\r\n        session = Session.builder.configs(json.load(open(\"connection.json\"))).create()\r\n        st.session_state['snowpark_session'] = session\r\n    else:\r\n        session = st.session_state['snowpark_session']\r\n    return session\r\n```\r\n\r\nSnowflake（SiS）で実行する場合は、次のように現在のSessionオブジェクトにアクセスします。\r\n\r\n```python\r\nsession = snowpark.session._get_active_session()\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## クリーンアップ\r\n\r\n「**データエンジニアリング**」セクションまたは「**データパイプライン**」セクションの一部として、2つのタスク`monthly_revenue_data_pipeline_task`と`campaign_spend_data_pipeline_task`を開始/再開した場合は、不要なリソース使用を回避するため、次のコマンドを実行してこれらのタスクを一時停止することが重要です。\r\n\r\nノートブックでSnowpark Python APIを使用する場合\r\n\r\n```sql\r\nsession.sql(\"alter task campaign_spend_data_pipeline_task suspend\").collect()\r\nsession.sql(\"alter task monthly_revenue_data_pipeline_task suspend\").collect()\r\n```\r\n\r\nSnowsightの場合\r\n\r\n```sql\r\nalter task campaign_spend_data_pipeline_task suspend;\r\nalter task monthly_revenue_data_pipeline_task suspend;\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## まとめとリソース\r\n\r\n\r\nおめでとうございます。Snowpark for Pythonとscikit-learnを使用して、データエンジニアリングタスクを正常に実行し、検索、ビデオ、ソーシャルメディア、メールなど複数のチャネルで変動する広告費予算の将来のROI（投資収益率）を予測する線形回帰モデルトレーニングしました。次に、そのモデルを使用して、ユーザー入力に基づいて新しい予算配分の予測を生成するStreamlitアプリケーションを作成しました。\r\n\r\nこのクイックスタートガイドに関するフィードバックをお待ちしています。こちらの[フィードバックフォーム](https://forms.gle/XKd8rXPUNs2G1yM28)からフィードバックをお寄せください。\r\n\r\n### 学習した内容\r\n\r\n- Snowpark DataFramesとAPIを利用してデータを分析し、データエンジニアリングタスクを実行する方法\r\n- 厳選されたSnowflake AnacondaチャネルからオープンソースのPythonライブラリを使用する方法\r\n- SnowflakeでSnowpark MLを使用してMLモデルをトレーニングする方法\r\n- オンライン推論とオフライン推論のそれぞれに、スカラーおよびベクトル化されたSnowpark Pythonユーザー定義関数（UDF）を作成する方法\r\n- Snowflakeタスクを作成してデータパイプラインとモデルの（再）トレーニングを自動化する方法\r\n- 推論にスカラーUDFを使用するStreamlitウェブアプリケーションを作成する方法\r\n\r\n### 関連リソース\r\n\r\n- [GitHubのソースコード](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn)\r\n- [上級：Snowpark for Pythonデータエンジニアリングガイド](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/)\r\n- [上級：Snowpark for Python機械学習ガイド](/en/developers/guides/getting-started-snowpark-machine-learning/)\r\n- [Snowpark for Pythonデモ](https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md)\r\n- [Snowpark for Python開発者ガイド](https://docs.snowflake.com/en/developer-guide/snowpark/python/index.html)\r\n- [Streamlitドキュメント](https://docs.streamlit.io/)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo 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