From 29d680b329be3c3c49ca10b0cdb2abdca8868d3d Mon Sep 17 00:00:00 2001 From: olprod Date: Fri, 20 Oct 2023 17:33:34 -0700 Subject: [PATCH] Copy Files From Source Repo (2023-10-20 17:33) --- .../Labs/08a-data-science-explore-data.md | 3 +- ...b-data-science-preprocess-data-wrangler.md | 14 +- Instructions/Labs/08c-data-science-train.md | 264 ++++++++++++++++++ .../Labs/12-query-data-in-kql-database.md | 67 +++++ .../Labs/Images/check-kql-sample-dataset.mp4 | Bin 0 -> 6871729 bytes .../Labs/Images/create-sample-data.png | Bin 0 -> 65719 bytes .../Labs/Images/data-wrangler-delete.png | Bin 21804 -> 10262 bytes .../Labs/Images/data-wrangler-summary.png | Bin 193362 -> 27326 bytes Instructions/Labs/Images/load-sample-data.png | Bin 0 -> 67394 bytes .../Labs/Images/select-kqldatabase.png | Bin 9574 -> 14473 bytes 10 files changed, 339 insertions(+), 9 deletions(-) create mode 100644 Instructions/Labs/08c-data-science-train.md create mode 100644 Instructions/Labs/12-query-data-in-kql-database.md create mode 100644 Instructions/Labs/Images/check-kql-sample-dataset.mp4 create mode 100644 Instructions/Labs/Images/create-sample-data.png create mode 100644 Instructions/Labs/Images/load-sample-data.png diff --git a/Instructions/Labs/08a-data-science-explore-data.md b/Instructions/Labs/08a-data-science-explore-data.md index 7909ccf..2b96ca3 100644 --- a/Instructions/Labs/08a-data-science-explore-data.md +++ b/Instructions/Labs/08a-data-science-explore-data.md @@ -134,8 +134,7 @@ lab: 1. 使用单元格输出下方的“ **+ 代码**”图标将新代码单元格添加到笔记本中,然后输入以下代码。 ```python - desc_stats = df.describe() - print(desc_stats) + df.describe() ``` 平均年龄约为 48.5 年,标准偏差为 13.1 年。 年龄最小的是 19 岁,年龄最大的是 79 岁。 平均 `BMI` 约为 26.4,根据 [WHO 标准](https://www.who.int/health-topics/obesity#tab=tab_1),属于**超重**类别。 最小值 `BMI` 为 18,最大值为 42.2。 diff --git a/Instructions/Labs/08b-data-science-preprocess-data-wrangler.md b/Instructions/Labs/08b-data-science-preprocess-data-wrangler.md index a6cb61a..5c363fa 100644 --- a/Instructions/Labs/08b-data-science-preprocess-data-wrangler.md +++ b/Instructions/Labs/08b-data-science-preprocess-data-wrangler.md @@ -142,7 +142,7 @@ lab: >注意:代码会自动复制到笔记本单元格,并可供使用。 -1. 将第 10 行和第 11 行替换为代码 `df = clean_data(df)`,因为数据整理器中生成的代码不会覆盖原始数据帧。 最终块应如下所示: +1. 将第 10 行和第 11 行替换为代码 `df = clean_data(df)`,因为数据整理器中生成的代码不会覆盖原始数据帧。 最终代码块应如下所示: ```python def clean_data(df): @@ -171,7 +171,7 @@ lab: 1. 在 `df` 数据帧的顶部菜单中启动数据整理程序。 -1. 选择网格上的 `Brand` 特征。 +1. 选择网格上的 `Brand` 特征。 1. 在“操作”面板上,展开“公式”,然后选择“独热编码” 。 @@ -183,7 +183,7 @@ lab: ## 排序和筛选操作 -假设我们需要查看特定商店的收入数据,然后对产品价格进行排序。 在以下步骤中,我们使用数据整理器筛选和分析 `df` 数据帧。 +假设我们需要查看特定商店的收入数据,然后对产品价格进行排序。 在以下步骤中,我们使用数据整理器筛选和分析 `df` 数据帧。 1. 针对 `df` 数据帧启动据整理器。 @@ -250,11 +250,11 @@ lab: 1. 为“聚合类型”属性选择“平均值” 。 -1. 选择**应用**。 +1. 选择**应用**。 -1. 选择“将代码添加到笔记本”。 +1. 选择“将代码添加到笔记本”。 -1. 将 `Brand` 变量转换中的代码与 `clean_data(df)` 函数中的聚合步骤生成的代码组合在一起。 最终块应如下所示: +1. 将 `Brand` 变量转换中的代码与 `clean_data(df)` 函数中的聚合步骤生成的代码组合在一起。 最终代码块应如下所示: ```python def clean_data(df): @@ -277,7 +277,7 @@ lab: ```python print(df) - ``` + ``` 结果: ``` diff --git a/Instructions/Labs/08c-data-science-train.md b/Instructions/Labs/08c-data-science-train.md new file mode 100644 index 0000000..e90a3c5 --- /dev/null +++ b/Instructions/Labs/08c-data-science-train.md @@ -0,0 +1,264 @@ +--- +lab: + title: 在 Microsoft Fabric 中训练和跟踪模型 + module: Train and track machine learning models with MLflow in Microsoft Fabric +--- + +# 在 Microsoft Fabric 中使用 MLflow 训练和跟踪机器学习模型 + +在此实验中,你将训练机器学习模型来预测糖尿病的定量度量。 将使用 scikit-learn 训练回归模型,并使用 MLflow 跟踪和比较模型。 + +完成此实验后,你将获得机器学习和模型跟踪的实践经验,并了解如何在 Microsoft Fabric 中使用笔记本、试验和模型 。 + +完成本实验室大约需要 45 分钟。 + +> 注意:完成本练习需要 Microsoft Fabric 许可证。 有关如何启用免费 Fabric 试用版许可证的详细信息,请参阅 [Fabric 入门](https://learn.microsoft.com/fabric/get-started/fabric-trial)。 执行此操作需要 Microsoft 学校或工作帐户 。 如果没有,可以[注册 Microsoft Office 365 E3 或更高版本的试用版](https://www.microsoft.com/microsoft-365/business/compare-more-office-365-for-business-plans)。 + +## 创建工作区 + +在 Fabric 中使用模型之前,在已启用的 Fabric 试用版中创建工作区。 + +1. 登录到 [Microsoft Fabric](https://app.fabric.microsoft.com) (`https://app.fabric.microsoft.com`),然后选择 Power BI。 +2. 在左侧菜单栏中,选择“工作区”(图标类似于 🗇)。 +3. 新建一个工作区并为其指定名称,并选择包含 Fabric 容量(试用版、高级版或 Fabric)的许可模式 。 +4. 打开新工作区时,它应为空,如下所示: + + ![Power BI 中空工作区的屏幕截图。](./Images/new-workspace.png) + +## 创建笔记本 + +若要训练模型,可以创建笔记本。 笔记本提供了一个交互式环境,可在其中编写和运行(多种语言的)代码。 + +1. 在 Fabric 门户左下角,选择“数据工程”图标并切换到“数据科学”体验 。 + +1. 在“数据科学”主页中,创建新的笔记本 。 + + 几秒钟后,一个包含单个单元格的新笔记本将会打开。 笔记本由一个或多个单元格组成,这些单元格可以包含代码或 markdown(格式化文本) 。 + +1. 选择第一个单元格(当前是代码单元格),然后在其右上角的动态工具栏中,使用 M↓ 按钮将单元格转换为 markdown 单元格。 + + 当单元格更改为 markdown 单元格时,它包含的文本将会呈现。 + +1. 使用 🖉(“编辑”)按钮将单元格切换到编辑模式,然后删除内容并输入以下文本: + + ```text + # Train a machine learning model and track with MLflow + ``` + +## 将数据加载到数据帧中 + +现在,你已准备好运行代码来获取数据和训练模型。 将使用 Azure 开放数据集中的[糖尿病数据集](https://learn.microsoft.com/azure/open-datasets/dataset-diabetes?tabs=azureml-opendatasets?azure-portal=true)。 加载数据后,将数据转换为 Pandas 数据帧:处理行和列中数据的常见结构。 + +1. 在笔记本中,使用最新单元格输出下方的“+ 代码”图标将新的代码单元格添加到笔记本,并在其中输入以下代码: + + ```python + # Azure storage access info for open dataset diabetes + blob_account_name = "azureopendatastorage" + blob_container_name = "mlsamples" + blob_relative_path = "diabetes" + blob_sas_token = r"" # Blank since container is Anonymous access + + # Set Spark config to access blob storage + wasbs_path = f"wasbs://%s@%s.blob.core.windows.net/%s" % (blob_container_name, blob_account_name, blob_relative_path) + spark.conf.set("fs.azure.sas.%s.%s.blob.core.windows.net" % (blob_container_name, blob_account_name), blob_sas_token) + print("Remote blob path: " + wasbs_path) + + # Spark read parquet, note that it won't load any data yet by now + df = spark.read.parquet(wasbs_path) + ``` + +1. 使用单元格左侧的“▷ 运行单元格”按钮运行单元格。 或者,可以按键盘上的 `SHIFT` + `ENTER` 来运行单元格。 + + > 注意:由于这是你第一次在此会话中运行 Spark 代码,因此必须启动 Spark 池。 这意味着会话中的第一次运行可能需要一分钟左右才能完成。 后续运行速度会更快。 + +1. 使用单元格输出下方的“+ 代码”图标将新的代码单元格添加到笔记本,并在其中输入以下代码: + + ```python + display(df) + ``` + +1. 单元格命令完成后,查看单元格下方的输出,该输出应如下所示: + + |年龄|SEX|BMI|BP|S1|S2|S3|S4|S5|S6|Y| + |---|---|---|--|--|--|--|--|--|--|--| + |59|2|32.1|101.0|157|93.2|38.0|4.0|4.8598|87|151| + |48|1|21.6|87.0|183|103.2|70.0|3.0|3.8918|69|75| + |72|2|30.5|93.0|156|93.6|41.0|4.0|4.6728|85|141| + |24|1|25.3|84.0|198|131.4|40.0|5.0|4.8903|89|206| + |50|1|23.0|101.0|192|125.4|52.0|4.0|4.2905|80|135| + | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | + + 输出显示糖尿病数据集的行和列。 + +1. 数据作为 Spark 数据帧加载。 Scikit-learn 将期望输入数据集是 Pandas 数据帧。 运行以下代码将数据集转换为 Pandas 数据帧: + + ```python + import pandas as pd + df = df.toPandas() + df.head() + ``` + +## 训练机器学习模型 + +现在,你已加载数据,可以使用它来训练机器学习模型并预测糖尿病的定量度量。 我们将使用 scikit-learn 库训练模型,并使用 MLflow 跟踪模型。 + +1. 运行以下代码,将数据拆分为训练数据集和测试数据集,并将特征与要预测的标签分开: + + ```python + from sklearn.model_selection import train_test_split + + print("Splitting data...") + X, y = df[['AGE','SEX','BMI','BP','S1','S2','S3','S4','S5','S6']].values, df['Y'].values + + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0) + ``` + +1. 在笔记本中添加另一个新代码单元格,在其中输入以下代码并运行它: + + ```python + import mlflow + experiment_name = "experiment-diabetes" + mlflow.set_experiment(experiment_name) + ``` + + 该代码创建名为 `experiment-diabetes` 的 MLflow 试验。 在此试验中跟踪模型。 + +1. 在笔记本中添加另一个新代码单元格,在其中输入以下代码并运行它: + + ```python + from sklearn.linear_model import LinearRegression + + with mlflow.start_run(): + mlflow.autolog() + + model = LinearRegression() + model.fit(X_train, y_train) + + mlflow.log_param("estimator", "LinearRegression") + ``` + + 代码使用线性回归训练回归模型。 使用 MLflow 自动记录参数、指标和项目。 此外,还需要记录一个名为 `estimator` 的参数,其值为 `LinearRegression`。 + +1. 在笔记本中添加另一个新代码单元格,在其中输入以下代码并运行它: + + ```python + from sklearn.tree import DecisionTreeRegressor + + with mlflow.start_run(): + mlflow.autolog() + + model = DecisionTreeRegressor(max_depth=5) + model.fit(X_train, y_train) + + mlflow.log_param("estimator", "DecisionTreeRegressor") + ``` + + 代码使用决策树回归量训练回归模型。 使用 MLflow 自动记录参数、指标和项目。 此外,还需要记录一个名为 `estimator` 的参数,其值为 `DecisionTreeRegressor`。 + +## 使用 MLflow 搜索和查看试验 + +使用 MLflow 训练和跟踪模型后,可以使用 MLflow 库检索试验及其详细信息。 + +1. 若要列出所有试验,请使用以下代码: + + ```python + import mlflow + experiments = mlflow.search_experiments() + for exp in experiments: + print(exp.name) + ``` + +1. 若要检索特定试验,可按名称获取试验: + + ```python + experiment_name = "experiment-diabetes" + exp = mlflow.get_experiment_by_name(experiment_name) + print(exp) + ``` + +1. 使用试验名称,可以检索该试验的所有作业: + + ```python + mlflow.search_runs(exp.experiment_id) + ``` + +1. 若要更轻松地比较作业运行和输出,可以配置搜索以对结果进行排序。 例如,以下单元格按 `start_time` 对结果进行排序,且仅显示最多 `2` 个结果: + + ```python + mlflow.search_runs(exp.experiment_id, order_by=["start_time DESC"], max_results=2) + ``` + +1. 最后,可以绘制多个模型的评估指标,以便轻松比较模型: + + ```python + import matplotlib.pyplot as plt + + df_results = mlflow.search_runs(exp.experiment_id, order_by=["start_time DESC"], max_results=2)[["metrics.training_r2_score", "params.estimator"]] + + fig, ax = plt.subplots() + ax.bar(df_results["params.estimator"], df_results["metrics.training_r2_score"]) + ax.set_xlabel("Estimator") + ax.set_ylabel("R2 score") + ax.set_title("R2 score by Estimator") + for i, v in enumerate(df_results["metrics.training_r2_score"]): + ax.text(i, v, str(round(v, 2)), ha='center', va='bottom', fontweight='bold') + plt.show() + ``` + + 输出应如下图所示: + + ![绘制的评估指标的屏幕截图。](./Images/plotted-metrics.png) + +## 探索试验 + +Microsoft Fabric 将跟踪所有试验,并支持直观地探索它们。 + +1. 导航到“数据科学”主页。 +1. 选择 `experiment-diabetes` 试验将其打开。 + + > 提示:如果看不到任何记录的试验运行,请刷新页面。 + +1. 选择“视图”选项卡。 +1. 选择“运行列表”。 +1. 通过选中每个框来选择两个最新运行。 + 因此,最后两个运行将在“指标比较”窗格中相互比较。 默认情况下,指标按运行名称绘制。 +1. 选择图形的 🖉(“编辑”)按钮,以可视化每个运行的平均绝对误差。 +1. 将可视化效果类型更改为 `bar`。 +1. 将 X 轴更改为 `estimator`。 +1. 选择“替换”并浏览新图形。 +1. (可选)可以对“指标比较”窗格中的其他图形重复这些步骤。 + +通过绘制每个记录的估算器的性能指标,可以查看哪种算法可生成更好的模型。 + +## 保存模型 + +比较了在试验运行中训练的机器学习模型后,可以选择性能最佳的模型。 若要使用性能最佳的模型,请保存模型并使用它生成预测。 + +1. 在试验概述中,确保已选择“视图”选项卡。 +1. 选择“运行详细信息”。 +1. 选择 R2 分数最高的运行。 +1. 在“另存为模型”框中选择“保存” 。 +1. 在新打开的弹出窗口中选择“创建新模型”。 +1. 选择 `model` 文件夹。 +1. 将模型命名为 `model-diabetes`,然后选择“保存”。 +1. 在创建模型时屏幕右上角显示的通知中选择“查看模型”。 还可刷新窗口。 已保存的模型在“模型版本”下链接。 + +请注意,模型、试验和试验运行是链接的,以便你查看模型的训练方式。 + +## 保存笔记本并结束 Spark 会话 + +完成模型训练和评估后,可以使用有意义的名称保存笔记本并结束 Spark 会话。 + +1. 在笔记本菜单栏中,使用 ⚙️“设置”图标查看笔记本设置。 +2. 将笔记本的“名称”设置为“训练和比较模型”,然后关闭设置窗格 。 +3. 在笔记本菜单上,选择“停止会话”以结束 Spark 会话。 + +## 清理资源 + +在本练习中,你已创建笔记本并训练了机器学习模型。 你已使用 scikit-learn 来训练模型,并使用 MLflow 来跟踪其性能。 + +如果已完成模型和试验,可以删除为本练习创建的工作区。 + +1. 在左侧栏中,选择工作区的图标以查看其包含的所有项。 +2. 在工具栏上的“...”菜单中,选择“工作区设置” 。 +3. 在“其他”部分中,选择“删除此工作区” 。 diff --git a/Instructions/Labs/12-query-data-in-kql-database.md b/Instructions/Labs/12-query-data-in-kql-database.md new file mode 100644 index 0000000..da2b5d0 --- /dev/null +++ b/Instructions/Labs/12-query-data-in-kql-database.md @@ -0,0 +1,67 @@ +--- +lab: + title: 在 KQL 数据库中查询数据 + module: Query data from a Kusto Query database in Microsoft Fabric +--- +# 在 Microsoft Fabric 中查询 Kusto 数据库入门 +KQL 查询集是一种工具,可用于查询 KQL 数据库、修改和显示来自其中的查询结果。 可以将 KQL 查询集中的每个选项卡链接到不同的 KQL 数据库,并保存查询以供将来使用或与他人共享进行数据分析。 还可以更换任何选项卡的 KQL 数据库,以比较来自不同数据源的查询结果。 + +KQL 查询集使用 Kusto 查询语言(与许多 SQL 函数兼容)来创建查询。 为深入了解 [kusto 查询 (KQL) 语言](https://learn.microsoft.com/en-us/azure/data-explorer/kusto/query/?context=%2Ffabric%2Fcontext%2Fcontext), + +完成本实验室大约需要 25 分钟。 + +## 创建工作区 + +在 Fabric 中处理数据之前,在已启用的 Fabric 试用版中创建工作区。 + +1. 登录到 [Microsoft Fabric](https://app.fabric.microsoft.com) (`https://app.fabric.microsoft.com`),然后选择 Power BI。 +2. 在左侧菜单栏中,选择“工作区”(图标类似于 🗇)。 +3. 新建一个工作区并为其指定名称,并选择包含 Fabric 容量(试用版、高级版或 Fabric)的许可模式 。 +4. 打开新工作区时,它应为空,如下所示: + + ![Power BI 中空工作区的屏幕截图。](./Images/new-workspace.png) + +在本实验室中,你将在 Fabric 中使用实时分析 (RTA),通过示例事件流创建 KQL 数据库。 实时分析 (RTA) 可以方便地提供一个示例数据集,供你用来探索 RTA 的功能。 你将使用此示例数据创建 KQL | SQL 查询和查询集,用于分析一些实时数据,还可以在下游进程中使用。 + +## 创建 KQL 数据库 + +1. 在“实时分析”中,选择“KQL 数据库”框 。 + + ![选择 kqldatabase 的图像](./Images/select-kqldatabase.png) + +2. 系统会提示为 KQL 数据库命名 + + ![名称 kqldatabase 的图像](./Images/name-kqldatabase.png) + +3. 为 KQL 数据库指定一个你能记住的名称(如 MyStockData),然后按“创建” 。 + +4. 在“数据库详细信息”面板中,选择铅笔图标以在 OneLake 中打开可用性。 + + ![“启用 onelake”的图像](./Images/enable-onelake-availability.png) + +5. 从“开始获取数据”的选项中选择“示例数据”框。 + + ![图像显示了一系列选择选项,并突出显示了示例数据](./Images/load-sample-data.png) + +6. 从示例数据的选项中选择“指标分析”框。 + + ![“选择实验室分析数据”的图像](./Images/create-sample-data.png) + +7. 加载数据后,数据加载到 KQL 数据库中。 为此,可以选择表右侧的省略号,导航到“查询表”,然后选择“显示任意 100 条记录”。 + +
+ +> 注:首次运行此功能时,可能需要几秒钟来分配计算资源。 + +## 方案 +在此方案中,你是一名分析师,负责查询一个要从 Fabric 环境实现的示例数据集。 + + + +Kusto 查询是读取数据、处理数据和显示结果的一种方法。 查询以易于使用的纯文本编写。 Kusto 查询可以有一个或多个以表或图的形式显示数据的语句。 + +Table 语句包含一些处理表数据的运算符。 每个运算符采用表作为输入,并输出一个表。 运算符之间由竖线 (|) 联接。 数据从一个运算符转移到另一个运算符。 每个运算符都以某种方式更改并传递数据。 + +可以将其想象成漏斗,一开始是整个数据表。 各个运算符筛选、排序或汇总数据。 运算符的顺序会产生影响,因为它们是一个接一个地按顺序工作。 在漏斗末尾,会获得最终输出。 + +这些运算符特定于 KQL,但可能与 SQL 或其他语言中的情况类似。 \ No newline at end of file diff --git a/Instructions/Labs/Images/check-kql-sample-dataset.mp4 b/Instructions/Labs/Images/check-kql-sample-dataset.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..a2f3b1f7bdaa8f50d87484a3ee6c647cf7115875 GIT binary patch literal 6871729 zcmV)SK(fC8000!2S(3sCWNbdL3EeuEThz3{s$%*MemK-P`E2oVe2B{3UE)}|b>dq2rSgN3j8U&qV%?JX2g#tfBep`6PZu3}G zBNhsv?7)7wsUye{PMc_!e1s{gp2>lWTc~T{&JM71V&cjf)##l~;q1n34dIFZ`EAex zNbc+YUuR3RcU(rIBK+H*)l1Qv*;Pa6G%>dUb<9pFs{IBfgS5Wb<^i?K?1`NB?>WGu zIZYN^)+q9kMLROf?#?JIc&qND;+LCClz~$qME8e)Ti5|-@yzFA$Vu!_G`OBFg1G}z 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