diff --git a/072-DataScienceInFabric/.wordlist.txt b/072-DataScienceInFabric/.wordlist.txt new file mode 100644 index 0000000000..4fcc448f98 --- /dev/null +++ b/072-DataScienceInFabric/.wordlist.txt @@ -0,0 +1,8 @@ +dataframe +DataFrame +DataFrame's +MLFlow +Leandro +interpretability +auc +repurpose diff --git a/072-DataScienceInFabric/Coach/README.md b/072-DataScienceInFabric/Coach/README.md new file mode 100644 index 0000000000..ea11225456 --- /dev/null +++ b/072-DataScienceInFabric/Coach/README.md @@ -0,0 +1,85 @@ +# What The Hack - Data Science In Microsoft Fabric + +## Introduction + +Welcome to the coach's guide for the Data Science In Microsoft Fabric What The Hack. Here you will find links to specific guidance for coaches for each of the challenges. + +**NOTE:** If you are a Hackathon participant, this is the answer guide. Don't cheat yourself by looking at these during the hack! Go learn something. :) + +## Coach's Guides + +- Challenge 00: **[Prerequisites - Ready, Set, GO!](./Solution-00.md)** + - Configure your Fabric workspace and gather your data +- Challenge 01: **[Bring your data to the OneLake](./Solution-01.md)** + - Creating a shortcut to the available data +- Challenge 02: **[Prepare your data for ML](./Solution-02.md)** + - Clean and transform the data into a useful format while leveraging Data Wrangler +- Challenge 03: **[Train and register the model](./Solution-03.md)** + - Train a machine learning model with ML Flow with the help of Copilot +- Challenge 04: **[Generate batch predictions](./Solution-04.md)** + - Score a static dataset with the model +- Challenge 05: **[Visualize predictions with a PowerBI report ](./Solution-05.md)** + - Visualize generated predictions by using a PowerBI report +- Challenge 06: **[Deploy the model to an AzureML real-time endpoint](./Solution-06.md)** + - Deploy the trained model to an AzureML endpoint for inference + +## Coach Prerequisites + +This hack has pre-reqs that a coach is responsible for understanding and/or setting up BEFORE hosting an event. Please review the [What The Hack Hosting Guide](https://aka.ms/wthhost) for information on how to host a hack event. + +The guide covers the common preparation steps a coach needs to do before any What The Hack event, including how to properly configure Microsoft Teams. + +### Student Resources + +Always refer students to the [What The Hack website](https://aka.ms/wth) for the student guide: [https://aka.ms/wth](https://aka.ms/wth) + +**NOTE:** Students should **not** be given a link to the What The Hack repo before or during a hack. The student guide does **NOT** have any links to the Coach's guide or the What The Hack repo on GitHub. + + +## Azure and Fabric Requirements + +This hack requires students to have access to Azure and Fabric. These requirements should be shared with a stakeholder in the organization that will be providing the licenses that will be used by the students. + +### Fabric and PowerBI licensing requirements: + +Each student will need access to Microsoft Fabric and be licensed to create PowerBI reports for this hack. The following are the options to complete these licensing requirements: + +1. **Recommended if available**: Individual [Fabric free trials](https://learn.microsoft.com/en-us/fabric/get-started/fabric-trial#start-the-fabric-capacity-trial). This will grant users access to creating the required Fabric items as well as the PowerBI report. **If previously used, the Fabric free trial may be unavailable** +2. Fabric Capacity and PowerBI Pro/Premium per user license. Each user would need their own PowerBI license but capacities could be shared and scaled up according to the needs. If running the hack on an individual basis, an F4 capacity would be adequate, and an F8 capacity would have generous compute power margin. **Alternatively, users can activate a [PowerBI Free Trial](https://learn.microsoft.com/en-us/power-bi/fundamentals/service-self-service-signup-for-power-bi) if available.** The PowerBI trial could be available even if the Fabric one is not. + + +### Azure licensing requirements + +There are 2 challenges that require access to Azure: + +- Challenge 1: Students are required to navigate an Azure ADLS Gen 2 account through the Azure Portal to learn how to set up a Fabric shortcut to an existing file. This challenge requires each student to have contributor permissions to the resource, but 1 single storage account/directory/file could be shared among all students, given that they will not modify it but rather just access and connect to it. + +- Challenge 6: Students are required to have Azure AI Developer access to an Azure Machine Learning resource. Each student will need to register their own model and create their own real-time endpoint, which is why it is **recommended to individually deploy an Azure ML workspace per student**. + +Given these requirements, each student could have their own Azure subscription or they could share access to a single subscription. + +These Azure resources can be deployed on an individual per-student basis using the `deployhack.sh` script included in the student resources folder. + +## Suggested Hack Agenda + +You may schedule this hack in any format, as long as the challenges are completed sequentially. + +Time estimate for each challenge: +- Challenge 00: 15 minutes +- Challenge 01: 30 minutes +- Challenge 02: 30 minutes +- Challenge 03: 45 minutes +- Challenge 04: 30 minutes +- Challenge 05: 30 minutes +- Challenge 06: 45 minutes + +## Repository Contents + +- `./Coach` + - Coach's Guide and related files +- `./Coach/Solutions` + - Solution files with completed example answers to challenges +- `./Student` + - Student's Challenge Guide +- `./Student/Resources` + - Student resource files, also available as a download link on Student Challenge 0 diff --git a/072-DataScienceInFabric/Coach/Solution-00.md b/072-DataScienceInFabric/Coach/Solution-00.md new file mode 100644 index 0000000000..0677130511 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solution-00.md @@ -0,0 +1,62 @@ +# Challenge 00 - Prerequisites - Ready, Set, GO! - Coach's Guide + +**[Home](./README.md)** - [Next Solution >](./Solution-01.md) + +## Introduction + +Thank you for participating in the Data Science in Microsoft Fabric What The Hack. Before you can hack, you will need to set up some prerequisites. + +## Common Prerequisites + +We have compiled a list of common tools and software that will come in handy to complete most What The Hack Azure-based hacks! + +You might not need all of them for the hack you are participating in. However, if you work with Azure on a regular basis, these are all things you should consider having in your toolbox. + + + +- [Azure Subscription](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-subscription) +- [Postman](https://www.postman.com/downloads/) +- [Managing Cloud Resources](../Student/000-HowToHack/WTH-Common-Prerequisites.md#managing-cloud-resources) +- [Azure Portal](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-portal) + - [Azure Cloud Shell](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-cloud-shell) + - [Azure CLI (optional)](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-cli) + - [Note for Windows Users](../Student/000-HowToHack/WTH-Common-Prerequisites.md#note-for-windows-users) + - [Azure PowerShell CmdLets](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-powershell-cmdlets) + +- [Azure Storage Explorer (optional)](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-storage-explorer) + +Additionally please refer to the [Coach Hack introduction](./README.md) for more information about licensing requirements and options +## Description + +Now that you have the common pre-requisites installed on your workstation, there are prerequisites specific to this hack. + +In Challenge 0 on the student guide, students are instructed to download the Resources folder [here](https://aka.ms/FabricdsWTHResources). This folder contains the notebooks students will be working with, as well as a shell script that they will use to deploy some needed Azure resources. + +The [coach solution notebooks](./Solutions/) are the completed versions of the student notebooks. The solutions can be used as a guide or uploaded to Fabric to complete each Challenge. + + +**NOTE:** The resources.zip folder also includes the heart.csv file. You can upload this data directly to the Fabric Lakehouse if you decide you want to go through this hack without needing an Azure subscription. However, this will skip half of Challenge 1 and the important concept of using shortcuts in Fabric. If you are going to be setting up the Azure resources and using the shortcut, ignore the heart.csv file. + +To begin setting up your Azure subscription for this hack, you will run a bash script that will deploy and configure a list of resources. You can find this script as the `HackSetup.sh` file in the resources folder. + - Download the setup file to your computer + - Go to the Azure portal and click on the cloud shell button on the top navigation bar, to the right of the Copilot button. + - **NOTE**: This script has been designed for the Azure CLI. It might fail to deploy if you attempt to run it from a local terminal. + - Once the cloud shell connects, make sure you are using a Bash shell. If you are not, click on the button on the top-right corner of the cloud shell to switch to bash. + - Click on the Manage Files button on the shell's navigation bar and select upload. Select the setup file from your computer. + - Run the `sh HackSetup.sh` command in your cloud shell. + - Follow the prompts in the shell. + +After setting up your Azure resources, head to [Microsoft Fabric](https://fabric.microsoft.com/). + - Create a new workspace by clicking on 'Workspaces' in the vertical menu on the left side of the screen. Use the 'New Workspace' button at the bottom of the list. + - Once you are inside your new workspace, select the Data Science experience using the button on the bottom left corner of the screen. + - At the top of the Data Science experience menu, check that you are still in the new workspace and select 'Import Notebook' from the top row of options. + - Follow the prompts to upload the 4 notebook (`.ipynb`) files contained within the resources folder. + + +## Success Criteria + +To complete this challenge successfully, you should be able to: + +- Verify that you have a storage account with the heart.csv data in a container +- Verify that you have a Fabric workspace where your 4 notebooks are available +- (Optional) Verify that your Azure ML workspace has correctly deployed (if completing Challenge) diff --git a/072-DataScienceInFabric/Coach/Solution-01.md b/072-DataScienceInFabric/Coach/Solution-01.md new file mode 100644 index 0000000000..7e2cf2f217 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solution-01.md @@ -0,0 +1,45 @@ +# Challenge 01 - Bring your data to the OneLake - Coach's Guide + +[< Previous Solution](./Solution-00.md) - **[Home](./README.md)** - [Next Solution >](./Solution-02.md) + +## Notes & Guidance + +In this challenge, hack participants must create a shortcut to the folder deployed in their Azure subscription on Challenge 0. This will allow them to use the data in Fabric without the need for replication. Once the shortcut is completed, participants will open Notebook 1 to load the csv file into a delta table for further modification on Notebook 2. + +### Sections + +1. Create a Lakehouse (non-notebook) +2. Create a Shortcut (non-notebook) +3. Read the .csv file into a dataframe in the notebook (Notebook 1) +4. Write the dataframe to the lakehouse as a delta table (Notebook 1) + +### Student step-by-step instructions (creating a shortcut) +- Creating a Lakehouse: + - Participants must create a lakehouse on the Fabric workspace they previously set up. In Fabric, navigate to the workspace. + - On the top left of the screen, select new and more options. + - On the data engineering section, select Lakehouse. Give the lakehouse a unique name and click on create. + +- Creating a Shortcut: + - On the Lakehouse navigator, use the left hand-side menu and click on the 3 dots (...) next to files. Click on "New shortcut" + - On the shortcut wizard, click on "Azure Data Lake Storage Gen2" + - Go to your Azure portal. The URL can be found on the **Settings>Endpoint** side menu of the Storage Account. In this menu, you will see a variety of endpoint Resource IDs and URLs. Find and copy the **data lake storage URL** from the list. Enter it into the wizard in Fabric. + - Create a new connection, give it a name and select "Account Key" as the authentication kind. + - Go back to your Azure portal. The Account Key can be found on in the **Security + Networking>Access keys** side menu of the Storage Account. Show and copy one of the keys. Enter it into the wizard in Fabric. + - Click on next to access the file explorer. Wait for the screen to load. + - On the side menu, expand the file-system folder. Select the check mark next to the "files" folder. + - Click next to move to the next screen, then click on create to create the shortcut. + - Verify that your shortcut is showing under the **Files** folder of the lakehouse navigator. You might need to click on the 3 dots and on refresh if your shortcut is not present initially. + +### Overview of student directions (running Notebook 1) +- This section of the challenge is notebook based. All the instructions and links required for participants to successfully complete this section can be found on Notebook 1 in the `student/resources.zip/notebooks` folder. +- To run the notebook, go to your Fabric workspace and select Notebook 1. Ensure that it is correctly attached to the lakehouse. You might need to connect to the lakehouse you previously created on the left-hand side file explorer menu. +- The students must follow the instructions, leverage the documentation and complete the code cells sequentially. + +### Coaches' guidance +- This challenge has 2 main sections, creating a shortcut and loading the files into delta tables. The first section must be completed before working on Notebook 1. +- The full version of Notebook 1, with all code cells filled in, can be found for reference in the `coach/solutions.zip` folder of this GitHub. +- The aim of this challenge, as noted in the student guide, is to understand lakehouses, shortcuts and the delta format. +- To assist students, coaches can clear up doubts regarding the Python syntax or how to get started with notebooks, but students should focus on learning how to set up shortcuts, navigate the Fabric UI and read/write to the delta lake. + +## Success criteria + - The heart.csv data is now saved as a delta table on the lakehouse diff --git a/072-DataScienceInFabric/Coach/Solution-02.md b/072-DataScienceInFabric/Coach/Solution-02.md new file mode 100644 index 0000000000..66764d2137 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solution-02.md @@ -0,0 +1,62 @@ +# Challenge 02 - Data preparation with Data Wrangler - Coach's Guide + +[< Previous Solution](./Solution-01.md) - **[Home](./README.md)** - [Next Solution >](./Solution-03.md) + +## Notes & Guidance + +In this challenge, hack participants must use Data Wrangler to prepare the heart dataset for model training. The purpose is to focus on transforming and preparing the data for the next challenges. They will have the flexibility to either write code in a notebook or leverage Data Wrangler’s intuitive interface to streamline the pre-processing tasks. + +### Sections + +1. Read the .csv file into a pandas dataframe in the notebook. (Notebook 2) +2. Launch the Data Wrangler and interact with the data cleaning operations (Notebook 2) +3. Apply the operations using python codes (Notebook 2) +4. Develop feature engineering using spark. (Notebook 2) +5. Write the dataframe to the lakehouse as a delta table. (Notebook 2) + +### Student step-by-step instructions +- Launching Data Wrangler: + - Participants must create a pandas dataframe the fabric notebook. It’s necessary to complete the first cell in the notebook 2. + - Once executed, under the notebook ribbon Home tab, select Launch Data Wrangler. You'll see a list of activated pandas DataFrames available for editing. + - Select the DataFrame you just created in last cell and open in Data Wrangler. From the Pandas dataframe list, select `df`. + + +- Data Cleaning Operations – (Data Wrangler) + - *Removing Unnecessary Columns* + - On the *Operations* panel, expand *Schema* and select *Drop columns*. + - Select `RowNumber`. This column will appear in red in the preview, to show they're changed by the code (in this case, dropped.) + - Select **Apply**, a new step is created in the **Cleaning steps panel** on the bottom left. + + - *Dropping Missing Values* + - On the **Operations** panel, select **Find and replace**, and then select **Drop missing values**. + - Select the `RestingBP`, `Cholesterol` and `FastingBS` columns. On the right left those are the ones that are pointed as missing values. + - Select **Apply**, a new step is created in the **Cleaning steps panel** on the bottom left. + + - *Dropping Duplicate Rows* + - On the **Operations** panel, select **Find and replace**, and then select **Drop duplicate rows**. + - Select **Apply**, a new step is created in the **Cleaning steps panel** on the bottom left. + +- Feature Engineering - (Notebook) + - This part is notebook based. Participants will work in cells 09, 10, and 11 to transform categorical values into numerical labels. + - You can also explore how to one-hot encode the categorical columns with Data Wrangler. However, this will not create labels in your existing columns, but rather a new column for each category with True and False values. Using this alternative format might need some modification to the code in the model training process. Please discuss this possibility with hack attendees to raise awareness of this Data Wrangler feature. + +### Overview of student directions (running Notebook 2) +- This section of the challenge is notebook based. All the instructions and links required for participants to successfully complete this section can be found on Notebook 2 in the `student/resources.zip/notebooks` folder. +- To run the notebook, go to your Fabric workspace and select Notebook 2. Ensure that it is correctly attached to the lakehouse. You might need to connect to the lakehouse you previously created on the left-hand side file explorer menu. +- The students must follow the instructions, leverage the documentation and complete the code cells sequentially. + +### Coaches' guidance + +- This challenge has 3 main sections, Data Wrangler operations, feature engineering and saving processed data to a delta table. +- The full version of Notebook 2, with all code cells filled in, can be found for reference in the coach/solutions.zip folder of this GitHub. +- The aim of this challenge, as noted in the student guide, is to understand data preparation using data wrangler and fabric notebooks. +- To assist students, coaches can clear up doubts regarding the Python syntax or how to get started with notebooks, but students should focus on learning how to operate data wrangler, navigate the Fabric UI, code in notebooks and read/write to the delta lake. + + +## Success criteria +- The heart dataset totally shaped, cleaned and prepared for the model training. +- No data duplicated or exceeded columns. +- No missing values. +- No categorical values. + + diff --git a/072-DataScienceInFabric/Coach/Solution-03.md b/072-DataScienceInFabric/Coach/Solution-03.md new file mode 100644 index 0000000000..480a12f557 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solution-03.md @@ -0,0 +1,28 @@ +# Challenge 03 - Train and register the model - Coach's Guide + +[< Previous Solution](./Solution-02.md) - **[Home](./README.md)** - [Next Solution >](./Solution-04.md) + +## Notes & Guidance + +In this challenge, participants must use the cleaned-up data from Challenge 2 to train, test and validate a machine learning model using MLFlow. The challenge concludes with saving the trained model, which will allow participants to load the model on Challenge 4 and generate predictions. + +### Sections +1. Creating an MLFlow experiment +2. Reading the delta table with Spark +3. Performing a random split of the data +4. Creating a run within the experiment to fit the model +5. Loading the saved model and generating predictions with the validation data + +### Overview of student directions: +- This is a notebook based challenge. All the instructions and links required for participants to successfully complete this challenge can be found on Notebook 3 in the `student/resources.zip/notebooks` folder. +- To run the notebook, go to your Fabric workspace and select Notebook 3. **Before running any cell, ensure that it is correctly attached to the lakehouse.** +- The students must follow the instructions, leverage the documentation and complete the code cells sequentially. + +### Coaches' guidance: +- The full version of Notebook 3, with all code cells filled in, can be found for reference in the `coach/solutions.zip` folder of this GitHub. +- There may be multiple ways to attain the same solution and multiple valid functions to use in each section, but not every combination of methods might yield the end result. +- The aim of this challenge, as noted in the student guide, is to practice leveraging MLFlow within Fabric. This specific hack uses `sklearn` but other ML frameworks can be used in Fabric as well. +- To assist students, coaches can clear up doubts/give hints on the specifics of splitting the data and fitting the model but students should focus on learning how to structure runs and experiments on MLFlow. + +## Success criteria + - The model has been trained and registered with MLFlow and it has been checked with testing and validation data diff --git a/072-DataScienceInFabric/Coach/Solution-04.md b/072-DataScienceInFabric/Coach/Solution-04.md new file mode 100644 index 0000000000..c4d9edf454 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solution-04.md @@ -0,0 +1,26 @@ +# Challenge 04 - Generate batch predictions - Coach's Guide + +[< Previous Solution](./Solution-03.md) - **[Home](./README.md)** - [Next Solution >](./Solution-05.md) + +## Notes & Guidance + +In this challenge, participants must used the model trained in Challenge 3 to generate predictions over a simulated set of data. Participants complete the challenge by making the new predictions available as a delta table in the lakehouse, which they will use on Challenge 6 to create a PowerBI report showing their findings. + +### Sections +1. Simulate input data to be used for predictions +2. Load the model and generate predictions +3. Format predictions and save them to a delta table + +### Overview of student directions: +- This is a notebook based challenge. All the instructions and links required for participants to successfully complete this challenge can be found on Notebook 4 in the `student/resources.zip/notebooks` folder. +- To run the notebook, go to your Fabric workspace and select Notebook 4. Ensure that it is correctly attached to the lakehouse. +- The students must follow the instructions, leverage the documentation and complete the code cells sequentially. + +### Coaches' guidance: +- The full version of Notebook 4, with all code cells filled in, can be found for reference in the `coach/solutions.zip` folder of this GitHub. +- The aim of this challenge, as noted in the student guide, is to learn how to use the PREDICT function from a Spark notebook with a pre-trained model. Simulating the data is a necessary previous step, but not the focus of this challenge. +- To assist students, coaches can clear up doubts/give hints on how to create the simulated data, but students should focus on learning how to load the model and generate predictions. + +## Success criteria + - The predictions have been saved as a delta table to the lakehouse + diff --git a/072-DataScienceInFabric/Coach/Solution-05.md b/072-DataScienceInFabric/Coach/Solution-05.md new file mode 100644 index 0000000000..dd7b5c5a13 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solution-05.md @@ -0,0 +1,44 @@ +# Challenge 05 - Visualize predictions with a Power BI report - Coach's Guide + +[< Previous Solution](./Solution-04.md) - **[Home](./README.md)** - [Next Solution >](./Solution-06.md) + +## Setup Steps + +- Create Semantic model +- Create PBI report + + +## Notes and Guidance +This challenge completes the hack by setting up a PowerBI report that shows the batch prediction data predicted by the trained model. + +### Sections +- Creating a Semantic model +- Creating a PowerBI report + +### Student step-by-step instructions + +- Creating the Semantic model: The easiest way to do so is from the Batch Prediction table stored in Lakehouse in Challenge 4: + + - Navigate to the batch prediction table made in the previous challenge by going to the lakehouse where it got stored. + - On the Lakehouse, create a semantic model required to build Power BI Report. Use the top ribbon on the lakehouse explorer and click on **New semantic model**. + - During semantic model creation, provide semantic model name, chose workspace (you can use the same one which we have been using for the lakehouse and notebooks) where you want to store the semantic model and select the `heartFailure_pred` table and click the confirm button to create the semantic model. + - It will take you to the semantic model page, on the top of the page, use the ribbon to click on the **New Report** button to create the PowerBI report. + + +- Create the Power BI report. + - Navigate to the **Visualizations** pane on the right side of your screen. From the group of icons, find the **Table** visual (on the 4th row) and click on it. + - Create a Table with the following settings: + - Data Values: on the **Columns** field of the Visualizations pane, drag and drop the **ID**, **age** and **predictions** columns from the `heartFailure_pred` table located in the **Data** pane to the rightmost of your screen. Click on the dropdown arrow next to the table name to access the columns. + - Make sure select the "Do not summarize" for all the columns. Once you place the columns in the Visualizations pane, use the dropdown arrow next to each column to select **Don't summarize** + - For prediction column, select the conditional formatting from the prediction column by clicking again on the dropdown arrow next to the column name under the data values field in visualization pane. Choose **Background color** from the list of options. + - Set the format style to **Rules**. Create 2 rules: if value equals to "0", show background color blue and if value equals to "1" show background color red. Click on **Ok** to close the window. + - This is the most basic visualization that student should create to identify patients at risk of heart failure. Students are encouraged in the challenge to create a variety of visualizations to explore other insights, such as how Age or Sex influence the chance of being at risk or how many total patients are at risk. + +### Coaches' guidance +- The purpose of this challenge is for students to understand how semantic models can be created from delta tables in Fabric, and how to build PowerBI reports from these. +- Students should focus on learning how to configure the semantic model and launch the PowerBI report. They should navigate the Fabric/PowerBI UI on their own. Coaches may help with any issues, such as the delta table not appearing in the modeling view, or giving ideas for what visuals to include in the report. +- The resulting final PowerBI report can take many different shapes, however, students should showcase how you can use the report to identify specific patients at risk. +- While the focus of working with PowerBI in this challenge is to get acquainted with the basic features, students can explore creative options to bring their data to life. + +## Success criteria +- The PowerBI report with the table identifying each patient predicted to be at risk diff --git a/072-DataScienceInFabric/Coach/Solution-06.md b/072-DataScienceInFabric/Coach/Solution-06.md new file mode 100644 index 0000000000..9798ded7a1 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solution-06.md @@ -0,0 +1,62 @@ +# Challenge 06 - Deploy the model to an AzureML real-time endpoint - Coach's Guide + +[< Previous Solution](./Solution-05.md) - **[Home](./README.md)** - [Next Solution >](./Solution-07.md) + +## Notes & Guidance + +### Sections +- Download the ml model from the Fabric workspace +- Upload the model to a real-time inference endpoint on azure machine learning studio +- Using postman, send sample data to it to test it out. + +### Student step-by-step instructions + +- Go to your data science workspace where all the notebooks are stored. +- That workspace will have the ml model which you created in previous challenge +- Exporting a model from Fabric is easy--just tap the Download ML model version button in the UI. + +![picture alt](../Images/image-10.png) + +- Fabric will combine the ML Model along with a Python Object Serialization file (a/k/a pickle file) having a `.pkl` extension, and YAML files (.yml) that describe to other platforms the structure and interface provided by the model. All these files are packaged into a .zip file, which will be placed in your Downloads folder. +- If you open the .zip file, you can review the model and metadata files before deploying them to other systems. Make sure you extract the folder before continuing, as you will not be able to upload a .zip file. + ![picture alt](../Images/image-11.png) +**NOTE**: due to some compatibility issues with MLFlow and Azure Machine Learning, you will need to modify some files locally before uploading your model to Azure Machine Learning. Once you have downloaded your model from Fabric, unzip the folder and open both the `conda.yaml` and `requirements.txt` files. Change the MLFlow version on both files to `2.7.0`. Save and close both files. +- After downloading the model, we have to deploy an Azure ML real-time inference endpoint to publish the model to the Internet via a RESTful web service. +- Navigate to your Azure Portal. Open the Azure Machine Learning workspace you previously deployed with the script in Challenge 0. +- Using the left menu in your workspace, navigate to **Assets**>**Models**. +- Register the model in the Azure ML workspace, and use the Azure ML Studio web UI to deploy the endpoint. +- To upload the model, use the **Register**/**From local files** menu in the Model List we navigated to (top left of the screen). + ![picture alt](../Images/image-12.png) + +- On the upload model wizard, make sure you select MLFlow as your model type and upload the unzipped folder. Give a name and version in the next settings screen. +- After uploading the files, the new model is available in the Models page of the Azure ML Workspace. +- Once the model is registered in the Azure ML workspace, click on the model name, and then select Real-time endpoint from the Deploy menu. + ![picture alt](../Images/image-14.png) + +- A menu opens up to configure the compute to use for the endpoint, and to specify the deployment name. +- Specify the compute size desired for the endpoint, and the names for the endpoint and deployment, then tap the Deploy button. + ![picture alt](../Images/Screenshot_26-6-2024_01752_ml.azure.com.jpeg) + +- After the deployment completes (10-15 minutes, typically), make a quick "smoke test" within Azure ML Studio to ensure the deployment is functional, and we're using the correct data structures when calling it. +- The index denotes how many groups of inputs are being sent to receive predictions. If you want to send a single set of inputs, use 0 as index. +- The data is your 11 inputs, in the order that you can see in the columns list. Make sure you send your inputs in a list within the existing list, such as shown in the screenshot below. + ![picture alt](../Images/Screenshot_26-6-2024_03016_ml.azure.com.jpeg) +- Once the interactive test succeeds, it's time to move on to consume the model from outside the Azure ML environment. +- The consume tab in the endpoint menu shows the endpoint url, keys and headers (in the code example) to make an API call. You can find the Body in the code example, or you can copy it from the test tab. Use POST as your method to get the prediction. +- Use postman to send data to deployed model endpoints to test the model. Follow the 3 following screenshots to configure your postman call. + ![picture alt](../Images/postman-token.png) + ![picture alt](../Images/postman-header.png) + ![picture alt](../Images/postman-body.png) + +### Coaches' guidance + +- **This challenge should be treated as optional**. This challenge goes beyond the scope of Microsoft Fabric to illustrate the next step to rendering an ML model trained in Fabric operational beyond analytics/reporting. +- This is an elaborate challenge that requires switching from Fabric to Azure. Documentation has been provided to students so that they can reference some of the actions needed. +- The goal is for students to understand the difference between Fabric Data Science and Azure ML studio. As a secondary objective, students should familiarize themselves with real-time endpoints in Azure ML and on how to send API requests. +- Coaches can provide guidance if students can't figure out the next step, but they should help students understand the process, not only getting to the end. +- When deploying the endpoint, there is no specific requirements for what type of compute to use. It is recommended to use the cheapest available and to ensure that students delete it after completing the hack to avoid incurring additional costs. + +## Success criteria + +- The real-time endpoint has been deployed on Azure ML and students are able to submit API calls to it. + diff --git a/072-DataScienceInFabric/Coach/Solutions/SOLUTION-01-Ingest-Heart-Failure-Dataset-to-Lakehouse.ipynb b/072-DataScienceInFabric/Coach/Solutions/SOLUTION-01-Ingest-Heart-Failure-Dataset-to-Lakehouse.ipynb new file mode 100644 index 0000000000..2e386ed912 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solutions/SOLUTION-01-Ingest-Heart-Failure-Dataset-to-Lakehouse.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["# Module 1: Read data from lakehouse and load into Delta format\n","\n","**Lakehouse**: A lakehouse is a collection of files/folders/tables that represent a database over a data lake used by the Spark engine and SQL engine for big data processing and that includes enhanced capabilities for ACID transactions when using the open-source Delta formatted tables.\n","\n","**Delta Lake**:Delta Lake is an open-source storage layer that brings ACID transactions, scalable metadata management, and batch and streaming data processing to Apache Spark. A Delta Lake table is a data table format that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata management.\n"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"c7b6575d-05eb-4434-a77d-a8127987e289"},{"cell_type":"markdown","source":["#### Pre-Requisites \n","\n","* A [Microsoft Fabric subscription](https://learn.microsoft.com/en-us/fabric/enterprise/licenses) or sign up for a free [Microsoft Fabric (Preview) trial](https://learn.microsoft.com/en-us/fabric/get-started/fabric-trial).\n","* Sign in to [Microsoft Fabric](https://fabric.microsoft.com/).\n","* Create or use an existing Fabric Workspace and Lakehouse, follow the steps here to [Create a Lakehouse in Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/data-engineering/create-lakehouse)\n","* Create shortcut to ADLS Gen2 account and load the the heart failure data into lakehouse file section\n","* After getting this notebook, attach the lakehouse with notebook."],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"4c4b9c95-8e28-4554-a8e0-8a8faf5a2d9f"},{"cell_type":"code","source":["# Read the heartfailure Dataset file\n","df = spark.read.format(\"csv\").option(\"header\",\"true\").option(\"inferSchema\", \"true\").load(\"Files/files/heart.csv\")\n","# df now is a Spark DataFrame containing CSV data from \"Files/heartdataset/heart.csv\".\n","display(df)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":3,"statement_ids":[3],"livy_statement_state":"available","session_id":"a7813a4a-e4f9-4c30-b218-1e03be07b4d9","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T13:47:36.9852067Z","session_start_time":"2024-07-15T13:47:37.2813228Z","execution_start_time":"2024-07-15T13:47:47.8515075Z","execution_finish_time":"2024-07-15T13:47:52.4384321Z","parent_msg_id":"7abfe83e-d5a5-4663-b0cc-c9e98b207e2b"},"text/plain":"StatementMeta(, a7813a4a-e4f9-4c30-b218-1e03be07b4d9, 3, Finished, Available, Finished)"},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.synapse.widget-view+json":{"widget_id":"d0caa3a6-8224-4d26-9f34-21dba475aefb","widget_type":"Synapse.DataFrame"},"text/plain":"SynapseWidget(Synapse.DataFrame, d0caa3a6-8224-4d26-9f34-21dba475aefb)"},"metadata":{}}],"execution_count":1,"metadata":{"collapsed":false,"microsoft":{"language_group":"synapse_pyspark","language":"python"}},"id":"d88f8805-7d68-4339-bc1e-f59e99480d0d"},{"cell_type":"code","source":["#print schema\n","df.printSchema()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":4,"statement_ids":[4],"livy_statement_state":"available","session_id":"a7813a4a-e4f9-4c30-b218-1e03be07b4d9","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T13:48:13.5365553Z","session_start_time":null,"execution_start_time":"2024-07-15T13:48:14.0119914Z","execution_finish_time":"2024-07-15T13:48:14.2615643Z","parent_msg_id":"fb7886a2-0f34-4efc-ab3a-13d521ebd116"},"text/plain":"StatementMeta(, a7813a4a-e4f9-4c30-b218-1e03be07b4d9, 4, Finished, Available, Finished)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["root\n |-- Age: integer (nullable = true)\n |-- Sex: string (nullable = true)\n |-- ChestPainType: string (nullable = true)\n |-- RestingBP: double (nullable = true)\n |-- Cholesterol: double (nullable = true)\n |-- FastingBS: double (nullable = true)\n |-- RestingECG: string (nullable = true)\n |-- MaxHR: integer (nullable = true)\n |-- ExerciseAngina: string (nullable = true)\n |-- Oldpeak: double (nullable = true)\n |-- ST_Slope: string (nullable = true)\n |-- HeartDisease: integer (nullable = true)\n |-- RowNumber: integer (nullable = true)\n\n"]}],"execution_count":2,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language_group":"synapse_pyspark","language":"python"}},"id":"480fbcc2-ff5e-4c85-a698-dce359509759"},{"cell_type":"markdown","source":["## Write Spark dataframe to lakehouse delta table"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"bbef6319-8f5b-463b-beec-b17539201579"},{"cell_type":"markdown","source":["**Enable Vorder and Optimized Delta Write**\n","\n","**Verti-Parquet or VOrder** “ Fabric includes Microsoft’s VertiParquet engine. VertiParquet writer optimizes the Delta Lake parquet files resulting in 3x-4x compression improvement and up to 10x performance acceleration over Delta Lake files not optimized using VertiParquet while still maintaining full Delta Lake and PARQUET format compliance.

\n","**Optimize write** “ Spark in Fabric includes an Optimize Write feature that reduces the number of files written and targets to increase individual file size of the written data. It dynamically optimizes files during write operations generating files with a default 128 MB size. The target file size may be changed per workload requirements using configurations.\n","\n","These configs can be applied at a session level(as spark.conf.set in a notebook cell) as demonstrated in the following code cell, or at workspace level which is applied automatically to all spark sessions created in the workspace. The workspace level Apache Spark configuration can be set at:\n","- _Workspace settings >> Data Engineering/Sceience >> Spark Compute >> Spark Properties >> Add_"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"548b1e20-ffe0-42ff-bdaf-53daab7a0d2f"},{"cell_type":"code","source":["spark.conf.set(\"sprk.sql.parquet.vorder.enabled\", \"true\") # Enable Verti-Parquet write\n","spark.conf.set(\"spark.microsoft.delta.optimizeWrite.enabled\", \"true\") # Enable automatic delta optimized write"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":5,"statement_ids":[5],"livy_statement_state":"available","session_id":"a7813a4a-e4f9-4c30-b218-1e03be07b4d9","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T13:48:33.256611Z","session_start_time":null,"execution_start_time":"2024-07-15T13:48:33.7002537Z","execution_finish_time":"2024-07-15T13:48:33.9516974Z","parent_msg_id":"58a18dd0-0af6-4dd8-b268-0f035af1c8a5"},"text/plain":"StatementMeta(, a7813a4a-e4f9-4c30-b218-1e03be07b4d9, 5, Finished, Available, Finished)"},"metadata":{}}],"execution_count":3,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language_group":"synapse_pyspark","language":"python"}},"id":"c8079c5e-2b34-4495-89b0-ff421750d88c"},{"cell_type":"code","source":["table_name = \"heartFailure\"\n","df.write.mode(\"overwrite\").format(\"delta\").save(f\"Tables/{table_name}\")\n","print(f\"Spark dataframe saved to delta table: {table_name}\")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":6,"statement_ids":[6],"livy_statement_state":"available","session_id":"a7813a4a-e4f9-4c30-b218-1e03be07b4d9","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T13:48:41.5698842Z","session_start_time":null,"execution_start_time":"2024-07-15T13:48:42.0294767Z","execution_finish_time":"2024-07-15T13:48:46.9311209Z","parent_msg_id":"bc0ced7a-033a-447c-8a09-c084c9cdc540"},"text/plain":"StatementMeta(, a7813a4a-e4f9-4c30-b218-1e03be07b4d9, 6, Finished, Available, Finished)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Spark dataframe saved to delta table: heartFailure\n"]}],"execution_count":4,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language_group":"synapse_pyspark","language":"python"}},"id":"916630ac-1b4d-4205-b5b2-981e3b20fff9"},{"cell_type":"code","source":["data_df = spark.read.format(\"delta\").load(\"Tables/heartFailure\")\n","display(data_df)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":7,"statement_ids":[7],"livy_statement_state":"available","session_id":"a7813a4a-e4f9-4c30-b218-1e03be07b4d9","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T13:49:09.8232331Z","session_start_time":null,"execution_start_time":"2024-07-15T13:49:10.2743984Z","execution_finish_time":"2024-07-15T13:49:12.8068039Z","parent_msg_id":"b2f0b20a-0de2-4880-a683-72cf6e1e5852"},"text/plain":"StatementMeta(, a7813a4a-e4f9-4c30-b218-1e03be07b4d9, 7, Finished, Available, 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You can either run code in a notebook, or use the Data Wrangler to generate code for you.\n","\n","**Data Wrangler** is a tool used in notebooks. It offers an easy-to-use interface for exploring data. This tool shows data in a grid format, offers automatic summary statistics, built-in visualizations, and a library of common data-cleaning operations. Each operation can be done in just a few clicks. It shows the changes to the data right away and creates code in pandas or PySpark that can be saved back to the notebook for future use.\n","\n","\n"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"22b6ce14-490c-4feb-a25e-ee52c88a6630"},{"cell_type":"markdown","source":["#### Launching Data Wrangler\n","\n","To explore and transform any pandas Dataframes in your notebook, launch Data Wrangler directly from the notebook.\n","\n",">[!NOTE]\n",">Data Wrangler can not be opened while the notebook kernel is busy. The cell execution must complete prior to launching Data Wrangler.\n","\n"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"29566045-0621-42a3-81be-0d0ec62d760c"},{"cell_type":"code","source":["import pandas as pd\n","\n","#Read the delta table\n","\n","data_df = spark.read.format(\"delta\").load(\"Tables/heartFailure\")\n","\n","# Convert to a pandas df (needed to use Data Wrangler)\n","df = data_df.toPandas()\n","display(df)\n"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":107,"statement_ids":[107],"livy_statement_state":"available","session_id":"5c80421a-3f4e-42ad-a379-8447cbc01755","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T14:36:28.4282286Z","session_start_time":null,"execution_start_time":"2024-07-15T14:36:28.8145249Z","execution_finish_time":"2024-07-15T14:36:43.2645781Z","parent_msg_id":"e5a09710-adb6-4106-aa70-25f95ffa7d75"},"text/plain":"StatementMeta(, 5c80421a-3f4e-42ad-a379-8447cbc01755, 107, Finished, Available, Finished)"},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.synapse.widget-view+json":{"widget_id":"4e4d314d-d410-427d-ae65-c9466a37b3d6","widget_type":"Synapse.DataFrame"},"text/plain":"SynapseWidget(Synapse.DataFrame, 4e4d314d-d410-427d-ae65-c9466a37b3d6)"},"metadata":{}}],"execution_count":7,"metadata":{"collapsed":false,"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"68ad6809-f71a-4e71-9ad4-af65ac2b2cf0"},{"cell_type":"markdown","source":["**NOTE**: To follow these instructions while working with Data Wrangler, duplicate this tab as once you open Data Wrangler you will not be able to see the notebook.\n","\n","1. Under the notebook ribbon Home tab, select Launch Data Wrangler. You'll see a list of activated pandas DataFrames available for editing.\n","\n","\n","2. Select the DataFrame you just created in last cell and open in Data Wrangler. From the Pandas dataframe list, select `df`.\n","\n","\n","![image-alt-text](https://github.com/lesantana/wthds/blob/main/1-fdatascience.png?raw=true)"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"e36657f1-edcf-40f4-9f1b-3c8dbad1040d"},{"cell_type":"markdown","source":["Data Wrangler launches and generates a descriptive overview of your data. The table in the middle shows each data column.\n","\n","![image-alt-text](https://github.com/lesantana/wthds/blob/main/2-fdatascience.png?raw=true)\n"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"358ff7dd-2b19-4226-a597-6f4741088eba"},{"cell_type":"markdown","source":["The Summary panel next to the table shows information about the DataFrame. When you select a column in the table, the summary updates with information about the selected column. In some instances, the data displayed and summarized will be a truncated view of your DataFrame. When this happens, you'll see warning image in the summary pane. But this is not the case in this datasframe.\n","\n",">Each operation you do can be applied in a matter of clicks, updating the data display in real time and generating code that you can save back to your notebook as a reusable function."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"9e8ef065-2952-410f-b395-304b9567f756"},{"cell_type":"markdown","source":["\n","### Let's start the data cleaning trought Data Wrangler\n","\n","> There are different preprocessing steps that is important for developing robust, efficient and reliable machine learning models. \n","\n"," \n"," ###### 1. Removing unnecessary columns from a dataset before training a machine learning model is a best practice that enhances model performance, improves interpretability & reduces complexity.\n"," a. On the **Operations** panel, expand **Schema** and select **Drop columns**.\n","\n"," b. Select **RowNumber**. This column will appear in red in the preview, to show they're changed by the code (in this case, dropped.)\n"," \n"," c. Select **Apply**, a new step is created in the **Cleaning steps panel** on the bottom left. \n","  \n","  \n","![image-alt-text](https://github.com/lesantana/wthds/blob/main/3-fdatascience.png?raw=true)\n","\n"," \n","###### 2. Machine learning algorithms do not support missing values, dropping rows with missing values ensures compatibility with a wide range of algorithms without needing additional imputation strategies.\n","\n"," a. On the **Operations** panel, select **Find and replace**, and then select **Drop missing values**.\n"," \n"," b. Select the **RestingBP**, **Cholesterol** and **FastingBS** columns. On the right left those are the ones that are pointed as missing values.\n","\n"," c. Select **Apply**, a new step is created in the **Cleaning steps panel** on the bottom left. \n","\n"," \n"," \n","![image-alt-text](https://github.com/lesantana/wthds/blob/main/4-fdatascience.png?raw=true)\n","\n"," \n","###### 3. Handling duplicate rows is an essential step in data preparation because it ensures data quality.\n","\n"," a. On the **Operations** panel, select **Find and replace**, and then select **Drop duplicate rows**.\n","\n"," b. Select **Apply**, a new step is created in the **Cleaning steps panel** on the bottom left. \n","\n"," \n"," \n"," ![image-alt-text](https://github.com/lesantana/wthds/blob/main/5-fdatascience.png?raw=true)\n","\n","  \n","\n","###### 4. Select **Add code to notebook** at the top left to close Data Wrangler and add the code automatically. The **Add code to notebook** wraps the code in a function.\n","\n","  \n"," ![image-alt-text](https://github.com/lesantana/wthds/blob/main/5.5.1-datascience.png?raw=true)\n"," ![image-alt-text](https://github.com/lesantana/wthds/blob/main/7-fdatascience.png?raw=true)\n"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"c6f4e0b4-3db3-473f-af58-3cee6454c757"},{"cell_type":"code","source":["# Code generated by Data Wrangler for pandas DataFrame\n","\n","def clean_data(df):\n"," # Drop column: 'RowNumber'\n"," df = df.drop(columns=['RowNumber'])\n"," # Drop rows with missing data in columns: 'RestingBP', 'Cholesterol', 'FastingBS'\n"," df = df.dropna(subset=['RestingBP', 'Cholesterol', 'FastingBS'])\n"," # Drop duplicate rows across all columns\n"," df = df.drop_duplicates()\n"," return df\n","\n","df_clean = clean_data(df.copy())\n","df_clean.head()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":108,"statement_ids":[108],"livy_statement_state":"available","session_id":"5c80421a-3f4e-42ad-a379-8447cbc01755","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T14:36:54.1749916Z","session_start_time":null,"execution_start_time":"2024-07-15T14:36:54.5849747Z","execution_finish_time":"2024-07-15T14:36:54.8211774Z","parent_msg_id":"5865df29-30b7-42c3-b3b1-fec2c9b019a3"},"text/plain":"StatementMeta(, 5c80421a-3f4e-42ad-a379-8447cbc01755, 108, Finished, Available, Finished)"},"metadata":{}},{"output_type":"execute_result","execution_count":323,"data":{"text/plain":" Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR \\\n1 60 M ASY 132.0 218.0 0.0 ST 140 \n2 63 M ASY 170.0 177.0 0.0 Normal 84 \n3 59 M ASY 122.0 233.0 0.0 Normal 117 \n4 58 M ASY 132.0 458.0 1.0 Normal 69 \n7 62 M ASY 158.0 210.0 1.0 Normal 112 \n\n ExerciseAngina Oldpeak ST_Slope HeartDisease \n1 Y 1.5 Down 1 \n2 Y 2.5 Down 1 \n3 Y 1.3 Down 1 \n4 N 1.0 Down 0 \n7 Y 3.0 Down 1 ","text/html":"

\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
AgeSexChestPainTypeRestingBPCholesterolFastingBSRestingECGMaxHRExerciseAnginaOldpeakST_SlopeHeartDisease
160MASY132.0218.00.0ST140Y1.5Down1
263MASY170.0177.00.0Normal84Y2.5Down1
359MASY122.0233.00.0Normal117Y1.3Down1
458MASY132.0458.01.0Normal69N1.0Down0
762MASY158.0210.01.0Normal112Y3.0Down1
\n
"},"metadata":{}}],"execution_count":8,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"3a247905-3524-475d-b01d-1a594ad7b6c1"},{"cell_type":"markdown","source":["## Feature Engineering\n","\n","###### In the feature engineering process, especially when dealing with categorical data, encoding is a crucial step. One of the simplest methods for converting categorical values into numerical values is using the **LabelEncoder**. Here’s how and why you would use LabelEncoder in the feature engineering process:\n","\n","\n","###### **Label encoding** is a process where categorical variables are converted into numerical labels. Each unique category value is assigned a unique integer. This method is useful for ordinal categorical data where the categories have a natural order."],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"4d1e9057-9dd4-4067-a11f-ae655ff53169"},{"cell_type":"markdown","source":["###### 1. Checking if datatypes are numerical"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"0ea7c801-19fa-4a9f-973d-c618398e4638"},{"cell_type":"code","source":["df_clean.dtypes"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":109,"statement_ids":[109],"livy_statement_state":"available","session_id":"5c80421a-3f4e-42ad-a379-8447cbc01755","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T14:37:06.6365861Z","session_start_time":null,"execution_start_time":"2024-07-15T14:37:07.0295926Z","execution_finish_time":"2024-07-15T14:37:07.2648728Z","parent_msg_id":"5e93f6dd-81a6-4fe5-aa57-4bfd8c246391"},"text/plain":"StatementMeta(, 5c80421a-3f4e-42ad-a379-8447cbc01755, 109, Finished, Available, Finished)"},"metadata":{}},{"output_type":"execute_result","execution_count":326,"data":{"text/plain":"Age int32\nSex object\nChestPainType object\nRestingBP float64\nCholesterol float64\nFastingBS float64\nRestingECG object\nMaxHR int32\nExerciseAngina object\nOldpeak float64\nST_Slope object\nHeartDisease int32\ndtype: object"},"metadata":{}}],"execution_count":9,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"71033523-a290-486b-b45e-22f3fcb6a26a"},{"cell_type":"markdown","source":["###### 2. Transforming categorical values"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"51b63b60-498d-4502-9bfa-f27bc1e53184"},{"cell_type":"code","source":["# Import the LabelEncoder class from the sklearn.preprocessing module.\n","# LabelEncoder is a tool provided by the scikit-learn library, a popular library in Python for machine learning, which is used to encode categorical labels into numerical values\"\n","# Creates an instance of the LabelEncoder class and assigns it to the variable lab.\n","\n","from sklearn.preprocessing import LabelEncoder\n","import pandas as pd\n","lab = LabelEncoder()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":110,"statement_ids":[110],"livy_statement_state":"available","session_id":"5c80421a-3f4e-42ad-a379-8447cbc01755","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T14:37:12.4049232Z","session_start_time":null,"execution_start_time":"2024-07-15T14:37:12.7926305Z","execution_finish_time":"2024-07-15T14:37:22.7588507Z","parent_msg_id":"976bcee1-9550-45a2-86d9-87cc7eeb3a50"},"text/plain":"StatementMeta(, 5c80421a-3f4e-42ad-a379-8447cbc01755, 110, Finished, Available, Finished)"},"metadata":{}}],"execution_count":10,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"0db68ba1-4a38-4c9e-935b-547dd959cdde"},{"cell_type":"markdown","source":["Code to transform the categorical values, please complete the code according to the steps. If needed, ask bing chat for help."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"adb87e41-e5c0-4a24-9840-443187c9b913"},{"cell_type":"code","source":["# 1.Iniatialize a dataframe to be cleaned.\n","data_df1 = df_clean\n","# 2.Split object and non-object data types. You could use variables and the functions Include and exclude for that.\n","obj = data_df1.select_dtypes(include='object')\n","not_obj = data_df1.select_dtypes(exclude='object')\n","# 3.Encode categorical columns \n","for i in range(0, obj.shape[1]):\n"," obj.iloc[:,i] = lab.fit_transform(obj.iloc[:,i])\n","# 4.Combine enconded and non-enconded data\n","df_new = pd.concat([obj, not_obj], axis=1)\n","# 4.Display the first 10 rows to evaluate\n","df_new.head(10)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":111,"statement_ids":[111],"livy_statement_state":"available","session_id":"5c80421a-3f4e-42ad-a379-8447cbc01755","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T14:37:29.6465123Z","session_start_time":null,"execution_start_time":"2024-07-15T14:37:30.030168Z","execution_finish_time":"2024-07-15T14:37:30.277126Z","parent_msg_id":"96811cec-baf0-40e0-80d7-990bf3c1f7f7"},"text/plain":"StatementMeta(, 5c80421a-3f4e-42ad-a379-8447cbc01755, 111, Finished, Available, Finished)"},"metadata":{}},{"output_type":"execute_result","execution_count":332,"data":{"text/plain":" Sex ChestPainType RestingECG ExerciseAngina ST_Slope Age RestingBP \\\n1 1 0 2 1 0 60 132.0 \n2 1 0 1 1 0 63 170.0 \n3 1 0 1 1 0 59 122.0 \n4 1 0 1 0 0 58 132.0 \n7 1 0 1 1 0 62 158.0 \n8 1 0 2 1 0 61 120.0 \n9 1 0 1 1 0 59 125.0 \n11 1 2 0 0 0 76 104.0 \n12 1 0 2 1 0 70 170.0 \n15 1 0 1 1 0 64 134.0 \n\n Cholesterol FastingBS MaxHR Oldpeak HeartDisease \n1 218.0 0.0 140 1.5 1 \n2 177.0 0.0 84 2.5 1 \n3 233.0 0.0 117 1.3 1 \n4 458.0 1.0 69 1.0 0 \n7 210.0 1.0 112 3.0 1 \n8 282.0 0.0 135 4.0 1 \n9 222.0 0.0 135 2.5 1 \n11 113.0 0.0 120 3.5 1 \n12 192.0 0.0 129 3.0 1 \n15 273.0 0.0 102 4.0 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SexChestPainTypeRestingECGExerciseAnginaST_SlopeAgeRestingBPCholesterolFastingBSMaxHROldpeakHeartDisease
11021060132.0218.00.01401.51
21011063170.0177.00.0842.51
31011059122.0233.00.01171.31
41010058132.0458.01.0691.00
71011062158.0210.01.01123.01
81021061120.0282.00.01354.01
91011059125.0222.00.01352.51
111200076104.0113.00.01203.51
121021070170.0192.00.01293.01
151011064134.0273.00.01024.01
\n
"},"metadata":{}}],"execution_count":11,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"29fc8ab5-21ab-4564-a6b0-94bba87ef247"},{"cell_type":"markdown","source":["**NOTE:** You can also explore how to one-hot encode the categorical columns with Data Wrangler. However, this will not create labels in your existing columns, but rather a new column for each category with True and False values. Using this alternative format might need some modification to the code in the model training process. Please discuss this possibility with hack attendees to raise awareness of this Data Wrangler feature."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"6e1c6f49-1b8f-4b74-8a01-525ad33627df"},{"cell_type":"markdown","source":["#### Save processed data to a Delta Table"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"52f09856-116f-4f11-aa8a-fa12f3f4d177"},{"cell_type":"markdown","source":["Refer to notebook 1 for more information about vorder and optimizedwrite"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"ffcd6a61-65fe-4300-b433-5573b294b9e5"},{"cell_type":"code","source":["spark.conf.set(\"sprk.sql.parquet.vorder.enabled\", \"true\") # Enable Verti-Parquet write\n","spark.conf.set(\"spark.microsoft.delta.optimizeWrite.enabled\", \"true\") # Enable automatic delta optimized write"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":112,"statement_ids":[112],"livy_statement_state":"available","session_id":"5c80421a-3f4e-42ad-a379-8447cbc01755","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T14:37:36.5895027Z","session_start_time":null,"execution_start_time":"2024-07-15T14:37:36.967572Z","execution_finish_time":"2024-07-15T14:37:37.209267Z","parent_msg_id":"da188c65-0e84-4738-946f-8562de47d439"},"text/plain":"StatementMeta(, 5c80421a-3f4e-42ad-a379-8447cbc01755, 112, Finished, Available, Finished)"},"metadata":{}}],"execution_count":12,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"86dd7ed8-8b4b-47a2-87a7-f443d8ae90ee"},{"cell_type":"markdown","source":["Refer to notebook 1 for more information on writing delta tables to the lakehouse."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"67fd5421-c851-4cae-b500-8c5962c8c79b"},{"cell_type":"code","source":["table_name = \"heartfailure_processed\"\n","data_df_processed = spark.createDataFrame(df_new)\n","data_df_processed.write.mode(\"overwrite\").format(\"delta\").save(f\"Tables/{table_name}\")\n","print(f\"Spark dataframe saved to delta table: {table_name}\")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":113,"statement_ids":[113],"livy_statement_state":"available","session_id":"5c80421a-3f4e-42ad-a379-8447cbc01755","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T14:37:39.8969956Z","session_start_time":null,"execution_start_time":"2024-07-15T14:37:40.2577227Z","execution_finish_time":"2024-07-15T14:37:46.5279878Z","parent_msg_id":"0b9ab835-873f-4ab5-a45d-95b4f2689f80"},"text/plain":"StatementMeta(, 5c80421a-3f4e-42ad-a379-8447cbc01755, 113, Finished, Available, Finished)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Spark dataframe saved to delta table: heartfailure_processed\n"]}],"execution_count":13,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"9a2df2c7-b352-4578-85ff-6b64b2b725b4"},{"cell_type":"code","source":["%%sql\n","\n","select * from heartfailure_processed limit 100;"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":114,"statement_ids":[114],"livy_statement_state":"available","session_id":"5c80421a-3f4e-42ad-a379-8447cbc01755","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T14:38:49.7330376Z","session_start_time":null,"execution_start_time":"2024-07-15T14:38:50.1627956Z","execution_finish_time":"2024-07-15T14:39:04.4881333Z","parent_msg_id":"71d6c4b0-f5ea-4ef9-b1e2-d593c74312b6"},"text/plain":"StatementMeta(, 5c80421a-3f4e-42ad-a379-8447cbc01755, 114, Finished, Available, 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\ No newline at end of file diff --git a/072-DataScienceInFabric/Coach/Solutions/SOLUTION-03-Train-Register-HeartFailurePredictionModel.ipynb b/072-DataScienceInFabric/Coach/Solutions/SOLUTION-03-Train-Register-HeartFailurePredictionModel.ipynb new file mode 100644 index 0000000000..492ab25032 --- /dev/null +++ b/072-DataScienceInFabric/Coach/Solutions/SOLUTION-03-Train-Register-HeartFailurePredictionModel.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["## Module 3: Train and register Heart Failure Prediction Machine Learning Model\n","In this module you will learn to train a machine learning model to predict the likelihood of an individual getting heart failure based on some historical diagnostic measurements available in the training dataset\n","\n","Once a model is trained, you will learn to register the trained model, and log hyperaparameters used and evaluation metrics using Fabric's native integration with the MLflow framework.\n","\n","[MLflow](https://mlflow.org/docs/latest/index.html) is an open source platform for managing the machine learning lifecycle with features like Tracking, Models, and Model Registry. MLflow is natively integrated with Fabric Data Science Experience."],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"f8990450-454f-43fb-9385-46ca1f71296b"},{"cell_type":"markdown","source":["### Import mlflow and create an experiment to log the run\n"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"a2d2f449-82e5-41e3-acce-5009f9f36986"},{"cell_type":"code","source":["# Create Experiment to Track and register model with mlflow\n","import mlflow\n","print(f\"mlflow lbrary version: {mlflow.__version__}\")\n","EXPERIMENT_NAME = \"heartfailure_prediction\"\n","mlflow.set_experiment(EXPERIMENT_NAME)\n","mlflow.autolog(exclusive=False)\n"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":null,"statement_ids":null,"livy_statement_state":null,"session_id":null,"state":"waiting","normalized_state":"waiting","queued_time":"2024-07-15T14:55:47.9686498Z","session_start_time":null,"execution_start_time":null,"execution_finish_time":null,"parent_msg_id":"67aeb653-2fdb-4926-9d6f-5ab47853bba6"},"text/plain":"StatementMeta(, , , Waiting, , Waiting)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["mlflow lbrary version: 2.6.0\n"]}],"execution_count":1,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"fca65cb6-306d-4dbd-97c4-d888b7385e2c"},{"cell_type":"code","source":["# Import the required libraries for model training\n","from sklearn.model_selection import train_test_split\n","from lightgbm import LGBMClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score, f1_score, precision_score, confusion_matrix, recall_score, roc_auc_score, classification_report"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":null,"statement_ids":null,"livy_statement_state":null,"session_id":null,"state":"waiting","normalized_state":"waiting","queued_time":"2024-07-15T14:56:01.1438707Z","session_start_time":null,"execution_start_time":null,"execution_finish_time":null,"parent_msg_id":"9d21d511-36c5-478a-9058-ee652cddb030"},"text/plain":"StatementMeta(, , , Waiting, , Waiting)"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["2024/07/15 14:56:11 INFO mlflow.tracking.fluent: Autologging successfully enabled for sklearn.\n"]},{"output_type":"stream","name":"stderr","text":["2024/07/15 14:56:11 INFO mlflow.tracking.fluent: Autologging successfully enabled for lightgbm.\n"]}],"execution_count":2,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"5fe6d340-a177-4fec-ae6b-bf430fd98e20"},{"cell_type":"markdown","source":["### Read data from lakehouse delta table (saved in previous module)"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"3dcec51a-236e-49c5-82a8-ae120908a2e7"},{"cell_type":"code","source":["data_df = spark.read.format(\"delta\").load(\"Tables/heartfailure_processed\")\n","data_df.printSchema()\n","display(data_df)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":null,"statement_ids":null,"livy_statement_state":null,"session_id":null,"state":"waiting","normalized_state":"waiting","queued_time":"2024-07-15T14:56:17.1761248Z","session_start_time":null,"execution_start_time":null,"execution_finish_time":null,"parent_msg_id":"1df6e988-fa94-4045-b444-0f94d6103d1e"},"text/plain":"StatementMeta(, , , Waiting, , Waiting)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["root\n |-- Sex: long (nullable = true)\n |-- ChestPainType: long (nullable = true)\n |-- RestingECG: long (nullable = true)\n |-- ExerciseAngina: long (nullable = true)\n |-- ST_Slope: long (nullable = true)\n |-- Age: integer (nullable = true)\n |-- RestingBP: double (nullable = true)\n |-- Cholesterol: double (nullable = true)\n |-- FastingBS: double (nullable = true)\n |-- MaxHR: integer (nullable = true)\n |-- Oldpeak: double (nullable = true)\n |-- HeartDisease: integer (nullable = true)\n\n"]},{"output_type":"display_data","data":{"application/vnd.synapse.widget-view+json":{"widget_id":"03b861ca-7b5e-466b-9b8f-9d260f378969","widget_type":"Synapse.DataFrame"},"text/plain":"SynapseWidget(Synapse.DataFrame, 03b861ca-7b5e-466b-9b8f-9d260f378969)"},"metadata":{}}],"execution_count":3,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"collapsed":false},"id":"dd7054e1-d116-4556-bdf8-8dd11e6f1eed"},{"cell_type":"markdown","source":["### Perform random split to get train and test datasets and identify feature columns to be used or Model Training"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"8fda7850-4185-4069-ac4f-e4ef26782e7c"},{"cell_type":"code","source":["data_df = data_df.toPandas()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":null,"statement_ids":null,"livy_statement_state":null,"session_id":null,"state":"waiting","normalized_state":"waiting","queued_time":"2024-07-15T14:56:31.6400458Z","session_start_time":null,"execution_start_time":null,"execution_finish_time":null,"parent_msg_id":"2ef0bac9-d66c-43c1-a064-ade68f15165f"},"text/plain":"StatementMeta(, , , Waiting, , Waiting)"},"metadata":{}}],"execution_count":5,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"1020a34d-117d-413f-b7c1-7bb3b200d724"},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","SEED = 12345\n","y = data_df[\"HeartDisease\"]\n","X = data_df.drop(\"HeartDisease\",axis=1)\n","# Split the dataset to 60%, 20%, 20% for training, validation, and test datasets\n","# Train-Test Separation\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=SEED)\n","# Train-Validation Separation\n","X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=SEED)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":null,"statement_ids":null,"livy_statement_state":null,"session_id":null,"state":"waiting","normalized_state":"waiting","queued_time":"2024-07-15T14:56:35.9316443Z","session_start_time":null,"execution_start_time":null,"execution_finish_time":null,"parent_msg_id":"2e460ba9-ea18-421a-b837-24ab024bff8b"},"text/plain":"StatementMeta(, , , Waiting, , Waiting)"},"metadata":{}}],"execution_count":6,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"c1ce97d1-f167-49b2-b9ea-bbbb528668d1"},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","from lightgbm import LGBMClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score, f1_score, precision_score, confusion_matrix, recall_score, roc_auc_score, classification_report\n","mlflow.sklearn.autolog(registered_model_name='rfc1_sm') # Register the trained model with autologging\n","rfc1_sm = RandomForestClassifier(max_depth=4, max_features=4, min_samples_split=3, random_state=1) # Pass hyperparameters\n","with mlflow.start_run(run_name=\"rfc1_sm\") as run:\n"," rfc1_sm_run_id = run.info.run_id # Capture run_id for model prediction later\n"," print(\"run_id: {}; status: {}\".format(rfc1_sm_run_id, run.info.status))\n"," rfc1_sm.fit(X_train,y_train) # Imbalanaced training data\n"," rfc1_sm.score(X_test,y_test)\n"," y_pred = rfc1_sm.predict(X_test)\n"," cr_rfc1_sm = classification_report(y_test, y_pred)\n"," cm_rfc1_sm = confusion_matrix(y_test, y_pred)\n"," roc_auc_rfc1_sm = roc_auc_score(y_train, rfc1_sm.predict_proba(X_train)[:, 1])"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":null,"statement_ids":null,"livy_statement_state":null,"session_id":null,"state":"waiting","normalized_state":"waiting","queued_time":"2024-07-15T14:56:40.3345305Z","session_start_time":null,"execution_start_time":null,"execution_finish_time":null,"parent_msg_id":"d523f8d4-779c-4ac4-bfa3-064b0ea62ed4"},"text/plain":"StatementMeta(, , , Waiting, , Waiting)"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["2024/07/15 14:56:41 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/mlflow/data/pandas_dataset.py:116: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n"]},{"output_type":"stream","name":"stdout","text":["run_id: 9111e9e2-ba82-4ba0-8a8f-f2c5d4ab8fb0; status: RUNNING\n"]},{"output_type":"stream","name":"stderr","text":["2024-07-15:14:56:41,868 WARNING [tracking_store.py:153] log_inputs not supported\n"]},{"output_type":"stream","name":"stderr","text":["2024/07/15 14:56:43 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/mlflow/models/signature.py:214: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n"]},{"output_type":"stream","name":"stderr","text":["2024/07/15 14:56:52 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\"\n"]},{"output_type":"stream","name":"stderr","text":["Registered model 'rfc1_sm' already exists. Creating a new version of this model...\n"]},{"output_type":"stream","name":"stderr","text":["2024/07/15 14:56:55 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation. Model name: rfc1_sm, version 2\nCreated version '2' of model 'rfc1_sm'.\n"]},{"output_type":"stream","name":"stderr","text":["2024/07/15 14:56:57 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/mlflow/data/pandas_dataset.py:116: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n2024-07-15:14:56:57,486 WARNING [tracking_store.py:153] log_inputs not supported\n"]},{"output_type":"stream","name":"stderr","text":["2024/07/15 14:56:57 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages/mlflow/data/pandas_dataset.py:116: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details.\"\n2024-07-15:14:56:57,541 WARNING [tracking_store.py:153] log_inputs not supported\n"]},{"output_type":"display_data","data":{"application/vnd.mlflow.run-widget+json":{"info":{"artifact_uri":"sds://onelakewestus3.pbidedicated.windows.net/a1efaa53-a101-4fb3-a1ba-7beb731708ad/f9869532-a5f0-4f51-9cd7-478d6d8e5c3c/9111e9e2-ba82-4ba0-8a8f-f2c5d4ab8fb0/artifacts","end_time":1721055418,"experiment_id":"cd275c89-df79-4d1e-808f-955592296261","lifecycle_stage":"active","run_id":"9111e9e2-ba82-4ba0-8a8f-f2c5d4ab8fb0","run_name":"","run_uuid":"9111e9e2-ba82-4ba0-8a8f-f2c5d4ab8fb0","start_time":1721055401,"status":"FINISHED","user_id":"7ebfac85-3ebb-440f-a743-e52052051f6a"},"data":{"metrics":{"training_f1_score":0.9098625711769114,"training_accuracy_score":0.9099756690997567,"training_precision_score":0.9100217879422928,"training_score":0.9099756690997567,"training_recall_score":0.9099756690997567,"training_log_loss":0.24901475491849967,"training_roc_auc":0.9750763941940412,"RandomForestClassifier_score_X_test":0.8188405797101449},"params":{"bootstrap":"True","ccp_alpha":"0.0","class_weight":"None","criterion":"gini","max_depth":"4","max_features":"4","max_leaf_nodes":"None","max_samples":"None","min_impurity_decrease":"0.0","min_samples_leaf":"1","min_samples_split":"3","min_weight_fraction_leaf":"0.0","n_estimators":"100","n_jobs":"None","oob_score":"False","random_state":"1","verbose":"0","warm_start":"False"},"tags":{"mlflow.user":"4b3a56ea-6f42-450e-b7c3-fb2932c7ac32","synapseml.notebook.artifactId":"38cc8efd-8656-42d9-bc54-bd58d4cbfa4b","synapseml.user.name":"System Administrator","synapseml.user.id":"819a2816-072c-4319-a551-5746ba7b8f71","synapseml.livy.id":"7f1fc87f-1288-499f-b3ff-7b2dd6398150","mlflow.runName":"rfc1_sm","estimator_name":"RandomForestClassifier","estimator_class":"sklearn.ensemble._forest.RandomForestClassifier","mlflow.rootRunId":"9111e9e2-ba82-4ba0-8a8f-f2c5d4ab8fb0","synapseml.experimentName":"heartfailure_prediction","synapseml.experiment.artifactId":"f9869532-a5f0-4f51-9cd7-478d6d8e5c3c"}},"inputs":{"dataset_inputs":[]}}},"metadata":{}}],"execution_count":7,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"884583a4-4ca3-45a8-a504-9590bd00d3e4"},{"cell_type":"code","source":["display(cm_rfc1_sm)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":null,"statement_ids":null,"livy_statement_state":null,"session_id":null,"state":"waiting","normalized_state":"waiting","queued_time":"2024-07-15T14:57:08.95194Z","session_start_time":null,"execution_start_time":null,"execution_finish_time":null,"parent_msg_id":"f510fcf2-b4b9-4321-bf90-85f00098b0ab"},"text/plain":"StatementMeta(, , , Waiting, , Waiting)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"array([[53, 17],\n [ 8, 60]])"},"metadata":{}}],"execution_count":8,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"2e0b9125-e89a-480f-af21-644e57fdca89"},{"cell_type":"code","source":[" display(roc_auc_rfc1_sm)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":null,"statement_ids":null,"livy_statement_state":null,"session_id":null,"state":"waiting","normalized_state":"waiting","queued_time":"2024-07-15T14:57:14.9619277Z","session_start_time":null,"execution_start_time":null,"execution_finish_time":null,"parent_msg_id":"e4a47ccd-eb0d-4305-9d0e-9a0229724d4f"},"text/plain":"StatementMeta(, , , Waiting, , Waiting)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"0.9750763941940412"},"metadata":{}}],"execution_count":9,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"366a5b4f-090b-49c3-9e35-68087f78f449"},{"cell_type":"markdown","source":["### Load the model to generate predictions and re-calculate accuracy with the validation dataset\n","\n"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"a27f2336-955b-41de-8155-b2c56a8507a5"},{"cell_type":"code","source":["load_model = mlflow.sklearn.load_model(f\"runs:/{rfc1_sm_run_id}/model\")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":12,"statement_ids":[12],"livy_statement_state":"available","session_id":"7f1fc87f-1288-499f-b3ff-7b2dd6398150","state":"finished","normalized_state":"finished","queued_time":"2024-07-15T15:07:29.428806Z","session_start_time":null,"execution_start_time":"2024-07-15T15:07:29.9193553Z","execution_finish_time":"2024-07-15T15:07:32.3519556Z","parent_msg_id":"f68ceb42-2b1b-4ce5-ac88-e22405414176"},"text/plain":"StatementMeta(, 7f1fc87f-1288-499f-b3ff-7b2dd6398150, 12, Finished, Available, Finished)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading artifacts: 0%| | 0/5 [00:00=2.4 in /home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages (from Faker==18.10.1) (2.8.2)\nRequirement already satisfied: six>=1.5 in /home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages (from python-dateutil>=2.4->Faker==18.10.1) (1.16.0)\nInstalling collected packages: Faker\nSuccessfully installed Faker-18.10.1\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\nNote: you may need to restart the kernel to use updated packages.\nWarning: PySpark kernel has been restarted to use updated packages.\n\n"]}],"execution_count":1,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"6b26247a-4751-48b9-ab13-d4d255184265"},{"cell_type":"code","source":["from pyspark.sql.types import StructType, StructField, IntegerType, DoubleType, StringType, LongType\n","\n","heartFailureDataSchema = StructType(\n","[\n"," StructField('Age', IntegerType(), True),\n"," StructField('Sex', IntegerType(), True),\n"," StructField('ChestPainType', IntegerType(), True),\n"," StructField('RestingBP', IntegerType(), True),\n"," StructField('Cholesterol', IntegerType(), True),\n"," StructField('FastingBS', IntegerType(), True),\n"," StructField('RestingECG', IntegerType(), True),\n"," StructField('MaxHR', IntegerType(), True),\n"," StructField('ExerciseAngina', IntegerType(), True),\n"," StructField('Oldpeak', DoubleType(), True),\n"," StructField('ST_Slope', IntegerType(), True)\n","]\n",")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":9,"statement_ids":[9],"livy_statement_state":"available","session_id":"56a1cf7b-7798-4dd8-bad2-032a69c3b978","state":"finished","normalized_state":"finished","queued_time":"2024-07-17T14:09:55.563128Z","session_start_time":null,"execution_start_time":"2024-07-17T14:10:00.9164154Z","execution_finish_time":"2024-07-17T14:10:01.1517454Z","parent_msg_id":"e75dbc48-e14a-4a78-bb3a-952fc2a10535"},"text/plain":"StatementMeta(, 56a1cf7b-7798-4dd8-bad2-032a69c3b978, 9, Finished, Available, Finished)"},"metadata":{}}],"execution_count":2,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"a9d0dbe0-4d7d-4854-9063-38a5ce632de6"},{"cell_type":"code","source":["from faker import Faker\n","\n","faker = Faker()\n","simulateRecordCount = 10\n","simData = []\n","\n","for i in range(simulateRecordCount):\n"," age = faker.random_int(54,70)\n"," RestingBP = faker.random_int(70, 170)\n"," Cholesterol = faker.random_int(100, 300)\n"," FastingBS= faker.random_int(0, 1) \n"," MaxHR = faker.random_int(100,200)\n"," OldPeak = faker.pyfloat(right_digits = 1, positive = True, max_value = 4.5)\n"," ChestPain = faker.random_int(0,3,1)\n"," Sex = faker.random_int(0,1)\n"," RestingECG = faker.random_int(0,2,1)\n"," ExerciseAngina = faker.random_int(0,1)\n"," StSlope= faker.random_int(0,1)\n"," simData.append((age, Sex,ChestPain, RestingBP,Cholesterol,FastingBS,RestingECG , MaxHR,ExerciseAngina,OldPeak, StSlope))\n","\n","df = spark.createDataFrame(data = simData, schema =heartFailureDataSchema)\n","display(df)\n","\n"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":10,"statement_ids":[10],"livy_statement_state":"available","session_id":"56a1cf7b-7798-4dd8-bad2-032a69c3b978","state":"finished","normalized_state":"finished","queued_time":"2024-07-17T14:11:00.8030572Z","session_start_time":null,"execution_start_time":"2024-07-17T14:11:01.2239151Z","execution_finish_time":"2024-07-17T14:11:03.7253792Z","parent_msg_id":"5e0018d7-2208-4186-a3b0-3bef59393ff9"},"text/plain":"StatementMeta(, 56a1cf7b-7798-4dd8-bad2-032a69c3b978, 10, Finished, Available, Finished)"},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.synapse.widget-view+json":{"widget_id":"8aa31ca2-b6d4-4069-9256-1186d99af1d7","widget_type":"Synapse.DataFrame"},"text/plain":"SynapseWidget(Synapse.DataFrame, 8aa31ca2-b6d4-4069-9256-1186d99af1d7)"},"metadata":{}}],"execution_count":3,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"collapsed":false,"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"8b77d29d-49b1-4787-b844-d33188ec1ec2"},{"cell_type":"markdown","source":["Transform dataframe to a Pandas df"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"dee20543-4c49-4f2b-af33-a4e99641670f"},{"cell_type":"code","source":["data_df = df.toPandas()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":11,"statement_ids":[11],"livy_statement_state":"available","session_id":"56a1cf7b-7798-4dd8-bad2-032a69c3b978","state":"finished","normalized_state":"finished","queued_time":"2024-07-17T14:11:26.1280735Z","session_start_time":null,"execution_start_time":"2024-07-17T14:11:26.5567956Z","execution_finish_time":"2024-07-17T14:11:28.0962601Z","parent_msg_id":"7fc4c0ae-5bbb-46df-98b2-c896d27743d4"},"text/plain":"StatementMeta(, 56a1cf7b-7798-4dd8-bad2-032a69c3b978, 11, Finished, Available, Finished)"},"metadata":{}}],"execution_count":4,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"13c2bc19-8e92-46d9-9aa6-efed7a1733b8"},{"cell_type":"markdown","source":["### Load trained and registered model to generate predictions"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"5c0b73c7-becb-4c1c-af17-bfed0c631b4a"},{"cell_type":"code","source":["from synapse.ml.predict import MLFlowTransformer\n","\n","model = MLFlowTransformer(\n"," inputCols=list(df.columns),\n"," outputCol='predictions',\n"," modelName='rfc1_sm',\n"," modelVersion=1\n",")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"statement_id":12,"statement_ids":[12],"livy_statement_state":"available","session_id":"56a1cf7b-7798-4dd8-bad2-032a69c3b978","state":"finished","normalized_state":"finished","queued_time":"2024-07-17T14:11:37.2228004Z","session_start_time":null,"execution_start_time":"2024-07-17T14:11:37.6101434Z","execution_finish_time":"2024-07-17T14:11:54.2918888Z","parent_msg_id":"425913ba-2454-4e0a-ae0f-20fca7f23da9"},"text/plain":"StatementMeta(, 56a1cf7b-7798-4dd8-bad2-032a69c3b978, 12, Finished, Available, Finished)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading artifacts: 0%| | 0/5 [00:00"},"metadata":{}}],"execution_count":11,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"sparksql","language_group":"synapse_pyspark"},"collapsed":false},"id":"4876b1ae-e785-412f-8244-6dd4f00a5e64"}],"metadata":{"language_info":{"name":"python"},"kernelspec":{"name":"synapse_pyspark","language":"Python","display_name":"Synapse 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+++ b/072-DataScienceInFabric/README.md @@ -0,0 +1,47 @@ +# What The Hack - Data Science In Microsoft Fabric + +## Introduction + +This Fabric Data Science guides you through the process of building an end-to-end ML deployment on Fabric. + +Contoso hospital has historical, anonymous heart condition data already present in their ADLS Gen 2 as part of their record-keeping solution. They want to leverage this data to create a new app that can help clinicians assess the heart failure risk of their patients, depending on a variety of factors. As an analyst at Contoso, you have been asked to use Microsoft Fabric to train a prediction model and deploy it on Realtime endpoints so that they can build an app on it which will be used by the doctors to predict the patient heart health. + +## Learning Objectives + +In this hack you will be learning how to best leverage Fabric for Data Science. This is not intended to be an in-depth tutorial around Machine Learning models. + +1. Use shortcuts and the OneLake +2. Work with data using Fabric Notebooks +3. Leverage tools such as Data Wrangler to simplify your tasks +4. Understand the different options to apply a trained ML model in Fabric and how to export it +5. Expose the insights from your predictions using PowerBI + +## Challenges + +- Challenge 00: **[Prerequisites - Ready, Set, GO!](Student/Challenge-00.md)** + - Configure your Fabric workspace and gather your data +- Challenge 01: **[Bring your data to the OneLake](Student/Challenge-01.md)** + - Creating a shortcut to the available data +- Challenge 02: **[Prepare your data for ML](Student/Challenge-02.md)** + - Clean and transform the data into a useful format while leveraging Data Wrangler +- Challenge 03: **[Train and register the model](Student/Challenge-03.md)** + - Train a machine learning model with ML Flow +- Challenge 04: **[Generate batch predictions](Student/Challenge-04.md)** + - Score a static dataset with the model +- Challenge 05: **[Create a PowerBI report with your predictions](Student/Challenge-05.md)** + - Build a PowerBI report with the scored data +- Challenge 06: **[(Optional) Deploy the model to an AzureML real-time endpoint](Student/Challenge-06.md)** + - Deploy the model you trained to AzureML and generate predictions via the API +## Prerequisites + +- Microsoft Fabric capacity/trial capacity. If running the hack on an individual basis, an F4 capacity would be adequate, and an F8 capacity would have generous compute power margin. +- PowerBI Pro or Premium per user subscription/trial (unless using Fabric trial capacity) +- Access to an Azure subscription to: + - Deploy a storage account to store the dataset (alternatively you can upload the dataset directly to Fabric) + - Deploy an AzureML workspace to host a real-time inference endpoint for Challenge 6 (optional) + +## Contributors + +- Pardeep Singla +- Juan Llovet +- Leandro Santana diff --git a/072-DataScienceInFabric/Student/Challenge-00.md b/072-DataScienceInFabric/Student/Challenge-00.md new file mode 100644 index 0000000000..23cfce4795 --- /dev/null +++ b/072-DataScienceInFabric/Student/Challenge-00.md @@ -0,0 +1,57 @@ +# Challenge 00 - Prerequisites - Ready, Set, GO! + +**[Home](../README.md)** - [Next Challenge >](./Challenge-01.md) + +## Introduction + +Thank you for participating in the Data Science in Microsoft Fabric What The Hack. Before you can hack, you will need to set up some prerequisites. + +## Common Prerequisites + +We have compiled a list of common tools and software that will come in handy to complete most What The Hack Azure-based hacks! + +You might not need all of them for the hack you are participating in. However, if you work with Azure on a regular basis, these are all things you should consider having in your toolbox. + + + +- [Azure Subscription](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-subscription) +- [Postman](https://www.postman.com/downloads/) +- [Managing Cloud Resources](../Student/000-HowToHack/WTH-Common-Prerequisites.md#managing-cloud-resources) +- [Azure Portal](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-portal) + - [Azure Cloud Shell](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-cloud-shell) + - [Azure CLI (optional)](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-cli) + - [Note for Windows Users](../Student/000-HowToHack/WTH-Common-Prerequisites.md#note-for-windows-users) + - [Azure PowerShell CmdLets](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-powershell-cmdlets) + +- [Azure Storage Explorer (optional)](../Student/000-HowToHack/WTH-Common-Prerequisites.md#azure-storage-explorer) + +Additionally, please refer to the [Hack Introduction](../README.md) for more information about licensing requirements. + +## Description + +Now that you have the common pre-requisites installed on your workstation, there are prerequisites specific to this hack. + +To get started, download and unzip the Resources folder [here](https://aka.ms/FabricdsWTHResources). This folder contains the notebooks you will be working with, as well as a shell script that you will use to deploy some needed Azure resources. + +**NOTE:** The Resources folder also includes the heart.csv file. You can upload this data directly to the Fabric Lakehouse if you decide you want to go through this hack without needing an Azure subscription. However, this will skip half of Challenge 1 and the important concept of using shortcuts in Fabric, as well as all of Challenge 6. If you are going to be setting up the Azure resources and using the shortcut, ignore the heart.csv file. + +First, head to [Microsoft Fabric](https://fabric.microsoft.com/). + - Create a new workspace by clicking on 'Workspaces' in the vertical menu on the left side of the screen. Use the 'New Workspace' button at the bottom of the list. + - Once you are inside your new workspace, select the Data Science experience using the button on the bottom left corner of the screen. + - At the top of the Data Science experience menu, check that you are still in the new workspace and select 'Import Notebook' from the top row of options. + - Follow the prompts to upload the 4 notebook `.ipynb` files contained within the resources folder. + +(**Skip if working only on Fabric**) To begin setting up your Azure subscription for this hack, you will run a bash script that will deploy and configure a list of resources. You can find this script as the `deployhack.sh` file in the resources folder you downloaded. + - Go to the Azure portal and click on the cloud shell button on the top navigation bar, to the right of the Copilot button. + - Once the cloud shell connects, make sure you are using a Bash shell. If you are not, click on the button on the top-left corner of the cloud shell to switch to bash. + - Click on the **Manage Files** button on the shell's navigation bar and select upload. Select the `deployhack.sh` file from your computer. + - Run the `sh deployhack.sh` command in your cloud shell. + - Follow the prompts in the shell. + +## Success Criteria + +To complete this challenge successfully, you should be able to: + +- Verify that you have a Fabric workspace where your 4 notebooks are available +- Verify that you have a storage account with the heart.csv data in a container (unless uploading data directly to Fabric) +- (Optional) Verify that your Azure ML workspace has correctly deployed (if completing Challenge 6) diff --git a/072-DataScienceInFabric/Student/Challenge-01.md b/072-DataScienceInFabric/Student/Challenge-01.md new file mode 100644 index 0000000000..cfd935bd1c --- /dev/null +++ b/072-DataScienceInFabric/Student/Challenge-01.md @@ -0,0 +1,55 @@ +# Challenge 01 - Bring your data to the OneLake + +[< Previous Challenge](./Challenge-00.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-02.md) + +## Introduction + +As you observed at the end of setup in Challenge 0, you have been provided with some anonymous historical data of patients who have suffered heart attacks. The csv file contains a set of conditions (or features) that these patients have had and an indication of whether they had a heart attack or not. The first step to working with this data is to bring it to Fabric's OneLake, from where you will be able to use it for your analysis and to train a machine learning model. + +## Description + +Your task in this challenge is to make your data available as a delta table in the Fabric OneLake. To do so, you must: +- Create a shortcut to the heart.csv file located in ADLS Gen2. +- Load the data to the lakehouse you will be using throughout this hack. + +To load the data to the lakehouse, you will be using a spark notebook. Open Notebook 1, that you uploaded to your Fabric workspace in Challenge 0. You will find more guidance and helpful links there. Additionally, visit the end of this challenge for documentation links on how to create a shortcut in Fabric. + +**NOTE**: If you skipped the Azure setup and are completing this challenge by only using Fabric, you will not need to create a shortcut. Instead, upload the csv file to your lakehouse and follow the instructions in Notebook 1. + +Notebook sections: +1. Read the .csv file into a dataframe in the notebook +2. Write the dataframe to the lakehouse as a delta table + +By the end of this challenge, you should be able to understand and know how to use: +- Fabric Shortcuts, how do they work, why are they beneficial and what is required to use them +- Fabric Lakehouses, their role, how to create a new one and their different components +- Delta, the concept of the delta lake, the delta parquet format and how to read/write to tables using spark + +## Success Criteria + +The heart.csv data is now saved as a delta table on the lakehouse in the same workspace where the notebooks are stored. + +## Learning Resources + +[Microsoft Fabric Lakehouse](https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-overview) + +[ADLS Shortcuts in Fabric](https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts#adls-shortcuts) + +Refer to Notebook 1 for more helpful links + + +## Tips + +- You will need to access your Azure subscription to complete this challenge, but you are not expected to change any settings. Requirements, such as the hierarchical namespace, have been taken care of by the setup script. +- Navigating the storage account can be complicated. Leverage Microsoft Learn to ensure you are bringing the right information over from Azure to Fabric when you set up the shortcut. You might need to do multiple attempts if you pick the wrong fields. +- If you do need to re-set the connection to the shortcut, make sure you are selecting "new connection" on the drop-down menu. The shortcut wizard will recognize the ADLS path and default to a previous connection instead of creating a new one. +- There are multiple ways to authenticate to ADLS from the shortcut menu. Some are easier to use than others. +- Notebooks depend on Lakehouses to read and write data. +- As you move on to using notebooks, most cells depend on each other. If you are getting an error on a cell that you think shouldn't be there, navigate upstream to try to find what is wrong. Leverage Microsoft Learn and the provided documentation to make sure you are using the right functions. Check that you are using the correct variable names. +- If you stop halfway through a notebook, your progress will be saved in the code cells but the variables might be deleted from memory. If that happens, run every cell from the start in succession to get back to the starting point. + +## Advanced Challenges (Optional) + +Interested in seeing the shortcut's low latency in action? + +Find another dataset of interest to you, save it in the same folder as heart.csv in your Azure storage account and watch the new file appear on Fabric. Explore the differences between files/shortcuts and actual tables in Fabric and what is needed to keep your data up to date at each stage. diff --git a/072-DataScienceInFabric/Student/Challenge-02.md b/072-DataScienceInFabric/Student/Challenge-02.md new file mode 100644 index 0000000000..2e16314370 --- /dev/null +++ b/072-DataScienceInFabric/Student/Challenge-02.md @@ -0,0 +1,60 @@ +# Challenge 02 - Data preparation with Data Wrangler + +[< Previous Challenge](./Challenge-01.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-03.md) + +## Introduction + +Welcome to Challenge 2! In this exercise, you will learn how to use Data Wrangler to prepare a heart dataset for model training. In the previous challenge, you mapped the data within One Lake. You will now focus on transforming and preparing your data for the next challenges. You have the flexibility to either write code in a notebook or leverage Data Wrangler’s intuitive interface to streamline the pre-processing tasks. + +## Description + +The main tasks in this challenge consist in different pre-processing steps that are important for developing robust, efficient and reliable machine learning models. You must ensure that you accomplish the following tasks during this challenge: +1. Removing unnecessary columns from a dataset. It is the best practice that enhances model performance, improves interpretability & reduces complexity. +2. Dropping rows with missing values ensure compatibility with a wide range of algorithms without needing additional imputation strategies. +3. Handling duplicate rows is an essential step in data preparation because it ensures data quality. +4. Adjusting data types. Machine learning algorithms operate on numerical data (integers, floats, etc.). If you feed them non-numeric data (e.g., strings), they won’t work. + +This is a notebook-based challenge. You will find all instructions as well as more guidance and helpful links in Notebook 2. Additionally, visit the end of this challenge for documentation links on how to get started with Data Wrangler in Microsoft Fabric. + +For most of these tasks you can use *Data Wrangler* to accelerate the data preparation process. *Data Wrangler* is a tool used in notebooks. It offers an easy-to-use interface for exploring data. This tool shows data in a grid format, offers automatic summary statistics, built-in visualizations, and a library of common data-cleaning operations. Each operation can be done in just a few clicks. It shows the changes to the data right away and creates code in `pandas` or `PySpark` that can be saved back to the notebook for future use. + +Another important step before the training model is the feature engineering. In the feature engineering process, especially when dealing with categorical data, encoding is a crucial step. One of the simplest methods for converting categorical values into numerical values is using the `LabelEncoder`. In this challenge you will need to figure out how to handle categorical values in the dataset. + +Notebook sections: +1. Read the .csv file into a pandas dataframe in the notebook. +2. Launch the Data Wrangler and interact with the data cleaning operations +3. Apply the operations using python codes +4. Develop feature engineering using spark. +5. Write the dataframe to the lakehouse as a delta table. + +By the end of this challenge, you should be able to understand and know how to use: +- Fabric Data Wrangler, how it works and how it can help you in your data preparation process. +- Fabric Notebooks, their role, how to create a new one and execute spark applications. + +## Success Criteria + +- The heart dataset totally shaped, cleaned and prepared for the model training. +- No data duplicated or exceeded columns. +- No missing values. +- No categorical values. +- The processed data saved to the Fabric Lakehouse as a Delta table. + +## Learning Resources + +[Data Science in Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/data-science/data-science-overview) + +[Accelerate Data prep with Data Wrangler](https://learn.microsoft.com/en-us/fabric/data-science/data-wrangler) + +[Pre-process data with Data Wrangler in Microsoft Fabric](https://learn.microsoft.com/en-us/training/modules/preprocess-data-with-data-wrangler-microsoft-fabric/) + +## Tips + +- You will need to access your Microsoft Fabric subscription or sign up for a free trial. +- Sign in to Microsoft Fabric and switch to the Synapse Data Science experience. +- You can launch Data Wrangler directly from a Fabric notebook to explore and transform pandas or Spark DataFrames. +- Use the Data Wrangler dropdown prompt under the notebook ribbon’s “Data” tab to browse active DataFrames available for editing. +- Note that Data Wrangler cannot be opened while the notebook kernel is busy; an executing cell must finish its execution first. +- When Data Wrangler loads, it displays a descriptive overview of the chosen DataFrame in the Summary panel. This overview includes information about the DataFrame’s dimensions, missing values, and more. +- Use Data Wrangler’s grid-like interface to apply various data-cleaning operations. The generated code can be saved back to the notebook as a reusable function. + + diff --git a/072-DataScienceInFabric/Student/Challenge-03.md b/072-DataScienceInFabric/Student/Challenge-03.md new file mode 100644 index 0000000000..7fe0cb7fa8 --- /dev/null +++ b/072-DataScienceInFabric/Student/Challenge-03.md @@ -0,0 +1,47 @@ +# Challenge 03 - Train and register the model + +[< Previous Challenge](./Challenge-02.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-04.md) + +## Introduction + +During Challenge 2, you cleaned up the data you will be working with. You explored the structure and statistics of the data and uncovered various inconsistencies, which you resolved. You must now use this data to train and register an ML model. To keep track of the model iterations, you will leverage Fabric's direct integration with MLFlow. Once you have a trained model you will save it for further prediction. + +## Description +Your task in this challenge is to train and register an ML model with MLFlow. This is a notebook-based challenge, you will find all instructions within Notebook 3. + +Notebook sections: +1. Creating an MLFlow experiment +2. Reading the delta table with Spark +3. Performing a random split of the data +4. Creating a run within the experiment to fit the model +5. Loading the saved model and generating predictions with the validation data + +By the end of this challenge, you should be able to understand and know how to use: +- MLFlow, what are experiments and runs, how to set and start them and how to load models +- `sklearn`, what are some basic notions of using this ML framework, how does it integrate with MLFlow and how that transfers to any other framework +- Basics of evaluating machine learning models, confusion matrices, classification reports and auc scores + + +## Success Criteria + +Your model has been trained and registered with MLFlow and you are able to load it and generate predictions with the validation data + +## Learning Resources + +Refer to Notebook 3 for helpful links + +## Tips + +- Notebooks depend on Lakehouses to read and write data. +- As you move on to using notebooks, most cells depend on each other. If you are getting an error on a cell that you think shouldn't be there, navigate upstream to try to find what is wrong. Leverage Microsoft Learn and the provided documentation to make sure you are using the right functions. Check that you are using the correct variable names. +- If you stop halfway through a notebook, your progress will be saved in the code cells but the variables might be deleted from memory. If that happens, run every cell from the start in succession to get back to the starting point. + +## Advanced Challenges (Optional) + +Wondering how MLFlow can assist in the process of model iteration and fine-tuning? + +Look back to the model set up. Understand what each of the stated hyperparameters do and how that affects the model generated. Set up another run in the experiment to create a version 2 of the model and try to switch some hyperparameters this time around. Look at the evaluation metrics. Can you perfect the model? + +Interested in learning more about how MLFlow is integrated with Fabric? + +Go back to your workspace and see what items have been automatically created. You should have a model and an experiment, and if you've had multiple runs you should see different model versions. diff --git a/072-DataScienceInFabric/Student/Challenge-04.md b/072-DataScienceInFabric/Student/Challenge-04.md new file mode 100644 index 0000000000..262f4fe0ec --- /dev/null +++ b/072-DataScienceInFabric/Student/Challenge-04.md @@ -0,0 +1,40 @@ +# Challenge 04 - Load the ML Model from Microsoft Fabric to Azure ML + +[< Previous Challenge](./Challenge-03.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-05.md) + +## Introduction + +During Challenge 3, you trained and registered a model. You validated it using a variety of metrics. You must now leverage this same model to generate predictions over new data. To do so, you will create a set of simulated input data to feed the model and receive a prediction. Once you have generated the predictions you will save them as a delta table for further use. + +## Description +Your task in this challenge is to obtain predictions from some simulated inputs This is a notebook-based challenge, you will find all instructions within Notebook 4. + +Notebook sections: +1. Simulate input data to be used for predictions +2. Load the model and generate predictions +3. Format predictions and save them to a delta table + +By the end of this challenge, you should be able to understand and know how to use: +- The PREDICT function, how to use the `MLFlowTransfomer` object, how to create predictions +- (Additional learnings, not core to the hack) The Python Faker library, how to use it to create simulated data to be used for model inference + + +## Success Criteria + +You have generated a set of predictions with the simulated data and have saved them as a delta table, making them available to create a PowerBI report + +## Learning Resources + +Refer to Notebook 4 for helpful links + +## Tips + +- Notebooks depend on Lakehouses to read and write data. +- As you move on to using notebooks, most cells depend on each other. If you are getting an error on a cell that you think shouldn't be there, navigate upstream to try to find what is wrong. Leverage Microsoft Learn and the provided documentation to make sure you are using the right functions. Check that you are using the correct variable names. +- If you stop halfway through a notebook, your progress will be saved in the code cells but the variables might be deleted from memory. If that happens, run every cell from the start in succession to get back to the starting point. + +## Advanced Challenges (Optional) + +Interested in how you can integrate the PREDICT function call into your analytics workflow? + +You can run Fabric notebooks from a pipeline, which means you can generate predictions over new data during the ETL process using Delta tables that you have created/uploaded. Explore how you can use the [notebook activity](https://learn.microsoft.com/en-us/fabric/data-factory/notebook-activity) to call a notebook from a pipeline and how you can [integrate a notebook](https://learn.microsoft.com/en-us/fabric/data-engineering/author-execute-notebook#integrate-a-notebook) by using parameters to pass along table names that the notebook can use for its inference and to write back the results. diff --git a/072-DataScienceInFabric/Student/Challenge-05.md b/072-DataScienceInFabric/Student/Challenge-05.md new file mode 100644 index 0000000000..7245ae47d6 --- /dev/null +++ b/072-DataScienceInFabric/Student/Challenge-05.md @@ -0,0 +1,39 @@ +# Challenge 05 - Visualize predictions with a Power BI report + +[< Previous Challenge](./Challenge-04.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-06.md) + +## Introduction + +During Challenge 4, you generated batch predictions over simulated patient data. This reflects the scenario of receiving new data of patients that are currently being monitored. You must now display the predictions in an insightful way that can show doctors which patients are at risk. To do so, you will be building a PowerBI report using the newly created predictions. + +## Description + +You've been tasked with creating some reporting using Power BI based on the data which got batch predicted and saved to a delta table in the last challenge. In this challenge, you must create a semantic model and the report and visualizations based on it. You will need to explore working on PowerBI to build the most helpful report you can based on the available data. This challenge does **not** require the use of a notebook. + +By the end of this challenge, you should be able to understand and know how to use: +- Fabric Semantic Models, how do you create one from existing data and what you can use it for. +- PowerBI basic reporting, how do you build a report from a semantic model and how different PowerBI visualizations can convey insights. + +## Success Criteria + +To complete this challenge, verify that: + +- You have a PowerBI report displaying the data from the batch predicted table. Your report must, at a minimum, be able to identify which patients are at risk of heart failure. You must use the id field for this purpose. + +## Learning Resources + +- [How to create semantic model in Fabric](https://microsoftlearning.github.io/mslearn-fabric/Instructions/Labs/14-create-a-star-schema-model.html) +- [How to create reports in Fabric](https://learn.microsoft.com/en-us/fabric/data-engineering/tutorial-lakehouse-build-report) + +## Tips + +- If you are unable to create a semantic model with your table, delete the delta table and re-run Notebook 3. Unfortunately, this is a [known issue](https://learn.microsoft.com/en-us/fabric/get-started/known-issues/known-issue-643-tables-not-available-semantic-model). +- PowerBI auto-aggregates data fields. Sometimes, you want different aggregations than the default or no aggregations at all. +- The way you organize data fields when building a PowerBI visualization affects the end result. Use the Data field to shape your visualization. +- PowerBI visualizations have extensive formatting options. Even the more traditional options can be enhanced with features such as conditional color. + +## Advanced Challenges (Optional) + +Get creative with the Power BI report! What kind of visuals can you create? + +There is a variety of data within the table that you saved in the previous challenge and PowerBI offers you many ways to show it. Explore the different options, including the [AI Visuals](https://learn.microsoft.com/en-us/power-bi/visuals/power-bi-visualization-decomposition-tree) (link redirects to decomposition tree, but feel free to explore the whole learn page) and try to build a report that conveys improved insights. diff --git a/072-DataScienceInFabric/Student/Challenge-06.md b/072-DataScienceInFabric/Student/Challenge-06.md new file mode 100644 index 0000000000..3170af1982 --- /dev/null +++ b/072-DataScienceInFabric/Student/Challenge-06.md @@ -0,0 +1,37 @@ +# Challenge 06 - Deploy the model to an AzureML real-time endpoint + +[< Previous Challenge](./Challenge-05.md) - **[Home](../README.md)** - [Next Challenge >](./Challenge-07.md) + +## Introduction +In Challenge 5 you created a PowerBI report showing which patients are at risk. However, part of the requirements of this solution is for an app to be able to obtain real-time predictions based on some input data. To do so, you will export and host in AzureML your previously trained ML Model so that you can serve it through an API endpoint. + +**NOTE**: you will make use of an AzureML workspace for this challenge. You must deploy a workspace if you didn't previously do so with the hack deployment script. + +## Description + +You have been tasked to deploy the machine learning model which you have trained in Microsoft Fabric to Azure ML learning for real time inference. In this challenge, you will deploy a Fabric model to Azure Machine Learning, and repurpose the model trained in Fabric for use by applications via scalable Azure compute clusters. The ability to export and repurpose models trained with MLFlow in Fabric opens endless opportunities! This challenge does **not** require the use of a notebook. + +By the end of this challenge, you should be able to understand and know how to use: +- MLFlow exported models, what the different files represent and how you can import them to a new location. +- AzureML real-time endpoints, how to create one with a custom model and how to use it via an API call. + +**NOTE**: due to some compatibility issues with MLFlow and Azure Machine Learning, you will need to modify some files locally before uploading your model to Azure Machine Learning. Once you have downloaded your model from Fabric, unzip the folder and open both the `conda.yaml` and `requirements.txt` files. Change the MLFlow version on both files to `2.7.0`. Save and close both files. + +## Success Criteria + +To complete this challenge, please verify that : + - ML model is ready and downloaded from Microsoft Fabric. + - Deployed Successfully on Azure ML real-time endpoints. + - Deployed model is predicting the heart failure prediction. Test the API call both within the AzureML studio and with Postman. + + +## Learning Resources + - [Difference between Data science in Fabric and Azure Machine Learning](https://www.linkedin.com/pulse/comparing-microsoft-fabric-azure-machine-learning-which-kim-berg) + - [How to deploy MLFlow model to Azure Machine learning](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2&tabs=studio) + +## Tips + +- Fabric stores everything you create as items in the workspace. +- Make sure you unzip the folder you will download from Fabric +- Once the AzureML endpoint is deployed, explore the example code snippets to understand what you need to send an inference API call + diff --git a/072-DataScienceInFabric/Student/Resources/.gitkeep b/072-DataScienceInFabric/Student/Resources/.gitkeep new file mode 100644 index 0000000000..e69de29bb2 diff --git a/072-DataScienceInFabric/Student/Resources/Notebooks/01-Ingest-Heart-Failure-Dataset-to-Lakehouse.ipynb b/072-DataScienceInFabric/Student/Resources/Notebooks/01-Ingest-Heart-Failure-Dataset-to-Lakehouse.ipynb new file mode 100644 index 0000000000..6029b448c9 --- /dev/null +++ b/072-DataScienceInFabric/Student/Resources/Notebooks/01-Ingest-Heart-Failure-Dataset-to-Lakehouse.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","id":"c7b6575d-05eb-4434-a77d-a8127987e289","metadata":{"nteract":{"transient":{"deleting":false}}},"source":["# Module 1: Read data from lakehouse and load into Delta format\n","\n","**Lakehouse**: A lakehouse is a collection of files/folders/tables that represent a database over a data lake used by the Spark engine and SQL engine for big data processing and that includes enhanced capabilities for ACID transactions when using the open-source Delta formatted tables.\n","\n","**Delta Lake**:Delta Lake is an open-source storage layer that brings ACID transactions, scalable metadata management, and batch and streaming data processing to Apache Spark. A Delta Lake table is a data table format that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata management.\n"]},{"cell_type":"markdown","id":"4c4b9c95-8e28-4554-a8e0-8a8faf5a2d9f","metadata":{"nteract":{"transient":{"deleting":false}}},"source":["#### Pre-Requisites \n","\n","* A [Microsoft Fabric subscription](https://learn.microsoft.com/en-us/fabric/enterprise/licenses) or sign up for a free [Microsoft Fabric (Preview) trial](https://learn.microsoft.com/en-us/fabric/get-started/fabric-trial).\n","* Sign in to [Microsoft Fabric](https://fabric.microsoft.com/).\n","* Create or use an existing Fabric Workspace and Lakehouse, follow the steps here to [Create a Lakehouse in Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/data-engineering/create-lakehouse)\n","* Create shortcut to ADLS Gen2 account and load the the heart failure data into lakehouse file section\n","* After getting this notebook, attach the lakehouse with notebook."]},{"cell_type":"markdown","id":"1aab8bb9-1963-4073-9f94-35b320055873","metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"source":["#### Part 1 Instructions:\n","\n","In the following code cell, load the Files/heart.csv into a dataframe. Display the dataframe to take a look at the data you will be working with. [Documentation link](https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-notebook-load-data). Make sure you automatically read the schema from the source as well. [Documentation link](https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option)"]},{"cell_type":"code","execution_count":2,"id":"d88f8805-7d68-4339-bc1e-f59e99480d0d","metadata":{"collapsed":false,"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"outputs":[{"data":{"application/vnd.livy.statement-meta+json":{"execution_finish_time":"2024-06-06T16:16:21.3831228Z","execution_start_time":"2024-06-06T16:16:18.112496Z","livy_statement_state":"available","parent_msg_id":"67f21f5a-d7de-4390-96fc-fb045108f704","queued_time":"2024-06-06T16:16:13.1526891Z","session_id":"5f849dc6-e766-4a8c-a84d-2e13caa6a6c2","session_start_time":null,"spark_pool":null,"state":"finished","statement_id":3,"statement_ids":[3]},"text/plain":["StatementMeta(, 5f849dc6-e766-4a8c-a84d-2e13caa6a6c2, 3, Finished, Available)"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.synapse.widget-view+json":{"widget_id":"6b2bf44b-b19c-4f50-8066-688b5e9d15e7","widget_type":"Synapse.DataFrame"},"text/plain":["SynapseWidget(Synapse.DataFrame, 6b2bf44b-b19c-4f50-8066-688b5e9d15e7)"]},"metadata":{},"output_type":"display_data"}],"source":["# Read the heartfailure Dataset file\n","df =\n","# df now is a Spark DataFrame containing CSV data from \"Files/heartdataset/heart.csv\".\n","display(df)"]},{"cell_type":"code","execution_count":3,"id":"480fbcc2-ff5e-4c85-a698-dce359509759","metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"outputs":[{"data":{"application/vnd.livy.statement-meta+json":{"execution_finish_time":"2024-06-06T16:21:35.8894537Z","execution_start_time":"2024-06-06T16:21:35.5921794Z","livy_statement_state":"available","parent_msg_id":"3bd8343c-1304-4ac8-87cf-5ba4aa1c1dfe","queued_time":"2024-06-06T16:21:35.1252762Z","session_id":"5f849dc6-e766-4a8c-a84d-2e13caa6a6c2","session_start_time":null,"spark_pool":null,"state":"finished","statement_id":4,"statement_ids":[4]},"text/plain":["StatementMeta(, 5f849dc6-e766-4a8c-a84d-2e13caa6a6c2, 4, Finished, Available)"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["root\n"," |-- Age: integer (nullable = true)\n"," |-- Sex: string (nullable = true)\n"," |-- ChestPainType: string (nullable = true)\n"," |-- RestingBP: integer (nullable = true)\n"," |-- Cholesterol: integer (nullable = true)\n"," |-- FastingBS: integer (nullable = true)\n"," |-- RestingECG: string (nullable = true)\n"," |-- MaxHR: integer (nullable = true)\n"," |-- ExerciseAngina: string (nullable = true)\n"," |-- Oldpeak: double (nullable = true)\n"," |-- ST_Slope: string (nullable = true)\n"," |-- HeartDisease: integer (nullable = true)\n","\n"]}],"source":["#Run this command to see the schema of your dataset\n","df.printSchema()"]},{"cell_type":"markdown","id":"bbef6319-8f5b-463b-beec-b17539201579","metadata":{"nteract":{"transient":{"deleting":false}}},"source":["## Write Spark dataframe to lakehouse delta table"]},{"cell_type":"markdown","id":"548b1e20-ffe0-42ff-bdaf-53daab7a0d2f","metadata":{"nteract":{"transient":{"deleting":false}}},"source":["**Enable Vorder and Optimized Delta Write**\n","\n","**Verti-Parquet or VOrder** “ Fabric includes Microsoft's VertiParquet engine. VertiParquet writer optimizes the Delta Lake parquet files resulting in 3x-4x compression improvement and up to 10x performance acceleration over Delta Lake files not optimized using VertiParquet while still maintaining full Delta Lake and PARQUET format compliance.

\n","**Optimize write** “ Spark in Fabric includes an Optimize Write feature that reduces the number of files written and targets to increase individual file size of the written data. It dynamically optimizes files during write operations generating files with a default 128 MB size. The target file size may be changed per workload requirements using configurations.\n","\n","These configs can be applied at a session level(as spark.conf.set in a notebook cell) as demonstrated in the following code cell, or at workspace level which is applied automatically to all spark sessions created in the workspace. The workspace level Apache Spark configuration can be set at:\n","- _Workspace settings >> Data Engineering/Sceience >> Spark Compute >> Spark Properties >> Add_\n","\n","[Learn more about V-Order and Optimized Delta Write](https://learn.microsoft.com/en-us/fabric/data-engineering/delta-optimization-and-v-order?tabs=sparksql)"]},{"cell_type":"code","execution_count":4,"id":"c8079c5e-2b34-4495-89b0-ff421750d88c","metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"outputs":[{"data":{"application/vnd.livy.statement-meta+json":{"execution_finish_time":"2024-06-06T16:23:03.4560196Z","execution_start_time":"2024-06-06T16:23:03.1473384Z","livy_statement_state":"available","parent_msg_id":"6d22bff6-70bb-4632-ba61-a0bc6d78a90d","queued_time":"2024-06-06T16:23:02.5957959Z","session_id":"5f849dc6-e766-4a8c-a84d-2e13caa6a6c2","session_start_time":null,"spark_pool":null,"state":"finished","statement_id":5,"statement_ids":[5]},"text/plain":["StatementMeta(, 5f849dc6-e766-4a8c-a84d-2e13caa6a6c2, 5, Finished, Available)"]},"metadata":{},"output_type":"display_data"}],"source":["#Run these configuration commands to set up your spark session\n","spark.conf.set(\"sprk.sql.parquet.vorder.enabled\", \"true\") # Enable Verti-Parquet write\n","spark.conf.set(\"spark.microsoft.delta.optimizeWrite.enabled\", \"true\") # Enable automatic delta optimized write"]},{"cell_type":"markdown","id":"379c7528-679b-4e4e-8b5f-cc9b19cc218e","metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"source":["#### Part 2 Instructions:\n","\n","Now, you will create a Delta table in your Lakehouse where you will store your dataset to be accessed at later notebooks. Write the data to a Delta table named \"heartFailure\" and then, once it has been saved, test reading the table and displaying the data in the second code cell. [Documentation link](https://learn.microsoft.com/en-us/training/modules/work-delta-lake-tables-fabric/3-create-delta-tables.) 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diff --git a/072-DataScienceInFabric/Student/Resources/Notebooks/02-data-analysis-preprocess_Data Wrangler.ipynb b/072-DataScienceInFabric/Student/Resources/Notebooks/02-data-analysis-preprocess_Data Wrangler.ipynb new file mode 100644 index 0000000000..ab0bc9c023 --- /dev/null +++ b/072-DataScienceInFabric/Student/Resources/Notebooks/02-data-analysis-preprocess_Data Wrangler.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["## Module 2: Perform data prep and cleansing using Apache Spark and Data Wrangler\n","\n","In this challenge we will prepare the dataset for the model training.\n","\n","Now that you have ingested and explored the data, you can transform the data. You can either run code in a notebook, or use the Data Wrangler to generate code for you.\n","\n","**Data Wrangler** is a tool used in notebooks. It offers an easy-to-use interface for exploring data. This tool shows data in a grid format, offers automatic summary statistics, built-in visualizations, and a library of common data-cleaning operations. Each operation can be done in just a few clicks. It shows the changes to the data right away and creates code in pandas or PySpark that can be saved back to the notebook for future use.\n","\n","\n","\n","\n"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"22b6ce14-490c-4feb-a25e-ee52c88a6630"},{"cell_type":"markdown","source":["#### Part 1 Launching Data Wrangler\n","\n","To explore and transform any pandas Dataframes in your notebook, launch Data Wrangler directly from the notebook.\n","\n",">[!NOTE]\n",">Data Wrangler can not be opened while the notebook kernel is busy. The cell execution must complete prior to launching Data Wrangler.\n","\n","Reference: [Lauching Data Wrangler](https://learn.microsoft.com/en-us/fabric/data-science/data-wrangler#launching-data-wrangler)\n","\n"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"29566045-0621-42a3-81be-0d0ec62d760c"},{"cell_type":"code","source":["import pandas as pd\n","\n","# Read a CSV into a Pandas DataFrame\n","\n","load_from_lakehouse= \n","\n","df=\n"],"outputs":[],"execution_count":null,"metadata":{"collapsed":false,"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"68ad6809-f71a-4e71-9ad4-af65ac2b2cf0"},{"cell_type":"markdown","source":["\n","### Part 2 Data cleaning trought Data Wrangler\n","\n","There are different preprocessing steps that is important for developing robust, efficient and reliable machine learning models. \n","\n"," \n"," 1. Removing unnecessary columns from a dataset before training a machine learning model is a best practice that enhances model performance, improves interpretability & reduces complexity.\n"," \n"," 2. Machine learning algorithms do not support missing values, dropping rows with missing values ensures compatibility with a wide range of algorithms without needing additional imputation strategies.\n"," \n"," 3. Handling duplicate rows is an essential step in data preparation because it ensures data quality.\n","  \n"," 4. Machine learning algorithms operate on numerical data (integers, floats, etc.). If you feed them non-numeric data (e.g., strings), they won’t work. You must adjust some columns data types.\n","  \n","\n","\n","Reference: [Browsing and applying data-cleaning operations](https://learn.microsoft.com/en-us/fabric/data-science/data-wrangler#browsing-data-cleaning-operations)\n"],"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"c6f4e0b4-3db3-473f-af58-3cee6454c757"},{"cell_type":"code","source":["# Code generated by Data Wrangler for pandas DataFrame\n"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"3a247905-3524-475d-b01d-1a594ad7b6c1"},{"cell_type":"markdown","source":["## Feature Engineering\n","\n","In the feature engineering process, especially when dealing with categorical data, encoding is a crucial step. One of the simplest methods for converting categorical values into numerical values is using the **LabelEncoder**. Here’s how and why you would use LabelEncoder in the feature engineering process:\n","\n","\n","**Label encoding** is a process where categorical variables are converted into numerical labels. Each unique category value is assigned a unique integer. This method is useful for ordinal categorical data where the categories have a natural order."],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"4d1e9057-9dd4-4067-a11f-ae655ff53169"},{"cell_type":"markdown","source":["1. Checking if datatypes are numerical"],"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"0ea7c801-19fa-4a9f-973d-c618398e4638"},{"cell_type":"code","source":["# Input the code to check the datatypes"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"71033523-a290-486b-b45e-22f3fcb6a26a"},{"cell_type":"markdown","source":["2. Transforming categorical values. Run the following cell as it is to get started with the encoding process."],"metadata":{"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"51b63b60-498d-4502-9bfa-f27bc1e53184"},{"cell_type":"code","source":["# Import the LabelEncoder class from the sklearn.preprocessing module.\n","# LabelEncoder is a tool provided by the scikit-learn library, a popular library in Python for machine learning, which is used to encode categorical labels into numerical values\"\n","# Creates an instance of the LabelEncoder class and assigns it to the variable lab.\n","\n","from sklearn.preprocessing import LabelEncoder\n","import pandas as pd\n","lab = LabelEncoder()"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"0db68ba1-4a38-4c9e-935b-547dd959cdde"},{"cell_type":"markdown","source":["Code to transform the categorical values, please complete the code according to the steps. Ask bing chat for help if needed. "],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"29102c1d-c938-43ff-aa8f-43e953c185db"},{"cell_type":"code","source":["# 1.Iniatialize a dataframe to be cleaned.\n","data_df1 = df_clean \n","# 2.Split object and non-object data types. You could use variables and the functions Include and exclude for that. \n","obj =\n","not_obj =\n","# 3.Encode categorical columns \n","for i in range(0, obj.shape[1]):\n"," obj.iloc[:,i] = lab.fit_transform(obj.iloc[:,i])\n","\n","# 4.Combine enconded and non-enconded data\n","df_new =\n","# 4.Display the first 10 rows to evaluate\n","head(df_new)\n"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"29fc8ab5-21ab-4564-a6b0-94bba87ef247"},{"cell_type":"markdown","source":["#### Save processed data to a Delta Table"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"52f09856-116f-4f11-aa8a-fa12f3f4d177"},{"cell_type":"markdown","source":["Refer to notebook 1 for more information about vorder and optimizedwrite"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"7f66c1cb-4d2b-443f-9136-97d52157a8cb"},{"cell_type":"code","source":["spark.conf.set(\"sprk.sql.parquet.vorder.enabled\", \"true\") # Enable Verti-Parquet write\n","spark.conf.set(\"spark.microsoft.delta.optimizeWrite.enabled\", \"true\") # Enable automatic delta optimized write"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"86dd7ed8-8b4b-47a2-87a7-f443d8ae90ee"},{"cell_type":"markdown","source":["Refer to notebook 1 for more information on writing delta tables to the lakehouse."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"70591db6-ec5d-43e8-8dc1-e30beb541913"},{"cell_type":"code","source":["# Saving the dataframe as a delta table, please complete the code on line 4. \n","table_name = \"heartfailure_processed\"\n","data_df_processed = spark.createDataFrame(df_new)\n","data_df_processed.\"please add your code here\""],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}}},"id":"9a2df2c7-b352-4578-85ff-6b64b2b725b4"},{"cell_type":"code","source":["%%sql\n","\n","select * from heartfailure_processed limit 100;"],"outputs":[],"execution_count":null,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false,"source_hidden":false},"microsoft":{"language":"sparksql","language_group":"synapse_pyspark"},"nteract":{"transient":{"deleting":false}},"sqlViewState":{"chartOptions":{"aggregationType":"count","binsNumber":10,"categoryFieldKeys":["9"],"chartType":"column","isStacked":false,"pivotFieldKey":"9","seriesFieldKeys":["8"],"wordFrequency":"-1"},"tableOptions":{}}},"id":"88b6c7b9-c13a-41af-882e-eec2d5b1a5a8"}],"metadata":{"kernel_info":{"name":"synapse_pyspark"},"kernelspec":{"name":"synapse_pyspark","language":"Python","display_name":"Synapse PySpark"},"language_info":{"name":"python"},"microsoft":{"language":"python","language_group":"synapse_pyspark","ms_spell_check":{"ms_spell_check_language":"en"}},"notebook_environment":{},"nteract":{"version":"nteract-front-end@1.0.0"},"widgets":{},"synapse_widget":{"state":{},"version":"0.1"},"save_output":true,"spark_compute":{"compute_id":"/trident/default","session_options":{"conf":{},"enableDebugMode":false}},"dependencies":{"lakehouse":{"default_lakehouse":"91f9e346-7d62-4063-bfd8-07035bb08131","known_lakehouses":[],"default_lakehouse_name":"Lakehouse","default_lakehouse_workspace_id":"a1efaa53-a101-4fb3-a1ba-7beb731708ad"}}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/072-DataScienceInFabric/Student/Resources/Notebooks/03-Train-Register-HeartFailurePredictionModel.ipynb b/072-DataScienceInFabric/Student/Resources/Notebooks/03-Train-Register-HeartFailurePredictionModel.ipynb new file mode 100644 index 0000000000..5291e23d2c --- /dev/null +++ b/072-DataScienceInFabric/Student/Resources/Notebooks/03-Train-Register-HeartFailurePredictionModel.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["## Module 3: Train and register Heart Failure Prediction Machine Learning Model\n","In this module you will learn to train a machine learning model to predict the likelihood of an individual getting heart failure based on some historical diagnostic measurements available in the training dataset\n","\n","Once a model is trained, you will learn to register the trained model, and log hyperaparameters used and evaluation metrics using Fabric's native integration with the MLflow framework.\n","\n","[MLflow](https://mlflow.org/docs/latest/index.html) is an open source platform for managing the machine learning lifecycle with features like Tracking, Models, and Model Registry. MLflow is natively integrated with Fabric Data Science Experience."],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"f8990450-454f-43fb-9385-46ca1f71296b"},{"cell_type":"markdown","source":["#### Import the required libraries\n"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"a2d2f449-82e5-41e3-acce-5009f9f36986"},{"cell_type":"code","source":["# Run this code cell to import the required libraries for model training\n","import mlflow\n","print(f\"mlflow lbrary version: {mlflow.__version__}\")\n","from sklearn.model_selection import train_test_split\n","from lightgbm import LGBMClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score, f1_score, precision_score, confusion_matrix, recall_score, roc_auc_score, classification_report\n"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"3f766ed0-1822-4de7-a442-f95c127f87cf","statement_id":4,"statement_ids":[4],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-19T13:27:51.7215161Z","session_start_time":null,"execution_start_time":"2024-06-19T13:27:52.1683263Z","execution_finish_time":"2024-06-19T13:28:06.5397558Z","parent_msg_id":"315cd3f0-0b99-40ef-8f74-603bc87b95f9"},"text/plain":"StatementMeta(, 3f766ed0-1822-4de7-a442-f95c127f87cf, 4, Finished, Available)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["mlflow lbrary version: 2.6.0\n"]}],"execution_count":2,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"fca65cb6-306d-4dbd-97c4-d888b7385e2c"},{"cell_type":"markdown","source":["#### Part 1 Instructions\n","In the cell below, create an mlflow experiment to track and register the model. The experiment name has been provided to you in the first variable. [Documentation link](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_experiment)"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"05f14c02-e1d4-4009-9310-b42148ca82b4"},{"cell_type":"code","source":["# Create Experiment to Track and register model with mlflow\n","EXPERIMENT_NAME = \"heartfailure_prediction\"\n","\n","#Enter code here to create an experiment\n","mlflow.\n","\n","#This line of code enables user-started runs to be tracked\n","mlflow.autolog(exclusive=False)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"3f766ed0-1822-4de7-a442-f95c127f87cf","statement_id":6,"statement_ids":[6],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-19T13:28:55.4267707Z","session_start_time":null,"execution_start_time":"2024-06-19T13:28:55.81991Z","execution_finish_time":"2024-06-19T13:29:06.6805267Z","parent_msg_id":"19045b0f-3f5e-4fe5-8446-1d7e962b914e"},"text/plain":"StatementMeta(, 3f766ed0-1822-4de7-a442-f95c127f87cf, 6, Finished, Available)"},"metadata":{}},{"output_type":"stream","name":"stderr","text":["2024/06/19 13:29:06 INFO mlflow.tracking.fluent: Autologging successfully enabled for lightgbm.\n2024/06/19 13:29:06 INFO mlflow.tracking.fluent: Autologging successfully enabled for sklearn.\n"]}],"execution_count":4,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"672e1230-71fa-475a-9fd5-fd9d3ec12ccb"},{"cell_type":"markdown","source":["#### Part 2 Instructions\n","Now, read the delta table that you saved into the lakehouse on notebook 2. Refer to previous notebooks if needed."],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"3dcec51a-236e-49c5-82a8-ae120908a2e7"},{"cell_type":"code","source":["#Enter code to read the delta table from the lakehouse\n","data_df = \n","\n","#Display the schema and a preview of the loaded dataframe\n","data_df.printSchema()\n","display(data_df)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"be009a0d-f936-4196-a3aa-31e411e3c3c2","statement_id":6,"statement_ids":[6],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T19:22:09.6333454Z","session_start_time":null,"execution_start_time":"2024-06-10T19:22:10.188978Z","execution_finish_time":"2024-06-10T19:22:16.8792153Z","parent_msg_id":"9878ff77-ba7a-4754-b602-ff1ce92e709e"},"text/plain":"StatementMeta(, be009a0d-f936-4196-a3aa-31e411e3c3c2, 6, Finished, Available)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["root\n |-- Sex: long (nullable = true)\n |-- ChestPainType: long (nullable = true)\n |-- RestingECG: long (nullable = true)\n |-- ExerciseAngina: long (nullable = true)\n |-- ST_Slope: long (nullable = true)\n |-- Age: integer (nullable = true)\n |-- RestingBP: integer (nullable = true)\n |-- Cholesterol: integer (nullable = true)\n |-- FastingBS: integer (nullable = true)\n |-- MaxHR: integer (nullable = true)\n |-- Oldpeak: double (nullable = true)\n |-- HeartDisease: integer (nullable = true)\n\n"]},{"output_type":"display_data","data":{"application/vnd.synapse.widget-view+json":{"widget_id":"fcf3065d-1f78-4673-9772-9febb4f656cc","widget_type":"Synapse.DataFrame"},"text/plain":"SynapseWidget(Synapse.DataFrame, fcf3065d-1f78-4673-9772-9febb4f656cc)"},"metadata":{}}],"execution_count":3,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"collapsed":false},"id":"dd7054e1-d116-4556-bdf8-8dd11e6f1eed"},{"cell_type":"markdown","source":["#### Part 3 instructions\n","You now have to perform a random split of your data to obtain a training and a testing sub-datasets. Run the following 2 code cells as they are, the comments explain what is happening at each stage."],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"8fda7850-4185-4069-ac4f-e4ef26782e7c"},{"cell_type":"code","source":["#First, convert your dataframe to a Pandas dataframe\n","data_df = data_df.toPandas()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"be009a0d-f936-4196-a3aa-31e411e3c3c2","statement_id":8,"statement_ids":[8],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T19:22:27.5391726Z","session_start_time":null,"execution_start_time":"2024-06-10T19:22:28.1427398Z","execution_finish_time":"2024-06-10T19:22:29.6978445Z","parent_msg_id":"414164e8-05d4-4d0f-baff-887982b07a9f"},"text/plain":"StatementMeta(, be009a0d-f936-4196-a3aa-31e411e3c3c2, 8, Finished, Available)"},"metadata":{}}],"execution_count":5,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"1020a34d-117d-413f-b7c1-7bb3b200d724"},{"cell_type":"code","source":["#Import the train_test_split function from sklearn\n","from sklearn.model_selection import train_test_split\n","\n","#Set the seed for the random splitter\n","SEED = 12345\n","\n","#Separate your dataframe into y, the feature data and x, the label data\n","y = data_df[\"HeartDisease\"]\n","X = data_df.drop(\"HeartDisease\",axis=1)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"be009a0d-f936-4196-a3aa-31e411e3c3c2","statement_id":9,"statement_ids":[9],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T19:22:44.9957125Z","session_start_time":null,"execution_start_time":"2024-06-10T19:22:45.5520859Z","execution_finish_time":"2024-06-10T19:22:45.8206494Z","parent_msg_id":"b30635d4-6504-4a17-adb0-8e47c36f4281"},"text/plain":"StatementMeta(, be009a0d-f936-4196-a3aa-31e411e3c3c2, 9, Finished, Available)"},"metadata":{}}],"execution_count":6,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"c1ce97d1-f167-49b2-b9ea-bbbb528668d1"},{"cell_type":"markdown","source":["Now, perform the split of data according to the following groups: 60% training (x_train and y_train), 20% testing (x_test and y_test) and 20% validation (x_val and y_val). Use the train_test_split function you previously imported. [Documentation Link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"584daef4-45ef-432e-bb5f-8345a8ee4ffc"},{"cell_type":"code","source":["# First split the data into training and testing. Make the training portion 60% and the rest testing. \n","\n","X_train, ... =\n","# Now, with your existing testing dataset, split it evenly into testing and validation.\n","\n","X_train, ... ="],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"2033814e-6f1f-444e-9551-0dea85651ad1"},{"cell_type":"markdown","source":["#### Part 4 Instructions\n","You will now create a run with the experiment you previously started. You will be using a random forest classifier from the SK Learn library. \n","\n","[Quick RandomForestClassifier function documentation](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)\n","\n","[In-depth explanation of random forest classifiers](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"93eece29-533c-4efb-93a9-09b467d18d08"},{"cell_type":"code","source":["#Import the required libraries by running this code cell\n","from sklearn.model_selection import train_test_split\n","from lightgbm import LGBMClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score, f1_score, precision_score, confusion_matrix, recall_score, roc_auc_score, classification_report"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"884583a4-4ca3-45a8-a504-9590bd00d3e4"},{"cell_type":"markdown","source":["Before starting, register the model in mlflow with autologging. Use the mlflow.sklearn.autolog function, which allows you to track sklearn models in mlflow. Make sure to use 'rfc1_sm' as your registered model name. [Documentation Link](https://mlflow.org/docs/latest/python_api/mlflow.sklearn.html#mlflow.sklearn.autolog)"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"2f0d75a3-9ccc-4e56-b559-3d771c8d235e"},{"cell_type":"code","source":["# Register the trained model with autologging\n","mlflow."],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"6c50c148-ff9e-4e2e-be3a-177aba988802"},{"cell_type":"markdown","source":["Now, set your registered model name to a Random Forest Classifier. Use the link under the Part 4 instructions for more information about the RandomForestClassfier function. For the hyperparameters, use a max depth of 4, a max features of 4, a minimum sample split of 3 and a random state of 1."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"873f2639-75c1-4841-83cc-2425f0b306d8"},{"cell_type":"code","source":["rfc1_sm = "],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"0a1c5fd0-5361-4c99-aa38-65ffb9fd14ce"},{"cell_type":"markdown","source":["Once you have set the model, you will start a training run in mlflow to fit your model to the data. Name your run 'rfc1_sm' [Documentation Link](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.start_run).\n","\n","You will need to perform a variety of different actions in the run, but they must all be in the same code cell. Here you will find a labeled list of helpful links for each step. You must add one line of code in between each comment line.\n","\n","1. [model.fit](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.fit)\n","1. [model.score](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.score)\n","1. [model.predict](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.predict)\n","1. [classification_report](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#classification-report)\n","1. [confusion_matrix](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#confusion-matrix)\n","1. [roc_auc_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#roc-auc-score) & [predict_proba](https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV.predict_proba)\n"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"6dbf7e20-7087-4f93-8d7f-b8d6a00df0f4"},{"cell_type":"code","source":["with \"use the start_run function here\" as run:\n"," rfc1_sm_run_id = #Enter code here to capture run id\n"," print(\"run_id: {}; status: {}\".format(rfc1_sm_run_id, run.info.status)) #This line prints the run ID and status\n"," #1 Fit the model on the train data you previously split\n"," \n"," #2 Score the model with the training data (x_train,...) to obtain the precision of the model\n"," \n"," #3 Generate predictions using the training feature data\n"," y_pred = \n"," #4 Create a classification report comparing your step 3 predictions (y_pred) with the actual values (y_train)\n"," cr_rfc1_sm = \n"," #5 Create a confusion matrix comparing your step 3 predictions with the actual values (y_train)\n"," cm_rfc1_sm = \n"," #6 Calculate the auc score with the training data. Use the predict_proba function for your predictions.\n"," roc_auc_rfc1_sm = "],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"f90ced8f-a251-49c0-aefe-f4e51955af7b"},{"cell_type":"markdown","source":["Display the classification report"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"64b377a9-3cc0-49ab-9b18-869d92294956"},{"cell_type":"code","source":["display(cr_rfc1_sm)"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"1817404a-fc23-4264-b138-1adf69bda2d9"},{"cell_type":"markdown","source":["Display the confusion matrix"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"2e1aa118-acd9-4804-ab8f-ee0caeb0eb2d"},{"cell_type":"code","source":["display(cm_rfc1_sm)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"be009a0d-f936-4196-a3aa-31e411e3c3c2","statement_id":11,"statement_ids":[11],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T19:25:09.515554Z","session_start_time":null,"execution_start_time":"2024-06-10T19:25:10.0610164Z","execution_finish_time":"2024-06-10T19:25:10.3453606Z","parent_msg_id":"e626b3c9-0ab4-4af2-b977-4b070ac05198"},"text/plain":"StatementMeta(, be009a0d-f936-4196-a3aa-31e411e3c3c2, 11, Finished, Available)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"array([[69, 18],\n [ 8, 89]])"},"metadata":{}}],"execution_count":8,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"2e0b9125-e89a-480f-af21-644e57fdca89"},{"cell_type":"markdown","source":["Display the auc score"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"e29c9975-d5db-4776-aef1-dbfdf643b747"},{"cell_type":"code","source":[" display(roc_auc_rfc1_sm)"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"be009a0d-f936-4196-a3aa-31e411e3c3c2","statement_id":12,"statement_ids":[12],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T19:26:34.2299026Z","session_start_time":null,"execution_start_time":"2024-06-10T19:26:34.7453269Z","execution_finish_time":"2024-06-10T19:26:35.0175372Z","parent_msg_id":"40faa8b0-b526-4095-853e-fdbe514f0e90"},"text/plain":"StatementMeta(, be009a0d-f936-4196-a3aa-31e411e3c3c2, 12, Finished, Available)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"0.9669833380099144"},"metadata":{}}],"execution_count":9,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"366a5b4f-090b-49c3-9e35-68087f78f449"},{"cell_type":"markdown","source":["#### Part 5 instructions\n","You have now a trained and saved model. While you have explored its performance with the training data, you will now complete a final check with the validation dataset. In this section you will also explore loading a previously trained model."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"3a23fa7d-db7b-4fc2-977b-5ae1dc1b4bfb"},{"cell_type":"markdown","source":["Load the model using MLFlow. [Documentation Link](https://mlflow.org/docs/latest/python_api/mlflow.sklearn.html#mlflow.sklearn.load_model)"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"6e799069-1b8e-4198-ad5b-fecc9849f78e"},{"cell_type":"code","source":["#Load the model\n","load_model="],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"ce3ed83f-e967-4284-aabf-feee5fc0508a"},{"cell_type":"markdown","source":["Now, generate predictions with the validation data (X_val). Refer to Part 4 for documentation."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"da13db15-4221-40ae-abc2-13ddd849f74a"},{"cell_type":"code","source":["#Generate predictions\n","y_pred_val="],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"e44611ff-38d5-4080-af56-e899d9ce3e78"},{"cell_type":"markdown","source":["To check the model accuracy, recalculate the clasification report, confusion matrix and auc score. Refer to Part 4 for documentation. Use y_val (actual values) and y_pred_val (predicted values)."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"d8b3e216-93ff-41e1-a066-27ca2bf1ad33"},{"cell_type":"code","source":["#Calculate the classification report\n","cr_val=\n","#Calculate the confusion matrix\n","cm_val=\n","#Calculate the auc score\n","roc_auc_val="],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"8968d800-a0eb-4fd8-b5a9-e63901263959"},{"cell_type":"markdown","source":["Display the classification report"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"226998b6-fd73-4438-9c3b-a7d76ad3b622"},{"cell_type":"code","source":["display(cr_val)"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"4e307f38-7b43-44e5-a82a-44c14b3f44ab"},{"cell_type":"markdown","source":["Display the confusion 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\ No newline at end of file diff --git a/072-DataScienceInFabric/Student/Resources/Notebooks/04-Perform-HeartFailure-Batch-Predictions.ipynb b/072-DataScienceInFabric/Student/Resources/Notebooks/04-Perform-HeartFailure-Batch-Predictions.ipynb new file mode 100644 index 0000000000..1ed7a026ac --- /dev/null +++ b/072-DataScienceInFabric/Student/Resources/Notebooks/04-Perform-HeartFailure-Batch-Predictions.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["## Module 4 - Simulate Input data, perform Batch Predictions and save predictions to Lakehouse"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"d0c8ef8e-c6e4-4600-ba71-5e9809ac8aa6"},{"cell_type":"markdown","source":["#### Install the required libraries and define the dataset schema"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"91085c58-4af6-46d9-9dec-d0c1cf8cecc3"},{"cell_type":"markdown","source":["\n","You will now use the [Faker](https://faker.readthedocs.io/en/master/) Python package to simulate heart failure diagnostic data. Python Libraries can be added in the Workspace Settings or installed inline using _%pip install Faker_. Read more on the public docs - [Manage Apache Spark libraries](https://learn.microsoft.com/en-us/fabric/data-engineering/library-management)"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"d6871701-3100-4625-ae77-0c70eb074410"},{"cell_type":"code","source":["#Install the required library\n","%pip install Faker==18.10.1"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"90dfa8b5-242d-46b7-962e-e13f54ac5fe3","statement_id":12,"statement_ids":[8,9,10,11,12],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T18:57:25.2794773Z","session_start_time":null,"execution_start_time":"2024-06-10T18:57:25.7980912Z","execution_finish_time":"2024-06-10T18:57:45.1978631Z","parent_msg_id":"0bb96cd7-dcd5-450a-a03b-3504f9d7ef07"},"text/plain":"StatementMeta(, 90dfa8b5-242d-46b7-962e-e13f54ac5fe3, 12, Finished, Available)"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Collecting Faker==18.10.1\n Downloading Faker-18.10.1-py3-none-any.whl (1.7 MB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m33.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: python-dateutil>=2.4 in /home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages (from Faker==18.10.1) (2.8.2)\nRequirement already satisfied: six>=1.5 in /home/trusted-service-user/cluster-env/trident_env/lib/python3.10/site-packages (from python-dateutil>=2.4->Faker==18.10.1) (1.16.0)\nInstalling collected packages: Faker\nSuccessfully installed Faker-18.10.1\n\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\nNote: you may need to restart the kernel to use updated packages.\nWarning: PySpark kernel has been restarted to use updated packages.\n\n"]}],"execution_count":6,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"6b26247a-4751-48b9-ab13-d4d255184265"},{"cell_type":"code","source":["#Import the required libraries\n","from pyspark.sql.types import StructType, StructField, IntegerType, DoubleType, StringType, LongType\n","from faker import Faker\n","import pandas\n","from synapse.ml.predict import MLFlowTransformer"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"b8fa0bb4-f63f-432e-89da-5e31d9722a38"},{"cell_type":"code","source":["#Define the dataset schema\n","\n","heartFailureDataSchema = StructType(\n","[\n"," StructField('Age', IntegerType(), True),\n"," StructField('Sex', IntegerType(), True),\n"," StructField('ChestPainType', IntegerType(), True),\n"," StructField('RestingBP', IntegerType(), True),\n"," StructField('Cholesterol', IntegerType(), True),\n"," StructField('FastingBS', IntegerType(), True),\n"," StructField('RestingECG', IntegerType(), True),\n"," StructField('MaxHR', IntegerType(), True),\n"," StructField('ExerciseAngina', IntegerType(), True),\n"," StructField('Oldpeak', DoubleType(), True),\n"," StructField('ST_Slope', IntegerType(), True)\n","]\n",")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"90dfa8b5-242d-46b7-962e-e13f54ac5fe3","statement_id":14,"statement_ids":[14],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T18:57:39.7509106Z","session_start_time":null,"execution_start_time":"2024-06-10T18:57:50.5493686Z","execution_finish_time":"2024-06-10T18:57:50.8581036Z","parent_msg_id":"c0fc1c14-55d0-4017-8534-84a68ee4f1f5"},"text/plain":"StatementMeta(, 90dfa8b5-242d-46b7-962e-e13f54ac5fe3, 14, Finished, Available)"},"metadata":{}}],"execution_count":7,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"a9d0dbe0-4d7d-4854-9063-38a5ce632de6"},{"cell_type":"markdown","source":["#### Part 1 instructions\n","\n","Simulate input heart failure diagnostic data to be used for predictions"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"47a943f9-25f5-4f30-bdac-fe50f83c92a6"},{"cell_type":"markdown","source":["First, set the simulated record count to 10 and set up a new empty list for the simulated data"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"adba6f14-cb65-4326-8160-c8adb4a24a6b"},{"cell_type":"code","source":["faker = Faker()\n","simulateRecordCount = 10\n","simData = []"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"collapsed":false,"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"8b77d29d-49b1-4787-b844-d33188ec1ec2"},{"cell_type":"markdown","source":["Now, you will loop through the 10 simulated elements to generate. For each element you need to generate a simulated value of each feature (age, bp, cholesterol...). \n","\n","Steps:\n","- Refer to the previously generated dataset schema to identify what kind of value (integer, float or categorical value) each feature is. \n","- Refer to the statistics of the data on notebook 2 to pick an adequate range of values for each feature.\n","- Pick one of the following faker functions according to the value type of each feature and set it to generate a value from the range you chose\n","\n","Faker functions:\n","\n","- For integers use [faker.randomint](https://faker.readthedocs.io/en/master/providers/baseprovider.html#faker.providers.BaseProvider.random_int)\n","- For categorical values use faker.randomint but make sure you are choosing the correct range\n","- For floats use [faker.pyfloat](https://faker.readthedocs.io/en/master/providers/faker.providers.python.html#faker.providers.python.Provider.pyfloat) making sure you are specifying the correct amount of decimals"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"2d3046af-cbdf-4493-af0e-331271b9efcb"},{"cell_type":"code","source":["\n","for i in range(simulateRecordCount):\n"," age = \n"," RestingBP = \n"," Cholesterol = \n"," FastingBS= \n"," MaxHR = \n"," OldPeak = \n"," ChestPain = \n"," Sex = \n"," RestingECG = \n"," ExerciseAngina = \n"," StSlope= \n"," #Adding all simulalted variables to the list\n"," simData.append((age, Sex,ChestPain, RestingBP,Cholesterol,FastingBS,RestingECG , MaxHR,ExerciseAngina,OldPeak, StSlope))\n","\n"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"ca6e2242-3dac-4f08-9a9f-5b729a719eff"},{"cell_type":"markdown","source":["To complete this section, create a dataframe with the data you have just simulated (simData) and the explicit schema you created previously (heartFailureDataSchema). [Documentation link](https://spark.apache.org/docs/latest/api/python/getting_started/quickstart_df.html#DataFrame-Creation)"],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"c9ace39e-9d85-4fd9-b8c0-8d5054b74ce7"},{"cell_type":"code","source":["df = \n","display(df)"],"outputs":[],"execution_count":null,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"b19195ba-e2bc-4e2d-8f73-693bd4484fec"},{"cell_type":"markdown","source":["Now, convert the dataframe to pandas."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"3d94e6e1-fedb-49fa-b471-2bc23c12853e"},{"cell_type":"code","source":["data_df = df.toPandas()"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"90dfa8b5-242d-46b7-962e-e13f54ac5fe3","statement_id":25,"statement_ids":[25],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T19:16:03.7141774Z","session_start_time":null,"execution_start_time":"2024-06-10T19:16:04.3586034Z","execution_finish_time":"2024-06-10T19:16:06.046793Z","parent_msg_id":"613c7204-a85a-4b16-b79e-9c473b026563"},"text/plain":"StatementMeta(, 90dfa8b5-242d-46b7-962e-e13f54ac5fe3, 25, Finished, Available)"},"metadata":{}}],"execution_count":18,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"13c2bc19-8e92-46d9-9aa6-efed7a1733b8"},{"cell_type":"markdown","source":["#### Part 2 Instructions \n","Load the trained and registered model to generate predictions"],"metadata":{"nteract":{"transient":{"deleting":false}}},"id":"5c0b73c7-becb-4c1c-af17-bfed0c631b4a"},{"cell_type":"markdown","source":["Load the model (rfc1_sm, version 1). From the [documentation link](https://learn.microsoft.com/en-us/fabric/data-science/model-scoring-predict#call-predict-from-a-notebook) go to step 3 to learn how to use the MLFlowTransformer function."],"metadata":{"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"}},"id":"9ba31d38-5d24-470d-9d13-06f84a479a5d"},{"cell_type":"code","source":["model = (\n","\n","\n","\n","\n",")"],"outputs":[{"output_type":"display_data","data":{"application/vnd.livy.statement-meta+json":{"spark_pool":null,"session_id":"90dfa8b5-242d-46b7-962e-e13f54ac5fe3","statement_id":26,"statement_ids":[26],"state":"finished","livy_statement_state":"available","queued_time":"2024-06-10T19:16:09.2760886Z","session_start_time":null,"execution_start_time":"2024-06-10T19:16:09.8941829Z","execution_finish_time":"2024-06-10T19:16:16.4018477Z","parent_msg_id":"021c8b5e-7411-483f-8164-61b83d11f42e"},"text/plain":"StatementMeta(, 90dfa8b5-242d-46b7-962e-e13f54ac5fe3, 26, Finished, Available)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading artifacts: 0%| | 0/5 [00:00"},"metadata":{}}],"execution_count":24,"metadata":{"jupyter":{"source_hidden":false,"outputs_hidden":false},"nteract":{"transient":{"deleting":false}},"microsoft":{"language":"python","language_group":"synapse_pyspark"},"collapsed":false},"id":"4876b1ae-e785-412f-8244-6dd4f00a5e64"}],"metadata":{"language_info":{"name":"python"},"kernelspec":{"name":"synapse_pyspark","language":"Python","display_name":"Synapse PySpark"},"widgets":{},"kernel_info":{"name":"synapse_pyspark"},"notebook_environment":{},"microsoft":{"language":"python","language_group":"synapse_pyspark","ms_spell_check":{"ms_spell_check_language":"en"}},"nteract":{"version":"nteract-front-end@1.0.0"},"synapse_widget":{"version":"0.1","state":{"b0d0740a-32d9-4c71-b469-da24206009bd":{"type":"Synapse.DataFrame","sync_state":{"table":{"rows":[{"0":"57","1":"0","2":"0","3":"117","4":"172","5":"1","6":"2","7":"138","8":"0","9":"2.9","10":"1","11":"1","index":1},{"0":"62","1":"1","2":"1","3":"86","4":"256","5":"0","6":"2","7":"180","8":"0","9":"0.6","10":"0","11":"0","index":2},{"0":"67","1":"1","2":"1","3":"158","4":"202","5":"0","6":"0","7":"106","8":"1","9":"1.0","10":"1","11":"0","index":3},{"0":"57","1":"0","2":"0","3":"87","4":"200","5":"1","6":"2","7":"167","8":"0","9":"3.9","10":"1","11":"1","index":4},{"0":"57","1":"0","2":"0","3":"95","4":"166","5":"1","6":"1","7":"167","8":"0","9":"3.1","10":"0","11":"1","index":5},{"0":"61","1":"0","2":"0","3":"70","4":"228","5":"0","6":"2","7":"126","8":"1","9":"0.1","10":"0","11":"1","index":6},{"0":"69","1":"1","2":"1","3":"78","4":"246","5":"0","6":"0","7":"159","8":"1","9":"2.6","10":"1","11":"0","index":7},{"0":"60","1":"0","2":"2","3":"138","4":"233","5":"1","6":"1","7":"135","8":"0","9":"1.1","10":"0","11":"0","index":8},{"0":"56","1":"0","2":"0","3":"74","4":"243","5":"1","6":"2","7":"198","8":"1","9":"3.1","10":"0","11":"1","index":9},{"0":"67","1":"1","2":"2","3":"78","4":"125","5":"0","6":"0","7":"171","8":"0","9":"3.2","10":"0","11":"0","index":10}],"schema":[{"key":"0","name":"Age","type":"int"},{"key":"1","name":"Sex","type":"int"},{"key":"2","name":"ChestPainType","type":"bigint"},{"key":"3","name":"RestingBP","type":"int"},{"key":"4","name":"Cholesterol","type":"int"},{"key":"5","name":"FastingBS","type":"int"},{"key":"6","name":"RestingECG","type":"int"},{"key":"7","name":"MaxHR","type":"int"},{"key":"8","name":"ExerciseAngina","type":"bigint"},{"key":"9","name":"Oldpeak","type":"double"},{"key":"10","name":"ST_Slope","type":"bigint"},{"key":"11","name":"predictions","type":"int"}],"truncated":false},"isSummary":false,"language":"scala"},"persist_state":{"view":{"type":"details","tableOptions":{},"chartOptions":{"chartType":"bar","categoryFieldKeys":["0"],"seriesFieldKeys":["0"],"aggregationType":"count","isStacked":false,"binsNumber":10,"wordFrequency":"-1"}}}}}},"save_output":true,"spark_compute":{"compute_id":"/trident/default","session_options":{"enableDebugMode":false,"conf":{}}},"dependencies":{"lakehouse":{"default_lakehouse":"87075977-c1b5-45fc-a88f-2a083d6b8023","default_lakehouse_name":"HeartFailureLakeHouse","default_lakehouse_workspace_id":"1852166c-bd3c-4dae-a90e-39c7373a88c8","known_lakehouses":[{"id":"87075977-c1b5-45fc-a88f-2a083d6b8023"}]}}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/072-DataScienceInFabric/Student/Resources/deployhack.sh b/072-DataScienceInFabric/Student/Resources/deployhack.sh new file mode 100644 index 0000000000..5b0d61d95d --- /dev/null +++ b/072-DataScienceInFabric/Student/Resources/deployhack.sh @@ -0,0 +1,57 @@ +#!/bin/bash + +read -p "Enter a prefix for your resource group and resources: " RESOURCEPREFIX +response=$(az storage account check-name --name $RESOURCEPREFIX) +available=$(jq -n --argjson data "$response" '$data.nameAvailable') + +until [[ $available == "true" ]] +do + echo "'$RESOURCEPREFIX' is not available as a storage account name. Please try a different name" + read -p "Enter a prefix for your resource group and resources: " RESOURCEPREFIX + response=$(az storage account check-name --name $RESOURCEPREFIX) + available=$(jq -n --argjson data "$response" '$data.nameAvailable') + +done + +RGNAME="$RESOURCEPREFIX-rg" +deploymentName="$RESOURCEPREFIX-deployment" + +read -p "Enter an Azure region to deploy to (i.e. 'eastus', 'westus', 'northeurope'): " LOCATION + +echo "Creating resource group '$RGNAME' in Azure region '$LOCATION'..." +az group create --location $LOCATION --name $RGNAME + +echo "Deploying Azure Storage Account where your training data will be located..." +az storage account create --name $RESOURCEPREFIX --resource-group $RGNAME --sku Standard_LRS + +echo "Validating storage account for ADLS Gen 2 migration... " +az storage account hns-migration start --request-type validation --name $RESOURCEPREFIX + +echo "Upgrading the Azure Storage Account to ADLS gen 2 to enable the creation of shortcuts..." +az storage account hns-migration start --request-type upgrade --name $RESOURCEPREFIX + +echo "Downloading the required data from the GitHub repo..." +wget https://raw.githubusercontent.com/pradeepsingla87/WhatTheHack/xxx-DataScience_In_MicrosoftFabric/072-DataScienceInFabric/Student/Resources/heartdata.csv + +echo "Creating a container to store the directory where the data will be uploaded..." +fsresponse=$(az storage fs create -n file-system --account-name $RESOURCEPREFIX) +until [[ $fsresponse != "" ]] +do + echo -e "\n\nAction failed. Trying again in 10 seconds..." + sleep 10 + fsresponse=$(az storage fs create -n file-system --account-name $RESOURCEPREFIX) +done + + +echo "Creating a file system in the previously created container..." +az storage fs directory create -n files -f file-system --account-name $RESOURCEPREFIX + +echo "Adding files to file system..." +az storage fs file upload -s "heartdata.csv" -p files/heart.csv -f file-system --account-name $RESOURCEPREFIX + +read -p "Would you like to proceed setting up the Azure Machine Learning workspace for Challenge 6 (Optional) (y/n) " CONTINUE + +if [[ $CONTINUE == "y" ]] +then +az ml workspace create -n $RESOURCEPREFIX -g $RGNAME +fi diff --git a/072-DataScienceInFabric/Student/Resources/heartdata.csv b/072-DataScienceInFabric/Student/Resources/heartdata.csv new file mode 100644 index 0000000000..03e2d06307 --- /dev/null +++ b/072-DataScienceInFabric/Student/Resources/heartdata.csv @@ -0,0 +1,1029 @@ 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+55,M,NAP,136.0,228.0,0.0,ST,124,Y,1.6,Flat,1,446 +54,M,ASY,130.0,0.0,0.0,ST,117,Y,1.4,Flat,1,447 +77,M,ASY,124.0,171.0,0.0,ST,110,Y,2.0,Up,1,448 +63,M,ASY,160.0,230.0,1.0,Normal,105,Y,1.0,Flat,1,449 +55,M,NAP,0.0,0.0,0.0,Normal,155,N,1.5,Flat,1,450 +52,M,NAP,122.0,0.0,0.0,Normal,110,Y,2.0,Down,1,451 +64,M,ASY,144.0,0.0,0.0,ST,122,Y,1.0,Flat,1,452 +60,M,ASY,140.0,281.0,,ST,118,Y,1.5,Flat,1,453 +60,M,ASY,,0.0,,Normal,133,Y,2.0,Up,0,454 +58,M,ASY,136.0,203.0,1.0,Normal,123,Y,1.2,Flat,1,455 +59,M,ASY,154.0,0.0,,ST,131,Y,1.5,Up,0,456 +61,M,NAP,120.0,0.0,0.0,Normal,80,Y,0.0,Flat,1,457 +40,M,ASY,125.0,0.0,,Normal,165,N,0.0,Flat,1,458 +61,M,ASY,134.0,0.0,1.0,ST,86,N,1.5,Flat,1,459 +41,M,ASY,104.0,0.0,0.0,ST,111,N,0.0,Up,0,460 +57,M,ASY,139.0,277.0,1.0,ST,118,Y,1.9,Flat,1,461 +63,M,ASY,136.0,0.0,0.0,Normal,84,Y,0.0,Flat,1,462 +59,M,ASY,122.0,233.0,0.0,Normal,117,Y,1.3,Down,1,463 +51,M,ASY,128.0,,0.0,Normal,107,N,0.0,Up,0,464 +59,M,NAP,131.0,0.0,0.0,Normal,128,Y,2.0,Down,1,465 +42,M,NAP,134.0,240.0,0.0,Normal,160,N,0.0,Up,0,466 +55,M,NAP,120.0,,0.0,ST,125,Y,2.5,Flat,1,467 +63,F,ATA,132.0,0.0,0.0,Normal,130,N,0.1,Up,0,468 +62,M,ASY,152.0,,0.0,ST,97,Y,1.6,Up,1,469 +56,M,ATA,124.0,224.0,1.0,Normal,161,N,2.0,Flat,0,470 +53,M,ASY,126.0,0.0,0.0,Normal,106,N,0.0,Flat,1,471 +68,M,ASY,,0.0,0.0,Normal,130,Y,3.0,Flat,1,472 +53,M,ASY,154.0,0.0,1.0,ST,140,Y,1.5,Flat,1,473 +60,M,NAP,141.0,316.0,1.0,ST,122,Y,1.7,Flat,1,474 +62,M,ATA,131.0,0.0,0.0,Normal,130,N,0.1,Up,0,475 +59,M,ASY,178.0,0.0,1.0,LVH,120,Y,0.0,Flat,1,476 +51,M,ASY,132.0,218.0,1.0,LVH,139,N,0.1,Up,0,477 +61,M,ASY,110.0,0.0,1.0,Normal,108,Y,2.0,Down,1,478 +57,M,ASY,130.0,311.0,1.0,ST,148,Y,2.0,Flat,1,479 +56,M,NAP,170.0,0.0,0.0,LVH,123,Y,2.5,Flat,1,480 +58,M,ATA,,0.0,1.0,Normal,110,Y,2.0,Flat,1,481 +69,M,NAP,140.0,0.0,1.0,ST,118,N,2.5,Down,1,482 +67,M,TA,142.0,270.0,1.0,Normal,125,N,2.5,Up,1,483 +58,M,ASY,120.0,0.0,0.0,LVH,106,Y,1.5,Down,1,484 +65,M,ASY,134.0,0.0,0.0,Normal,112,Y,1.1,Flat,1,485 +63,M,ATA,,217.0,1.0,ST,128,Y,1.2,Flat,1,486 +55,M,ATA,,,1.0,ST,180,N,0.4,Up,0,487 +57,M,ASY,140.0,,0.0,ST,144,Y,2.0,Flat,1,488 +65,M,TA,140.0,252.0,0.0,Normal,135,N,0.3,Up,0,489 +54,M,ASY,136.0,220.0,0.0,Normal,140,Y,3.0,Flat,1,490 +72,M,NAP,120.0,214.0,0.0,Normal,102,Y,1.0,Flat,1,491 +75,M,ASY,170.0,,1.0,ST,108,N,0.0,Flat,1,492 +49,M,TA,130.0,0.0,0.0,ST,145,N,3.0,Flat,1,493 +51,M,NAP,137.0,,0.0,Normal,127,Y,1.7,Flat,1,494 +60,M,ASY,142.0,,0.0,Normal,110,Y,2.5,Flat,1,495 +64,F,ASY,142.0,276.0,0.0,Normal,140,Y,1.0,Flat,1,496 +58,M,ASY,132.0,458.0,1.0,Normal,69,N,1.0,Down,0,497 +61,M,ASY,,241.0,0.0,Normal,148,Y,3.0,Down,1,498 +67,M,ASY,160.0,384.0,1.0,ST,130,Y,0.0,Flat,1,499 +62,M,ASY,135.0,297.0,0.0,Normal,130,Y,1.0,Flat,1,500 +65,M,ASY,,248.0,0.0,Normal,140,Y,4.0,Down,1,501 +63,M,ASY,130.0,308.0,0.0,Normal,138,Y,2.0,Flat,1,502 +69,M,ASY,140.0,208.0,0.0,ST,140,Y,2.0,Flat,1,503 +51,M,ASY,132.0,227.0,1.0,ST,138,N,0.2,Up,0,504 +62,M,ASY,158.0,210.0,1.0,Normal,112,Y,3.0,Down,1,505 +55,M,NAP,136.0,245.0,1.0,ST,131,Y,1.2,Flat,1,506 +75,M,ASY,136.0,225.0,0.0,Normal,112,Y,3.0,Flat,1,507 +40,M,NAP,106.0,240.0,0.0,Normal,80,Y,0.0,Up,0,508 +67,M,ASY,120.0,0.0,1.0,Normal,150,N,1.5,Down,1,509 +58,M,ASY,110.0,198.0,0.0,Normal,110,N,0.0,Flat,1,510 +60,M,ASY,136.0,195.0,0.0,Normal,126,N,0.3,Up,0,511 +63,M,ASY,160.0,,1.0,ST,88,Y,2.0,Flat,1,512 +35,M,NAP,123.0,161.0,0.0,ST,153,N,-0.1,Up,0,513 +62,M,TA,112.0,258.0,0.0,ST,150,Y,1.3,Flat,1,514 +43,M,ASY,122.0,0.0,0.0,Normal,120,N,0.5,Up,1,515 +63,M,NAP,130.0,0.0,1.0,ST,160,N,3.0,Flat,0,516 +68,M,NAP,150.0,195.0,1.0,Normal,132,N,0.0,Flat,1,517 +65,M,ASY,150.0,235.0,0.0,Normal,120,Y,1.5,Flat,1,518 +48,M,NAP,102.0,0.0,1.0,ST,110,Y,1.0,Down,1,519 +63,M,ASY,96.0,305.0,,ST,121,Y,1.0,Up,1,520 +64,M,ASY,130.0,223.0,0.0,ST,128,N,0.5,Flat,0,521 +61,M,ASY,120.0,282.0,0.0,ST,135,Y,4.0,Down,1,522 +50,M,ASY,144.0,349.0,0.0,LVH,120,Y,1.0,Up,1,523 +59,M,ASY,124.0,160.0,0.0,Normal,117,Y,1.0,Flat,1,524 +55,M,ASY,150.0,160.0,0.0,ST,150,N,0.0,Up,0,525 +45,M,NAP,130.0,236.0,0.0,Normal,144,N,0.1,Up,0,526 +65,M,ASY,,,0.0,LVH,113,Y,1.7,Flat,1,527 +61,M,ATA,139.0,283.0,0.0,Normal,135,N,0.3,Up,0,528 +49,M,NAP,131.0,142.0,0.0,Normal,127,Y,1.5,Flat,1,529 +72,M,ASY,143.0,211.0,0.0,Normal,109,Y,1.4,Flat,1,530 +50,M,ASY,133.0,,0.0,Normal,128,Y,1.1,Flat,1,531 +64,M,ASY,,306.0,1.0,ST,115,Y,1.8,Flat,1,532 +55,M,ASY,116.0,186.0,1.0,ST,102,N,0.0,Flat,1,533 +63,M,ASY,110.0,252.0,0.0,ST,140,Y,2.0,Flat,1,534 +59,M,ASY,125.0,222.0,0.0,Normal,135,Y,2.5,Down,1,535 +56,M,ASY,130.0,0.0,0.0,LVH,122,Y,1.0,Flat,1,536 +62,M,NAP,133.0,0.0,1.0,ST,119,Y,1.2,Flat,1,537 +74,M,ASY,,258.0,1.0,ST,130,Y,4.0,Down,1,538 +54,M,ASY,130.0,202.0,1.0,Normal,112,Y,2.0,Flat,1,539 +57,M,ASY,110.0,197.0,0.0,LVH,100,N,0.0,Up,0,540 +62,M,NAP,138.0,204.0,,ST,122,Y,1.2,Flat,1,541 +76,M,NAP,104.0,113.0,0.0,LVH,120,N,3.5,Down,1,542 +54,F,ASY,,,0.0,Normal,105,Y,1.5,Flat,1,543 +70,M,ASY,170.0,192.0,0.0,ST,129,Y,3.0,Down,1,544 +61,F,ATA,140.0,298.0,,Normal,120,Y,0.0,Up,0,545 +48,M,ASY,132.0,272.0,0.0,ST,139,N,0.2,Up,0,546 +48,M,NAP,132.0,220.0,1.0,ST,162,N,0.0,Flat,1,547 +61,M,TA,142.0,200.0,,ST,100,N,1.5,Down,1,548 +66,M,ASY,112.0,261.0,0.0,Normal,140,N,1.5,Up,1,549 +68,M,TA,139.0,181.0,1.0,ST,135,N,0.2,Up,0,550 +55,M,ASY,172.0,260.0,0.0,Normal,73,N,2.0,Flat,1,551 +62,M,NAP,120.0,220.0,0.0,LVH,86,N,0.0,Up,0,552 +71,M,NAP,,221.0,0.0,Normal,108,Y,1.8,Flat,1,553 +74,M,TA,145.0,216.0,1.0,Normal,116,Y,1.8,Flat,1,554 +53,M,NAP,155.0,175.0,1.0,ST,160,N,0.3,Up,0,555 +58,M,NAP,150.0,219.0,,ST,118,Y,0.0,Flat,1,556 +75,M,ASY,160.0,310.0,,Normal,112,Y,2.0,Down,0,557 +56,M,NAP,137.0,208.0,1.0,ST,122,Y,1.8,Flat,1,558 +58,M,NAP,137.0,232.0,0.0,ST,124,Y,1.4,Flat,1,559 +64,M,ASY,134.0,273.0,0.0,Normal,102,Y,4.0,Down,1,560 +54,M,NAP,133.0,203.0,,ST,137,N,0.2,Up,0,561 +54,M,ATA,132.0,182.0,0.0,ST,141,N,0.1,Up,0,562 +59,M,ASY,140.0,274.0,,Normal,154,Y,2.0,Flat,0,563 +55,M,ASY,135.0,204.0,1.0,ST,126,Y,1.1,Flat,1,564 +57,M,ASY,144.0,270.0,1.0,ST,160,Y,2.0,Flat,1,565 +61,M,ASY,141.0,292.0,0.0,ST,115,Y,1.7,Flat,1,566 +41,M,ASY,150.0,,0.0,Normal,128,Y,1.5,Flat,0,567 +71,M,ASY,130.0,221.0,0.0,ST,115,Y,0.0,Flat,1,568 +38,M,ASY,110.0,289.0,,Normal,105,Y,1.5,Down,1,569 +55,M,ASY,158.0,217.0,0.0,Normal,110,Y,2.5,Flat,1,570 +56,M,ASY,128.0,,,ST,119,Y,2.0,Down,1,571 +69,M,ASY,,110.0,1.0,Normal,109,Y,1.5,Flat,1,572 +64,M,ASY,150.0,193.0,0.0,ST,135,Y,0.5,Flat,1,573 +72,M,ASY,160.0,,,LVH,130,N,1.5,Flat,1,574 +69,M,ASY,142.0,210.0,1.0,ST,112,Y,1.5,Flat,1,575 +56,M,ASY,137.0,282.0,1.0,Normal,126,Y,1.2,Flat,1,576 +62,M,ASY,139.0,170.0,0.0,ST,120,Y,3.0,Flat,1,577 +67,M,ASY,146.0,369.0,0.0,Normal,110,Y,1.9,Flat,1,578 +57,M,ASY,156.0,173.0,0.0,LVH,119,Y,3.0,Down,1,579 +69,M,ASY,145.0,289.0,1.0,ST,110,Y,1.8,Flat,1,580 +51,M,ASY,131.0,152.0,1.0,LVH,130,Y,1.0,Flat,1,581 +48,M,ASY,140.0,208.0,0.0,Normal,159,Y,1.5,Up,1,582 +69,M,ASY,122.0,216.0,,LVH,84,Y,0.0,Flat,1,583 +69,M,NAP,142.0,271.0,0.0,LVH,126,N,0.3,Up,0,584 +64,M,ASY,141.0,244.0,1.0,ST,116,Y,1.5,Flat,1,585 +57,M,ATA,180.0,285.0,1.0,ST,120,N,0.8,Flat,1,586 +53,M,ASY,124.0,243.0,0.0,Normal,122,Y,2.0,Flat,1,587 +37,M,NAP,118.0,240.0,0.0,LVH,165,N,1.0,Flat,0,588 +67,M,ASY,140.0,219.0,0.0,ST,122,Y,2.0,Flat,1,589 +74,M,NAP,140.0,237.0,1.0,Normal,94,N,0.0,Flat,1,590 +63,M,ATA,136.0,165.0,0.0,ST,133,N,0.2,Up,0,591 +58,M,ASY,100.0,213.0,0.0,ST,110,N,0.0,Up,0,592 +61,M,ASY,190.0,287.0,1.0,LVH,150,Y,2.0,Down,1,593 +64,M,ASY,130.0,258.0,1.0,LVH,130,N,0.0,Flat,1,594 +58,M,ASY,160.0,256.0,1.0,LVH,113,Y,1.0,Up,1,595 +60,M,ASY,130.0,186.0,,LVH,140,Y,0.5,Flat,1,596 +57,M,ASY,,264.0,0.0,LVH,100,N,0.0,Flat,1,597 +55,M,NAP,133.0,185.0,0.0,ST,136,N,0.2,Up,0,598 +55,M,ASY,120.0,,0.0,LVH,127,Y,1.7,Down,1,599 +56,M,ASY,,,1.0,Normal,98,N,1.5,Flat,1,600 +57,M,ASY,130.0,207.0,0.0,ST,96,Y,1.0,Flat,0,601 +61,M,NAP,140.0,284.0,0.0,Normal,123,Y,1.3,Flat,1,602 +61,M,NAP,120.0,337.0,,Normal,98,Y,0.0,Flat,1,603 +74,M,ASY,155.0,310.0,0.0,Normal,112,Y,1.5,Down,1,604 +68,M,NAP,134.0,254.0,1.0,Normal,151,Y,0.0,Up,0,605 +51,F,ASY,114.0,258.0,1.0,LVH,96,N,1.0,Up,0,606 +62,M,ASY,160.0,254.0,,ST,108,Y,3.0,Flat,1,607 +53,M,ASY,144.0,300.0,1.0,ST,128,Y,1.5,Flat,1,608 +62,M,ASY,158.0,,,ST,138,Y,0.0,Flat,1,609 +46,M,ASY,134.0,310.0,0.0,Normal,126,N,0.0,Flat,1,610 +54,F,ASY,127.0,333.0,1.0,ST,154,N,0.0,Flat,1,611 +62,M,TA,135.0,139.0,0.0,ST,137,N,0.2,Up,0,612 +55,M,ASY,122.0,223.0,1.0,ST,100,N,0.0,Flat,1,613 +58,M,ASY,140.0,385.0,1.0,LVH,135,N,0.3,Up,0,614 +62,M,ATA,120.0,254.0,0.0,LVH,93,Y,0.0,Flat,1,615 +70,M,ASY,130.0,322.0,0.0,LVH,109,N,2.4,Flat,1,616 +67,F,NAP,,564.0,,LVH,160,N,1.6,Flat,0,617 +57,M,ATA,124.0,261.0,0.0,Normal,141,N,0.3,Up,1,618 +64,M,ASY,,263.0,0.0,Normal,105,Y,0.2,Flat,0,619 +74,F,ATA,120.0,269.0,0.0,LVH,121,Y,0.2,Up,0,620 +65,M,ASY,120.0,177.0,0.0,Normal,140,N,0.4,Up,0,621 +56,M,NAP,130.0,256.0,,LVH,142,Y,0.6,Flat,1,622 +59,M,ASY,110.0,239.0,,LVH,142,Y,1.2,Flat,1,623 +60,M,ASY,140.0,293.0,0.0,LVH,170,N,1.2,Flat,1,624 +63,F,ASY,150.0,407.0,0.0,LVH,154,N,4.0,Flat,1,625 +59,M,ASY,135.0,234.0,0.0,Normal,161,N,0.5,Flat,0,626 +53,M,ASY,142.0,226.0,0.0,LVH,111,Y,0.0,Up,0,627 +44,M,NAP,140.0,235.0,0.0,LVH,180,N,0.0,Up,0,628 +61,M,TA,134.0,234.0,0.0,Normal,145,N,2.6,Flat,1,629 +57,F,ASY,128.0,,0.0,LVH,159,N,0.0,Up,0,630 +71,F,ASY,112.0,149.0,0.0,Normal,125,N,1.6,Flat,0,631 +46,M,ASY,140.0,,0.0,Normal,120,Y,1.8,Flat,1,632 +53,M,ASY,140.0,203.0,1.0,LVH,155,Y,3.1,Down,1,633 +64,M,TA,110.0,211.0,0.0,LVH,144,Y,1.8,Flat,0,634 +40,M,TA,140.0,199.0,0.0,Normal,178,Y,1.4,Up,0,635 +67,M,ASY,120.0,229.0,,LVH,129,Y,2.6,Flat,1,636 +48,M,ATA,130.0,245.0,0.0,LVH,180,N,0.2,Flat,0,637 +43,M,ASY,115.0,303.0,,Normal,181,N,1.2,Flat,0,638 +47,M,ASY,,204.0,0.0,Normal,143,N,0.1,Up,0,639 +54,F,ATA,132.0,288.0,1.0,LVH,159,Y,0.0,Up,0,640 +48,F,NAP,130.0,,0.0,Normal,139,N,0.2,Up,0,641 +46,F,ASY,138.0,243.0,0.0,LVH,152,Y,0.0,Flat,0,642 +51,F,NAP,120.0,295.0,0.0,LVH,157,N,0.6,Up,0,643 +58,M,NAP,112.0,230.0,0.0,LVH,165,N,2.5,Flat,1,644 +71,F,NAP,110.0,265.0,1.0,LVH,130,N,0.0,Up,0,645 +57,M,NAP,128.0,229.0,0.0,LVH,150,N,0.4,Flat,1,646 +66,M,ASY,160.0,228.0,0.0,LVH,138,N,2.3,Up,0,647 +37,F,NAP,120.0,215.0,0.0,Normal,170,N,0.0,Up,0,648 +59,M,ASY,170.0,,0.0,LVH,140,Y,3.4,Down,1,649 +50,M,ASY,144.0,,0.0,LVH,126,Y,0.9,Flat,1,650 +48,M,ASY,130.0,256.0,1.0,LVH,150,Y,0.0,Up,1,651 +61,M,ASY,140.0,,0.0,LVH,138,Y,1.9,Up,1,652 +59,M,TA,160.0,273.0,0.0,LVH,125,N,0.0,Up,1,653 +42,M,NAP,130.0,180.0,0.0,Normal,150,N,0.0,Up,0,654 +48,M,ASY,122.0,222.0,0.0,LVH,186,N,0.0,Up,0,655 +40,M,ASY,,223.0,0.0,Normal,181,N,0.0,Up,1,656 +62,F,ASY,124.0,209.0,0.0,Normal,163,N,0.0,Up,0,657 +44,M,NAP,,233.0,0.0,Normal,179,Y,0.4,Up,0,658 +46,M,ATA,101.0,197.0,1.0,Normal,156,N,0.0,Up,0,659 +59,M,NAP,126.0,218.0,1.0,Normal,134,N,2.2,Flat,1,660 +58,M,NAP,140.0,211.0,1.0,LVH,165,N,0.0,Up,0,661 +49,M,NAP,118.0,149.0,0.0,LVH,126,N,0.8,Up,1,662 +44,M,ASY,110.0,197.0,0.0,LVH,177,N,0.0,Up,1,663 +66,M,ATA,160.0,246.0,,Normal,120,Y,0.0,Flat,1,664 +65,F,ASY,,225.0,,LVH,114,N,1.0,Flat,1,665 +42,M,ASY,136.0,315.0,0.0,Normal,125,Y,1.8,Flat,1,666 +52,M,ATA,,205.0,1.0,Normal,184,N,0.0,Up,0,667 +65,F,NAP,140.0,417.0,1.0,LVH,157,N,0.8,Up,0,668 +63,F,ATA,140.0,195.0,0.0,Normal,179,N,0.0,Up,0,669 +45,F,ATA,130.0,234.0,0.0,LVH,175,N,0.6,Flat,0,670 +41,F,ATA,105.0,198.0,0.0,Normal,168,N,0.0,Up,0,671 +61,M,ASY,138.0,166.0,0.0,LVH,125,Y,3.6,Flat,1,672 +60,F,NAP,120.0,,1.0,Normal,96,N,0.0,Up,0,673 +59,F,ASY,,249.0,0.0,Normal,143,Y,0.0,Flat,1,674 +62,M,ATA,,,0.0,LVH,103,N,1.4,Flat,1,675 +57,M,NAP,150.0,126.0,1.0,Normal,173,N,0.2,Up,0,676 +51,F,ASY,130.0,305.0,0.0,Normal,142,Y,1.2,Flat,1,677 +44,M,NAP,120.0,226.0,0.0,Normal,169,N,0.0,Up,0,678 +60,F,TA,150.0,240.0,0.0,Normal,171,N,0.9,Up,0,679 +63,M,TA,145.0,233.0,1.0,LVH,150,N,2.3,Down,0,680 +57,M,ASY,,276.0,0.0,LVH,112,Y,0.6,Flat,1,681 +51,M,ASY,140.0,261.0,0.0,LVH,186,Y,0.0,Up,0,682 +58,F,ATA,136.0,319.0,1.0,LVH,152,N,0.0,Up,1,683 +44,F,NAP,118.0,242.0,0.0,Normal,149,N,0.3,Flat,0,684 +47,M,NAP,108.0,243.0,0.0,Normal,152,N,0.0,Up,1,685 +61,M,ASY,,260.0,0.0,Normal,140,Y,3.6,Flat,1,686 +57,F,ASY,120.0,354.0,,Normal,163,Y,0.6,Up,0,687 +70,M,ATA,156.0,245.0,0.0,LVH,143,N,0.0,Up,0,688 +76,F,NAP,140.0,197.0,0.0,ST,116,N,1.1,Flat,0,689 +67,F,ASY,106.0,223.0,0.0,Normal,142,N,0.3,Up,0,690 +45,M,ASY,142.0,309.0,,LVH,147,Y,0.0,Flat,1,691 +45,M,ASY,104.0,208.0,0.0,LVH,148,Y,3.0,Flat,0,692 +39,F,NAP,,199.0,0.0,Normal,179,N,0.0,Up,0,693 +42,F,NAP,120.0,209.0,0.0,Normal,173,N,0.0,Flat,0,694 +56,M,ATA,120.0,236.0,0.0,Normal,178,N,0.8,Up,0,695 +58,M,ASY,146.0,218.0,0.0,Normal,105,N,2.0,Flat,1,696 +35,M,ASY,120.0,198.0,0.0,Normal,130,Y,1.6,Flat,1,697 +58,M,ASY,150.0,270.0,0.0,LVH,111,Y,0.8,Up,1,698 +41,M,NAP,130.0,214.0,0.0,LVH,168,N,2.0,Flat,0,699 +57,M,ASY,110.0,,0.0,Normal,126,Y,1.5,Flat,0,700 +42,M,TA,,244.0,0.0,LVH,178,N,0.8,Up,0,701 +62,M,ATA,128.0,208.0,,LVH,140,N,0.0,Up,0,702 +59,M,TA,178.0,270.0,0.0,LVH,145,N,4.2,Down,0,703 +41,F,ATA,126.0,,0.0,Normal,163,N,0.0,Up,0,704 +50,M,ASY,150.0,243.0,0.0,LVH,128,N,2.6,Flat,1,705 +59,M,ATA,140.0,221.0,0.0,Normal,164,Y,0.0,Up,0,706 +61,F,ASY,,330.0,0.0,LVH,169,N,0.0,Up,1,707 +54,M,ASY,124.0,266.0,,LVH,109,Y,2.2,Flat,1,708 +54,M,ASY,110.0,206.0,0.0,LVH,108,Y,0.0,Flat,1,709 +52,M,ASY,125.0,212.0,0.0,Normal,168,N,1.0,Up,1,710 +47,M,ASY,110.0,275.0,0.0,LVH,118,Y,1.0,Flat,1,711 +66,M,ASY,120.0,302.0,0.0,LVH,151,N,0.4,Flat,0,712 +58,M,ASY,100.0,234.0,0.0,Normal,156,N,0.1,Up,1,713 +64,F,NAP,140.0,313.0,0.0,Normal,133,N,0.2,Up,0,714 +50,F,ATA,120.0,244.0,0.0,Normal,162,N,1.1,Up,0,715 +44,F,NAP,108.0,141.0,0.0,Normal,175,N,0.6,Flat,0,716 +67,M,ASY,120.0,237.0,0.0,Normal,71,N,1.0,Flat,1,717 +49,F,ASY,130.0,269.0,0.0,Normal,163,N,0.0,Up,0,718 +57,M,ASY,165.0,289.0,1.0,LVH,124,N,1.0,Flat,1,719 +63,M,ASY,130.0,254.0,0.0,LVH,147,N,1.4,Flat,1,720 +48,M,ASY,124.0,274.0,0.0,LVH,166,N,0.5,Flat,1,721 +51,M,NAP,100.0,222.0,0.0,Normal,143,Y,1.2,Flat,0,722 +60,F,ASY,150.0,258.0,0.0,LVH,157,N,2.6,Flat,1,723 +59,M,ASY,140.0,,,Normal,162,Y,0.0,Up,1,724 +45,F,ATA,112.0,160.0,0.0,Normal,138,N,0.0,Flat,0,725 +55,F,ASY,180.0,327.0,0.0,ST,117,Y,3.4,Flat,1,726 +41,M,ATA,110.0,235.0,0.0,Normal,153,N,0.0,Up,0,727 +60,F,ASY,158.0,305.0,0.0,LVH,161,N,0.0,Up,1,728 +54,F,NAP,135.0,304.0,1.0,Normal,170,N,0.0,Up,0,729 +42,M,ATA,120.0,295.0,0.0,Normal,162,N,0.0,Up,0,730 +49,F,ATA,134.0,271.0,0.0,Normal,162,N,0.0,Flat,0,731 +46,M,ASY,120.0,249.0,0.0,LVH,144,N,0.8,Up,1,732 +56,F,ASY,200.0,288.0,,LVH,133,Y,4.0,Down,1,733 +66,F,TA,150.0,226.0,,Normal,114,N,2.6,Down,0,734 +56,M,ASY,130.0,283.0,1.0,LVH,103,Y,1.6,Down,1,735 +49,M,NAP,,188.0,0.0,Normal,139,N,2.0,Flat,1,736 +54,M,ASY,122.0,286.0,0.0,LVH,116,Y,3.2,Flat,1,737 +57,M,ASY,152.0,274.0,0.0,Normal,88,Y,1.2,Flat,1,738 +65,F,NAP,160.0,360.0,0.0,LVH,151,N,0.8,Up,0,739 +54,M,NAP,125.0,273.0,0.0,LVH,152,N,0.5,Down,0,740 +54,F,NAP,160.0,201.0,0.0,Normal,163,N,0.0,Up,0,741 +62,M,ASY,120.0,267.0,0.0,Normal,99,Y,1.8,Flat,1,742 +52,F,NAP,,196.0,0.0,LVH,169,N,0.1,Flat,0,743 +52,M,ATA,134.0,,0.0,Normal,158,N,0.8,Up,0,744 +60,M,ASY,117.0,230.0,1.0,Normal,160,Y,1.4,Up,1,745 +63,F,ASY,108.0,269.0,0.0,Normal,169,Y,1.8,Flat,1,746 +66,M,ASY,112.0,212.0,,LVH,132,Y,0.1,Up,1,747 +42,M,ASY,140.0,226.0,0.0,Normal,178,N,0.0,Up,0,748 +64,M,ASY,120.0,246.0,0.0,LVH,96,Y,2.2,Down,1,749 +54,M,NAP,150.0,232.0,0.0,LVH,165,N,1.6,Up,0,750 +46,F,NAP,142.0,177.0,0.0,LVH,160,Y,1.4,Down,0,751 +67,F,NAP,152.0,277.0,0.0,Normal,172,N,0.0,Up,0,752 +56,M,ASY,125.0,249.0,1.0,LVH,144,Y,1.2,Flat,1,753 +34,F,ATA,118.0,210.0,0.0,Normal,192,N,0.7,Up,0,754 +57,M,ASY,132.0,207.0,0.0,Normal,168,Y,0.0,Up,0,755 +64,M,ASY,145.0,212.0,0.0,LVH,132,N,2.0,Flat,1,756 +59,M,ASY,138.0,271.0,0.0,LVH,182,N,0.0,Up,0,757 +50,M,NAP,140.0,233.0,0.0,Normal,163,N,0.6,Flat,1,758 +51,M,TA,125.0,213.0,0.0,LVH,125,Y,1.4,Up,0,759 +54,M,ATA,192.0,283.0,0.0,LVH,195,N,0.0,Up,1,760 +53,M,ASY,123.0,282.0,0.0,Normal,95,Y,2.0,Flat,1,761 +52,M,ASY,112.0,230.0,0.0,Normal,160,N,0.0,Up,1,762 +40,M,ASY,110.0,167.0,0.0,LVH,114,Y,2.0,Flat,1,763 +58,M,NAP,132.0,224.0,,LVH,173,N,3.2,Up,1,764 +41,F,NAP,112.0,268.0,0.0,LVH,172,Y,0.0,Up,0,765 +41,M,NAP,112.0,250.0,0.0,Normal,179,N,0.0,Up,0,766 +50,F,NAP,120.0,219.0,,Normal,158,N,1.6,Flat,0,767 +54,F,NAP,108.0,267.0,0.0,LVH,167,N,0.0,Up,0,768 +64,F,ASY,130.0,303.0,0.0,Normal,122,N,2.0,Flat,0,769 +51,F,NAP,130.0,256.0,0.0,LVH,149,N,0.5,Up,0,770 +46,F,ATA,105.0,204.0,0.0,Normal,172,N,0.0,Up,0,771 +55,M,ASY,140.0,217.0,0.0,Normal,111,Y,5.6,Down,1,772 +45,M,ATA,128.0,308.0,0.0,LVH,170,N,0.0,Up,0,773 +56,M,TA,120.0,193.0,0.0,LVH,162,N,1.9,Flat,0,774 +66,F,ASY,178.0,228.0,1.0,Normal,165,Y,1.0,Flat,1,775 +38,M,TA,120.0,231.0,0.0,Normal,182,Y,3.8,Flat,1,776 +62,F,ASY,150.0,244.0,0.0,Normal,154,Y,1.4,Flat,1,777 +55,M,ATA,130.0,262.0,,Normal,155,N,0.0,Up,0,778 +58,M,ASY,128.0,259.0,0.0,LVH,130,Y,3.0,Flat,1,779 +43,M,ASY,110.0,211.0,0.0,Normal,161,N,0.0,Up,0,780 +64,F,ASY,180.0,325.0,0.0,Normal,154,Y,0.0,Up,0,781 +50,F,ASY,110.0,254.0,0.0,LVH,159,N,0.0,Up,0,782 +53,M,NAP,130.0,197.0,1.0,LVH,152,N,1.2,Down,0,783 +45,F,ASY,138.0,236.0,0.0,LVH,152,Y,0.2,Flat,0,784 +65,M,TA,138.0,282.0,,LVH,174,N,1.4,Flat,1,785 +69,M,TA,160.0,234.0,1.0,LVH,131,N,0.1,Flat,0,786 +69,M,NAP,140.0,254.0,0.0,LVH,146,N,2.0,Flat,1,787 +67,M,ASY,100.0,299.0,0.0,LVH,125,Y,0.9,Flat,1,788 +68,F,NAP,120.0,211.0,0.0,LVH,115,N,1.5,Flat,0,789 +34,M,TA,118.0,182.0,0.0,LVH,174,N,0.0,Up,0,790 +62,F,ASY,138.0,294.0,,Normal,106,N,1.9,Flat,1,791 +51,M,ASY,140.0,298.0,0.0,Normal,122,Y,4.2,Flat,1,792 +46,M,NAP,150.0,231.0,0.0,Normal,147,N,3.6,Flat,1,793 +67,M,ASY,125.0,254.0,1.0,Normal,163,N,0.2,Flat,1,794 +50,M,NAP,129.0,196.0,0.0,Normal,163,N,0.0,Up,0,795 +42,M,NAP,120.0,,1.0,Normal,194,N,0.8,Down,0,796 +56,F,ASY,,409.0,0.0,LVH,150,Y,1.9,Flat,1,797 +41,M,ASY,110.0,172.0,0.0,LVH,158,N,0.0,Up,1,798 +42,F,ASY,102.0,265.0,0.0,LVH,122,N,0.6,Flat,0,799 +53,M,NAP,130.0,246.0,,LVH,173,N,0.0,Up,0,800 +43,M,NAP,,315.0,0.0,Normal,162,N,1.9,Up,0,801 +56,M,ASY,132.0,184.0,0.0,LVH,105,Y,2.1,Flat,1,802 +52,M,ASY,108.0,233.0,1.0,Normal,147,N,0.1,Up,0,803 +62,F,ASY,140.0,394.0,0.0,LVH,157,N,1.2,Flat,0,804 +70,M,NAP,160.0,269.0,0.0,Normal,112,Y,2.9,Flat,1,805 +54,M,ASY,140.0,239.0,0.0,Normal,160,N,1.2,Up,0,806 +70,M,ASY,145.0,,0.0,Normal,125,Y,2.6,Down,1,807 +54,M,ATA,108.0,309.0,0.0,Normal,156,N,0.0,Up,0,808 +35,M,ASY,126.0,282.0,0.0,LVH,156,Y,0.0,Up,1,809 +48,M,NAP,,255.0,1.0,Normal,175,N,0.0,Up,0,810 +55,F,ATA,,,0.0,LVH,161,N,1.4,Flat,0,811 +58,F,ASY,,248.0,0.0,LVH,122,N,1.0,Flat,0,812 +54,F,NAP,110.0,214.0,0.0,Normal,158,N,1.6,Flat,0,813 +69,F,TA,140.0,239.0,0.0,Normal,151,N,1.8,Up,0,814 +77,M,ASY,125.0,304.0,0.0,LVH,162,Y,0.0,Up,1,815 +68,M,NAP,118.0,277.0,0.0,Normal,151,N,1.0,Up,0,816 +58,M,ASY,125.0,300.0,0.0,LVH,171,N,0.0,Up,1,817 +60,M,ASY,125.0,258.0,0.0,LVH,141,Y,2.8,Flat,1,818 +51,M,ASY,140.0,,0.0,Normal,173,Y,1.6,Up,1,819 +55,M,ASY,160.0,289.0,0.0,LVH,145,Y,0.8,Flat,1,820 +52,M,TA,152.0,,1.0,Normal,178,N,1.2,Flat,0,821 +60,F,NAP,102.0,318.0,0.0,Normal,160,N,0.0,Up,0,822 +58,M,NAP,105.0,240.0,0.0,LVH,154,Y,0.6,Flat,0,823 +64,M,NAP,125.0,309.0,0.0,Normal,131,Y,1.8,Flat,1,824 +37,M,NAP,130.0,,0.0,Normal,187,N,3.5,Down,0,825 +59,M,TA,170.0,288.0,0.0,LVH,159,N,0.2,Flat,1,826 +51,M,NAP,125.0,245.0,1.0,LVH,166,N,2.4,Flat,0,827 +43,F,NAP,122.0,213.0,0.0,Normal,165,N,0.2,Flat,0,828 +58,M,ASY,128.0,216.0,0.0,LVH,131,Y,2.2,Flat,1,829 +29,M,ATA,130.0,204.0,0.0,LVH,202,N,0.0,Up,0,830 +41,F,ATA,,204.0,,LVH,172,N,1.4,Up,0,831 +63,F,NAP,135.0,252.0,0.0,LVH,172,N,0.0,Up,0,832 +51,M,NAP,94.0,227.0,0.0,Normal,154,Y,0.0,Up,0,833 +54,M,NAP,120.0,258.0,0.0,LVH,147,N,0.4,Flat,0,834 +44,M,ATA,120.0,220.0,,Normal,170,N,0.0,Up,0,835 +54,M,ASY,110.0,239.0,,Normal,126,Y,2.8,Flat,1,836 +65,M,ASY,135.0,,0.0,LVH,127,N,2.8,Flat,1,837 +57,M,NAP,150.0,168.0,0.0,Normal,174,N,1.6,Up,0,838 +63,M,ASY,130.0,330.0,1.0,LVH,132,Y,1.8,Up,1,839 +35,F,ASY,138.0,183.0,0.0,Normal,182,N,1.4,Up,0,840 +41,M,ATA,135.0,203.0,0.0,Normal,132,N,0.0,Flat,0,841 +62,F,NAP,130.0,263.0,0.0,Normal,97,N,1.2,Flat,1,842 +43,F,ASY,132.0,341.0,1.0,LVH,136,Y,3.0,Flat,1,843 +58,F,TA,150.0,283.0,1.0,LVH,162,N,1.0,Up,0,844 +52,M,TA,118.0,186.0,0.0,LVH,190,N,0.0,Flat,0,845 +61,F,ASY,145.0,307.0,0.0,LVH,146,Y,1.0,Flat,1,846 +39,M,ASY,,219.0,0.0,Normal,140,N,1.2,Flat,1,847 +45,M,ASY,115.0,260.0,0.0,LVH,185,N,0.0,Up,0,848 +52,M,ASY,128.0,255.0,0.0,Normal,161,Y,0.0,Up,1,849 +62,M,NAP,130.0,231.0,0.0,Normal,146,N,1.8,Flat,0,850 +62,F,ASY,160.0,164.0,0.0,LVH,145,N,6.2,Down,1,851 +53,F,ASY,,234.0,0.0,LVH,160,N,0.0,Up,0,852 +43,M,ASY,,177.0,0.0,LVH,120,Y,2.5,Flat,1,853 +47,M,NAP,138.0,257.0,0.0,LVH,156,N,0.0,Up,0,854 +52,M,ATA,120.0,325.0,0.0,Normal,172,N,0.2,Up,0,855 +68,M,NAP,180.0,,1.0,LVH,150,Y,1.6,Flat,1,856 +39,M,NAP,140.0,321.0,0.0,LVH,182,N,0.0,Up,0,857 +53,F,ASY,130.0,264.0,0.0,LVH,143,N,0.4,Flat,0,858 +62,F,ASY,140.0,268.0,0.0,LVH,160,N,3.6,Down,1,859 +51,F,NAP,,308.0,0.0,LVH,142,N,1.5,Up,0,860 +60,M,ASY,130.0,253.0,0.0,Normal,144,Y,1.4,Up,1,861 +65,M,ASY,110.0,248.0,,LVH,158,N,0.6,Up,1,862 +65,F,NAP,155.0,269.0,0.0,Normal,148,N,0.8,Up,0,863 +60,M,NAP,140.0,185.0,,LVH,155,N,3.0,Flat,1,864 +60,M,ASY,145.0,282.0,,LVH,142,Y,2.8,Flat,1,865 +54,M,ASY,120.0,188.0,0.0,Normal,113,N,1.4,Flat,1,866 +44,M,ATA,130.0,219.0,0.0,LVH,188,N,0.0,Up,0,867 +44,M,ASY,112.0,290.0,0.0,LVH,153,N,0.0,Up,1,868 +51,M,NAP,110.0,175.0,0.0,Normal,123,N,0.6,Up,0,869 +59,M,NAP,150.0,,1.0,Normal,157,N,1.6,Up,0,870 +71,F,ATA,160.0,302.0,0.0,Normal,162,N,0.4,Up,0,871 +61,M,NAP,,243.0,1.0,Normal,137,Y,1.0,Flat,0,872 +55,M,ASY,,353.0,0.0,Normal,132,Y,1.2,Flat,1,873 +64,M,NAP,,335.0,0.0,Normal,158,N,0.0,Up,1,874 +43,M,ASY,150.0,247.0,,Normal,171,N,1.5,Up,0,875 +58,F,NAP,120.0,340.0,0.0,Normal,172,N,0.0,Up,0,876 +60,M,ASY,,206.0,0.0,LVH,132,Y,2.4,Flat,1,877 +58,M,ATA,,284.0,0.0,LVH,160,N,1.8,Flat,1,878 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+58,M,ATA,130.0,,,Normal,150,N,0.0,Up,0,957 +32,F,ATA,105.0,198.0,0.0,Normal,165,N,0.0,Up,0,958 +63,M,ASY,96.0,,0.0,ST,121,Y,1.0,Up,1,959 +54,M,ASY,,365.0,0.0,ST,134,N,1.0,Up,0,960 +48,M,ATA,100.0,159.0,0.0,Normal,100,N,0.0,Up,0,961 +53,F,ATA,140.0,216.0,0.0,Normal,142,Y,2.0,Flat,0,962 +57,M,NAP,105.0,0.0,1.0,Normal,148,N,0.3,Flat,1,963 +48,M,ASY,160.0,193.0,0.0,Normal,102,Y,3.0,Flat,1,964 +38,M,NAP,,0.0,0.0,Normal,128,Y,0.0,Flat,1,965 +66,F,TA,150.0,226.0,0.0,Normal,114,N,2.6,Down,0,966 +54,F,ATA,120.0,273.0,0.0,Normal,150,N,1.5,Flat,0,967 +59,M,TA,178.0,270.0,0.0,LVH,145,N,4.2,Down,0,968 +54,M,ASY,130.0,242.0,0.0,Normal,91,Y,1.0,Flat,1,969 +62,F,ASY,120.0,0.0,1.0,ST,123,Y,1.7,Down,1,970 +57,M,ASY,,264.0,0.0,LVH,100,N,0.0,Flat,1,971 +69,M,NAP,142.0,271.0,0.0,LVH,126,N,0.3,Up,0,972 +45,M,NAP,135.0,,0.0,Normal,110,N,0.0,Up,0,973 +39,M,ASY,110.0,,0.0,Normal,132,N,0.0,Up,0,974 +49,M,ASY,128.0,212.0,0.0,Normal,96,Y,0.0,Flat,1,975 +59,M,NAP,131.0,0.0,0.0,Normal,128,Y,2.0,Down,1,976 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