Reporting Forecast

Update Microsoft Visual C++

 Precondition: This step may be optional, depending on previously installed .NET and Visual C packages on your computer. We added it as a safety precaution in case you encounter unexpected errors in the previous or upcoming steps.

1. If necessary, update here https://support.microsoft.com/en-ca/help/2977003/the-latest-supported-visual-c-downloads,
   1. For 64-bit Systems download and install: vc_redist.x64.exe
   2. For 32-bit Systems download and install: vc_redist.x86.exe

In most cases you can simply execute and perform the setup. It should automatically detect if you need any updates and can be installed "over" existing packages.

Python Setup

1. Go to python.org
2. Download the Python Python 3.7.9 - Aug. 17, 2020 version in the menu "Downloads" → A python.exe is being downloaded. 
   Newer versions may work, but are untested. We experienced issues with the most recent versions.
   

3. Start the install wizard by opening the executable.

   

   Add to Enviroment Variables

   Important: Make sure "Add Python to PATH" (Windows Variables) is enabled.

   Otherwise Power BI and GIT might not find Python automatically. → You will have to add the PATH later manually or reinstall.
   

4. Click through the remaining install wizard until "Install Now".

Install GitHub

 Precondition: Step 1 must be completed successfully.

1. Go to gitforwindows.org 
2. Download the Installer files
3. Start install wizard by opening the file. We recommend the following settings: 
   
   1. Default branch name for new repositories: "main"
   2. 
   3. Terminal Emulator: "Use Windows default console window"
      
   4. Choose setup default settings for anything else during installation (options, path command line, openSSL library)
      
      

4. Once installed, open GitBash via your Start menu → A command prompt opens.
   

   

   Tip

   Note that GIT looks similar to a DOS Prompt, with some key differences: 

   • you can paste your clipboard (e.g. commands shown below) via right click into the GIT bash window.
   • GIT will pause operation when you click anywhere within the window itself. To resume, click on the window and move the cursor with a direction key.
5. Create an SSH Key to get the credentials for the Luware Python package. You may also refer to → documentation on Generating a new SSH Key. 
   

   ---

   Why is this necessary? With this key we uniquely identify your contact and repository to connect you to the Luware GIT repository for the latest files. This procedure only needs to be done once, unless your reporting user's contact address changes.
   

   ---

   
   

   1. Within GitBash enter: 

      ssh-keygen -t ed25519 -C "your_email@example.com"

      BASH

      Important: Substitute the Mail address with your own. This is required to generate a random key unique to you.
      
      

   2. Continue pressing Return until you see an "randomart" image as follows:
      
      
      

6. Next is verifying your key and getting it ready for transfer:

   1. Check if the key is now active and running by typing: 
      

      eval `ssh-agent -s`

      BASH

      → You will see a response like ‘Agent pid 1234’ or other number
      
      

   2. Now add the SSH private key to the ssh-agent by typing
      

      ssh-add ~/.ssh/id_ed25519

      BASH

      → Identity added: <Your Info> will be shown.
      
      

   3. Copy the key to your clipboard via:

      clip < ~/.ssh/id_ed25519.pub

      BASH

      → The key is now in your clipboard and can be pasted anywhere with CTRL+V. 
      Tip:  by default the generated key is also stored in C:\Users\<YourUserName>\.ssh in a .pub file which can be opened in any text editor.
      
      

7. The result will look like this, of course with a key unique to you. 

   ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIPaqFG9B0PLynvcDOxufWC0341WClag4F7/GdWVgXfLu your_email@example.com

   CODE

   Paste this Key into an email and send it to: mjakob@luware.com

Install and Update Luware Library

 Precondition: Previous steps must be completed successfully. Luware must also have confirmed your SSH key so your local GIT repository can connect to ours.

Data Import

Precondition

• For this Step Power BI should recognize the Python PATH automatically (→ See "Python Setup" above).
   Encountered problems? Check out the Python section of the official Microsoft Power BI documentation.

• For the Python scripts to work properly in the Power BI service, all data sources need to be set to "public".
   You can find these settings also under: File > Options > Data sources settings 
  

  

  When you see this pop-up, choose "Public"

B: Transform Variant

 This variant is recommended for advanced users who want to transform and export data to their needs. This variant is mutually exclusive to → Variant A:

1. Go back to the main "Fact", "Service Task", and "Applied Steps" entries list.

   

   Until we add the additional features, the two entries "Merged Queries" and "Expanded data (1)" do not exist yet.

2. Between the Applied Steps "Source" - which is the final step loading the dataset -  and the "Grouped Rows" step you can filter rows for the tasks to forecast.

   

3. In the popup you can add filter requirements in the "Basic" section, or go to "Advanced". See example below.

   

4. In this example we grouped rows to count the total # of tasks per day.

   

5. Then we sorted rows from the beginning to now.

   

6. In order to merge the imported file with the current "Applied Step", click on "Merge Queries".

   

7. In the Popup, select the Dataset to import:

   1. click on the "date" columns, in order to match the two datasets.

   2. Then choose "full outer join" and click "OK".

      

8. Click on the two arrows at the column's header (data_inc_feature).

   1. Select the Feature to merge

   2. Untick the "Use original column name as prefix".

   3. Continue with "OK".

      
9. The next step is the Python code. This step should be inserted for the customer already. In the "Transform" menu tab, go to "Run Python script", copy/paste the code. Click "OK".

10. Click on "ci_df" to select the dataset with the forecast, this creates automatically the last step.
    

    

Create Visualization

 After the query and code preparations are done the visualizations can now be created.

1. Choose the forecast results from "ServiceTask", prediction, down and up (confidence intervals), and the date.
    No need to add an additional "feature" anymore as is already included in the forecast
2. Add additional data as described in chapter "Data Import" above, then exit ans save.

3. Now create Visualizations, with Python not Power BI. Click on "Python visual" and add the Values "DateId", "Totals" and the imported Feature.

   

4. Choose which visualization should be shown. Note that ... 

   1. ... there are 4 visualizations to choose from (see lines beginning with "plot_")

   2. ... only one of the last 4 lines can be shown at the same time. 
      A hashtag in front of the statement will "comments" a line out as seen in the example below.
      Comment 3 lines out (or delete those lines) and leave one line with the visualizatuib function.

      
5. Don"t forget ot Save and Close your Report to not having to make the changes again next time.
    If you want to keep different visualizations, save multiple variants of your prediction report.