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.env

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# Leave this set to 0 on the GitHub repo so the unit and
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# integration tests do not need to have wheels (until we
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# find a convenient way to use wheels on GitHub)
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TAG=1.1.a0
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TAG=0.20
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USE_WHEELS=1

.github/workflows/aliro_tests.yml

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# Controls when the action will run.
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on:
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push:
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branches:
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- master
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pull_request:
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branches:
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- master
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# Allows you to run this workflow manually from the Actions tab
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workflow_dispatch:

.gitignore

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.xz
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*.exe
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*.mp4
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# *.mp4
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package-lock.json
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package.json
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package-copy.json

aliroed-examples/1_installation.md

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# AliroEd
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## AliroEd Installation Requirements
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- [Raspberry Pi 400](https://www.raspberrypi.com/products/raspberry-pi-400/)
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- A computer running Windows 10 or higher
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- A MicroSD Card Reader
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- MicroSD Card
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- Minimum capacity: 32GB
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- **Note:** There are different speed classes for MicroSD Cards, Application
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Performance Class 1 (A1) and Application Performance 2 (A2). A2 cards are
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**highly recommended** as these are much faster than A1 cards.
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- A copy of the [aliro-imager.exe](http://52.35.223.86/education/#download)
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## AliroEd Installation
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### Windows
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1. Download a copy of the [aliro-imager.exe](http://52.35.223.86/education/#download)
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2. Insert the MicroSD Card in your card reader.
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3. Double-click the downloaded **aliro-imager-\*.exe** on you computer. If prompted to allow
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the application to run, select **Yes**. You may need to enter your computer's
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**Administrator** password to continue.
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4. Follow the prompts to proceed with the installation.
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![Aliro Imager Install](https://media.githubusercontent.com/media/EpistasisLab/Aliro/master/docs/source/_static/aliro_imager_install.png?raw=true "Aliro Imager Install")
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5. Once installed, you can run the **AliroEd Imager** from the Start Menu. When
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the program starts up you will see this screen:
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![Aliro Imager Start](https://media.githubusercontent.com/media/EpistasisLab/Aliro/master/docs/source/_static/aliro_imager_start.png?raw=true "Aliro Imager Start")
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6. Click the **CHOOSE STORAGE** button and select your MicroSD Card from the
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popup menu.
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![Aliro Imager Choose Storage](https://media.githubusercontent.com/media/EpistasisLab/Aliro/master/docs/source/_static/aliro_imager_choose_storage.png?raw=true "Aliro Imager Choose Storage")
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7. Click the **WRITE** button to begin writing the Operatying System to your
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MicroSD Card.
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![Aliro Imager Write](https://media.githubusercontent.com/media/EpistasisLab/Aliro/master/docs/source/_static/aliro_imager_write.png?raw=true "Aliro Imager Write")
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- **NOTE** that this will **format** your MicroSD Card and all existing data will
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be erased. Click **Yes** at the prompt to proceed.
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![Aliro Imager Format](https://media.githubusercontent.com/media/EpistasisLab/Aliro/master/docs/source/_static/aliro_imager_format.png?raw=true "Aliro Imager Format")
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- This writing process may take several minutes, the progress will be shown
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on the **AliroEd Imager.**
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![Aliro Imager Finish](https://media.githubusercontent.com/media/EpistasisLab/Aliro/master/docs/source/_static/aliro_imager_finish.png?raw=true "Aliro Imager Finish")
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8. Insert the MicroSD Card into your Raspberry Pi 400 and start it up.
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9. When the Operating System has finished starting up, double-click the
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AliroEd Icon on the Destop or launch the Web Browser.
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Now that Aliro is up and running, you are ready to run experiments, AliroEd
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comes preloaded with some data sets for you to experiment with. Other datasets
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can be downloaded from the
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[Penn Machine Learning Benchmarks](https://github.com/EpistasisLab/pmlb)

aliroed-examples/2_experiment.md

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# Running experiments on AliroEd
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This guide shows you how to construct your own experiment, construct an AI experiment, and view and download results. It assumes that you have launched AliroEd in your Raspberry Pi.
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1. **Introduction page** The first page you will see when AliroEd (in the Rapsberry Pi) launches is the introduction page. You can browse this site while AliroEd is loading.
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<!-- ![AliroEd Intropage](https://media.githubusercontent.com/media/EpistasisLab/Aliro/master/docs/source/_static/1_aliroed_intropage.png?raw=true "AliroEd Intropage") -->
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![AliroEd Intropage](https://raw.githubusercontent.com/EpistasisLab/Aliro/master/docs/source/_static/1_aliroed_intropage.png "AliroEd Intropage")
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2. **Ready to Run** On the top-right corner of the introductory page you will see a message saying **Loading AliroEd**. Once AliroEd is loaded, this message will change to **Aliro is ready to run**. This is a link, click on it and will take you to the Aliro landing page (step 3).
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![AliroEd Ready to Run](https://raw.githubusercontent.com/EpistasisLab/Aliro/master/docs/source/_static/2_aliroed_readytorun.png "AliroEd Ready to Run")
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3. **Aliro landing page** This page shows you some preloaded datasets.
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**Note the tooltips that will also guide you through buiding experiments.**
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![AliroEd Landing Page](https://raw.githubusercontent.com/EpistasisLab/Aliro/master/docs/source/_static/3_aliroed_landingpage.png "AliroEd Landing Page")
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4. **Building your own experiment** Click _Build New Experiment_ at the bottom of your dataset box.
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This takes you to the experiment building page.
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![AliroEd Building Experiment](https://raw.githubusercontent.com/EpistasisLab/Aliro/master/docs/source/_static/4_aliroed_buildingownexperiment.png "AliroEd Building Experiment")
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5. **Experiments page** The Experiments page lists information about current experiments, including start time, score, and prediction type. By filtering by Algorithm, you can see the algorithm’s parameters for each experiment. In the Actions column on the right, you may download files to reproduce your results:
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* **Download model** downloads a pickled model file that contains the trained model. Use it to run analysis on more datasets.
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* **Download script** gives you a Python script to reproduce the analysis in the experiment.
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![AliroEd Experiments Page](https://raw.githubusercontent.com/EpistasisLab/Aliro/master/docs/source/_static/5_aliroed_experimentspage.png "AliroEd Experiments Page")
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6. **Experiment results** The Results page summarizes the experiment results through figures and statistics. The algorithm configuration and runtime information is displayed on the left. On the right, the training and testing balances accuracies are shown. For every experiment, the top 10 most important features are shown in a bar plot on the bottom left. For every classification problem, a confusion matrix is shown. For binary classification, an ROC curve and AUC score is provided.
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![AliroEd Experiment Results](https://raw.githubusercontent.com/EpistasisLab/Aliro/master/docs/source/_static/6_aliroed_experimentresults.png "AliroEd Experiments Results")
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7. **AI Analysis** To allow the AI to choose experiments for you, simply toggle on the AI button in the upper right corner of the dataset box. The AI will then run one or more experiments for which it chooses its own algorithms to run. Progress can be viewed from the Experiments page. To configure the AI settings (for example, the number of experiments to run or time limits), see the configuration file in config/ai.env.
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![AliroEd AI Analysis](https://raw.githubusercontent.com/EpistasisLab/Aliro/master/docs/source/_static/7_aliroed_ai.png "AliroEd AI Analysis")
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8. **Uploading your own dataset** Upload a dataset by clicking the "Add New Dataset" button on the **Aliro Landing Page**.
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This will take you to the Dataset Upload screen where you can select a .csv or .tsv file to upload.
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You will see a preview of the data.
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Specify the name of the dependent column, as well as any nominal or ordinal features.
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Some example datasets can be found in the [Penn Machine Learning Benchmarks github repository](https://epistasislab.github.io/pmlb)
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![AliroEd Uploading Datasets](https://raw.githubusercontent.com/EpistasisLab/Aliro/master/docs/source/_static/8_aliroed_addnewdataset.png "AliroEd Uploading Datasets")

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