Add Data

Upon accessing the project dashboard, your initial task is to upload pertinent data sets. These may encompass data utilized for training, testing, validation, production, or any other data integral to your project's scope and requirements.

Additional data can be uploaded through the 'Data Settings' component on the main menu.

NOTE:During the initial data upload process, whether through the dashboard interface or API integration, it is imperative to begin by uploading at least one sample dataset from the dashboard. Subsequently, users can proceed to define the requisite data settings. Additional datasets may also be uploaded seamlessly via the API following this initial setup.

To initiate the data upload process, begin by selecting the upload type from the dropdown menu, which offers the options of 'Data', 'Data Description' and ‘Feature mapping’. In Data description, users can add description for the data columns. 

For data uploads, it's necessary to specify the 'Upload Tag' from the dropdown, where you can specify the data type - Training, Testing, Validation, or you can choose to add a custom tag as well.

Users have the flexibility to upload files either by dragging and dropping them or by selecting the CSV file for uploading directly. After adding the file, proceed by selecting the 'Upload File' option to initiate the upload process.

NOTE:CSV files are limited to 200 MB for default server and 1 GB for custom server on workspace/ project

Once the upload is complete, you will be directed to ‘Data Config’ to configure the details.  

NOTE:When uploading data, if you receive an error message stating that the file already exists, you can navigate to the 'File Info' section and delete the existing file, if the processing is not completed.

Data Config.

Data Configuration serves as the foundational framework encompassing crucial high-level details essential for all subsequent operations within the project, and cannot be changed once set. 

1. Begin by specifying the project type, which may involve either classification or regression tasks
2. Define the ‘Unique identifier’ - Assign a unique identifier to each data point within the dataset. This identifier distinguishes individual data entries and aids in data management and analysis.
3. Select the true label - Identify the true label, which represents the target variable to be predicted. For instance, in a real estate dataset, the true label could denote the 'Sale Price' of a property.
4. If applicable, choose the predicted label from the provided dropdown menu. This step is important when evaluating predictions generated by an existing model.
5. Feature Exclusion: Select features (data points) to be excluded. There might be multiple features within your project. You can exclude the features that might not be relevant to your project from the ‘Features exclude’ option. You can see all the features included and excluded on the right.

NOTE: In cases where your data includes duplicate unique identifiers, you have the option to eliminate them by selecting the provided checkbox.

True and Predicted label

The predicted label is essential if you intend for the XAI model to explain the predictions generated by your model. If the predicted label is not explicitly defined, AryaXAI will automatically select the true label to construct the XAI model.

Once the above steps are completed, select ‘Save Initial Configuration'.

NOTE: When defining data features, specifically the data settings, it should be noted that these settings serve as the foundation for training the explainable model. The feature selection conducted during this stage should closely align with the final set of features utilized in your model. This alignment ensures consistency and accuracy in the interpretability analysis provided by AryaXAI.

Upon submission of the Project Configuration, you will be directed to the 'Project Summary' page. This page provides access to the Project Summary, Data Diagnostics, and Model Performance.