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.

AryaXAI provides users the option to upload the data either manually, or via SDK.

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.

Upload Data manually

To initiate the data upload process through manual upload, select the 'Add data manually' option. 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 file size limits vary based on your subscription plan. To check your current plan, navigate to Profile Settings and select the Plans tab.

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.
6. Select the 'Drop Duplicate Unique Identifier' checkbox to ensure duplicates are removed.
7. Additionally, we recommend selecting the 'Handle Errors' checkbox to automatically remove null values from your data. If this option is not selected, null values may be included, potentially causing errors during model development.

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.

File Info

Detailed information about all uploaded files can be found in the ‘File Info’ tab under ‘Data Settings.’ This includes metadata such as file size, number of rows and columns, along with detailed statistics like data points, data types (numerical or categorical), missing values percentage, and the minimum and maximum values for each data point.

The 'Tag Data’ table below shows the uploaded files. You can use the ‘Feature Name’ filter to sort and view data based on specific columns in the uploaded file.

Add data through SDK

To add data through the AryaXAI python package:

1. To begin using the AryaXAI SDK, follow the installation instructions to set it up in your project. After installation, you will be able to utilize the features effectively
2. Next, import the AryaXAI SDK into your application. Once imported, authenticate your session by providing your API key to ensure secure access to the SDK's functionalities.
3. Configure the project
4. Upload your file