Tabular dataset

Functionality for tabular data is implemented. Dataset selection provides a prediction of malignancy of breast cancer dataset and a stroke prediction dataset which the user can access visually using either the plots and the features based on his needs but also the actual data in the form of a table that is depicted in the page. Training of the classifiers for the tabular dataset has been implemented for some time now and produces adequete results. A user can choose the type of preprocessing to do, the classifier and the train/test ratio. Visualizing the training results is available through the charts.html page (Pre trained models and visualization) from where plots like Feature importance PCA classification reports and others are loaded. For counterfactuals explanations DICEMl is improved using not only the datapoint to compute the counterfactuals of, but also the features to vary parameter which can be exploited using the additional functionality of the dashboard that provides for selection of specific features to vary, select/deselect and sort based on the importance.

Timeseries dataset

Utilising timeseries datasets is furtherly improved in this version. In the dataset selection the user can pick from 6 different datasets provided by Wildboar and observe the data for each of them. Moving to training, Wildboar and Glacier classifiers are added and preprocessing is allowed. The available classifiers are RSF and KNN and 1dCNN. For counterfactuals and explainability the user can pick the pre trained classifier, decide on a example timeseries entry based on the class and see the computed counterfactual live.

For Glacier things are a bit more complicated and definetely need further improvement. Training of Glacier for now can only be 1dCNN with little configuration. A user can also decide on an autoencoder. After the train the user can access the counterfactuals section from where using the pre trained classifier (1dCNN) he can run some experiments for specific contraints types. These experiments are saved cand can be accessed using the displayed load button.To try the experiments live the user is asked to choose a class label of the example they would like to try, and pick an actual entry from a series of example entries which will be displayed after its selection. "Run Counterfactual" button will accessd the experiments data and plot the respective computed counterfactual generated by that specific method.

For wildboar the procedure is the same except for some extra configuration during the training where a test ratio and some prepropcessing can be selected.

Import

Importing a dataset is available in this version using the dataset selection navigator. It is important to state the type of the dataset (timeseries or tabular) for the backend. Timeseries and tabular dataset imports are available and imported datasets are saved and can be reused later if the user likes to. From there depending on the type of the datasets the respective functionalities are available.