# List of published AITs

Qunomon provides readily available AITs on GitHub that are copyright-free. 
Users can directly utilize these in Qunomon, modify the code to develop new AITs, or prepare for registration in Qunomon by using git clone as per the tutorials.

``` important:: External link to GitHub.
```

``` note:: For details on the AIT, please check develop/my_ait.ipynb.
```

* alyz_dataset_table_counts_attr_coverage
  * Focuses on the number of records for attribute combinations within the data to assess for rare cases or imbalanced trends.
  * [link](https://github.com/aistairc/Qunomon_AIT_alyz_dataset_table_counts_attr_coverage)
* alyz_dataset_table_counts_comb_all_attr
  * Calculate the counts of attribute values and the percentage of occurrences by the combination of attributes in the table data.
  * Based on this summary information, the distribution trend of the frequency of occurrence of the table data can be inferred.
  * [link](https://github.com/aistairc/Qunomon_AIT_alyz_dataset_table_counts_comb_all_attr)
* alyz_dataset_table_counts_comb_two_attr
  * Specifies unnecessary (improbable) attribute value combinations in table data and calculates how much of this unwanted data is contained, along with the count and proportion of attribute values.
  * AIT users can use this summary information to understand the unhealthy trends of attribute values based on their occurrences.
  * [link](https://github.com/aistairc/Qunomon_AIT_alyz_dataset_table_counts_comb_two_attr)
* alyz_dataset_table_counts_inde_attr_by_chi2
  * For assessing data validity, calculates the independence of labels and each attribute in a CSV dataset using the chi-squared statistic.
  * [link](https://github.com/aistairc/Qunomon_AIT_alyz_dataset_table_counts_inde_attr_by_chi2)
* eval_correctness_image_classifier_pytorch
  * Splits a dataset randomly and calculates the accuracy of the model for each split dataset.
  * Low variance in accuracy suggests that the model has acquired a generalizable performance across the dataset.
  * [link](https://github.com/aistairc/Qunomon_AIT_eval_correctness_image_classifier_pytorch)
* eval_dataset_image_diversity_vae
  * Uses a VAE model, trained on features of training image data, to calculate the feature values of evaluation training data.
  * Smaller difference in feature values indicates that the evaluation image data comprehensively covers the features of the training data.
  * [link](https://github.com/aistairc/Qunomon_AIT_eval_dataset_image_diversity_vae)
* eval_dataset_image_3features_kld
  * Inputs two groups of image data and calculates the KLD (KL Divergence, KL information) for the distribution of brightness, contrast, and exposure between them.
  * KLD close to zero indicates that the two image groups replicate the same features.
  * [link](https://github.com/aistairc/Qunomon_AIT_eval_dataset_image_3features_kld)
* eval_model_image_classify_acc_adversarial_example
  * Generates adversarial sample images from input images and calculates accuracy information (Accuracy, Precision, Recall, F-value, AUC) for the input model (an image classification model trained on input images).
  * These accuracy metrics allow the evaluation of the accuracy and stability of machine learning models.
  * [link](https://github.com/aistairc/Qunomon_AIT_eval_model_image_classify_acc_adversarial_example)
* eval_model_peformance_pytorch
  * Given a dataset and a PyTorch classification model, evaluates the inference accuracy of the model from the dataset's inference results.
  * Calculates accuracy, AP (average precision), and balanced accuracy for inferences on the dataset, assessing the model's inference precision.
  * [link](https://github.com/aistairc/Qunomon_AIT_eval_model_peformance_pytorch)
* eval_noise_score_aquavs
  * To evaluate the stability of the model, we will validate with labels that have added noise.
  * Using the latent representations from the SVAE, we will measure the "noise score" of each sample in the input dataset to detect anomalies.
  * [link](https://github.com/aistairc/Qunomon_AIT_eval_noise_score_aquavs)
* eval_surprise_adequacy
  * We are calculating the Surprise Adequacy (SA) of the input VAE model.
  * SA evaluates the activation traces of each neuron for each sample in the input data.
  * [link](https://github.com/aistairc/Qunomon_AIT_eval_surprise_adequacy)
* eval_processcheck_problem_domain_analysis
  * A checklist method is used to examine whether the dataset used for the machine learning system satisfies the sufficiency of problem domain analysis.
  * The checklist [checklist.csv] can be downloaded from the link below.
  * [link](https://github.com/aistairc/Qunomon_AIT_eval_processcheck_problem_domain_analysis)