################################################################################### # # # XAI_CLASSIFICATION_MIXED_DATA README # # # ################################################################################### +=================================================================================+ | # DEPENDENCIES # | +=================================================================================+ | Due to the different implemetation stages of the XAI techniques used, there is a| | large discrepency of package, python verions etc... needed to run the code. | | | | We recommended that you create four virtual environments to run this code. | | | | The dependencies for each recommended environment: | | The dependency files should be read in and the packages installed using pip. | +=================================================================================+ +---------------------------------------------------------------------------------+ | Environment | Python version | Associated package dependency file | | | | | +------------------------+----------------+---------------------------------------+ | anchor_image_explainer | 3.9.6 | anchor_image_package_dependencies.txt | +------------------------+----------------+---------------------------------------+ | lime_image_explainer | 3.9.5 | lime_image_package_dependencies.txt | +------------------------+----------------+---------------------------------------+ | shap_image_explainer | 3.6.8 | shap_image_package_dependencies.txt | +------------------------+----------------+---------------------------------------+ | text_explainer | 3.6.13 | text_package_dependencies.txt | +------------------------+----------------+---------------------------------------+ +=================================================================================+ | # DATASETS # | +=================================================================================+ | Six datasets are needed to run this project, the table below lists them with a | | link to where they can be installed. | | | | Once downloaded they should be unpacked and placed into the associated folder | | within the datasets folder. | +=================================================================================+ +=================================================================================+ | # CLASS AND FUNCTION EXPLANATION # | +=================================================================================+ | We briefly explain the classes and the functions of our implemenation | | | | For ease of explanation we explain image & text explanation code seperately. | | The classes are described in the two tables below with each instance in logical | | order to run and generate explanations. | +=================================================================================+ +---------------------------------------------------------------------------------------------------------------------+ | IMAGE EXPLANATIONS (FOLDER: image_classification) | +-------------------------------+------------------------------------+------------------------------------------------+ | File | Function | Purpose | | | | | +-------------------------------+------------------------------------+------------------------------------------------+ | image_dataset_1_processing.py | - get_dataset_1() | The functions, load, process and return the | | image_dataset_2_processing.py | - get_dataset_2() | image datasets ready for training and generat- | | image_dataset_2_processing.py | - get_dataset_3() | ing image explanations. | +-------------------------------+------------------------------------+------------------------------------------------+ | image_perturbation.py | - image_perturbation() | The image_perturbation function takes as input | | | | an image and returns a perturbed image. | +-------------------------------+------------------------------------+------------------------------------------------+ | model_and_plot.py | - binary_dataset_creation() | The binary_dataset_creation function loads the | | | - img_classification_model() | dataset passed to it using an ImageGenerator. | | | - plot_accruracy_loss_multiple() | | | | | The img_classification_model function calls | | | | the training of the image classification model | | | | | | | | The plot_accuracy_loss_multiple model is the | | | | implementation which generates graphs of the | | | | model's loss and accuracy. | +-------------------------------+------------------------------------+------------------------------------------------+ | model_training.py | N/A | Running this file, trains and saves all the | | | | image classification models and plots their | | | | accuracy and loss. | +-------------------------------+------------------------------------+------------------------------------------------+ | anchor_image_explanation.py | - extracting_anchors_explanation() | This method generates Anchor explanations from | | | | instances of the data passed to it. | +-------------------------------+------------------------------------+------------------------------------------------+ | lime_image_explanation.py | - extracting_lime_explanation() | This method generates LIME explanations from | | | | instances of the data passed to it. | +-------------------------------+------------------------------------+------------------------------------------------+ | shap_image_explanation.py | - extracting_shap_explanation() | This method generates SHAP explanations from | | | | instances of the data passed to it. | +-------------------------------+------------------------------------+------------------------------------------------+ +--------------------------------------------------------------------------------------------------------------------------+ | TEXT EXPLANATIONS (FOLDER: Text_classification) | +-------------------------------+------------------------------------+-----------------------------------------------------+ | File | Function | Purpose | | | | | +-------------------------------+------------------------------------+-----------------------------------------------------+ | image_dataset_1_processing.py | - dataset_1_build() | dataset_1_build - Loads in data from csv. | | | - get_1_preprocessing() | | | | - get_dataset_1() | get_1_preprocessing - Data cleaning by dropping | | | | NaN values, converting emojis, regex and stopwords. | | | | | | | | get_dataset_1 - Binarizers and tokenizes data and | | | | returns data and data features. | +-------------------------------+------------------------------------+-----------------------------------------------------+ | image_dataset_2_processing.py | - dataset_2_build() | dataset_2_build - Loads in data from csv. | | | - get_2_preprocessing() | | | | - get_dataset_2() | get_2_preprocessing - Data cleaning by dropping | | | | NaN values, converting emojis, regex and stopwords. | | | | | | | | get_dataset_2 - Binarizers and tokenizes data and | | | | returns data and data features. | +-------------------------------+------------------------------------+-----------------------------------------------------+ | image_dataset_3_processing.py | - dataset_3_build() | dataset_3_build - Loads in data from csv. | | | - get_3_preprocessing() | | | | - get_dataset_3() | get_3_preprocessing - Data cleaning by dropping | | | | NaN values, regex and stopwords. | | | | | | | | get_dataset_3 - Binarizers and tokenizes data and | | | | returns data and data features. | +-------------------------------+------------------------------------+-----------------------------------------------------+ | text_perturbation.py | - text_perturbation() | The text_perturbation function takes as input | | | | a text instance and returns a perturbed text. | +-------------------------------+------------------------------------+-----------------------------------------------------+ | model_and_plot.py | - binary_dataset_creation() | The binary_dataset_creation function loads the | | | - lstm_model() | dataset passed to it using a Generator. | | | - plot_accruracy_loss_multiple() | | | | | The lstm_model function calls the training of the | | | | text classification model | | | | | | | | The plot_accuracy_loss_multiple model is the | | | | implementation which generates graphs of the | | | | model's loss and accuracy. | +-------------------------------+------------------------------------+-----------------------------------------------------+ | model_training.py | N/A | Running this file, trains and saves all the | | | | image classification models and plots their | | | | accuracy and loss. | +-------------------------------+------------------------------------+-----------------------------------------------------+ | anchor_image_explanation.py | - extracting_anchors_explanation() | This function generates Anchor explanations from | | | | instances of the data passed to it. | +-------------------------------+------------------------------------+-----------------------------------------------------+ | lime_image_explanation.py | - lime_text_explainer() | This function generates LIME explanations from | | | | instances of the data passed to it. | +-------------------------------+------------------------------------+-----------------------------------------------------+ | shap_image_explanation.py | - shap_explainer() | This function generates SHAP explanations from | | | | instances of the data passed to it. | +-------------------------------+------------------------------------+-----------------------------------------------------+