eazyml-xai-image 0.0.46

eazyml-image-xai provides APIs for explainable AI (XAI)

EazyML Responsible-AI: Image XAI

This package focuses on segmentation prediction, explainability, active learning and online learning for image dataset.

Features

• Active learning focuses on reducing the amount of labeled data required to train the model while maximizing performance, making it particularly useful when labeling data is expensive or time-consuming. By prioritizing uncertain or diverse examples, active learning accelerates model improvement and enhances efficiency.
• Online learning is a machine learning approach where models are trained incrementally as data becomes available, rather than using a fixed, pre-existing dataset. This method is well-suited for dynamic environments, enabling real-time updates and adaptability to new patterns or changes in data streams.

Installation

User installation

The easiest way to install Image XAI is using pip:

pip install -U eazyml-xai-image

Dependencies

EazyML Image XAI requires :

• tensorflow
• segmentation-models==1.0.1
• lime
• opencv-python
• flask
• pyyaml

Usage

It provides following apis :

1. ez_image_active_learning : This APIs sorts test images based on explainability scores for the model’s predictions. If a “query count” is specified in the options, it returns the indices and corresponding scores for that number of inputs.

   ez_image_active_learning(
           filenames=['..', '..'],
           model_path='path_of_model',
           predicted_filenames=['path_of_model_prediction_file_names'],
           options={
               "query_count": 10,
               "training_data_path": "path/to/training/data.csv",
               "score_strategy": "weighted-moments",
               "al_strategy": "pool-based",
               "xai_strategy": "gradcam",
               "gradcam_layer": "layer_name",
               "model_num": "1"
           }
       )
   

2. ez_image_model_evaluate : This APIs validates a model using provided data and returns the model evaluation.

   ez_image_model_evaluate(
           validation_data_path='path_of_new_data_for_validation',
           model_path='path_of_model',
           options={
               "required_functions": {
                   "loss_fn": '...',
                   "metric_fns": '...',
                   "input_preprocess_fn": '',
                   "label_preprocess_fn": '',
                   "output_process_fn": ''
                   },
               "batch_size": 32,
               "log_file": "path/to/log/file"
           })
   

3. ez_image_online_learning : This APIs updates a given model using new training data and saves the updated model. The update process adapts based on the Online Learning strategy or optimizes performance on provided validation data.

   ez_image_online_learning(
           validation_data_path='path_of_new_data_for_validation',
           model_path='path_of_model',
           options={
               "required_functions": {
                   "loss_fn": '...',
                   "metric_fns": '...',
                   "input_preprocess_fn": '',
                   "label_preprocess_fn": '',
                   "output_process_fn": ''
               },
               "batch_size": 32,
               "log_file": "path/to/log/file"
           }
       )
   

4. ez_xai_image_explain : This APIs provides confidence scores and image explanations for model predictions. It can process a single image or multiple images, returning explanations for all predictions.

   ez_xai_image_explain(
           filenames=['..', '..'],
           model_path='path_of_model',
           predicted_filenames=['path_of_model_prediction_file_names'],
           options={
               "training_data_path": "...",
               "score_strategy": "weighted-moments",
               "xai_strategy": "gradcam",
               "xai_image_path": "...",
               "gradcam_layer": "layer_name",
               "model_num": "1",
               "required_functions": {...}
           }
       )
   

You can find more information in the documentation.

Useful links, other packages from EazyML family

• Documentation

• Homepage

• If you have questions or would like to discuss a use case, please contact us here

• Here are the other packages from EazyML suite:

  • eazyml-automl: eazyml-automl provides a suite of APIs for training, optimizing and validating machine learning models with built-in AutoML capabilities, hyperparameter tuning, and cross-validation.
  • eazyml-data-quality: eazyml-data-quality provides APIs for comprehensive data quality assessment, including bias detection, outlier identification, and drift analysis for both data and models.
  • eazyml-counterfactual: eazyml-counterfactual provides APIs for optimal prescriptive analytics, counterfactual explanations, and actionable insights to optimize predictive outcomes to align with your objectives.
  • eazyml-insight: eazyml-insight provides APIs to discover patterns, generate insights, and mine rules from your datasets.
  • eazyml-xai: eazyml-xai provides APIs for explainable AI (XAI), offering human-readable explanations, feature importance, and predictive reasoning.
  • eazyml-xai-image: eazyml-xai-image provides APIs for image explainable AI (XAI).

License

This project is licensed under the Proprietary License.

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