omnixai 1.1.2

OmniXAI: An Explainable AI Toolbox

OmniXAI: A Library for Explainable AI

Table of Contents

1. Introduction
2. Installation
3. Getting Started
4. Documentation
5. Tutorials
6. Dashboard Demo
7. How to Contribute
8. Technical Report and Citing OmniXAI

Introduction

OmniXAI (short for Omni eXplainable AI) is a Python machine-learning library for explainable AI (XAI), offering omni-way explainable AI and interpretable machine learning capabilities to address many pain points in explaining decisions made by machine learning models in practice. OmniXAI aims to be a one-stop comprehensive library that makes explainable AI easy for data scientists, ML researchers and practitioners who need explanation for various types of data, models and explanation methods at different stages of ML process:

OmniXAI includes a rich family of explanation methods integrated in a unified interface, which supports multiple data types (tabular data, images, texts, time-series), multiple types of ML models (traditional ML in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of diverse explaination methods including "model-specific" and "model-agnostic" methods (such as feature-attribution explanation, counterfactual explanation, gradient-based explanation, etc). For practitioners, OmniXAI provides an easy-to-use unified interface to generate the explanations for their applications by only writing a few lines of codes, and also a GUI dashboard for visualization for obtaining more insights about decisions.

The following table shows the supported explanation methods and features in our library. We will continue improving this library to make it more comprehensive in the future, e.g., supporting more explanation methods for vision, NLP and time-series tasks.

Method	Model Type	Explanation Type	EDA	Tabular	Image	Text	Timeseries
Feature analysis	NA	Global	✅
Feature selection	NA	Global	✅
Prediction metrics	Black box	Global		✅	✅	✅	✅
Partial dependence plots	Black box	Global		✅
Accumulated local effects	Black box	Global		✅
Sensitivity analysis	Black box	Global		✅
LIME	Black box	Local		✅	✅	✅
SHAP	Black box*	Local		✅	✅	✅	✅
Integrated gradient	Torch or TF	Local		✅	✅	✅
Counterfactual	Black box*	Local		✅	✅	✅	✅
Contrastive explanation	Torch or TF	Local			✅
Grad-CAM, Grad-CAM++	Torch or TF	Local			✅
Learning to explain	Black box	Local		✅	✅	✅
Linear models	Linear models	Global and Local		✅
Tree models	Tree models	Global and Local		✅

SHAP accepts black box models for tabular data, PyTorch/Tensorflow models for image data, transformer models for text data. Counterfactual accepts black box models for tabular, text and time-series data, and PyTorch/Tensorflow models for image data.

The following table shows the comparison between our toolkit/library and other existing XAI toolkits/libraries in literature:

Data Type	Method	OmniXAI	InterpretML	AIX360	Eli5	Captum	Alibi	explainX
Tabular	LIME	✅	✅	✅		✅
	SHAP	✅	✅	✅		✅	✅	✅
	PDP	✅	✅
	ALE	✅					✅
	Sensitivity	✅	✅
	Integrated gradient	✅				✅	✅
	Counterfactual	✅					✅
	Linear models	✅	✅	✅	✅		✅	✅
	Tree models	✅	✅	✅	✅		✅	✅
	L2X	✅
Image	LIME	✅				✅
	SHAP	✅				✅
	Integrated gradient	✅				✅	✅
	Grad-CAM, Grad-CAM++	✅			✅	✅
	CEM	✅		✅			✅
	Counterfactual	✅					✅
	L2X	✅
Text	LIME	✅			✅	✅
	SHAP	✅				✅
	Integrated gradient	✅				✅	✅
	L2X	✅
	Counterfactual	✅
Timeseries	SHAP	✅
	Counterfactual	✅

Installation

You can install omnixai from PyPI by calling pip install omnixai. You may install from source by cloning the OmniXAI repo, navigating to the root directory, and calling pip install ., or pip install -e . to install in editable mode. You may install additional dependencies:

• For plotting & visualization: Calling pip install omnixai[plot], or pip install .[plot] from the root directory of the repo.
• For vision tasks: Calling pip install omnixai[vision], or pip install .[vision] from the root directory of the repo.
• For NLP tasks: Calling pip install omnixai[nlp], or pip install .[nlp] from the root directory of the repo.
• Install all the dependencies: Calling pip install omnixai[all], or pip install .[all] from the root directory of the repo.

Getting Started

For example code and an introduction to the library, see the Jupyter notebooks in tutorials, and the guided walkthrough here. A dashboard demo can be found here.

Some examples:

1. Tabular classification
2. Tabular regression
3. Image classification
4. Text classification
5. Time-series anomaly detection

To get started, we recommend the linked tutorials in tutorials. In general, we recommend using TabularExplainer, VisionExplainer, NLPExplainer and TimeseriesExplainer for tabular, vision, NLP and time-series tasks, respectively, and using DataAnalyzer and PredictionAnalyzer for feature analysis and prediction result analysis. To generate explanations, one only needs to specify

• The ML model to explain: e.g., a scikit-learn model, a tensorflow model, a pytorch model or a black-box prediction function.
• The pre-processing function: i.e., converting raw input features into the model inputs.
• The post-processing function (optional): e.g., converting the model outputs into class probabilities.
• The explainers to apply: e.g., SHAP, MACE, Grad-CAM.

Let's take the income prediction task as an example. The dataset used in this example is for income prediction. We recommend using data class Tabular to represent a tabular dataset. To create a Tabular instance given a pandas dataframe, one needs to specify the dataframe, the categorical feature names (if exists) and the target/label column name (if exists).

from omnixai.data.tabular import Tabular
# Load the dataset
feature_names = [
   "Age", "Workclass", "fnlwgt", "Education",
   "Education-Num", "Marital Status", "Occupation",
   "Relationship", "Race", "Sex", "Capital Gain",
   "Capital Loss", "Hours per week", "Country", "label"
]
df = pd.DataFrame(
  np.genfromtxt('adult.data', delimiter=', ', dtype=str),
  columns=feature_names
)
tabular_data = Tabular(
   df,
   categorical_columns=[feature_names[i] for i in [1, 3, 5, 6, 7, 8, 9, 13]],
   target_column='label'
)

The package omnixai.preprocessing provides several useful preprocessing functions for a Tabular instance. TabularTransform is a special transform designed for processing tabular data. By default, it converts categorical features into one-hot encoding, and keeps continuous-valued features. The method transform of TabularTransform transforms a Tabular instance to a numpy array. If the Tabular instance has a target/label column, the last column of the numpy array will be the target/label. One can also apply any customized preprocessing functions instead of using TabularTransform. After data preprocessing, we train a XGBoost classifier for this task.

from omnixai.preprocessing.tabular import TabularTransform
# Data preprocessing
transformer = TabularTransform().fit(tabular_data)
class_names = transformer.class_names
x = transformer.transform(tabular_data)
# Split into training and test datasets
train, test, train_labels, test_labels = \
    sklearn.model_selection.train_test_split(x[:, :-1], x[:, -1], train_size=0.80)
# Train an XGBoost model (the last column of `x` is the label column after transformation)
model = xgboost.XGBClassifier(n_estimators=300, max_depth=5)
model.fit(train, train_labels)
# Convert the transformed data back to Tabular instances
train_data = transformer.invert(train)
test_data = transformer.invert(test)

To initialize TabularExplainer, we need to set the following parameters:

• explainers: The names of the explainers to apply, e.g., ["lime", "shap", "mace", "pdp"].
• data: The data used to initialize explainers. data is the training dataset for training the machine learning model. If the training dataset is too large, data can be a subset of it by applying omnixai.sampler.tabular.Sampler.subsample.
• model: The ML model to explain, e.g., a scikit-learn model, a tensorflow model or a pytorch model.
• preprocess: The preprocessing function converting the raw inputs (A Tabular instance) into the inputs of model.
• postprocess (optional): The postprocessing function transforming the outputs of model to a user-specific form, e.g., the predicted probability for each class. The output of postprocess should be a numpy array.
• mode: The task type, e.g., "classification" or "regression".

The preprocessing function takes a Tabular instance as its input and outputs the processed features that the ML model consumes. In this example, we simply call transformer.transform. If one uses some customized transforms on pandas dataframes, the preprocess function has format: lambda z: some_transform(z.to_pd()). If the output of model is not a numpy array, postprocess needs to be set to convert it into a numpy array.

from omnixai.explainers.tabular import TabularExplainer
# Initialize a TabularExplainer
explainers = TabularExplainer(
  explainers=["lime", "shap", "mace", "pdp", "ale"], # The explainers to apply
  mode="classification",                             # The task type
  data=train_data,                                   # The data for initializing the explainers
  model=model,                                       # The ML model to explain
  preprocess=lambda z: transformer.transform(z),     # Converts raw features into the model inputs
  params={
     "mace": {"ignored_features": ["Sex", "Race", "Relationship", "Capital Loss"]}
  }                                                  # Additional parameters
)

In this example, LIME, SHAP and MACE generate local explanations while PDP (partial dependence plot) generates global explanations. explainers.explain returns the local explanations generated by the three methods given the test instances, and explainers.explain_global returns the global explanations generated by PDP. TabularExplainer hides all the details behind the explainers, so we can simply call these two methods to generate explanations.

# Generate explanations
test_instances = test_data[:5]
local_explanations = explainers.explain(X=test_instances)
global_explanations = explainers.explain_global(
    params={"pdp": {"features": ["Age", "Education-Num", "Capital Gain",
                                 "Capital Loss", "Hours per week", "Education",
                                 "Marital Status", "Occupation"]}}
)

Similarly, we create a PredictionAnalyzer for computing performance metrics for this classification task. To initialize PredictionAnalyzer, we set the following parameters:

• mode: The task type, e.g., "classification" or "regression".
• test_data: The test dataset, which should be a Tabular instance.
• test_targets: The test labels or targets. For classification, test_targets should be integers (processed by a LabelEncoder) and match the class probabilities returned by the ML model.
• preprocess: The preprocessing function converting the raw data (a Tabular instance) into the inputs of model.
• postprocess (optional): The postprocessing function transforming the outputs of model to a user-specific form, e.g., the predicted probability for each class. The output of postprocess should be a numpy array.

from omnixai.explainers.prediction import PredictionAnalyzer

analyzer = PredictionAnalyzer(
    mode="classification",
    test_data=test_data,                           # The test dataset (a `Tabular` instance)
    test_targets=test_labels,                      # The test labels (a numpy array)
    model=model,                                   # The ML model
    preprocess=lambda z: transformer.transform(z)  # Converts raw features into the model inputs
)
prediction_explanations = analyzer.explain()

Given the generated explanations, we can launch a dashboard (a Dash app) for visualization by setting the test instance, the local explanations, the global explanations, the prediction metrics, the class names, and additional parameters for visualization (optional).

from omnixai.visualization.dashboard import Dashboard
# Launch a dashboard for visualization
dashboard = Dashboard(
   instances=test_instances,                        # The instances to explain
   local_explanations=local_explanations,           # Set the local explanations
   global_explanations=global_explanations,         # Set the global explanations
   prediction_explanations=prediction_explanations, # Set the prediction metrics
   class_names=class_names                          # Set class names
)
dashboard.show()                                    # Launch the dashboard

After opening the Dash app in the browser, we will see a dashboard showing the explanations:

How to Contribute

We welcome the contribution from the open-source community to improve the library!

To add a new explanation method/feature into the library, please follow the template and steps demonstrated in this documentation.

Technical Report and Citing OmniXAI

You can find more details in our technical report: https://arxiv.org/abs/2206.01612

If you're using OmniXAI in your research or applications, please cite using this BibTeX:

@article{wenzhuo2022-omnixai,
  author    = {Wenzhuo Yang and Hung Le and Silvio Savarese and Steven Hoi},
  title     = {OmniXAI: A Library for Explainable AI},
  year      = {2022},
  doi       = {10.48550/ARXIV.2206.01612},
  url       = {https://arxiv.org/abs/2206.01612},
  archivePrefix = {arXiv},
  eprint    = {206.01612},
}

Contact Us

If you have any questions, comments or suggestions, please do not hesitate to contact us at [e-mail].

License

BSD 3-Clause License