An interpretable and easy-to-understand python version of the Anchor explanation method from Anchors: High-Precision Model-Agnostic Explanations. Our implementation is inspired by this code. Furthermore, it supports optimization with the SMAC optimizer besides KL-divergence. The code is unit tested and the tests can be run with pytest.
This section describes how to get started explaining your own black-box models.
To install the required packages we recommend using Conda. Our used environment can be easily installed with conda.
- Clone the repo
git clone https://github.com/automl-classroom/iml-ws21-projects-risingnumpygods.git
- Install conda environment
conda env create -f environment.yml
- Activate the environment
conda activate iAnchor
- Go to the project directory and run
pytest tests/*
We provided an example jupyter notebook for different use cases. You can find the notebooks here. The notebooks cover the following use cases:
- Explanation of image prediction
- Explanation of tabular prediction
- Explanation of text prediction
Suppose you want to explain a tabular instance prediction. You can get an explanation with a few lines of code.
import numpy as np
import sklearn
from Anchor.anchor import Anchor, Tasktype
# Load dataset
data = np.genfromtxt('../datasets/titanic.txt', delimiter=',')
y_train = data[:, -1]
X_train = data[:, :-1]
# Train classifier
c = sklearn.ensemble.RandomForestClassifier(n_estimators=100, n_jobs=5, random_state=123)
c.fit(X_train, y_train)
print('Train', sklearn.metrics.accuracy_score(y_train, c.predict(X_train)))
# Init explainer for specific task
explainer = Anchor(Tasktype.TABULAR)
# Get explanation for desired instance
task_paras = {"dataset": X_train, "column_names": ["PcClass", "Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked"]}
method_paras = {"beam_size": 1, "desired_confidence": 1.0}
anchor = explainer.explain_instance(
input=X_train[759].reshape(1, -1),
predict_fn=c.predict,
method="beam",
task_specific=task_paras,
method_specific=method_paras,
num_coverage_samples=100,
)
# Visualize explanation
visu = explainer.visualize(anchor, X_train[759])
print(visu)For more advanced usage and architecture insights you can look at the docs.