Skip to main content

Explainable Artificial Intelligence for dynamic and Incremental models.

Project description

PyPi PyPi_status
  </a>
  <!-- License -->
  <a href= mit_license

ixai: Incremental Explainable Artificial Intelligence

This is the first iteration of our incremental explanation package.

Currently, it includes two explanation methods: PFI and SAGE.

Please look at the examples in the examples directory.

Please help us in improving our work by contributing or pointing to issues. We will update this iteration soon with further information.

🛠 Installation

ixai is intended to work with Python 3.8 and above. Installation can be done via pip:

pip install ixai

📊 Quickstart

Basic Classification

>>> from river.metrics import Accuracy
>>> from river.ensemble import AdaptiveRandomForestClassifier
>>> from river.datasets.synth import Agrawal

>>> from ixai.explainer import IncrementalPFI

>>> stream = Agrawal(classification_function=2)
>>> feature_names = list([x_0 for x_0, _ in stream.take(1)][0].keys())

>>> model = AdaptiveRandomForestClassifier(n_models=10, max_depth=10, leaf_prediction='mc')

>>> incremental_pfi = IncrementalPFI(
...     model_function=model.predict_one,
...     loss_function=Accuracy(),
...     feature_names=feature_names,
...     smoothing_alpha=0.001,
...     n_inner_samples=5
... )

>>> training_metric = Accuracy()
>>> for (n, (x, y)) in enumerate(stream, start=1)
...     y_pred = model.predict_one(x)       # inference
...     training_metric.update(y, y_pred)   # update score
...     incremental_pfi.explain_one(x, y)   # explaining
...     model.learn_one(x, y)               # learning
...     if n % 1000 == 0:
...         print(f"{n}: Accuracy: {training_metric.get():.3f}, PFI: {incremental_pfi.importance_values}")

1000: Accuracy: 0.785, PFI: {'age': 0.22, 'elevel': 0.18, 'zipcode': -0.07, 'salary': 0.04, 'commission': 0.05, 'loan': -0.06, 'car': 0.02, 'hyears': 0.03, 'hvalue': 0.03}
2000: Accuracy: 0.841, PFI: {'age': 0.26, 'elevel': 0.21, 'zipcode': -0.01, 'salary': 0.02, 'commission': 0.03, 'loan': -0.02, 'car': 0.02, 'hyears': 0.04, 'hvalue': 0.02}
3000: Accuracy: 0.921, PFI: {'age': 0.28, 'elevel': 0.24, 'zipcode': -0.00, 'salary': 0.00, 'commission': 0.01, 'loan': -0.01, 'car': 0.01, 'hyears': 0.01, 'hvalue': 0.00}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ixai-0.1.3.tar.gz (30.3 kB view hashes)

Uploaded Source

Built Distribution

ixai-0.1.3-py3-none-any.whl (46.8 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page